Integrative analyses of hypoxia-related genes and mechanisms associated with Allergic Rhinitis

Integrative analyses of hypoxia-related genes and mechanisms associated with Allergic Rhinitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrative analyses of hypoxia-related genes and mechanisms associated with Allergic Rhinitis Shiyun Shao, Kunchen Wei, Xiao Feng, Guanhui Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4096488/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In the realm of immunological disorders, allergic rhinitis (AR) persists as a prevalent condition, yet its molecular underpinnings remain only partially deciphered, necessitating deeper exploration. This study pioneers in bridging this knowledge gap, unveiling intricate molecular markers and pathways pivotal to AR's pathophysiology, thereby steering the scientific community towards novel diagnostic and prognostic frontiers. Employing rigorous bioinformatics analyses, similar to methodologies applied in studies on endometriosis and age-related macular degeneration, we delved into the molecular landscape, identifying 21 hypoxia-related differential expression genes (HRDEGs) and constructing a robust LASSO diagnostic model, a methodology that stands out for its precision in capturing clinical heterogeneity. Methods Our approach encompassed a comprehensive analysis of differential gene expressions, focusing particularly on HRDEGs, and their subsequent integration into a logistic regression model to ascertain their diagnostic and prognostic efficacy. Key findings revealed a high expression of genes such as CPT1C and MMP1 in the AR group, underscoring their significance in AR's molecular signature. Furthermore, the constructed LASSO model demonstrated high accuracy, highlighting genes like CPT1C, CWF19L1, MED17, and MMP1 as reliable biomarkers. Results Interestingly, the study also unearthed a nuanced interplay between AR and other systemic conditions, suggesting that the molecular mechanisms underlying allergic inflammation could influence the pathophysiology of various respiratory diseases3. These insights not only contribute to the academic discourse but also hold profound therapeutic potential, particularly in the realm of personalized medicine. Conclusions In conclusion, this research illuminates the molecular complexities of AR, offering substantial evidence for the involvement of specific genes and pathways in its pathogenesis. The implications of these discoveries are far-reaching, promising to revolutionize AR management through more tailored therapeutic strategies and underscoring the need for further investigations in larger, more diverse cohorts. Biological sciences/Biochemistry Biological sciences/Computational biology and bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Introduction Allergic rhinitis (AR) is a prevalent health condition characterized by inflammation of the nasal mucosa in response to allergen exposure, posing a significant health burden worldwide [ 1 , 2 ]. AR's diagnosis primarily relies on subjective symptom reporting, which can lead to both overdiagnosis and underdiagnosis. This diagnostic challenge arises due to the overlap of AR symptoms with other respiratory conditions, such as non-allergic rhinitis and sinusitis [ 3 ]. Consequently, there is a critical need for objective and precise diagnostic tools to differentiate AR from other similar conditions accurately. Furthermore, AR's pathophysiology involves a complex interplay of immunological mechanisms, and its exact etiology is not fully understood. Recent research has highlighted an intriguing aspect of AR—the potential association with hypoxia-related genes [ 4 ]. Hypoxia is a condition characterized by insufficient oxygen supply to tissues and cells, known for its crucial roles in various physiological and pathological processes. It has been extensively studied in contexts such as tumorigenesis, wound healing, and inflammation. However, its role in allergic rhinitis has only recently begun to emerge as an area of interest. Phenotypically, hypoxia manifests as an oxygen-deficient microenvironment within tissues and organs, triggering various adaptive responses at the cellular and molecular levels. The relevance of hypoxia extends to several diseases that share similarities or associations with AR, such as asthma and chronic obstructive pulmonary disease (COPD). Studies in these related conditions have highlighted the potential involvement of hypoxia-related pathways in disease pathogenesis and progression. In light of these considerations, this study aims to address the diagnostic challenges associated with AR by developing a diagnostic model based on hypoxia-related genes. To investigate the potential correlation between the expression of hypoxia-related genes and the presence of AR. Then develop a diagnostic model utilizing hypoxia-related gene expression profiles to distinguish AR from other respiratory conditions accurately. We will conduct a comprehensive analysis of gene expression data from AR patients and individuals with similar respiratory conditions, incorporating dataset GSE51392 and potentially other relevant datasets. Differential expression analyses, Gene Set Enrichment Analysis (GSEA), and machine learning techniques will be employed to identify key hypoxia-related genes and construct the diagnostic model. The study anticipates identifying hypoxia-related genes associated with AR, shedding light on the molecular mechanisms underlying the disease. The development of a diagnostic model based on hypoxia-related gene expression profiles holds the potential to significantly improve the accuracy of AR diagnosis, addressing a critical clinical need. Materials and Methods 1.1 Data Download We from the GEO database [ 5 ] ( https://www.ncbi.nlm.nih.gov/geo/ ) using the R package GEOquery [ 6 ] download the Allergic rhinitis, Allergic rhinitis, AR) data set GSE51392 [ 7 ], GSE46171 [ 8 ]. Datasets GSE51392 and GSE46171 are from Homo sapiens. The data platform GSE51392 is GPL13158 [HT_HG-U133_Plus_PM] Affymetrix HT HG-U133 + PM Array Plate. The data set GSE51392 excludes the asthma data, leaving a total of 44 sample data. A total of 44 samples were included, including 20 samples of allergic rhinitis and 24 samples of Control group. The tissue source was primary nasal and bronchial epithelial cells. GSE46171 data selection platform was GPL16981 Agilent-020087 human whole genome 4x44K (Probe Name version) data, the GSE46171 data set excluded asthma part of the data, a total of 19 samples data. The GSE46171 dataset included gene expression profiles of 5 allergic rhinitis samples and 14 Control samples, and the tissue source was Nasal mucosa. The specific information of the data set can be found in Table 1 . We collected Hypoxia related genes (HRGs) from the GeneCards[ 9 ] database, which provides comprehensive information on human genes ( https://www.genecards.org/ ). Using the term "Hypoxia" as the search keyword and "Protein Coding" as the screening criterion, we obtained 6094 hypoxia-related genes (HRGs, mRNA). In addition, we also used "Hypoxia" as a search term on the MSigDB (Molecular Signatures Database) [ 10 ] database website. Twenty-seven Hypoxia related genes (HRGs) were collected from the "BIOCARTA VEGF PATHWAY" reference gene set. After combined deduplication, we obtained a total of 6099 Hypoxia related genes (HRGs), the detailed information is shown in Table S1 . 1.2 Differential analysis of dataset GSE51392 and GSE46171 The data of datasets GSE51392 and GSE46171 have been standardized and can be used directly. The boxplots of datasets GSE51392 and GSE46171 are shown in the Supplementary materials (Figure S1 - S2 ). Subsequently, according to the grouping information (AR/Control) in the data sets GSE51392 and GSE46171, we used the R package limma[ 11 ] for differential analysis to obtain differentially expressed genes. Finally, we will GSE51392 data sets, be all | GSE46171 variance analysis logFC | and pvalue > 0 < 0.05 DEGs HRGs take intersection get oxygen related differentially expressed genes (HRDEGs), By R package ggplot2 map volcanic present the results of variance analysis and R package pheatmap draw oxygen differentially expressed genes related to heat map, at the same time draw oxygen differentially expressed genes related to the correlation between the heat map. The group comparison map was used to show the expression trend of differentially expressed genes related to hypoxia. The differentially expressed genes related to hypoxia with the same expression trend and statistical significance in GSE51392 and GSE46171 datasets were selected for chromosome localization map and subsequent analysis. 1.3 GSEA enrichment analysis Gene Set Enrichment Analysis (GSEA) [ 12 ] is a computational method proposed by the Broad Institute to determine whether a predefined set of genes shows statistical differences between two biological states. It is commonly used to estimate changes in the activity of pathways and biological processes in expression data set samples. In order to study the biological process of difference between two groups of samples, we based on gene expression profile datasets, downloaded from MSigDB database [ 13 ] the reference gene set "c2. Cp. All. V2022.1. Hs. Symbols. The GMT [all Canonical Pathways] (3050)", The GSEA method included in the R package clusterProfiler was used for enrichment analysis and visualization of the dataset. The parameters used in this GSEA enrichment analysis are as follows: The seed was 2022, the number of computation was 5000, the number of genes in each gene set was at least 10, and the number of genes in each gene set was at most 500. The p-value correction method was Benjamini-Hochberg (BH). The screening criteria for significant enrichment were Padj < 0.05 and FDR value (qvalue) < 0.05. 1.4 GSVA enrichment analysis Gene Set Variation Analysis (GSVA) [ 14 ] is a non-parametric unsupervised analysis method, which is mainly used to evaluate the enrichment results of gene set of microarray nuclear transcriptome by converting the expression matrix of gene between different samples into the expression matrix of gene set between samples. Thus, we can evaluate whether different pathways are enriched in different samples. We get in MSigDB database "c2. All. V2023.1. Hs. Symbols. GMT" gene set to data set GSE51392 and disease among the GSE46171 normal (AR/Control) group analyses GSVA all genes, pvalue < 0.05 was used as the screening criterion for significant enrichment. Then, the pathways that met the requirements in GSVA enrichment analysis results were sorted in descending logFC order, and the top 10 and bottom 10 pathways were selected for results display. 1.5 Construct the diagnostic model LASSO regression is a commonly used machine learning algorithm to construct diagnostic models, which is mostly used to construct prognostic diagnostic models or screen variables. On the basis of linear regression, it uses regularization to solve the overfitting situation in the process of curve fitting by adding a penalty term (lambda × absolute value of slope), and improves the generalization ability of the model. In order to obtain the prognostic model in dataset GSE51392, we used glmnet package [ 15 ] based on the expression level of HRDEGs in dataset GSE51392, set the seed to 2022, ten-fold cross validation, and set the seed to be 2022. LASSO[ 16 ] (Least absolute shrinkage and selection operator) regression was performed to obtain the related HRDEGs with nonzero coefficient corresponding to the lambda value of the best evaluation index. Subsequently, based on the hypoxia related differentially expressed genes (HRDEGs), the SVM model was constructed by SVM (Support Vector Machine) [ 17 ] algorithm, and the hypoxia related differentially expressed genes (HRDEGs) were screened based on the number of genes with the highest accuracy and the lowest error rate. The genes selected by LASSO analysis and SVM analysis were used for subsequent analysis. logistic regression analysis was performed on HRDEGs screened by LASSO analysis and SVM analysis, and pvalue < 0.05 was used as the criterion to screen HRDEGs and construct a logistic regression model. The HRDEGs screened by single factor in logistic regression analysis were used as key genes for subsequent analysis. Then, based on the results of logistic regression analysis, we used the R package rms to construct a nomogram (nomogram). The Nomogram is a graph that uses a cluster of disjoint line segments to represent the functional relationship between multiple independent variables in the rectangular coordinate system of the plane. Based on the multi-factor regression analysis, a certain scale was set to characterize the various variables in the multi-factor regression model, and the total score was finally calculated to predict the probability of the occurrence of events. The Calibration Curve was drawn by Calibration analysis to evaluate the accuracy and discrimination of the logistic regression model based on key genes. The calibration curve is to evaluate the prediction effect of the model on the actual outcome by plotting the fitting of the actual probability and the model predicted probability under different conditions in the graph. We used the R package "rms" to construct the calibration curve. Decision curve analysis (DCA) is a simple method to evaluate clinical prediction models, diagnostic tests and molecular markers. We used the R package ggDCA[ 18 ] to draw DCA diagrams to evaluate the model. 1.6 Differential expression and GSEA analysis related to high and low risk groups of diagnostic model. In order to identify the potential mechanism of key genes and related biological characteristics and pathways in the high and low risk groups of the logistic regression model of GSE51392 and GSE46171, we first used limma package to compare the data set GSE51392 and GSE46171 with the data set GSE51392. GSE46171 was analyzed for differences between High and Low risk groups (High/Low), and then GSEA analysis was performed on them. Download the reference gene from MSigDB database [ 13 ] set "c2. Cp. All. V2022.1. Hs. Symbols. The GMT [all Canonical Pathways] (3050)", The enrichment analysis and visualization of the dataset were performed using the GSEA method included in the R package clusterProfiler. The parameters used in this GSEA enrichment analysis are as follows: Each gene set contains at least 10 genes, and the maximum number of genes is 500. The p-value correction method is Benjamini-Hochberg (BH). The screening criteria for significant enrichment are Padj < 0.05 and FDR value (qvalue) < 0.05. 1.7 GOKEGG enrichment analysis Gene Ontology (GO) [ 19 ] analysis is a common method for large-scale functional enrichment studies, including biological process (BP), molecular function (molecular function, MF) and cellular component (CC). The Kyoto Encyclopedia of Genes and Genomes (KEGG) [ 20 ] is a widely used database storing information on genomes, biological pathways, diseases and drugs. We used R package clusterProfiler[ 21 ] to perform GO and KEGG annotation analysis of key genes. The entry screening criteria were Padj < 0.05 and FDR value (qvalue) < 0.05, and the P value correction method was Benjamini-Hochberg (BH). Finally, the R package Pathview[ 22 ] was used to visualize the pathway map related to the pathway (KEGG) enrichment analysis. 1.8 ROC Curve Receiver operating characteristic curve (ROC) [ 23 ] : A coordinate schema-based analysis tool that can be used to select the best model, discard the second-best model, or set the best threshold in the same model. ROC curve is a comprehensive indicator of continuous variables reflecting sensitivity and specificity, and reflects the relationship between sensitivity and specificity by composition method. The area under the ROC curve is generally between 0.5 and 1. The closer the AUC is to 1, the better the diagnostic effect. When AUC was between 0.5 and 0.7, the accuracy was low, when AUC was between 0.7 and 0.9, the accuracy was moderate, and when AUC was above 0.9, the accuracy was high. We used the pROC package to draw the receiver operating characteristic (ROC) curves of key genes in different groups (AR/Control) and calculated the area under the curve (AUC) to evaluate the diagnostic effect of key gene expression on disease. 1.9 Functional similarity analysis The semantic comparison of Gene Ontology (GO) annotations provides a quantitative method for calculating the similarity between genes and genomes, and has become an important basis for many bioinformatics analysis methods. The GOSemSim package [ 24 ] was used to calculate the GO semantic similarity of key genes, and the geometric mean values of the key genes obtained from the dataset GSE51392 at the BP, CC and MF levels were further calculated to obtain the final score. Finally, the functional similarity analysis results were analyzed by the ggplot package. The Spearman method was used to explore the correlation between genes. 1.10 Immune infiltration analysis Single-sample gene set enrichment analysis (ssGSEA; single-sample gene-set enrichment analysis (SSGSEA) can quantify the relative abundance of each gene in a dataset sample. Markers for each infiltrating immune cell type, such as Activated CD8 T cell, Activated dendritic cell, Gamma delta T cell, Natural killer cell, The enrichment scores calculated by ssGSEA analysis were used to represent the relative abundance of each immune cell infiltration in each sample. The enrichment score of dataset GSE51392 was calculated by ssGSEA algorithm analysis in R package GSVA package [ 14 ] to represent the infiltration level of different types of immune cells in each sample. Then we combined the gene expression matrix of data set GSE51392 to calculate the correlation between immune cells and key genes in the High/Low risk group of the logistic regression model of data set GSE51392, and the R package ggplot2 was used to draw correlation maps. All the above correlation analyses were calculated by pearson algorithm. CIBERSORT[ 25 ] is an immune infiltration analysis algorithm that deconvolutes the transcriptome expression matrix based on the principle of linear support vector regression to estimate the composition and abundance of immune cells in mixed cells. We upload the data of GSE51392 gene expression matrix to CIBERSORT, combine with LM22 feature gene matrix to screen out the data of immune fine abundance and greater than zero, and finally obtain and display the specific results of immune cell infiltration abundance matrix. The proportion of immune cell infiltration abundance among the samples in the High/Low risk groups of the logistic regression model in dataset GSE51392 was displayed by stacking bar charts. Then the correlation between the abundance and immune cells greater than zero in the High/Low risk groups of the logistic regression model in GSE51392 was visualized by the R package ggplot2. Then we combined the gene expression matrix of the data set GSE51392 to calculate the correlation between immune cells and key genes in the High/Low risk groups of the logistic regression model of the data set GSE51392, and the R package ggplot2 was used to draw the correlation map. All the above correlation analyses were calculated by pearson algorithm. 1.11 Interaction network analysis of key genes ENCORI [ 26 ] database ( https://starbase.sysu.edu.cn/ ) is a starBase database version 3.0, ENCORI database of micrornas - mRNA interaction is based on the CLIP - seq and degradation group sequencing plants (for) of data mining, It provides a variety of visual interfaces for exploring the targets of miRNA. We used ENCORI database to predict the mirnas that interacted with Key genes, retained the interaction pairs recorded in at least three databases, and then plotted the mRNA-miRNA interaction network in Cytoscape. CHIPBase database [ 27 ] (version 3.0) ( https://encori : / / rna.sysu.edu.cn/chipbase/) from the DNA binding protein ChIP - seq data identified in thousands of combining base sequence matrix and its binding sites, and forecasts the millions of Transcription factor (Transcription factors, TF) and gene Transcription regulation between relations. HTFtarget database [ 28 ] ( http://bioinfo.life.hust.edu.cn/hTFtarget .) is a human transcription factor (TF) and the corresponding control target data integrated database. We searched CHIPBase (version 3.0) and hTFtarget database to find transcription factors (TFS) that bind to key genes and kept the common parts in both databases. The public Comparative Toxicogenomics Database (CTD) [ 29 ] ( http://ctdbase.org/ ) is an innovative digital ecosystem that links chemicals, genes, phenotypes, diseases and known toxicological information, To facilitate the understanding of human health related information database. We used the CTD database to predict potential drugs or small molecule compounds that would interact with Key genes, and used "Reference Count" > 1 as the screening criterion to screen mRNA-drugs interaction pairs. Cytoscape software was used to visualize the mRNA-drugs interaction network. 1.12 Spatial protein structure of key genes Proteins are essential for life, and knowledge of their structure can facilitate an understanding of alignment function. Alphafold website ( https://www.alphafold.ebi.ac.uk/ ) [ 30 ] first proposed can under the situation of no homologous template based on the calculation method to predict protein structure with atomic precision, predict the structure of the cover 98.5% of the known human proteins and other biological the same proportion of protein. We used the Alphafold2 website to predict the protein structures of key genes (mrnas) in the PPI network and presented the results. 1.13 Statistical analysis All data processing and analysis were performed with the use of R software (Version 4.2.3). Continuous variables are presented as mean ± SD. Continuous variables were compared between the two groups with the use of the Wilcoxon rank sum test, and statistical significance was estimated for normally distributed variables with the use of an independent Student's t-test. The Kruskal-Wallis test was used for comparisons of three or more groups. The chi-square test or Fisher's exact test was used to compare and analyze statistical significance between the two groups of categorical variables. receiver operating characteristic (ROC) curves were based on the R package pROC. If not specified, the results were calculated by spearman correlation analysis and all P statistics are two-sided. A P value of less than 0.05 was considered to indicate statistical significance. Results 2.1 Analysis flow chart The technical route of this analysis is shown in the figure below (Fig. 1 ). 2.2 Difference analysis of dataset GSE51392 and GSE46171 To analyze the differences in gene expression between different groups (AR /Control) of the GSE51392 and GSE46171 datasets, limma package was used to perform differential analysis on GSE51392 and GSE46171 datasets to obtain the differentially expressed genes (DEGs) between different groups of AR datasets (AR /Control). The results are as follows: GSE51392 data sets were obtained 19921 differentially expressed genes, which meet the | logFC | and pvalue > 0 < 0.05 threshold gene has 1504, under the threshold, high expression in AR group (the Control group of middle and lower expression, logFC is positive, raised genes) there are 685 in number, Low expression in AR group (the Control group increased, logFC negative) there are 819 in number, we will data set GSE51392 variance analysis results to plot the chart (Fig. 2 a). And GSE46171 data sets were obtained 19449 differentially expressed genes, which meet the | logFC | and pvalue > 0 < 0.05 threshold gene has 761, under the threshold, high expression in AR group (the Control group of middle and lower expression, logFC is positive, raised genes) there are 353 in number, Low expression in AR group (the Control group increased, logFC negative) there are 408 in number, we will data set GSE51392 variance analysis results to plot the chart (Fig. 2 b). To obtain Hypoxia related differentially expressed genes (HRDEGs), We first put the data sets GSE51392 data sets and meet all | GSE46171 data set logFC | and pvalue > 0 < 0.05 threshold of differentially expressed genes (differentially expressed genes, Relevant genes (DEGs) and oxygen Hypoxia related genes, HRGs) intersection, received 21 differentially expressed genes related Hypoxia (Hypoxia related differentially expressed genes. HRDEGs) and the Venn diagram (Fig. 2 C) was drawn. The 21 HRDEGs are: AMOT, ANKRD39, ATF4, CPT1C, CWF19L1, DDX52, DIP2A, EED, GIPR, IGFBP3, LRPAP1, MED13L, MED17, MEX3A, MFAP1, MMP1, NCDN, NECAB2, PSMA7, PSMC3, PSMD13. According to the results obtained by Venn diagram, the differential expression of 21 HRDEGs in different groups (AR /Control) of GSE51392 dataset and GSE46171 dataset was analyzed, and the R package pheatmap was used to draw a heatmap to show the specific differential analysis results (Fig. 2 D-E). The expression of 21 HRDEGs in different groups (AR /Control) of GSE51392 data set (Fig. 2 D) and GSE46171 data set (Fig. 2 E) were significantly different. In addition, the correlation heatmap was used to show the correlation between the differentially expressed genes related to hypoxia in GSE51392 dataset and GSE46171 dataset (Fig. 2 F-G). Group comparison plots were also drawn to show the expression of 21 hypoxia-related differentially expressed genes in datasets GSE51392 and GSE46171 (Fig. 3 A-B). It can be seen from the figure that the hypoxia-related differentially expressed genes with the same expression trend and statistical significance (p < 0.05) in dataset GSE51392 and dataset GSE46171 were CPT1C, CWF19L1, DDX52, MED17, and MMP1. In GSE51392 and GSE46171, the genes CPT1C and MMP1 were highly expressed in the AR group, and the genes CWF19L1, DDX52 and MED17 were lowly expressed in the AR group compared with the Control group. The five HRDEGs (CPT1C, CWF19L1, DDX52, MED17, MMP1) were further analyzed. In order to analyze the position of the 5 HRDEGs on the human chromosome, we also used the RCircos package to annotate the position of the 5 HRDEGs (Fig. 3 C). According to Fig. 3 C, the genes MED17 and MMP1 were distributed on the 11th chromosome, indicating that these two genes were more closely related than other HRDEGs. 2.3 GSEA enrichment analysis To determine the effect of hypoxia-associated differentially expressed genes (HRDEGs) expression levels on the occurrence of AR, Gene Set Enrichment Analysis (GSEA) was used to analyze all gene expression and involved biological processes of AR/Control group samples in dataset GSE51392 and dataset GSE46171, respectively. Padj < 0.05 and FDR value (qvalue) < 0.05 were used as the screening criteria for significant enrichment of the relationship between cellular components and molecular functions. The results showed that the genes of AR/Control group in dataset GSE51392 were significantly enriched in WP_EPITHELIAL_TO_MESENCHYMAL_TRANSITION_IN_COLORECTAL_CANCER, and WP_epithelial_to_mesenchymal_transition_in_Colorectal_cancer. WP_VITAMIN_A_AND_CAROTENOID_METABOLISM, WP_CANONICAL_AND_NONCANONICAL_NOTCH_SIGNALING, REACTOME_MET_PROMOTES_CELL_MOTILITY, REACTOME_ASSEMBLY_OF_COLLAGEN_FIBRILS_AND_OTHER_MULTIMERIC_STRUCTURES, WP_INFLAMMATORY_RESPONSE_PATHWAY (Fig. 4 A-G, Table 2 ). The results showed that the genes of AR/Control group in dataset GSE46171 were significantly enriched in WP_OXIDATIVE_DAMAGE_RESPONSE, PID_IL12_2PATHWAY, WP_VITAMIN_B12_METABOLISM, and WP_oxidative_damage_response. REACTOME_COSTIMULATION_BY_THE_CD28_FAMILY, WP_INTERACTIONS_BETWEEN_IMMUNE_CELLS_AND_MICRORNAS_IN_TUMOR_MICROENVIRONMENT, REACTOME_INTERLEUKIN_10_SIGNALING (Fig. 5 A-G, Table 3 ). 2.4 GSVA enrichment analysis In order to explore the c2. All. V2023.1. Hs. Symbols. The GMT gene set in the data set GSE51392 disease is normal (AR/Control) group, the difference of the sample Gene Set Variation Analysis (GSVA) was then performed to analyze the expression of all genes in dataset GSE51392. According to the results of gene set variation analysis (GSVA), the pvalue was < 0.05 The differential expression of the top 10 and the bottom 10 pathways between allergic rhinitis (AR) and Control (Control) groups was analyzed by logFC sorting and visualized by heat map (Fig. 6 A) and group comparison map (Fig. 6 B) (Table 6 ). The results of gene set variation analysis (GSVA) showed that 20 pathways were statistically significant (pvalue < 0.05) in the sensitive rhinitis (AR) group and the Control (Control) group, respectively: BIOCARTA_RAN_PATHWAY, REACTOME_GDP_FUCOSE_BIOSYNTHESIS, REACTOME_HYALURONAN_BIOSYNTHESIS_AND_EXPORT, REACTOME_VITAMIN_B1_THIAMIN_METABOLISM, REACTOME_SCAVENGING_BY_CLASS_F_RECEPTORS, WP_HIF1A_AND_PPARG_REGULATION_OF_GLYCOLYSIS, REACTOME_METALLOTHIONEINS_BIND_METALS, KRISHNAN_FURIN_TARGETS_UP, BIOCARTA_PROTEASOME_PATHWAY, REACTOME_SYNTHESIS_OF_DOLICHYL_PHOSPHATE REACTOME_ACTIVATED_NTRK2_SIGNALS_THROUGH_FYN, REACTOME_UPTAKE_OF_DIETARY_COBALAMINS_INTO_ENTEROCYTES WP_MIR222_IN_EXERCISEINDUCED_CARDIAC_GROWTH, HASLINGER_B_CLL_WITH_MUTATED_VH_GENES, IWANAGA_E2F1_TARGETS_NOT_INDUCED_BY_SERUM, MCCOLLUM_GELDANAMYCIN_RESISTANCE_DN, REACTOME_SIGNALING_BY_NOTCH1_T_7_9_NOTCH1_M1580_K2555_TRANSLOCATION_MUTANT, BIOCARTA_SALMONELLA_PATHWAY, RAFFEL_VEGFA_TARGETS_UP, REACTOME_ACYL_CHAIN_REMODELING_OF_DAG_AND_TAG. In order to explore the c2. All. V2023.1. Hs. Symbols. The GMT gene set in the data set GSE46171 disease is normal (AR/Control) group, the difference of the sample Gene Set Variation Analysis (GSVA) was then performed to analyze the expression of all genes in dataset GSE46171. According to the results of gene set variation analysis (GSVA), the pvalue was < 0.05 The differential expression of the top 10 and the bottom 10 pathways between allergic rhinitis (AR) and Control (Control) groups was analyzed by logFC sorting and visualized by heat map (Fig. 7 A) and group comparison map (Fig. 7 B) (Table 7 ). The results of gene set variation analysis (GSVA) showed that 15 pathways were statistically significant (pvalue < 0.05) in the sensitive rhinitis (AR) group and the Control (Control) group, respectively: REACTOME_CONJUGATION_OF_BENZOATE_WITH_GLYCINE, REACTOME_TYROSINE_CATABOLISM, WP_TYROSINE_METABOLISM_AND_RELATED_DISORDERS, REACTOME_PROPIONYL_COA_CATABOLISM, BIOCARTA_EEA1_PATHWAY, BYSTRYKH_HEMATOPOIESIS_STEM_CELL_FGF3, HOLLEMAN_DAUNORUBICIN_B_ALL_DN, DASU_IL6_SIGNALING_DN, REACTOME_ESTROGEN_BIOSYNTHESIS, REACTOME_DEFECTIVE_F9_ACTIVATION, REACTOME_RUNX3_REGULATES_WNT_SIGNALING, REACTOME_NECTIN_NECL_TRANS_HETERODIMERIZATION, TONKS_TARGETS_OF_RUNX1_RUNX1T1_FUSION_SUSTAINED_IN_MONOCYTE_DN, REACTOME_EPITHELIAL_MESENCHYMAL_TRANSITION_EMT_DURING_GASTRULATION, PASTURAL_RIZ1_TARGETS_DN. 2.5 Construct diagnostic model To determine the diagnostic value of five HRDEGs (CPT1C, CWF19L1, DDX52, MED17, MMP1) in dataset GSE51392, LASSO regression analysis was used to construct a diagnostic model for HRDEGs (Fig. 8 A). In addition, we also visualized the LASSO regression results to obtain the LASSO variable trajectory map (Fig. 8 B). According to the figure, the LASSO diagnostic model we constructed was composed of a total of 5 HRDEGs, which were CPT1C, CWF19L1, DDX52, MED17, and MMP1. At the same time, the SVM model was constructed based on 5 HRDEGs and SVM (Support Vector Machine) algorithm, and the number of genes with the highest accuracy (Fig. 8 C) and the lowest error rate (Fig. 8 D) was obtained. The results showed that when the number of genes was 4 (CPT1C, CWF19L1, MED17, MMP1), the accuracy of SVM model was the highest. We intersected the genes obtained by the two algorithms to obtain four intersected HRDEGs (CPT1C, CWF19L1, MED17, MMP1). The four HRDEGs (CPT1C, CWF19L1, MED17, MMP1) obtained by the intersection of LASSO and SVM algorithms were analyzed by logistic regression and the logistic regression model was constructed. Logistics regression analysis finally included 4 HRDEGs as key genes (CPT1C, CWF19L1, MED17, MMP1) for analysis. We sorted out the results of univariate logistic regression analysis and presented them in the form of forest map (Fig. 8 E). Then we conducted nomogram analysis to judge the diagnostic ability of the model and drew a nomogram of logistic predictive value to show the contribution of the four key genes to the diagnostic model (Fig. 8 F). The results showed that the expression level of MED17 had a higher utility for the diagnostic model than other key genes. The Calibration Curve plot of the diagnostic model (Fig. 8 G) showed that the calibration curve shown by the dotted line was slightly deviated from the diagonal line of the ideal model, but was close to consistent. We also used decision curve analysis (DCA) to evaluate the role of the diagnostic model in clinical utility and presented the results (Fig. 8 H). In the DCA figure, when the line of the model is higher than that of All positive and all negative in a certain range, the larger the range is, the greater the net benefit will be, and the better the performance of the model will be. The results show that (Fig. 8 G-H), the line of the model is more stable than all positive and all negative in a certain range, and the net income of the model is more, and the effect of the model is better. Finally, we performed functional similarity analysis on these four key genes (CPT1C, CWF19L1, DDX52, MED17, MMP1), and then visualized the functional similarity analysis results between the key genes through the cloud and rain diagram (Fig. 8 I). The results showed that among the four key genes, Among the four key genes, the functional similarity value between CWF19L1 and other key genes was the highest. 2.6 Diagnosis ROC We also analyzed the diagnostic value of logistic regression model linear predictors in dataset GSE51392, the receiver operating characteristic curve (ROC) of the logistic regression model linear predictors was drawn for the data set GSE51392 (Fig. 9 A). The ROC curve results showed that, the logistic linear predictors of the logistic regression model in GSE51392 had high diagnostic accuracy (AUC > 0.9). In addition, ROC curves were drawn for the four key genes (CPT1C, CWF19L1, MED17, MMP1) in dataset GSE51392 (Fig. 9 B-E). The results of ROC curve showed that the diagnostic effect of four key genes (CPT1C, CWF19L1, MED17, MMP1) had a certain degree of accuracy (AUC: 0.7–0.9). 2.7 GSEA enrichment analysis based on high and low risk scores of key genes Firstly, we divided the allergic rhinitis (AR) samples of GSE51392 and GSE46171 into High/Low risk groups according to the median predicted value of the logistic regression model (linear predictors). The formula for calculating the predictive value (linear predictors) score in the logistic regression model is as follows: Gene Set Enrichment Analysis (GSEA) was used to analyze the relationship between the expression of all genes and the biological processes, cellular components and molecular functions of the samples in the GSE51392High/Low group. Padj < 0.05 and FDR value (qvalue) < 0.05 were used as the screening criteria for significant enrichment. The results showed that the genes in the High/Low group samples of dataset GSE51392 were significantly enriched in REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING, and the genes in the high/low group were significantly enriched in reactome_interleukin_4_and_Interleukin_13_signaling. REACTOME_KERATAN_SULFATE_KERATIN_METABOLISM, WP_RETINOL_METABOLISM, REACTOME_DECTIN_2_FAMILY, WP_IL1_AND_MEGAKARYOCYTES_IN_OBESITY, REACTOME_INTERLEUKIN_10_SIGNALING, WP_VITAMIN_A_AND_CAROTENOID_METABOLISM pathway (Fig. 10 A-H, Table 4 ). 2.8 Gene function enrichment analysis (GO) and pathway enrichment analysis (KEGG) Gene ontology (GO) and pathway (KEGG) enrichment analysis were used to further explore the biological process (BP), cellular component (CC) of key genes (CPT1C, CWF19L1, MED17, MMP1), The relationship between molecular function (MF) and biological Pathway (Pathway) and allergic rhinitis (AR). The four key genes were used for gene ontology (GO) and pathway (KEGG) enrichment analysis, and the specific results are shown in Table 5 . The results showed that the four key genes were mainly enriched in cellular response to UV-A, carnitine metabolic process, response to UV-A, and carnitine metabolic process. amino-acid betaine metabolic process and other biological processes (BP); post-mRNA release spliceosomal complex, AMPA glutamate receptor complex, core mediator complex, ionotropic glutamate receptor complex, mediator complex, neurotransmitter receptor complex and other cellular components (CC); nuclear vitamin D receptor binding, nuclear thyroid hormone receptor binding, palmitoyltransferase activity, O-acyltransferase activity, nuclear receptor coactivator activity and other molecular functions (MF). At the same time, it was also enriched in PPAR signaling pathway and other biological pathways. The results of gene ontology (GO) and pathway (KEGG) enrichment analysis were visualized by bar diagram (Fig. 11 A) and bubble diagram (Fig. 11 B). At the same time, the network diagram of biological process (BP), cell component (CC), molecular function (MF) and biological Pathway (Pathway) was drawn according to gene ontology (GO) and pathway (KEGG) enrichment analysis (Fig. 11 C-F). The lines show the corresponding molecules and the annotations of the corresponding entries, and the larger the nodes, the more molecules the entries contain. Finally, the R package Pathview was used to visualize the pathway map related to the pathway (KEGG) enrichment analysis results (Fig. 12 ). 2.9 GSVA enrichment analysis based on high and low risk scores of key genes In order to explore the c2. All. V2023.1. Hs. Symbols. The GMT gene set in the data set GSE51392 of High and Low risk group (High/Low) group, the difference of the sample Gene Set Variation Analysis (GSVA) was performed on the expression of all genes in the AR samples of dataset GSE51392. According to the results of gene set variation analysis (GSVA), the pvalue was < 0.05 The differential expression of the top 10 and the bottom 10 pathways between the high-risk group (High) and the low-risk group (Low) was analyzed by logFC sorting and visualized by heat map (Fig. 13A) and group comparison map (Fig. 13B) (Table 8 ). The results of gene set variation analysis (GSVA) showed that 18 pathways were statistically significant (pvalue < 0.05) in the allergic rhinitis (AR) group and the Control (Control) group, respectively: WP_DDX1_AS_A_REGULATORY_COMPONENT_OF_THE_DROSHA_MICROPROCESSOR, TERAMOTO_OPN_TARGETS_CLUSTER_3, BIOCARTA_RAN_PATHWAY, REACTOME_TRNA_PROCESSING_IN_THE_MITOCHONDRION, REACTOME_SENSING_OF_DNA_DOUBLE_STRAND_BREAKS, SCIAN_CELL_CYCLE_TARGETS_OF_TP53_AND_TP73_UP, WP_HIF1A_AND_PPARG_REGULATION_OF_GLYCOLYSIS, WP_EXRNA_MECHANISM_OF_ACTION_AND_BIOGENESIS, REACTOME_TRAFFICKING_OF_MYRISTOYLATED_PROTEINS_TO_THE_CILIUM, PID_ARF6_DOWNSTREAM_PATHWAY, REACTOME_SARS_COV_2_TARGETS_PDZ_PROTEINS_IN_CELL_CELL_JUNCTION, BIOCARTA_CB1R_PATHWAY, KEGG_CIRCADIAN_RHYTHM_MAMMAL, REACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_TO_LIMIT_CHOLESTEROL_UPTAKE, WP_TRANSCRIPTIONAL_CASCADE_REGULATING_ADIPOGENESIS, CHOI_ATL_ACUTE_STAGE, WP_MIR222_IN_EXERCISEINDUCED_CARDIAC_GROWTH, REACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_LINKED_TO_TRIGLYCERIDE_LIPOLYSIS_IN_ADIPOSE. 2.10 Analysis of ssGSEA immune infiltration between High/Low risk groups in GSE51392 dataset To further explore the correlation between the expression levels of four key genes (GZMK, IFNB1, LY96, VPREB3) in AR samples in dataset GSE51392 and 28 immune cell infiltration grades in ssGSEA algorithm, Firstly, we used ssGSEA algorithm to calculate the infiltration abundance of 28 immune cells in the samples between the High and Low risk groups of the model in dataset GSE51392. Then we used pearson algorithm to analyze the correlation between the expression levels of four key genes in AR samples in dataset GSE51392 and the infiltration degree results of 28 immune cells. P < 0.05 was used as the standard for screening and the results were displayed by correlation dot plot (Fig. 14 A). The results showed that the expression of MMP1 in the 4 key genes in data set GSE51392 was mostly correlated with 28 immune cell infiltration grades. Then we selected two pairs of gene immune cell pairs with the highest correlation and two pairs with the lowest correlation and presented the results respectively by drawing the correlation scatter plot (Fig. 14 B-E). The two pairs of gene immune cell pairs with the highest positive correlation in GSE51392 dataset were shown by correlation scatter plot (Fig. 14 B, R = 0.769, P < 0.001). MMP1 and Natural killer cell (Fig. 14 C, R = 0.760, P < 0.001) and MMP1 and Immature dendritic cell (Fig. 14 D, R = -0.733, P < 0.001) had the highest negative correlation. P < 0.001). 14E, R = -0.788, P < 0.001). 2.11 CIBERSORT immune infiltration analysis between High and Low risk (High/Low) groups of GSE51392 dataset To explore the CIBERSORT immune infiltration analysis between the High and Low risk groups of the model GSE51392 dataset, We used the CIBERSORT algorithm to calculate the correlation between the expression profile data of 22 immune cells and High/Low groups for the samples of High/Low groups in dataset GSE51392. According to the results of immune infiltration analysis, 19 immune cells with abundance greater than or equal to 0 were selected: B cells naive, B cells memory, Plasma cells, T cells CD8, T cells CD4 naive, T cells CD4 memory resting, T cells follicular helper, T cells regulatory (Tregs), T cells gamma delta, NK cells resting, NK cells activated, Monocytes, Macrophages M0, Macrophages M1, Macrophages M2, Dendritic cells activated, Mast cells activated, Eosinophils, Neutrophils. We plotted the immune cell infiltration of these 19 immune cells in the dataset GSE51392AR sample data grouped by High/Low risk (Fig. 15 A) in the form of stacked bar graphs. Then we further calculated the correlation between the 19 immune cells with abundance greater than 0 in the low-risk group and the high-risk group samples and presented the results (Fig. 15 B-C). The results showed that in the low-risk group of dataset GSE51392, the number of positive and negative correlations between the infiltration abundance of the 19 immune cells was equal. Among them, the positive correlation between Dendritic cells activated and Eosinophils was the highest, and the negative correlation between B cells naive and Eosinophils was the highest (Fig. 15 B). In the high risk group of dataset GSE51392, the number of positive and negative correlations between infiltration abundance of 19 immune cells was comparable (Fig. 15 C), among which the positive correlation between immune cells Macrophages M1 and B cells naive was the highest. The highest negative correlation was found between T cells regulatory T cells (Tregs) and T cells follicular helper. Finally, we also calculated the correlation between the abundance of infiltration of these 19 immune cells and the expression levels of 4 genes (CPT1C, CWF19L1, MED17, MMP1) that were significantly different between the high and low risk groups of AR samples in dataset GSE51392. P < 0.05 was used as the standard for screening and the results were presented by correlation graph (Fig. 15 D-E). In data set GSE51392, the results of the low-risk group showed the abundance of six immune cell infiltration (Mast cells activated, T cells CD8, Plasma cells, Macrophages M1, T cells CD4 naive, T cells activated, T cells CD8, plasma cells). Macrophages M0) and the expression of 2 genes (CWF19L1, MMP1) were significantly correlated (P < 0.05). The results of the high-risk group showed that there was a significant correlation between the abundance of three immune cell infiltration (Eosinophils, Macrophages M0, T cells CD4 memory resting) and the expression of two genes (CWF19L1, MMP1) (P < 0.05). 2.12 Interaction network analysis of key genes We used the mRNA-miRNA data from the ENCORI database to predict mirnas that interacted with key genes (CPT1C, CWF19L1, MED17, MMP1) and retained only those interactions that had been documented in at least three databases. Then, Cytoscape software was used to draw the mRNA-miRNA interaction network for visualization (Fig. 16 A), and the red squares in the mRNA-miRNA interaction network were mrnas. Blue squares are mirnas. According to the mRNA-miRNA interaction network, our mRNA-miRNA interaction network is composed of 3 Key genes (CWF19L1, MED17, MMP1) and 50 miRNA molecules, which constitute a total of 50 mRNA-miRNA interaction relationships. The specific mRNA-miRNA interaction relationships are shown in Supplementary Table S2 . Transcription factors (TFS) control gene expression by interacting with target genes (mrnas) at the post-transcriptional stage. We searched for Transcription factors (TFS) that bind to key genes through CHIPBase database and hTFtarget database. The interactions found in the two databases were downloaded and intercrossed with four key genes. Then Cytoscape software was used to draw the mRNA-TF interaction network for visualization (Fig. 16 B). The red squares in the mRNA-TF interaction network were mrnas. Blue circles are mirnas. Finally, 4 key genes (CPT1C, CWF19L1, MED17, MMP1) and 41 transcription factors (TFS) were obtained to form a total of 57 mRNA-TF interaction relationships. The interaction relationships are shown in the Supplementary Table (Table S3 ). We used the CTD database to predict potential drugs or small molecule compounds that interacted with key genes (CPT1C, CWF19L1, MED17, MMP1), and used "Reference Count" > 1 as the screening criterion to screen mRNA-drugs interaction pairs. Cytoscape software was used to visualize the mRNA-drugs interaction network (Fig. 16 C). The red squares in the mRNA-drugs interaction network are mrnas. Blue triangles are drugs. According to the mRNA-drugs interaction network, our mRNA-drugs interaction network is composed of 3 mrnas (CPT1C, MED17, MMP1) and 36 drugs molecules, which constitute a total of 36 mRNA-drugs interaction relationships. The mRNA-drugs interaction relationship is shown in Supplementary Table S4 . 2.13 Spatial protein structure of key genes AlphaFold Protein Structure Database Database ( https://www.alphafold.ebi.ac.uk/ ) contains AlphaFold ai system about 350000 Protein Structure prediction, It covers humans as well as 20 model organisms commonly used in biological research (E. coli, Drosophila, zebrafish, mouse...).. In terms of the human proteome, AlphaFold made predictions about the structure of 98.5 percent of human proteins. The protein results of four key genes (CPT1C, CWF19L1, MED17, MMP1) were analyzed and displayed using the AlphaFold website (Fig. 17 A-D). Discussion Allergic Rhinitis (AR) is a common inflammatory disorder of the nasal mucosa, characterized by symptoms such as sneezing, nasal itching, congestion, and rhinorrhea. It is triggered by allergens like pollen, dust mites, and animal dander. AR not only affects the quality of life of patients but also poses a significant economic burden due to its high prevalence and associated medical costs. The pathogenesis of AR is multifaceted, with genetic predisposition, environmental factors, and immune dysregulation playing crucial roles [ 31 ]. Among these, the role of hypoxia in the progression of AR has recently garnered attention, prompting us to delve deeper into its association. Our study embarked on a comprehensive research to elucidate the hypoxia-related genes and mechanisms associated with AR. In our study investigating the intricate relationship between allergic rhinitis (AR) and hypoxia-related genes, we took a comprehensive approach to improve result accuracy. One of our key strategies involved identifying differential genes associated with AR, specifically focusing on genes with known relevance to asthma. The rationale behind this approach lies in the shared immunological aspects between AR and asthma, where both conditions involve complex immune responses in the upper and lower respiratory tract. Our analysis led to the identification of several crucial genes, namely CPT1C, CWF19L1, MED17, and MMP1, which exhibited significantly higher expression levels in the early stages of AR compared to established disease states. This observation highlights the importance of these genes in the initial phases of the disease, potentially contributing to the onset and progression of AR. Previous studies have indicated the involvement of CPT1C in fatty acid metabolism and energy production within cells [ 32 ]. Dysregulation of these processes can lead to cellular stress and inflammation, which are common features in allergic conditions. CWF19L1 has been linked to the regulation of immune responses and inflammatory pathways. Its elevated expression in the early stages of AR suggests a potential role in the initiation of allergic reactions. MED17 is part of the mediator complex involved in gene transcription. Dysregulation of this complex can impact the expression of genes related to immune responses, potentially influencing the early phases of AR [ 33 ]. MMP1 is known for its role in tissue remodeling and extracellular matrix degradation. Elevated MMP1 expression in the early stages of AR might contribute to tissue changes in the nasal mucosa characteristic of allergic inflammation [ 34 ]. It is noteworthy that these genes, including CPT1C, CWF19L1, MED17, and MMP1, have been previously identified as having important roles in various diseases. While their specific functions in AR have not been fully understood before our study, their significance in other contexts underscores their potential relevance in AR pathogenesis. For instance, CPT1C has been associated with metabolic disorders and neurological diseases, emphasizing its versatile role in cellular functions. CWF19L1 has been linked to immune-related processes in autoimmune diseases, indicating its broader involvement in immune regulation. MED17's importance in gene transcription regulation has been demonstrated in cancer and developmental disorders. MMP1, as a member of the matrix metalloproteinase family, has been extensively studied in tissue remodeling processes in cancer and inflammatory diseases. The finding that these genes are upregulated in early AR stages aligns with prior research suggesting their importance in immune responses and allergic diseases. However, their specific roles in AR's pathogenesis, especially in the early phases, remain incompletely understood. Our study extends this understanding and highlights their potential significance as early diagnostic markers or therapeutic targets. Our foremost discovery centers on specific genes, particularly CWF19L1 and MMP1, which exhibited pronounced correlations with immune cell infiltration in both low-risk and high-risk groups of AR patients. These findings underscore the complex interplay between the immune microenvironment within the nasal mucosa and hypoxia-related genes. This observation raises intriguing questions about the potential contributions of gene dysregulation to the immunopathology of AR. The identification of these correlations highlights the pivotal roles played by hypoxia-related genes, such as CWF19L1 and MMP1, in shaping the immunological landscape of AR. Our enrichment analyses provided a deeper understanding of the biological implications of our HRDEGs. The significant involvement of our HRDEGs in processes like cellular response to UV-A, carnitine metabolic process, and pathways like PPAR signaling pathway suggests a multifaceted role of these genes in AR11. Their involvement in various biological processes and pathways underscores their potential diagnostic and therapeutic value. The differential expression patterns of HRDEGs in various phenotypically related samples, as revealed by GSEA and GSVA, further emphasize their significance in AR's pathogenesis. Furthermore, our study has unveiled distinct immune cell subtypes that display significant associations with the expression of hypoxia-related genes. This revelation points to the substantial influence exerted by these genes on the immune microenvironment within the nasal mucosa during AR. This phenomenon deepens our understanding of how genes like CWF19L1 and MMP1 may impact immune responses and inflammation in AR. The interplay between hypoxia-related pathways and the immune system, as evidenced in our study, could contribute to the exacerbation of AR symptoms and the progression of the disease. Additionally, our research underscores the importance of patient stratification in the context of AR. We have observed distinct correlations in immune cell infiltration and gene expression patterns between low-risk and high-risk groups of AR patients. This suggests that different subgroups of AR patients may exhibit unique immunological profiles. Therefore, personalized approaches to diagnosis and treatment may be necessary to address the diverse needs of AR patients effectively. Our analysis heavily relies on publicly available gene expression datasets, specifically GSE51392 and GSE46171. While these datasets provide a wealth of information, they are not without limitations. Variations in sample size, experimental conditions, and data preprocessing may introduce biases or confounding factors that could impact our results. While our study highlights the potential roles of CPT1C, CWF19L1, MED17, and MMP1 in AR, functional validation experiments are required to confirm their specific contributions to disease pathogenesis. Conclusion Our study sheds light on the molecular intricacies of AR, providing robust evidence for the role of CPT1C, CWF19L1, MED17, and MMP1 in its pathogenesis. The potential diagnostic and prognostic applications of these findings could revolutionize AR management, moving towards more personalized and effective therapeutic strategies. Further studies are necessary to validate these biomarkers in larger cohorts and diverse populations, and to explore their therapeutic implications in AR and beyond. Abbreviations allergic rhinitis (AR),hypoxia-related differential expression genes (HRDEGs),chronic obstructive pulmonary disease (COPD),Gene Set Enrichment Analysis (GSEA),Hypoxia related genes (HRGs),Benjamini-Hochberg (BH),Gene Set Variation Analysis (GSVA),Support Vector Machine (SVM),Decision curve analysis (DCA),Gene Ontology (GO),biological process (BP), ,molecular function (MF) ,cellular component (CC).,Kyoto Encyclopedia of Genes and Genomes (KEGG),Receiver operating characteristic curve (ROC),Single-sample gene set enrichment analysis (ssGSEA),Transcription factors (TF),differentially expressed genes (DEGs), Declarations Acknowledgements Not applicable Authors' contributions Guanhui Huang and Shiyun Shao designed the research study. Shiyun Shao analyzed and interpreted data. Shiyun Shao and Xiao Feng wrote the manuscript. All authors have read and approved the final manuscript. Funding Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Declarations Not applicable Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Data availability statement Data are available in a public, open access repository, Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as supplemental information, The datasets (GE0 data) and (TCGA LIHC data) for this study can be found in the GE0 (https://www.ncbi.nlm.nih.gov/) and TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga). Ethical approval This study does not contain any studies with human participants or animals performed by any of the authors. References Greiner, A. N., Hellings, P.W., Rotiroti, G. & Scadding, G. K. Allergic rhinitis. Lancet 378, 21 12–2122 (2011). Bousquet, J. et al. Allergic Rhinitis and its Impact on Asthma (ARIA) 2008 update. Allergy 63 (Suppl. 86), 8–160 (2008). Rondón C, Campo P, Togias A, Fokkens WJ, Durham SR, Powe DG, et al. Local allergic rhinitis: concept, pathophysiology, and management. J Allergy Clin Immunol. (2012) 129:1460–7. Riechelmann H, Deutschle T, Rozsasi A, Keck T, Polzehl D, Burner H. Nasal biomarker profiles in acute and chronic rhinosinusitis. 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Nur Husna SM, Tan H-TT, Md Shukri N, Mohd Ashari NS, Wong KK. Allergic rhinitis: a clinical and pathophysiological overview. Front Med. (2022) 9:940. 10.3389/fmed.2022.874114 Casas M, Fadó R, Domínguez JL, Roig A, Kaku M, Chohnan S, et al. Sensing of nutrients by CPT1C controls SAC1 activity to regulate AMPA receptor trafficking. J Cell Biol. 2020;219. Agostini A, Marchetti D, Izzi C, Cocco I, Pinelli L, Accorsi P, Iascone Maria R, Giordano L (2018) Expanding the phenotype of MED 17 mutations: description of two new cases and review of the literature. Am J Med Genet B Neuropsychiatr Genet 177(8):687–690 Trivedi V, Boire A, Tchernychev B, Kaneider NC, Leger AJ, O'Callaghan K, Covic L, Kuliopulos A (2009) Platelet matrix metalloprotease‐1 mediates thrombogenesis by activating PAR1 at a cryptic ligand site. Cell 137:332‐343 Tables Table 1 GSE46171 Dataset and GSE51392 Dataset information list. GSE51392 GSE46171 Platform GPL4133 GPL16981 Species Homo sapiens Homo sapiens Tissue primary nasal and bronchial epithelial cells Nasal mucosa Samples in AR group 20 5 Samples in Control group 24 14 Reference [ 3 ] [ 4 ] AR, Allergic rhinitis. Table 2 GSEA enrichment analysis GSE51392 Dataset (AR/Control). ID setSize enrichmentScore NES pvalue Padj qvalue WP_INFLAMMATORY_RESPONSE_PATHWAY 30 0.666560056 1.952780071 0.000455789 0.005956374 0.004590169 REACTOME_ASSEMBLY_OF_COLLAGEN_FIBRILS_AND_OTHER_MULTIMERIC_STRUCTURES 58 0.587477157 1.951917827 0.000484027 0.005956374 0.004590169 REACTOME_MET_PROMOTES_CELL_MOTILITY 41 0.598192736 1.852394168 0.001421801 0.012378737 0.009539442 WP_CANONICAL_AND_NONCANONICAL_NOTCH_SIGNALING 27 0.623219847 1.783101052 0.005447118 0.03463308 0.026689336 WP_VITAMIN_A_AND_CAROTENOID_METABOLISM 41 0.568928216 1.761772161 0.003791469 0.02632847 0.020289544 WP_EPITHELIAL_TO_MESENCHYMAL_TRANSITION_IN_COLORECTAL_CANCER 155 0.445312168 1.728936154 0.000519481 0.006254277 0.004819742 REACTOME_TRIGLYCERIDE_METABOLISM 35 0.568071148 1.716408199 0.006993007 0.042609351 0.032836101 REACTOME_CIRCADIAN_CLOCK 67 0.493525859 1.687625261 0.004341534 0.029013585 0.022358777 WP_FOCAL_ADHESION_PI3KAKTMTORSIGNALING_PATHWAY 300 0.387154464 1.626321882 0.000583771 0.006781566 0.005226087 REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING 107 0.436384315 1.613352282 0.00297619 0.021825397 0.016819334 KEGG_TGF_BETA_SIGNALING_PATHWAY 83 0.43445153 1.543296064 0.008099095 0.047938928 0.036943239 WP_PI3KAKT_SIGNALING_PATHWAY 330 0.326276774 1.386288178 0.005973716 0.037219692 0.028682661 REACTOME_REGULATION_OF_TP53_ACTIVITY_THROUGH_PHOSPHORYLATION 89 0.446033194 1.537415637 0.00685401 0.041865032 0.032262505 REACTOME_SIGNALING_BY_HEDGEHOG 144 0.418636859 1.541565111 0.00195122 0.015749129 0.012136772 REACTOME_FC_EPSILON_RECEPTOR_FCERI_SIGNALING 135 0.43930619 1.60613929 0.001624959 0.013881955 0.01069787 REACTOME_TRANSCRIPTIONAL_REGULATION_BY_TP53 326 0.407200723 1.640555217 0.00030248 0.005029273 0.003875716 REACTOME_GLUCOSE_METABOLISM 86 0.480216954 1.645489893 0.002737851 0.020439331 0.015751188 WP_ONECARBON_METABOLISM 29 0.586934892 1.646161759 0.008208423 0.048407607 0.037304418 REACTOME_SIGNALING_BY_NOTCH4 81 0.500632652 1.699183736 0.001371742 0.012355619 0.009521627 REACTOME_HEDGEHOG_ON_STATE 83 0.502260096 1.708996286 0.001033414 0.010013803 0.00771695 REACTOME_BUTYRATE_RESPONSE_FACTOR_1_BRF1_BINDS_AND_DESTABILIZES_MRNA 17 0.685664368 1.726907669 0.006783292 0.041535131 0.032008273 WP_BIOMARKERS_FOR_PYRIMIDINE_METABOLISM_DISORDERS 14 0.717041282 1.728327532 0.005673759 0.035890495 0.02765834 WP_DISORDERS_OF_FOLATE_METABOLISM_AND_TRANSPORT 13 0.734216043 1.730813502 0.004270463 0.028748553 0.022154536 REACTOME_FATTY_ACIDS 15 0.708400424 1.731393325 0.005761613 0.036292192 0.0279679 WP_OXIDATIVE_PHOSPHORYLATION 36 0.587460002 1.732428583 0.004889976 0.031990735 0.024653062 REACTOME_KSRP_KHSRP_BINDS_AND_DESTABILIZES_MRNA 17 0.693863229 1.747557241 0.004284184 0.028748553 0.022154536 WP_PATHWAYS_OF_NUCLEIC_ACID_METABOLISM_AND_INNATE_IMMUNE_SENSING 14 0.725987912 1.74989213 0.003546099 0.025124587 0.019361794 REACTOME_CELLULAR_RESPONSE_TO_HYPOXIA 70 0.536062954 1.781896873 0.001028807 0.010013803 0.00771695 REACTOME_HEDGEHOG_OFF_STATE 108 0.506388474 1.794839279 0.000335683 0.005029273 0.003875716 GSEA: Gene Set Enrichment Analysis. AR, Allergic rhinitis. Table 3 GSEA enrichment analysis GSE46171 Dataset (AR/Control). ID setSize enrichmentScore NES pvalue Padj qvalue BIOCARTA_IL2_PATHWAY 22 0.669896141 1.781086615 0.003517749 0.049228718 0.042271231 BIOCARTA_IL2RB_PATHWAY 37 0.609653299 1.801248514 0.002707581 0.04163324 0.035749221 KEGG_DRUG_METABOLISM_CYTOCHROME_P450 65 0.699887884 2.607747568 0.00070373 0.017019329 0.01461399 KEGG_GLUTATHIONE_METABOLISM 50 0.553174462 1.972029165 0.00128866 0.024135726 0.020724628 KEGG_JAK_STAT_SIGNALING_PATHWAY 146 0.449220608 1.640282917 0.000750939 0.017482133 0.015011386 KEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 62 0.522294365 1.934112871 0.000693481 0.016935901 0.014542352 KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY 125 0.532479724 1.90815374 0.000254972 0.00973294 0.008357385 KEGG_PROPANOATE_METABOLISM 31 0.639132652 2.051845741 0.000581734 0.0151576 0.013015379 KEGG_RIBOFLAVIN_METABOLISM 14 0.750882424 1.814795755 0.002314815 0.037960657 0.032595683 NABA_ECM_AFFILIATED 151 0.435177866 1.599109185 0.000993295 0.020968631 0.018005138 PID_IL12_2PATHWAY 62 0.647919946 2.093091347 0.000280899 0.00973294 0.008357385 PID_IL12_STAT4_PATHWAY 32 0.667486075 1.928667211 0.00060241 0.0151576 0.013015379 PID_IL2_1PATHWAY 55 0.57521809 1.823282845 0.000570125 0.0151576 0.013015379 PID_IL23_PATHWAY 37 0.619323753 1.82982031 0.001504212 0.027152113 0.023314709 REACTOME_COSTIMULATION_BY_THE_CD28_FAMILY 69 0.643039636 2.11494122 0.000277316 0.00973294 0.008357385 REACTOME_DECTIN_2_FAMILY 24 0.674026137 1.824802607 0.002857143 0.043134199 0.03703805 REACTOME_INTERLEUKIN_10_SIGNALING 44 0.764336594 2.334262803 0.000292141 0.00973294 0.008357385 REACTOME_INTERLEUKIN_2_FAMILY_SIGNALING 44 0.617873212 1.886967688 0.000584283 0.0151576 0.013015379 REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING 107 0.519509497 1.82583856 0.000261028 0.00973294 0.008357385 REACTOME_NEUTROPHIL_DEGRANULATION 453 0.362463986 1.454386695 0.0004302 0.012285945 0.010549575 REACTOME_SIGNALING_BY_INTERLEUKINS 441 0.46620367 1.868743165 0.000215332 0.00973294 0.008357385 WP_FOLATE_METABOLISM 68 0.581901484 1.909247333 0.000556019 0.0151576 0.013015379 WP_IL18_SIGNALING_PATHWAY 263 0.447838977 1.729202463 0.000230574 0.00973294 0.008357385 WP_IL2_SIGNALING_PATHWAY 42 0.58043378 1.757547639 0.003513909 0.049228718 0.042271231 WP_IL3_SIGNALING_PATHWAY 48 0.583495125 1.806188014 0.001158413 0.022543802 0.01935769 WP_IL7_SIGNALING_PATHWAY 25 0.654055801 1.787985774 0.003146633 0.046656328 0.040062396 WP_INTERACTIONS_BETWEEN_IMMUNE_CELLS_AND_MICRORNAS_ IN_TUMOR_MICROENVIRONMENT 28 0.758784196 2.120293275 0.000311818 0.00973294 0.008357385 WP_INTERACTIONS_OF_NATURAL_KILLER_CELLS_IN_PANCREATIC_CANCER 26 0.718894349 1.976938159 0.000313775 0.00973294 0.008357385 GSEA: Gene Set Enrichment Analysis. AR, Allergic rhinitis. Table 4 GSEA enrichment analysis GSE51392 Dataset (High/Low). ID setSize enrichmentScore NES pvalue Padj qvalue WP_VITAMIN_A_AND_CAROTENOID_METABOLISM 41 0.65834 1.91785 0.000367 0.007779 0.006057 REACTOME_INTERLEUKIN_10_SIGNALING 42 0.65233 1.913143 0.000365 0.007779 0.006057 WP_IL1_AND_MEGAKARYOCYTES_IN_OBESITY 24 0.694568 1.819214 0.002625 0.024347 0.018958 REACTOME_DECTIN_2_FAMILY 24 0.67506 1.768117 0.005249 0.039743 0.030947 WP_RETINOL_METABOLISM 14 0.759148 1.766216 0.005124 0.039196 0.030521 REACTOME_KERATAN_SULFATE_KERATIN_METABOLISM 33 0.630227 1.763419 0.002968 0.026722 0.020808 REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING 107 0.508895 1.745545 0.000672 0.009142 0.007118 REACTOME_INTERLEUKIN_17_SIGNALING 70 0.540842 1.736866 0.001405 0.016321 0.012709 WP_NOVEL_INTRACELLULAR_COMPONENTS_OF_RIGILIKE_RECEPTOR_PATHWAY 57 0.557471 1.733715 0.002481 0.023447 0.018258 WP_INTERLEUKIN1_IL1_STRUCTURAL_PATHWAY 49 0.572533 1.728509 0.002172 0.021749 0.016935 REACTOME_SIGNALING_BY_INTERLEUKINS 435 0.429654 1.720506 0.000295 0.007779 0.006057 WP_IL6_SIGNALING_PATHWAY 43 0.575937 1.700628 0.005111 0.039196 0.030521 PID_TGFBR_PATHWAY 53 0.551155 1.688757 0.005348 0.040163 0.031274 KEGG_EPITHELIAL_CELL_SIGNALING_IN_HELICOBACTER_PYLORI_INFECTION 67 0.514999 1.644888 0.004569 0.036293 0.02826 WP_OVERVIEW_OF_PROINFLAMMATORY_AND_PROFIBROTIC_MEDIATORS 108 0.476856 1.636307 0.002366 0.022679 0.01766 REACTOME_NEGATIVE_REGULATION_OF_THE_PI3K_AKT_NETWORK 112 0.465466 1.601566 0.003375 0.029132 0.022685 REACTOME_NEUTROPHIL_DEGRANULATION 448 0.380399 1.525489 0.000294 0.007779 0.006057 WP_IL18_SIGNALING_PATHWAY 257 0.398587 1.518449 0.001232 0.015008 0.011686 REACTOME_METABOLISM_OF_AMINO_ACIDS_AND_DERIVATIVES 345 0.32572 1.35498 0.005245 0.039743 0.030947 REACTOME_HEDGEHOG_OFF_STATE 108 0.4189 1.5148 0.004895 0.037908 0.029518 REACTOME_METABOLISM_OF_VITAMINS_AND_COFACTORS 174 0.39365 1.52304 0.00155 0.017275 0.013451 REACTOME_METABOLISM_OF_NUCLEOTIDES 84 0.44655 1.55677 0.007163 0.049882 0.038842 REACTOME_HEDGEHOG_ON_STATE 83 0.4583 1.59646 0.005218 0.039743 0.030947 REACTOME_REGULATION_OF_TP53_ACTIVITY 152 0.42386 1.60867 0.001002 0.012387 0.009645 KEGG_PYRIMIDINE_METABOLISM 91 0.46526 1.64485 0.002422 0.022986 0.017899 KEGG_GLUTATHIONE_METABOLISM 47 0.52997 1.67299 0.005755 0.041794 0.032544 REACTOME_GLYCOLYSIS 67 0.50068 1.68661 0.002318 0.022504 0.017524 REACTOME_METABOLISM_OF_FAT_SOLUBLE_VITAMINS 47 0.53539 1.69008 0.003984 0.032796 0.025537 GSEA: Gene Set Enrichment Analysis. AR, Allergic rhinitis. Table 5 GOKEGG enrichment analysis results of key genes. ONTOLOGY ID Description GeneRatio BgRatio Padj qvalue GO:0071492 cellular response to UV-A 2023/1/4 11/18800 0.042445771 0.01806203 GO:0009437 carnitine metabolic process 2023/1/4 13/18800 0.042445771 0.01806203 GO:0070141 response to UV-A 2023/1/4 14/18800 0.042445771 0.01806203 GO:0006577 amino-acid betaine metabolic process 2023/1/4 17/18800 0.042445771 0.01806203 GO:0071014 post-mRNA release spliceosomal complex 2023/1/4 12/19594 0.033570481 0.012849945 GO:0032281 AMPA glutamate receptor complex 2023/1/4 26/19594 0.033570481 0.012849945 GO:0070847 core mediator complex 2023/1/4 26/19594 0.033570481 0.012849945 GO:0008328 ionotropic glutamate receptor complex 2023/1/4 40/19594 0.033570481 0.012849945 GO:0016592 mediator complex 2023/1/4 40/19594 0.033570481 0.012849945 GO:0098878 neurotransmitter receptor complex 2023/1/4 45/19594 0.033570481 0.012849945 GO:0042809 nuclear vitamin D receptor binding 2023/1/4 15/18410 0.043606365 0.015300479 GO:0046966 nuclear thyroid hormone receptor binding 2023/1/4 29/18410 0.043606365 0.015300479 GO:0016409 palmitoyltransferase activity 2023/1/4 37/18410 0.043606365 0.015300479 GO:0008374 O-acyltransferase activity 2023/1/4 53/18410 0.043606365 0.015300479 GO:0030374 nuclear receptor coactivator activity 2023/1/4 56/18410 0.043606365 0.015300479 hsa03320 PPAR signaling pathway 2023/2/3 75/8164 0.00372525 0.001045684 GO: Gene Ontology; BP: biological process; CC: cellular component; MF: molecular function. KEGG, Kyoto Encyclopedia of Genes and Genomes; HRDEGs: Hypoxia related differentially expressed genes. Table 6 GSVA enrichment analysis GSE51392 Dataset (AR/Control). Description logFC AveExpr pvalue Padj B BIOCARTA_RAN_PATHWAY 0.525135498 0.060408483 0.000369704 0.102567442 0.065399945 REACTOME_GDP_FUCOSE_BIOSYNTHESIS 0.494935488 0.052760525 0.001843162 0.144382393 1.296180124 REACTOME_HYALURONAN_BIOSYNTHESIS_AND_EXPORT 0.48076637 0.017851579 0.000484021 0.103370692 0.163859188 REACTOME_VITAMIN_B1_THIAMIN_METABOLISM 0.440463907 0.040010092 0.000523276 0.104095716 0.230160452 REACTOME_SCAVENGING_BY_CLASS_F_RECEPTORS 0.438245156 0.041222039 0.001853124 0.144382393 1.300721362 WP_HIF1A_AND_PPARG_REGULATION_OF_GLYCOLYSIS 0.430002944 0.059643458 0.002730179 0.16926399 1.626519519 REACTOME_METALLOTHIONEINS_BIND_METALS 0.425443321 0.004987262 0.002192415 0.14707766 1.442242878 KRISHNAN_FURIN_TARGETS_UP 0.422525194 0.018526141 0.000241664 0.102567442 0.427693778 BIOCARTA_PROTEASOME_PATHWAY 0.413234007 0.010243535 0.000436415 0.102567442 0.075795113 REACTOME_SYNTHESIS_OF_DOLICHYL_PHOSPHATE 0.409841535 0.017384575 0.000787373 0.108791455 0.577092001 REACTOME_ACTIVATED_NTRK2_SIGNALS_THROUGH_FYN 0.322204621 0.018684376 0.00835996 0.284238642 2.558347055 REACTOME_UPTAKE_OF_DIETARY_COBALAMINS_INTO_ENTEROCYTES 0.328707605 0.018156917 0.005884076 0.255041706 2.267635169 WP_MIR222_IN_EXERCISEINDUCED_CARDIAC_GROWTH 0.34017406 0.033472873 0.025134585 0.407042385 3.454921363 HASLINGER_B_CLL_WITH_MUTATED_VH_GENES 0.346572259 0.033810457 0.000175691 0.102567442 0.699718012 IWANAGA_E2F1_TARGETS_NOT_INDUCED_BY_SERUM 0.346988782 0.037419643 0.003113511 0.178930462 1.736672117 MCCOLLUM_GELDANAMYCIN_RESISTANCE_DN 0.363548547 0.017672553 0.003181744 0.181247788 1.754830864 REACTOME_SIGNALING_BY_NOTCH1_T_7_9_NOTCH1_M1580_K2555_TRANSLOCATION_MUTANT 0.384539971 0.041012383 0.002762457 0.16926399 1.636380584 BIOCARTA_SALMONELLA_PATHWAY 0.387108633 0.000644908 0.001795885 0.144382393 1.274286206 RAFFEL_VEGFA_TARGETS_UP 0.398727993 0.000480108 0.000116088 0.102567442 1.05362427 REACTOME_ACYL_CHAIN_REMODELING_OF_DAG_AND_TAG 0.417025868 0.054218414 0.002355923 0.154539005 1.502714701 GSVA, Gene Set Variation Analysis. AR, Allergic rhinitis. Table 7 GSVA enrichment analysis GSE46171 Dataset (AR/Control). Description logFC AveExpr pvalue Padj B REACTOME_CONJUGATION_OF_BENZOATE_WITH_GLYCINE 0.595565102 0.008578752 0.00870236 0.999613633 3.843513513 REACTOME_TYROSINE_CATABOLISM 0.589010196 0.000586248 0.003634967 0.999613633 3.65611297 WP_TYROSINE_METABOLISM_AND_RELATED_DISORDERS 0.589010196 0.000586248 0.003634967 0.999613633 3.65611297 REACTOME_PROPIONYL_COA_CATABOLISM 0.579674819 0.015489779 0.006191191 0.999613633 3.769919353 WP_VITAMIN_B6DEPENDENT_AND_RESPONSIVE_DISORDERS 0.572600547 0.001174523 0.008510005 0.999613633 3.838664068 BIOCARTA_EEA1_PATHWAY 0.560448638 0.06113302 0.013010775 0.999613633 3.931152954 WP_MITOCHONDRIAL_FATTY_ACID_SYNTHESIS_PATHWAY 0.552447976 0.040223951 0.017189948 0.999613633 3.992212062 BYSTRYKH_HEMATOPOIESIS_STEM_CELL_FGF3 0.544340166 0.009628858 0.004481797 0.999613633 3.700666183 HOLLEMAN_DAUNORUBICIN_B_ALL_DN 0.538029479 0.001067863 0.002643783 0.999613633 3.588916666 REACTOME_ELECTRIC_TRANSMISSION_ACROSS_GAP_JUNCTIONS 0.53589791 0.004473138 0.007343354 0.999613633 3.80673564 DASU_IL6_SIGNALING_DN 0.596624569 0.05516285 0.003763735 0.999613633 3.663499896 REACTOME_ESTROGEN_BIOSYNTHESIS 0.605676089 0.003460789 0.002547141 0.999613633 3.581101298 TERAO_AOX4_TARGETS_HG_DN 0.623790464 0.009049224 0.005846149 0.999613633 3.757584629 REACTOME_DEFECTIVE_F9_ACTIVATION 0.624236148 0.011580347 0.001843722 0.999613633 3.513676435 REACTOME_MELANIN_BIOSYNTHESIS 0.667760074 0.03889754 0.00737566 0.999613633 3.807684598 REACTOME_RUNX3_REGULATES_WNT_SIGNALING 0.668552147 0.051733539 0.001046724 0.999613633 3.39737633 REACTOME_NECTIN_NECL_TRANS_HETERODIMERIZATION 0.694189583 0.004087266 0.001471802 0.999613633 3.467111067 TONKS_TARGETS_OF_RUNX1_RUNX1T1_FUSION_SUSTAINED_IN_MONOCYTE_DN 0.707953147 0.010196513 0.000278063 0.999613633 3.134668042 REACTOME_EPITHELIAL_MESENCHYMAL_TRANSITION_EMT_DURING_GASTRULATION 0.717933346 0.020688586 0.000730368 0.999613633 3.324695139 PASTURAL_RIZ1_TARGETS_DN 0.780084907 0.008469229 0.000453448 0.999613633 3.229972844 GSVA, Gene Set Variation Analysis. AR, Allergic rhinitis. Table 8 GSVA enrichment analysis GSE51392 Dataset (High/Low). Description logFC AveExpr pvalue Padj B WP_DDX1_AS_A_REGULATORY_COMPONENT_OF_THE_DROSHA_MICROPROCESSOR 0.795388063 0.007994751 3.60 e-05 0.026000459 2.213482441 TERAMOTO_OPN_TARGETS_CLUSTER_3 0.787785391 0.02590135 2.35 e-05 0.02551622 2.588641155 BIOCARTA_RAN_PATHWAY 0.72397089 0.053694304 6.62 e-05 0.035827387 1.680489297 REACTOME_TRNA_PROCESSING_IN_THE_MITOCHONDRION 0.713694676 0.04679351 0.005048406 0.144285396 2.112889359 REACTOME_SENSING_OF_DNA_DOUBLE_STRAND_BREAKS 0.684455895 0.035375588 0.000123689 0.045838432 1.131984604 REACTOME_SYNTHESIS_OF_WYBUTOSINE_AT_G37_OF_TRNA_PHE 0.683242921 0.02622534 0.005236533 0.145948682 2.144507668 SCIAN_CELL_CYCLE_TARGETS_OF_TP53_AND_TP73_UP 0.661655652 0.002683472 2.75 e-05 0.02551622 2.449821566 WP_HIF1A_AND_PPARG_REGULATION_OF_GLYCOLYSIS 0.637320249 0.006749236 0.000475165 0.093506699 0.050019304 WP_EXRNA_MECHANISM_OF_ACTION_AND_BIOGENESIS 0.607471091 0.065281905 0.004912566 0.143059196 2.089307701 REACTOME_TRAFFICKING_OF_MYRISTOYLATED_PROTEINS_TO_THE_CILIUM 0.603478448 0.039468854 0.000674943 0.102781069 0.357977658 MARIADASON_RESPONSE_TO_BUTYRATE_CURCUMIN_SULINDAC_TSA_2 0.597928931 0.052527585 0.004910055 0.143059196 2.088865684 PID_ARF6_DOWNSTREAM_PATHWAY 0.600107433 0.028323103 3.24 e-05 0.026000459 2.307341622 REACTOME_SARS_COV_2_TARGETS_PDZ_PROTEINS_IN_CELL_CELL_JUNCTION 0.602187996 0.071782598 0.001898367 0.106897356 1.263001767 BIOCARTA_CB1R_PATHWAY 0.605786256 0.009720656 0.001955004 0.106897356 1.288653764 KEGG_CIRCADIAN_RHYTHM_MAMMAL 0.647403084 0.064496609 0.000113337 0.045838432 1.208731102 REACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_ TO_LIMIT_CHOLESTEROL_UPTAKE 0.655877067 0.036726028 0.000413866 0.08958829 0.071236732 WP_TRANSCRIPTIONAL_CASCADE_REGULATING_ADIPOGENESIS 0.672488886 0.051226134 7.30 e-05 0.036455996 1.595029512 CHOI_ATL_ACUTE_STAGE 0.677490069 0.001964758 0.000657202 0.102781069 0.334614683 WP_MIR222_IN_EXERCISEINDUCED_CARDIAC_GROWTH 0.822295138 0.070516027 4.70 e-05 0.030543939 1.980164233 REACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_LINKED_ TO_TRIGLYCERIDE_LIPOLYSIS_IN_ADIPOSE 0.83270275 0.038784008 8.00 e-06 0.02551622 3.526886046 GSVA, Gene Set Variation Analysis. AR, Allergic rhinitis. Additional Declarations No competing interests reported. Supplementary Files FigureS1boxplotbeforeGSE51392.pdf figureS2boxplotbeforeGSE461711.pdf TableS1.xlsx TableS2.docx TableS3.docx TableS4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4096488","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286184660,"identity":"4186efd1-7c19-4d03-bbc8-842a7778580f","order_by":0,"name":"Shiyun Shao","email":"","orcid":"","institution":"Hospital of PLA 75th Group Army","correspondingAuthor":false,"prefix":"","firstName":"Shiyun","middleName":"","lastName":"Shao","suffix":""},{"id":286184661,"identity":"a720476e-374d-4944-bae3-f0b50235c428","order_by":1,"name":"Kunchen Wei","email":"","orcid":"","institution":"Changzheng Hospital, Navy Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kunchen","middleName":"","lastName":"Wei","suffix":""},{"id":286184662,"identity":"f99b90ba-0812-4b02-b9bd-de34dddd0af2","order_by":2,"name":"Xiao Feng","email":"","orcid":"","institution":"Changzheng Hospital, Navy Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Feng","suffix":""},{"id":286184663,"identity":"ee539c53-3d13-40c6-afce-504f86f4de62","order_by":3,"name":"Guanhui Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3QsUrEQBCA4QmBicVw125Q4issBNLeq8wSSKUi2KQ48ETJFndim8e48sqEg71m7VOu+AKms1Kv90hiZ7FfPT/MDIDn/UMYPbZOfeH94qBbx+VyPJmRyeUHzhiszaWzZjxJxFUW15gwdJzFb0/hhMUEpylRdhNsmqJUK4S5XvNwQk69kyjuwujBdGp3AcK+boeTiPcpSRNU1BSdsghSXI8koKpz4u9gIzi7VVU4ITnLMa4bVPUxgWkJmVD2K0wl2VywNTR6y6V+6d3xUYmMdNt/lstkrp+Hk1/ob+Oe53neST9VuksHmm/2jQAAAABJRU5ErkJggg==","orcid":"","institution":"Hospital of PLA 75th Group Army","correspondingAuthor":true,"prefix":"","firstName":"Guanhui","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-03-14 01:31:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4096488/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4096488/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53926307,"identity":"a2f19b6c-344f-49a4-a287-1c8fab08443b","added_by":"auto","created_at":"2024-04-02 09:59:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110796,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical Roadmap\u003c/p\u003e\n\u003cp\u003eAR, Allergic rhinitis. GO: Gene Ontology. KEGG: Kyoto Encyclopedia of Genes and Genomes. GSEA: Gene Set Enrichment Analysis. GSVA, Gene Set Variation Analysis. ROC: Receiver Operating Characteristic curve. HRGs: Hypoxia related genes; HRDEGs: Hypoxia related differentially expressed genes. ssGSEA: Single-Sample Gene-Set Enrichment Analysis; LASSO, Least Absolute Shrinkage And Selection Operator. SVM, Support Vector Machine. TF, Transcription Factor.\u003c/p\u003e","description":"","filename":"figure1300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/0fb8a6ad19fbff3f644d26d6.png"},{"id":53926308,"identity":"33dad0af-db3c-40c2-9839-2228f51a23ac","added_by":"auto","created_at":"2024-04-02 09:59:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":452447,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression analysis of GSE51392 dataset and GSE46171 dataset\u003c/p\u003e\n\u003cp\u003eA-b. Volcano plot of differentially expressed genes analysis between different groups (AR /Control) of GSE51392 dataset (A) and GSE46171 dataset (B). C. Venn diagram. D-e. Complex numerical heat maps of HRDEGs between different groups (AR /Control) in the GSE51392 dataset (D) and GSE46171 dataset (E). F-g. Correlation heatmap of HRDEGs inGSE51392 dataset (F) and GSE46171 dataset (G). AR, Allergic rhinitis; DEGs, differentially expressed genes; HRGs, Hypoxia related genes; HRDEGs, Hypoxia related differentially expressed genes.\u003c/p\u003e","description":"","filename":"figure2300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/763105a61bbe8318c8484d0b.png"},{"id":53926312,"identity":"c1842c3b-573b-446f-ac0b-b54c1fde9ba3","added_by":"auto","created_at":"2024-04-02 09:59:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":336151,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of data set GSE51392\u003c/p\u003e\n\u003cp\u003eA. Group comparison of hypoxia-related differentially expressed genes between the AR group and the Control group in dataset GSE51392. B. Group comparison diagram of hypoxia-related differentially expressed genes between AR group and Control group in dataset GSE46171. C. Chromosomal mapping of five hypoxia-related differentially expressed genes. AR, Allergic rhinitis. DEGs: Differentially expressed genes; HRGs: Hypoxia related genes; HRDEGs: Hypoxia related differentially expressed genes. The symbol * is equivalent to P \u0026lt; 0.05 and statistically significant; The symbol ** is equivalent to P \u0026lt; 0.01, which is highly statistically significant; The symbol *** is equivalent to P \u0026lt; 0.001 and highly statistically significant.\u003c/p\u003e","description":"","filename":"figure3300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/695a84efe74ecc0e844fb140.png"},{"id":53926313,"identity":"5eac9c7c-8783-4732-b9b0-fd03dd9b474b","added_by":"auto","created_at":"2024-04-02 09:59:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":265540,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA enrichment analysis of dataset GSE51392\u003c/p\u003e\n\u003cp\u003eA. GSEA enrichment analysis of the AR/Control group samples in dataset GSE51392 showed six main biological characteristics. B-g. The genes of AR/Control group in dataset GSE51392 were significantly enriched in WP_EPITHELIAL_TO_MESENCHYMAL_TRANSITION_IN_COLORECTAL_CANCER (B). WP_VITAMIN_A_AND_CAROTENOID_METABOLISM (C), WP_CANONICAL_AND_NONCANONICAL_NOTCH_SIGNALING (D), REACTOME_MET_PROMOTES_CELL_MOTILITY (E), REACTOME_ASSEMBLY_OF_COLLAGEN_FIBRILS_AND_OTHER_MULTIMERIC_STRUCTURES (F), WP_INFLAMMATORY_RESPONSE_PATHWAY (G) and other pathways. The significant enrichment screening criteria for GSEA enrichment analysis were Padj \u0026lt; 0.05 and FDR value (qvalue) \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"figure4300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/536bdb6d54e529980230cdbc.png"},{"id":53927836,"identity":"0352a30b-bad1-4684-afe8-0a11bd89ca87","added_by":"auto","created_at":"2024-04-02 10:15:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":247171,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA enrichment analysis of dataset GSE46171\u003c/p\u003e\n\u003cp\u003eA. Six main biological characteristics of GSEA enrichment analysis between AR/Control group samples in dataset GSE46171. B-g. WP_OXIDATIVE_DAMAGE_RESPONSE (B), PID_IL12_2PATHWAY (C), WP_oxidative_damage_response (B) and PID_IL12_2pathway (C) were significantly enriched in AR/Control group samples in dataset GSE46171. WP_VITAMIN_B12_METABOLISM (D), REACTOME_COSTIMULATION_BY_THE_CD28_FAMILY (E), WP_INTERACTIONS_BETWEEN_IMMUNE_CELLS_AND_MICRORNAS_IN_TUMOR_MICROENVIRONMENT (F), REACTOME_INTERLEUKIN_10_SIGNALING (G) and other pathways. The significant enrichment screening criteria for GSEA enrichment analysis were Padj \u0026lt; 0.05 and FDR value (qvalue) \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"figure5300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/30895e6afad51a42e18909d8.png"},{"id":53926321,"identity":"feefe2b1-778d-4fd2-a6b0-a8fbd42ca188","added_by":"auto","created_at":"2024-04-02 09:59:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":226771,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA analysis\u003c/p\u003e\n\u003cp\u003eA. Heat map showing the results of GSVA analysis of the disease normal (AR/Control) group samples of dataset GSE51392. B. Group comparison diagram showing the GSVA analysis results of the disease normal (AR/Control)group samples of dataset GSE51392. The symbol * is equivalent to P \u0026lt; 0.05, which is statistically significant; The symbol ** is equivalent to P \u0026lt; 0.01, which is highly statistically significant; *** represents P \u0026lt; 0.001, which is highly statistically significant. GSVA, Gene Set Variation Analysis. AR, Allergic rhinitis.\u003c/p\u003e","description":"","filename":"figure6300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/f972af8e47ec9c2bf4a90320.png"},{"id":53926324,"identity":"643daf28-9183-4ba6-9e3f-299c565a55e2","added_by":"auto","created_at":"2024-04-02 09:59:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":208675,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA analysis\u003c/p\u003e\n\u003cp\u003eA. Heat map presentation of results from GSVA analysis of the disease normal (AR/Control) group samples of dataset GSE46171. B. Group comparison diagram of GSVA analysis results of disease normal (AR/Control)group samples of dataset GSE46171. The symbol * is equivalent to P \u0026lt; 0.05, which is statistically significant; The symbol ** is equivalent to P \u0026lt; 0.01, which is highly statistically significant; *** represents P \u0026lt; 0.001, which is highly statistically significant. GSVA, Gene Set Variation Analysis. AR, Allergic rhinitis.\u003c/p\u003e","description":"","filename":"figure7300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/e4490e4d0948391818a0b1ab.png"},{"id":53927211,"identity":"14080cfc-12ae-4625-835b-6f5d74fe5462","added_by":"auto","created_at":"2024-04-02 10:07:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":240498,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of HRDEGs diagnostic model\u003c/p\u003e\n\u003cp\u003eA. LASSO diagnostic model diagram of HRDEGs. B. Variable trajectory plot of the LASSO regression diagnostic model. C. The number of genes with the highest accuracy obtained by SVM algorithm, D. The number of genes with the lowest error rate obtained by SVM algorithm. E. Collated forest plot of the results of logistic regression analysis. F. Nomogram of logistic regression model. G. Calibration curve of logistic predictive value scoring model. The main function of the calibration curve is to fit and evaluate the prediction accuracy of logistic diagnostic model. There is a high degree of overlap between the fitted curve and the predicted curve in the figure, indicating that the logistic diagnostic model has a good diagnostic effect. H. DCA plot, the x axis of DCA plot represents the Threshold Probability or threshold probability, and the y axis represents the net benefit. The results can be judged by observing the range of x values in which the line of the model can be stably higher than the All line and the none line. The larger the range of x values, the better the effect of the model. I. Functional similarity analysis chart. LASSO, Least Absolute Shrinkage and Selection Operator. SVM, Support Vector Machine. DCA, decision curve analysis.\u003c/p\u003e","description":"","filename":"figure8300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/556da74fd2b4049ea7d0d420.png"},{"id":53926317,"identity":"d6c749fa-2531-47ca-abb8-6e79b5dffc67","added_by":"auto","created_at":"2024-04-02 09:59:32","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":73943,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve\u003c/p\u003e\n\u003cp\u003eA. ROC curve results of logistic regression model linear predictorsin dataset GSE51392 are shown. B-F. The ROC curve results of four key genes (CPT1C, CWF19L1, MED17, MMP1) with AR and Control as outcome variables showed that the area under the ROC curve values were generally between 0.5 and 1. The closer the AUC is to 1, the better the diagnostic effect is. When AUC was between 0.5 and 0.7, the accuracy was low, when AUC was between 0.7 and 0.9, the accuracy was moderate, and when AUC was above 0.9, the accuracy was high. TPR, true positive rate. FPR, false positive rate. ROC, receiver operating characteristic curve.\u003c/p\u003e","description":"","filename":"figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/25d50aedcd00eec3655d615e.png"},{"id":53926314,"identity":"af4498dc-e594-4aa5-909b-3901a8fb2667","added_by":"auto","created_at":"2024-04-02 09:59:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":315347,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA analysis of high and lowrisk groups\u003c/p\u003e\n\u003cp\u003eA. There were seven main biological characteristics in GSEA enrichment analysis between High/Lowgroup samples in dataset GSE51392. B-h. In dataset GSE51392, the genes between the High/Lowgroup samples were significantly enriched in REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING (B), REACTOME_KERATAN_SULFATE_KERATIN_METABOLISM (C), WP_RETINOL_METABOLISM (D), REACTOME_DECTIN_2_FAMILY (E), WP_IL1_AND_MEGAKARYOCYTES_IN_OBESITY (F), REACTOME_INTERLEUKIN_10_SIGNALING (G), WP_VITAMIN_A_AND_CAROTENOID_METABOLISM (H) pathway. The significant enrichment screening criteria for GSEA enrichment analysis were Padj \u0026lt; 0.05 and FDR value (qvalue) \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/1a2f0ff1d8f19b79a610664d.png"},{"id":53926326,"identity":"11caaadc-b64d-4b6c-83c9-3c4a94d5444d","added_by":"auto","created_at":"2024-04-02 09:59:33","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":243574,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analysis of key genes\u003c/p\u003e\n\u003cp\u003eA-b. The results of GO and KEGG enrichment analysis of key genes are presented in bar graphs (A) and bubble plots (B). C-f. Loop network diagram of GO and KEGG enrichment analysis results of key genes BP (C), CC (D), MF (E), and KEGG (F). In the bar graph (A) and bubble graph (B), the ordinate is the GO terms, and the abscissa represents the -log10 (Padj) size and enrichment factor GeneRatio of different GO terms. The blue dots in the ring network diagram (C, D, E, F) represent specific genes, and the orange circles represent specific pathways. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; The screening criteria for GO enrichment items were pvalue \u0026lt; 0.05 and FDR value (qvalue) \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"figure11300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/99745d5d8223b4c4d4d84d90.png"},{"id":53927210,"identity":"e5f6639b-41c6-4a75-9af1-4dc35898a1e6","added_by":"auto","created_at":"2024-04-02 10:07:32","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":96525,"visible":true,"origin":"","legend":"\u003cp\u003ePathway map of KEGG analysis results of key genes\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment (KEGG) analysis results of key genes showed PPAR signaling pathway.\u003c/p\u003e","description":"","filename":"figure12300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/2c43a9c93e6356dedf97c6d0.png"},{"id":53926311,"identity":"e8cb3ad6-f0de-4d6d-9fa3-49753448a25d","added_by":"auto","created_at":"2024-04-02 09:59:31","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":223993,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA analysis\u003c/p\u003e\n\u003cp\u003eA. Heat map showing the results of GSVA analysis between high and low risk groups of the prognostic model in the AR sample of dataset GSE51392. B. In the AR sample of dataset GSE51392, the group comparison diagram of GSVA analysis results between high and low risk groups of prognostic model is presented. The symbol * is equivalent to P \u0026lt; 0.05, which is statistically significant; The symbol ** is equivalent to P \u0026lt; 0.01, which is highly statistically significant; *** represents P \u0026lt; 0.001, which is highly statistically significant. GSVA, Gene Set Variation Analysis.\u003c/p\u003e","description":"","filename":"figure13300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/e7a1065074ef988b861e0b38.png"},{"id":53926328,"identity":"265254b5-f176-4e85-a22c-a975354d1cf7","added_by":"auto","created_at":"2024-04-02 09:59:33","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":215198,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of key genes and immune cells\u003c/p\u003e\n\u003cp\u003eA. Correlation analysis results of key genes and immune cells correlation dot plot display. B-e. Scatter plot results of correlation between MMP1 and Type 17 T helper cell (B), MMP1 and Neutrophil (C), MMP1 and Immature dendritic cell (D), MMP1 and Memory B cell (E). P≥ 0.05, no statistical significance; P \u0026lt; 0.05, statistically significant; P \u0026lt; 0.01, highly statistically significant; P \u0026lt; 0.001, highly statistically significant. The absolute value of the correlation coefficient (r) in the scatter plot of correlation was more than 0.8, indicating a strong correlation. Moderate correlation was defined as an absolute value between 0.5 and 0.8. 0.3-0.5 is weak correlation; Values below 0.3 are considered weak or uncorrelated.\u003c/p\u003e","description":"","filename":"figure14300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/704cda3ef6f0b37a1827f73a.png"},{"id":53927209,"identity":"3e383bf0-b1da-4179-9f61-d224e0420785","added_by":"auto","created_at":"2024-04-02 10:07:32","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":411105,"visible":true,"origin":"","legend":"\u003cp\u003eCIBERSORT immune cell infiltration analysis of GSE51392 samples in the dataset\u003c/p\u003e\n\u003cp\u003eA. Stacking plot of CIBERSORT immune cell infiltration analysis results between High and Low risk (High/Low) groups in dataset GSE51392. B-c. The results of correlation analysis of immune cell infiltration abundance in dataset GSE51392low-risk group (B) and high-risk group (C) are presented. D-e. Correlation map between immune cells and key genes in the low risk group (D) and high risk group (E) of dataset GSE51392. The symbol ns is equivalent to P≥ 0.05, which has no statistical significance; The symbol * is equivalent to P \u0026lt; 0.05, which is statistically significant; The symbol ** is equivalent to P \u0026lt; 0.01, which is highly statistically significant; The symbol *** is equivalent to P \u0026lt; 0.001 and highly statistically significant.\u003c/p\u003e","description":"","filename":"figure15300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/3e41aaa855eb05d229efba7b.png"},{"id":53926316,"identity":"94c7ccd1-d765-4442-97eb-9533bdea4c68","added_by":"auto","created_at":"2024-04-02 09:59:32","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":574044,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction network of mRNA-miRNA, mRNA-TF and mRNA-drug of key genes\u003c/p\u003e\n\u003cp\u003eA-c. mRNA-miRNA (A), mRNA-TF (B), mRNA-drug (C) of the four key genes. The red squares in the mRNA-miRNA (B) interaction network are mrnas and the blue square blocks are mirnas. Red squares in mRNA-TF (D) interaction network are mrnas; Blue circles are transcription factors (TFS). Red squares in the mRNA-drug (C) interaction network are mrnas. Blue triangles are drugs. TF, Transcription factors.\u003c/p\u003e","description":"","filename":"figure16300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/17c9f0b38e46192923bf9422.png"},{"id":53926327,"identity":"1adc7ef4-34b9-4cd5-b692-9061d7915a2a","added_by":"auto","created_at":"2024-04-02 09:59:33","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":825478,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial protein structure of key genes\u003c/p\u003e\n\u003cp\u003eA-f. Presentation of the protein structures of CPT1C (A), CWF19L1 (B), MED17 (C), and MMP1 (D). The confidence score per residue (pLDDT) generated by the AlphaFold website was between 0 and 100. Some regions below 50 pLDDT may be isolated unstructured regions, and when pLDDT \u0026lt; 50 (red area), the model confidence is very low; When 50 \u0026lt; pLDDT \u0026lt; 70 (yellow area), the model confidence is low; When 70 \u0026lt; pLDDT \u0026lt; 90 (light blue area), the model confidence was normal. When 90 \u0026lt; pLDDT (blue area), the model confidence is very high.\u003c/p\u003e","description":"","filename":"figure17300ppi.png","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/987d24a5be06753418cbeeff.png"},{"id":58993261,"identity":"e310549a-adcb-499e-b42f-07b246e09dee","added_by":"auto","created_at":"2024-06-25 05:32:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5457052,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/a46a50e0-8eff-4971-a5f2-48ae8d90facf.pdf"},{"id":53926305,"identity":"3f853f30-c073-419a-89b2-36f332c33343","added_by":"auto","created_at":"2024-04-02 09:59:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7247,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1boxplotbeforeGSE51392.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/ceccb9ee01eb000dda648b9a.pdf"},{"id":53927196,"identity":"bf38b13e-179d-4de4-9221-4606102e073d","added_by":"auto","created_at":"2024-04-02 10:07:31","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5688,"visible":true,"origin":"","legend":"","description":"","filename":"figureS2boxplotbeforeGSE461711.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/783eda73a6d63e7815db55fe.pdf"},{"id":53927195,"identity":"989fc732-a6c8-47c9-a6ab-5dedae272a05","added_by":"auto","created_at":"2024-04-02 10:07:31","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":77811,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/7f53897593cce9c9a9a37540.xlsx"},{"id":53926319,"identity":"a9de8f18-1656-4070-86f1-f5157e70656a","added_by":"auto","created_at":"2024-04-02 09:59:32","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17128,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/260da9441d5f8f30569c6f47.docx"},{"id":53926325,"identity":"580073c4-5c4e-4799-80ba-a64a312f5375","added_by":"auto","created_at":"2024-04-02 09:59:33","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17013,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/20ccff528c463afbce3cbe17.docx"},{"id":53926323,"identity":"28b9296c-d893-4b6a-8e38-8e93b49a8387","added_by":"auto","created_at":"2024-04-02 09:59:32","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":17047,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4096488/v1/8736643f3d28fb7af7cdc8ca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative analyses of hypoxia-related genes and mechanisms associated with Allergic Rhinitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAllergic rhinitis (AR) is a prevalent health condition characterized by inflammation of the nasal mucosa in response to allergen exposure, posing a significant health burden worldwide [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. AR's diagnosis primarily relies on subjective symptom reporting, which can lead to both overdiagnosis and underdiagnosis. This diagnostic challenge arises due to the overlap of AR symptoms with other respiratory conditions, such as non-allergic rhinitis and sinusitis [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, there is a critical need for objective and precise diagnostic tools to differentiate AR from other similar conditions accurately.\u003c/p\u003e\n\u003cp\u003eFurthermore, AR's pathophysiology involves a complex interplay of immunological mechanisms, and its exact etiology is not fully understood. Recent research has highlighted an intriguing aspect of AR\u0026mdash;the potential association with hypoxia-related genes [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Hypoxia is a condition characterized by insufficient oxygen supply to tissues and cells, known for its crucial roles in various physiological and pathological processes. It has been extensively studied in contexts such as tumorigenesis, wound healing, and inflammation. However, its role in allergic rhinitis has only recently begun to emerge as an area of interest.\u003c/p\u003e\n\u003cp\u003ePhenotypically, hypoxia manifests as an oxygen-deficient microenvironment within tissues and organs, triggering various adaptive responses at the cellular and molecular levels. The relevance of hypoxia extends to several diseases that share similarities or associations with AR, such as asthma and chronic obstructive pulmonary disease (COPD). Studies in these related conditions have highlighted the potential involvement of hypoxia-related pathways in disease pathogenesis and progression.\u003c/p\u003e\n\u003cp\u003eIn light of these considerations, this study aims to address the diagnostic challenges associated with AR by developing a diagnostic model based on hypoxia-related genes. To investigate the potential correlation between the expression of hypoxia-related genes and the presence of AR. Then develop a diagnostic model utilizing hypoxia-related gene expression profiles to distinguish AR from other respiratory conditions accurately. We will conduct a comprehensive analysis of gene expression data from AR patients and individuals with similar respiratory conditions, incorporating dataset GSE51392 and potentially other relevant datasets. Differential expression analyses, Gene Set Enrichment Analysis (GSEA), and machine learning techniques will be employed to identify key hypoxia-related genes and construct the diagnostic model. The study anticipates identifying hypoxia-related genes associated with AR, shedding light on the molecular mechanisms underlying the disease. The development of a diagnostic model based on hypoxia-related gene expression profiles holds the potential to significantly improve the accuracy of AR diagnosis, addressing a critical clinical need.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e1.1 Data Download\u003c/p\u003e\n\u003cp\u003eWe from the GEO database [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) using the R package GEOquery [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e] download the Allergic rhinitis, Allergic rhinitis, AR) data set GSE51392 [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e], GSE46171 [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Datasets GSE51392 and GSE46171 are from Homo sapiens. The data platform GSE51392 is GPL13158 [HT_HG-U133_Plus_PM] Affymetrix HT HG-U133\u0026thinsp;+\u0026thinsp;PM Array Plate. The data set GSE51392 excludes the asthma data, leaving a total of 44 sample data. A total of 44 samples were included, including 20 samples of allergic rhinitis and 24 samples of Control group. The tissue source was primary nasal and bronchial epithelial cells. GSE46171 data selection platform was GPL16981 Agilent-020087 human whole genome 4x44K (Probe Name version) data, the GSE46171 data set excluded asthma part of the data, a total of 19 samples data. The GSE46171 dataset included gene expression profiles of 5 allergic rhinitis samples and 14 Control samples, and the tissue source was Nasal mucosa. The specific information of the data set can be found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eWe collected Hypoxia related genes (HRGs) from the GeneCards[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] database, which provides comprehensive information on human genes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003c/span\u003e). Using the term \"Hypoxia\" as the search keyword and \"Protein Coding\" as the screening criterion, we obtained 6094 hypoxia-related genes (HRGs, mRNA). In addition, we also used \"Hypoxia\" as a search term on the MSigDB (Molecular Signatures Database) [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] database website. Twenty-seven Hypoxia related genes (HRGs) were collected from the \"BIOCARTA VEGF PATHWAY\" reference gene set. After combined deduplication, we obtained a total of 6099 Hypoxia related genes (HRGs), the detailed information is shown in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e1.2 Differential analysis of dataset GSE51392 and GSE46171\u003c/p\u003e\n\u003cp\u003eThe data of datasets GSE51392 and GSE46171 have been standardized and can be used directly. The boxplots of datasets GSE51392 and GSE46171 are shown in the Supplementary materials (Figure\u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). Subsequently, according to the grouping information (AR/Control) in the data sets GSE51392 and GSE46171, we used the R package limma[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] for differential analysis to obtain differentially expressed genes. Finally, we will GSE51392 data sets, be all | GSE46171 variance analysis logFC | and pvalue\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026thinsp;\u0026lt;\u0026thinsp;0.05 DEGs HRGs take intersection get oxygen related differentially expressed genes (HRDEGs), By R package ggplot2 map volcanic present the results of variance analysis and R package pheatmap draw oxygen differentially expressed genes related to heat map, at the same time draw oxygen differentially expressed genes related to the correlation between the heat map. The group comparison map was used to show the expression trend of differentially expressed genes related to hypoxia. The differentially expressed genes related to hypoxia with the same expression trend and statistical significance in GSE51392 and GSE46171 datasets were selected for chromosome localization map and subsequent analysis.\u003c/p\u003e\n\u003cp\u003e1.3 GSEA enrichment analysis\u003c/p\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA) [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] is a computational method proposed by the Broad Institute to determine whether a predefined set of genes shows statistical differences between two biological states. It is commonly used to estimate changes in the activity of pathways and biological processes in expression data set samples. In order to study the biological process of difference between two groups of samples, we based on gene expression profile datasets, downloaded from MSigDB database [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] the reference gene set \"c2. Cp. All. V2022.1. Hs. Symbols. The GMT [all Canonical Pathways] (3050)\", The GSEA method included in the R package clusterProfiler was used for enrichment analysis and visualization of the dataset. The parameters used in this GSEA enrichment analysis are as follows: The seed was 2022, the number of computation was 5000, the number of genes in each gene set was at least 10, and the number of genes in each gene set was at most 500. The p-value correction method was Benjamini-Hochberg (BH). The screening criteria for significant enrichment were Padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR value (qvalue)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003cp\u003e1.4 GSVA enrichment analysis\u003c/p\u003e\n\u003cp\u003eGene Set Variation Analysis (GSVA) [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] is a non-parametric unsupervised analysis method, which is mainly used to evaluate the enrichment results of gene set of microarray nuclear transcriptome by converting the expression matrix of gene between different samples into the expression matrix of gene set between samples. Thus, we can evaluate whether different pathways are enriched in different samples. We get in MSigDB database \"c2. All. V2023.1. Hs. Symbols. GMT\" gene set to data set GSE51392 and disease among the GSE46171 normal (AR/Control) group analyses GSVA all genes, pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as the screening criterion for significant enrichment. Then, the pathways that met the requirements in GSVA enrichment analysis results were sorted in descending logFC order, and the top 10 and bottom 10 pathways were selected for results display.\u003c/p\u003e\n\u003cp\u003e1.5 Construct the diagnostic model\u003c/p\u003e\n\u003cp\u003eLASSO regression is a commonly used machine learning algorithm to construct diagnostic models, which is mostly used to construct prognostic diagnostic models or screen variables. On the basis of linear regression, it uses regularization to solve the overfitting situation in the process of curve fitting by adding a penalty term (lambda \u0026times; absolute value of slope), and improves the generalization ability of the model. In order to obtain the prognostic model in dataset GSE51392, we used glmnet package [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] based on the expression level of HRDEGs in dataset GSE51392, set the seed to 2022, ten-fold cross validation, and set the seed to be 2022. LASSO[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] (Least absolute shrinkage and selection operator) regression was performed to obtain the related HRDEGs with nonzero coefficient corresponding to the lambda value of the best evaluation index. Subsequently, based on the hypoxia related differentially expressed genes (HRDEGs), the SVM model was constructed by SVM (Support Vector Machine) [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] algorithm, and the hypoxia related differentially expressed genes (HRDEGs) were screened based on the number of genes with the highest accuracy and the lowest error rate. The genes selected by LASSO analysis and SVM analysis were used for subsequent analysis.\u003c/p\u003e\n\u003cp\u003elogistic regression analysis was performed on HRDEGs screened by LASSO analysis and SVM analysis, and pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as the criterion to screen HRDEGs and construct a logistic regression model. The HRDEGs screened by single factor in logistic regression analysis were used as key genes for subsequent analysis. Then, based on the results of logistic regression analysis, we used the R package rms to construct a nomogram (nomogram). The Nomogram is a graph that uses a cluster of disjoint line segments to represent the functional relationship between multiple independent variables in the rectangular coordinate system of the plane. Based on the multi-factor regression analysis, a certain scale was set to characterize the various variables in the multi-factor regression model, and the total score was finally calculated to predict the probability of the occurrence of events. The Calibration Curve was drawn by Calibration analysis to evaluate the accuracy and discrimination of the logistic regression model based on key genes. The calibration curve is to evaluate the prediction effect of the model on the actual outcome by plotting the fitting of the actual probability and the model predicted probability under different conditions in the graph. We used the R package \"rms\" to construct the calibration curve. Decision curve analysis (DCA) is a simple method to evaluate clinical prediction models, diagnostic tests and molecular markers. We used the R package ggDCA[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] to draw DCA diagrams to evaluate the model.\u003c/p\u003e\n\u003cp\u003e1.6 Differential expression and GSEA analysis related to high and low risk groups of diagnostic model.\u003c/p\u003e\n\u003cp\u003eIn order to identify the potential mechanism of key genes and related biological characteristics and pathways in the high and low risk groups of the logistic regression model of GSE51392 and GSE46171, we first used limma package to compare the data set GSE51392 and GSE46171 with the data set GSE51392. GSE46171 was analyzed for differences between High and Low risk groups (High/Low), and then GSEA analysis was performed on them. Download the reference gene from MSigDB database [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] set \"c2. Cp. All. V2022.1. Hs. Symbols. The GMT [all Canonical Pathways] (3050)\", The enrichment analysis and visualization of the dataset were performed using the GSEA method included in the R package clusterProfiler. The parameters used in this GSEA enrichment analysis are as follows: Each gene set contains at least 10 genes, and the maximum number of genes is 500. The p-value correction method is Benjamini-Hochberg (BH). The screening criteria for significant enrichment are Padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR value (qvalue)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003cp\u003e1.7 GOKEGG enrichment analysis\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] analysis is a common method for large-scale functional enrichment studies, including biological process (BP), molecular function (molecular function, MF) and cellular component (CC). The Kyoto Encyclopedia of Genes and Genomes (KEGG) [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] is a widely used database storing information on genomes, biological pathways, diseases and drugs. We used R package clusterProfiler[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] to perform GO and KEGG annotation analysis of key genes. The entry screening criteria were Padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR value (qvalue)\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the P value correction method was Benjamini-Hochberg (BH). Finally, the R package Pathview[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] was used to visualize the pathway map related to the pathway (KEGG) enrichment analysis.\u003c/p\u003e\n\u003cp\u003e1.8 ROC Curve\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic curve (ROC) [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] : A coordinate schema-based analysis tool that can be used to select the best model, discard the second-best model, or set the best threshold in the same model. ROC curve is a comprehensive indicator of continuous variables reflecting sensitivity and specificity, and reflects the relationship between sensitivity and specificity by composition method. The area under the ROC curve is generally between 0.5 and 1. The closer the AUC is to 1, the better the diagnostic effect. When AUC was between 0.5 and 0.7, the accuracy was low, when AUC was between 0.7 and 0.9, the accuracy was moderate, and when AUC was above 0.9, the accuracy was high. We used the pROC package to draw the receiver operating characteristic (ROC) curves of key genes in different groups (AR/Control) and calculated the area under the curve (AUC) to evaluate the diagnostic effect of key gene expression on disease.\u003c/p\u003e\n\u003cp\u003e1.9 Functional similarity analysis\u003c/p\u003e\n\u003cp\u003eThe semantic comparison of Gene Ontology (GO) annotations provides a quantitative method for calculating the similarity between genes and genomes, and has become an important basis for many bioinformatics analysis methods. The GOSemSim package [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] was used to calculate the GO semantic similarity of key genes, and the geometric mean values of the key genes obtained from the dataset GSE51392 at the BP, CC and MF levels were further calculated to obtain the final score. Finally, the functional similarity analysis results were analyzed by the ggplot package. The Spearman method was used to explore the correlation between genes.\u003c/p\u003e\n\u003cp\u003e1.10 Immune infiltration analysis\u003c/p\u003e\n\u003cp\u003eSingle-sample gene set enrichment analysis (ssGSEA; single-sample gene-set enrichment analysis (SSGSEA) can quantify the relative abundance of each gene in a dataset sample. Markers for each infiltrating immune cell type, such as Activated CD8 T cell, Activated dendritic cell, Gamma delta T cell, Natural killer cell, The enrichment scores calculated by ssGSEA analysis were used to represent the relative abundance of each immune cell infiltration in each sample. The enrichment score of dataset GSE51392 was calculated by ssGSEA algorithm analysis in R package GSVA package [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] to represent the infiltration level of different types of immune cells in each sample. Then we combined the gene expression matrix of data set GSE51392 to calculate the correlation between immune cells and key genes in the High/Low risk group of the logistic regression model of data set GSE51392, and the R package ggplot2 was used to draw correlation maps. All the above correlation analyses were calculated by pearson algorithm.\u003c/p\u003e\n\u003cp\u003eCIBERSORT[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] is an immune infiltration analysis algorithm that deconvolutes the transcriptome expression matrix based on the principle of linear support vector regression to estimate the composition and abundance of immune cells in mixed cells. We upload the data of GSE51392 gene expression matrix to CIBERSORT, combine with LM22 feature gene matrix to screen out the data of immune fine abundance and greater than zero, and finally obtain and display the specific results of immune cell infiltration abundance matrix. The proportion of immune cell infiltration abundance among the samples in the High/Low risk groups of the logistic regression model in dataset GSE51392 was displayed by stacking bar charts. Then the correlation between the abundance and immune cells greater than zero in the High/Low risk groups of the logistic regression model in GSE51392 was visualized by the R package ggplot2. Then we combined the gene expression matrix of the data set GSE51392 to calculate the correlation between immune cells and key genes in the High/Low risk groups of the logistic regression model of the data set GSE51392, and the R package ggplot2 was used to draw the correlation map. All the above correlation analyses were calculated by pearson algorithm.\u003c/p\u003e\n\u003cp\u003e1.11 Interaction network analysis of key genes\u003c/p\u003e\n\u003cp\u003eENCORI [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://starbase.sysu.edu.cn/\u003c/span\u003e\u003c/span\u003e) is a starBase database version 3.0, ENCORI database of micrornas - mRNA interaction is based on the CLIP - seq and degradation group sequencing plants (for) of data mining, It provides a variety of visual interfaces for exploring the targets of miRNA. We used ENCORI database to predict the mirnas that interacted with Key genes, retained the interaction pairs recorded in at least three databases, and then plotted the mRNA-miRNA interaction network in Cytoscape.\u003c/p\u003e\n\u003cp\u003eCHIPBase database [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] (version 3.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://encori\u003c/span\u003e\u003c/span\u003e: / / rna.sysu.edu.cn/chipbase/) from the DNA binding protein ChIP - seq data identified in thousands of combining base sequence matrix and its binding sites, and forecasts the millions of Transcription factor (Transcription factors, TF) and gene Transcription regulation between relations. HTFtarget database [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/hTFtarget\u003c/span\u003e\u003c/span\u003e.) is a human transcription factor (TF) and the corresponding control target data integrated database. We searched CHIPBase (version 3.0) and hTFtarget database to find transcription factors (TFS) that bind to key genes and kept the common parts in both databases.\u003c/p\u003e\n\u003cp\u003eThe public Comparative Toxicogenomics Database (CTD) [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.org/\u003c/span\u003e\u003c/span\u003e) is an innovative digital ecosystem that links chemicals, genes, phenotypes, diseases and known toxicological information, To facilitate the understanding of human health related information database. We used the CTD database to predict potential drugs or small molecule compounds that would interact with Key genes, and used \"Reference Count\" \u0026gt; 1 as the screening criterion to screen mRNA-drugs interaction pairs. Cytoscape software was used to visualize the mRNA-drugs interaction network.\u003c/p\u003e\n\u003cp\u003e1.12 Spatial protein structure of key genes\u003c/p\u003e\n\u003cp\u003eProteins are essential for life, and knowledge of their structure can facilitate an understanding of alignment function. Alphafold website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.alphafold.ebi.ac.uk/\u003c/span\u003e\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] first proposed can under the situation of no homologous template based on the calculation method to predict protein structure with atomic precision, predict the structure of the cover 98.5% of the known human proteins and other biological the same proportion of protein. We used the Alphafold2 website to predict the protein structures of key genes (mrnas) in the PPI network and presented the results.\u003c/p\u003e\n\u003cp\u003e1.13 Statistical analysis\u003c/p\u003e\n\u003cp\u003eAll data processing and analysis were performed with the use of R software (Version 4.2.3). Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Continuous variables were compared between the two groups with the use of the Wilcoxon rank sum test, and statistical significance was estimated for normally distributed variables with the use of an independent Student's t-test. The Kruskal-Wallis test was used for comparisons of three or more groups. The chi-square test or Fisher's exact test was used to compare and analyze statistical significance between the two groups of categorical variables. receiver operating characteristic (ROC) curves were based on the R package pROC. If not specified, the results were calculated by spearman correlation analysis and all P statistics are two-sided. A P value of less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e2.1 Analysis flow chart\u003c/p\u003e\n\u003cp\u003eThe technical route of this analysis is shown in the figure below (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e2.2 Difference analysis of dataset GSE51392 and GSE46171\u003c/p\u003e\n\u003cp\u003eTo analyze the differences in gene expression between different groups (AR /Control) of the GSE51392 and GSE46171 datasets, limma package was used to perform differential analysis on GSE51392 and GSE46171 datasets to obtain the differentially expressed genes (DEGs) between different groups of AR datasets (AR /Control). The results are as follows: GSE51392 data sets were obtained 19921 differentially expressed genes, which meet the | logFC | and pvalue \u0026gt; 0 \u0026lt; 0.05 threshold gene has 1504, under the threshold, high expression in AR group (the Control group of middle and lower expression, logFC is positive, raised genes) there are 685 in number, Low expression in AR group (the Control group increased, logFC negative) there are 819 in number, we will data set GSE51392 variance analysis results to plot the chart (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\n\u003cp\u003eAnd GSE46171 data sets were obtained 19449 differentially expressed genes, which meet the | logFC | and pvalue \u0026gt; 0 \u0026lt; 0.05 threshold gene has 761, under the threshold, high expression in AR group (the Control group of middle and lower expression, logFC is positive, raised genes) there are 353 in number, Low expression in AR group (the Control group increased, logFC negative) there are 408 in number, we will data set GSE51392 variance analysis results to plot the chart (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eTo obtain Hypoxia related differentially expressed genes (HRDEGs), We first put the data sets GSE51392 data sets and meet all | GSE46171 data set logFC | and pvalue \u0026gt; 0 \u0026lt; 0.05 threshold of differentially expressed genes (differentially expressed genes, Relevant genes (DEGs) and oxygen Hypoxia related genes, HRGs) intersection, received 21 differentially expressed genes related Hypoxia (Hypoxia related differentially expressed genes. HRDEGs) and the Venn diagram (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC) was drawn. The 21 HRDEGs are: AMOT, ANKRD39, ATF4, CPT1C, CWF19L1, DDX52, DIP2A, EED, GIPR, IGFBP3, LRPAP1, MED13L, MED17, MEX3A, MFAP1, MMP1, NCDN, NECAB2, PSMA7, PSMC3, PSMD13. According to the results obtained by Venn diagram, the differential expression of 21 HRDEGs in different groups (AR /Control) of GSE51392 dataset and GSE46171 dataset was analyzed, and the R package pheatmap was used to draw a heatmap to show the specific differential analysis results (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD-E). The expression of 21 HRDEGs in different groups (AR /Control) of GSE51392 data set (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD) and GSE46171 data set (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE) were significantly different. In addition, the correlation heatmap was used to show the correlation between the differentially expressed genes related to hypoxia in GSE51392 dataset and GSE46171 dataset (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF-G).\u003c/p\u003e\n\u003cp\u003eGroup comparison plots were also drawn to show the expression of 21 hypoxia-related differentially expressed genes in datasets GSE51392 and GSE46171 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). It can be seen from the figure that the hypoxia-related differentially expressed genes with the same expression trend and statistical significance (p \u0026lt; 0.05) in dataset GSE51392 and dataset GSE46171 were CPT1C, CWF19L1, DDX52, MED17, and MMP1. In GSE51392 and GSE46171, the genes CPT1C and MMP1 were highly expressed in the AR group, and the genes CWF19L1, DDX52 and MED17 were lowly expressed in the AR group compared with the Control group. The five HRDEGs (CPT1C, CWF19L1, DDX52, MED17, MMP1) were further analyzed.\u003c/p\u003e\n\u003cp\u003eIn order to analyze the position of the 5 HRDEGs on the human chromosome, we also used the RCircos package to annotate the position of the 5 HRDEGs (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). According to Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, the genes MED17 and MMP1 were distributed on the 11th chromosome, indicating that these two genes were more closely related than other HRDEGs.\u003c/p\u003e\n\u003cp\u003e2.3 GSEA enrichment analysis\u003c/p\u003e\n\u003cp\u003eTo determine the effect of hypoxia-associated differentially expressed genes (HRDEGs) expression levels on the occurrence of AR, Gene Set Enrichment Analysis (GSEA) was used to analyze all gene expression and involved biological processes of AR/Control group samples in dataset GSE51392 and dataset GSE46171, respectively. Padj \u0026lt; 0.05 and FDR value (qvalue) \u0026lt; 0.05 were used as the screening criteria for significant enrichment of the relationship between cellular components and molecular functions. The results showed that the genes of AR/Control group in dataset GSE51392 were significantly enriched in WP_EPITHELIAL_TO_MESENCHYMAL_TRANSITION_IN_COLORECTAL_CANCER, and WP_epithelial_to_mesenchymal_transition_in_Colorectal_cancer. WP_VITAMIN_A_AND_CAROTENOID_METABOLISM, WP_CANONICAL_AND_NONCANONICAL_NOTCH_SIGNALING, REACTOME_MET_PROMOTES_CELL_MOTILITY, REACTOME_ASSEMBLY_OF_COLLAGEN_FIBRILS_AND_OTHER_MULTIMERIC_STRUCTURES, WP_INFLAMMATORY_RESPONSE_PATHWAY (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-G, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe results showed that the genes of AR/Control group in dataset GSE46171 were significantly enriched in WP_OXIDATIVE_DAMAGE_RESPONSE, PID_IL12_2PATHWAY, WP_VITAMIN_B12_METABOLISM, and WP_oxidative_damage_response. REACTOME_COSTIMULATION_BY_THE_CD28_FAMILY, WP_INTERACTIONS_BETWEEN_IMMUNE_CELLS_AND_MICRORNAS_IN_TUMOR_MICROENVIRONMENT, REACTOME_INTERLEUKIN_10_SIGNALING (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-G, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e2.4 GSVA enrichment analysis\u003c/p\u003e\n\u003cp\u003eIn order to explore the c2. All. V2023.1. Hs. Symbols. The GMT gene set in the data set GSE51392 disease is normal (AR/Control) group, the difference of the sample Gene Set Variation Analysis (GSVA) was then performed to analyze the expression of all genes in dataset GSE51392. According to the results of gene set variation analysis (GSVA), the pvalue was \u0026lt; 0.05 The differential expression of the top 10 and the bottom 10 pathways between allergic rhinitis (AR) and Control (Control) groups was analyzed by logFC sorting and visualized by heat map (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA) and group comparison map (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB) (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The results of gene set variation analysis (GSVA) showed that 20 pathways were statistically significant (pvalue \u0026lt; 0.05) in the sensitive rhinitis (AR) group and the Control (Control) group, respectively: BIOCARTA_RAN_PATHWAY, REACTOME_GDP_FUCOSE_BIOSYNTHESIS, REACTOME_HYALURONAN_BIOSYNTHESIS_AND_EXPORT, REACTOME_VITAMIN_B1_THIAMIN_METABOLISM, REACTOME_SCAVENGING_BY_CLASS_F_RECEPTORS, WP_HIF1A_AND_PPARG_REGULATION_OF_GLYCOLYSIS, REACTOME_METALLOTHIONEINS_BIND_METALS, KRISHNAN_FURIN_TARGETS_UP, BIOCARTA_PROTEASOME_PATHWAY, REACTOME_SYNTHESIS_OF_DOLICHYL_PHOSPHATE REACTOME_ACTIVATED_NTRK2_SIGNALS_THROUGH_FYN, REACTOME_UPTAKE_OF_DIETARY_COBALAMINS_INTO_ENTEROCYTES WP_MIR222_IN_EXERCISEINDUCED_CARDIAC_GROWTH, HASLINGER_B_CLL_WITH_MUTATED_VH_GENES, IWANAGA_E2F1_TARGETS_NOT_INDUCED_BY_SERUM, MCCOLLUM_GELDANAMYCIN_RESISTANCE_DN, REACTOME_SIGNALING_BY_NOTCH1_T_7_9_NOTCH1_M1580_K2555_TRANSLOCATION_MUTANT, BIOCARTA_SALMONELLA_PATHWAY, RAFFEL_VEGFA_TARGETS_UP, REACTOME_ACYL_CHAIN_REMODELING_OF_DAG_AND_TAG.\u003c/p\u003e\n\u003cp\u003eIn order to explore the c2. All. V2023.1. Hs. Symbols. The GMT gene set in the data set GSE46171 disease is normal (AR/Control) group, the difference of the sample Gene Set Variation Analysis (GSVA) was then performed to analyze the expression of all genes in dataset GSE46171. According to the results of gene set variation analysis (GSVA), the pvalue was \u0026lt; 0.05 The differential expression of the top 10 and the bottom 10 pathways between allergic rhinitis (AR) and Control (Control) groups was analyzed by logFC sorting and visualized by heat map (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA) and group comparison map (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB) (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The results of gene set variation analysis (GSVA) showed that 15 pathways were statistically significant (pvalue \u0026lt; 0.05) in the sensitive rhinitis (AR) group and the Control (Control) group, respectively: REACTOME_CONJUGATION_OF_BENZOATE_WITH_GLYCINE, REACTOME_TYROSINE_CATABOLISM, WP_TYROSINE_METABOLISM_AND_RELATED_DISORDERS, REACTOME_PROPIONYL_COA_CATABOLISM, BIOCARTA_EEA1_PATHWAY, BYSTRYKH_HEMATOPOIESIS_STEM_CELL_FGF3, HOLLEMAN_DAUNORUBICIN_B_ALL_DN, DASU_IL6_SIGNALING_DN, REACTOME_ESTROGEN_BIOSYNTHESIS, REACTOME_DEFECTIVE_F9_ACTIVATION, REACTOME_RUNX3_REGULATES_WNT_SIGNALING, REACTOME_NECTIN_NECL_TRANS_HETERODIMERIZATION, TONKS_TARGETS_OF_RUNX1_RUNX1T1_FUSION_SUSTAINED_IN_MONOCYTE_DN, REACTOME_EPITHELIAL_MESENCHYMAL_TRANSITION_EMT_DURING_GASTRULATION, PASTURAL_RIZ1_TARGETS_DN.\u003c/p\u003e\n\u003cp\u003e2.5 Construct diagnostic model\u003c/p\u003e\n\u003cp\u003eTo determine the diagnostic value of five HRDEGs (CPT1C, CWF19L1, DDX52, MED17, MMP1) in dataset GSE51392, LASSO regression analysis was used to construct a diagnostic model for HRDEGs (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). In addition, we also visualized the LASSO regression results to obtain the LASSO variable trajectory map (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB). According to the figure, the LASSO diagnostic model we constructed was composed of a total of 5 HRDEGs, which were CPT1C, CWF19L1, DDX52, MED17, and MMP1. At the same time, the SVM model was constructed based on 5 HRDEGs and SVM (Support Vector Machine) algorithm, and the number of genes with the highest accuracy (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC) and the lowest error rate (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD) was obtained. The results showed that when the number of genes was 4 (CPT1C, CWF19L1, MED17, MMP1), the accuracy of SVM model was the highest. We intersected the genes obtained by the two algorithms to obtain four intersected HRDEGs (CPT1C, CWF19L1, MED17, MMP1). The four HRDEGs (CPT1C, CWF19L1, MED17, MMP1) obtained by the intersection of LASSO and SVM algorithms were analyzed by logistic regression and the logistic regression model was constructed. Logistics regression analysis finally included 4 HRDEGs as key genes (CPT1C, CWF19L1, MED17, MMP1) for analysis. We sorted out the results of univariate logistic regression analysis and presented them in the form of forest map (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eThen we conducted nomogram analysis to judge the diagnostic ability of the model and drew a nomogram of logistic predictive value to show the contribution of the four key genes to the diagnostic model (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eF). The results showed that the expression level of MED17 had a higher utility for the diagnostic model than other key genes.\u003c/p\u003e\n\u003cp\u003eThe Calibration Curve plot of the diagnostic model (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eG) showed that the calibration curve shown by the dotted line was slightly deviated from the diagonal line of the ideal model, but was close to consistent. We also used decision curve analysis (DCA) to evaluate the role of the diagnostic model in clinical utility and presented the results (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eH). In the DCA figure, when the line of the model is higher than that of All positive and all negative in a certain range, the larger the range is, the greater the net benefit will be, and the better the performance of the model will be. The results show that (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eG-H), the line of the model is more stable than all positive and all negative in a certain range, and the net income of the model is more, and the effect of the model is better.\u003c/p\u003e\n\u003cp\u003eFinally, we performed functional similarity analysis on these four key genes (CPT1C, CWF19L1, DDX52, MED17, MMP1), and then visualized the functional similarity analysis results between the key genes through the cloud and rain diagram (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eI). The results showed that among the four key genes, Among the four key genes, the functional similarity value between CWF19L1 and other key genes was the highest.\u003c/p\u003e\n\u003cp\u003e2.6 Diagnosis ROC\u003c/p\u003e\n\u003cp\u003eWe also analyzed the diagnostic value of logistic regression model linear predictors in dataset GSE51392, the receiver operating characteristic curve (ROC) of the logistic regression model linear predictors was drawn for the data set GSE51392 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA). The ROC curve results showed that, the logistic linear predictors of the logistic regression model in GSE51392 had high diagnostic accuracy (AUC \u0026gt; 0.9). In addition, ROC curves were drawn for the four key genes (CPT1C, CWF19L1, MED17, MMP1) in dataset GSE51392 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB-E). The results of ROC curve showed that the diagnostic effect of four key genes (CPT1C, CWF19L1, MED17, MMP1) had a certain degree of accuracy (AUC: 0.7–0.9).\u003c/p\u003e\n\u003cp\u003e2.7 GSEA enrichment analysis based on high and low risk scores of key genes\u003c/p\u003e\n\u003cp\u003eFirstly, we divided the allergic rhinitis (AR) samples of GSE51392 and GSE46171 into High/Low risk groups according to the median predicted value of the logistic regression model (linear predictors). The formula for calculating the predictive value (linear predictors) score in the logistic regression model is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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6twlSdu5CR9UP5fVL2q3jNHlVb9fVoLO7MKgn2Dqgb2zqT4FNYIMheA7umTyg3G1pQd79aLt8YPg63oJcCM/qh35QM2c0Thr1J9o0K+Xms9Xmz27775Jh9f4hG+zgkxnc2XuTK84uox8Ue1javuYtzWuejTm/d7WPOpRv8b0kvU5XVt3pRvr+lbuZTev9rFf9bxxxrp9rGdcumBQdTDW6daXLObUj2x250j3XDaRlbz6RgnXqF/zVHO/8Km7V6s62EfuJvJwVFySDdXa7xqHNK5aPiHnMRGOPNPPWailMoRVZ4s+1rxSsKdEEfk9c7HR2Z/Z1flwGfmq2sfU9jFva1z1aMz7va151KN+ja3ee9e5t52kbS+xg8hzWTlgs8/sMnH5uayzMrq8Cjh+gJV0jPSRKBHwRsGuzmN9XdCSHOuuLxQSu/orVQ3Ke/3QWkcs8ZPPVsHf6pvm0A9L1oMdWI7sMYYu50jbf3EY7ZvseY1N9M0KMt0a9cKtL0NkR/7LjmyqVj9119eNr8r53KofHaNPncfz1nmoc/bsBXNhvXUvq43umQSDj5dRQsidgEHdM/rwpxb2t+t3OcaZ74Vz7ueUMc69y3G+ubMq4l3PmM7e1i9a6MEX1yndqzrwAZbUrB1eo9gkf1mHF3yozJQs+X5rL5hf16y1dHfRbdFePXeSc47qc530Vf8Z786Nz6tt7bVqH+/6uvFVOZ9L2+fRHn3qPJ61r3A4d0nSdm7CB9WvgDVzXzJ+oSWvb50cZsYJxp7EcSH8khOYCEgqtGtgY4x+dCOP7lGp+pFTwJe/vCSQ0zMyW34QEBWQsU+btY18Qb4GUenQHDigw/3o1oXcVlAQa/ySftqsU0X75rw1VmuY+dw6zrO4dmPw5KO16SU8Wge23J7aqmWjPqufejbmcqO2z/e2y4/6WRdjYu9zurbku7GuT3vXje3pkx7tA7XfUc6ar5Gkxvexuzuyj5zuiPpqXfUzrrPLmJ6rLs4sfmGfMjp7umPPhQb/YA9brLu7Cys6qup61+s4z/iMTbHHNs9at+bo7tV+ZPl0ZcSjymr/dS/rOOeXu4odj9vIaa7PISZXP9HBXtX5Pk9t5Py8qa3a5dSudZWt41vPPt/bPm/Ur3u8eu9d5952kra9xG5EXhdOgcWXTZAhOHGA+XCxefbAx3wussbrhaZffXqRS59qjcs2LwL9Mja7HEpS8Kv6xBj92OCFQABSWfEDHloXOmi7Demidn0euHjhYFvrpN1xdl172vhT9TtLrb/7dcHtuP+ssxbG3Q766kvLzwHrRYY9mO2f2xEj71O7G6OPolqye2qf623XMeqHM0xWi4L9qjzcsP0Q5wVf2Q/0UfsZoe1r5Ez5ue/OrBIG5vFBhr5a0KUzyFnwwn3UmUKmOyucO/ld4450YX/GCB3SP0pctnTI1n1quIgBdRdHdDZqfEG2csMH5Jx95xcyW7EbHujBTsdQfsuWaj8/2HA5nmG+WqSzk+/G6KOo7uZt9flcb/u8UT9r33PvXefedpK2vcRuSJ7A0AWHG0KQpV6YwChIdm5VWZ63PjM9W3OrPXSRTOx5OXX2t/pyL7cIZfzoBLq7NVpTlb3PvUW39Nxn/mPce60/SZtIpH6JAN+4+LY0+ib60oR0hMANEyBZe4xv27mXN3zIsvSrI/BY914LT9ImEqlbAjqQq3/OapWkMwSeOAH+7MS37fqnrHMtO/fyXGSjNwTWCTz2vcezJG3r+3Ozkvy/htH/HblZKFl4CPw/ARIo/v/PY/8inXuZIxgClyNwqXufpO1yex7LIRACIRACIRACIbBMIEnbMqoIhkAIhEAIhEAIhMDlCCRpuxz7WA6BEAiBEAiBEAiBZQJJ2pZRRTAEQiAEQiAEQiAELkcgSdvl2MdyCIRACIRACIRACCwTSNK2jCqCIRACIRACIRACIXA5AknaLsc+lkMgBEIgBEIgBEJgmUCStmVUEQyBEAiBEAiBEAiByxFI0nY59rEcAiEQAiEQAiEQAssEkrQto4pgCIRACIRACIRACFyOQJK2y7GP5RAIgRAIgRAIgRBYJpCkbRlVBEMgBEIgBEIgBELgcgSStF2OfSyHQAiEQAiEQAiEwDKBJG3LqCIYAiEQAiEQAiEQApcjkKTtcuxjOQRCIARCIARCIASWCSRpW0YVwRAIgRAIgRAIgRC4HIEkbZdjH8shEAIhEAIhEAIhsEwgSdsyqtsU/P6HH+/+/o/37n76+ZfbBJBVh8AJBL786uu79z/48O7X334/QUumhkAIhMD/CCRpy0kYEuCF8/Y77949e/bHUCYDIXBpAq+8+trJLuzR0cl2fXKKLzxvvPlWvvgISOonTWB2F/YsfFVPJ9f17bF9zbJJ2q55dy7oG78M8KLJLwQX3ISYXiKwGqBncrOx6kQn2/X5PH0B8r5Ltbd8vZRfsfs0COw5XzPZ2ZiT6uS6Pp+j9qqc5K+hTtJ2DbtwhT589PEnd3yOUPg18BrKp599fsefk08p33z73d0X//zXKSqe9FyC7OqnAzEK0qP+qkNyKz7UuXwJInm7ZJH/l/Qhtp8OgZV7IJnRqkdnctTveiQjG7Pa53lbOrzvmttJ2q55dy7kG38O5SCfmoDgPjpIqnSZaJOYbBUSly55oU+6VI+SNv4sReL5+l//9sLLkvWRYNGPDmrkRn8G5v/08fHCumTf607HyA/0+Vy1V/i4L12b/0eFPtZWi/aXcU/MxUN+qNbaSTjUp5pEhD3p1o1d1t7tz4gf+vYWfFktkpX/s7rTqfl1bNTvcnDqWLjMOdtbPnLu4I8cNXs3KvwCr7uFPOuq8YL5nB3GOVs1YdUY4/6pcviAb5zpWrp4gC4/16sxiDOMjbqOahO/dSfq2N5nGGnt//7Pf1+aTpxiHH7aj4e42+gbxbwR65ecG3Tg754ieXGY1VWv5q72Vzk9j/Ro/JrqJG3XtBtn9IUgQ4BbKVzahzjEBHaCg+wSFAkU6B695JnDCwAZzXOf6VsJmAR+vSiqLQIhLyX96ZcgjSz9tRBIGas2mbPCaOYHttCx9ZKoPq08s2b8FgOfA0P6awLR7Tv++9ph5M9aH7oqZ9ixvo4TPtSXFHuCvq0inbN6S0cd73zsZFxur32dmcqp2tGz5PV8au2+V126q+KvhGzkK2dA+4cMz+yfF86Ekif0Yt/POvKuH5mqg3HFDD93skOfkhn6WAd2dLe1LsUS1+e28Ys7UX2UHdWjeKDx+9SKR93a6cMn7qYKfp96t7Ve7Q+6nU3HWvZrjX9bnzpn5RmdsyKbktFzV0tmVG/ZGs27RH+StktQv4BNLqEC15Z55OoLfWtON969dNTngVZzCbAEEYLHyF982wooCkidDWx1uumr3+SZD4fOptYh37t6yw/mECyQO0dBNzydF2x5EdTkC/sra5qx0EscXcjxolnRiTwvbF5E+Le3PETA3dKhcdX46G33edTP2hhb3e9Vdm571B75JHn2zpMGJT9K4iQ3qut54u5gU8kT89DvSULVxTjnxgvy6OrOncupXc97x1B9ig+cU9md7c8sHsj+fWps43e1TT+f2o8N+upa995t9ow7p7KXtebVGt8eosz0aEw19rzt9kf9LjObX+Uu/Zyk7dI78Ej2u0RlZLoGg5HcVj9BhKCggMjzqu6Rv+jyJKTzgXEC0qiggxeEXigEcfyk9kLCNnphKPC7fG1v+YE8AaXarXrqM/JiWsf8Gd34T6218hKGTcexrgmZaqebh01YdftSdbp/3mb+bM+QZR17P7Lh89SnmjGKavV3tcu4ztru5srGaiK0ym5ky/vdb+9Xm72r++d3V3K15lyR8PGFR2cMGc5JtYlMtSF9+pWXGNGV0blzWeZis/tVSud4KwYxH+5dmcWDTp6+0XpdHt/4wIf4qEKM0pqqT/h5yt3GBrZYUy0rrJmDD3s/bsvner90e13H9YwOFddX25KZ1a5rJnfpsSRtl96BR7JP8FDg2jKJ7Eqw2dLDOIGc4K9LRJAYBWbXN/JXv8gQ0NCpQKq52KOfBIAx2tivL0rG5RN1HVcgRW/3siFgolc28Md1rPrBPOlA3yxxIXAzTo1/vCxne8q6KLDUywB7+Ma8usfodSa0q/5unmxUffRL53NHBv9IBr/2Fq1R9db8Ts77vN3p8nFvu+yoHxnGKlOf621x8b77tmc+oZO9q/vH88xXxtDLueV8+b3WmPtLX7Whcfp1RtXn9Wyu5JDhfNfCucJHfOXDfXNfXZ5xuNeCbj6ULh64PLqRoWZd6ON5VKRb+42/xBLx6Hyij+LcVu62fCCxhYnWpH5q+kb75HJdW36p7mRqXyfrfd6ezR3JjfpnuurYNT0nabum3XhAX7h0HNbZpwtOuMDc0aXdq5cAqcCgYEbfVsGO5o1kpY/go6LAR0LDOIXgBwclBQQsBTjGNUff0EnIfP20+SA38ol+bDDXdc78eC5o/8gP5owKMqyHNZPAaY2dPP5QeAEgj169CPDX14ic7EsXMnW93TzkxUhzVVed6vcan2YvNZf1ttZHn7e7Z81bkasymlv1Ijf6+BxvI1+ZalysRjrpr3umuVs1c2el2z/6Rr66LvzmPrlvzKs2R2eHe4ms7o7rVns0V+PU+NDdnT0xCD9Yj5f7xAPWhM/cO7go9rhetZHjQ2ENtD0+dT6J7d67zTw+siMfvMa+76WPzdryCRlvd8+uZ0W2ymi+99MefSQ/q13XTO7SY0naLr0Dj2R/NQDjDi/R+1zauhQCSr0IejHVwFjnrvpb9enZ9ZHY4IdsdgELe1ozCcTo8iu4un61maPxFT80z2v3w/vV5qUkGdnSWK2dPS8PPnoxdoG587nq7OYhw4tRCaHP2dKpF7b2xufO2tofl9F6VfuY2j7mbY2rHo15v7c1j3rUrzHuxUrZYreiw2VmfulMuXx3T3zc2/Wuc06qvVFcGfW7/tG5k4ySl/olpvqFvLh2Zw6fa/994gE6mAfDrS9XrI0PhfvNPfUvMZ1Pzvah7rZYbrGWnNf44z4xpmfVLu9tH/e2y7g+73d5b49kvN/bo7kucy3tJG3XshNn9oOgrMCwZQq5lV/DtvQQgOplmAVM17fqr/QpGaHGpp7RWRMDglxl0b205M9sTDLU2NU3/RU/fK7a2PKArX5q1up/HuWFJHsup7azZx66VbrALJaSoeYluDpPv1T6/E6nj+MXL7Y9RetS7XO7vm58Vc7n0vZ5tEefOo/n+uWhk/G+LXYuu9J236t83Qf52u1pncszcq5fz/4LEzEFO15kh7M8K915dfnRl4a9MYg1wH1WtuIBa+IOUyNLLOi+0MgGa1M8Eg/3ofPJWd/3bst+rbdYV3n5otrHuz4fpy0Z1XVcz92499EefaRjVLuekcy19Cdpu5adOLMfBA8Fhi1TCrhbclvjSpYIWAQjPrT9Jc1l6fwaBWHklaigj+BYE0yeFTQ7m/hA4qbASM3z6MXRBWn6tC44sAZ01JfUzA/m4Kvm4Afr23pRItcx8/1g3bNARKCv3Lp9xw5rVakvCOawblh0RTq1RpfBxxl3l6XNenxNaquWfH1Wv3T489626/a26xn1a39Z90qR/IrsqszIN33J0N1in/2ecg58Lm3dF/aWs+TnBH/YW84/ZXQO0Ot2Ruuo/ricOHVnjD581V3t4oF06c5oXeqvdRcPqoyeKxP1e83aRvdZPsFPRX16rjX6Rnfbv8zWeXqesZYMNVzrmVB/lfPn2nYddWzr2ed62+eN+iWzNS65a6mTtF3LTpzZD4LHKDBU0woKBMNTC8GGAMLF4EMQ9+BKn/ulgCF55tKnQvAlyGu86kOOwES/y3iwYn3o4aWCDPr0spId1QRw6fFgjryvi3blteUH8uyL9M/8kD+rtdZPXYuvyZMt8ZA/qvXi8XkaY92+f7LF2n1t6ObZ94H1089+3KfIh25uN0YfRXU3b6vP53rb54364QSv1QKfka5VHZ3cSCf7q7tV9wrffR6yOv/soZahqZMAAAKpSURBVJIit8deSwa9nnhIDjvd+dG428E+epjjhfm1z8e3YhCyzPfzzzO2a6EPP/h041V+6xlG0ud3Q/O0H/im8VPuNvpGZYX1aC79WkcnMxqjX3O7eVt9mj/T4TJV32ysyl7Lc5K2a9mJK/ODIOwv9CtzL+6EwPOXxCqGGpx53vp0uqVna67kXAcvzId40bvOtEPgWgh0Z37mW5W/z52SjvvMnfl2zWNJ2q55dy7oG7+G8e3OfxW7oDsxHQKHJqBfMQ69iDgfAiFwcQJJ2i6+BdfrgF409/3z1fWuLJ6FwOMR4M9a/MqmP289nuVYCoEQeGoEkrQ9tR194PXo/13lhfPAYKPuJgjwxYf/g5RfrG9iu7PIEDg7gSRtZ0ccAyEQAiEQAiEQAiFwOoEkbaczjIYQCIEQCIEQCIEQODuBJG1nRxwDIRACIRACIRACIXA6gSRtpzOMhhAIgRAIgRAIgRA4O4EkbWdHHAMhEAIhEAIhEAIhcDqBJG2nM4yGEAiBEAiBEAiBEDg7gSRtZ0ccAyEQAiEQAiEQAiFwOoEkbaczjIYQCIEQCIEQCIEQODuBJG1nRxwDIRACIRACIRACIXA6gSRtpzOMhhAIgRAIgRAIgRA4O4EkbWdHHAMhEAIhEAIhEAIhcDqBJG2nM4yGEAiBEAiBEAiBEDg7gSRtZ0ccAyEQAiEQAiEQAiFwOoEkbaczjIYQCIEQCIEQCIEQODuBJG1nRxwDIRACIRACIRACIXA6gSRtpzOMhhAIgRAIgRAIgRA4O4EkbWdHHAMhEAIhEAIhEAIhcDqBJG2nM4yGEAiBEAiBEAiBEDg7gSRtZ0ccAyEQAiEQAiEQAiFwOoEkbaczjIYQCIEQCIEQCIEQODuBJG1nRxwDIRACIRACIRACIXA6gf8DqEd4Lu3oB2AAAAAASUVORK5CYII=\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA) was used to analyze the relationship between the expression of all genes and the biological processes, cellular components and molecular functions of the samples in the GSE51392High/Low group. Padj \u0026lt; 0.05 and FDR value (qvalue) \u0026lt; 0.05 were used as the screening criteria for significant enrichment. The results showed that the genes in the High/Low group samples of dataset GSE51392 were significantly enriched in REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING, and the genes in the high/low group were significantly enriched in reactome_interleukin_4_and_Interleukin_13_signaling. REACTOME_KERATAN_SULFATE_KERATIN_METABOLISM, WP_RETINOL_METABOLISM, REACTOME_DECTIN_2_FAMILY, WP_IL1_AND_MEGAKARYOCYTES_IN_OBESITY, REACTOME_INTERLEUKIN_10_SIGNALING, WP_VITAMIN_A_AND_CAROTENOID_METABOLISM pathway (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eA-H, Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e2.8 Gene function enrichment analysis (GO) and pathway enrichment analysis (KEGG)\u003c/p\u003e\n\u003cp\u003eGene ontology (GO) and pathway (KEGG) enrichment analysis were used to further explore the biological process (BP), cellular component (CC) of key genes (CPT1C, CWF19L1, MED17, MMP1), The relationship between molecular function (MF) and biological Pathway (Pathway) and allergic rhinitis (AR). The four key genes were used for gene ontology (GO) and pathway (KEGG) enrichment analysis, and the specific results are shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The results showed that the four key genes were mainly enriched in cellular response to UV-A, carnitine metabolic process, response to UV-A, and carnitine metabolic process. amino-acid betaine metabolic process and other biological processes (BP); post-mRNA release spliceosomal complex, AMPA glutamate receptor complex, core mediator complex, ionotropic glutamate receptor complex, mediator complex, neurotransmitter receptor complex and other cellular components (CC); nuclear vitamin D receptor binding, nuclear thyroid hormone receptor binding, palmitoyltransferase activity, O-acyltransferase activity, nuclear receptor coactivator activity and other molecular functions (MF). At the same time, it was also enriched in PPAR signaling pathway and other biological pathways. The results of gene ontology (GO) and pathway (KEGG) enrichment analysis were visualized by bar diagram (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eA) and bubble diagram (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eB). At the same time, the network diagram of biological process (BP), cell component (CC), molecular function (MF) and biological Pathway (Pathway) was drawn according to gene ontology (GO) and pathway (KEGG) enrichment analysis (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eC-F). The lines show the corresponding molecules and the annotations of the corresponding entries, and the larger the nodes, the more molecules the entries contain. Finally, the R package Pathview was used to visualize the pathway map related to the pathway (KEGG) enrichment analysis results (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e2.9 GSVA enrichment analysis based on high and low risk scores of key genes\u003c/p\u003e\n\u003cp\u003eIn order to explore the c2. All. V2023.1. Hs. Symbols. The GMT gene set in the data set GSE51392 of High and Low risk group (High/Low) group, the difference of the sample Gene Set Variation Analysis (GSVA) was performed on the expression of all genes in the AR samples of dataset GSE51392. According to the results of gene set variation analysis (GSVA), the pvalue was \u0026lt; 0.05 The differential expression of the top 10 and the bottom 10 pathways between the high-risk group (High) and the low-risk group (Low) was analyzed by logFC sorting and visualized by heat map (Fig. 13A) and group comparison map (Fig. 13B) (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The results of gene set variation analysis (GSVA) showed that 18 pathways were statistically significant (pvalue \u0026lt; 0.05) in the allergic rhinitis (AR) group and the Control (Control) group, respectively: WP_DDX1_AS_A_REGULATORY_COMPONENT_OF_THE_DROSHA_MICROPROCESSOR, TERAMOTO_OPN_TARGETS_CLUSTER_3, BIOCARTA_RAN_PATHWAY, REACTOME_TRNA_PROCESSING_IN_THE_MITOCHONDRION, REACTOME_SENSING_OF_DNA_DOUBLE_STRAND_BREAKS, SCIAN_CELL_CYCLE_TARGETS_OF_TP53_AND_TP73_UP, WP_HIF1A_AND_PPARG_REGULATION_OF_GLYCOLYSIS, WP_EXRNA_MECHANISM_OF_ACTION_AND_BIOGENESIS, REACTOME_TRAFFICKING_OF_MYRISTOYLATED_PROTEINS_TO_THE_CILIUM, PID_ARF6_DOWNSTREAM_PATHWAY, REACTOME_SARS_COV_2_TARGETS_PDZ_PROTEINS_IN_CELL_CELL_JUNCTION, BIOCARTA_CB1R_PATHWAY, KEGG_CIRCADIAN_RHYTHM_MAMMAL, REACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_TO_LIMIT_CHOLESTEROL_UPTAKE, WP_TRANSCRIPTIONAL_CASCADE_REGULATING_ADIPOGENESIS, CHOI_ATL_ACUTE_STAGE, WP_MIR222_IN_EXERCISEINDUCED_CARDIAC_GROWTH, REACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_LINKED_TO_TRIGLYCERIDE_LIPOLYSIS_IN_ADIPOSE.\u003c/p\u003e\n\u003cp\u003e2.10 Analysis of ssGSEA immune infiltration between High/Low risk groups in GSE51392 dataset\u003c/p\u003e\n\u003cp\u003eTo further explore the correlation between the expression levels of four key genes (GZMK, IFNB1, LY96, VPREB3) in AR samples in dataset GSE51392 and 28 immune cell infiltration grades in ssGSEA algorithm, Firstly, we used ssGSEA algorithm to calculate the infiltration abundance of 28 immune cells in the samples between the High and Low risk groups of the model in dataset GSE51392. Then we used pearson algorithm to analyze the correlation between the expression levels of four key genes in AR samples in dataset GSE51392 and the infiltration degree results of 28 immune cells. P \u0026lt; 0.05 was used as the standard for screening and the results were displayed by correlation dot plot (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003eA). The results showed that the expression of MMP1 in the 4 key genes in data set GSE51392 was mostly correlated with 28 immune cell infiltration grades.\u003c/p\u003e\n\u003cp\u003eThen we selected two pairs of gene immune cell pairs with the highest correlation and two pairs with the lowest correlation and presented the results respectively by drawing the correlation scatter plot (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003eB-E). The two pairs of gene immune cell pairs with the highest positive correlation in GSE51392 dataset were shown by correlation scatter plot (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003eB, R = 0.769, P \u0026lt; 0.001). MMP1 and Natural killer cell (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003eC, R = 0.760, P \u0026lt; 0.001) and MMP1 and Immature dendritic cell (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003eD, R = -0.733, P \u0026lt; 0.001) had the highest negative correlation. P \u0026lt; 0.001). 14E, R = -0.788, P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e2.11 CIBERSORT immune infiltration analysis between High and Low risk (High/Low) groups of GSE51392 dataset\u003c/p\u003e\n\u003cp\u003eTo explore the CIBERSORT immune infiltration analysis between the High and Low risk groups of the model GSE51392 dataset, We used the CIBERSORT algorithm to calculate the correlation between the expression profile data of 22 immune cells and High/Low groups for the samples of High/Low groups in dataset GSE51392. According to the results of immune infiltration analysis, 19 immune cells with abundance greater than or equal to 0 were selected: B cells naive, B cells memory, Plasma cells, T cells CD8, T cells CD4 naive, T cells CD4 memory resting, T cells follicular helper, T cells regulatory (Tregs), T cells gamma delta, NK cells resting, NK cells activated, Monocytes, Macrophages M0, Macrophages M1, Macrophages M2, Dendritic cells activated, Mast cells activated, Eosinophils, Neutrophils. We plotted the immune cell infiltration of these 19 immune cells in the dataset GSE51392AR sample data grouped by High/Low risk (Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003eA) in the form of stacked bar graphs. Then we further calculated the correlation between the 19 immune cells with abundance greater than 0 in the low-risk group and the high-risk group samples and presented the results (Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003eB-C). The results showed that in the low-risk group of dataset GSE51392, the number of positive and negative correlations between the infiltration abundance of the 19 immune cells was equal. Among them, the positive correlation between Dendritic cells activated and Eosinophils was the highest, and the negative correlation between B cells naive and Eosinophils was the highest (Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003eB). In the high risk group of dataset GSE51392, the number of positive and negative correlations between infiltration abundance of 19 immune cells was comparable (Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003eC), among which the positive correlation between immune cells Macrophages M1 and B cells naive was the highest. The highest negative correlation was found between T cells regulatory T cells (Tregs) and T cells follicular helper.\u003c/p\u003e\n\u003cp\u003eFinally, we also calculated the correlation between the abundance of infiltration of these 19 immune cells and the expression levels of 4 genes (CPT1C, CWF19L1, MED17, MMP1) that were significantly different between the high and low risk groups of AR samples in dataset GSE51392. P \u0026lt; 0.05 was used as the standard for screening and the results were presented by correlation graph (Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003eD-E). In data set GSE51392, the results of the low-risk group showed the abundance of six immune cell infiltration (Mast cells activated, T cells CD8, Plasma cells, Macrophages M1, T cells CD4 naive, T cells activated, T cells CD8, plasma cells). Macrophages M0) and the expression of 2 genes (CWF19L1, MMP1) were significantly correlated (P \u0026lt; 0.05). The results of the high-risk group showed that there was a significant correlation between the abundance of three immune cell infiltration (Eosinophils, Macrophages M0, T cells CD4 memory resting) and the expression of two genes (CWF19L1, MMP1) (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e2.12 Interaction network analysis of key genes\u003c/p\u003e\n\u003cp\u003eWe used the mRNA-miRNA data from the ENCORI database to predict mirnas that interacted with key genes (CPT1C, CWF19L1, MED17, MMP1) and retained only those interactions that had been documented in at least three databases. Then, Cytoscape software was used to draw the mRNA-miRNA interaction network for visualization (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003eA), and the red squares in the mRNA-miRNA interaction network were mrnas. Blue squares are mirnas. According to the mRNA-miRNA interaction network, our mRNA-miRNA interaction network is composed of 3 Key genes (CWF19L1, MED17, MMP1) and 50 miRNA molecules, which constitute a total of 50 mRNA-miRNA interaction relationships. The specific mRNA-miRNA interaction relationships are shown in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTranscription factors (TFS) control gene expression by interacting with target genes (mrnas) at the post-transcriptional stage. We searched for Transcription factors (TFS) that bind to key genes through CHIPBase database and hTFtarget database. The interactions found in the two databases were downloaded and intercrossed with four key genes. Then Cytoscape software was used to draw the mRNA-TF interaction network for visualization (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003eB). The red squares in the mRNA-TF interaction network were mrnas. Blue circles are mirnas. Finally, 4 key genes (CPT1C, CWF19L1, MED17, MMP1) and 41 transcription factors (TFS) were obtained to form a total of 57 mRNA-TF interaction relationships. The interaction relationships are shown in the Supplementary Table (Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe used the CTD database to predict potential drugs or small molecule compounds that interacted with key genes (CPT1C, CWF19L1, MED17, MMP1), and used \"Reference Count\" \u0026gt; 1 as the screening criterion to screen mRNA-drugs interaction pairs. Cytoscape software was used to visualize the mRNA-drugs interaction network (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003eC). The red squares in the mRNA-drugs interaction network are mrnas. Blue triangles are drugs. According to the mRNA-drugs interaction network, our mRNA-drugs interaction network is composed of 3 mrnas (CPT1C, MED17, MMP1) and 36 drugs molecules, which constitute a total of 36 mRNA-drugs interaction relationships. The mRNA-drugs interaction relationship is shown in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e2.13 Spatial protein structure of key genes\u003c/p\u003e\n\u003cp\u003eAlphaFold Protein Structure Database Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.alphafold.ebi.ac.uk/\u003c/span\u003e\u003c/span\u003e) contains AlphaFold ai system about 350000 Protein Structure prediction, It covers humans as well as 20 model organisms commonly used in biological research (E. coli, Drosophila, zebrafish, mouse...).. In terms of the human proteome, AlphaFold made predictions about the structure of 98.5 percent of human proteins. The protein results of four key genes (CPT1C, CWF19L1, MED17, MMP1) were analyzed and displayed using the AlphaFold website (Fig. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003eA-D).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAllergic Rhinitis (AR) is a common inflammatory disorder of the nasal mucosa, characterized by symptoms such as sneezing, nasal itching, congestion, and rhinorrhea. It is triggered by allergens like pollen, dust mites, and animal dander. AR not only affects the quality of life of patients but also poses a significant economic burden due to its high prevalence and associated medical costs. The pathogenesis of AR is multifaceted, with genetic predisposition, environmental factors, and immune dysregulation playing crucial roles [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Among these, the role of hypoxia in the progression of AR has recently garnered attention, prompting us to delve deeper into its association.\u003c/p\u003e \u003cp\u003eOur study embarked on a comprehensive research to elucidate the hypoxia-related genes and mechanisms associated with AR. In our study investigating the intricate relationship between allergic rhinitis (AR) and hypoxia-related genes, we took a comprehensive approach to improve result accuracy. One of our key strategies involved identifying differential genes associated with AR, specifically focusing on genes with known relevance to asthma. The rationale behind this approach lies in the shared immunological aspects between AR and asthma, where both conditions involve complex immune responses in the upper and lower respiratory tract.\u003c/p\u003e \u003cp\u003eOur analysis led to the identification of several crucial genes, namely CPT1C, CWF19L1, MED17, and MMP1, which exhibited significantly higher expression levels in the early stages of AR compared to established disease states. This observation highlights the importance of these genes in the initial phases of the disease, potentially contributing to the onset and progression of AR.\u003c/p\u003e \u003cp\u003ePrevious studies have indicated the involvement of CPT1C in fatty acid metabolism and energy production within cells [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Dysregulation of these processes can lead to cellular stress and inflammation, which are common features in allergic conditions. CWF19L1 has been linked to the regulation of immune responses and inflammatory pathways. Its elevated expression in the early stages of AR suggests a potential role in the initiation of allergic reactions. MED17 is part of the mediator complex involved in gene transcription. Dysregulation of this complex can impact the expression of genes related to immune responses, potentially influencing the early phases of AR [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. MMP1 is known for its role in tissue remodeling and extracellular matrix degradation. Elevated MMP1 expression in the early stages of AR might contribute to tissue changes in the nasal mucosa characteristic of allergic inflammation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is noteworthy that these genes, including CPT1C, CWF19L1, MED17, and MMP1, have been previously identified as having important roles in various diseases. While their specific functions in AR have not been fully understood before our study, their significance in other contexts underscores their potential relevance in AR pathogenesis. For instance, CPT1C has been associated with metabolic disorders and neurological diseases, emphasizing its versatile role in cellular functions. CWF19L1 has been linked to immune-related processes in autoimmune diseases, indicating its broader involvement in immune regulation. MED17's importance in gene transcription regulation has been demonstrated in cancer and developmental disorders. MMP1, as a member of the matrix metalloproteinase family, has been extensively studied in tissue remodeling processes in cancer and inflammatory diseases.\u003c/p\u003e \u003cp\u003eThe finding that these genes are upregulated in early AR stages aligns with prior research suggesting their importance in immune responses and allergic diseases. However, their specific roles in AR's pathogenesis, especially in the early phases, remain incompletely understood. Our study extends this understanding and highlights their potential significance as early diagnostic markers or therapeutic targets.\u003c/p\u003e \u003cp\u003eOur foremost discovery centers on specific genes, particularly CWF19L1 and MMP1, which exhibited pronounced correlations with immune cell infiltration in both low-risk and high-risk groups of AR patients. These findings underscore the complex interplay between the immune microenvironment within the nasal mucosa and hypoxia-related genes. This observation raises intriguing questions about the potential contributions of gene dysregulation to the immunopathology of AR. The identification of these correlations highlights the pivotal roles played by hypoxia-related genes, such as CWF19L1 and MMP1, in shaping the immunological landscape of AR.\u003c/p\u003e \u003cp\u003eOur enrichment analyses provided a deeper understanding of the biological implications of our HRDEGs. The significant involvement of our HRDEGs in processes like cellular response to UV-A, carnitine metabolic process, and pathways like PPAR signaling pathway suggests a multifaceted role of these genes in AR11. Their involvement in various biological processes and pathways underscores their potential diagnostic and therapeutic value. The differential expression patterns of HRDEGs in various phenotypically related samples, as revealed by GSEA and GSVA, further emphasize their significance in AR's pathogenesis.\u003c/p\u003e \u003cp\u003eFurthermore, our study has unveiled distinct immune cell subtypes that display significant associations with the expression of hypoxia-related genes. This revelation points to the substantial influence exerted by these genes on the immune microenvironment within the nasal mucosa during AR. This phenomenon deepens our understanding of how genes like CWF19L1 and MMP1 may impact immune responses and inflammation in AR. The interplay between hypoxia-related pathways and the immune system, as evidenced in our study, could contribute to the exacerbation of AR symptoms and the progression of the disease.\u003c/p\u003e \u003cp\u003eAdditionally, our research underscores the importance of patient stratification in the context of AR. We have observed distinct correlations in immune cell infiltration and gene expression patterns between low-risk and high-risk groups of AR patients. This suggests that different subgroups of AR patients may exhibit unique immunological profiles. Therefore, personalized approaches to diagnosis and treatment may be necessary to address the diverse needs of AR patients effectively.\u003c/p\u003e \u003cp\u003eOur analysis heavily relies on publicly available gene expression datasets, specifically GSE51392 and GSE46171. While these datasets provide a wealth of information, they are not without limitations. Variations in sample size, experimental conditions, and data preprocessing may introduce biases or confounding factors that could impact our results. While our study highlights the potential roles of CPT1C, CWF19L1, MED17, and MMP1 in AR, functional validation experiments are required to confirm their specific contributions to disease pathogenesis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study sheds light on the molecular intricacies of AR, providing robust evidence for the role of CPT1C, CWF19L1, MED17, and MMP1 in its pathogenesis. The potential diagnostic and prognostic applications of these findings could revolutionize AR management, moving towards more personalized and effective therapeutic strategies. Further studies are necessary to validate these biomarkers in larger cohorts and diverse populations, and to explore their therapeutic implications in AR and beyond.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eallergic rhinitis (AR),hypoxia-related differential expression genes (HRDEGs),chronic obstructive pulmonary disease (COPD),Gene Set Enrichment Analysis (GSEA),Hypoxia related genes (HRGs),Benjamini-Hochberg (BH),Gene Set Variation Analysis (GSVA),Support Vector Machine (SVM),Decision curve analysis (DCA),Gene Ontology (GO),biological process (BP), ,molecular function (MF) ,cellular component (CC).,Kyoto Encyclopedia of Genes and Genomes (KEGG),Receiver operating characteristic curve (ROC),Single-sample gene set enrichment analysis (ssGSEA),Transcription factors (TF),differentially expressed genes (DEGs),\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuanhui Huang and Shiyun Shao designed the research study. Shiyun Shao analyzed and interpreted data. Shiyun Shao and Xiao Feng wrote the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available in a public, open access repository, Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as supplemental information, The datasets (GE0 data) and (TCGA LIHC data) for this study can be found in the GE0 (https://www.ncbi.nlm.nih.gov/) and TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGreiner, A. N., Hellings, P.W., Rotiroti, G. \u0026amp; Scadding, G. K. Allergic rhinitis. Lancet 378, 21 12\u0026ndash;2122 (2011).\u003c/li\u003e\n\u003cli\u003eBousquet, J. et al. Allergic Rhinitis and its Impact on Asthma (ARIA) 2008 update. 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Am J Med Genet B Neuropsychiatr Genet 177(8):687\u0026ndash;690\u003c/li\u003e\n\u003cli\u003eTrivedi V, Boire A, Tchernychev B, Kaneider NC, Leger AJ, O\u0026apos;Callaghan K, Covic L, Kuliopulos A (2009) Platelet matrix metalloprotease‐1 mediates thrombogenesis by activating PAR1 at a cryptic ligand site. Cell 137:332‐343\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGSE46171 Dataset and GSE51392 Dataset information list.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGSE51392\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGSE46171\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlatform\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPL4133\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPL16981\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpecies\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHomo sapiens\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHomo sapiens\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eprimary nasal and bronchial epithelial cells\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNasal\u0026nbsp;mucosa\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSamples in AR group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSamples in Control group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAR, Allergic rhinitis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGSEA enrichment analysis GSE51392 Dataset (AR/Control).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esetSize\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eenrichmentScore\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePadj\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eqvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_INFLAMMATORY_RESPONSE_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.666560056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.952780071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000455789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005956374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004590169\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_ASSEMBLY_OF_COLLAGEN_FIBRILS_AND_OTHER_MULTIMERIC_STRUCTURES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.587477157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.951917827\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000484027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005956374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004590169\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_MET_PROMOTES_CELL_MOTILITY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.598192736\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.852394168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001421801\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012378737\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009539442\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_CANONICAL_AND_NONCANONICAL_NOTCH_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.623219847\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.783101052\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005447118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03463308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026689336\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_VITAMIN_A_AND_CAROTENOID_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.568928216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.761772161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003791469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02632847\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020289544\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_EPITHELIAL_TO_MESENCHYMAL_TRANSITION_IN_COLORECTAL_CANCER\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.445312168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.728936154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000519481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006254277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004819742\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_TRIGLYCERIDE_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.568071148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.716408199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006993007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.042609351\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.032836101\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_CIRCADIAN_CLOCK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.493525859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.687625261\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004341534\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.029013585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022358777\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_FOCAL_ADHESION_PI3KAKTMTORSIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.387154464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.626321882\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000583771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006781566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005226087\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.436384315\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.613352282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00297619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.021825397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.016819334\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_TGF_BETA_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.43445153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.543296064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008099095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.047938928\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.036943239\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_PI3KAKT_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e330\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.326276774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.386288178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005973716\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.037219692\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028682661\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_REGULATION_OF_TP53_ACTIVITY_THROUGH_PHOSPHORYLATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.446033194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.537415637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00685401\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.041865032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.032262505\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SIGNALING_BY_HEDGEHOG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.418636859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.541565111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00195122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015749129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012136772\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_FC_EPSILON_RECEPTOR_FCERI_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.43930619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.60613929\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001624959\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013881955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01069787\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_TRANSCRIPTIONAL_REGULATION_BY_TP53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e326\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.407200723\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.640555217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00030248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005029273\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003875716\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_GLUCOSE_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.480216954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.645489893\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002737851\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020439331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015751188\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_ONECARBON_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.586934892\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.646161759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008208423\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.048407607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.037304418\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SIGNALING_BY_NOTCH4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.500632652\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.699183736\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001371742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012355619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009521627\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_HEDGEHOG_ON_STATE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.502260096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.708996286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001033414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010013803\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00771695\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_BUTYRATE_RESPONSE_FACTOR_1_BRF1_BINDS_AND_DESTABILIZES_MRNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.685664368\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.726907669\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006783292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.041535131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.032008273\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_BIOMARKERS_FOR_PYRIMIDINE_METABOLISM_DISORDERS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.717041282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.728327532\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005673759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.035890495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02765834\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_DISORDERS_OF_FOLATE_METABOLISM_AND_TRANSPORT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.734216043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.730813502\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004270463\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028748553\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022154536\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_FATTY_ACIDS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.708400424\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.731393325\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005761613\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.036292192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0279679\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_OXIDATIVE_PHOSPHORYLATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.587460002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.732428583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004889976\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.031990735\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.024653062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_KSRP_KHSRP_BINDS_AND_DESTABILIZES_MRNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.693863229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.747557241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004284184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028748553\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022154536\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_PATHWAYS_OF_NUCLEIC_ACID_METABOLISM_AND_INNATE_IMMUNE_SENSING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.725987912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.74989213\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003546099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025124587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.019361794\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_CELLULAR_RESPONSE_TO_HYPOXIA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.536062954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.781896873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001028807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010013803\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00771695\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_HEDGEHOG_OFF_STATE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.506388474\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.794839279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000335683\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005029273\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003875716\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGSEA: Gene Set Enrichment Analysis. AR, Allergic rhinitis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGSEA enrichment analysis GSE46171 Dataset (AR/Control).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esetSize\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eenrichmentScore\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePadj\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eqvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIOCARTA_IL2_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.669896141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.781086615\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003517749\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.049228718\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.042271231\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIOCARTA_IL2RB_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.609653299\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.801248514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002707581\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04163324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.035749221\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_DRUG_METABOLISM_CYTOCHROME_P450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.699887884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.607747568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00070373\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.017019329\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01461399\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_GLUTATHIONE_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.553174462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.972029165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00128866\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.024135726\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020724628\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_JAK_STAT_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.449220608\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.640282917\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000750939\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.017482133\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015011386\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.522294365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.934112871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000693481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.016935901\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.014542352\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.532479724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.90815374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000254972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00973294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008357385\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_PROPANOATE_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.639132652\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.051845741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000581734\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0151576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013015379\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_RIBOFLAVIN_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.750882424\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.814795755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002314815\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.037960657\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.032595683\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNABA_ECM_AFFILIATED\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.435177866\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.599109185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000993295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020968631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018005138\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePID_IL12_2PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.647919946\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.093091347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000280899\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00973294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008357385\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePID_IL12_STAT4_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.667486075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.928667211\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00060241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0151576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013015379\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePID_IL2_1PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.57521809\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.823282845\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000570125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0151576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013015379\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePID_IL23_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.619323753\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.82982031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001504212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.027152113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023314709\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_COSTIMULATION_BY_THE_CD28_FAMILY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.643039636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.11494122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000277316\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00973294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008357385\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_DECTIN_2_FAMILY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.674026137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.824802607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002857143\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.043134199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03703805\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_INTERLEUKIN_10_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.764336594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.334262803\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000292141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00973294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008357385\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_INTERLEUKIN_2_FAMILY_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.617873212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.886967688\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000584283\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0151576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013015379\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.519509497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.82583856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000261028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00973294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008357385\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_NEUTROPHIL_DEGRANULATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e453\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.362463986\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.454386695\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0004302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012285945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010549575\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SIGNALING_BY_INTERLEUKINS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e441\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.46620367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.868743165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000215332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00973294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008357385\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_FOLATE_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.581901484\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.909247333\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000556019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0151576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013015379\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_IL18_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e263\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.447838977\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.729202463\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000230574\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00973294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008357385\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_IL2_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58043378\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.757547639\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003513909\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.049228718\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.042271231\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_IL3_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.583495125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.806188014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001158413\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022543802\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01935769\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_IL7_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.654055801\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.787985774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003146633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.046656328\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.040062396\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_INTERACTIONS_BETWEEN_IMMUNE_CELLS_AND_MICRORNAS_\u003cbr /\u003eIN_TUMOR_MICROENVIRONMENT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.758784196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.120293275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000311818\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00973294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008357385\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_INTERACTIONS_OF_NATURAL_KILLER_CELLS_IN_PANCREATIC_CANCER\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.718894349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.976938159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000313775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00973294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008357385\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGSEA: Gene Set Enrichment Analysis. AR, Allergic rhinitis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGSEA enrichment analysis GSE51392 Dataset (High/Low).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esetSize\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eenrichmentScore\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePadj\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eqvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_VITAMIN_A_AND_CAROTENOID_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.91785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006057\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_INTERLEUKIN_10_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65233\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.913143\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006057\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_IL1_AND_MEGAKARYOCYTES_IN_OBESITY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.694568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.819214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.024347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018958\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_DECTIN_2_FAMILY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.67506\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.768117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005249\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.039743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030947\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_RETINOL_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.759148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.766216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005124\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.039196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030521\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_KERATAN_SULFATE_KERATIN_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.630227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.763419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026722\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020808\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.508895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.745545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000672\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007118\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_INTERLEUKIN_17_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.540842\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.736866\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.016321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012709\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_NOVEL_INTRACELLULAR_COMPONENTS_OF_RIGILIKE_RECEPTOR_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.557471\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.733715\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023447\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018258\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_INTERLEUKIN1_IL1_STRUCTURAL_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.572533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.728509\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.021749\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.016935\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SIGNALING_BY_INTERLEUKINS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.429654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.720506\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006057\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_IL6_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.575937\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.700628\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.039196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030521\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePID_TGFBR_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.551155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.688757\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005348\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.040163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.031274\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_EPITHELIAL_CELL_SIGNALING_IN_HELICOBACTER_PYLORI_INFECTION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.514999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.644888\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004569\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.036293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02826\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_OVERVIEW_OF_PROINFLAMMATORY_AND_PROFIBROTIC_MEDIATORS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.476856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.636307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022679\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01766\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_NEGATIVE_REGULATION_OF_THE_PI3K_AKT_NETWORK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.465466\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.601566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.029132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022685\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_NEUTROPHIL_DEGRANULATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e448\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.380399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.525489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006057\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_IL18_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.398587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.518449\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001232\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011686\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_METABOLISM_OF_AMINO_ACIDS_AND_DERIVATIVES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.32572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.35498\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005245\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.039743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030947\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_HEDGEHOG_OFF_STATE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4189\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.5148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.037908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.029518\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_METABOLISM_OF_VITAMINS_AND_COFACTORS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e174\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.39365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.52304\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.017275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013451\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_METABOLISM_OF_NUCLEOTIDES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.44655\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.55677\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.049882\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.038842\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_HEDGEHOG_ON_STATE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.59646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.039743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030947\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_REGULATION_OF_TP53_ACTIVITY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.42386\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.60867\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009645\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_PYRIMIDINE_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.46526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.64485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002422\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022986\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.017899\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_GLUTATHIONE_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.52997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.67299\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.041794\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.032544\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_GLYCOLYSIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.50068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.68661\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002318\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022504\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.017524\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_METABOLISM_OF_FAT_SOLUBLE_VITAMINS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.53539\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.69008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003984\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.032796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025537\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGSEA: Gene Set Enrichment Analysis. AR, Allergic rhinitis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGOKEGG enrichment analysis results of key genes.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eONTOLOGY\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eGeneRatio\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBgRatio\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePadj\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eqvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0071492\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ecellular response to UV-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e11/18800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.042445771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.01806203\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0009437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ecarnitine metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e13/18800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.042445771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.01806203\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0070141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eresponse to UV-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e14/18800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.042445771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.01806203\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0006577\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eamino-acid betaine metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17/18800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.042445771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.01806203\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0071014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003epost-mRNA release spliceosomal complex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e12/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.033570481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.012849945\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0032281\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eAMPA glutamate receptor complex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e26/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.033570481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.012849945\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0070847\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ecore mediator complex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e26/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.033570481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.012849945\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0008328\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eionotropic glutamate receptor complex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e40/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.033570481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.012849945\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0016592\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003emediator complex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e40/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.033570481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.012849945\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0098878\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eneurotransmitter receptor complex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e45/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.033570481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.012849945\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0042809\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003enuclear vitamin D receptor binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e15/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.043606365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.015300479\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0046966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003enuclear thyroid hormone receptor binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e29/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.043606365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.015300479\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0016409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003epalmitoyltransferase activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e37/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.043606365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.015300479\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0008374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eO-acyltransferase activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e53/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.043606365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.015300479\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003enuclear receptor coactivator activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/1/4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e56/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.043606365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.015300479\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa03320\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePPAR signaling pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023/2/3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e75/8164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.00372525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.001045684\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGO: Gene Ontology; BP: biological process; CC: cellular component; MF: molecular function. KEGG, Kyoto Encyclopedia of Genes and Genomes; HRDEGs: Hypoxia related differentially expressed genes.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGSVA enrichment analysis GSE51392 Dataset (AR/Control).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003elogFC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAveExpr\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePadj\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIOCARTA_RAN_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.525135498\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.060408483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000369704\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102567442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.065399945\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_GDP_FUCOSE_BIOSYNTHESIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.494935488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.052760525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001843162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.144382393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.296180124\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_HYALURONAN_BIOSYNTHESIS_AND_EXPORT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48076637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017851579\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000484021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103370692\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.163859188\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_VITAMIN_B1_THIAMIN_METABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.440463907\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040010092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000523276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.104095716\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.230160452\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SCAVENGING_BY_CLASS_F_RECEPTORS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.438245156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041222039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001853124\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.144382393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.300721362\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_HIF1A_AND_PPARG_REGULATION_OF_GLYCOLYSIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.430002944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.059643458\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002730179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16926399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.626519519\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_METALLOTHIONEINS_BIND_METALS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.425443321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004987262\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002192415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14707766\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.442242878\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKRISHNAN_FURIN_TARGETS_UP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.422525194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018526141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000241664\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102567442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.427693778\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIOCARTA_PROTEASOME_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.413234007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010243535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000436415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102567442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.075795113\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SYNTHESIS_OF_DOLICHYL_PHOSPHATE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.409841535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017384575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000787373\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.108791455\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.577092001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_ACTIVATED_NTRK2_SIGNALS_THROUGH_FYN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.322204621\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018684376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00835996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.284238642\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.558347055\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_UPTAKE_OF_DIETARY_COBALAMINS_INTO_ENTEROCYTES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.328707605\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018156917\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005884076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.255041706\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.267635169\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_MIR222_IN_EXERCISEINDUCED_CARDIAC_GROWTH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.34017406\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.033472873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025134585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.407042385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.454921363\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHASLINGER_B_CLL_WITH_MUTATED_VH_GENES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.346572259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.033810457\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000175691\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102567442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.699718012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIWANAGA_E2F1_TARGETS_NOT_INDUCED_BY_SERUM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.346988782\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.037419643\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003113511\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.178930462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.736672117\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCCOLLUM_GELDANAMYCIN_RESISTANCE_DN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.363548547\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017672553\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003181744\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.181247788\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.754830864\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SIGNALING_BY_NOTCH1_T_7_9_NOTCH1_M1580_K2555_TRANSLOCATION_MUTANT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.384539971\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041012383\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002762457\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16926399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.636380584\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIOCARTA_SALMONELLA_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.387108633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000644908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001795885\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.144382393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.274286206\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRAFFEL_VEGFA_TARGETS_UP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.398727993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000480108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000116088\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102567442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05362427\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_ACYL_CHAIN_REMODELING_OF_DAG_AND_TAG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.417025868\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.054218414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002355923\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.154539005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.502714701\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGSVA, Gene Set Variation Analysis. AR, Allergic rhinitis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGSVA enrichment analysis GSE46171 Dataset (AR/Control).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003elogFC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAveExpr\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePadj\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_CONJUGATION_OF_BENZOATE_WITH_GLYCINE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.595565102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008578752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00870236\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.843513513\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_TYROSINE_CATABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.589010196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000586248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003634967\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.65611297\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_TYROSINE_METABOLISM_AND_RELATED_DISORDERS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.589010196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000586248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003634967\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.65611297\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_PROPIONYL_COA_CATABOLISM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.579674819\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015489779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006191191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.769919353\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_VITAMIN_B6DEPENDENT_AND_RESPONSIVE_DISORDERS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.572600547\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001174523\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008510005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.838664068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIOCARTA_EEA1_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.560448638\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06113302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013010775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.931152954\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_MITOCHONDRIAL_FATTY_ACID_SYNTHESIS_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.552447976\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040223951\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017189948\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.992212062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBYSTRYKH_HEMATOPOIESIS_STEM_CELL_FGF3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.544340166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009628858\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004481797\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.700666183\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHOLLEMAN_DAUNORUBICIN_B_ALL_DN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.538029479\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001067863\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002643783\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.588916666\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_ELECTRIC_TRANSMISSION_ACROSS_GAP_JUNCTIONS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.53589791\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004473138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007343354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.80673564\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDASU_IL6_SIGNALING_DN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.596624569\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05516285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003763735\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.663499896\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_ESTROGEN_BIOSYNTHESIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.605676089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003460789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002547141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.581101298\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTERAO_AOX4_TARGETS_HG_DN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.623790464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009049224\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005846149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.757584629\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_DEFECTIVE_F9_ACTIVATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.624236148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011580347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001843722\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.513676435\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_MELANIN_BIOSYNTHESIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.667760074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03889754\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00737566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.807684598\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_RUNX3_REGULATES_WNT_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.668552147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051733539\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001046724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.39737633\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_NECTIN_NECL_TRANS_HETERODIMERIZATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.694189583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004087266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001471802\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.467111067\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTONKS_TARGETS_OF_RUNX1_RUNX1T1_FUSION_SUSTAINED_IN_MONOCYTE_DN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.707953147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010196513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000278063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.134668042\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_EPITHELIAL_MESENCHYMAL_TRANSITION_EMT_DURING_GASTRULATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.717933346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020688586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000730368\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.324695139\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePASTURAL_RIZ1_TARGETS_DN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.780084907\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008469229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000453448\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999613633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.229972844\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGSVA, Gene Set Variation Analysis. AR, Allergic rhinitis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGSVA enrichment analysis GSE51392 Dataset (High/Low).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003elogFC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAveExpr\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePadj\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_DDX1_AS_A_REGULATORY_COMPONENT_OF_THE_DROSHA_MICROPROCESSOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.795388063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007994751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.60 e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.026000459\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.213482441\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTERAMOTO_OPN_TARGETS_CLUSTER_3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.787785391\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02590135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.35 e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02551622\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.588641155\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIOCARTA_RAN_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72397089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.053694304\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.62 e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.035827387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.680489297\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_TRNA_PROCESSING_IN_THE_MITOCHONDRION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.713694676\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04679351\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005048406\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.144285396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.112889359\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SENSING_OF_DNA_DOUBLE_STRAND_BREAKS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.684455895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.035375588\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000123689\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045838432\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.131984604\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SYNTHESIS_OF_WYBUTOSINE_AT_G37_OF_TRNA_PHE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.683242921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02622534\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005236533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.145948682\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.144507668\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSCIAN_CELL_CYCLE_TARGETS_OF_TP53_AND_TP73_UP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.661655652\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002683472\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.75 e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02551622\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.449821566\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_HIF1A_AND_PPARG_REGULATION_OF_GLYCOLYSIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.637320249\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006749236\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000475165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.093506699\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050019304\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_EXRNA_MECHANISM_OF_ACTION_AND_BIOGENESIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.607471091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.065281905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004912566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.143059196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.089307701\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_TRAFFICKING_OF_MYRISTOYLATED_PROTEINS_TO_THE_CILIUM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.603478448\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.039468854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000674943\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102781069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.357977658\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMARIADASON_RESPONSE_TO_BUTYRATE_CURCUMIN_SULINDAC_TSA_2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.597928931\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.052527585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004910055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.143059196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.088865684\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePID_ARF6_DOWNSTREAM_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.600107433\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028323103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.24 e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.026000459\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.307341622\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SARS_COV_2_TARGETS_PDZ_PROTEINS_IN_CELL_CELL_JUNCTION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.602187996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.071782598\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001898367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.106897356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.263001767\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBIOCARTA_CB1R_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.605786256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009720656\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001955004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.106897356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.288653764\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_CIRCADIAN_RHYTHM_MAMMAL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.647403084\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.064496609\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000113337\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045838432\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.208731102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_\u003cbr /\u003eTO_LIMIT_CHOLESTEROL_UPTAKE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.655877067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036726028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000413866\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08958829\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.071236732\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_TRANSCRIPTIONAL_CASCADE_REGULATING_ADIPOGENESIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.672488886\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051226134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.30 e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036455996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.595029512\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHOI_ATL_ACUTE_STAGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.677490069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001964758\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000657202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102781069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.334614683\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_MIR222_IN_EXERCISEINDUCED_CARDIAC_GROWTH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.822295138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.070516027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.70 e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030543939\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.980164233\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_LINKED_\u003cbr /\u003eTO_TRIGLYCERIDE_LIPOLYSIS_IN_ADIPOSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.83270275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.038784008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.00 e-06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02551622\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.526886046\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGSVA, Gene Set Variation Analysis. AR, Allergic rhinitis.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4096488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4096488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the realm of immunological disorders, allergic rhinitis (AR) persists as a prevalent condition, yet its molecular underpinnings remain only partially deciphered, necessitating deeper exploration. This study pioneers in bridging this knowledge gap, unveiling intricate molecular markers and pathways pivotal to AR's pathophysiology, thereby steering the scientific community towards novel diagnostic and prognostic frontiers. Employing rigorous bioinformatics analyses, similar to methodologies applied in studies on endometriosis and age-related macular degeneration, we delved into the molecular landscape, identifying 21 hypoxia-related differential expression genes (HRDEGs) and constructing a robust LASSO diagnostic model, a methodology that stands out for its precision in capturing clinical heterogeneity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur approach encompassed a comprehensive analysis of differential gene expressions, focusing particularly on HRDEGs, and their subsequent integration into a logistic regression model to ascertain their diagnostic and prognostic efficacy. Key findings revealed a high expression of genes such as CPT1C and MMP1 in the AR group, underscoring their significance in AR's molecular signature. Furthermore, the constructed LASSO model demonstrated high accuracy, highlighting genes like CPT1C, CWF19L1, MED17, and MMP1 as reliable biomarkers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInterestingly, the study also unearthed a nuanced interplay between AR and other systemic conditions, suggesting that the molecular mechanisms underlying allergic inflammation could influence the pathophysiology of various respiratory diseases3. These insights not only contribute to the academic discourse but also hold profound therapeutic potential, particularly in the realm of personalized medicine.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn conclusion, this research illuminates the molecular complexities of AR, offering substantial evidence for the involvement of specific genes and pathways in its pathogenesis. The implications of these discoveries are far-reaching, promising to revolutionize AR management through more tailored therapeutic strategies and underscoring the need for further investigations in larger, more diverse cohorts.\u003c/p\u003e","manuscriptTitle":"Integrative analyses of hypoxia-related genes and mechanisms associated with Allergic Rhinitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-02 09:59:25","doi":"10.21203/rs.3.rs-4096488/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b95ca24-23c0-46c6-b039-e203700dea27","owner":[],"postedDate":"April 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30122032,"name":"Biological sciences/Biochemistry"},{"id":30122033,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2024-06-25T05:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-02 09:59:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4096488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4096488","identity":"rs-4096488","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}