Identification and clinical validation of endoplasmic reticulum genes related to pulmonary tuberculosis

Identification and clinical validation of endoplasmic reticulum genes related to pulmonary tuberculosis | 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 Identification and clinical validation of endoplasmic reticulum genes related to pulmonary tuberculosis min li, Yuxiu wang, Ling-feng Min, Meiying Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6582977/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Tuberculosis caused by Mycobacterium tuberculosis is a serious infectious disease. Previous studies have shown that endoplasmic reticulum (ER) stress plays an important role in various infectious diseases. This study aims to identify potential ER stress-related genes in tuberculosis by analyzing differentially expressed genes (DEGs) and to explore the role of ER stress in Mycobacterium tuberculosis infection. This study identified 10 endoplasmic reticulum stress-related differentially expressed genes (ERSRDEGs) by standardizing and analyzing the differential expression of the dataset GSE114911. GO and KEGG enrichment analysis found that ERSRDEGs are significantly involved in neutrophil migration 17/TNF signaling pathway. Protein-protein interaction network identified four hub genes ( IL-1B , CCL20, IL-1A , TNF), among which IL-1B showed highly significant differential expression in the independent dataset GSE147964, demonstrating excellent diagnostic performance (AUC = 0.93), and was validated by ELISA for its high expression in the serum of tuberculosis patients. Immune infiltration analysis showed that the infiltration of M1 macrophages increased in the tuberculosis infection group, and IL-1B was strongly positively correlated with M1 macrophages. In addition, the study also analyzed the correlation between IL-1A and IL-1B with clinical indicators (inflammatory factors, D-dimer). According to the analysis results, IL-1B was positively correlated with IL-6, TNF, and IFN-γ, while IL-1A was positively correlated with D-dimer.Our findings emphasize the critical role of ER stress-related genes in the pathophysiology of Mycobacterium tuberculosis infection. Health sciences/Biomarkers/Diagnostic markers Health sciences/Diseases/Infectious diseases/Tuberculosis Endoplasmic reticulum stress pulmonary tuberculosis bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a serious infectious disease that has long posed a significant threat to human health, primarily due to the widespread prevalence of MTB infections 1 . In 2023, there were 10.8 million new cases of tuberculosis globally, resulting in 1.25 million unfortunate deaths, which has profound implications for public health and economic stability. Despite the availability of effective anti-tuberculosis treatment regimens, management strategies have become increasingly complex due to the long treatment duration, significant side effects, and the emergence of multidrug-resistant (MDR) strains. These factors underscore the importance of in-depth research into the pathogenesis of tuberculosis, particularly regarding host immune responses and the exploration of potential biomarkers for early diagnosis. Growing evidence suggests that the occurrence and development of tuberculosis may be closely related to the imbalance of functional endoplasmic reticulum stress (ERS), which plays a significant role in the pathophysiological processes of tuberculosis. When the accumulation of misfolded proteins within cells exceeds the homeostasis that the endoplasmic reticulum can maintain, it triggers ER stress and activates a cellular response known as the unfolded protein response (UPR) 2 ༎Pathogen infections that cause endoplasmic reticulum stress have been widely reported in bacteria, viruses, fungi, and protozoan parasites, including infectious diseases such as tuberculosis 3 . Previous studies have found associations between ER stress and various inflammatory diseases, highlighting its potential importance in tuberculosis 4 . First reported in 2010, most genes related to ER stress are upregulated in Mycobacterium tuberculosis (MTB) granulomas in humans and mice 5 . Subsequently, Mycobacterium bovis, Mycobacterium smegmatis, and Mycobacterium avium have also been determined to cause endoplasmic reticulum stress during the infection process 5 . However, the specific role of ER stress and its related genes in tuberculosis has not been thoroughly explored. Investigating the expression profiles of ER stress-related genes during Mycobacterium tuberculosis infection may provide new insights into the pathogenesis of this disease and propose new therapeutic strategies 6 . To address this knowledge blank, this study employs bioinformatics methods to conduct an in-depth analysis of publicly available datasets, with a particular focus on the expression profiles of ER stress-related genes in tuberculosis patients. By integrating GEO database, our goal is to identify differentially expressed ER stress-related genes (ERSRDEGs) in tuberculosis patients compared to healthy controls. By correlating gene expression data with functional enrichment analysis and protein-protein interaction networks, we hope to elucidate the complex molecular mechanisms between endoplasmic reticulum stress and tuberculosis infection. In summary, Our study aims to paving the way for innovative diagnostic and therapeutic strategies to address this ongoing global health threat 7 . Materials and Methods 1.1 Research Data 1.1.1 Main Public Database Information The GEO database (https://www.ncbi.nlm.nih.gov/geo/) collects omics data from global research institutions, including gene expression profiles, transcriptomic data, non-coding RNA data, and methylation profiles obtained through microarray or next-generation sequencing. Two tuberculosis-related datasets (GSE114911 and GSE147964) were acquired from this repository. The GeneCards database (https://www.genecards.org) integrates multi-omics annotations of human genes. Querying with "Endoplasmic Reticulum Stress" and filtering for protein-coding genes with Relevance Score >1 identified 2,371 endoplasmic reticulum stress-related genes (ERSRGs). Corresponding gene sets were extracted from MSigDB using the same keyword. 1.1.2 Blood Samples and Medical Records The design and procedures of this study comply with the Declaration of Helsinki, and the research has been approved by the Ethics Committee of Suzhou Fifth People's Hospital (Approval No.: ZF-2024-008-01). All participants signed informed consent forms. Whole blood samples from tuberculosis patients and healthy controls were obtained from residual clinical specimens at Suzhou Fifth People's Hospital. Inclusion Criteria (1) Study groups: Active tuberculosis (ATB) patients and healthy controls (HC); (2) Age range: 18–75 years; (3) ATB diagnosis: Clinical symptoms (cough, hemoptysis, weight loss, chest pain, fever, night sweats, dyspnea) with bacteriological confirmation by sputum smear, culture, or Xpert MTB/RIF assay; (4) HC definition: No tuberculosis symptoms, comorbidities, negative T-SPOT.TB/PPD, normal blood indices, and unremarkable chest radiography. Exclusion Criteria (1) Autoimmune diseases; (2) Immunosuppressive therapy; (3) HIV infection or malignancies; (4) Age 75 years. Sample Grouping Based on clinical manifestations, bacteriological evidence, and interferon-gamma release assay (IGRA) results, samples were stratified into ATB group (n=40) and HC group (n=40). Sample Collection and Processing (1) Peripheral blood was collected from ATB patients undergoing treatment and HCs receiving routine checkups at Suzhou Fifth People's Hospital between November 2024 and February 2025. (2) Blood samples were centrifuged at 3,000 ×g for 3 min. Plasma aliquots were stored at -80°C. 1.2 Research methods 1.2.1Data Download From a GEO database 8 （https://www.ncbi.nlm.nih.gov/geo/）download the dataset GSE114911 (pulmonary tuberculosis caused by Mycobacterium tuberculosis ) and the dataset GSE147964 (as the validation set) 9 . The samples of datasets GSE114911 and GSE147964 were all from Homo sapiens. The specific information is shown in Table 1. All M. tuberculosis Infected samples and Normal samples were included in this study. Endoplasmic reticulum stress related genes (ERSRGs) were collected by GeneCards database（https://www.genecards.org/） 10 and the MSigDB database 11 (https://www.gsea-msigdb.org/gsea/msigdb). The GeneCards database provides comprehensive information on human genes, and we used the term "Endoplasmic Reticulum Stress" as a search term, a total of 2371 endoplasmic reticulum stress-related genes (ERSRGs) were obtained after keeping only those with "Protein Coding" and "Relevance Score > 1". Similarly, the set of ERSRGs was searched in MSigDB database with "Endoplasmic Reticulum Stress" as the key word, and a total of 2208 ERSRGs were included. In addition, "Endoplasmic Reticulum Stress-related" as well as "Endoplasmic Reticulum Stress-associated "as keywords in PubMed website (https://pubmed.ncbi.nlm.nih.gov/) has been published literature 12-14 ，with a total of 47 ERSRGs. A total of 3992 ERSRGs were obtained after combined deduplication. Detailed information is provided in Table S1. The datasets GSE114911 and GSE147964 were standardized, and the probes were annotated and processed respectively using the R package limma 15 . Principal Component Analysis (PCA) was performed on the expression matrix of dataset GSE114911 to verify the effect of standardization. Principal Component Analysis (PCA) 16 is a method of data dimensionality reduction. The feature vectors (components) of the data were extracted from the high-dimensional data and transformed into the low-dimensional data and these features were displayed in two-dimensional or three-dimensional graphs. 1.2.2 ER stress-related differentially expressed genes in MTB infection According to the grouping of samples in dataset GSE114911, the samples were divided into Mycobacterium tuberculosis infection (Infected) group and Normal group, respectively. Differential analysis of genes in the Mycobacterium tuberculosis infected group and the normal group using the R package limma 15 . The threshold value of |logFC| > 1 and p value 1 and p value < 0.05 were Up-regulated DEGs. Genes with logFC < -1 and p value < 0.05 were Down-regulated DEGs. The results of differential analysis were used to draw the volcano plot by the R package ggplot2 (Version 3.5.1). To obtain Endoplasmic Reticulum Stress Related Differentially Expressed Genes (ERSRDEGs) associated with Mycobacterium tuberculosis infection, all DEGs in dataset GSE114911 were interfaced with ERSRGs and drawn Venn diagram to ERSRDEGs. The R package pheatmap (Version 1.0.12) was used to draw the heatmap and the R package RCircos 17 1.2.3 Gene ontology (GO) and pathway (KEGG) enrichment analysis Gene Ontology (GO) analysis 18 is a commonly used method for conducting large-scale functional enrichment studies, including Biological Process (BP), Cell Component (CC), and Molecular Function (MF) . The Kyoto Encyclopedia of Genes and Genomes (KEGG) 19 is a widely used database that stores information about genomes, biological pathways, diseases, and drugs. We performed GO and KEGG of ERSRDEGs using the R package clusterProfiler 20 . The entry screening criteria were adj.p < 0.05 and FDR value (q value) < 0.25, and the p value correction method was Benjamini-Hochberg (BH). 1.2.4 Gene Set Enrichment Analysis (GSEA) Gene Set Enrichment Analysis (GSEA) 21 is used to evaluate the distribution trend of genes , thereby determining their contribution to the phenotype. In this study, the genes of dataset GSE114911 were first ranked according to logFC value, and then the R package clusterProfiler 20 (Version 4.12.0) is used to perform Gene Set Enrichment Analysis (GSEA) for all genes in the dataset GSE114911. The parameters used in the GSEA were as follows: the seed was 2024, the minimum number of genes contained in each gene set was 10, and the maximum number of genes contained in each gene set was 500. gene set “c2” was obtained through the Molecular Signatures Database (MSigDB) for GSEA. The screening criteria for GSEA were adj.p < 0.05. p value correction method was Benjamini-Hochberg (BH). 1.2.5 Protein-protein Interaction (PPI) Network The Protein-protein Interaction Network (PPI Network) is composed of proteins and proteins that interact with each other and participate in biological signaling, gene expression regulation, All aspects of life processes such as energy and substance metabolism and cell cycle regulation. Systematic analysis of the interaction of proteins in biological systems is of great significance for understanding the working principle of proteins in biological systems, understanding the reaction mechanism of biological signals and energy and substance metabolism under special physiological conditions such as diseases, and understanding the functional relationship between proteins. In this study, the STRING database 22 （https://cn.string-db.org/）was applied based on ERSRDEGs with a minimum interaction score greater than 0.4. The closely connected local regions in the PPI Network) may represent molecular complexes with specific biological functions. Genes in PPI network that have interaction with other genes were selected for subsequent analysis. We used the Cytoscape 23 software to visualize this network. All of five algorithms from the CytoHubba 24 , namely, Maximal Clique Centrality (MCC), Degree, Maximum Neighborhood Component (MNC), Edge Percolated Component (EPC), Closeness. The scores of ERSRDEGs were calculated by Closeness, and then the top 5 ERSRDEGs were selected according to the scores. Finally, the genes obtained by five different algorithms were interfaced and analyzed by Venn diagram. The intersection genes of the algorithms were used as ER stress-related hub genes. The GeneMANIA database 25 gives a list of query genes, GeneMANIA finds functionally similar genes using a large set of genomics and proteomics data. In this mode, it weights each functional genomic dataset according to the predicted value of the query. Another use of GeneMANIA is gene function prediction. Given a query gene, GeneMANIA finds genes that are likely to share functions with it, based on how the gene interacts with it. We predicted the functionally similar genes of hub genes through GeneMANIA online website to construct a PPI Network. 1.2.6 Construction of regulatory network Transcription factors (TFs) control gene expression through their interactions with hub genes at the post-transcriptional stage. By ChIPBase database 26 , the mRNA-TF Regulatory Network was visualized by Cytoscape software. In addition, miRNA plays an important regulatory role in the process of biological development and evolution. In order to analysis the hub genes and the relationship between miRNA, using StarBase v3.0 database 27 , the mRNA-miRNA Regulatory Network was visualized by Cytoscape software. long non-coding RNA (lncRNA) plays an important regulatory role in the process of biological development and evolution, and can regulate a variety of target genes. In order to analyze the interaction between hub genes and miRNA and lncRNA, using StarBase v3.0 database, the ceRNA Regulatory Network was visualized by Cytoscape software. 1.2.7 Validation of differential expression of Hub Genes and ROC curve analysis In order to further explore the difference in the expression of hub genes between the Infected group and the Normal group of Mycobacterium tuberculosis in data sets GSE114911 and GSE147964, a group comparison map was drawn based on the expression of hub genes. Finally, Use R package pROC 28 , AUC was used to curve of the hub genes and calculate the area under the ROC curve to evaluate the diagnostic effect on the occurrence of Mycobacterium tuberculosis infection. The AUC of the ROC curve is generally between 0.5 and 1. The closer the AUC is to 1, the better the diagnostic performance. 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. 1.2.8 Validation of Clinical Specimens Between December 2024 to February 2025, the peripheral blood samples from patients undergoing TB screening at Fifth People's Hospital of Suzhou were collected. The inclusion criteria for ATB and HC samples included: Patients with ATB are diagnosed based on clinical signs, such as cough, hemoptysis, loss of weight, chest pain, fever, night sweat, and shortness of breath, with bacteriological evidence by any of smear, culture or Xpert from sputum,; healthy individuals are without clinical TB symptoms and negative for IGRA test. Each research participant signed an informed consent form and agreed to participate in the study. The exclusion criteria included: the study subjects are suffering from autoimmune diseases; receiving any immunosuppressive drug treatment; being positive for HIV test and having malignant tumor. According to clinical signs, bacteriological evidence and IGRA test, these samples were divided into two groups:ATB group (40cases), and HC group (40 cases). The Ethics Committee of Fifth People's Hospital of Suzhou has granted approval for the conduct of this study, ensuring its ethical compliance and scientific validity (Ethical approval no. 1.2.9 Enzyme-linked immunosorbent assay (ELISA) The PPI network identified four core genes, among which IL-1A and IL-1B are the intersection of the mRNA-miRNA, mRNA-TF, and ceRNA regulatory networks. Additionally, IL-1B shows a highly significant expression difference in the independent dataset GSE147964, with excellent diagnostic performance (AUC=0.93). Therefore, we chose IL-1A and IL-1B for EILSA validation.Therefore, we used 40 blood samples from tuberculosis patients and 40 samples from healthy controls for validation. The patients were from the Fifth People's Hospital of Suzhou. The candidate biomarkers were validated using a human IL-1A and IL-1B ELISA kit (USCN Life Science; Wuhan, China). The experiments were conducted according to the manufacturer's instructions. 1.2.10 CIBERSORT immune infiltration analysis CIBERSORT 29 is based on linear support vector regression to deconvolute the transcriptome expression matrix to estimate the composition and abundance of immune cells in a mixture of cells. The CIBERSORT algorithm, combined with the LM22 feature gene matrix, was used to filter out the data with immune cell enrichment score greater than zero, and the specific results of immune cell infiltration matrix in the dataset GSE114911 were finally obtained, and the proportion bar chart was drawn for display. Then, the R package ggplot2 (Version 3.5.1) was used to draw group comparison plots to show the expression differences of immune cells in the Infected group and the Normal group of the dataset GSE114911. Subsequently, the immune cells with significant differences in the two groups were screened for subsequent analysis. The correlation between immune cells was calculated based on Spearman algorithm, and the R package pheatmap (Version 1.0.12) was used to draw the correlation heatmap to show the correlation analysis results of immune cells themselves. The correlation between hub genes and immune cells was calculated based on Spearman algorithm, and the correlation bubble plot was drawn using R package ggplot2 (Version 3.5.1) to show the correlation analysis results between hub genes and immune cells. 1.2.11 Statistical analysis All data processing and analysis in this article were based on R software (Version 4.4.0). For comparisons of continuous variables between two groups, statistical significance of normally distributed variables was estimated by independent Student's T-Test, unless otherwise specified. The Mann-Whitney U Test method (Wilcoxon Rank Sum Test) was used to analyze the differences between the variables that were not normally distributed. Kruskal-Wallis test was used for comparison of three or more groups. Spearman correlation analysis was used to calculate the correlation coefficient between different genes. All statistical p values were two-sided if not specified, and a p value of less than 0.05 was considered to indicate statistical significance. Results 2.1 Standardization of the dataset for MTB infection Firstly, the R package limma was used to standardize the M. tuberculosis infection dataset GSE114911. Distribution boxplots were used to compare the data sets before and after standardization (Fig.1A-B), and principal component analysis (PCA) was used to compare the principal component distribution of the samples before and after standardization (Fig.1C). The results showed that the data distribution of the standardized dataset was more regular. 2.2 Endoplasmic reticulum stress-related differentially expressed genes associated with Mycobacterium tuberculosis infection The data of dataset GSE114911 were divided into Mycobacterium tuberculosis Infected group and Normal group. we analyzed the difference of gene expression values between the Infected group and the Normal group in the data set GSE114911, there were 23 up-regulated DEGs and 1 down-regulated DEG. The volcano diagram was drawn according to the difference analysis results of this data set (Fig.2A). In order to obtain endoplasmic reticulum stress-related differentially expressed genes (ERSRDEGs), all the DEGs were interleaved with ERSRGs and Venn diagram was drawn (Fig.2B). A total of 10 ERSRDEGs were obtained. According to the intersection results, the expression differences between different sample groups were analyzed, and the R package pheatmap was used to draw a heatmap to show the expression of ERSRDEGs (Fig.2C). Finally, the location of ERSRDEGs on the human chromosome was analyzed by the R package RCircos, and the chromosome localization map was drawn (Fig.2D). The chromosomal mapping showed that ERSRDEGs were distributed on chromosome 1, 2, 4, 5, 6, 7, 8, 10, 12, 15, 17 and 19, and hub genes (see 2.6) were concentrated on chromosome 2, with a total of 3 genes, respectively: IL-1A, IL-1B and CCL20 ; The other hub gene, located on chromosome 6, is TNF . 2.3 GO and KEGG enrichment analysis The 10 ERSRDEGs were used for gene ontology (GO) and pathway (KEGG) enrichment analysis, and the specific results are shown in Table 2. The results showed that the 10 ERSRDEGs were mainly enriched in neutrophil migration, granulocyte migration, leukocyte migration, and neutrophil migration in Mycobacterium tuberculosis infection. cellular response to lipopolysaccharide and cellular response to molecule of bacterial origin and other biological processes (BP); external side of plasma membrane cell component (CC); cytokine activity, cytokine receptor binding, growth factor receptor binding, chemokine activity, chemokine receptor binding and other molecular functions (MF). It was also enriched in Cytokine-cytokine receptor interaction, Rheumatoid arthritis, IL-17 signaling pathway, TNF signaling pathway, Pertussis and other biological pathways (KEGG). The results of enrichment analysis were visualized by bar graphs (Fig.3A). At the same time, the network diagram of enrichment analysis (Fig.3B-D) was drawn. Since only one item related to cell component (CC) was enriched, no network diagram was made for this item. The line shows the corresponding gene and the annotation of the corresponding entry. The larger the pathway node is, the more genes are enriched in the pathway. The color of the gene node shows the logFC of the gene, red indicates up-regulated genes, and blue indicates down-regulated genes. 2.4 GSEA enrichment analysis To determine the effects of the expression levels of all genes in dataset GSE114911 on Mycobacterium tuberculosis infection, gene set enrichment analysis (GSEA) was used to study the relationship between the expression levels of all genes in dataset GSE114911 and the biological processes, cellular components and molecular functions they played. The detailed results are shown in Table 3. The results showed that the Up-regulated genes in dataset GSE114911 were significantly enriched in OVERVIEW OF PROINFLAMMATORY AND PROFIBROTIC MEDIATORS (Fig.4A). ZHANG RESPONSE TO IKK INHIBITOR AND TNF UP (Fig.4B) and other biologically relevant functions and signaling pathways, Down-regulated genes were significantly enriched in CREIGHTON ENDOCRINE THERAPY RESISTANCE 2 (Fig.4C), NEUT SENGUPTA NASOPHARYNGEAL CARCINOMA DN (Fig.4D) and other biologically related functions and signaling pathways. 2.5 Construction of protein-protein interaction network and screening of Hub genes Firstly, PPI Network of 10 ERSRDEGs was constructed using STRING database and visualized using Cytoscape software (Fig.5A). PPI Network showed that the 10 ERSRDEGs were all related. Subsequently, using CytoHubba, the scores of 10 ERSRDEGs were calculated, screen the top 5 genes, take the intersection, and the intersection was taken and the Venn diagram was drawn (Fig.5B), in which the color of the circles represents the different algorithms. Finally, four hub genes were screened out, namely: IL-1B, CCL20, IL-1A, and TNF. Finally, the interaction network of four hub genes and their functionally similar genes was predicted and constructed by GeneMANIA website (Fig.5C). The lines with different colors represent the co-expression between them and share information such as protein domains. Among them, there were 4 hub genes and 20 functionally similar proteins. 2.6 Construction of regulatory networks Firstly, miRNA related to hub gene s ( IL-1B, CCL20, IL-1A, TNF ) were obtained through StarBase database. The mRNA-miRNA Regulatory Network was constructed and visualized by Cytoscape software (Fig.6A). Among them, there were 3 hub genes and 14 miRNAs, and the specific information is shown in Table 4. Then, the transcription factors (TFs) combined with Hub Genes were obtained through the ChIPBase database, and the mRNA-TF Regulatory Network was constructed (Fig.6B). Among them, a total of2 hub genes and 19 TFs were included, and the specific information is shown in Table 5. Finally, the lncRNAs related to hub genes ( IL-1B, CCL20, IL-1A, TNF ) and the lncRNAs related to miRNAs obtained through StarBase database. The ceRNA Regulatory Network was constructed. There were 4 hub genes, a total of16 miRNAs and 2 lncRNAs in ceRNA regulatory network（Fig.6C）. 2.7 Differential expression verification and ROC curve analysis of Hub Genes To explore and verify the differential expression of hub genes in the Mycobacterium tuberculosis infection dataset, The group comparison figure (Fig.7A-B) shows the difference analysis results of the expression levels of four hub genes in the Infected group and the Normal group of Mycobacterium tuberculosis infection in dataset GSE114911 and dataset GSE147964, respectively. The results of differential analysis showed that the expression levels of four hub genes in the Infected group and the Normal group of Mycobacterium tuberculosis in dataset GSE114911 were significantly different (p value < 0.05). In dataset GSE147964, only one hub gene, IL-1B , showed a statistically significant difference in expression between the Infected group and the Normal group (p value < 0.05). Finally, the R package pROC was used to draw the ROC curve based on the expression levels of hub genes in dataset GSE114911 and dataset GSE147964. The ROC curve (Fig. 7c-d) shows that in dataset GSE114911 (Fig.7C), the expression levels of these four hub genes showed certain accuracy in the classification of MTB Infected group and Normal group (0.7 < AUC < 0.9); In dataset GSE147964 (Fig.7D), among the four hub genes, the expression level of CCL20 showed a low accuracy in the classification of MTB infection group and Normal group (0.5 < AUC < 0.7); The expression level of TNF showed a certain accuracy in the classification of Mycobacterium tuberculosis infection group and Normal group (0.7 < AUC 0.9) in the classification of Mycobacterium tuberculosis infection (Infected) and Normal groups. 2.8 Validation of potential biomarkers expression through ELISA The PPI network identified four core genes, among which IL-1A and IL-1B are the intersection of the mRNA-miRNA, mRNA-TF, and ceRNA regulatory networks. In the datasets GSE114911 and GSE147964, the levels of IL-1B in tuberculosis patients were found to be upregulated compared to the healthy control group. The expression difference of IL-1B in the independent dataset GSE147964 was highly significant, with excellent diagnostic performance (AUC=0.93). Therefore, we chose IL-1A and IL-1B for clinical validation. The experimental results showed that IL-1B was upregulated in the tuberculosis group, which is consistent with our bioinformatics analysis results (Fig 8 A, B). The ROC curve showed an AUC of 0.0716 (Figure 8C), demonstrating that IRF1 could be a diagnostic biomarker of TB. 2.9 Analysis of the correlation between the expression levels of ERS-related genes and clinical indicators. The correlation between the expression levels of ERS-related genes and clinical indicators (inflammatory factors, D-dimer) was analyzed, as shown in the results of Fig.9According to the results, IL-1B is positively correlated with IL6, TNF , and IFN-γ (Fig.9A, B, and C), and IL-1A is positively correlated with D-dimer (P<0.05) (Fig.9D). 2.10 CIBERSORT immune infiltration analysis According to the results of immune infiltration analysis, the bar chart of the proportion of immune cells was drawn (Fig.10A). Then, the immune cells with p value < 0.05 were screened by group comparison chart and the expression differences in the infiltration abundance of immune cells in different groups were shown. The grouping comparison chart (Fig.10B) showed that the eight types of immune cells, including B cells memory, NK cells activated, Macrophages M0, Macrophages M1, Macrophages M2, Mast cells resting, Mast cells activated and Eosinophils were significantly different between the Infected group and the Normal group (p value < 0.05). Then, the correlation results of the 22 immune cells infected with M. tuberculosis in the immune infiltration analysis of dataset GSE114911 were shown by correlation heatmap (Fig.10C). Then, the correlation between hub genes and 8 immune cells that were significantly different between the Infected group and the Normal group were analyzed and displayed by correlation bubble plot (Fig.10D). Table 1 GEO Microarray Chip Information GSE114911 GSE147964 Platform GPL6480 GPL23126 Species Homo sapiens Homo sapiens Tissue lung tissue blood samples Samples in Infected group 33 10 Samples in Normal group 19 10 Reference PMID: 29977236 GEO, Gene Expression Omnibus. Table 2 Results of GO and KEGG Enrichment Analysis for ERSRDEGs ONTOLOGY ID Description GeneRatio BgRatio pvalue padj qvalue BP GO:1990266 neutrophil migration 6/10 129/18888 1.85E-11 2.13E-08 5.83E-09 BP GO:0097530 granulocyte migration 6/10 154/18888 5.45E-11 3.13E-08 8.57E-09 BP GO:0050900 leukocyte migration 7/10 396/18888 1.92E-10 7.36E-08 2.01E-08 BP GO:0071222 cellular response to lipopolysaccharide 6/10 225/18888 5.40E-10 1.49E-07 4.07E-08 BP GO:0071219 cellular response to molecule of bacterial origin 6/10 238/18888 7.57E-10 1.49E-07 4.07E-08 CC GO:0009897 external side of plasma membrane 3/10 405/19894 9.04E-04 1.45E-02 1.05E-02 MF GO:0005125 cytokine activity 9/10 238/18522 8.12E-17 2.52E-15 8.55E-16 MF GO:0005126 cytokine receptor binding 9/10 273/18522 2.84E-16 4.41E-15 1.50E-15 MF GO:0070851 growth factor receptor binding 4/10 138/18522 5.98E-07 6.18E-06 2.10E-06 MF GO:0008009 chemokine activity 3/10 49/18522 2.06E-06 1.60E-05 5.42E-06 MF GO:0042379 chemokine receptor binding 3/10 74/18522 7.20E-06 4.46E-05 1.52E-05 KEGG hsa04060 Cytokine-cytokine receptor interaction 9/10 298/8848 4.81E-13 3.80E-11 1.27E-11 KEGG hsa05323 Rheumatoid arthritis 7/10 94/8848 1.42E-12 5.63E-11 1.87E-11 KEGG hsa04657 IL-17 signaling pathway 6/10 95/8848 2.65E-10 6.98E-09 2.32E-09 KEGG hsa04668 TNF signaling pathway 6/10 119/8848 1.05E-09 2.07E-08 6.90E-09 KEGG hsa05133 Pertussis 5/10 78/8848 1.14E-08 1.80E-07 5.99E-08 GO, Gene Ontology; BP, Biological Process; CC, Cellular Component; MF, Molecular Function; KEGG, Kyoto Encyclopedia of Genes and Genomes; ERSRDEGs, Endoplasmic Reticulum Stress Related Differentially Expressed Genes. Table 3 Results of GSEA for GSE114911 ID Set Size Enrichment Score NES p value p adjust q value WP_OVERVIEW_OF_PROINFLAMMATORY_AND_PROFIBROTIC_MEDIATORS 117 0.7997 3.0553 1.00E-10 1.09E-08 8.89E-09 ZHANG_RESPONSE_TO_IKK_INHIBITOR_AND_TNF_UP 207 0.7260 2.9950 1.00E-10 1.09E-08 8.89E-09 LINDSTEDT_DENDRITIC_CELL_MATURATION_A 59 0.8635 2.9564 1.00E-10 1.09E-08 8.89E-09 BLANCO_MELO_COVID19_SARS_COV_2_INFECTION_CALU3_CELLS_UP 301 0.6827 2.9426 1.00E-10 1.09E-08 8.89E-09 SANA_TNF_SIGNALING_UP 79 0.8076 2.9329 1.00E-10 1.09E-08 8.89E-09 BLANCO_MELO_HUMAN_PARAINFLUENZA_VIRUS_3_INFECTION_A594_CELLS_UP 182 0.7147 2.9014 1.00E-10 1.09E-08 8.89E-09 ALTEMEIER_RESPONSE_TO_LPS_WITH_MECHANICAL_VENTILATION 117 0.7508 2.8687 1.00E-10 1.09E-08 8.89E-09 KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION 251 0.6774 2.8632 1.00E-10 1.09E-08 8.89E-09 SEKI_INFLAMMATORY_RESPONSE_LPS_UP 72 0.8002 2.8464 1.00E-10 1.09E-08 8.89E-09 REACTOME_INTERLEUKIN_10_SIGNALING 44 0.8719 2.8319 1.00E-10 1.09E-08 8.89E-09 SENGUPTA_NASOPHARYNGEAL_CARCINOMA_DN 275 -0.7248 -3.1974 1.00E-10 1.09E-08 8.89E-09 CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_2 337 -0.5475 -2.4669 1.00E-10 1.09E-08 8.89E-09 KEGG_LYSOSOME 118 -0.5861 -2.3168 1.85E-10 1.82E-08 1.49E-08 KEGG_MEDICUS_VARIANT_MUTATION_CAUSED_ABERRANT_SOD1_TO_RETROGRADE_AXONAL_TRANSPORT 27 -0.7365 -2.2351 2.87E-06 0.000102 8.35E-05 SANA_TNF_SIGNALING_DN 80 -0.5863 -2.1766 2.70E-07 1.26E-05 1.03E-05 LAIHO_COLORECTAL_CANCER_SERRATED_DN 79 -0.5787 -2.1434 2.34E-07 1.10E-05 9.02E-06 FOROUTAN_INTEGRATED_TGFB_EMT_DN 70 -0.5864 -2.1412 1.24E-06 4.95E-05 4.05E-05 APPEL_IMATINIB_RESPONSE 31 -0.6844 -2.1296 1.44E-05 0.000402 0.000329 RICKMAN_HEAD_AND_NECK_CANCER_D 32 -0.6709 -2.0959 4.06E-05 0.000974 0.000797 FOROUTAN_TGFB_EMT_DN 102 -0.5334 -2.0735 5.10E-07 2.17E-05 1.77E-05 GSEA, Gene Set Enrichment Analysis; NES, Normalized Enrichment Score. Table 4 mRNA-miRNA Interaction of hub genes mRNA miRNA CCL20 hsa-miR-5579-3p IL-1A hsa-miR-30a-5p IL-1A hsa-miR-192-5p IL-1A hsa-miR-30c-5p IL-1A hsa-miR-30d-5p IL-1A hsa-miR-181c-5p IL-1A hsa-miR-30b-5p IL-1A hsa-miR-125b-5p IL-1A hsa-miR-125a-5p IL-1A hsa-miR-30e-5p IL-1A hsa-miR-181d-5p IL-1A hsa-miR-532-5p IL-1A hsa-miR-670-5p IL-1B hsa-miR-101-3p miRNA, microRNA. Table 5 mRNA-TF Interaction of hub genes mRNA TF IL-1A ATF4 IL-1A CEBPA IL-1A CEBPB IL-1A EP300 IL-1A ERG IL-1A FOS IL-1A GABPA IL-1A GATA2 IL-1A HNF4A IL-1A JUND IL-1A MAX IL-1A MYC IL-1A RELA IL-1A SPI1 IL-1A TEAD4 IL-1A USF1 IL-1B SPI1 IL-1B TAL1 IL-1B CEBPB TF, Transcription Factors. Discussion Tuberculosis (TB) is a disease caused by Mycobacterium tuberculosis and remains a major threat to global health, affecting millions of people each year 30 . The disease primarily manifests as pulmonary tuberculosis, leading to severe respiratory complications and causing high morbidity and mortality rates in low- and middle-income countries 31 . Despite some progress in diagnosis and treatment, the emergence of multidrug-resistant strains poses significant challenges to management strategies, highlighting the urgent need for a deeper understanding of the fundamental biological mechanisms driving TB pathology and host responses 32 .This study aims to explore the role of endoplasmic reticulum (ER) stress in TB infection, with a particular focus on the differential expression of ER stress-related genes associated with Mycobacterium tuberculosis. By utilizing publicly available data and employing bioinformatics methods, we identified several key genes related to ER stress that may serve as potential biomarkers for tuberculosis. The results indicate that these differentially expressed genes not only deepen our understanding of the mechanisms of tuberculosis but also provide new therapeutic targets for improving disease management and patient prognosis. In the context of Mycobacterium tuberculosis infection, the analysis of differentially expressed genes (DEGs) reveals important information about the potential molecular mechanisms of the disease. We identified 24 significantly upregulated DEGs, highlighting the inflammatory response triggered by the pathogen. Notably, based on bioinformatics analysis, the IL-1B levels in tuberculosis patients were found to be upregulated compared to healthy controls in the test sets GSE114911 and GSE147964. Therefore, we chose to use ELISA to further identify the IL-1B expression levels associated with endoplasmic reticulum stress (ERS). The results showed that the expression level of IL-1B was consistent with the bioinformatics mRNA microarray analysis results. Several studies have proposed ERS as a potential target for tuberculosis treatment, and in tuberculosis patients, the level of IL-1B is significantly elevated, closely related to the severity of the condition 33 . Research indicates that IL-1B enhances the expression of pro-inflammatory factors by activating the NF-κB and MAPK signaling pathways, leading to a stronger inflammatory response. As our research found, IL-1B is positively correlated with other inflammatory factors such as IL-6, TNF-A , and IFN-γ, and studies have shown that IL-1B can increase the release of these inflammatory factors. These cytokines can activate ERS and UPR. This inflammatory microenvironment not only helps to resist infection by Mycobacterium tuberculosis.This inflammatory microenvironment not only helps to resist Mycobacterium tuberculosis infection 34 ;moreover, studies have shown that the overexpression of IL-1B is associated with the pathological progression of tuberculosis, and inhibiting IL-1B activity may help alleviate the inflammatory response and improve the condition. For example, the use of IL-1B monoclonal antibodies (such as Canakinumab) in animal models has shown significant reductions in tuberculosis-related inflammatory markers and improvements in pulmonary pathological changes 35 . In clinical studies, the application of anti- IL-1B therapy has also shown potential benefits for tuberculosis patients, especially in those who respond poorly to conventional anti-tuberculosis treatment 36 . These studies provide strong evidence for IL-1B as a therapeutic target, suggesting that it may play an important role in the treatment of pulmonary tuberculosis. Pathway enrichment analysis particularly highlights the roles of neutrophil migration and cytokine-cytokine receptor interactions, revealing key aspects of the immune response to tuberculosis. The enrichment of DEGs in these pathways indicates that the effective mobilization of neutrophils by the immune system is crucial for controlling the infection. Neutrophils are typically the first responders to bacterial infections, and their migration to the site of infection is vital for effective pathogen clearance 37 . Furthermore, the cytokine-cytokine receptor interaction pathway underscores the complex signaling network that regulates the immune response 38 . These interactions are essential for fine-tuning the immune response, ensuring that it is neither overly aggressive nor insufficient, to prevent tissue damage. A deeper understanding of these pathways will provide important insights for potential therapeutic strategies to modulate the immune response in tuberculosis patients. The results of CIBERSORT analysis reveal changes in the immune cell composition in the context of tuberculosis infection. Consistent with previous findings 39–42 , significant changes in immune cell abundance were observed between healthy individuals and the active tuberculosis (ATB) cohort. The results showed that memory B cells, activated NK cells, M0 macrophages, M1 macrophages, M2 macrophages, resting mast cells, activated mast cells, and eosinophils had statistically significant differences (p value < 0.05) between the infected group and the normal group, suggesting a potential association of these cells with the occurrence and progression of tuberculosis. Research shows that the number and activity of mast cells in the lung tissue of tuberculosis patients are significantly increased, which may be related to the infection of Mycobacterium tuberculosis and the inflammatory response it triggers 43 . Mast cells sense the presence of pathogens through their surface receptors and respond rapidly in the early stages of infection by releasing a large amount of inflammatory mediators, thereby regulating the local immune response 44 . In addition, mast cells also regulate the activity of T cells and B cells through interactions with other immune cells, further influencing the progression and outcome of tuberculosis 45 . Consistent with previous research findings, our results showed that the number of activated mast cells in the tuberculosis infection group was significantly higher than that in the healthy control group, with statistical significance (p value < 0.05). The constructed PPI network reveals the complex relationships. Significant interactions among these proteins suggest that they may work synergistically to regulate the cellular response to Mycobacterium tuberculosis infection 46 . For instance, central genes such as IL-1B and CCL20 not only participate in inflammatory signaling but may also interact with other proteins that regulate the ER stress pathway, indicating their multifaceted roles in the infection response 47 . This network analysis is crucial for elucidating the regulatory mechanisms involved in the pathogenesis of tuberculosis and may guide future research to identify new therapeutic targets that disrupt these interactions 48,49 . Understanding the dynamics of the PPI network may also facilitate the discovery of potential biomarkers for the diagnosis and monitoring of tuberculosis, thereby improving clinical management of the disease 50 . ROC curve analysis demonstrates the diagnostic potential of central genes, showcasing their relevance as biomarkers for tuberculosis. The ability of these genes to distinguish between infected and non-infected individuals, along with their acceptable area under the curve (AUC), indicates that they can be integrated into diagnostic tests to enhance early detection of the disease 51,52 . Early diagnosis is critical in the management of tuberculosis, especially in the context of rising rates of drug-resistant strains. Furthermore, the predictive value of these biomarkers may pave the way for personalized treatment approaches, predicting patient responses to therapy based on their gene expression profiles. This aligns with the current trend in precision medicine 53 . Previous studies have shown that tuberculosis can induce a systemic hypercoagulable state, characterized by significantly elevated plasma D-dimer levels, especially in patients with active tuberculosis and severe tuberculosis 54 . This is closely related to the severity, activity, and prognosis of tuberculosis patients. This study found a positive correlation between IL-1A and D-dimer, suggesting that the ESR-related gene IL-1A may be closely related to the severity and prognosis of tuberculosis, and thus IL-1A may be a prognostic marker for tuberculosis. However, the qPCR and ELISA results for our target gene IL-1A were contrary to the results from the bioinformatics testing set, which may be related to the heterogeneity of the samples and the potential suppression of protein post-translational degradation. Therefore, further expansion of the experimental sample is needed to validate its expression levels in tuberculosis patients. Our research also has some limitations. First, more gene chip samples from tuberculosis patients are needed to fully elucidate the molecular mechanisms of tuberculosis occurrence and development. Second, many biomarkers related to tuberculosis have yet to be characterized, requiring further experimental validation and bioinformatics analysis to study genes associated with tuberculosis. The specific mechanisms need to be further explored in future animal models. Summary: This study reveals the important roles of ER stress-related differentially expressed genes associated with Mycobacterium tuberculosis infection and provides potential biomarkers and targets for future diagnostic and therapeutic strategies. These findings contribute to a better understanding of the mechanisms of Mycobacterium tuberculosis infection and support efforts to address public health challenges. Declarations Acknowledgments We would like to thank all participants and data collectors involved in this study. Data Sharing Statement The corresponding author will provide supplementary materials and all other data used in this study upon reasonable request. Ethical approval and informed consent Te clinical study, which included blood samples and information of participants, was reviewed and approved by the Ethics Committee of Fifth People's Hospital of Suzhou, Department of Pulmonology, Suzhou, Jiangsu, People's Republic of China (Approval No: ZF-2024-008-01) .All procedures were carried out in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to study procedures. Author Contributions All authors made significant contributions to various key aspects of this study, including conception, research design, execution, data acquisition, analysis and interpretation, as well as other areas such as manuscript writing (drafting, revising, or critically reviewing the article), final approval of the version to be published, agreement on the journal to which the article should be submitted, and agreement to be accountable for all aspects of the report. 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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-6582977","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":455521698,"identity":"d5d4826e-44b2-4464-b54f-53dd3f0787dc","order_by":0,"name":"min li","email":"","orcid":"","institution":"Northern Jiangsu People’s Hospital (Clinical Medical College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"min","middleName":"","lastName":"li","suffix":""},{"id":455521699,"identity":"4cd35b03-5987-470f-ab9a-cea132becd5a","order_by":1,"name":"Yuxiu wang","email":"","orcid":"","institution":"Northern Jiangsu People’s Hospital (Clinical Medical College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yuxiu","middleName":"","lastName":"wang","suffix":""},{"id":455521700,"identity":"25f8fb23-cd31-476b-9c27-91483cd8e551","order_by":2,"name":"Ling-feng Min","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie2PoQoCQRCGRwQtq1vnEHwDYS0mwVcZy1kUTGLQw3QXxG7wIYzGwQMtKza5YLgrdqNF3EOzt1FwP/jhD//HMAAOxy9ShhITgDS1lNK0a6VArngLU1WqfbtDDG+l4mXhvnjeimrM2W6OshFdplRhkNGSviqduE7c10f0NnqSkLgC6tO2QBGK++EhUMnQTwhvoHBkp2DPKGNSsbUyQ4WDAxDZK4yYDMtI7IviX866nT3CAOV6kN0fz25TRqvvyofYRKi8CZt5TmBSTW3XDofD8We8AImKUEJmPFavAAAAAElFTkSuQmCC","orcid":"","institution":"Northern Jiangsu People’s Hospital (Clinical Medical College of Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Ling-feng","middleName":"","lastName":"Min","suffix":""},{"id":455521701,"identity":"0a579321-2d83-480d-b551-1e83f1880066","order_by":3,"name":"Meiying Wu","email":"","orcid":"","institution":"Fifth People's Hospital of Suzhou","correspondingAuthor":false,"prefix":"","firstName":"Meiying","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-05-03 08:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6582977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6582977/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-29599-7","type":"published","date":"2025-12-07T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82793337,"identity":"9fbaab51-7b70-46b1-ae04-f7fd5a0f5933","added_by":"auto","created_at":"2025-05-15 10:24:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72247,"visible":true,"origin":"","legend":"\u003cp\u003eNormalization of GSE114911\u003c/p\u003e\n\u003cp\u003eA. Boxplot of distribution of GSE114911 dataset before normalization. B. Distribution boxplot of the normalized dataset GSE114911. C. PCA plot of the normalized dataset GSE114911. PCA, Principal Component Analysis. Infected samples with M. tuberculosis are in red, and Normal samples are in blue.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/646c8da2e548fdea603f5890.jpg"},{"id":82793339,"identity":"2a9d16f6-5ded-4e68-8939-144364ddabbc","added_by":"auto","created_at":"2025-05-15 10:24:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89682,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Gene Expression Analysis\u003c/p\u003e\n\u003cp\u003eA. Volcano plot of differentially expressed genes analysis between the Infected and Normal groups of Mycobacterium tuberculosis in dataset GSE114911. B. Venn diagram of DEGs and endoplasmic reticulum stress-related genes (ERSRGs) in dataset GSE114911. C. Heat map of ERSRDEGs in dataset GSE114911. D. Chromosomal mapping of ERSRDEGs. DEGs, Differentially Expressed Genes; ERSRGs, Endoplasmic Reticulum Stress Related Genes; ERSRDEGs, Endoplasmic Reticulum Stress Related Differentially Expressed Genes. The light red is the MTB Infected group, and the light blue is the Normal group. In the heat map, red represents high expression and blue represents low expression.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/55bd57b59fbe39d2fc3e2cdf.jpg"},{"id":82795091,"identity":"b064cb85-087a-462c-83c5-e46a4932d028","added_by":"auto","created_at":"2025-05-15 10:32:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113458,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG Enrichment Analysis for ERSRDEGs\u003c/p\u003e\n\u003cp\u003eA. Bar graph of GO and KEGG enrichment analysis results of ERSRDEGs. B-d. GO and KEGG of ERSRDEGs network diagram showing BP (B), MF (C) and KEGG (D). The light brown nodes represent entries, and the lines indicate the corresponding genes and the annotations of the corresponding entries. The larger the pathway node is, the more genes are enriched in the pathway. The color of the gene node shows the logFC of the gene, red denotes up-regulated genes, and blue denotes down-regulated genes. ERSRDEGs, Endoplasmic Reticulum Stress Related Differentially Expressed Genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, Biological Process; MF, Molecular Function. The color of the columns in the bar graph represents the size of the adj.p value, the redder the adj.p value is smaller, and the bluer the adj.p value is larger. The screening criteria for GO and KEGG were adj.p \u0026lt; 0.05 and FDR value (q value) \u0026lt; 0.25, and the p value correction method was Benjamini-Hochberg (BH).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/0e2379563a76e4741a4771e8.jpg"},{"id":82793341,"identity":"f049a27b-a018-49f3-b445-0a82e462e54f","added_by":"auto","created_at":"2025-05-15 10:24:34","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101607,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA for GSE114911\u003c/p\u003e\n\u003cp\u003eA-d. Gene set enrichment analysis (GSEA) showed that up-regulated genes were significantly enriched in OVERVIEW OF PROINFLAMMATORY AND PROFIBROTIC MEDIATORS (A), ZHANG RESPONSE TO IKK INHIBITOR AND TNF UP (B) and other biological related functions and signaling pathways, down-regulated genes were significantly enriched in CREIGHTON ENDOCRINE THERAPY RESISTANCE 2 (C), NEUT SENGUPTA NASOPHARYNGEAL CARCINOMA DN (D). GSEA, Gene Set Enrichment Analysis; NES, Normalized Enrichment Score. The screening criteria of gene set enrichment analysis (GSEA) were adj.p \u0026lt; 0.05 and FDR value (q value) \u0026lt; 0.25, and the p value correction method was Benjamini-Hochberg (BH).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/0cc1f51287f9144e0aac6b06.jpg"},{"id":82795092,"identity":"05d3f6e3-fa15-4c8e-bf46-3f9e7e1102d6","added_by":"auto","created_at":"2025-05-15 10:32:34","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":95972,"visible":true,"origin":"","legend":"\u003cp\u003ePPI Network Analysis for ERSRDEGs\u003c/p\u003e\n\u003cp\u003eA. PPI Network of ERSRDEGs calculated from STRING database. B. Intersection Venn diagram of the top 5 genes with scores of 10ERSRDEGs calculated by MCC, Degree, MNC, EPC and Closeness algorithms of CytoHubba plugin. C. GeneMANIA website predicted the interaction network of functionally similar genes of ERSRDEGs. The circles in the figure show the studied ERSRDEGs and their functionally similar genes, and the corresponding colors of the lines represent the interconnected functions. PPI, Protein-protein Interaction Network; ERSRDEGs, Endoplasmic Reticulum Stress Related Differentially Expressed Genes; MCC, Maximal Clique Centrality; MNC, Maximum Neighborhood Component; EPC, Edge Percolated Component.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/ea13cd9efcc466c1ca029f35.jpg"},{"id":82793345,"identity":"c54ded23-1a8c-4c61-90c8-7d5e20345aee","added_by":"auto","created_at":"2025-05-15 10:24:34","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":66240,"visible":true,"origin":"","legend":"\u003cp\u003eRegulatory Network of Hub Genes\u003c/p\u003e\n\u003cp\u003eA. mRNA-miRNA Regulatory Network of Hub Genes. B. mRNA-TF Regulatory Network of Hub Genes. C. ceRNA Regulatory Network of Hub Genes (Hub Genes) orange is mRNA, light blue is miRNA, light yellow is TF, dark blue is lncRNA. miRNA, microRNA; TF, Transcription factors; ceRNA, competing endogenous RNA, lncRNA, long non-coding RNA.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/8e8fa6ee39e65b5a9961341d.jpg"},{"id":82795093,"identity":"0f1f4b5d-dea2-4b54-bc6f-00e9ad00b3d0","added_by":"auto","created_at":"2025-05-15 10:32:34","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":72353,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Expression Validation and ROC Curve Analysis\u003c/p\u003e\n\u003cp\u003eA. Cluster comparison diagram of Hub Genes in dataset GSE114911 of Mycobacterium tuberculosis infection (Infected) group and Normal group. B. The grouping comparison of Hub Genes in the Mycobacterium tuberculosis infection (Infected) group and the Normal group in the dataset GSE147964. C-d. ROC curves of four Hub Genes (Hub Genes) in dataset GSE114911 (C) and dataset GSE147964 (D). ** stands for p value \u0026lt; 0.01, highly statistically significant; *** represents p value \u0026lt; 0.001 and extremely statistically significant. When AUC \u0026gt; 0.5, it indicates that the expression of the molecule is a trend to promote the occurrence of the event, and the closer the AUC is to 1, the better the diagnostic effect. AUC had low accuracy in the range of 0.5 to 0.7, and AUC had moderate accuracy in the range of 0.7 to 0.9. ROC, Receiver Operating Characteristic; AUC, Area Under the Curve; TPR, True Positive Rate; FPR, False Positive Rate. Group comparison boxplots (A-B), red represents the MTB infection (Infected) group and blue represents the Normal (Normal) group.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/a2abf151dc4d3434a109e956.jpg"},{"id":82796884,"identity":"0262f72d-838d-4519-a4c0-dcd89c563e98","added_by":"auto","created_at":"2025-05-15 10:40:34","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":31835,"visible":true,"origin":"","legend":"\u003cp\u003eResults of\u003cem\u003e IL-1A\u003c/em\u003e and\u003cem\u003e IL-1B\u003c/em\u003eEILSA and the ROC curve of \u003cem\u003eIL-1B.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA.The expression levels of \u003cem\u003eIL-1A\u003c/em\u003e in the serum of tuberculosis patients and healthy controls ;\u003c/p\u003e\n\u003cp\u003eB .The expression levels of \u003cem\u003eIL-1B\u003c/em\u003e in the serum of tuberculosis patients and healthy controls ;\u003c/p\u003e\n\u003cp\u003eC. ROC curve of \u003cem\u003eIL-1B\u003c/em\u003e ;** represents p value \u0026lt; 0.01; *** represents p value \u0026lt; 0.001；HC means healthy control,TB means tuberculosis.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/3576dacb94a4d4e8995978c0.jpg"},{"id":82793348,"identity":"1f271b3c-39c0-4d49-8c63-587940d8b0dc","added_by":"auto","created_at":"2025-05-15 10:24:34","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":31692,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of \u003cem\u003eIL-1A\u003c/em\u003e and \u003cem\u003eIL-1B\u003c/em\u003e with clinical indicators.\u003c/p\u003e\n\u003cp\u003eA. \u003cem\u003eIL-1B\u003c/em\u003e is positively correlated with\u003cem\u003e IL-6\u003c/em\u003e. B.\u003cem\u003eIL-1B\u003c/em\u003eis positively correlated with \u003cem\u003eTNFA\u003c/em\u003e. C. \u003cem\u003eIL-1B \u003c/em\u003eis positively correlated with \u003cem\u003eIFN-γ\u003c/em\u003e. D \u003cem\u003eIL-1A\u003c/em\u003e is positively correlated with D-dimer.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/186694bdcd8eed989387952d.jpg"},{"id":82793349,"identity":"1fbdb5aa-8bee-4fe0-9264-9386caeff136","added_by":"auto","created_at":"2025-05-15 10:24:34","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":110734,"visible":true,"origin":"","legend":"\u003cp\u003eImmune Infiltration Analysis by CIBERSORT Algorithm\u003c/p\u003e\n\u003cp\u003eA. Bar chart of the abundance of immune infiltration in the Infected group and the Normal group of the dataset GSE114911. B. The grouping comparison of immune cell infiltration abundance in the Mycobacterium tuberculosis infection (Infected) group and the Normal group in dataset GSE114911. C. Correlation heatmap of immune cells in immune cell infiltration abundance matrix. D. Bubble plot of correlation between Hub Genes and immune cell infiltration abundance in dataset GSE114911. * represents p value \u0026lt; 0.05, indicating statistical significance; ** represents p value \u0026lt; 0.01, highly statistically significant; *** represents p value \u0026lt; 0.001 and extremely statistically significant. The light red is the Mycobacterium tuberculosis Infected group, and the light blue is the Normal group. Red is positive correlation, blue is negative correlation. The depth of the color represents the strength of the correlation.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/ca8c745b7998d198d91d9c6a.jpg"},{"id":97724619,"identity":"9fc4a879-0839-41f2-9267-b6c783e619c1","added_by":"auto","created_at":"2025-12-08 16:12:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1576488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/c7676e47-a3bf-4aef-95bb-14cf04d01bd0.pdf"},{"id":82793338,"identity":"70ccd2db-7c84-4c8d-aeff-285b9ed968f3","added_by":"auto","created_at":"2025-05-15 10:24:34","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28327,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-6582977/v1/490bc2c348771a55357f3684.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIdentification and clinical validation of endoplasmic reticulum genes related to pulmonary tuberculosis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a serious infectious disease that has long posed a significant threat to human health, primarily due to the widespread prevalence of MTB infections\u003csup\u003e1\u003c/sup\u003e. In 2023, there were 10.8\u0026nbsp;million new cases of tuberculosis globally, resulting in 1.25\u0026nbsp;million unfortunate deaths, which has profound implications for public health and economic stability. Despite the availability of effective anti-tuberculosis treatment regimens, management strategies have become increasingly complex due to the long treatment duration, significant side effects, and the emergence of multidrug-resistant (MDR) strains. These factors underscore the importance of in-depth research into the pathogenesis of tuberculosis, particularly regarding host immune responses and the exploration of potential biomarkers for early diagnosis. Growing evidence suggests that the occurrence and development of tuberculosis may be closely related to the imbalance of functional endoplasmic reticulum stress (ERS), which plays a significant role in the pathophysiological processes of tuberculosis.\u003c/p\u003e \u003cp\u003eWhen the accumulation of misfolded proteins within cells exceeds the homeostasis that the endoplasmic reticulum can maintain, it triggers ER stress and activates a cellular response known as the unfolded protein response (UPR)\u003csup\u003e2\u003c/sup\u003e༎Pathogen infections that cause endoplasmic reticulum stress have been widely reported in bacteria, viruses, fungi, and protozoan parasites, including infectious diseases such as tuberculosis\u003csup\u003e3\u003c/sup\u003e. Previous studies have found associations between ER stress and various inflammatory diseases, highlighting its potential importance in tuberculosis\u003csup\u003e4\u003c/sup\u003e. First reported in 2010, most genes related to ER stress are upregulated in Mycobacterium tuberculosis (MTB) granulomas in humans and mice\u003csup\u003e5\u003c/sup\u003e. Subsequently, Mycobacterium bovis, Mycobacterium smegmatis, and Mycobacterium avium have also been determined to cause endoplasmic reticulum stress during the infection process\u003csup\u003e5\u003c/sup\u003e. However, the specific role of ER stress and its related genes in tuberculosis has not been thoroughly explored. Investigating the expression profiles of ER stress-related genes during Mycobacterium tuberculosis infection may provide new insights into the pathogenesis of this disease and propose new therapeutic strategies\u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address this knowledge blank, this study employs bioinformatics methods to conduct an in-depth analysis of publicly available datasets, with a particular focus on the expression profiles of ER stress-related genes in tuberculosis patients. By integrating GEO database, our goal is to identify differentially expressed ER stress-related genes (ERSRDEGs) in tuberculosis patients compared to healthy controls. By correlating gene expression data with functional enrichment analysis and protein-protein interaction networks, we hope to elucidate the complex molecular mechanisms between endoplasmic reticulum stress and tuberculosis infection. In summary, Our study aims to paving the way for innovative diagnostic and therapeutic strategies to address this ongoing global health threat\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e1.1 Research Data\u003c/p\u003e\n\u003cp\u003e1.1.1 Main Public Database Information\u003c/p\u003e\n\u003cp\u003eThe GEO database (https://www.ncbi.nlm.nih.gov/geo/) collects omics data from global research institutions, including gene expression profiles, transcriptomic data, non-coding RNA data, and methylation profiles obtained through microarray or next-generation sequencing. Two tuberculosis-related datasets (GSE114911 and GSE147964) were acquired from this repository.\u003c/p\u003e\n\u003cp\u003eThe GeneCards database (https://www.genecards.org) integrates multi-omics annotations of human genes. Querying with \u0026quot;Endoplasmic Reticulum Stress\u0026quot; and filtering for protein-coding genes with Relevance Score \u0026gt;1 identified 2,371 endoplasmic reticulum stress-related genes (ERSRGs). Corresponding gene sets were extracted from MSigDB using the same keyword.\u003c/p\u003e\n\u003cp\u003e1.1.2 Blood Samples and Medical Records\u003c/p\u003e\n\u003cp\u003eThe design and procedures of this study comply with the Declaration of Helsinki, and the research has been approved by the Ethics Committee of Suzhou Fifth People\u0026apos;s Hospital (Approval No.: ZF-2024-008-01). All participants signed informed consent forms. Whole blood samples from tuberculosis patients and healthy controls were obtained from residual clinical specimens at Suzhou Fifth People\u0026apos;s Hospital.\u003c/p\u003e\n\u003cp\u003eInclusion Criteria\u003c/p\u003e\n\u003cp\u003e(1) Study groups: Active tuberculosis (ATB) patients and healthy controls (HC);\u003c/p\u003e\n\u003cp\u003e(2) Age range: 18\u0026ndash;75 years;\u003c/p\u003e\n\u003cp\u003e(3) ATB diagnosis: Clinical symptoms (cough, hemoptysis, weight loss, chest pain, fever, night sweats, dyspnea) with bacteriological confirmation by sputum smear, culture, or Xpert MTB/RIF assay;\u003c/p\u003e\n\u003cp\u003e(4) HC definition: No tuberculosis symptoms, comorbidities, negative T-SPOT.TB/PPD, normal blood indices, and unremarkable chest radiography.\u003c/p\u003e\n\u003cp\u003eExclusion Criteria\u003c/p\u003e\n\u003cp\u003e(1) Autoimmune diseases;\u003c/p\u003e\n\u003cp\u003e(2) Immunosuppressive therapy;\u003c/p\u003e\n\u003cp\u003e(3) HIV infection or malignancies;\u003c/p\u003e\n\u003cp\u003e(4) Age \u0026lt;16 or \u0026gt;75 years.\u003c/p\u003e\n\u003cp\u003eSample Grouping\u003c/p\u003e\n\u003cp\u003eBased on clinical manifestations, bacteriological evidence, and interferon-gamma release assay (IGRA) results, samples were stratified into ATB group (n=40) and HC group (n=40).\u003c/p\u003e\n\u003cp\u003eSample Collection and Processing\u003c/p\u003e\n\u003cp\u003e(1) Peripheral blood was collected from ATB patients undergoing treatment and HCs receiving routine checkups at Suzhou Fifth People\u0026apos;s Hospital between November 2024 and February 2025.\u003c/p\u003e\n\u003cp\u003e(2) Blood samples were centrifuged at 3,000 \u0026times;g for 3 min. Plasma aliquots were stored at -80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e1.2 Research methods\u003c/p\u003e\n\u003cp\u003e1.2.1Data Download\u003c/p\u003e\n\u003cp\u003eFrom a GEO database\u003csup\u003e8\u003c/sup\u003e（https://www.ncbi.nlm.nih.gov/geo/）download the dataset GSE114911 (pulmonary tuberculosis caused by Mycobacterium tuberculosis ) and the dataset GSE147964 (as the validation set)\u003csup\u003e9\u003c/sup\u003e. The samples of datasets GSE114911 and GSE147964 were all from Homo sapiens. The specific information is shown in Table 1.\u0026nbsp;All M. tuberculosis Infected samples and Normal samples were included in this study.\u003c/p\u003e\n\u003cp\u003eEndoplasmic reticulum stress related genes (ERSRGs) were collected by GeneCards database（https://www.genecards.org/）\u003csup\u003e10\u003c/sup\u003eand the MSigDB database\u003csup\u003e11\u003c/sup\u003e (https://www.gsea-msigdb.org/gsea/msigdb). The GeneCards database provides comprehensive information on human genes, and we used the term \u0026quot;Endoplasmic Reticulum Stress\u0026quot; as a search term, a total of 2371 endoplasmic reticulum stress-related genes (ERSRGs) were obtained after keeping only those with \u0026quot;Protein Coding\u0026quot; and \u0026quot;Relevance Score \u0026gt; 1\u0026quot;. Similarly, the set of ERSRGs was searched in MSigDB database with \u0026quot;Endoplasmic Reticulum Stress\u0026quot; as the key word, and a total of 2208 ERSRGs were included. In addition, \u0026quot;Endoplasmic Reticulum Stress-related\u0026quot; as well as \u0026quot;Endoplasmic Reticulum Stress-associated \u0026quot;as keywords in PubMed website (https://pubmed.ncbi.nlm.nih.gov/) has been published literature\u003csup\u003e12-14\u003c/sup\u003e，with a total of 47 ERSRGs.\u0026nbsp;A total of 3992 ERSRGs were obtained after combined deduplication. Detailed information is provided in Table S1.\u003c/p\u003e\n\u003cp\u003eThe datasets GSE114911 and GSE147964 were standardized, and the probes were annotated and processed respectively using the R package limma \u003csup\u003e15\u003c/sup\u003e. Principal Component Analysis (PCA) was performed on the expression matrix of dataset GSE114911 to verify the effect of standardization.\u0026nbsp;Principal Component Analysis (PCA)\u003csup\u003e16\u003c/sup\u003e is a method of data dimensionality reduction. The feature vectors (components) of the data were extracted from the high-dimensional data and transformed into the low-dimensional data and these features were displayed in two-dimensional or three-dimensional graphs.\u003c/p\u003e\n\u003cp\u003e1.2.2 ER stress-related differentially expressed genes in MTB infection\u003c/p\u003e\n\u003cp\u003eAccording to the grouping of samples in dataset GSE114911, the samples were divided into Mycobacterium tuberculosis infection (Infected) group and Normal group, respectively. \u0026nbsp;Differential analysis of genes in the Mycobacterium tuberculosis infected group and the normal group using the R package limma\u003csup\u003e15\u003c/sup\u003e.\u0026nbsp;The threshold value of |logFC| \u0026gt; 1 and p value \u0026lt; 0.05 was set as the Differentially Expressed Genes (DEGs). Genes with logFC \u0026gt; 1 and p value \u0026lt; 0.05 were Up-regulated DEGs. Genes with logFC \u0026lt; -1 and p value \u0026lt; 0.05 were Down-regulated DEGs. The results of differential analysis were used to draw the volcano plot by the R package ggplot2 (Version 3.5.1).\u003c/p\u003e\n\u003cp\u003eTo obtain Endoplasmic Reticulum Stress Related Differentially Expressed Genes (ERSRDEGs) associated with Mycobacterium tuberculosis infection, all DEGs in dataset GSE114911 were interfaced with ERSRGs and drawn Venn diagram to ERSRDEGs. The R package pheatmap (Version 1.0.12) was used to draw the heatmap and the R package RCircos\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e1.2.3 Gene ontology (GO) and pathway (KEGG) enrichment analysis\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) analysis\u003csup\u003e18\u003c/sup\u003e is a commonly used method for conducting large-scale functional enrichment studies, including Biological Process (BP), Cell Component (CC), and Molecular Function (MF) .\u0026nbsp;The Kyoto Encyclopedia of Genes and Genomes (KEGG)\u003csup\u003e19\u003c/sup\u003e is a widely used database that stores information about genomes, biological pathways, diseases, and drugs. We performed GO and KEGG of ERSRDEGs using the R package clusterProfiler\u003csup\u003e20\u003c/sup\u003e. The entry screening criteria were adj.p \u0026lt; 0.05 and FDR value (q value) \u0026lt; 0.25, and the p value correction method was Benjamini-Hochberg (BH).\u003c/p\u003e\n\u003cp\u003e1.2.4 Gene Set Enrichment Analysis (GSEA)\u003c/p\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA)\u003csup\u003e21\u003c/sup\u003e is used to evaluate the distribution trend of genes , thereby determining their contribution to the phenotype. In this study, the genes of dataset GSE114911 were first ranked according to logFC value, and then the R package clusterProfiler\u003csup\u003e20\u003c/sup\u003e (Version 4.12.0) is used to perform Gene Set Enrichment Analysis (GSEA) for all genes in the dataset GSE114911. The parameters used in the GSEA were as follows: the seed was 2024, the minimum number of genes contained in each gene set was 10, and the maximum number of genes contained in each gene set was 500. gene set \u0026ldquo;c2\u0026rdquo; was obtained through the Molecular Signatures Database (MSigDB) for GSEA. The screening criteria for GSEA were adj.p \u0026lt; 0.05. p value correction method was Benjamini-Hochberg (BH).\u003c/p\u003e\n\u003cp\u003e1.2.5 Protein-protein Interaction (PPI) Network\u003c/p\u003e\n\u003cp\u003eThe Protein-protein Interaction Network (PPI Network) is composed of proteins and proteins that interact with each other and participate in biological signaling, gene expression regulation, All aspects of life processes such as energy and substance metabolism and cell cycle regulation. Systematic analysis of the interaction of proteins in biological systems is of great significance for understanding the working principle of proteins in biological systems, understanding the reaction mechanism of biological signals and energy and substance metabolism under special physiological conditions such as diseases, and understanding the functional relationship between proteins. In this study, the STRING database\u003csup\u003e22\u003c/sup\u003e（https://cn.string-db.org/）was applied based on ERSRDEGs with a minimum interaction score greater than 0.4. The closely connected local regions in the PPI Network) may represent molecular complexes with specific biological functions. Genes in PPI network that have interaction with other genes were selected for subsequent analysis. We used the Cytoscape\u003csup\u003e23\u003c/sup\u003e software to visualize this network.\u003c/p\u003e\n\u003cp\u003eAll of five algorithms from the CytoHubba\u003csup\u003e24\u003c/sup\u003e, namely, Maximal Clique Centrality (MCC), Degree, Maximum Neighborhood Component (MNC), Edge Percolated Component (EPC), Closeness. The scores of ERSRDEGs were calculated by Closeness, and then the top 5 ERSRDEGs were selected according to the scores. Finally, the genes obtained by five different algorithms were interfaced and analyzed by Venn diagram. The intersection genes of the algorithms were used as ER stress-related hub genes.\u003c/p\u003e\n\u003cp\u003eThe GeneMANIA database\u003csup\u003e25\u003c/sup\u003e gives a list of query genes, GeneMANIA finds functionally similar genes using a large set of genomics and proteomics data. In this mode, it weights each functional genomic dataset according to the predicted value of the query. Another use of GeneMANIA is gene function prediction. Given a query gene, GeneMANIA finds genes that are likely to share functions with it, based on how the gene interacts with it. We predicted the functionally similar genes of hub genes through GeneMANIA online website to construct a PPI Network.\u003c/p\u003e\n\u003cp\u003e1.2.6 Construction of regulatory network\u003c/p\u003e\n\u003cp\u003eTranscription factors (TFs) control gene expression through their interactions with hub genes at the post-transcriptional stage. By ChIPBase database\u003csup\u003e26\u003c/sup\u003e,\u0026nbsp;the mRNA-TF Regulatory Network was visualized by Cytoscape software.\u003c/p\u003e\n\u003cp\u003eIn addition, miRNA plays an important regulatory role in the process of biological development and evolution. In order to analysis the hub genes and the relationship between miRNA, using StarBase v3.0 database\u003csup\u003e27\u003c/sup\u003e,\u0026nbsp;the mRNA-miRNA Regulatory Network was visualized by Cytoscape software.\u003c/p\u003e\n\u003cp\u003elong non-coding RNA (lncRNA) plays an important regulatory role in the process of biological development and evolution, and can regulate a variety of target genes. In order to analyze the interaction between hub genes and miRNA and lncRNA, using StarBase v3.0 database, the ceRNA Regulatory Network was visualized by Cytoscape software.\u003c/p\u003e\n\u003cp\u003e1.2.7 Validation of differential expression of Hub Genes and ROC curve analysis\u003c/p\u003e\n\u003cp\u003eIn order to further explore the difference in the expression of hub genes between the Infected group and the Normal group of Mycobacterium tuberculosis in data sets GSE114911 and GSE147964, a group comparison map was drawn based on the expression of hub genes. Finally, Use R package pROC\u003csup\u003e28\u003c/sup\u003e,\u0026nbsp;AUC was used to curve of the hub genes and calculate the area under the ROC curve to\u0026nbsp;evaluate the diagnostic effect on the occurrence of Mycobacterium tuberculosis infection. The AUC of the ROC curve is generally between 0.5 and 1. The closer the AUC is to 1, the better the diagnostic performance. 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.\u003c/p\u003e\n\u003cp\u003e1.2.8 Validation of Clinical Specimens\u003c/p\u003e\n\u003cp\u003eBetween December 2024 to February 2025, the peripheral blood samples from patients undergoing TB screening at Fifth People\u0026apos;s Hospital of Suzhou were collected. The inclusion criteria for ATB and HC samples included: \u0026nbsp;Patients with ATB are diagnosed based on clinical signs, such as cough, hemoptysis, loss of weight, chest pain, fever, night sweat, and shortness of breath, with bacteriological evidence by any of smear, culture or Xpert from sputum,; healthy individuals are without clinical TB symptoms and negative for IGRA test. Each research participant signed an informed consent form and agreed to participate in the study. The exclusion criteria included: the study subjects are suffering from autoimmune diseases; receiving any immunosuppressive drug treatment; being positive for HIV test and having malignant tumor. According to clinical signs, bacteriological evidence and IGRA test, these samples were divided into two groups:ATB group (40cases), and HC group (40 cases). The Ethics Committee of Fifth People\u0026apos;s Hospital of Suzhou has granted approval for the conduct of this study, ensuring its ethical compliance and scientific validity (Ethical approval no.\u003c/p\u003e\n\u003cp\u003e1.2.9 Enzyme-linked immunosorbent assay (ELISA) \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PPI network identified four core genes, among which\u003cem\u003e\u0026nbsp;IL-1A\u003c/em\u003e and\u003cem\u003e\u0026nbsp;IL-1B\u003c/em\u003e are the intersection of the mRNA-miRNA, mRNA-TF, and ceRNA regulatory networks. Additionally, \u003cem\u003eIL-1B\u003c/em\u003e shows a highly significant expression difference in the independent dataset GSE147964, with excellent diagnostic performance (AUC=0.93). Therefore, we chose\u003cem\u003e\u0026nbsp;IL-1A\u003c/em\u003e and \u003cem\u003eIL-1B\u0026nbsp;\u003c/em\u003efor EILSA validation.Therefore, we used 40 blood samples from tuberculosis patients and 40 samples from healthy controls for validation. The patients were from the Fifth People\u0026apos;s Hospital of Suzhou. The candidate biomarkers were validated using a human \u003cem\u003eIL-1A\u003c/em\u003e and \u003cem\u003eIL-1B\u003c/em\u003e ELISA kit (USCN Life Science; Wuhan, China). The experiments were conducted according to the manufacturer\u0026apos;s instructions.\u003c/p\u003e\n\u003cp\u003e1.2.10 CIBERSORT immune infiltration analysis\u003c/p\u003e\n\u003cp\u003eCIBERSORT\u003csup\u003e29\u003c/sup\u003e is based on linear support vector regression to deconvolute the transcriptome expression matrix to estimate the composition and abundance of immune cells in a mixture of cells. The CIBERSORT algorithm, combined with the LM22 feature gene matrix, was used to filter out the data with immune cell enrichment score greater than zero, and the specific results of immune cell infiltration matrix in the dataset GSE114911 were finally obtained, and the proportion bar chart was drawn for display. Then, the R package ggplot2 (Version 3.5.1) was used to draw group comparison plots to show the expression differences of immune cells in the Infected group and the Normal group of the dataset GSE114911. Subsequently, the immune cells with significant differences in the two groups were screened for subsequent analysis. The correlation between immune cells was calculated based on Spearman algorithm, and the R package pheatmap (Version 1.0.12) was used to draw the correlation heatmap to show the correlation analysis results of immune cells themselves. The correlation between hub genes and immune cells was calculated based on Spearman algorithm, and the correlation bubble plot was drawn using R package ggplot2 (Version 3.5.1) to show the correlation analysis results between hub genes and immune cells.\u003c/p\u003e\n\u003cp\u003e1.2.11 Statistical analysis\u003c/p\u003e\n\u003cp\u003eAll data processing and analysis in this article were based on R software (Version 4.4.0). For comparisons of continuous variables between two groups, statistical significance of normally distributed variables was estimated by independent Student\u0026apos;s T-Test, unless otherwise specified. The Mann-Whitney U Test method (Wilcoxon Rank Sum Test) was used to analyze the differences between the variables that were not normally distributed. Kruskal-Wallis test was used for comparison of three or more groups. Spearman correlation analysis was used to calculate the correlation coefficient between different genes. All statistical p values were two-sided if not specified, and a p value of less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e2.1 Standardization of the dataset for MTB infection\u003c/p\u003e\n\u003cp\u003eFirstly, the R package limma was used to standardize the M. tuberculosis infection dataset GSE114911. Distribution boxplots were used to compare the data sets before and after standardization (Fig.1A-B), and principal component analysis (PCA) was used to compare the principal component distribution of the samples before and after standardization (Fig.1C). The results showed that the data distribution of the standardized dataset was more regular.\u003c/p\u003e\n\u003cp\u003e2.2 Endoplasmic reticulum stress-related differentially expressed genes associated with Mycobacterium tuberculosis infection\u003c/p\u003e\n\u003cp\u003eThe data of dataset GSE114911 were divided into Mycobacterium tuberculosis Infected group and Normal group. we analyzed the difference of gene expression values between the Infected group and the Normal group in the data set GSE114911, there were 23 up-regulated DEGs and 1 down-regulated DEG. The volcano diagram was drawn according to the difference analysis results of this data set (Fig.2A).\u003c/p\u003e\n\u003cp\u003eIn order to obtain endoplasmic reticulum stress-related differentially expressed genes (ERSRDEGs), all the DEGs were interleaved with ERSRGs and Venn diagram was drawn (Fig.2B). A total of 10 ERSRDEGs were obtained. According to the intersection results, the expression differences between different sample groups were analyzed, and the R package pheatmap was used to draw a heatmap to show the expression of ERSRDEGs (Fig.2C). Finally, the location of ERSRDEGs on the human chromosome was analyzed by the R package RCircos, and the chromosome localization map was drawn (Fig.2D). The chromosomal mapping showed that ERSRDEGs were distributed on chromosome 1, 2, 4, 5, 6, 7, 8, 10, 12, 15, 17 and 19, and hub genes (see 2.6) were concentrated on chromosome 2, with a total of 3 genes, respectively: \u003cem\u003eIL-1A, IL-1B and CCL20\u003c/em\u003e; The other hub gene, located on chromosome 6, is \u003cem\u003eTNF\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e2.3 GO and KEGG enrichment analysis\u003c/p\u003e\n\u003cp\u003eThe 10 ERSRDEGs were used for gene ontology (GO) and pathway (KEGG) enrichment analysis, and the specific results are shown in Table 2. The results showed that the 10 ERSRDEGs were mainly enriched in neutrophil migration, granulocyte migration, leukocyte migration, and neutrophil migration in Mycobacterium tuberculosis infection. cellular response to lipopolysaccharide and cellular response to molecule of bacterial origin and other biological processes (BP); external side of plasma membrane cell component (CC); cytokine activity, cytokine receptor binding, growth factor receptor binding, chemokine activity, chemokine receptor binding and other molecular functions (MF). It was also enriched in Cytokine-cytokine receptor interaction, Rheumatoid arthritis, IL-17 signaling pathway, TNF signaling pathway, Pertussis and other biological pathways (KEGG). The results of enrichment analysis were visualized by bar graphs (Fig.3A).\u003c/p\u003e\n\u003cp\u003eAt the same time, the network diagram of enrichment analysis (Fig.3B-D) was drawn. Since only one item related to cell component (CC) was enriched, no network diagram was made for this item. The line shows the corresponding gene and the annotation of the corresponding entry. The larger the pathway node is, the more genes are enriched in the pathway. The color of the gene node shows the logFC of the gene, red indicates up-regulated genes, and blue indicates down-regulated genes.\u003c/p\u003e\n\u003cp\u003e2.4 GSEA enrichment analysis\u003c/p\u003e\n\u003cp\u003eTo determine the effects of the expression levels of all genes in dataset GSE114911 on Mycobacterium tuberculosis infection, gene set enrichment analysis (GSEA) was used to study the relationship between the expression levels of all genes in dataset GSE114911 and the biological processes, cellular components and molecular functions they played. The detailed results are shown in Table 3. The results showed that the Up-regulated genes in dataset GSE114911 were significantly enriched in OVERVIEW OF PROINFLAMMATORY AND PROFIBROTIC MEDIATORS (Fig.4A). ZHANG RESPONSE TO IKK INHIBITOR AND TNF UP (Fig.4B) and other biologically relevant functions and signaling pathways, Down-regulated genes were significantly enriched in CREIGHTON ENDOCRINE THERAPY RESISTANCE 2 (Fig.4C), NEUT SENGUPTA NASOPHARYNGEAL CARCINOMA DN (Fig.4D) and other biologically related functions and signaling pathways.\u003c/p\u003e\n\u003cp\u003e2.5 Construction of protein-protein interaction network and screening of Hub genes\u003c/p\u003e\n\u003cp\u003eFirstly, PPI Network of 10 ERSRDEGs was constructed using STRING database and visualized using Cytoscape software (Fig.5A). PPI Network showed that the 10 ERSRDEGs were all related.\u003c/p\u003e\n\u003cp\u003eSubsequently, using CytoHubba, the scores of 10 ERSRDEGs were calculated, screen the top 5 genes, take the intersection, and the intersection was taken and the Venn diagram was drawn (Fig.5B), in which the color of the circles represents the different algorithms. Finally, four hub genes were screened out, namely: \u003cem\u003eIL-1B, CCL20, IL-1A, and TNF.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFinally, the interaction network of four hub genes and their functionally similar genes was predicted and constructed by GeneMANIA website (Fig.5C). The lines with different colors represent the co-expression between them and share information such as protein domains. Among them, there were 4 hub genes and 20 functionally similar proteins.\u003c/p\u003e\n\u003cp\u003e2.6 Construction of regulatory networks\u003c/p\u003e\n\u003cp\u003eFirstly, miRNA related to hub gene\u003cem\u003es\u003c/em\u003e (\u003cem\u003eIL-1B, CCL20, IL-1A, TNF\u003c/em\u003e) were obtained through StarBase database. The mRNA-miRNA Regulatory Network was constructed and visualized by Cytoscape software (Fig.6A). Among them, there were 3 hub genes and 14 miRNAs, and the specific information is shown in Table 4.\u003c/p\u003e\n\u003cp\u003eThen, the transcription factors (TFs) combined with Hub Genes were obtained through the ChIPBase database, and the mRNA-TF Regulatory Network was constructed (Fig.6B). Among them, a total of2 hub genes and 19 TFs were included, and the specific information is shown in Table 5.\u003c/p\u003e\n\u003cp\u003eFinally, the lncRNAs related to hub genes (\u003cem\u003eIL-1B, CCL20, IL-1A, TNF\u003c/em\u003e) and the lncRNAs related to miRNAs obtained through StarBase database. The ceRNA Regulatory Network was constructed. There were 4 hub genes, \u0026nbsp;a total of16 miRNAs and 2 lncRNAs in ceRNA regulatory network（Fig.6C）.\u003c/p\u003e\n\u003cp\u003e2.7 Differential expression verification and ROC curve analysis of Hub Genes\u003c/p\u003e\n\u003cp\u003eTo explore and verify the differential expression of hub genes in the Mycobacterium tuberculosis infection dataset, The group comparison figure (Fig.7A-B) shows the difference analysis results of the expression levels of four hub genes in the Infected group and the Normal group of Mycobacterium tuberculosis infection in dataset GSE114911 and dataset GSE147964, respectively. The results of differential analysis showed that the expression levels of four hub genes in the Infected group and the Normal group of Mycobacterium tuberculosis in dataset GSE114911 were significantly different (p value \u0026lt; 0.05). In dataset GSE147964, only one hub gene, \u003cem\u003eIL-1B\u003c/em\u003e, showed a statistically significant difference in expression between the Infected group and the Normal group (p value \u0026lt; 0.05). Finally, the R package pROC was used to draw the ROC curve based on the expression levels of hub genes in dataset GSE114911 and dataset GSE147964. The ROC curve (Fig. 7c-d) shows that in dataset GSE114911 (Fig.7C), the expression levels of these four hub genes showed certain accuracy in the classification of MTB Infected group and Normal group (0.7 \u0026lt; AUC \u0026lt; 0.9); In dataset GSE147964 (Fig.7D), among the four hub genes, the expression level of CCL20 showed a low accuracy in the classification of MTB infection group and Normal group (0.5 \u0026lt; AUC \u0026lt; 0.7); The expression level of TNF showed a certain accuracy in the classification of Mycobacterium tuberculosis infection group and Normal group (0.7 \u0026lt; AUC \u0026lt; 0.9); The expression level of \u003cem\u003eIL-1B\u003c/em\u003e showed a high accuracy (AUC \u0026gt; 0.9) in the classification of Mycobacterium tuberculosis infection (Infected) and Normal groups.\u003c/p\u003e\n\u003cp\u003e2.8 Validation of potential biomarkers expression through ELISA\u003c/p\u003e\n\u003cp\u003eThe PPI network identified four core genes, among which \u003cem\u003eIL-1A\u003c/em\u003e and\u003cem\u003e\u0026nbsp;IL-1B\u0026nbsp;\u003c/em\u003eare the intersection of the mRNA-miRNA, mRNA-TF, and ceRNA regulatory networks.\u0026nbsp;In the datasets GSE114911 and GSE147964, the levels of\u003cem\u003e\u0026nbsp;IL-1B\u0026nbsp;\u003c/em\u003ein tuberculosis patients were found to be upregulated compared to the healthy control group. The expression difference of \u003cem\u003eIL-1B\u003c/em\u003e in the independent dataset GSE147964 was highly significant, with excellent diagnostic performance (AUC=0.93). Therefore, we chose \u003cem\u003eIL-1A\u0026nbsp;\u003c/em\u003eand \u003cem\u003eIL-1B\u0026nbsp;\u003c/em\u003efor clinical validation. The experimental results showed that \u003cem\u003eIL-1B\u003c/em\u003e was upregulated in the tuberculosis group, which is consistent with our bioinformatics analysis results (Fig 8 A, B). The ROC curve showed an AUC of 0.0716 (Figure 8C), demonstrating that IRF1 could be a diagnostic biomarker of TB.\u003c/p\u003e\n\u003cp\u003e2.9 Analysis of the correlation between the expression levels of ERS-related genes and clinical indicators.\u003c/p\u003e\n\u003cp\u003eThe correlation between the expression levels of ERS-related genes and clinical indicators (inflammatory factors, D-dimer) was analyzed, as shown in the results of Fig.9According to the results, \u003cem\u003eIL-1B\u003c/em\u003e is positively correlated with \u003cem\u003eIL6, TNF\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;IFN-\u0026gamma;\u0026nbsp;\u003c/em\u003e(Fig.9A, B, and C), and\u003cem\u003e\u0026nbsp;IL-1A\u003c/em\u003e is positively correlated with D-dimer (P\u0026lt;0.05) (Fig.9D).\u003c/p\u003e\n\u003cp\u003e2.10 CIBERSORT immune infiltration analysis\u003c/p\u003e\n\u003cp\u003eAccording to the results of immune infiltration analysis, the bar chart of the proportion of immune cells was drawn (Fig.10A). Then, the immune cells with p value \u0026lt; 0.05 were screened by group comparison chart and the expression differences in the infiltration abundance of immune cells in different groups were shown. The grouping comparison chart (Fig.10B) showed that the eight types of immune cells, including B cells memory, NK cells activated, Macrophages M0, Macrophages M1, Macrophages M2, Mast cells resting, Mast cells activated and Eosinophils were significantly different between the Infected group and the Normal group (p value \u0026lt; 0.05). Then, the correlation results of the 22 immune cells infected with M. tuberculosis in the immune infiltration analysis of dataset GSE114911 were shown by correlation heatmap (Fig.10C). Then, the correlation between hub genes and 8 immune cells that were significantly different between the Infected group and the Normal group were analyzed and displayed by correlation bubble plot (Fig.10D).\u003c/p\u003e\n\u003cp\u003eTable 1 GEO Microarray Chip Information\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eGSE114911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eGSE147964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eGPL6480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eGPL23126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eTissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003elung tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eblood samples\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eSamples in Infected group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eSamples in Normal group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003ePMID: 29977236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGEO, Gene Expression Omnibus.\u003c/p\u003e\n\u003cp\u003eTable 2 Results of GO and KEGG Enrichment Analysis for ERSRDEGs\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"887\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eONTOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eGeneRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eBgRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003epadj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:1990266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003eneutrophil migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e129/18888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.85E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.13E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e5.83E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0097530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003egranulocyte migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e154/18888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5.45E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e3.13E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e8.57E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0050900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003eleukocyte migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e396/18888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.92E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7.36E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.01E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0071222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003ecellular response to lipopolysaccharide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e225/18888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5.40E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.49E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e4.07E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0071219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003ecellular response to molecule of bacterial origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e238/18888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7.57E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.49E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e4.07E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0009897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003eexternal side of plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e405/19894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e9.04E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.45E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.05E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0005125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003ecytokine activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e238/18522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e8.12E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.52E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e8.55E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0005126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003ecytokine receptor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e273/18522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.84E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e4.41E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.50E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0070851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003egrowth factor receptor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e138/18522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5.98E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6.18E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.10E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0008009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003echemokine activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e49/18522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.06E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.60E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e5.42E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGO:0042379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003echemokine receptor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e74/18522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7.20E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e4.46E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.52E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ehsa04060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003eCytokine-cytokine receptor interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e298/8848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e4.81E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e3.80E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.27E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ehsa05323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003eRheumatoid arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e94/8848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.42E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5.63E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.87E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ehsa04657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003eIL-17 signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e95/8848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.65E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6.98E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.32E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ehsa04668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003eTNF signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e119/8848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.05E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.07E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e6.90E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ehsa05133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003ePertussis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e78/8848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.14E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.80E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e5.99E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGO, Gene Ontology; BP, Biological Process; CC, Cellular Component; MF, Molecular Function; KEGG, Kyoto Encyclopedia of Genes and Genomes; ERSRDEGs, Endoplasmic Reticulum Stress Related Differentially Expressed Genes.\u003c/p\u003e\n\u003cp\u003eTable 3 Results of GSEA for GSE114911\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003eSet Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eEnrichment Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ep adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eq value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eWP_OVERVIEW_OF_PROINFLAMMATORY_AND_PROFIBROTIC_MEDIATORS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.7997\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3.0553\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eZHANG_RESPONSE_TO_IKK_INHIBITOR_AND_TNF_UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.7260\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.9950\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eLINDSTEDT_DENDRITIC_CELL_MATURATION_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.8635\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.9564\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eBLANCO_MELO_COVID19_SARS_COV_2_INFECTION_CALU3_CELLS_UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.6827\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.9426\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eSANA_TNF_SIGNALING_UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.8076\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.9329\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eBLANCO_MELO_HUMAN_PARAINFLUENZA_VIRUS_3_INFECTION_A594_CELLS_UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.7147\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.9014\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eALTEMEIER_RESPONSE_TO_LPS_WITH_MECHANICAL_VENTILATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.7508\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.8687\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eKEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.6774\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.8632\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eSEKI_INFLAMMATORY_RESPONSE_LPS_UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.8002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.8464\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eREACTOME_INTERLEUKIN_10_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.8719\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.8319\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eSENGUPTA_NASOPHARYNGEAL_CARCINOMA_DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.7248\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-3.1974\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eCREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.5475\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-2.4669\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eKEGG_LYSOSOME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.5861\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-2.3168\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.85E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.82E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.49E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eKEGG_MEDICUS_VARIANT_MUTATION_CAUSED_ABERRANT_SOD1_TO_RETROGRADE_AXONAL_TRANSPORT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.7365\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-2.2351\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.87E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.000102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.35E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eSANA_TNF_SIGNALING_DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.5863\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-2.1766\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.70E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.26E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.03E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eLAIHO_COLORECTAL_CANCER_SERRATED_DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.5787\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-2.1434\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.34E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.10E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.02E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eFOROUTAN_INTEGRATED_TGFB_EMT_DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.5864\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-2.1412\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.24E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4.95E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4.05E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eAPPEL_IMATINIB_RESPONSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.6844\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-2.1296\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.44E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.000402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.000329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eRICKMAN_HEAD_AND_NECK_CANCER_D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.6709\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-2.0959\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4.06E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.000974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.000797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.8877%;\"\u003e\n \u003cp\u003eFOROUTAN_TGFB_EMT_DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3534%;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.5334\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-2.0735\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5.10E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.17E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.77E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGSEA, Gene Set Enrichment Analysis; NES, Normalized Enrichment Score.\u003c/p\u003e\n\u003cp\u003eTable 4 mRNA-miRNA Interaction of hub genes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003emRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eCCL20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-5579-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-30a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-192-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-30c-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-30d-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-181c-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-30b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-125b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-125a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-30e-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-181d-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-532-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-670-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ehsa-miR-101-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003emiRNA, microRNA.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 mRNA-TF Interaction of hub genes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003emRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eATF4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eCEBPA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eCEBPB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eEP300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eERG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eFOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eGABPA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eGATA2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eHNF4A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eJUND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eMAX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eMYC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eRELA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eSPI1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eTEAD4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eUSF1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eSPI1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eTAL1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eIL-1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eCEBPB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTF, Transcription Factors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTuberculosis (TB) is a disease caused by Mycobacterium tuberculosis and remains a major threat to global health, affecting millions of people each year\u003csup\u003e30\u003c/sup\u003e. The disease primarily manifests as pulmonary tuberculosis, leading to severe respiratory complications and causing high morbidity and mortality rates in low- and middle-income countries\u003csup\u003e31\u003c/sup\u003e. Despite some progress in diagnosis and treatment, the emergence of multidrug-resistant strains poses significant challenges to management strategies, highlighting the urgent need for a deeper understanding of the fundamental biological mechanisms driving TB pathology and host responses\u003csup\u003e32\u003c/sup\u003e.This study aims to explore the role of endoplasmic reticulum (ER) stress in TB infection, with a particular focus on the differential expression of ER stress-related genes associated with Mycobacterium tuberculosis. By utilizing publicly available data and employing bioinformatics methods, we identified several key genes related to ER stress that may serve as potential biomarkers for tuberculosis. The results indicate that these differentially expressed genes not only deepen our understanding of the mechanisms of tuberculosis but also provide new therapeutic targets for improving disease management and patient prognosis.\u003c/p\u003e \u003cp\u003eIn the context of Mycobacterium tuberculosis infection, the analysis of differentially expressed genes (DEGs) reveals important information about the potential molecular mechanisms of the disease. We identified 24 significantly upregulated DEGs, highlighting the inflammatory response triggered by the pathogen. Notably, based on bioinformatics analysis, the \u003cem\u003eIL-1B\u003c/em\u003e levels in tuberculosis patients were found to be upregulated compared to healthy controls in the test sets GSE114911 and GSE147964. Therefore, we chose to use ELISA to further identify the \u003cem\u003eIL-1B\u003c/em\u003e expression levels associated with endoplasmic reticulum stress (ERS). The results showed that the expression level of \u003cem\u003eIL-1B\u003c/em\u003e was consistent with the bioinformatics mRNA microarray analysis results. Several studies have proposed ERS as a potential target for tuberculosis treatment, and in tuberculosis patients, the level of \u003cem\u003eIL-1B\u003c/em\u003e is significantly elevated, closely related to the severity of the condition\u003csup\u003e33\u003c/sup\u003e. Research indicates that \u003cem\u003eIL-1B\u003c/em\u003e enhances the expression of pro-inflammatory factors by activating the NF-κB and MAPK signaling pathways, leading to a stronger inflammatory response. As our research found, \u003cem\u003eIL-1B\u003c/em\u003e is positively correlated with other inflammatory factors such as \u003cem\u003eIL-6, TNF-A\u003c/em\u003e, and IFN-γ, and studies have shown that \u003cem\u003eIL-1B\u003c/em\u003e can increase the release of these inflammatory factors. These cytokines can activate ERS and UPR. This inflammatory microenvironment not only helps to resist infection by Mycobacterium tuberculosis.This inflammatory microenvironment not only helps to resist Mycobacterium tuberculosis infection \u003csup\u003e34\u003c/sup\u003e;moreover, studies have shown that the overexpression of \u003cem\u003eIL-1B\u003c/em\u003e is associated with the pathological progression of tuberculosis, and inhibiting \u003cem\u003eIL-1B\u003c/em\u003e activity may help alleviate the inflammatory response and improve the condition. For example, the use of \u003cem\u003eIL-1B\u003c/em\u003e monoclonal antibodies (such as Canakinumab) in animal models has shown significant reductions in tuberculosis-related inflammatory markers and improvements in pulmonary pathological changes \u003csup\u003e35\u003c/sup\u003e. In clinical studies, the application of anti-\u003cem\u003eIL-1B\u003c/em\u003e therapy has also shown potential benefits for tuberculosis patients, especially in those who respond poorly to conventional anti-tuberculosis treatment \u003csup\u003e36\u003c/sup\u003e. These studies provide strong evidence for \u003cem\u003eIL-1B\u003c/em\u003e as a therapeutic target, suggesting that it may play an important role in the treatment of pulmonary tuberculosis.\u003c/p\u003e \u003cp\u003ePathway enrichment analysis particularly highlights the roles of neutrophil migration and cytokine-cytokine receptor interactions, revealing key aspects of the immune response to tuberculosis. The enrichment of DEGs in these pathways indicates that the effective mobilization of neutrophils by the immune system is crucial for controlling the infection. Neutrophils are typically the first responders to bacterial infections, and their migration to the site of infection is vital for effective pathogen clearance\u003csup\u003e37\u003c/sup\u003e. Furthermore, the cytokine-cytokine receptor interaction pathway underscores the complex signaling network that regulates the immune response\u003csup\u003e38\u003c/sup\u003e. These interactions are essential for fine-tuning the immune response, ensuring that it is neither overly aggressive nor insufficient, to prevent tissue damage. A deeper understanding of these pathways will provide important insights for potential therapeutic strategies to modulate the immune response in tuberculosis patients.\u003c/p\u003e \u003cp\u003eThe results of CIBERSORT analysis reveal changes in the immune cell composition in the context of tuberculosis infection. Consistent with previous findings \u003csup\u003e39\u0026ndash;42\u003c/sup\u003e, significant changes in immune cell abundance were observed between healthy individuals and the active tuberculosis (ATB) cohort. The results showed that memory B cells, activated NK cells, M0 macrophages, M1 macrophages, M2 macrophages, resting mast cells, activated mast cells, and eosinophils had statistically significant differences (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the infected group and the normal group, suggesting a potential association of these cells with the occurrence and progression of tuberculosis. Research shows that the number and activity of mast cells in the lung tissue of tuberculosis patients are significantly increased, which may be related to the infection of Mycobacterium tuberculosis and the inflammatory response it triggers\u003csup\u003e43\u003c/sup\u003e. Mast cells sense the presence of pathogens through their surface receptors and respond rapidly in the early stages of infection by releasing a large amount of inflammatory mediators, thereby regulating the local immune response\u003csup\u003e44\u003c/sup\u003e. In addition, mast cells also regulate the activity of T cells and B cells through interactions with other immune cells, further influencing the progression and outcome of tuberculosis\u003csup\u003e45\u003c/sup\u003e. Consistent with previous research findings, our results showed that the number of activated mast cells in the tuberculosis infection group was significantly higher than that in the healthy control group, with statistical significance (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe constructed PPI network reveals the complex relationships. Significant interactions among these proteins suggest that they may work synergistically to regulate the cellular response to Mycobacterium tuberculosis infection\u003csup\u003e46\u003c/sup\u003e. For instance, central genes such as \u003cem\u003eIL-1B\u003c/em\u003e and CCL20 not only participate in inflammatory signaling but may also interact with other proteins that regulate the ER stress pathway, indicating their multifaceted roles in the infection response\u003csup\u003e47\u003c/sup\u003e. This network analysis is crucial for elucidating the regulatory mechanisms involved in the pathogenesis of tuberculosis and may guide future research to identify new therapeutic targets that disrupt these interactions\u003csup\u003e48,49\u003c/sup\u003e. Understanding the dynamics of the PPI network may also facilitate the discovery of potential biomarkers for the diagnosis and monitoring of tuberculosis, thereby improving clinical management of the disease\u003csup\u003e50\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eROC curve analysis demonstrates the diagnostic potential of central genes, showcasing their relevance as biomarkers for tuberculosis. The ability of these genes to distinguish between infected and non-infected individuals, along with their acceptable area under the curve (AUC), indicates that they can be integrated into diagnostic tests to enhance early detection of the disease\u003csup\u003e51,52\u003c/sup\u003e. Early diagnosis is critical in the management of tuberculosis, especially in the context of rising rates of drug-resistant strains. Furthermore, the predictive value of these biomarkers may pave the way for personalized treatment approaches, predicting patient responses to therapy based on their gene expression profiles. This aligns with the current trend in precision medicine\u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that tuberculosis can induce a systemic hypercoagulable state, characterized by significantly elevated plasma D-dimer levels, especially in patients with active tuberculosis and severe tuberculosis\u003csup\u003e54\u003c/sup\u003e. This is closely related to the severity, activity, and prognosis of tuberculosis patients. This study found a positive correlation between \u003cem\u003eIL-1A\u003c/em\u003e and D-dimer, suggesting that the ESR-related gene \u003cem\u003eIL-1A\u003c/em\u003e may be closely related to the severity and prognosis of tuberculosis, and thus IL-1A may be a prognostic marker for tuberculosis. However, the qPCR and ELISA results for our target gene \u003cem\u003eIL-1A\u003c/em\u003e were contrary to the results from the bioinformatics testing set, which may be related to the heterogeneity of the samples and the potential suppression of protein post-translational degradation. Therefore, further expansion of the experimental sample is needed to validate its expression levels in tuberculosis patients.\u003c/p\u003e \u003cp\u003eOur research also has some limitations. First, more gene chip samples from tuberculosis patients are needed to fully elucidate the molecular mechanisms of tuberculosis occurrence and development. Second, many biomarkers related to tuberculosis have yet to be characterized, requiring further experimental validation and bioinformatics analysis to study genes associated with tuberculosis. The specific mechanisms need to be further explored in future animal models.\u003c/p\u003e \u003cp\u003eSummary: This study reveals the important roles of ER stress-related differentially expressed genes associated with Mycobacterium tuberculosis infection and provides potential biomarkers and targets for future diagnostic and therapeutic strategies. These findings contribute to a better understanding of the mechanisms of Mycobacterium tuberculosis infection and support efforts to address public health challenges.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe would like to thank all participants and data collectors involved in this study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Sharing Statement \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe corresponding author will provide supplementary materials and all other data used in this study upon reasonable request. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval and informed consent\u003c/p\u003e\n\u003cp\u003eTe clinical study, which included blood samples and information of participants, was reviewed and approved by the Ethics Committee of Fifth People's Hospital of Suzhou, Department of Pulmonology, Suzhou, Jiangsu, People's Republic of China (Approval No: ZF-2024-008-01) .All procedures were carried out in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to study procedures.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors made significant contributions to various key aspects of this study, including conception, research design, execution, data acquisition, analysis and interpretation, as well as other areas such as manuscript writing (drafting, revising, or critically reviewing the article), final approval of the version to be published, agreement on the journal to which the article should be submitted, and agreement to be accountable for all aspects of the report. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisclosure \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;the GeO database provides access to the datasets examined in this study (http://www.ncbi.nlm.nih.gov/geo/). Please contact the corresponding author if you have any more questions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCastro-Lima, V. A. C.\u003cem\u003e et al.\u003c/em\u003e Prevalence of latent Mycobacterium tuberculosis infection in hematopoietic stem cell transplantation comparing tuberculin skin test and interferon-gamma release assay. \u003cem\u003eEuropean journal of clinical microbiology \u0026amp; infectious diseases : official publication of the European Society of Clinical Microbiology\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 899-902, doi:10.1007/s10096-023-04613-w (2023).\u003c/li\u003e\n\u003cli\u003eYuen, W. L. P. \u0026amp; Loo, W. L. 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Previous studies have shown that endoplasmic reticulum (ER) stress plays an important role in various infectious diseases. This study aims to identify potential ER stress-related genes in tuberculosis by analyzing differentially expressed genes (DEGs) and to explore the role of ER stress in Mycobacterium tuberculosis infection. This study identified 10 endoplasmic reticulum stress-related differentially expressed genes (ERSRDEGs) by standardizing and analyzing the differential expression of the dataset GSE114911. GO and KEGG enrichment analysis found that ERSRDEGs are significantly involved in neutrophil migration 17/TNF signaling pathway. Protein-protein interaction network identified four hub genes (\u003cem\u003eIL-1B\u003c/em\u003e, CCL20, \u003cem\u003eIL-1A\u003c/em\u003e, TNF), among which \u003cem\u003eIL-1B\u003c/em\u003e showed highly significant differential expression in the independent dataset GSE147964, demonstrating excellent diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.93), and was validated by ELISA for its high expression in the serum of tuberculosis patients. Immune infiltration analysis showed that the infiltration of M1 macrophages increased in the tuberculosis infection group, and \u003cem\u003eIL-1B\u003c/em\u003e was strongly positively correlated with M1 macrophages. In addition, the study also analyzed the correlation between \u003cem\u003eIL-1A\u003c/em\u003e and IL-1B with clinical indicators (inflammatory factors, D-dimer). According to the analysis results, IL-1B was positively correlated with IL-6, TNF, and IFN-γ, while \u003cem\u003eIL-1A\u003c/em\u003e was positively correlated with D-dimer.Our findings emphasize the critical role of ER stress-related genes in the pathophysiology of Mycobacterium tuberculosis infection.\u003c/p\u003e","manuscriptTitle":"Identification and clinical validation of endoplasmic reticulum genes related to pulmonary tuberculosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 10:24:29","doi":"10.21203/rs.3.rs-6582977/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-10T11:17:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T03:27:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-27T13:16:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T05:44:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269218108202955643205447696257693027936","date":"2025-05-21T06:39:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264417010017021773286610911803780606974","date":"2025-05-12T17:15:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50984124231266713571801870066382305757","date":"2025-05-11T07:26:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242230163427026602708465888744113806685","date":"2025-05-09T06:00:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-09T05:54:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-09T05:44:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-06T11:55:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-05T12:30:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-03T08:28:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a749b9e-3572-49b3-b231-e7d2f7dd6bae","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48475727,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":48475728,"name":"Health sciences/Diseases/Infectious diseases/Tuberculosis"}],"tags":[],"updatedAt":"2025-12-08T16:09:53+00:00","versionOfRecord":{"articleIdentity":"rs-6582977","link":"https://doi.org/10.1038/s41598-025-29599-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-07 15:58:07","publishedOnDateReadable":"December 7th, 2025"},"versionCreatedAt":"2025-05-15 10:24:29","video":"","vorDoi":"10.1038/s41598-025-29599-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-29599-7","workflowStages":[]},"version":"v1","identity":"rs-6582977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6582977","identity":"rs-6582977","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}