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

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

scholarly journals Potential Biological Molecules and Immune Cell for Rheumatoid Arthritis Synovial Lesions

2021 ◽  
Author(s):  
Mingyi Yang ◽  
Yani Su ◽  
Yao Ma ◽  
Ke Xu ◽  
Aihaiti Yirixiati ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Ontology ◽  
Dendritic Cells ◽  
B Cells ◽  
Signaling Pathway ◽  
Synovial Tissue ◽  
Immune Cell ◽  
Hub Genes ◽  
Data Set ◽  
Synovial Lesions

Abstract Objective: To study the potential biomarkers and related pathways in rheumatoid arthritis (RA) synovial lesions, as well as immune cell, to provide theoretical basis and research directions for the mechanism and treatment of RA. Methods: Download the RA synovial tissue microarray data set (GSE77298, GSE55457 and GSE55235) from Gene Expression Omnibus (GEO), The“limma” package of R to identify differentially expressed genes (DEGs), DAVIA Perform GO (Gene Ontology, gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes, Kyoto Gene Encyclopedia) enrichment analysis. STRING (Search Tool for the Retrieval of Interacting Genes) constructs a Protein Protein Interaction Network (PPI), R screens Hub genes, and mines targeted miRNAs of Hub genes based on multiple databases. In R,the immune cell of RA synovial tissue samples are obtained through the three packages of "e1071", "parallel" and "preprocessCore" with "CIBERSORT" software. Results: Ten Hub genes (KIAA0101, FOXM1, EGFR, CDC20, BUB1B, TYMS, TOP2A, RRM2, JUN and CCNA2) and 2 key miRNAs (miR-520d-5p and miR) related to RA synovial lesions were finally identified -139-5p), significantly enriched in epithelial cell signaling, ECM-receptor interaction, estrogen signaling pathway, cell cycle, ErbB signaling pathway and GnRH signaling pathway in Helicobacter pylori infection. Immune cell analysis found that resting dendritic cells, B cells memory, dendritic cells activated, plasma cells, macrophages M1, mast cells resting and T cells regulatory have high expression in RA, while neutrophils, B cells naive and natural killer cells activated have low expression in RA. Conclusion:The Hub genes, key miRNAs, related pathways, and immune cell obtained in this study provide a certain basis for the etiology and treatment of RA synovial lesions.

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

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

scholarly journals Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8390 ◽  
Author(s):  
Weisong Cai ◽  
Haohuan Li ◽  
Yubiao Zhang ◽  
Guangtao Han
Keyword(s):  
T Cells ◽  
Mast Cells ◽  
Signaling Pathway ◽  
Synovial Tissue ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Normal Controls

Background Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological change of OA. However, the molecular mechanisms of synovitis in OA are still not well understood. Objective This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. Materials and Methods The gene expression profiles of GSE12021, GSE55235 and GSE55457 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional enrichment analyses were performed. A protein-protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of synovial tissue between OA and normal controls. Results A total of 106 differentially expressed genes, including 68 downregulated genes and 38 upregulated genes, were detected. The PPI network was assessed, and the most significant module containing 14 hub genes was identified. Gene Ontology analysis revealed that the hub genes were significantly enriched in immune cell chemotaxis and cytokine activity. KEGG pathway analysis showed that the hub genes were significantly enriched in the rheumatoid arthritis signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction signaling pathway. The immune infiltration profiles varied significantly between osteoarthritis and normal controls. Compared with normal tissue, OA synovial tissue contained a higher proportion of memory B cells, naive CD4+ T cells, regulatory T cells, resting dendritic cells and resting mast cells, while naive CD4+ T cells, activated NK cells, activated mast cells and eosinophils contributed to a relatively lower portion (P > 0.05). Finally, the expression levels of 11 hub genes were confirmed by RT-PCR. Conclusion The hub genes and the difference in immune infiltration in synovial tissue between osteoarthritis and normal controls might provide new insight for understanding OA development.

scholarly journals Mechanism of miR-218-5p in autophagy, apoptosis and oxidative stress in rheumatoid arthritis synovial fibroblasts is mediated by KLF9 and JAK/STAT3 pathways

2021 ◽  
pp. jim-2020-001437
Author(s):  
Ming Chen ◽  
Minghui Li ◽  
Na Zhang ◽  
Wenwen Sun ◽  
Hui Wang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Oxidative Stress ◽  
Signaling Pathway ◽  
Synovial Tissue ◽  
Synovial Fibroblasts ◽  
Reverse Transcription Pcr ◽  
Stat3 Signaling ◽  
Stat3 Signaling Pathway ◽  
Interfering Rna ◽  
And Oxidative Stress

This study was aimed to investigate the effects of miR-218-5p on the proliferation, apoptosis, autophagy, and oxidative stress of rheumatoid arthritis synovial fibroblasts (RASFs), and the related mechanisms. Quantitative reverse transcription–PCR showed that the expression of miR-218-5p in rheumatoid arthritis synovial tissue was significantly higher than that in healthy synovial tissue. Compared with healthy synovial fibroblasts, miR-218-5p expression was obviously upregulated in RASFs, while KLF9 protein expression was markedly downregulated. Mechanistically, miR-218-5p could directly bind to the 3′ untranslated region of KLF9 to inhibit the expression of KLF9. Additionally, transfection of miR-218-5p small interfering RNA (siRNA) inhibited the proliferation but promoted apoptosis and autophagy of RASFs. Simultaneously, miR-218-5p silencing reduced reactive oxygen species and malondialdehyde levels and increased superoxide dismutase and glutathione peroxidase activity to improve oxidative stress in RASFs. More importantly, the introduction of KLF9 siRNA reversed the effects of miR-218-5p siRNA transfection on RASF proliferation, apoptosis, autophagy, and oxidative stress. What is more, silencing miR-218-5p inhibited the activation of JAK2/STAT3 signaling pathway by targeting KLF9. Collectively, knockdown of miR-218-5p could regulate the proliferation, apoptosis, autophagy and oxidative stress of RASFs by increasing the expression of KLF9 and inhibiting the activation of the JAK2/STAT3 signaling pathway, which may provide a potential target for the mechanism research of RA.

scholarly journals Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Yanzhi Ge ◽  
Zuxiang Chen ◽  
Yanbin Fu ◽  
Xiujuan Xiao ◽  
Haipeng Xu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Quantitative Polymerase Chain Reaction ◽  
Functional Enrichment ◽  
Proximal Promoter ◽  
Integrated Analysis ◽  
Data Sets ◽  
Hub Genes ◽  
Multiple Gene

Abstract Background Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with similar clinical phenotypes. This study aimed to determine the mechanistic similarities and differences between OA and RA by integrated analysis of multiple gene expression data sets. Methods Microarray data sets of OA and RA were obtained from the Gene Expression Omnibus (GEO). By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein–protein interaction (PPI) network analysis of DEGs were conducted to determine hub genes and pathways. The “Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)” algorithm was employed to evaluate the immune infiltration cells (IICs) profiles in OA and RA. Moreover, mouse models of RA and OA were established, and selected hub genes were verified in synovial tissues with quantitative polymerase chain reaction (qPCR). Results A total of 1116 DEGs were identified between OA and RA. GO functional enrichment analysis showed that DEGs were enriched in regulation of cell morphogenesis involved in differentiation, positive regulation of neuron differentiation, nuclear speck, RNA polymerase II transcription factor complex, protein serine/threonine kinase activity and proximal promoter sequence-specific DNA binding. KEGG pathway analysis showed that DEGs were enriched in EGFR tyrosine kinase inhibitor resistance, ubiquitin mediated proteolysis, FoxO signaling pathway and TGF-beta signaling pathway. Immune cell infiltration analysis identified 9 IICs with significantly different distributions between OA and RA samples. qPCR results showed that the expression levels of the hub genes (RPS6, RPS14, RPS25, RPL11, RPL27, SNRPE, EEF2 and RPL19) were significantly increased in OA samples compared to their counterparts in RA samples (P < 0.05). Conclusion This large-scale gene analyses provided new insights for disease-associated genes, molecular mechanisms as well as IICs profiles in OA and RA, which may offer a new direction for distinguishing diagnosis and treatment between OA and RA.

scholarly journals Identification of the Crucial Gene in Overflow Arteriovenous Fistula by Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhengde Zhao ◽  
Qining Fu ◽  
Liangzhu Hu ◽  
Yangdong Liu
Keyword(s):  
Signaling Pathways ◽  
Signaling Pathway ◽  
Intimal Hyperplasia ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Mapk Signaling Pathway ◽  
Biological Processes ◽  
Hub Genes ◽  
Av Fistula ◽  
Core Genes

Objective: The aim was to study the preliminary screening of the crucial genes in intimal hyperplasia in the venous segment of arteriovenous (AV) fistula and the underlying potential molecular mechanisms of intimal hyperplasia with bioinformatics analysis.Methods: The gene expression profile data (GSE39488) was analyzed to identify differentially expressed genes (DEGs). We performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of DEGs. Gene set enrichment analysis (GSEA) was used to understand the potential activated signaling pathway. The protein–protein interaction (PPI) network was constructed with the STRING database and Cytoscape software. The Venn diagram between 10 hub genes and gene sets of 4 crucial signaling pathways was used to obtain core genes and relevant potential pathways. Furthermore, GSEAs were performed to understand their biological functions.Results: A total of 185 DEGs were screened in this study. The main biological function of the 111 upregulated genes in AV fistula primarily concentrated on cell proliferation and vascular remodeling, and the 74 downregulated genes in AV fistula were enriched in the biological function mainly relevant to inflammation. GSEA found four signaling pathways crucial for intimal hyperplasia, namely, MAPK, NOD-like, Cell Cycle, and TGF-beta signaling pathway. A total of 10 hub genes were identified, namely, EGR1, EGR2, EGR3, NR4A1, NR4A2, DUSP1, CXCR4, ATF3, CCL4, and CYR61. Particularly, DUSP1 and NR4A1 were identified as core genes that potentially participate in the MAPK signaling pathway. In AV fistula, the biological processes and pathways were primarily involved with MAPK signaling pathway and MAPK-mediated pathway with the high expression of DUSP1 and were highly relevant to cell proliferation and inflammation with the low expression of DUSP1. Besides, the biological processes and pathways in AV fistula with the high expression of NR4A1 similarly included the MAPK signaling pathway and the pathway mediated by MAPK signaling, and it was mainly involved with inflammation in AV fistula with the low expression of NR4A1.Conclusion: We screened four potential signaling pathways relevant to intimal hyperplasia and identified 10 hub genes, including two core genes (i.e., DUSP1 and NR4A1). Two core genes potentially participate in the MAPK signaling pathway and might serve as the therapeutic targets of intimal hyperplasia to prevent stenosis after AV fistula creation.

scholarly journals Identification of Common Mechanisms and Key Genes in the Chronic Obstructive Pulmonary Disease and Acute Myocardial Infarction

2021 ◽  
Author(s):  
Fengshou Chen ◽  
Haijia Hou ◽  
Bing Tang
Keyword(s):  
Myocardial Infarction ◽  
Chronic Obstructive Pulmonary Disease ◽  
Acute Myocardial Infarction ◽  
Pulmonary Disease ◽  
Signaling Pathway ◽  
Chronic Obstructive ◽  
Biological Processes ◽  
Hub Genes ◽  
Obstructive Pulmonary Disease ◽  
The Relationship

Abstract Background: Chronic obstructive pulmonary disease (COPD) and acute myocardial infarction (AMI) have a strong association. We aimed to study the relationships between COPD and AMI, and reveal potential therapeutic targets and biomarkers. Materials and methods: The dataset GSE38974 and GSE60993 were downloaded from the Gene Expression Omnibus (GEO) database to analyze the intersections among differentially expressed genes (DEGs). Common DEGs were identified and performed functional enrichment analyses. The hub genes were obtained based on the protein-protein interaction (PPI) network by cytoHubba in Cytoscape software. The receiver operator characteristic (ROC) curve analysis was applied to identify the diagnosis efficacy of hub genes. The relationship between hub genes and these two diseases in the CTD database were validated. Finally, the transcription factors (TFs) corresponding to hub genes were also analyzed. Results: In our study, sixty-five common DEGs were obtained in COPD and AMI. GO enrichment analysis indicated that inflammation or apoptotic biological processes are significant enriched biological processes. Common DEGs were mostly enriched in pathways including apoptosis, HIF-1 signaling pathway, TNF signaling pathway, and cytokine-cytokine receptor interaction. MMP9, SOCS3, MCL1, ERBB2 and S100A12 were identified as the hub genes. Furthermore, we found that the expression of hub genes was significantly associated with a diagnosis efficacy of COPD and AMI. We also validated the relationship between the hub genes and these two diseases in the CTD database. We also found that ELK1, ETV4, STAT3 and TFAP2A were significant TFs, which interacted with the hub genes. Conclusion: In conclusion, our study revealed the communal DEGs and related mechanisms between the pathophysiology of COPD and AMI. MMP9, SOCS3, MCL1, ERBB2 and S100A12 were identified as the hub genes that are associated with COPD and AMI. Our study provides new ideas and evidence for further exploration of the mechanisms and treatment of COPD and AMI.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
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.

scholarly journals Identification of key genes and multiple molecular pathways of metastatic process in prostate cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7899
Author(s):  
Lihuang Guo ◽  
Mingyue Lin ◽  
Zhenbo Cheng ◽  
Yi Chen ◽  
Yue Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cell Cycle ◽  
Therapeutic Targets ◽  
Phase Cell ◽  
Cycle Phase ◽  
Molecular Pathways ◽  
Hub Genes ◽  
Tissue Samples ◽  
Key Genes

Background Cancer metastasis is well known as the most adverse outcome and the major cause of mortality in cancer patients, including prostate cancer (PCa). There are no credible predictors, to this day, that can reflect the metastatic ability of localized PCa. In the present study, we firstly identified the differentially expressed genes (DEGs) and molecular pathways involved in the metastaic process of PCa by comparing gene expressions of metastaic PCa with localized PCa directly, with the purpose of identifying potential markers or therapeutic targets. Methods The gene expression profiles (GSE6919 and GSE32269) were downloaded from the Gene Expression Omnibus database, which contained 141 tissue samples, including 87 primary localized PCa samples and 54 metastaic PCa samples. After data processing, DEGs were identified by R language using the Student’s t-test adjusted via the Beniamini–Hochberg method. Subsequently, the gene ontology functional and pathway enrichment analyses of DEGs were performed and the protein–protein interaction network was constructed. Hub genes were identified using the plug-in cytoHubba in Cytoscape software by MCC and degree. Furthermore, validation and prognostic significance analysis of the hub genes were performed by UALCAN and gene expression profiling interactive analysis (GEPIA). Results A total of 90 DEGs were identified between localized and metastaic PCa, which consisted of 47 upregulated and 43 downregulated genes. The enriched functions and pathways of the DEGs include catabolic process, cell cycle, response to steroid hormone, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. A total of 10 genes were identified as hub genes and biological process analysis of hub genes showed that cell cycle phase, cell division, and mitotic cell cycle process were mainly enriched. The expression of hub genes were confirmed in metastaic PCa when compared with localized PCa tissues by The Cancer Genome Atlas database. Moreover, the disease-free survival analysis of hub genes revealed that these genes may play an important role in invasion, progression or recurrence. Therefore, these hub genes might be the key genes contributed to tumor progression or metastasis in PCa and provide candidate therapeutic targets for PCa. Conclusions The present study identified some DEGs between localized and metastaic PCa tissue samples. These key genes might be potential therapeutic targets and biomarkers for the metastaic process of PCa.

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