Identification of hub genes with prognostic values in gastric cancer by bioinformatics analysis

Abstract Gastric cancer remains one of the five major malignant tumors in the world, posing a great threat to public life and health. As gene sequencing technology develops, it is urgent to find out specific molecular markers for cancer therapy. In this study, datasets of GSE13911, GSE30727, GSE63089 and GSE118916 were investigated by bioinformatics analysis, and differentially expressed genes (DEGs) between GC tissues and normal tissues were screened for potential cancer therapeutic targets. Furthermore, the GSE63089 dataset was analyzed by Weighted Gene Co-expression Network Analysis (WGCNA), and the highly related genes were clustered. Then, the hub genes were searched using co-expression network and Molecular Complex Detection (MCODE) plug-in from Cytoscape software. Finally, ASPM, COL11A1 and CDC20 were obtained by intersection of hub genes and DEGs. The expressions of ASPM, COL11A1 and CDC20 gene in gastric cancer tissues and normal tissues from TCGA database were detected. For these genes, the least absolute shrink and selection operator (LASSO) Cox expression analysis was used to establish the prognostic risk model. COL11A1 and CDC20 genes were identified as candidate prognostic risk markers for GC. Analysis using qRT-PCR has shown that COL11A1 and CDC20 were significant differentially expressed between gastric cancer tissues and normal gastric tissues (P < 0.01). In conclusion, our study identifies specific DEGs involved in ECM process and metabolism by cytochrome P450 process, and these DEGs may be potential targets for GC therapy. The model constructed by COL11A1 and CDC20 genes can predict the prognosis risk of GC patients. Taken together, these findings provide reference for further analyses of key alterations during GC progression.

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Identification of hub genes in gastric cancer by integrated bioinformatics analysis

Translational Cancer Research ◽

10.21037/tcr-20-3540 ◽

2021 ◽

Vol 0 (0) ◽

pp. 0-0

Author(s):

Feng Sun ◽

Chen Zhang ◽

Shichao Ai ◽

Zhijian Liu ◽

Xiaofeng Lu

Keyword(s):

Gastric Cancer ◽

Bioinformatics Analysis ◽

Hub Genes

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Identification of hub genes and discovery of promising compounds in gastric cancer based on bioinformatics analysis

Biomarkers in Medicine ◽

10.2217/bmm-2019-0608 ◽

2020 ◽

Vol 14 (12) ◽

pp. 1069-1084

Author(s):

Jiani Huang ◽

Fang Wen ◽

Wenjie Huang ◽

Yingfeng Bai ◽

Xiaona Lu ◽

...

Keyword(s):

Gastric Cancer ◽

Bioinformatics Analysis ◽

Gastric Carcinogenesis ◽

Gene Expression Omnibus ◽

Overall Survival Rate ◽

Comprehensive Understanding ◽

Hub Genes ◽

Poor Overall Survival ◽

Microarray Datasets ◽

Molecular Compounds

Aim: To explore the mechanism of gastric carcinogenesis by mining potential hub genes and to search for promising small-molecular compounds for gastric cancer (GC). Materials & methods: The microarray datasets were downloaded from Gene Expression Omnibus database and the genes and compounds were analyzed by bioinformatics-related tools and software. Results: Six hub genes ( MKI67, PLK1, COL1A1, TPX2, COL1A2 and SPP1) related to the prognosis of GC were confirmed to be upregulated in GC and their high expression was correlated with poor overall survival rate in GC patients. In addition, eight candidate compounds with potential anti-GC activity were identified, among which resveratrol was closely correlated with six hub genes. Conclusion: Six hub genes identified in the present study may contribute to a more comprehensive understanding of the mechanism of gastric carcinogenesis and the predicted potential of resveratrol may provide valuable clues for the future development of targeted anti-GC inhibitors.

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Bioinformatics Analysis and Identification of Potential Biomarkers in Gastric Cancer

10.21203/rs.3.rs-111288/v1 ◽

2020 ◽

Author(s):

Jingdi Yang ◽

Bo Peng ◽

Xianzheng Qin ◽

Tian Zhou

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Differentially Expressed Genes ◽

Bioinformatics Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Hub Genes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

Abstract Background: Although the morbidity and mortality of gastric cancer are declining, gastric cancer is still one of the most common causes of death. Early detection of gastric cancer is of great help to improve the survival rate, but the existing biomarkers are not sensitive to diagnose early gastric cancer. The aim of this study is to identify the novel biomarkers for gastric cancer.Methods: Three gene expression profiles (GSE27342, GSE63089, GSE33335) were downloaded from Gene Expression Omnibus database to select differentially expressed genes. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to explore the biological functions of differentially expressed genes. Cytoscape was utilized to construct protein-protein interaction network and hub genes were analyzed by plugin cytoHubba of Cytoscape. Furthermore, Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter were used to verify the identified hub genes.Results: 35 overlapping differentially expressed genes were screened from gene expression datasets, which consisted of 11 up-regulated genes and 24 down-regulated genes. Gene Ontology functional enrichment analysis revealed that differentially expressed genes were significantly enriched in digestion, regulation of biological quality, response to hormone and steroid hormone, and homeostatic process. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed differentially expressed genes were enriched in the secretion of gastric acid and collecting duct acid, leukocyte transendothelial migration and ECM-receptor interaction. According to protein-protein interaction network, 10 hub genes were identified by Maximal Clique Centrality method.Conclusion: By using bioinformatics analysis, COL1A1, BGN, THY1, TFF2 and SST were identified as the potential biomarkers for early detection of gastric cancer.

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Bioinformatics Analysis Identifies CPZ as a Tumor Immunology Biomarker For Gastric Cancer

Current Bioinformatics ◽

10.2174/1574893615999200707145643 ◽

2020 ◽

Vol 15 ◽

Author(s):

Yuan Gu ◽

Ying Gao ◽

Xiaodan Tang ◽

Huizhong Xia ◽

Kunhe Shi

Keyword(s):

Gastric Cancer ◽

T Cells ◽

Signaling Pathway ◽

Tumor Immunology ◽

Bioinformatics Analysis ◽

Human Cancer ◽

Disease Free Survival ◽

Kaplan Meier ◽

Potential Biomarker ◽

Pathway Regulation

Background: Gastric cancer (GC) is one of the most common malignancies worldwide. However, the biomarkers for the prognosis and diagnosis of Gastric cancer were still need. Objective: The present study aimed to evaluate whether CPZ could be a potential biomarker for GC. Method: Kaplan-Meier plotter (http://kmplot.com/analysis/) was used to determine the correlation between CPZ expression and overall survival (OS) and disease-free survival (DFS) time in GC [9]. We analyzed CPZ expression in different types of cancer and the correlation of CPZ expression with the abundance of immune infiltrates, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells, via gene modules using TIMER Database. Results: The present study identified that CPZ was overexpressed in multiple types of human cancer, including Gastric cancer. We found that overexpression of CPZ correlates to the poor prognosis of patients with STAD. Furthermore, our analyses show that immune infiltration levels and diverse immune marker sets are correlated with levels of CPZ expression in STAD. Bioinformatics analysis revealed that CPZ was involved in regulating multiple pathways, including PI3K-Akt signaling pathway, cGMP-PKG signaling pathway, Rap1 signaling pathway, TGF-beta signaling pathway, regulation of cell adhesion, extracellular matrix organization, collagen fibril organization, collagen catabolic process. Conclusion: This study for the first time provides useful information to understand the potential roles of CPZ in tumor immunology and validate it to be a potential biomarker for GC.

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The meta and bioinformatics analysis of GRP78 expression in gastric cancer

Oncotarget ◽

10.18632/oncotarget.20318 ◽

2017 ◽

Vol 8 (42) ◽

pp. 73017-73028 ◽

Cited By ~ 2

Author(s):

Hua-Chuan Zheng ◽

Bao-Cheng Gong ◽

Shuang Zhao

Keyword(s):

Gastric Cancer ◽

Bioinformatics Analysis

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Identification of chemoresistance-associated microRNAs and hub genes in breast cancer using bioinformatics analysis

Investigational New Drugs ◽

10.1007/s10637-020-01059-1 ◽

2021 ◽

Author(s):

Ming Wu ◽

Yujie Zhao ◽

Nanxi Peng ◽

Zuo Tao ◽

Bo Chen

Keyword(s):

Breast Cancer ◽

Bioinformatics Analysis ◽

Hub Genes

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Bioinformatics analysis of the prognosis and biological significance of VCAN in gastric cancer

Immunity Inflammation and Disease ◽

10.1002/iid3.414 ◽

2021 ◽

Author(s):

Xiao‐yan Huang ◽

Jin‐jian Liu ◽

Xiong Liu ◽

Yao‐hui Wang ◽

Wei Xiang

Keyword(s):

Gastric Cancer ◽

Bioinformatics Analysis ◽

Biological Significance

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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

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