ArticleJournal of biomedical research2020
Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis.
Article in Journal of biomedical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Esketamine Enhances the Chemosensitivity of Colorectal Adenocarcinoma Cells to 5-Fluorouracil via AMPK/mTOR/HMMR Signaling Pathway.Oncology research · 2026Article
- Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures.NPJ precision oncology · 2025Article
- Clinacanthus nutans (Burm.f.) Lindau facilitates cuproptosis and ameliorates colon cancer progression by inhibiting PDE3B-mediated Apelin pathway.Discover oncology · 2025Article
- Microarray analysis points to LMNB1 and JUN as potential target genes for predicting metastasis promotion by etoposide in colorectal cancer.Scientific reports · 2024Article
- Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents.BMC medical genomics · 2023Article
- Systematic Analysis of the Clinical Significance of Hyaluronan-Mediated Motility Receptor in Colorectal Cancer.Frontiers in molecular biosciences · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients. In this study, mRNA microarray datasets GSE113513, GSE21510, GSE44076, and GSE32323 were obtained from the Gene Expression Omnibus (GEO) and analyzed with bioinformatics to identify hub genes in CRC development. Differentially expressed genes (DEGs) were analyzed using the GEO2R tool. Gene ontology (GO) and KEGG analyses were performed through the DAVID database. STRING database and Cytoscape software were used to construct a protein-protein interaction (PPI) network and identify key modules and hub genes. Survival analyses of the DEGs were performed on GEPIA database. The Connectivity Map database was used to screen potential drugs. A total of 865 DEGs were identified, including 374 upregulated and 491 downregulated genes. These DEGs were mainly associated with metabolic pathways, pathways in cancer, cell cycle and so on. The PPI network was identified with 863 nodes and 5817 edges. Survival analysis revealed that
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