ArticleDiscover oncology2026
Identification of senescence-related genes as diagnostic biomarkers for gastric cancer using bioinformatics and machine learning.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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6 authors.
Funding
Abstract
Gastric cancer (GC) presents a significant global health challenge with a poor prognosis due to late detection. This study combines single-cell RNA sequencing and bulk transcriptomics to identify senescence-related gastric cancer genes (SGCGs) as diagnostic biomarkers for GC. Using weighted gene co-expression network analysis (WGCNA) and machine learning-based feature selection, we identified 20 core SAGs enriched in mitochondrial and cell cycle pathways. An RF-XGBoost ensemble model achieved high predictive accuracy (ROC = 0.841), with PNPT1 emerging as a key driver through SHAP analysis. Experimental validation confirmed overexpression of PNPT1 in GC cells, with its expression correlating with age-related progression. A web-based Shiny app was developed to support clinical risk stratification. These findings highlight the importance of SGCGs in GC development and offer a translational tool for early detection and personalized treatment.
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