Identification of glycolysis-related genes in pulpitis by bioinformatics analysis

Identification of glycolysis-related genes in pulpitis by bioinformatics analysis | 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 Research Article Identification of glycolysis-related genes in pulpitis by bioinformatics analysis Chaoran Liang, Qiang Wang, Mengyin Luan, Yatong Li, Jingjing Zong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4802823/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background: Pulpitis, a common inflammation of the dental pulp, involves intricate mechanisms that are not yet fully understood. Our study aims to elucidate the alterations in genetic transcription linked to glycolysis in pulpitis and their impact on biological pathways and molecular networks. Methods: Gene expression data was collected from the GEO database. Glycolysis-related genes were identified through databases like GeneCards and MsigDB. To understand the roles of these genes, GO, KEGG pathway enrichment, and GSEA were carried out. The PPI network was constructed with STRING, and central genes were determined using cytoHubba algorithms. mRNA-miRNA and mRNA-TF regulatory interactions were obtained from TarBase, ChIPBase, and hTFtarget. We assessed differential expression of the hub genes between groups, and conducted ROC curve analysis. ssGSEA was used to examine immune cell infiltration, with pheatmap illustrating associations between hub genes and immune cells. All statistical analyses were performed using R. Results: Our analysis revealed 3480 differentially expressed genes (DEGs) in pulpitis, comprising 1591 upregulated and 1889 downregulated genes. Among these, 63 glycolysis-related differentially expressed genes (GRDEGs) were predominantly located on chromosome 11. These GRDEGs were enriched in energy metabolism processes, organelle compartments, and molecular functions, implicating key pathways in the pathology of pulpitis. PPI network analysis identified eight hub genes— HIF1A , LDHA , HK2 , STAT3 , TALDO1 , PPARG , ALDOC , and PFKP . Additionally, ssGSEA uncovered notable differences in the infiltration levels of 28 types of immune cells between pulpitis and control samples, suggesting alterations in the immune response related to pulpitis. Conclusion: Our research offers new perspectives into the molecular mechanisms of pulpitis, particularly regarding glycolytic pathways. These results may help identify better diagnostic markers and therapeutic targets for managing pulpitis. Future studies should aim to validate these potential biomarkers and investigate their functional roles in the etiology of disease. Pulpitis Glycolysis Differential Expression Bioinformatics Diagnostic Biomarkers Immune Infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The pulp tissue, situated within the dental pulp cavity, plays a crucial role in dentin production, nutrient provision, and the support and protection of the tooth. When the dentin layer is compromised, the dental pulp initiates a complex defensive response, activating microbial-related humoral and cellular immune mechanisms that result in initial reversible local inflammation [ 1 ]. Pulpitis, an inflammatory condition affecting the dental pulp, is primarily induced by bacterial infection [ 2 ]. If left unaddressed, pulpitis can progress to cause severe pain, tooth loss, and potentially life-threatening systemic infections [ 3 ]. Currently, root canal therapy is the main treatment for pulpitis. However, the procedure is often invasive and may not be appropriate for all patients [ 4 ]. The clinical diagnosis of pulpitis primarily depends on subjective symptoms and diagnostic methods, which unfortunately show limited correlation with the histological state of the dental pulp [ 5 ]. Hence, there is a critical necessity to comprehend the molecular mechanisms associated with pulpitis to enhance diagnostic precision and treatment efficacy. In 2021, Xin et al. [ 6 ] highlighted that chemokine–associated genes, including CXCL10 , CXCL1 , CCL5 , and CXCR4 , could be possible biomarkers for identifying pulpitis. In another study, that Xu et al. [ 7 ] identified several genes linked to m6A modification, including RBM15B , YTHDF1 , METTL16 , METTL3 , METTL14 , and ALKBH5 , as useful markers for pulpitis. Nonetheless, the pathogenesis of pulpitis is multifactorial, and numerous biological pathways remain uncharted. Glycolysis is a metabolic pathway in which glucose or glycogen is enzymatically split to form pyruvate and lactic acid, producing ATP under anaerobic conditions or hypoxia [ 8 ]. Previous studies have shown that the dysregulation of glycolysis-related genes is involved in the development of inflammatory diseases [ 9 , 10 ]. Current research has identified glycolysis as a critical mediator of macrophage pyroptosis in periodontitis, with glycolysis inhibitors proposed as potential agents for controlling inflammation and minimizing periodontal tissue injury [ 11 ]. Nevertheless, the correlation between glycolysis and pulpitis remains undefined, necessitating further investigation. Our research conducted an in-depth examination of gene expression patterns associated with pulpitis, concentrating on glycolysis-related pathways. By comparing significantly Differentially Expressed Genes (DEGs) between pulpitis-associated and glycolysis-related datasets, we identified crucial biomarkers. Following this, we carried out Gene Ontology (GO) term enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA) based on the DEGs. Leveraging Cytoscape, we generated a Protein-Protein Interaction (PPI) network and identified eight hub genes. This study provides fresh perspectives on the molecular underpinnings of pulpitis while uncovering promising markers for timely diagnosis and precision medicine approaches. Methods Data Download The datasets GSE77459 [ 12 ] and GSE92681 [ 13 ], relevant to pulpitis, were obtained via the GEO database utilizing the R package GEOquery [ 14 ]. The samples from GSE77459 and GSE92681 were all derived from homo sapiens, specifically from human dental pulp tissue. The chip platforms used for GSE77459 and GSE92681 were GPL17692 and GPL16956, respectively. Detailed information is provided in Table 1 . Dataset GSE77459 comprised 6 samples of pulpitis and 6 control samples, while GSE92681 included 7 pulpitis samples and 5 control samples. All samples, both pulpitis and control, were included in the study. Table 1 GEO Microarray Chip Information GSE77459 GSE92681 Platform GPL17692 GPL16956 Type Array Array Species Homo sapiens Homo sapiens Tissue Pulp Pulp Samples in Disease group 6 7 Samples in Control group 6 5 Reference PMID: 33011745 PMID: 29079059 GEO, Gene Expression Omnibus。 Glycolysis-Related Genes (GRGs) were collected through GeneCards database [ 15 ]. By using "Glycolysis" as a search term and filtering for GRGs categorized as "Protein Coding" with a relevance score exceeding 3, we identified a total of 101 GRGs. Additionally, querying "Glycolysis" in the MsigDB website [ 16 , 17 ] yielded a comprehensive set of glycolysis-related gene sets from various databases (biocarta glycolysis pathway, hallmark glycolysis, kegg glycolysis gluconeogenesis, reactome glycolysis, reactome regulation of glycolysis by fructose 2 6 bis phosphate metabolism, wp aerobic glycolysis, wp glycolysis and gluconeogenesis, wp glycolysis in senescence,wp hif1a and pparg regulation of glycolysis), resulting in the identification of 302 GRGs. After removing redundancies, 338 distinct GRGs were identified, with specifics listed in Table S1 . The R package sva [ 18 ] was used to perform batch correction on GSE77459 and GSE92681, merging them into an integrated dataset comprising 13 pulpitis samples and 11 control samples. Subsequently, the merged data was standardized using the R package limma [ 19 ], with probes annotated and data normalized. Pulpitis-related glycolysis-related differentially expressed genes Based on the sample grouping from the combined datasets, samples were categorized into either the pulpitis or control group. Using the R package limma, differential expression analysis was performed to compare the pulpitis and control groups, with DEGs defined by | logFC | > 0 and adj. P < 0.05. To identify Glycolysis-Related Differentially Expressed Genes (GRDEGs) associated with pulpitis, we intersected all DEGs with GRGs. Heat maps were generated using the R package pheatmap, and chromosomal genetic location mapping was performed using RCircos [ 20 ]. The volcano plot was created using the R package ggplot2 to visualize the differential expression results. Functional enrichment analysis To clarify the biological relevance of GRDEGs, GO annotation was employed [ 21 ]. The signaling pathways associated with GRDEGs were explored using the KEGG [ 22 ]. The R package clusterProfiler [ 23 ] was used to perform GO and KEGG enrichment analyses of GRDEGs, considering adj. P < 0.05 and FDR value (q value) < 0.25 as significant. Gene Set Enrichment Analysis GSEA assesses how genes are distributed within predetermined gene sets [ 24 ]. The R package clusterProfiler was used to conduct GSEA on the entire gene set within the integrated datasets. Adj. P < 0.05 and q value < 0.25 were used to establish statistical significance. PPI network and hub gene identification Interactions among identified and forecasted proteins can be investigated using the STRING database [ 25 ]. For this research, the PPI network was constructed with the STRING database, with an interaction threshold of 0.700. Five algorithms from Cytoscape's [ 26 ] cytoHubba [ 27 ] plugin were utilized: Maximal Clique Centrality (MCC), Degree, Maximum Neighborhood Component (MNC), Edge Percolated Component (EPC), and Closeness [ 28 ]. Scores were computed for GRDEGs in the PPI network. The highest-scoring 10 GRDEGs were subsequently determined. Lastly, a Venn diagram was created to compare the genes identified by the five algorithms, and the overlapping genes identified by these algorithms were considered glycolysis-related hub genes. Construction of regulatory network The TarBase database [ 29 ] was used to identify miRNAs associated with hub genes, mapping the relationship between hub genes and miRNAs. The mRNA-miRNA regulatory network was constructed using Cytoscape software. To study the role of transcription factors in the regulation of hub genes, information from ChIPBase [ 30 ] and hTFtarget [ 31 ] databases was used, and the mRNA-TF regulatory network was depicted using Cytoscape software. Differential expression and ROC analysis of hub genes To better understand the differential expression of the hub genes in the pulpitis and control groups of the combined datasets, a group comparison map was made based on the expression level of the hub genes. Subsequently, the R package pROC was used to create Receiver Operating Characteristic (ROC) curves for hub genes and calculate the associated Area Under the Curve (AUC) values. AUC values between 0.5 and 0.7 indicated low accuracy, values between 0.7 and 0.9 indicated moderate accuracy, and values greater than 0.9 indicated high accuracy. Immune infiltration analysis We used single-sample Gene Set Enrichment Analysis (ssGSEA) [ 32 ] to quantify the extent of immune cell infiltration. The ssGSEA enrichment scores allowed visualization of immune cell infiltration levels per sample, excluding with those having P < 0.05 to build the infiltration matrix for immune cells. Moreover, we used the R package ggplot2 to generate plots comparing ssGSEA immune infiltration between groups. Heatmaps depicting correlations were created using R package pheatmap, depicting correlations of hub genes with infiltrating immune cells based on ssGSEA in pulpitis and control samples. Statistical analysis We processed and analyzed the data using R software (v4.3.1). Continuous data were reported as mean ± SD. The Wilcoxon rank sum test allowed comparison between the two groups. We computed correlation coefficients among various molecules using Spearman's method, and statistical significance was set at adj. P < 0.05 except if stated otherwise. Results Technology Roadmap Merging of pulpitis datasets The R package sva was employed to adjust for batch effects present in the pulpitis datasets (GSE77459, GSE92681), resulting in merged datasets from GEO. We assessed the pre- and post-correction datasets using box plots showing distributions and PCA plots (Fig. 2 A-D). The grouped boxplot and PCA plots demonstrated effective reduction of batch effects in the Pulpitis dataset following correction. Pulpitis-related glycolysis-related differentially expressed genes In total, we found 3480 DEGs, with 1591 upregulated and 1889 downregulated, as shown in the volcano plot (Fig. 3 A). To pinpoint genes with significant expression changes, those meeting | logFC | > 0 and adj. P < 0.05 criteria were selected through intersection of DEGs and GRGs using a Venn diagram (Fig. 3 B). This analysis identified 63 genes meeting these criteria, detailed in Table S2 . Subsequently, GRDEG analysis across different sample groups in the combined datasets was conducted based on the intersection results (Fig. 3 C). The chromosomal genetic locations of 63 GRDEGs were then examined (Fig. 3 D). The map revealed that a significant number of GRDEGs were situated on chromosome 11, including genes such as SLC2A3 , FKBP4 , YAP1 , UCP2 , PC , CD44 , LDHA , NUP94 and TALDO1 . Analysis of GRDEGs for functional enrichment We investigated the Biological Processes (BP), Molecular Functions (MF), Cellular Components (CC) and biological pathways linked to 63 GRDEGs using GO and KEGG enrichment analysis, and the specific results are shown in Table 2 . Our findings suggest that the GRDEGs are mainly involved in BP associated with pyruvate metabolism, glycolysis, and the generation of metabolic precursors and energy. They are also enriched in CC such as intracellular organelle lumens and MF including carbohydrate binding and isomerase activity. KEGG analysis revealed that GRDEGs participate in key metabolic and signaling pathways, including glucose metabolism, hypoxia response, and carbon utilization. The GO and KEGG enrichment analysis results were graphically represented (Fig. 4 A-B). A network diagram depicting the relationships between the enriched terms and pathways was also created (Fig. 4 C-F). Table 2 Result of GO and KEGG Enrichment Analysis for GRDEGs ONTOLOGY ID GeneRatio BgRatio P value adj. P q value BP GO:0006090 14/63 106/18800 4.014E-19 9.4972E-16 6.114E-16 BP GO:0006091 19/63 494/18800 1.4011E-15 1.3151E-12 8.4663E-13 BP GO:0006096 11/63 81/18800 2.4125E-15 1.3151E-12 8.4663E-13 BP GO:0016052 13/63 152/18800 2.7709E-15 1.3151E-12 8.4663E-13 BP GO:0006757 11/63 82/18800 2.7792E-15 1.3151E-12 8.4663E-13 CC GO:0034774 6/63 322/19594 0.00058131 0.02989102 0.02584562 CC GO:0060205 6/63 325/19594 0.00061027 0.02989102 0.02584562 CC GO:0031983 6/63 327/19594 0.00063019 0.02989102 0.02584562 CC GO:0090575 5/63 230/19594 0.00086209 0.02989102 0.02584562 CC GO:0005667 7/63 483/19594 0.00088961 0.02989102 0.02584562 MF GO:0048029 5/63 71/18410 4.3686E-06 0.00077862 0.00058218 MF GO:0005536 3/63 11/18410 6.1795E-06 0.00077862 0.00058218 MF GO:0016853 6/63 155/18410 1.4783E-05 0.00124176 0.00092847 MF GO:0017056 3/63 28/18410 0.0001177 0.00741513 0.00554431 MF GO:0033293 4/63 81/18410 0.00017005 0.00807198 0.00603544 KEGG hsa00010 10/51 67/8164 6.7666E-12 1.0827E-09 7.9775E-10 KEGG hsa04066 11/51 109/8164 4.3893E-11 3.5114E-09 2.5874E-09 KEGG hsa01200 10/51 115/8164 1.6302E-09 8.6946E-08 6.4065E-08 GO, Gene Ontology;BP, Biological Process༛CC, Cellular Component༛MF, Molecular Function༛KEGG, Kyoto Encyclopedia of Genes and Genomes༛ GRDEGs, Glycolysis-Related Differentially Expressed Genes。 Gene Set Enrichment Analysis (GSEA) GSEA findings were visualized in a bubble plot (Fig. 5 A). Table 3 provides a summary of the detailed GSEA results. GSEA showed that the genes in the combined datasets were significantly enriched in various signaling cascades and biological processes, such as Jak Stat signaling, Tak1 dependent Ikk and Nf Kappa B activation, negative regulation of the Pi3k Akt network, dysregulation of Hippomerlin signaling, the Mapk signaling pathway, and Fceri mediated Mapk activation (Fig. 5 B-G). Table 3 Results of GSEA for Combined Datasets ID setSize EnrichmentScore NES P value adj. P q value WP_OVERVIEW_OF_PROINFLAMMATORY_AND_PROFIBROTIC_MEDIATORS 113 0.79849 2.84547 1.00E-10 8.38E-09 6.57E-09 REACTOME_IMMUNOREGULATORY_INTERACTIONS_BETWEEN_A_LYMPHOID_AND_A_NON_LYMPHOID_CELL 116 0.76925 2.75771 1.00E-10 8.38E-09 6.57E-09 WP_EXTRAFOLLICULAR_B_CELL_ACTIVATION_BY_SARSCOV2 66 0.83452 2.74739 1.00E-10 8.38E-09 6.57E-09 KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION 237 0.70544 2.74225 1.00E-10 8.38E-09 6.57E-09 WP_NETWORK_MAP_OF_SARSCOV2_SIGNALING_PATHWAY 203 0.71046 2.71883 1.00E-10 8.38E-09 6.57E-09 REACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES 49 0.85806 2.69917 1.00E-10 8.38E-09 6.57E-09 REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING 102 0.76476 2.69181 1.00E-10 8.38E-09 6.57E-09 KEGG_CHEMOKINE_SIGNALING_PATHWAY 171 0.70496 2.64823 1.00E-10 8.38E-09 6.57E-09 REACTOME_INTERLEUKIN_10_SIGNALING 42 0.86787 2.64032 1.00E-10 8.38E-09 6.57E-09 REACTOME_NEUTROPHIL_DEGRANULATION 430 0.63813 2.59588 1.00E-10 8.38E-09 6.57E-09 REACTOME_SIGNALING_BY_INTERLEUKINS 426 0.63802 2.59482 1.00E-10 8.38E-09 6.57E-09 WP_TYROBP_CAUSAL_NETWORK_IN_MICROGLIA 56 0.78595 2.51623 1.00E-10 8.38E-09 6.57E-09 REACTOME_CELL_SURFACE_INTERACTIONS_AT_THE_VASCULAR_WALL 132 0.68277 2.49264 1.00E-10 8.38E-09 6.57E-09 WP_SARSCOV2_INNATE_IMMUNITY_EVASION_AND_CELLSPECIFIC_IMMUNE_RESPONSE 64 0.75520 2.47953 1.00E-10 8.38E-09 6.57E-09 KEGG_JAK_STAT_SIGNALING_PATHWAY 145 0.59371 2.19889 3.38E-10 2.28E-08 1.79E-08 REACTOME_TAK1_DEPENDENT_IKK_AND_NF_KAPPA_B_ACTIVATION 43 0.59764 1.83074 5.59E-04 5.08E-03 3.98E-03 REACTOME_NEGATIVE_REGULATION_OF_THE_PI3K_AKT_NETWORK 106 0.49835 1.76228 9.46E-05 1.15E-03 9.01E-04 WP_HIPPOMERLIN_SIGNALING_DYSREGULATION 110 0.47991 1.70400 5.60E-04 5.08E-03 3.98E-03 REACTOME_FCERI_MEDIATED_MAPK_ACTIVATION 34 0.64417 1.87970 3.77E-04 3.67E-03 2.87E-03 WP_MAPK_SIGNALING_PATHWAY 236 0.37321 1.45133 4.20E-03 2.77E-02 2.17E-02 GSEA, Gene Set Enrichment Analysis。 Building the PPI Network and identifying hub genes We constructed a PPI network of the 63 GRDEGs using the STRING database (Fig. 6 A). The PPI network analysis showed that 47 GRDEGs had notable interactions (Table S3 ). We then computed scores for the 47 GRDEGs using five different algorithms. For each algorithm, we selected the top 10 scoring GRDEGs and used them to generate PPI networks (Fig. 6 B-F). A Venn diagram was created to identify genes common across the different algorithms (Fig. 6 G). The resulting intersection genes were found to be glycolysis-related hub genes, including HIF1A , LDHA , HK2 , STAT3 , TALDO1 , PPARG , ALDOC , and PFKP . Construction of regulatory network Initially, we obtained miRNAs linked to central genes from the TarBase database, enabling us to construct and visualize the mRNA-miRNA regulatory network (Fig. 7 A). This network encompassed 8 hub genes and 54 miRNAs, with detailed information available in Table S4 . Next, we determined Transcription Factors (TFs) interacting with central genes using the ChIPBase and hTFtarget databases. The overlap of these databases allowed us to visualize the mRNA-TF regulatory network (Fig. 7 B). This network featured 6 hub genes and 57 TFs, with specific details provided in Table S5 . Differential expression and ROC analysis of hub genes To confirm differential expression of the 8 central genes ( ALDOC , HIF1A , HK2 , LDHA , PFKP , PPARG , STAT3 , and TALDO1 ), we employed the Wilcoxon rank-sum test and visualized the results using group comparison plots (Fig. 8 A). We found highly significant differences ( P < 0.001) in the expression of HIF1A , LDHA , HK2 , STAT3 , TALDO1 , PPARG , and ALDOC when comparing pulpitis and control samples. Additionally, PFKP showed a very significant difference in expression ( P < 0.01) between the pulpitis and control groups. We used ROC curve analysis (Fig. 8 B-J) to evaluate the diagnostic performance of the eight central genes with significant differential expression. Our findings revealed that ALDOC (AUC = 0.951), HIF1A (AUC = 0.923), HK2 (AUC = 0.923), PPARG (AUC = 0.909), STAT3 (AUC = 0.923), and TALDO1 (AUC = 0.930) demonstrated high diagnostic accuracy for pulpitis, with AUC values exceeding 0.9 (Fig. 8 B-D, G-I). LDHA (AUC = 0.888) and PFKP (AUC = 0.874) showed moderate diagnostic accuracy for pulpitis, with AUC values between 0.7 and 0.9 (Fig. 8 E, F). Immune infiltration analysis The ssGSEA method was utilized to evaluate the infiltration levels of 28 different immune cell populations in the merged datasets. The group contrast diagram (Fig. 9 A) shows notable variances in immune infiltration abundance for these cell populations when comparing the pulpitis and control cohorts ( P < 0.05). The research uncovered considerable differences in the abundance levels of various immune cell types, such as Activated B cells, Regulatory T cells (Tregs), T follicular helper cells (Tfh), when contrasting the pulpitis and control groups ( P < 0.001). The pulpitis and control cohorts also demonstrated statistically significant variances in the prevalence of Central memory CD8 + T cells and Type 2 T helper cells ( P < 0.01). Furthermore, CD56 bright natural killer cells were found to be significantly more abundant in the pulpitis cohort relative to the control group ( P < 0.05). Next, a correlation heatmap (Fig. 9 B) was generated to visualize the correlation analysis of infiltration abundance for the 28 immune cell types that showed significant variations in their infiltration levels across the merged datasets. The findings revealed positive correlations among all 28 immune cell populations. Notably, CD56 bright natural killer cells, Type 17 T helper cells, Type 2 T helper cells, and Effector memory CD8 + T cells displayed moderate positive correlations with other immune cell types, whereas strong positive correlations were found among the remaining immune cell populations. Lastly, the correlation heatmap (Fig. 9 C) illustrated the associations between the 8 hub genes in the merged datasets and the infiltration abundance of the 28 immune cell types. The analysis showed that ALDOC had moderate to strong negative correlations with the infiltration levels of all immune cell populations. Positive correlations were observed between HK2 , STAT3 , TALDO1 , and the infiltration abundance of all immune cell types. HIF1A was positively associated with the infiltration of almost all the immune cell types, but not with Type 1 T helper cells, Tregs, MDSCs, and Gamma-delta T cells. LDHA had a moderate to high positive relationship with the infiltration level of Gamma-delta T cells, MDSCs, and immature dendritic cells. Discussion Pulpitis, defined as the inflammation of the dental pulp, is a common disease that significantly impact the health of patients. Symptoms include toothache, discomfort during intake of hot or cold foods and beverages, and in severe cases, abscesses and general sepsis [ 33 ]. Clinicians face challenges in accurately determining the degree of dental pulp inflammation due to the lack of effective diagnostic methods, which adversely affects the outcomes of vital pulp therapy [ 34 ]. Glycolysis is one of the most important pathways of cellular energy metabolism, connected with several inflammatory diseases [ 35 ]. However, the present literature lacks a definite connection between glycolysis and pulpitis. The purpose of this research is to establish the relationship between alterations in glycolytic gene expression and pulpitis, with a view to enhancing disease diagnosis and management. Identifying key regulatory genes could reveal new biomarkers and therapeutic targets, thereby enhancing the diagnostic and prognostic accuracy of pulpitis. This, in turn, may help in the preservation of teeth and support oral and maxillofacial health. Analysis of GEO datasets GSE77459 and GSE92681 showed that the expression levels of GRDEGs like PPBP and BIK were up-regulated in pulpitis, and the expressions of PRKAA2 , GLCE , and VLDLR were downregulated, suggesting the involvement of energy metabolism in the development of pulpitis. Enrichment analysis identified that these GRDEGs are predominantly clustered with biological processes, cellular structures, and molecular activities related to energy production, consistent with the high energy demands required for sustaining inflammatory responses [ 36 ]. Notably, several GRDEGs located on chromosome 11 may play pivotal roles in modulating the glycolytic pathway during pulpitis development. The recognition of hub genes, including HIF1A , LDHA , HK2 , STAT3 , TALDO1 , PPARG , ALDOC , and PFKP , emphasizes their pivotal position within the PPI network and underscores their potential influence on disease progression. For instance, HIF1A is well-known to regulate cellular reactions to low oxygen tension and has relation to several inflammatory disorders due to its ability in controlling anaerobic respiration [ 37 ]. Also, LDHA and HK2 are glycolytic enzymes that increase in activity in inflamed tissue [ 38 ]. In addition, analysis of immune cell infiltration between the pulpitis and control groups revealed differences in the levels of 28 different immune cell types, including macrophages, dendritic cells, T lymphocytes, and B lymphocytes, which are involved in the processes of inflammation and tissue repair. For example, macrophages display various roles ranging from aggressive (M1) to the reparative (M2), which can affect the course of pulpitis [ 39 ]. Also, dendritic cells are crucial for antigen presentation and initiating specific immune responses, potentially influencing the chronicity of inflammatory conditions such as pulpitis [ 40 ]. Moreover, T lymphocyte subsets including Th1, Th2, Th17, and Treg are seen in pulpitis, indicating the intricate nature of the adaptive immune response. Th17 cells are linked with inflammation through IL-17 secretion, while Tregs suppress the immune responses to maintain tissue health [ 41 ]. Stewardship of these counterpoised influences may significantly affect the course of the disease. The correlation between immunological cell types and hub genes discussed in this study gives some clues to possible molecular targets for future treatments. For instance, HIF1A , which is a transcription factor for hypoxia and inflammation, correlates with immune cell infiltration, indicating that dental pulp cells acclimatize to hypoxic conditions in an inflamed environment [ 42 ]. Also, the activation of STAT3 is associated with both pro-inflammatory mechanisms and anti-inflammatory actions [ 43 ], suggesting the complex role in the development of pulpitis. This study, through bioinformatics analysis, has identified multiple interactions of immune cells in pulpitis, paving the way for identifying specific diagnostic markers and treatment strategies. However, there are some limitations that should be taken into consideration regarding the present study and the findings made on differential gene expression associated with pulpitis and their possible functions and roles in biological processes and molecular pathways. Firstly, our findings were not validated in wet-laboratory experiments. Secondly, the sample size was relatively low, which might have repercussions concerning the generalizability of the results. Finally, despite efforts to correct batch effects using computational methods, the combination of multiple datasets can introduce variability which may influence the robustness of our conclusions. Conclusion In short, our bioinformatics study identified genes related to pulpitis and their role in glycolysis pathways. Enrichment analyses revealed important biological processes, and the PPI network identified hub genes including HIF1A , LDHA , HK2 , STAT3 , TALDO1 , PPARG , ALDOC , and PFKP . The interactions between mRNA-miRNA, mRNA-TF, and hub genes illustrate how upstream signals can modulate gene expression. Some hub genes have potential as diagnostic markers. Computational examination of immune cell infiltrates uncovered alterations in the immune cellular landscape between pulpitis and control samples. These findings indicate promising molecular markers and therapeutic targets for improving pulpitis diagnosis and treatment. Abbreviations GEO Gene Expression Omnibus GO Gene Ontology BP Biological Processes CC Cellular Components MF Biological Processes KEGG Kyoto Encyclopedia of Genes and Genomes GSEA Gene Set Enrichment Analysis PPI Protein-Protein Interaction ssGSEA single-sample Gene Set Enrichment Analysis DEGs Differentially Expressed Genes GRGs Glycolysis-Related Genes GRDEGs Glycolysis-Related Differentially Expressed Genes MCC Maximal Clique Centrality MNC Maximum Neighborhood Component EPC Edge Percolated Component ROC Receiver Operating Characteristic AUC Area Under the Curve TFs Transcription Factors Tregs Regulatory T cells Tfh T follicular helper cells Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Authors' information 1 Department of Endodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases Address: No.44 − 1 Wenhua Road West, 250012, Jinan, Shandong, China; Fax: +86 53188382923 2 Jinan Key Medical and Health Laboratory of Oral Diseases and Tissue Regeneration, Shandong Provincial Key Medical and Health Laboratory of Oral Diseases and Tissue Regeneration, Jinan Stomatological Hospital, Jinan 250001, Shandong Province, China Funding No funding. Author Contribution XW and QW designed and supervised the study. CL and QW performed the analysis and prepared the manuscript. ML helped to draft the manuscript. YL and JZ helped to critically revised the manuscript. All authors were actively involved with their work on this manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are available in GEO DataSets repository, https://www.ncbi.nlm.nih.gov/gds References Yu C, Abbott PV. An overview of the dental pulp: its functions and responses to injury. Aust Dent J. 2007;52(1 Suppl):S4–16. Kim Y, Park JS, Park HJ, Kim MK, Kim YI, Bae SK, Kim HJ, Jeong CH, Bae MK. Pentraxin 3 Modulates the Inflammatory Response in Human Dental Pulp Cells. J Endod. 2018;44(12):1826–31. Bertossi D, Barone A, Iurlaro A, Marconcini S, De Santis D, Finotti M, Procacci P. Odontogenic Orofacial Infections. J Craniofac Surg. 2017;28(1):197–202. Lu Y, Liu Z, Huang J, Liu C. Therapeutic effect of one-time root canal treatment for irreversible pulpitis. J Int Med Res. 2020;48(2):300060519879287. Mejàre IA, Axelsson S, Davidson T, Frisk F, Hakeberg M, Kvist T, Norlund A, Petersson A, Portenier I, Sandberg H, et al. 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Supplementary Files TableS1GRGs.csv TableS2GRDEGs.csv TableS3PPIGRDEGs.xlsx TableS4mRNAmiRNA.csv TableS5mRNATF.csv Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 30 Jul, 2024 Submission checks completed at journal 30 Jul, 2024 First submitted to journal 25 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4802823","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333867905,"identity":"c14c9b4a-ce27-462c-8880-9eb0c86b1967","order_by":0,"name":"Chaoran Liang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Chaoran","middleName":"","lastName":"Liang","suffix":""},{"id":333867906,"identity":"eec360fb-07f7-4724-8011-301b890148bd","order_by":1,"name":"Qiang Wang","email":"","orcid":"","institution":"Jinan Stomatological Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Wang","suffix":""},{"id":333867907,"identity":"b840bb40-bdee-442a-a0a4-b80ab5b7d07e","order_by":2,"name":"Mengyin Luan","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Mengyin","middleName":"","lastName":"Luan","suffix":""},{"id":333867908,"identity":"b9acfebe-8bcb-4e21-9083-27740e0e181a","order_by":3,"name":"Yatong Li","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yatong","middleName":"","lastName":"Li","suffix":""},{"id":333867909,"identity":"74b21dc6-995c-47de-a5a5-b4e48a4a3a23","order_by":4,"name":"Jingjing Zong","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Zong","suffix":""},{"id":333867910,"identity":"7b68f680-2174-44b9-be21-c3697e55ad68","order_by":5,"name":"Xiaoying Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACZhBhAOV8QOUSoYVxBlFaULTzEKPF4Djzw8c8BXfsNhw/e/i1zR/rxAb25m0SDDV3cGqRbGYzNuYxeJa84UxemnVuW3piA8+xMgmGY89wauFnZjCT5jE4nGx2IMfMOLfhcGKDRI6ZBGPDYZxa2JjZv0G0nH9jZmzxB6hF/g1+LfzMPGBb7Mxu5Bg/ZmAD2cKDX4tkM0+x4RyDwwn2N96YMfa2pRu38aQVWyQcw63F4PzxjQ/e/DlsL9mfY/zhxx9r2X72wxtvfKjBrQUEmHgYGBIbgP6SAPsORCTg1QCM9B8MDPZAmvkDAYWjYBSMglEwQgEA321Sh8ojaYcAAAAASUVORK5CYII=","orcid":"","institution":"Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-07-25 15:09:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4802823/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4802823/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63359171,"identity":"74724a4d-383b-4969-a47c-05352a49e37f","added_by":"auto","created_at":"2024-08-27 09:53:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":149686,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Chart for the Comprehensive Analysis of GRDEGs\u003c/p\u003e\n\u003cp\u003eDEGs, Differentially Expressed Genes; GRGs, Glycolysis Related Genes; GRDEGs, Glycolysis Related Differentially Expressed Genes; GSEA, Gene Set Enrichment Analysis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, Protein-Protein Interaction; ROC Curve, Receiver Operating Characteristic Curve; TF, Transcription Factor; ssGSEA, single-sample Gene-Set Enrichment Analysis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/08513b650249c51d883dbc3c.png"},{"id":63357952,"identity":"b717dbfd-0720-4957-bc54-e87d62818f93","added_by":"auto","created_at":"2024-08-27 09:37:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93143,"visible":true,"origin":"","legend":"\u003cp\u003eBatch Effects Removal of GSE77459 and GSE92681\u003c/p\u003e\n\u003cp\u003eA. Box plot of dataset distribution before going to batch. B. The box plot of the combined datasets distribution after batch processing. C. PCA plot of the datasets before debatching. D. PCA plot of the combined datasets after debatching. PCA, Principal Component Analysis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/b96c82eb944d80b3399ca97d.png"},{"id":63358596,"identity":"f50aeff6-551f-49b4-b026-6b67346ba876","added_by":"auto","created_at":"2024-08-27 09:45:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":693112,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially Expressed Genes Analysis\u003c/p\u003e\n\u003cp\u003eA. Volcano plot of DEGs analysis between pulpitis group and control group in combined datasets. B. DEGs and GRGs Venn diagram in combined datasets. C. Heat map of expression values of GRDEGs associated with combined datasets. D. Chromosomal genetic location map of GRDEGs. DEGs, Differentially Expressed Genes; GRGs, Glycolysis Related Genes; Genes GRDEGs, Glycolysis-Related Differentially Expressed.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/79409406141a077bc7987fab.png"},{"id":63358598,"identity":"465fb219-e89b-4854-af7d-fc7d774827d9","added_by":"auto","created_at":"2024-08-27 09:45:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":365133,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG Enrichment Analysis for GRDEGs\u003c/p\u003e\n\u003cp\u003eA. Bar graph of GO and KEGG enrichment analysis results of GRDEGs. B. Bubble plot of GO and KEGG enrichment analysis results of GRDEGs. C. GO enrichment analysis Biological Process (BP) network of GRDEGs. D. GO enrichment analysis Cellular Component (CC) network diagram of GRDEGs. E. GO enrichment analysis Molecular Function (MF) network of GRDEGs. F. KEGG enrichment analysis network diagram of GRDEGs. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. The screening criteria for GO enrichment analysis were adj. \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05 and q value \u0026lt; 0.25.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/dd2692c9c5c297dada846d1f.png"},{"id":63358599,"identity":"65bc76a9-897e-4996-b31d-2be487af2c09","added_by":"auto","created_at":"2024-08-27 09:45:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":267397,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA for Combined Datasets\u003c/p\u003e\n\u003cp\u003eA. Bubble plot of GSEA results of combined datasets. B-G. GSEA showed that pulpitis was significantly enriched in Jak Stat Signaling Pathway (B), Tak1 Dependent Ikk and Nf Kappa B Activation (C). Negative Regulation of the Pi3k Akt Network (D), Hippomerlin Signaling Dysregulation (E), Mapk Signaling Pathway (F) and Fceri Mediated Mapk Activation (G). GSEA, Gene Set Enrichment Analysis. The screening criteria of GSEA were adj. \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 and q value \u0026lt; 0.25.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/aef44b7199fad48be410c1f6.png"},{"id":63357960,"identity":"d847802a-02ee-423d-b301-af0ceecd3a76","added_by":"auto","created_at":"2024-08-27 09:37:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":516663,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network and Hub Genes Analysis\u003c/p\u003e\n\u003cp\u003eA. PPI network of GRDEGs calculated from STRING database. B-F. PPI network of Top10 GRDEGs calculated by 5 algorithms of the cytoHubba plugin, including Closeness (B), Degree (C), EPC (D), MCC (E), and MNC (F). G. Venn diagram of Top10 GRDEGs for the 5 algorithms. PPI network, Protein-Protein Interaction network. EPC, Edge Percolated Component. MCC, Maximal Clique Centrality. MNC, Maximum Neighborhood Component.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/a25045b365c5e00243e76888.png"},{"id":63357964,"identity":"f76ee896-bdc4-411b-a151-90d044467243","added_by":"auto","created_at":"2024-08-27 09:37:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":391605,"visible":true,"origin":"","legend":"\u003cp\u003eRegulatory network of Hub Genes\u003c/p\u003e\n\u003cp\u003eA. The mRNA-miRNA regulatory network of hub genes. B. mRNA-TF regulatory network of hub genes. TF, Transcription Factor.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/dbe2ba55540b009bd639402d.png"},{"id":63357953,"identity":"996d2f04-0012-4226-a5f9-7bfd543705ce","added_by":"auto","created_at":"2024-08-27 09:37:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":205425,"visible":true,"origin":"","legend":"\u003cp\u003eExpression Difference and ROC Curve Analysis\u003c/p\u003e\n\u003cp\u003eA. Group comparison diagram of hub genes in the combined datasets. B-I. ROC curves of hub genes \u003cem\u003eALDOC\u003c/em\u003e (B), \u003cem\u003eHIF1A\u003c/em\u003e (C), \u003cem\u003eHK2\u003c/em\u003e (D), \u003cem\u003eLDHA\u003c/em\u003e (E), \u003cem\u003ePFKP\u003c/em\u003e(F), \u003cem\u003ePPARG\u003c/em\u003e (G), \u003cem\u003eSTAT3\u003c/em\u003e (H) and \u003cem\u003eTALDO1\u003c/em\u003e (I) with significant differences in expression values in group comparison plots. ROC, Receiver Operating Characteristic. AUC, Area Under the Curve. ** represents \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, highly statistically significant; *** represents \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 and highly statistically significant.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/5941512a880673b627596d51.png"},{"id":63358595,"identity":"e2a2c8cb-3dd3-4424-a146-f3c8f267031a","added_by":"auto","created_at":"2024-08-27 09:45:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":452570,"visible":true,"origin":"","legend":"\u003cp\u003eCombined Datasets Immune Infiltration Analysis by ssGSEA Algorithm\u003c/p\u003e\n\u003cp\u003eA. Group comparison of immune cells in the pulpitis group and the control group. B. Heat map of correlation in the combined datasets for immune cell infiltration abundance with the significant difference in infiltration abundance in group comparison plots. C. Heat map of the correlation between hub genes and 28 immune cell infiltration abundance in the combined datasets. ssGSEA, single-sample Gene-Set Enrichment Analysis. * represents \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, statistically significant; ** represents \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, highly statistically significant; *** represents \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, extremely statistically significant. The absolute value of the correlation coefficient (γ value) below 0.3 was weak or no correlation, between 0.3 and 0.5 was weak correlation, between 0.5 and 0.8 was moderate correlation, and more than 0.8 was strong correlation.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/02b6b0446079bcfb65b8e2a4.png"},{"id":63359774,"identity":"a86cce0a-8104-4fe0-9a63-778feeb87c07","added_by":"auto","created_at":"2024-08-27 10:01:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3330851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/9a8986a8-6e88-4b22-839e-2d0e1051676b.pdf"},{"id":63357965,"identity":"6f361c8e-b4a4-45e4-a90c-0280be77e8ea","added_by":"auto","created_at":"2024-08-27 09:37:52","extension":"csv","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":3000,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1GRGs.csv","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/8bf31164c3cdd63dfc88867e.csv"},{"id":63358600,"identity":"1c9d0950-8f56-4920-ba97-3d75ed17c0f8","added_by":"auto","created_at":"2024-08-27 09:45:52","extension":"csv","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":7606,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2GRDEGs.csv","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/08589005a0e2b19b4c84eae6.csv"},{"id":63357957,"identity":"48fc7ef6-1cae-4b09-8949-84735224c893","added_by":"auto","created_at":"2024-08-27 09:37:51","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":10573,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3PPIGRDEGs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/78f270f3eff9450aacc9c2d3.xlsx"},{"id":63357963,"identity":"a0f2ce9a-80ff-4bc2-9549-83a82d1c2570","added_by":"auto","created_at":"2024-08-27 09:37:51","extension":"csv","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":2392,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4mRNAmiRNA.csv","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/b336669015547e4c515f6be4.csv"},{"id":63357962,"identity":"e5175ddf-2524-43ff-9b3e-6e051345e3ac","added_by":"auto","created_at":"2024-08-27 09:37:51","extension":"csv","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":1081,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5mRNATF.csv","url":"https://assets-eu.researchsquare.com/files/rs-4802823/v1/19fd9f8fffaded349cb4d44a.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of glycolysis-related genes in pulpitis by bioinformatics analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe pulp tissue, situated within the dental pulp cavity, plays a crucial role in dentin production, nutrient provision, and the support and protection of the tooth. When the dentin layer is compromised, the dental pulp initiates a complex defensive response, activating microbial-related humoral and cellular immune mechanisms that result in initial reversible local inflammation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Pulpitis, an inflammatory condition affecting the dental pulp, is primarily induced by bacterial infection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. If left unaddressed, pulpitis can progress to cause severe pain, tooth loss, and potentially life-threatening systemic infections [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, root canal therapy is the main treatment for pulpitis. However, the procedure is often invasive and may not be appropriate for all patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The clinical diagnosis of pulpitis primarily depends on subjective symptoms and diagnostic methods, which unfortunately show limited correlation with the histological state of the dental pulp [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Hence, there is a critical necessity to comprehend the molecular mechanisms associated with pulpitis to enhance diagnostic precision and treatment efficacy.\u003c/p\u003e \u003cp\u003eIn 2021, Xin et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] highlighted that chemokine\u0026ndash;associated genes, including \u003cem\u003eCXCL10\u003c/em\u003e, \u003cem\u003eCXCL1\u003c/em\u003e, \u003cem\u003eCCL5\u003c/em\u003e, and \u003cem\u003eCXCR4\u003c/em\u003e, could be possible biomarkers for identifying pulpitis. In another study, that Xu et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] identified several genes linked to m6A modification, including \u003cem\u003eRBM15B\u003c/em\u003e, \u003cem\u003eYTHDF1\u003c/em\u003e, \u003cem\u003eMETTL16\u003c/em\u003e, \u003cem\u003eMETTL3\u003c/em\u003e, \u003cem\u003eMETTL14\u003c/em\u003e, and \u003cem\u003eALKBH5\u003c/em\u003e, as useful markers for pulpitis. Nonetheless, the pathogenesis of pulpitis is multifactorial, and numerous biological pathways remain uncharted.\u003c/p\u003e \u003cp\u003eGlycolysis is a metabolic pathway in which glucose or glycogen is enzymatically split to form pyruvate and lactic acid, producing ATP under anaerobic conditions or hypoxia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous studies have shown that the dysregulation of glycolysis-related genes is involved in the development of inflammatory diseases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Current research has identified glycolysis as a critical mediator of macrophage pyroptosis in periodontitis, with glycolysis inhibitors proposed as potential agents for controlling inflammation and minimizing periodontal tissue injury [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Nevertheless, the correlation between glycolysis and pulpitis remains undefined, necessitating further investigation.\u003c/p\u003e \u003cp\u003eOur research conducted an in-depth examination of gene expression patterns associated with pulpitis, concentrating on glycolysis-related pathways. By comparing significantly Differentially Expressed Genes (DEGs) between pulpitis-associated and glycolysis-related datasets, we identified crucial biomarkers. Following this, we carried out Gene Ontology (GO) term enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA) based on the DEGs. Leveraging Cytoscape, we generated a Protein-Protein Interaction (PPI) network and identified eight hub genes. This study provides fresh perspectives on the molecular underpinnings of pulpitis while uncovering promising markers for timely diagnosis and precision medicine approaches.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Download\u003c/h2\u003e \u003cp\u003eThe datasets GSE77459 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and GSE92681 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], relevant to pulpitis, were obtained via the GEO database utilizing the R package GEOquery [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The samples from GSE77459 and GSE92681 were all derived from homo sapiens, specifically from human dental pulp tissue. The chip platforms used for GSE77459 and GSE92681 were GPL17692 and GPL16956, respectively. Detailed information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Dataset GSE77459 comprised 6 samples of pulpitis and 6 control samples, while GSE92681 included 7 pulpitis samples and 5 control samples. All samples, both pulpitis and control, were included in the study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGEO Microarray Chip Information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE77459\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE92681\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL17692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL16956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArray\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomo sapiens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHomo sapiens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePulp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePulp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples in Disease group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples in Control group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID: 33011745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMID: 29079059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eGEO, Gene Expression Omnibus。\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGlycolysis-Related Genes (GRGs) were collected through GeneCards database [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. By using \"Glycolysis\" as a search term and filtering for GRGs categorized as \"Protein Coding\" with a relevance score exceeding 3, we identified a total of 101 GRGs. Additionally, querying \"Glycolysis\" in the MsigDB website [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] yielded a comprehensive set of glycolysis-related gene sets from various databases (biocarta glycolysis pathway, hallmark glycolysis, kegg glycolysis gluconeogenesis, reactome glycolysis, reactome regulation of glycolysis by fructose 2 6 bis phosphate metabolism, wp aerobic glycolysis, wp glycolysis and gluconeogenesis, wp glycolysis in senescence,wp hif1a and pparg regulation of glycolysis), resulting in the identification of 302 GRGs. After removing redundancies, 338 distinct GRGs were identified, with specifics listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe R package sva [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] was used to perform batch correction on GSE77459 and GSE92681, merging them into an integrated dataset comprising 13 pulpitis samples and 11 control samples. Subsequently, the merged data was standardized using the R package limma [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], with probes annotated and data normalized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePulpitis-related glycolysis-related differentially expressed genes\u003c/h2\u003e \u003cp\u003eBased on the sample grouping from the combined datasets, samples were categorized into either the pulpitis or control group. Using the R package limma, differential expression analysis was performed to compare the pulpitis and control groups, with DEGs defined by | logFC | \u0026gt; 0 and adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eTo identify Glycolysis-Related Differentially Expressed Genes (GRDEGs) associated with pulpitis, we intersected all DEGs with GRGs. Heat maps were generated using the R package pheatmap, and chromosomal genetic location mapping was performed using RCircos [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The volcano plot was created using the R package ggplot2 to visualize the differential expression results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo clarify the biological relevance of GRDEGs, GO annotation was employed [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The signaling pathways associated with GRDEGs were explored using the KEGG [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The R package clusterProfiler [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was used to perform GO and KEGG enrichment analyses of GRDEGs, considering adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR value (q value)\u0026thinsp;\u0026lt;\u0026thinsp;0.25 as significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGSEA assesses how genes are distributed within predetermined gene sets [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The R package clusterProfiler was used to conduct GSEA on the entire gene set within the integrated datasets. Adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and q value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were used to establish statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePPI network and hub gene identification\u003c/h2\u003e \u003cp\u003eInteractions among identified and forecasted proteins can be investigated using the STRING database [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For this research, the PPI network was constructed with the STRING database, with an interaction threshold of 0.700. Five algorithms from Cytoscape's [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] cytoHubba [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] plugin were utilized: Maximal Clique Centrality (MCC), Degree, Maximum Neighborhood Component (MNC), Edge Percolated Component (EPC), and Closeness [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Scores were computed for GRDEGs in the PPI network. The highest-scoring 10 GRDEGs were subsequently determined. Lastly, a Venn diagram was created to compare the genes identified by the five algorithms, and the overlapping genes identified by these algorithms were considered glycolysis-related hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of regulatory network\u003c/h2\u003e \u003cp\u003eThe TarBase database [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was used to identify miRNAs associated with hub genes, mapping the relationship between hub genes and miRNAs. The mRNA-miRNA regulatory network was constructed using Cytoscape software. To study the role of transcription factors in the regulation of hub genes, information from ChIPBase [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and hTFtarget [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] databases was used, and the mRNA-TF regulatory network was depicted using Cytoscape software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression and ROC analysis of hub genes\u003c/h2\u003e \u003cp\u003eTo better understand the differential expression of the hub genes in the pulpitis and control groups of the combined datasets, a group comparison map was made based on the expression level of the hub genes. Subsequently, the R package pROC was used to create Receiver Operating Characteristic (ROC) curves for hub genes and calculate the associated Area Under the Curve (AUC) values. AUC values between 0.5 and 0.7 indicated low accuracy, values between 0.7 and 0.9 indicated moderate accuracy, and values greater than 0.9 indicated high accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e \u003cp\u003eWe used single-sample Gene Set Enrichment Analysis (ssGSEA) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] to quantify the extent of immune cell infiltration. The ssGSEA enrichment scores allowed visualization of immune cell infiltration levels per sample, excluding with those having \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to build the infiltration matrix for immune cells. Moreover, we used the R package ggplot2 to generate plots comparing ssGSEA immune infiltration between groups. Heatmaps depicting correlations were created using R package pheatmap, depicting correlations of hub genes with infiltrating immune cells based on ssGSEA in pulpitis and control samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe processed and analyzed the data using R software (v4.3.1). Continuous data were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. The Wilcoxon rank sum test allowed comparison between the two groups. We computed correlation coefficients among various molecules using Spearman's method, and statistical significance was set at adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 except if stated otherwise.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eTechnology Roadmap\u003c/h2\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eMerging of pulpitis datasets\u003c/h2\u003e\n\u003cp\u003eThe R package sva was employed to adjust for batch effects present in the pulpitis datasets (GSE77459, GSE92681), resulting in merged datasets from GEO. We assessed the pre- and post-correction datasets using box plots showing distributions and PCA plots (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-D). The grouped boxplot and PCA plots demonstrated effective reduction of batch effects in the Pulpitis dataset following correction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003ePulpitis-related glycolysis-related differentially expressed genes\u003c/h2\u003e\n\u003cp\u003eIn total, we found 3480 DEGs, with 1591 upregulated and 1889 downregulated, as shown in the volcano plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). To pinpoint genes with significant expression changes, those meeting | logFC | \u0026gt; 0 and adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 criteria were selected through intersection of DEGs and GRGs using a Venn diagram (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). This analysis identified 63 genes meeting these criteria, detailed in Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e. Subsequently, GRDEG analysis across different sample groups in the combined datasets was conducted based on the intersection \u003cspan class=\"InternalRef\"\u003eresults\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). The chromosomal genetic locations of 63 GRDEGs were then examined (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). The map revealed that a significant number of GRDEGs were situated on chromosome 11, including genes such as \u003cem\u003eSLC2A3\u003c/em\u003e, \u003cem\u003eFKBP4\u003c/em\u003e, \u003cem\u003eYAP1\u003c/em\u003e, \u003cem\u003eUCP2\u003c/em\u003e, \u003cem\u003ePC\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003eLDHA\u003c/em\u003e, \u003cem\u003eNUP94\u003c/em\u003e and \u003cem\u003eTALDO1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eAnalysis of GRDEGs for functional enrichment\u003c/h2\u003e\n\u003cp\u003eWe investigated the Biological Processes (BP), Molecular Functions (MF), Cellular Components (CC) and biological pathways linked to 63 GRDEGs using GO and KEGG enrichment analysis, and the specific results are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Our findings suggest that the GRDEGs are mainly involved in BP associated with pyruvate metabolism, glycolysis, and the generation of metabolic precursors and energy. They are also enriched in CC such as intracellular organelle lumens and MF including carbohydrate binding and isomerase activity. KEGG analysis revealed that GRDEGs participate in key metabolic and signaling pathways, including glucose metabolism, hypoxia response, and carbon utilization. The GO and KEGG enrichment analysis results were graphically represented (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). A network diagram depicting the relationships between the enriched terms and pathways was also created (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC-F).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResult of GO and KEGG Enrichment Analysis for GRDEGs\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eONTOLOGY\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGeneRatio\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBgRatio\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eadj. \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eq value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0006090\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106/18800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.014E-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.4972E-16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.114E-16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0006091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e494/18800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4011E-15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3151E-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.4663E-13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0006096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81/18800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.4125E-15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3151E-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.4663E-13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0016052\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e152/18800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7709E-15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3151E-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.4663E-13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0006757\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82/18800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7792E-15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3151E-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.4663E-13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0034774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e322/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00058131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02989102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02584562\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0060205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e325/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00061027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02989102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02584562\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0031983\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e327/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00063019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02989102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02584562\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0090575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e230/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00086209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02989102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02584562\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e483/19594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00088961\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02989102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02584562\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0048029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3686E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00077862\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00058218\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005536\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.1795E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00077862\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00058218\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0016853\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4783E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00124176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00092847\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0017056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001177\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00741513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00554431\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0033293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4/63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81/18410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00017005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00807198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00603544\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa00010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10/51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67/8164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.7666E-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0827E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9775E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa04066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11/51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109/8164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3893E-11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5114E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5874E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa01200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10/51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e115/8164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6302E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.6946E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.4065E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eGO, Gene Ontology;BP, Biological Process༛CC, Cellular Component༛MF, Molecular Function༛KEGG, Kyoto Encyclopedia of Genes and Genomes༛ GRDEGs, Glycolysis-Related Differentially Expressed Genes。\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003eGene Set Enrichment Analysis (GSEA)\u003c/h2\u003e\n\u003cp\u003eGSEA findings were visualized in a bubble plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides a summary of the detailed GSEA results. GSEA showed that the genes in the combined datasets were significantly enriched in various signaling cascades and biological processes, such as Jak Stat signaling, Tak1 dependent Ikk and Nf Kappa B activation, negative regulation of the Pi3k Akt network, dysregulation of Hippomerlin signaling, the Mapk signaling pathway, and Fceri mediated Mapk activation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB-G).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of GSEA for Combined Datasets\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esetSize\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEnrichmentScore\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eadj. \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eq value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_OVERVIEW_OF_PROINFLAMMATORY_AND_PROFIBROTIC_MEDIATORS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79849\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.84547\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_IMMUNOREGULATORY_INTERACTIONS_BETWEEN_A_LYMPHOID_AND_A_NON_LYMPHOID_CELL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76925\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.75771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_EXTRAFOLLICULAR_B_CELL_ACTIVATION_BY_SARSCOV2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83452\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.74739\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.70544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.74225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_NETWORK_MAP_OF_SARSCOV2_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.71883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.69917\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.69181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_CHEMOKINE_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.70496\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.64823\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_INTERLEUKIN_10_SIGNALING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86787\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.64032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_NEUTROPHIL_DEGRANULATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e430\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.63813\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.59588\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_SIGNALING_BY_INTERLEUKINS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.63802\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.59482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_TYROBP_CAUSAL_NETWORK_IN_MICROGLIA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.51623\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_CELL_SURFACE_INTERACTIONS_AT_THE_VASCULAR_WALL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.68277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.49264\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_SARSCOV2_INNATE_IMMUNITY_EVASION_AND_CELLSPECIFIC_IMMUNE_RESPONSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.75520\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.47953\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.38E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG_JAK_STAT_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.59371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.19889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.38E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.28E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.79E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_TAK1_DEPENDENT_IKK_AND_NF_KAPPA_B_ACTIVATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.59764\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.83074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.59E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.08E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.98E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_NEGATIVE_REGULATION_OF_THE_PI3K_AKT_NETWORK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.49835\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.76228\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.46E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.01E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_HIPPOMERLIN_SIGNALING_DYSREGULATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.47991\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.70400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.08E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.98E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREACTOME_FCERI_MEDIATED_MAPK_ACTIVATION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.64417\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.87970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.77E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.67E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.87E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWP_MAPK_SIGNALING_PATHWAY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e236\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.37321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.45133\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.20E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.77E-02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.17E-02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eGSEA, Gene Set Enrichment Analysis。\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003eBuilding the PPI Network and identifying hub genes\u003c/h2\u003e\n\u003cp\u003eWe constructed a PPI network of the 63 GRDEGs using the STRING database (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). The PPI network analysis showed that 47 GRDEGs had notable interactions (Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). We then computed scores for the 47 GRDEGs using five different algorithms. For each algorithm, we selected the top 10 scoring GRDEGs and used them to generate PPI networks (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB-F). A Venn diagram was created to identify genes common across the different algorithms (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eG). The resulting intersection genes were found to be glycolysis-related hub genes, including \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eLDHA\u003c/em\u003e, \u003cem\u003eHK2\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, \u003cem\u003eTALDO1\u003c/em\u003e, \u003cem\u003ePPARG\u003c/em\u003e, \u003cem\u003eALDOC\u003c/em\u003e, and \u003cem\u003ePFKP\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eConstruction of regulatory network\u003c/h2\u003e\n\u003cp\u003eInitially, we obtained miRNAs linked to central genes from the TarBase database, enabling us to construct and visualize the mRNA-miRNA regulatory network (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). This network encompassed 8 hub genes and 54 miRNAs, with detailed information available in Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e. Next, we determined Transcription Factors (TFs) interacting with central genes using the ChIPBase and hTFtarget databases. The overlap of these databases allowed us to visualize the mRNA-TF regulatory network (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). This network featured 6 hub genes and 57 TFs, with specific details provided in Table \u003cspan class=\"InternalRef\"\u003eS5\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003eDifferential expression and ROC analysis of hub genes\u003c/h2\u003e\n\u003cp\u003eTo confirm differential expression of the 8 central genes (\u003cem\u003eALDOC\u003c/em\u003e, \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eHK2\u003c/em\u003e, \u003cem\u003eLDHA\u003c/em\u003e, \u003cem\u003ePFKP\u003c/em\u003e, \u003cem\u003ePPARG\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, and \u003cem\u003eTALDO1\u003c/em\u003e), we employed the Wilcoxon rank-sum test and visualized the results using group comparison plots (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). We found highly significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the expression of \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eLDHA\u003c/em\u003e, \u003cem\u003eHK2\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, \u003cem\u003eTALDO1\u003c/em\u003e, \u003cem\u003ePPARG\u003c/em\u003e, and \u003cem\u003eALDOC\u003c/em\u003e when comparing pulpitis and control samples. Additionally, \u003cem\u003ePFKP\u003c/em\u003e showed a very significant difference in expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between the pulpitis and control groups. We used ROC curve analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB-J) to evaluate the diagnostic performance of the eight central genes with significant differential expression. Our findings revealed that \u003cem\u003eALDOC\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.951), \u003cem\u003eHIF1A\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.923), \u003cem\u003eHK2\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.923), \u003cem\u003ePPARG\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.909), \u003cem\u003eSTAT3\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.923), and \u003cem\u003eTALDO1\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.930) demonstrated high diagnostic accuracy for pulpitis, with AUC values exceeding 0.9 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB-D, G-I). \u003cem\u003eLDHA\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.888) and \u003cem\u003ePFKP\u003c/em\u003e (AUC\u0026thinsp;=\u0026thinsp;0.874) showed moderate diagnostic accuracy for pulpitis, with AUC values between 0.7 and 0.9 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eE, F).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e\n\u003cp\u003eThe ssGSEA method was utilized to evaluate the infiltration levels of 28 different immune cell populations in the merged datasets. The group contrast diagram (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA) shows notable variances in immune infiltration abundance for these cell populations when comparing the pulpitis and control cohorts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The research uncovered considerable differences in the abundance levels of various immune cell types, such as Activated B cells, Regulatory T cells (Tregs), T follicular helper cells (Tfh), when contrasting the pulpitis and control groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The pulpitis and control cohorts also demonstrated statistically significant variances in the prevalence of Central memory CD8\u0026thinsp;+\u0026thinsp;T cells and Type 2 T helper cells (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, CD56 bright natural killer cells were found to be significantly more abundant in the pulpitis cohort relative to the control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eNext, a correlation heatmap (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB) was generated to visualize the correlation analysis of infiltration abundance for the 28 immune cell types that showed significant variations in their infiltration levels across the merged datasets. The findings revealed positive correlations among all 28 immune cell populations. Notably, CD56 bright natural killer cells, Type 17 T helper cells, Type 2 T helper cells, and Effector memory CD8\u0026thinsp;+\u0026thinsp;T cells displayed moderate positive correlations with other immune cell types, whereas strong positive correlations were found among the remaining immune cell populations.\u003c/p\u003e\n\u003cp\u003eLastly, the correlation heatmap (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eC) illustrated the associations between the 8 hub genes in the merged datasets and the infiltration abundance of the 28 immune cell types. The analysis showed that \u003cem\u003eALDOC\u003c/em\u003e had moderate to strong negative correlations with the infiltration levels of all immune cell populations. Positive correlations were observed between \u003cem\u003eHK2\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, \u003cem\u003eTALDO1\u003c/em\u003e, and the infiltration abundance of all immune cell types. \u003cem\u003eHIF1A\u003c/em\u003e was positively associated with the infiltration of almost all the immune cell types, but not with Type 1 T helper cells, Tregs, MDSCs, and Gamma-delta T cells. \u003cem\u003eLDHA\u003c/em\u003e had a moderate to high positive relationship with the infiltration level of Gamma-delta T cells, MDSCs, and immature dendritic cells.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePulpitis, defined as the inflammation of the dental pulp, is a common disease that significantly impact the health of patients. Symptoms include toothache, discomfort during intake of hot or cold foods and beverages, and in severe cases, abscesses and general sepsis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Clinicians face challenges in accurately determining the degree of dental pulp inflammation due to the lack of effective diagnostic methods, which adversely affects the outcomes of vital pulp therapy [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Glycolysis is one of the most important pathways of cellular energy metabolism, connected with several inflammatory diseases [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, the present literature lacks a definite connection between glycolysis and pulpitis. The purpose of this research is to establish the relationship between alterations in glycolytic gene expression and pulpitis, with a view to enhancing disease diagnosis and management. Identifying key regulatory genes could reveal new biomarkers and therapeutic targets, thereby enhancing the diagnostic and prognostic accuracy of pulpitis. This, in turn, may help in the preservation of teeth and support oral and maxillofacial health.\u003c/p\u003e \u003cp\u003eAnalysis of GEO datasets GSE77459 and GSE92681 showed that the expression levels of GRDEGs like \u003cem\u003ePPBP\u003c/em\u003e and \u003cem\u003eBIK\u003c/em\u003e were up-regulated in pulpitis, and the expressions of \u003cem\u003ePRKAA2\u003c/em\u003e, \u003cem\u003eGLCE\u003c/em\u003e, and \u003cem\u003eVLDLR\u003c/em\u003e were downregulated, suggesting the involvement of energy metabolism in the development of pulpitis. Enrichment analysis identified that these GRDEGs are predominantly clustered with biological processes, cellular structures, and molecular activities related to energy production, consistent with the high energy demands required for sustaining inflammatory responses [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Notably, several GRDEGs located on chromosome 11 may play pivotal roles in modulating the glycolytic pathway during pulpitis development.\u003c/p\u003e \u003cp\u003eThe recognition of hub genes, including \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eLDHA\u003c/em\u003e, \u003cem\u003eHK2\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, \u003cem\u003eTALDO1\u003c/em\u003e, \u003cem\u003ePPARG\u003c/em\u003e, \u003cem\u003eALDOC\u003c/em\u003e, and \u003cem\u003ePFKP\u003c/em\u003e, emphasizes their pivotal position within the PPI network and underscores their potential influence on disease progression. For instance, \u003cem\u003eHIF1A\u003c/em\u003e is well-known to regulate cellular reactions to low oxygen tension and has relation to several inflammatory disorders due to its ability in controlling anaerobic respiration [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Also, \u003cem\u003eLDHA\u003c/em\u003e and \u003cem\u003eHK2\u003c/em\u003e are glycolytic enzymes that increase in activity in inflamed tissue [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, analysis of immune cell infiltration between the pulpitis and control groups revealed differences in the levels of 28 different immune cell types, including macrophages, dendritic cells, T lymphocytes, and B lymphocytes, which are involved in the processes of inflammation and tissue repair. For example, macrophages display various roles ranging from aggressive (M1) to the reparative (M2), which can affect the course of pulpitis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Also, dendritic cells are crucial for antigen presentation and initiating specific immune responses, potentially influencing the chronicity of inflammatory conditions such as pulpitis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, T lymphocyte subsets including Th1, Th2, Th17, and Treg are seen in pulpitis, indicating the intricate nature of the adaptive immune response. Th17 cells are linked with inflammation through IL-17 secretion, while Tregs suppress the immune responses to maintain tissue health [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Stewardship of these counterpoised influences may significantly affect the course of the disease.\u003c/p\u003e \u003cp\u003eThe correlation between immunological cell types and hub genes discussed in this study gives some clues to possible molecular targets for future treatments. For instance, \u003cem\u003eHIF1A\u003c/em\u003e, which is a transcription factor for hypoxia and inflammation, correlates with immune cell infiltration, indicating that dental pulp cells acclimatize to hypoxic conditions in an inflamed environment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Also, the activation of \u003cem\u003eSTAT3\u003c/em\u003e is associated with both pro-inflammatory mechanisms and anti-inflammatory actions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], suggesting the complex role in the development of pulpitis. This study, through bioinformatics analysis, has identified multiple interactions of immune cells in pulpitis, paving the way for identifying specific diagnostic markers and treatment strategies.\u003c/p\u003e \u003cp\u003eHowever, there are some limitations that should be taken into consideration regarding the present study and the findings made on differential gene expression associated with pulpitis and their possible functions and roles in biological processes and molecular pathways. Firstly, our findings were not validated in wet-laboratory experiments. Secondly, the sample size was relatively low, which might have repercussions concerning the generalizability of the results. Finally, despite efforts to correct batch effects using computational methods, the combination of multiple datasets can introduce variability which may influence the robustness of our conclusions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn short, our bioinformatics study identified genes related to pulpitis and their role in glycolysis pathways. Enrichment analyses revealed important biological processes, and the PPI network identified hub genes including \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eLDHA\u003c/em\u003e, \u003cem\u003eHK2\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, \u003cem\u003eTALDO1\u003c/em\u003e, \u003cem\u003ePPARG\u003c/em\u003e, \u003cem\u003eALDOC\u003c/em\u003e, and \u003cem\u003ePFKP\u003c/em\u003e. The interactions between mRNA-miRNA, mRNA-TF, and hub genes illustrate how upstream signals can modulate gene expression. Some hub genes have potential as diagnostic markers. Computational examination of immune cell infiltrates uncovered alterations in the immune cellular landscape between pulpitis and control samples. These findings indicate promising molecular markers and therapeutic targets for improving pulpitis diagnosis and treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological Processes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCellular Components\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological Processes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein-Protein Interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003essGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-sample Gene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGRGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycolysis-Related Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGRDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycolysis-Related Differentially Expressed Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximal Clique Centrality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMNC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum Neighborhood Component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEdge Percolated Component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTFs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscription Factors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTregs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegulatory T cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTfh\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT follicular helper cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors' information\u003c/h2\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eDepartment of Endodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University \u0026amp; Shandong Key Laboratory of Oral Tissue Regeneration \u0026amp; Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration \u0026amp; Shandong Provincial Clinical Research Center for Oral Diseases\u003c/p\u003e \u003cp\u003eAddress: No.44\u0026thinsp;\u0026minus;\u0026thinsp;1 Wenhua Road West, 250012, Jinan, Shandong, China; Fax: +86 53188382923\u003c/p\u003e \u003cp\u003e \u003csup\u003e2\u003c/sup\u003eJinan Key Medical and Health Laboratory of Oral Diseases and Tissue Regeneration, Shandong Provincial Key Medical and Health Laboratory of Oral Diseases and Tissue Regeneration, Jinan Stomatological Hospital, Jinan 250001, Shandong Province, China\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXW and QW designed and supervised the study. CL and QW performed the analysis and prepared the manuscript. ML helped to draft the manuscript. YL and JZ helped to critically revised the manuscript. All authors were actively involved with their work on this manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the current study are available in GEO DataSets repository, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYu C, Abbott PV. An overview of the dental pulp: its functions and responses to injury. 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Blood. 2014;123(19):2978\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulpitis, Glycolysis, Differential Expression, Bioinformatics, Diagnostic Biomarkers, Immune Infiltration","lastPublishedDoi":"10.21203/rs.3.rs-4802823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4802823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Pulpitis, a common inflammation of the dental pulp, involves intricate mechanisms that are not yet fully understood. Our study aims to elucidate the alterations in genetic transcription linked to glycolysis in pulpitis and their impact on biological pathways and molecular networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Gene expression data was collected from the GEO database. Glycolysis-related genes were identified through databases like GeneCards and MsigDB. To understand the roles of these genes, GO, KEGG pathway enrichment, and GSEA were carried out. The PPI network was constructed with STRING, and central genes were determined using cytoHubba algorithms. mRNA-miRNA and mRNA-TF regulatory interactions were obtained from TarBase, ChIPBase, and hTFtarget. We assessed differential expression of the hub genes between groups, and conducted ROC curve analysis. ssGSEA was used to examine immune cell infiltration, with pheatmap illustrating associations between hub genes and immune cells. All statistical analyses were performed using R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our analysis revealed 3480 differentially expressed genes (DEGs) in pulpitis, comprising 1591 upregulated and 1889 downregulated genes. Among these, 63 glycolysis-related differentially expressed genes (GRDEGs) were predominantly located on chromosome 11. These GRDEGs were enriched in energy metabolism processes, organelle compartments, and molecular functions, implicating key pathways in the pathology of pulpitis. PPI network analysis identified eight hub genes—\u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eLDHA\u003c/em\u003e, \u003cem\u003eHK2\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, \u003cem\u003eTALDO1\u003c/em\u003e,\u003cem\u003ePPARG\u003c/em\u003e, \u003cem\u003eALDOC\u003c/em\u003e, and \u003cem\u003ePFKP\u003c/em\u003e. Additionally, ssGSEA uncovered notable differences in the infiltration levels of 28 types of immune cells between pulpitis and control samples, suggesting alterations in the immune response related to pulpitis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our research offers new perspectives into the molecular mechanisms of pulpitis, particularly regarding glycolytic pathways. These results may help identify better diagnostic markers and therapeutic targets for managing pulpitis. Future studies should aim to validate these potential biomarkers and investigate their functional roles in the etiology of disease.\u003c/p\u003e","manuscriptTitle":"Identification of glycolysis-related genes in pulpitis by bioinformatics analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 09:37:45","doi":"10.21203/rs.3.rs-4802823/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-07-30T16:53:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-30T14:29:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2024-07-25T15:08:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5021214-9636-40dc-8375-731427e8f624","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-27T09:37:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 09:37:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4802823","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4802823","identity":"rs-4802823","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}