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

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

10.21203/rs.2.20729/v3 ◽

2020 ◽

Author(s):

Ming Chen ◽

Junkai Zeng ◽

Yeqing Yang ◽

Buling Wu

Keyword(s):

Gene Expression ◽

Dental Pulp ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Biological Pathways ◽

Signalling Pathway ◽

Diagnostic Biomarker ◽

Hub Genes ◽

Go Analysis ◽

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.

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

10.21203/rs.2.20729/v2 ◽

2020 ◽

Author(s):

Ming Chen ◽

Junkai Zeng ◽

Yeqing Yang ◽

Buling Wu

Keyword(s):

Gene Expression ◽

Dental Pulp ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Biological Pathways ◽

Signalling Pathway ◽

Diagnostic Biomarker ◽

Hub Genes ◽

Go Analysis ◽

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.

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

10.21203/rs.2.20729/v1 ◽

2020 ◽

Author(s):

Ming Chen ◽

Junkai Zeng ◽

Yeqing Yang ◽

Buling Wu

Keyword(s):

Gene Expression ◽

Signaling Pathway ◽

Dental Pulp ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Biological Pathways ◽

Gene Set Enrichment Analysis ◽

Diagnostic Biomarker ◽

Hub Genes ◽

Geo Database

Abstract Background Pulpitis is known as an inflammatory disease classified by the level of inflammation. The existed traditional methods of evaluating status of dental pulp tissue in clinical practice still have some shortages and limitations. Immediate and accurate diagnosis of pulpitis is essential to the choice of treatment. Through integrating different datasets from Gene Expression Omnibus (GEO) database, we analyzed the merged expression matrix of pulpitis, aiming to identified biological pathways and diagnostic biomarker of pulpitis.Methods By integrating two datasets (GSE77459 and GSE92681) in GEO database using sva and limma packages, differentially expressed genes (DEGs) of pulpitis were identified. Then DEGs were used to analyze biological pathways of dental pulp inflammation with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the 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 472 DEGs consisting of 396 upregulated and 76 downregulated genes were found in pulpitis tissue. DEGs in GO analysis were enriched in biological processes about inflammation and in KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signaling pathway and NF-κB signaling pathway. GSEA results provided further functional annotations including complement system, IL6/JAK/STAT3 signaling pathway and inflammatory response pathways. According to the degrees of nodes in PPI network, 10 hub genes were obtained and 8 diagnostic biomarker candidates were screened, including PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1.

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Bioinformatic Identification of Hub Genes and Biological Pathways for Sepsis-Associated Acute Lung Injury

10.21203/rs.3.rs-623296/v1 ◽

2021 ◽

Author(s):

Chao Zhang ◽

Feng Xu ◽

Fang Fang

Keyword(s):

Gene Expression ◽

Acute Lung Injury ◽

Lung Injury ◽

Function Analysis ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Biological Pathways ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Hub Genes

Abstract Background: Sepsis-associated acute lung injury (ALI) is a potentially lethal complication associated with a poor prognosis and high mortality worldwide, especially in the outbreak of COVID-19. However, the fundamental mechanisms of this complication were still not fully elucidated. Thus, we conducted this study to identify hub genes and biological pathways of sepsis-associated ALI, mainly focus on two pathways of LPS and HMGB1. Methods: Gene expression profile GSE3037 were downloaded from Gene Expression Omnibus (GEO) database, including 8 patients with sepsis-induced acute lung injury, with 8 unstimulated blood neutrophils, 8 LPS- induced neutrophils and 8 HMGB1-induced neutrophils. Diﬀerentially expressed genes (DEGs) identifications, Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, Gene Set Enrichment Analysis (GSEA) and protein-protein interaction (PPI) network constructions were performed to obtain hub genes and relevant biological pathways.Results: We identified 534 and 317 DEGs for LPS- and HMGB1-induced ALI, respectively. The biological pathways involved in LPS- and HMGB1-induced ALI were also identified accordingly. By PPI network analysis, we found that ten hub genes for LPS-induced ALI (CXCL8, TNF, IL6, IL1B, ICAM1, CXCL1, CXCL2, IL1A, IL1RN and CXCL3) and another ten hub genes for HMGB1-induced ALI (CCL20, CXCL2, CXCL1, CCL4, CXCL3, CXCL9, CCL21, CXCR6, KNG1 and SST). Furthermore, by combining analysis, the results revealed that genes of TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3 were potential biomarkers for sepsis-associated ALI. Conclusions: Our study revealed that ten hub genes associated with sepsis-induced ALI were TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3, which may serve as genetic biomarkers and be further verified in prospective experimental trials.

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

10.21203/rs.3.rs-1066425/v1 ◽

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.

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Integrated Analysis of Hub Genes and Pathways In Esophageal Carcinoma Based on NCBI’s Gene Expression Omnibus (GEO) Database: A Bioinformatics Analysis

Medical Science Monitor ◽

10.12659/msm.923934 ◽

2020 ◽

Vol 26 ◽

Cited By ~ 1

Author(s):

Tan Yu-jing ◽

Tang Wen-jing ◽

Tang Biao

Keyword(s):

Gene Expression ◽

Esophageal Carcinoma ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Integrated Analysis ◽

Hub Genes ◽

Geo Database

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Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis

PeerJ ◽

10.7717/peerj.10419 ◽

2020 ◽

Vol 8 ◽

pp. e10419

Author(s):

Jingyi Ding ◽

Yanxi Liu ◽

Yu Lai

Keyword(s):

Gene Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Prognostic Biomarkers ◽

Ductal Adenocarcinoma ◽

Receptor Interaction ◽

Analysis Tool ◽

Ppi Network ◽

Hub Genes

Background Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neoplasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC. Methods The microarray datasets GSE28735, GSE62165, and GSE91035 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website. Results A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI network and then identified key modules. From the PPI network, 225 nodes and 803 edges were selected. The most significant module, which comprised 11 DEGs, was identified using the Molecular Complex Detection plugin. The top 20 hub genes, which were filtered by the CytoHubba plugin, comprised FN1, COL1A1, COL3A1, BGN, POSTN, FBN1, COL5A2, COL12A1, THBS2, COL6A3, VCAN, CDH11, MMP14, LTBP1, IGFBP5, ALB, CXCL12, FAP, MATN3, and COL8A1. These genes were validated using The Cancer Genome Atlas (TCGA) and Genotype–Tissue Expression (GTEx) databases, and the encoded proteins were subsequently validated using the HPA website. The GO analysis results showed that the most significantly enriched biological process, cellular component, and molecular function terms among the 20 hub genes were cell adhesion, proteinaceous extracellular matrix, and calcium ion binding, respectively. The KEGG pathway analysis showed that the 20 hub genes were mainly enriched in ECM–receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and protein digestion and absorption. These findings indicated that FBN1 and COL8A1 appear to be involved in the progression of PDAC. Moreover, patient survival analysis performed via the GEPIA using TCGA and GTEx databases demonstrated that the expression levels of COL12A1 and MMP14 were correlated with a poor prognosis in PDAC patients (p < 0.05). Conclusions The results demonstrated that upregulation of MMP14 and COL12A1 is associated with poor overall survival, and these might be a combination of prognostic biomarkers in PDAC.

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POS0851 IDENTIFICATION OF HUB GENES AND PATHWAYS IN DERMATOMYOSITIS BY BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.2026 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 680.1-680

Author(s):

C. Zheng ◽

S. X. Zhang ◽

R. Zhao ◽

L. Cheng ◽

T. Kong ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathway ◽

Muscle Tissue ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Shanxi Province ◽

Pathway Enrichment Analysis ◽

Type I ◽

Hub Genes ◽

Key Genes

Background:Dermatomyositis (DM) is a chronic systemic autoimmune disease characterized by inflammatory infiltrates in the skin and muscle1. The genes and pathways in the inflamed myopathies in patients with DM are poorly understood2.Objectives:To identify the key genes and pathways associated with DM and further discover its pathogenesis.Methods:Muscle tissue gene expression profile (GSE143323) were acquired from the GEO database, which included 39 DM samples and 20 normal samples. The differentially expressed genes (DEGs) in DM muscle tissue were screened by adopting the R software. Gene ontology (GO) and Kyoto Encyclopedia of Genome (KEGG) pathway enrichment analysis was performed by Metascape online analysis tool. A protein-protein interaction (PPI) network was then constructed by STRING software using the genes in significantly different pathways. Network of DEGs was analyzed by Cytoscape software. And degree of nodes was used to screen key genes.Results:Totally, 126 DEGs were obtained, which contained 122 up-regulated and 4 down-regulated. GO analysis revealed that most of the DEGs were significantly enriched in type I interferon signaling pathway, response to interferon-gamma, collagen-containing extracellular matrix, response to interferon-alpha and bacterium, positive regulation of cell death, leukocyte chemotaxis. KEGG pathway analysis showed that upregulated DEGs enhanced pathways associated with the hepatitis C, complement and coagulation cascades, p53 signaling pathway, RIG-I-like receptor signaling, Osteoclast differentiation, and AGE-RAGE signaling pathway. Ten hub genes were identified in DM, they were ISG15, IRF7, STAT1, MX1, OASL, OAS2, OAS1, OAS3, GBP1, and IRF9 according to the Cytoscape software and cytoHubba plugin.Conclusion:The findings from this bioinformatics network analysis study identified the key hub genes that might provide new molecular markers for its diagnosis and treatment.References:[1]Olazagasti JM, Niewold TB, Reed AM. Immunological biomarkers in dermatomyositis. Curr Rheumatol Rep 2015;17(11):68. doi: 10.1007/s11926-015-0543-y [published Online First: 2015/09/26].[2]Chen LY, Cui ZL, Hua FC, et al. Bioinformatics analysis of gene expression profiles of dermatomyositis. Mol Med Rep 2016;14(4):3785-90. doi: 10.3892/mmr.2016.5703 [published Online First: 2016/09/08].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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POS0742 SCREENING AND BIOINFORMATICS ANALYSIS OF HUB GENES AND PATHWAYS FOR PRIMARY SJÖGREN’S SYNDROME BASED ON GEO DATABASE

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.2021 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 622.2-623

Author(s):

T. Kong ◽

S. X. Zhang ◽

S. Song ◽

X. Sun ◽

C. Zheng ◽

...

Keyword(s):

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Sjogren's Syndrome ◽

Primary Sjögren’S Syndrome ◽

Primary Sjogren’S Syndrome ◽

Hub Genes ◽

Primary Sjögren's Syndrome ◽

Pathway Enrichment ◽

Geo Database

Background:Primary Sjögren’s syndrome (pSS) is an autoimmune disease that featured as lymphoplasmacytic infiltration of the exocrine glands leading to sicca symptoms1. However, its underlying molecular mechanisms remain elusive.Objectives:This study aims to identify differentially expressed genes (DEGs) and pathways associated with the progression of pSS using bioinformatics analysis and explore its pathogenesis.Methods:The pSS-associated gene chip data set GSE66795 was obtained from the Gene Expression Omnibus (GEO) database, which included 131 cases of fully-phenotyped pSS patients’ whole blood samples and 29 cases of control samples. DEGs were screened Using R software. Online tool Metascape2 was used to make Gene Ontology (GO) and KEGG pathway enrichment. The PPI network was performed using String database. Hub genes were identified by Cytoscape.Results:A total of 108 DEGs were captured, including 101 up-regulated genes and 7 down-regulated genes. GO enrichment showed that these DEGs were primarily enriched in defense response to virus, response to interferon-gamma, regulation of innate immune response, response to interferon-beta, double-stranded RNA binding, response to interferon-alpha. KEGG pathway enrichment analysis showed these DEGs were principally enriched in Influenza A, RIG-I-like receptor signaling pathway, necroptosis, Staphylococcus aureus infection. Finally, 9 hub genes (STAT1, IRF7, OAS2, GBP1, OAS1, IFIT3, IFIH1, OAS3, DDX60) had highest degree value.Conclusion:The findings identified molecular mechanisms and the key hub genes that may involve in the occurrence and development of pSS.References:[1]Francois H, Mariette X. Renal involvement in primary Sjogren syndrome. Nat Rev Nephrol 2016;12(2):82-93. doi: 10.1038/nrneph.2015.174 [published Online First: 2015/11/17].[2]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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