Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

查看全文
  • Xiaobo Li,
  • Zhenggen Piao,
  • Mengyue Lei,
  • Dongyuan Xu,
  • TouFeng Jin,
  • Lan Liu

摘要

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.