ArticleGenes2023
Network-Based and Machine-Learning Approaches Identify Diagnostic and Prognostic Models for EMT-Type Gastric Tumors.
Article in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- GLI3 in cancer: a context-dependent regulator with diagnostic, prognostic, and therapeutic potential.Frontiers in immunology · 2026Review
- Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma.NPJ systems biology and applications · 2025Article
- Regulator of cullins-1 predicts a poor prognosis and regulates epithelial-mesenchymal transition process through GSK-3β/Wnt signaling in renal cell carcinoma.Translational andrology and urology · 2025Article
- Potential molecular metabolic mechanisms underlying the effects of cimifugin in gastric cancer through single-cell and bulk RNA sequencing combined with network pharmacology.Journal of gastrointestinal oncology · 2024Article
- Comprehensive analysis of PDE2A: a novel biomarker for prognostic value and immunotherapeutic potential in human cancers.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2024Article
- AI-Powered Insights into Drug Resistance in Gastric Cancer: A Path Toward Precision Therapy.Iranian journal of pharmaceutical research : IJPRReview
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The microsatellite stable/epithelial-mesenchymal transition (MSS/EMT) subtype of gastric cancer represents a highly aggressive class of tumors associated with low rates of survival and considerably high probabilities of recurrence. In the era of precision medicine, the accurate and prompt diagnosis of tumors of this subtype is of vital importance. In this study, we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify a differentially expressed co-expression module of mRNAs in EMT-type gastric tumors. Using network analysis and linear discriminant analysis, we identified mRNA motifs and microRNA-based models with strong prognostic and diagnostic relevance: three models comprised of (i) the microRNAs miR-199a-5p and miR-141-3p, (ii) EVC/EVC2/GLI3, and (iii) PDE2A/GUCY1A1/GUCY1B1 gene expression profiles distinguish EMT-type tumors from other gastric tumors with high accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.995, AUC = 0.9742, and AUC = 0.9717; respectively). Additionally, the DMD/ITGA1/CAV1 motif was identified as the top motif with consistent relevance to prognosis (hazard ratio > 3). Molecular functions of the members of the identified models highlight the central roles of MAPK, Hh, and cGMP/cAMP signaling in the pathology of the EMT subtype of gastric cancer and underscore their potential utility in precision therapeutic approaches.
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