ArticleMedical science monitor : international medical journal of experimental and clinical research2021
Identification of Potential Biomarkers Associated with Prognosis in Gastric Cancer via Bioinformatics Analysis.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Patient-derived cell lines unveil COL1A2 as a predictor of docetaxel resistance in breast cancer.Frontiers in oncology · 2025Article
- Definition of a Multi-Omics Signature for Esophageal Adenocarcinoma Prognosis Prediction.Cancers · 2024Article
- Article
- Identification of differentially expressed genes associated with the pathogenesis of gastric cancer by bioinformatics analysis.BMC medical genomics · 2023Article
- A Novel Gene List Identifies Tumors with a Stromal-Mesenchymal Phenotype and Worse Prognosis in Gastric Cancer.Cancers · 2023Article
- The identification of hub biomarkers and pathways in lung cancer and prognostic evaluation.Translational cancer research · 2022Article
- TWIST1-EP300 Expedites Gastric Cancer Cell Resistance to Apatinib by Activating the Expression of COL1A2.Analytical cellular pathology (Amsterdam) · 2022Article
- Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival.Frontiers in medicine · 2021Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide. We aimed to identify differentially expressed genes (DEGs) and their potential mechanisms associated with the prognosis of GC patients. MATERIAL AND METHODS This study was based on gene profiling information for 37 paired samples of GC and adjacent normal tissues from the GSE118916, GSE79973, and GSE19826 datasets in the Gene Expression Omnibus database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to investigate the biological role of the DEGs. The protein-protein interaction (PPI) network was constructed by Cytoscape, and the Kaplan-Meier plotter was used for prognostic analysis. RESULTS We identified 119 DEGs, including 21 upregulated and 98 downregulated genes, in GC. The 21 upregulated genes were mainly enriched in extracellular matrix-receptor interaction, focal adhesion, and transforming growth factor-ß signaling, while the 98 downregulated genes were significantly associated with gastric acid secretion, retinol metabolism, and metabolism of xenobiotics by cytochrome P450. Thirty hub DEGs were obtained for further analysis. Twenty-five of the 30 hub DEGs were significantly associated with the prognosis of GC, and 21 of the 25 hub DEGs showed consistent expression trends within the 3 profile datasets. KEGG reanalysis of these 21 hub DEGs showed that COL1A1, COL1A2, COL2A1, COL11A1, THBS2, and SPP1 were mainly enriched in the extracellular matrix-receptor interaction pathways. CONCLUSIONS We identified 6 genes that were significantly related to the prognosis of GC patients. These genes and pathways could serve as potential prognostic markers and be used to develop treatments for GC patients.
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