scholarly journals PD-1 Coexpression Gene Analysis and the Regulatory Network in Endometrial Cancer Based on Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lina Wang ◽  
Zhen Liu ◽  
Wenwen Zhang ◽  
Aihua Zhang ◽  
Pengpeng Qu
Keyword(s):  
Endometrial Cancer ◽  
Bioinformatics Analysis ◽  
Cell Structure ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Developed Countries ◽  
Node Degree ◽  
Hub Genes ◽  
Tumor Immune Escape ◽  
Coexpressed Genes

Gynecological malignancies are tumors of the female reproductive system, mainly cervical cancer, endometrial cancer, and ovarian cancer. Endometrial cancer (EC) is the most common gynecological malignant tumor in developed countries. The aim of this study was to construct a network of programmed cell death protein 1 (PD-1) coexpressed genes through bioinformatics analysis and screen the potential biomarkers of PD-1 in endometrial cancer. In addition, genes and pathways involved in PD-1 and modulating tumor immune status were identified. We select the EC transcriptomic dataset in TCGA to retrieve gene sets on the cBioPortal platform, and the PD-1 coexpressed genes were obtained on the platform. GO and KEGG enrichment analysis of coexpressed genes was performed using the DAVID database. The target protein-protein interaction (PPI) network was constructed using Cytoscape 3.7.1 software, and the hub genes were then screened. A total of 976 coexpression genes were obtained. The enrichment analysis showed that PD-1 coexpressed genes were significantly enriched in overall components of the cell structure, the interaction of cytokines with cytokine receptors, chemokine signaling pathways, and cell adhesion molecules (CAMs). Ten hub genes were obtained by node degree analysis. CD3E gene is involved in the prognosis and immune process of EC, and the expression level is related to PD-1 (Pearson correlation coefficient is 0.82, P < 0.01 ). Patients with low CD3E gene expression in EC have a poor prognosis. The coexpression hub genes of PD-1 are related to immunity, in which CD3E is a prognostic marker that is involved in the PD-1/PD-L1-induced tumor immune escape. This study provides a new area to study the mechanism of PD-1/PD-L1 in EC and the precise treatment with targeted drugs.

scholarly journals Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ming Chen ◽  
Junkai Zeng ◽  
Yeqing Yang ◽  
Buling Wu
Keyword(s):  
Gene Expression ◽  
Dental Pulp ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Signalling Pathway ◽  
Diagnostic Biomarker ◽  
Hub Genes ◽  
Geo Database

Abstract Background Pulpitis is an inflammatory disease, the grade of which is classified according to the level of inflammation. Traditional methods of evaluating the status of dental pulp tissue in clinical practice have limitations. The rapid and accurate diagnosis of pulpitis is essential for determining the appropriate treatment. By integrating different datasets from the Gene Expression Omnibus (GEO) database, we analysed a merged expression matrix of pulpitis, aiming to identify biological pathways and diagnostic biomarkers of pulpitis. Methods By integrating two datasets (GSE77459 and GSE92681) in the GEO database using the sva and limma packages of R, differentially expressed genes (DEGs) of pulpitis were identified. Then, the DEGs were analysed to identify biological pathways of dental pulp inflammation with Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Protein–protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. Results A total of 470 DEGs comprising 394 upregulated and 76 downregulated genes were found in pulpitis tissue. GO analysis revealed that the DEGs were enriched in biological processes related to inflammation, and the enriched pathways in the KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signalling pathway and NF-κB signalling pathway. The GSEA results provided further functional annotations, including complement system, IL6/JAK/STAT3 signalling pathway and inflammatory response pathways. According to the degrees of nodes in the PPI network, 10 hub genes were identified, and 8 diagnostic biomarker candidates were screened: PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1. Conclusions With bioinformatics analysis of merged datasets, biomarker candidates of pulpitis were screened and the findings may be as reference to develop a new method of pulpitis diagnosis.

scholarly journals Increased IGFBP7 Expression Correlates with Poor Prognosis and Immune Infiltration in Gastric Cancer: An Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Qiaoyun Zhao ◽  
Rulin Zhao ◽  
Conghua Song ◽  
Huan Wang ◽  
Jianfang Rong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Coexpressed Genes

Abstract Background Insulin-like growth factor binding protein-7 (IGFBP7) contributes to multiple biological processes in various tumors. However, the role of IGFBP7 in gastric cancer (GC) is still undetermined. The study aims to explore the role of IGFBP7 in GC via an integrated bioinformatics analysis.Methods IGFBP7 expression levels in GC and its normal gastric tissues were analyzed using multiple databases, including the Tumor Immune Estimation Resource (TIMER), Oncomine, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The methylation analysis was conducted with MEXPRESS, UALCAN and Xena online tools. The survival analysis was conducted using the Kaplan-Meier Plotter and Gene Expression Profiling Interactive Analysis (GEPIA) databases. Coexpressed genes of IGFBP7 were selected with the cBioPortal tool and enrichment analysis was conducted with the clusterProfiler package in R software. Gene set enrichment analysis (GSEA) was performed to explore the IGFBP7-related biological processes involved in GC. Correlations between IGFBP7 and immune cell infiltrates were analyzed using the TIMER database.Results IGFBP7 expression was significantly upregulated in GC and correlated with stage, grade, tumor status and Helicobacter pylori infection. High IGFBP7 expression and low IGFBP7 methylation levels were significantly associated with short survival of patients with GC. Univariate and multivariate analyses revealed that IGFBP7 was an independent risk factor for GC. The coexpressed genes LHFPL6, SEPTIN4, HSPB2, LAYN and GGT5 predicted unfavorable outcomes of GC. Enrichment analysis showed that the coexpressed genes were involved in extracellular matrix (ECM)-related processes. GSEA indicated that IGFBP7 was positively related to ECM and inflammation-related pathways. TIMER analysis indicated that the IGFBP7 expression level was strongly correlated with genes related to various infiltrating immune cells in GC, especially with gene markers of tumor associated macrophages (TAMs).Conclusions We demonstrate that increased IGFBP7 expression correlates with poor prognosis and immune cell infiltration in GC. IGFBP7 might be a potential biomarker for the diagnosis and targeted therapy for GC.

scholarly journals Identification of Potential Autophagy-Related Genes in Steroid-Induced Osteonecrosis of the Femoral Head

2021 ◽  
Author(s):  
XueZhen LIANG ◽  
Di LUO ◽  
Yan-Rong CHEN ◽  
Jia-Cheng LI ◽  
Bo-Zhao YAN ◽  
...  
Keyword(s):  
Femoral Head ◽  
Correlation Analysis ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
R Software ◽  
Pathway Enrichment

Abstract Purpose: Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people. Previous experimental studies had shown that autophagy might be involved in the pathological process of SONFH, but the pathogenesis of autophagy in SONFH remained unclear. We aim to identify and validate the key potential autophagy-related genes of SONFH to further illustrate the mechanism of autophagy in SONFH through bioinformatics analysis. Methods: The mRNA expression profile dataset GSE123568 was download from Gene Expression Omnibus (GEO) database, including 10 non-SONFH (following steroid administration) samples and 30 SONFH samples. The autophagy-related genes were obtained from the Human Autophagy Database (HADb). The autophagy-related genes of SONFH were screened by intersecting GSE123568 dataset with autophagy genes. The differentially expressed autophagy-related genes of SONFH were identified by R software. Besides, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted for the differentially expressed autophagy-related genes of SONFH by R software. Then, the correlation analysis between the expression levels of differentially expressed autophagy-related genes of SONFH was confirmed by R software. Moreover, the protein–protein interaction (PPI) network were analyzed by the Search Tool for the Retrieval of Interacting Genes (STRING), and the significant gene cluster modules were identified by the MCODE Cytoscape plugin, and hub genes of differentially expressed autophagy-related genes of SONFH were screened by the CytoHubba Cytoscape plugin. Finally, the expression levels of hub genes of differentially expressed autophagy-related genes of SONFH was validated in hip articular cartilage specimens from necrosis femur head (NFH) by GSE74089 dataset. Results: A total of 34 differentially expressed autophagy-related genes were identified between the peripheral blood of SONFH samples and non-SONFH Samples based on the defined criteria, including 25 up-regulated genes and 9 down-regulated genes. The GO and KEGG pathway enrichment analysis revealed that these 34 differentially expressed autophagy-related genes of SONFH were concentrated in death domain receptors, FOXO signaling pathway and apoptosis. The correlation analysis revealed a significant correlation among the 34 differentially expressed autophagy-related genes of SONFH. The PPI results demonstrated that the 34 differentially expressed autophagy-related genes interacted with each other. There were 10 hub genes identified by the MCC algorithms of Cytohubba. The results of GSE74089 dataset showed TNFSF10, PTEN and CFLAR were significantly upregulated while BCL2L1 were significantly downregulated in the hip cartilage specimens, which were consistent with the GSE123568 dataset. Conclusions: There were 34 potential autophagy-related genes of SONFH identified using bioinformatics analysis. TNFSF10, PTEN, CFLAR and BCL2L1 might serve as potential drug targets and biomarkers by regulating autophagy. These results would expand new insights into the autophagy-related understanding of SONFH and might be useful in the diagnosis and prognosis of SONFH.

scholarly journals Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huan Deng ◽  
Qingqing Hang ◽  
Dijian Shen ◽  
Yibi Zhang ◽  
Ming Chen
Keyword(s):  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Transforming Growth Factor ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Prognostic Indicators ◽  
Hub Genes ◽  
Coexpressed Genes

Abstract Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

scholarly journals Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Network Modules ◽  
Gene Regulatory

Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were then performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis and RT-PCR confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

Integrated Bioinformatics Analysis Identifies Hub Genes Associated with Viral Infection and Alzheimer’s Disease

2021 ◽  
pp. 1-9
Author(s):  
Xiaoru Sun ◽  
Hui Zhang ◽  
Dongdong Yao ◽  
Yaru Xu ◽  
Qi Jing ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Viral Infection ◽  
Bioinformatics Analysis ◽  
Expression Patterns ◽  
Temporal Cortex ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Barr Virus

Background: Alzheimer’s disease (AD) is a fatal neurodegenerative disease, the etiology of which is unclear. Previous studies have suggested that some viruses are neurotropic and associated with AD. Objective: By using bioinformatics analysis, we investigated the potential association between viral infection and AD. Methods: A total of 5,066 differentially expressed genes (DEGs) in the temporal cortex between AD and control samples were identified. These DEGs were then examined via weighted gene co-expression network analysis (WGCNA) and clustered into modules of genes with similar expression patterns. Of identified modules, module turquoise had the highest correlation with AD. The module turquoise was further characterized using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. Results: Our results showed that the KEGG pathways of the module turquoise were mainly associated with viral infection signaling, specifically Herpes simplex virus, Human papillomavirus, and Epstein-Barr virus infections. A total of 126 genes were enriched in viral infection signaling pathways. In addition, based on values of module membership and gene significance, a total of 508 genes within the module were selected for further analysis. By intersecting these 508 genes with those 126 genes enriched in viral infection pathways, we identified 4 hub genes that were associated with both viral infection and AD: TLR2, COL1A2, NOTCH3, and ZNF132. Conclusion: Through bioinformatics analysis, we demonstrated a potential link between viral infection and AD. These findings may provide a platform to further our understanding of AD pathogenesis.

Potential Biomarkers of Acute Myocardial Infarction Based on Co-Expression Network Analysis

2020 ◽  
Author(s):  
Zhiwen Ding ◽  
Zhaohui Hu ◽  
Xiangjun Ding ◽  
Yuyao Ji ◽  
Guiyuan li ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Control Group ◽  
Hub Genes ◽  
And Control ◽  
Potential Biomarkers

Abstract Background: Acute myocardial infarction (AMI) is a common cause of death in many countries. Analyzing the potential biomarkers of AMI is crucial to understanding the molecular mechanism of disease. However, specific diagnostic biomarkers have not been fully elucidated, and candidate regulatory targets for AMI have not been determined.Methods: In this study, AMI gene chip data GSE48060, blood samples from normal cardiac function controls (n = 21) and AMI patients (n = 26) were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) of AMI and control group were identified with Online tool GEO2R. the genes co-expressed with were found. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, MR screening and cutoff threshold screening. Then, GO database was used to analyze the function and pathway enrichment of genes in the most important modules. KEGG DISEASE and BioCyc were used to analyze the hub gene in the module to determine important sub-pathways. In addition, the expression of hub genes were certified by RT-qPCR in AMI and control specimens.Results: This study identified 52 DEGs, including 26 up-regulated genes and 26 down-regulated genes. Co-expression network analysis of 52 DEGs revealed that there are mainly three up-regulated genes (AKR1C3, RPS24 and P2RY12) and three down-regulated genes (ACSL1, B3GNT5 and MGAM) as key hub genes in the co-expression network. Furthermore, GO enrichment analysis was performed on all AMI co-expression network genes and found to be functionally enriched mainly in RAGE receptor binding and negative regulation of T cell cytokine production. In addition, through KEGG DISEASE and BioCyc analysis, the functions of genes RPS24 and P2RY12 were enriched in cardiovascular diseases, AKR1C3 was enriched in cardenolide biosynthesis, MGAM was enriched in glycogenolysis, B3GNT5 was enriched in glycosphingolipids biosynthesis, and ACSL1 enriched in icosapentaenoate biosynthesis II. Conclusion: The hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential targets of AMI and have potential application value in the diagnosis of AMI.

scholarly journals Bioinformatics analysis on enrichment analysis of potential hub genes in breast cancer

2021 ◽  
Vol 10 (5) ◽  
pp. 2399-2408
Author(s):  
Limin Wei ◽  
Yukun Wang ◽  
Dan Zhou ◽  
Xinyang Li ◽  
Ziming Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Hub Genes

scholarly journals Identification of differentially expressed genes in Cushing's disease by integrated bioinformatics analysis

2020 ◽  
Author(s):  
Tingting Lv ◽  
Haoying Yu ◽  
Shuyue Ren ◽  
Jingrong Wang ◽  
Lan Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Cushing’S Disease ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Cushing's Disease ◽  
Age Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Differential Genes

Abstract Background Cushing's disease is a rare and little-known disease, and the individualization of drug treatment varies greatly. Studies have shown that the gene expression profile of Cushing's disease is related to its clinical characteristics. Therefore, the study aims to identify key differential genes between the age and size of tumors through bioinformatics technology, thus providing a theoretical basis for personalized targeted therapy of Cushing's disease. Methods Downloading the gene expression microarray (GSE93825) data from the Gene Expression Omnibus (GEO) database and obtaining differentially expressed genes (DEGs) of different tumor sizes and ages through GEO2R. The DAVID database, Cytoscape and String platforms were utilized for functional enrichment analysis and protein-protein interaction (PPI) network analysis on selected differential genes. Results First, 96 DEGs were identified between macroadenoma (MAC) and microadenoma (MIC), which initially proved the different gene expression characteristics between them. Second, a total of 2128 DEGs were identified in MAC age group. The top five hub genes of the PPI network were GNGT2, LPAR3, PDYN, GRM3, and HTR1D. A total of 16 DEGs were identified in MIC age group. In addition, 88 DEGs were identified in younger MAC and MIC groups. The top five hub genes included LEP, PTGS2, STAT6, CXCL12, and ITPKB. 299 DEGs were identified in senior MAC and MIC groups. The first five hub genes were CCR7, LPAR2, CXCR5, ADCY3, and TAS2R14. By virtue of DAVID and Cytoscape software, the function enrichment analysis and core module analysis were performed successfully. Conclusions In summary, our research shows through bioinformatics analysis that different gene expression profiles of Cushing's disease are related to the size and age of the tumor, which may provide new insights into the molecular pathogenesis of Cushing's disease. These hub genes may be used for accurate diagnosis and treatment of Cushing's disease.

scholarly journals Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets

2020 ◽  
Author(s):  
Ming Chen ◽  
Junkai Zeng ◽  
Yeqing Yang ◽  
Buling Wu
Keyword(s):  
Gene Expression ◽  
Dental Pulp ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Signalling Pathway ◽  
Diagnostic Biomarker ◽  
Hub Genes ◽  
Geo Database

Abstract Background: Pulpitis is an inflammatory disease, the grade of which is classified according to the level of inflammation. Traditional methods of evaluating the status of dental pulp tissue in clinical practice have limitations. The rapid and accurate diagnosis of pulpitis is essential for determining the appropriate treatment. By integrating different datasets from the Gene Expression Omnibus (GEO) database, we analysed a merged expression matrix of pulpitis, aiming to identify biological pathways and diagnostic biomarkers of pulpitis. Methods: By integrating two datasets (GSE77459 and GSE92681) in the GEO database using the sva and limma packages of R, differentially expressed genes (DEGs) of pulpitis were identified. Then, the DEGs were analysed to identify biological pathways of dental pulp inflammation with Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Protein–protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape.Results: A total of 470 DEGs comprising 394 upregulated and 76 downregulated genes were found in pulpitis tissue. GO analysis revealed that the DEGs were enriched in biological processes related to inflammation, and the enriched pathways in the KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signalling pathway and NF-κB signalling pathway. The GSEA results provided further functional annotations, including complement system, IL6/JAK/STAT3 signalling pathway and inflammatory response pathways. According to the degrees of nodes in the PPI network, 10 hub genes were identified, and 8 diagnostic biomarker candidates were screened: PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1.Conclusions: With bioinformatics analysis of merged datasets, biomarker candidates of pulpitis were screened and the findings may be as reference to develop a new method of pulpitis diagnosis.

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