Bioinformatics Analysis Identifies Potential Ferroptosis Key Genes in the Pathogenesis of Pulmonary Fibrosis

Objective: Ferroptosis has an important role in developing pulmonary fibrosis. The present project aimed to identify and validate the potential ferroptosis-related genes in pulmonary fibrosis by bioinformatics analyses and experiments.Methods: First, the pulmonary fibrosis tissue sequencing data were obtained from Gene Expression Omnibus (GEO) and FerrDb databases. Bioinformatics methods were used to analyze the differentially expressed genes (DEGs) between the normal control group and the pulmonary fibrosis group and extract ferroptosis-related DEGs. Hub genes were screened by enrichment analysis, protein-protein interaction (PPI) analysis, and random forest algorithm. Finally, mouse pulmonary fibrosis model was made for performing an exercise intervention and the hub genes’ expression was verified through qRT-PCR.Results: 13 up-regulated genes and 7 down-regulated genes were identified as ferroptosis-related DEGs by comparing 103 lung tissues with idiopathic pulmonary fibrosis (IPF) and 103 normal lung tissues. PPI results indicated the interactions among these ferroptosis-related genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment and Genome-Ontology (GO) enrichment analyses showed that these ferroptosis-related genes involved in the organic anion transport, response to hypoxia, response to decrease oxygen level, HIF-1 signaling pathway, renal cell carcinoma, and arachidonic acid metabolism signaling pathway. The confirmed genes using PPI analysis and random forest algorithm included CAV1, NOS2, GDF15, HNF4A, and CDKN2A. qRT-PCR of the fibrotic lung tissues from the mouse model showed that the mRNA levels of NOS2 and GDF15 were up-regulated, while CAV1 and CDKN2A were down-regulated. Also, treadmill training led to an increased expression of CAV1 and CDKN2A and a decrease in the expression of NOS2 and GDF15.Conclusion: Using bioinformatics analysis, 20 potential genes were identified to be associated with ferroptosis in pulmonary fibrosis. CAV1, NOS2, GDF15, and CDKN2A were demonstrated to be influencing the development of pulmonary fibrosis by regulating ferroptosis. These findings suggested that, as an aerobic exercise treatment, treadmill training reduced ferroptosis in the pulmonary fibrosis tissues, and thus, reduces inflammation in the lungs. Aerobic exercise training initiate concomitantly with induction of pulmonary fibrosis reduces ferroptosis in lung. These results may develop our knowledge about pulmonary fibrosis and may contribute to its treatment.

Download Full-text

POS0458 IDENTIFICATION OF HUB GENES AND MOLECULAR PATHWAYS IN PATIENTS WITH RHEUMATOID ARTHRITIS BY BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.1938 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 460.1-460

Author(s):

L. Cheng ◽

S. X. Zhang ◽

S. Song ◽

C. Zheng ◽

X. Sun ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Signaling Pathway ◽

Synovial Tissue ◽

Bioinformatics Analysis ◽

Metabolic Process ◽

Shanxi Province ◽

Molecular Pathways ◽

Biological Processes ◽

Hub Genes ◽

Tissue Samples

Background:Rheumatoid arthritis (RA) is a chronic, inflammatory synovitis based systemic disease of unknown etiology1. The genes and pathways in the inflamed synovium of RA patients are poorly understood.Objectives:This study aims to identify differentially expressed genes (DEGs) associated with the progression of synovitis in RA using bioinformatics analysis and explore its pathogenesis2.Methods:RA expression profile microarray data GSE89408 were acquired from the public gene chip database (GEO), including 152 synovial tissue samples from RA and 28 healthy synovial tissue samples. The DEGs of RA synovial tissues were screened by adopting the R software. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Protein-protein interaction (PPI) networks were assembled with Cytoscape software.Results:A total of 654 DEGs (268 up-regulated genes and 386 down-regulated genes) were obtained by the differential analysis. The GO enrichment results showed that the up-regulated genes were significantly enriched in the biological processes of myeloid leukocyte activation, cellular response to interferon-gamma and immune response-regulating signaling pathway, and the down-regulated genes were significantly enriched in the biological processes of extracellular matrix, retinoid metabolic process and regulation of lipid metabolic process. The KEGG annotation showed the up-regulated genes mainly participated in the staphylococcus aureus infection, chemokine signaling pathway, lysosome signaling pathway and the down-regulated genes mainly participated in the PPAR signaling pathway, AMPK signaling pathway, ECM-receptor interaction and so on. The 9 hub genes (PTPRC, TLR2, tyrobp, CTSS, CCL2, CCR5, B2M, fcgr1a and PPBP) were obtained based on the String database model by using the Cytoscape software and cytoHubba plugin3.Conclusion:The findings identified the molecular mechanisms and the key hub genes of pathogenesis and progression of RA.References:[1]Xiong Y, Mi BB, Liu MF, et al. Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis. Med Sci Monit 2019;25:2246-56. doi: 10.12659/MSM.915451 [published Online First: 2019/03/28][2]Mun S, Lee J, Park A, et al. Proteomics Approach for the Discovery of Rheumatoid Arthritis Biomarkers Using Mass Spectrometry. Int J Mol Sci 2019;20(18) doi: 10.3390/ijms20184368 [published Online First: 2019/09/08][3]Zhu N, Hou J, Wu Y, et al. Identification of key genes in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis. Medicine (Baltimore) 2018;97(22):e10997. doi: 10.1097/MD.0000000000010997 [published Online First: 2018/06/01]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

Download Full-text

Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2020.631775 ◽

2021 ◽

Vol 7 ◽

Author(s):

Tao Yan ◽

Shijie Zhu ◽

Miao Zhu ◽

Chunsheng Wang ◽

Changfa Guo

Keyword(s):

Atrial Fibrillation ◽

Network Analysis ◽

Molecular Mechanism ◽

Immune Cells ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Correlation Network ◽

Hub Genes ◽

Qrt Pcr ◽

Gene Correlation

Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF.Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR).Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis.Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.

Download Full-text

Identification of potential diagnostic and prognostic biomarkers for LUAD based on TCGA and GEO databases

Bioscience Reports ◽

10.1042/bsr20204370 ◽

2021 ◽

Author(s):

Qiangqiang Zheng ◽

Shihui Min ◽

Qinghua Zhou

Keyword(s):

Signaling Pathway ◽

Clinical Features ◽

Enrichment Analysis ◽

Expression Level ◽

Normal Lung ◽

Receptor Interaction ◽

Hub Genes ◽

Kaplan Meier ◽

Viral Carcinogenesis ◽

Relationship Of

Accumulating evidence has demonstrated that gene alterations play a crucial role in LUAD development, progression, and prognosis. The current study aimed to identify the hub genes associated with LUAD. In the present study, we used TCGA database to screen the hub genes. Then, we validated the results by GEO datasets. Finally, we used cBioPortal, UALCAN, qRT-PCR, HPA database, TCGA database, and Kaplan-Meier plotter database to estimate the gene mutation, gene transcription, protein expression, clinical features of hub genes in patients with LUAD. A total of 5,930 DEGs were screened out in TCGA database. Enrichment analysis revealed that DEGs were involved in the transcriptional misregulation in cancer, viral carcinogenesis, cAMP signaling pathway, calcium signaling pathway, and ECM-receptor interaction. The combining results of MCODE and CytoHubba showed that ADCY8, ADRB2, CALCA, GCG, GNGT1, and NPSR1 were hub genes. Then, we verified the above results by GSE118370, GSE136043, and GSE140797 datasets. Compared with normal lung tissues, the expression level of ADCY8 and ADRB2 were lower in LUAD tissues, but the expression level of CALCA, GCG, GNGT1, and NPSR1 were higher. In the prognosis analyses, the low expression of ADCY8 and ADRB2 and the high expression of CALCA, GCG, GNGT1, and NPSR1 were correlated with poor OS and poor PFS. The significant differences in the relationship of the expression of 6 hub genes and clinical features were observed. In conclusion, 6 hub genes will not only contribute to elucidating the pathogenesis of LUAD, and may be potential therapeutic targets for LUAD.

Download Full-text

Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis

Cardiovascular Therapeutics ◽

10.1155/2019/8490707 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Da-Qiu Chen ◽

Xiang-Sheng Kong ◽

Xue-Bin Shen ◽

Mao-Zhi Huang ◽

Jian-Ping Zheng ◽

...

Keyword(s):

Gene Expression ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Signaling Pathway ◽

Differentially Expressed Genes ◽

Bioinformatics Analysis ◽

Differentially Expressed ◽

High Morbidity ◽

Hub Genes ◽

Module Analysis

Background. Acute myocardial infarction (AMI) is a common disease with high morbidity and mortality around the world. The aim of this research was to determine the differentially expressed genes (DEGs), which may serve as potential therapeutic targets or new biomarkers in AMI. Methods. From the Gene Expression Omnibus (GEO) database, three gene expression profiles (GSE775, GSE19322, and GSE97494) were downloaded. To identify the DEGs, integrated bioinformatics analysis and robust rank aggregation (RRA) method were applied. These DEGs were performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using Clusterprofiler package. In order to explore the correlation between these DEGs, the interaction network of protein-protein internet (PPI) was constructed using the STRING database. Utilizing the MCODE plug-in of Cytoscape, the module analysis was performed. Utilizing the cytoHubba plug-in, the hub genes were screened out. Results. 57 DEGs in total were identified, including 2 down- and 55 upregulated genes. These DEGs were mainly enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and so on. The module analysis filtered out 18 key genes, including Cxcl5, Arg1, Cxcl1, Spp1, Selp, Ptx3, Tnfaip6, Mmp8, Serpine1, Ptgs2, Il6, Il1r2, Il1b, Ccl3, Ccr1, Hmox1, Cxcl2, and Ccl2. Ccr1 was the most fundamental gene in PPI network. 4 hub genes in total were identified, including Cxcl1, Cxcl2, Cxcl5, and Mmp8. Conclusion. This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI.

Download Full-text

Mechanism of interaction between TM4SF1 and jak2-stat3 signaling pathway in lung cancer cells and expression analysis in non-small cell lung cancer

10.21203/rs.3.rs-23716/v1 ◽

2020 ◽

Author(s):

Yumeng Niu ◽

Hailong Deng ◽

Lipeng Li ◽

Weikang Chen ◽

Yuxuan Wang ◽

...

Keyword(s):

Lung Cancer ◽

Signaling Pathway ◽

A549 Cells ◽

Normal Lung ◽

Expression Levels ◽

Qrt Pcr ◽

Stat3 Signaling ◽

Skin Cancers ◽

Stat3 Signaling Pathway ◽

Cancer Tissues

Abstract Background According to the latest data released in 2018, it is estimated that there will be 18.1 million new cancer cases worldwide (excluding 1.7 million non-melanoma skin cancers) and 9.6 million cancer deaths (excluding 950 non-melanoma skin cancers) Million cases). Among them, the incidence of lung cancer (11.6% of the total number of cases) and mortality (18.4% of the total number of cancer deaths, which are expected to cause 1.8 million deaths) are the first. In recent years, studies have found TM4SF1 play an important role in the development process of many tumors.Methods Sixty-one patients with NSCLC who underwent surgical resection of cancer tissues, para-carcinoma tissues, and 10 normal lung tissues removed from benign lung disease (Jun/2018-Dec/2018) were collected. Real-time immunofluorescence quantitative PCR (qRT-PCR) and Western blot were used to detect the expression of TM4SF1 in NSCLC tissues (CT), para-carcinoma tissue (PCT), and normal lung tissues(NLT). TM4SF1 gene was overexpressed in lung cancer A549 cells using lentiviral transfection technology, qRT-PCR and Western blot were used to detect whether TM4SF1 gene was successfully expressed in lung cancer A549 cells, and Transwell was used to detect the effect of TM4SF1 overexpression on A549 migration. JAK2-STAT3 signal pathway interference reagent AG490 was used to analyze the expression levels of Stat3 and downstream Sox2 genes in the overexpression group, blank group, negative control group and their corresponding treatment groups TM4SF1, JAK2-STAT3 signal pathway using real-time qRT-PCR. Analyze the relevance of these three indicators at the same time.Results The expression levels of TM4SF1 mRNA and protein in cancer tissues were significantly higher than those in adjacent cancer tissues (P<0.05) and normal lung tissue specimens (P <0.05). The expression of TM4SF1 was not significantly associated with the age and sex of patients, but was associated with tumor size, degree of differentiation, lymph node metastasis, and clinical stage were related (P<0.05). TM4SF1 was successfully overexpressed in A549 cells. After overexpressing TM4SF1, the ability to migrate of A549 cells was significantly enhanced, and the expression levels of Stat3 and downstream Sox2 in the JAK2-STAT3 signaling pathway were up-regulated. The expression of TM4SF1, Stat3 and Sox2 at the mRNA level showed a positive correlation trend (P<0.01).Conclusion TM4SF1 is highly expressed in NSCLC, and its expression level is closely related to many clinical staging indicators. Overexpression of this gene can promote the migration of A549 cells and up-regulate the expression levels of Stat3 and downstream Sox2 in the JAK2-STAT3 signaling pathway. The expressions of TM4SF1, Stat3 and Sox2 were positively correlated in A549 cells. TM4SF1 may promote the occurrence, development and distant metastasis of NSCLC through this pathway. TM4SF1 may become a potential therapeutic target for NSCLC.

Download Full-text

Identification of Hub Genes and Key Pathways of Paraquat Induced Human Embryonic Pulmonary Fibrosis by Bioinformatics Analysis and Vitro Experiments

10.21203/rs.3.rs-73540/v1 ◽

2020 ◽

Author(s):

Yue Fu ◽

Xiang Xia Zeng ◽

Jin Lun Hu ◽

Mei Yan ◽

CHun Ming Xie ◽

...

Keyword(s):

Gene Expression ◽

Pulmonary Fibrosis ◽

Lung Fibrosis ◽

Bioinformatics Analysis ◽

Core Promoter ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Hub Genes

Abstract Background: Paraquat is highly toxic pesticide, which usually led to acute lung injury and subsequently develop pulmonary fibrosis, the exact mechanisms of PQ-induced lung fibrosis remain largely unclear and no specific drugs for this disease have been approved. Methods: Our study aimed to identify its potential mechanism though modeling study in vitro and bioinformatics analysis. Gene expression datasets associated with PQ-induced lung fibrosis were obtained from the Gene Expression Omnibus and differentially expressed genes (DEGs) were identified using GEO2R. Functional enrichment analyses were performed using the Database for Annotation. Results: The DEGs in the two datasets, of which 92 overlapping genes were found in two microarray datasets. Functional analysis demonstrated that the 92 DEGs were enriched in the ‘TNF signaling pathway’, ‘CXCR chemokine receptor binding’, and ‘core promoter binding’. Moreover, nine hub genes were identified from a protein‑protein interaction network. Conclusions: This integrative analysis firstly identified candidate genes and pathways in PQ-induced lung fibrosis, as well as benefit to research novel approaches for treating for control of PQ-induced pulmonary fibrosis.

Download Full-text

Integrative analysis of miRNA-mRNA network in high altitude retinopathy by bioinformatics analysis

Bioscience Reports ◽

10.1042/bsr20200776 ◽

2021 ◽

Author(s):

Tong Su ◽

Chufeng Gu ◽

Deji Draga ◽

Chuandi Zhou ◽

Thashi Lhamo ◽

...

Keyword(s):

High Altitude ◽

Bioinformatics Analysis ◽

Cell Model ◽

Expression Profiles ◽

Oxygen Deficiency ◽

Differentially Expressed ◽

Hub Genes ◽

Bioinformatics Analyses ◽

Effective Prevention ◽

Qrt Pcr

High-altitude retinopathy (HAR) is an ocular manifestation of acute oxygen deficiency at high altitudes. Although the pathophysiology of HAR has been revealed by many studies in recent years, the molecular mechanism is not yet clear. Our study aimed to systematically identify the genes and miRNA and explore the potential biomarkers associated with HAR by integrated bioinformatics analysis. The mRNA and miRNA expression profiles were obtained from the GEO database. We performed Gene Ontology (GO) functional annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Potential target gene analysis and miRNA-mRNA network analysis were also conducted. Quantitative RT-PCR (qRT-PCR) was used to validate the results of the bioinformatics analysis. Through a series of bioinformatics analyses and experiments, we selected 16 differentially expressed miRNAs (DE-miRNAs) and 157 differentially expressed genes (DEGs) related to AMS and constructed a miRNA-mRNA network containing 240 relationship pairs. The hub genes were filtered from the PPI network: IL7R, FOS, IL10, FCGR2A, DDX3X, CDK1, BCL11B and HNRNPH1, which were all downregulated in the AMS group. Then, 9 upregulated DE-miRNAs and 8 hub genes were verified by qRT-PCR in our hypoxia-induced HAR cell model. The expression of miR-3177-3p, miR-369-3p, miR-603, miR-495, miR-4791, miR-424-5p, FOS, IL10 and IL7R was consistent with our bioinformatics results. In conclusion, FOS, IL10, IL-7R and 7 DE-miRNAs may participate in the development of HAR. Our findings will contribute to the identification of biomarkers and promote the effective prevention and treatment of HAR in the future.

Download Full-text

Bioinformatic Analysis Reveals Key Genes and Pathways in Aging Brain of Senescence-accelerated Mouse P8 (SAMP8)

CNS & Neurological Disorders - Drug Targets ◽

10.2174/1871527317666180816094741 ◽

2018 ◽

Vol 17 (9) ◽

pp. 712-722 ◽

Cited By ~ 5

Author(s):

Jiaqi Li ◽

Yuzhi Zhou ◽

Guanhua Du ◽

Xuemei Qin ◽

Li Gao

Keyword(s):

Signaling Pathway ◽

Gene Network ◽

Brain Aging ◽

Aging Brain ◽

Hub Genes ◽

Qrt Pcr ◽

Key Genes ◽

Samp8 Mice ◽

Key Pathways ◽

Senescence Accelerated Mouse

Background: Aging is a complex process accompanied with the decline of the different physiological functions. Numerous differentially expressed genes (DEGs) have been found in the aging brain of senescence-accelerated mouse P8 (SAMP8), however, it was challenging to screen out the crucial ones. Objective: This study aimed to explore the crucial genes and pathways in aging brain of SAMP8 mice, which would be beneficial for understanding the pathogenesis of brain aging. Methods: Firstly, 430 genes that are differentially expressed in SAMP8 mice versus SAMR1 mice were obtained from 9 gene expression studies, and gene-gene network was constructed. Clustering analysis and topological analysis were used to single out the hub genes from this network. Secondly, pathway enrichment analysis was utilized to identify the key pathways from the 430 DEGs, and the DEGs in key pathways were considered as functional genes. Thirdly, the inner-network between hub genes and functional genes was constructed, and the key genes were predicted. Parts of the key genes were experimentally verified by quantitative real-time PCR (qRT-PCR), and the associated transcription factors (TFs) were predicted. Results: Our results revealed that 12 crucial genes might affect brain aging, including Trp53, Bcl2, Tnf, Casp9, Fos, Il6, Ptgs2, Il1b, Bdnf, Cdkn1a, Pik3c3, Rps6ka1, among which Casp9, Fos, Ptgs2, Cdkn1a, Pik3c3, and Rps6ka1 had been verified by qRT-PCR in 10-moth-old SAMP8 mice. Five functional groups including mitogen-activated protein kinase (MAPK) signaling pathway, neurotrophin signaling pathway, Hepatitis B, Alzheimer's disease and Oxytocin signaling pathway were significantly changed during aging process in SAMP8 mice. Two key transcription factors of c-Fos and C/EBPbeta were predicted by constructing a TF-target gene network. Conclusion: These putative genes and pathways are closely related to brain senescence and our results would gain new insight into the pathogenesis of brain aging.

Download Full-text

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

Download Full-text