ArticleBiomedical reports2020
Identification of potential key genes in gastric cancer using bioinformatics analysis.
Article in Biomedical reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 24 citations in OpenAlex.
- AI-Assisted Detection of Early Gastric Cancer via Visualization of Mucosal Acidity Compromise During Endoscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Identification of gastric cancer biomarkers throughBiochemistry and biophysics reports · 2024Article
- Insights into the Gene Expression Profile of Classical Hodgkin Lymphoma: A Study towards Discovery of Novel Therapeutic Targets.Molecules (Basel, Switzerland) · 2024Article
- Early diagnostic and prognostic biomarkers for gastric cancer: systems-level molecular basis of subsequent alterations in gastric mucosa from chronic atrophic gastritis to gastric cancer.Journal, genetic engineering & biotechnology · 2023Article
- CGB5, INHBA and TRAJ19 Hold Prognostic Potential as Immune Genes for Patients with Gastric Cancer.Digestive diseases and sciences · 2023Article
- Identification of gene profiles related to the development of oral cancer using a deep learning technique.BMC medical genomics · 2023Article
- Current Insights on the Impact of Proteomics in Respiratory Allergies.International journal of molecular sciences · 2022Review
- Collagen Family and Other Matrix Remodeling Proteins Identified by Bioinformatics Analysis as Hub Genes Involved in Gastric Cancer Progression and Prognosis.International journal of molecular sciences · 2022Article
- DNA Hypomethylation Is Associated with the Overexpression of INHBA in Upper Tract Urothelial Carcinoma.International journal of molecular sciences · 2022Article
- Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis.Frontiers in genetics · 2022Article
- Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis.Frontiers in genetics · 2022Article
- Evaluation of a five-gene signature associated with stromal infiltration for diffuse large B-cell lymphoma.World journal of clinical cases · 2021Article
- Identification of key methylation differentially expressed genes in posterior fossa ependymoma based on epigenomic and transcriptome analysis.Journal of translational medicine · 2021Article
- α-Enolase Lies Downstream of mTOR/HIF1α and Promotes Thyroid Carcinoma Progression by Regulating CST1.Frontiers in cell and developmental biology · 2021Article
- Identification of Differentially Expressed Genes Reveals BGN Predicting Overall Survival and Tumor Immune Infiltration of Gastric Cancer.Computational and mathematical methods in medicine · 2021Article
- Identification and Validation of SNP-Containing Genes With Prognostic Value in Gastric CancerFrontiers in oncology · 2021Article
- The Reprimo-Like Gene Is an Epigenetic-Mediated Tumor Suppressor and a Candidate Biomarker for the Non-Invasive Detection of Gastric Cancer.International journal of molecular sciences · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: 'extracellular matrix organization', 'collagen catabolic process' and 'cell adhesion'. These three KEGG pathways were significantly enriched in the categories: 'ECM-receptor interaction', 'protein digestion and absorption', and 'focal adhesion'. Based on Oncomine, expression of
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