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April 16, 2026Discover Oncology0 citationsOpen Access

Identifying Senescence-Related Genes as Biomarkers for Gastric Cancer

Identification of senescence-related genes as diagnostic biomarkers for gastric cancer using bioinformatics and machine learning

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Authors

XLXiaobo LiZPZhe PiaoMLMengyue Lei

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Overview

Bioinformatics and machine learning reveal senescence-related biomarkers in gastric cancer, suggesting new diagnostic tools.

Key Points

  • The research aims to identify senescence-related genes as diagnostic biomarkers for gastric cancer using advanced computational methods.
  • Combined single-cell RNA sequencing and bulk transcriptomics analysis
  • Employed weighted gene co-expression network analysis (WGCNA)
  • Utilized machine learning feature selection techniques
  • Developed an RF-XGBoost ensemble model for predictive accuracy
  • Created a web-based Shiny app for clinical risk stratification
  • Identified 20 core senescence-related gastric cancer genes (SGCGs)
  • Achieved predictive accuracy of ROC = 0.841 using RF-XGBoost model
  • PNPT1 was highlighted as a key driver based on SHAP analysis
  • Experimental validation showed PNPT1 overexpression in gastric cancer cells
  • Expression of PNPT1 correlates with age-related progression of the disease

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69e07e582f7e8953b7cbf6c3https://doi.org/10.1007/s12672-026-04967-5
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