Integration of gene expression data identifies key genes and pathways in colorectal cancer

被引:0
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作者
Hossein Hozhabri
Ali Lashkari
Seyed-Morteza Razavi
Ali Mohammadian
机构
[1] University of Tehran,Institute of Biochemistry and Biophysics
[2] Kharazmi University,Department of Cell and Molecular Biology, Faculty of Biological Sciences
[3] Salari Institute of Cognitive and Behavioral Disorders (SICBD),Systems Biology Research Lab, Bioinformatics Group
[4] Systems Biology of Next Generation Company (SBNGC),Department of Medical Biotechnology, Faculty of Medical Sciences
[5] Tarbiat Modares University,undefined
来源
Medical Oncology | 2021年 / 38卷
关键词
Bioinformatics analysis; Colorectal cancer; Differentially expressed gene; Microarray;
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学科分类号
摘要
Colorectal cancer (CRC) is one of the most common malignant tumor and prevalent cause of cancer-related death worldwide. In this study, we analyzed the gene expression profiles of patients with CRC with the aim of better understanding the molecular mechanism and key genes in CRC. Four gene expression profiles including, GSE9348, GSE41328, GSE41657, and GSE113513 were downloaded from GEO database. The data were processed using R programming language, in which 319 common differentially expressed genes including 94 up-regulated and 225 down-regulated were identified. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were conducted to find the most significant enriched pathways in CRC. Based on the GO and KEGG pathway analysis, the most important dysregulated pathways were regulation of cell proliferation, biocarbonate transport, Wnt, and IL-17 signaling pathways, and nitrogen metabolism. The protein–protein interaction (PPI) network of the DEGs was constructed using Cytoscape software and hub genes including MYC, CXCL1, CD44, MMP1, and CXCL12 were identified as the most critical hub genes. The present study enhances our understanding of the molecular mechanisms of the CRC, which might potentially be applied in the treatment strategies of CRC as molecular targets and diagnostic biomarkers.
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