ArticleFrontiers in oncology2021
Screening and Identification of Key Biomarkers of Gastric Cancer: Three Genes Jointly Predict Gastric Cancer.
Article in Frontiers in oncology, 2021. 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, 11 citations in OpenAlex.
- Feasibility of isosulfan blue in sentinel lymph node visualization for gastric cancer.Annals of medicine and surgery (2012) · 2025Article
- Correlation between dyslipidaemia and gastric cancer: pathogenesis to prevention and treatment strategies.Lipids in health and disease · 2025Review
- Article
- AGT May Serve as a Prognostic Biomarker and Correlated with Immune Infiltration in Gastric Cancer.International journal of general medicine · 2022Article
- Anti-POSTN and Anti-TIMP1 Autoantibodies as Diagnostic Markers in Esophageal Squamous Cell Carcinoma.Frontiers in genetics · 2022Article
- Overexpression of NREP Promotes Migration and Invasion in Gastric Cancer Through Facilitating Epithelial-Mesenchymal Transition.Frontiers in cell and developmental biology · 2021Article
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGastric cancer (GC) is one of the most common cancers all over the world, causing high mortality. Gastric cancer screening is one of the effective strategies used to reduce mortality. We expect that good biomarkers can be discovered to diagnose and treat gastric cancer as early as possible.
methodsWe download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between gastric cancer and adjacent normal tissues were detected to explore biomarkers that may play an important role in gastric cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlap genes were conducted by the Metascape online database; the protein-protein interaction (PPI) network was constructed by the STRING online database, and we screened the hub genes of the PPI network using the Cytoscape software. The survival curve analysis was conducted by km-plotter and the stage plots of hub genes were created by the GEPIA online database. PCR, WB, and immunohistochemistry were used to verify the expression of hub genes. A neural network model was established to quantify the predictors of gastric cancer.
resultsThe relative expression level of cadherin-3 (CDH3), lymphoid enhancer-binding factor 1 (LEF1), and matrix metallopeptidase 7 (MMP7) were significantly higher in gastric samples, compared with the normal groups (p<0.05). Receiver operator characteristic (ROC) curves were constructed to determine the effect of the three genes' expression on gastric cancer, and the AUC was used to determine the degree of confidence: CDH3 (AUC = 0.800, P<0.05, 95% CI =0.857-0.895), LEF1 (AUC=0.620, P<0.05, 95%CI=0.632-0.714), and MMP7 (AUC=0.914, P<0.05, 95%CI=0.714-0.947). The high-risk warning indicator of gastric cancer contained 8<CDH3<15 and 10<expression of LEF1<16.
conclusionsCDH3, LEF1, and MMP7 can be used as candidate biomarkers to construct a neural network model from hub genes, which may be helpful for the early diagnosis of gastric cancer.
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