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A novel machine learning-based predictive model for gastric cancer.

Jianxu Yuan1, Dalin Zhou2, Shengjie Yu2

  • 1Department of Surgery, Xinqiao Hospital of Army Medical University, Army Medical University, Chongqing, China.

Translational Cancer Research
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

This study identified four key genes (INHBA, CLDN1, LY6E, SERPINE1) as potential biomarkers for gastric cancer (GC) using machine learning. These findings offer a theoretical basis for improved GC prevention and treatment strategies.

Keywords:
Gastric cancer (GC)SHapley Additive exPlanation (SHAP)least absolute shrinkage and selection operator regression (LASSO regression)random forest (RF)support vector machine (SVM)

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Gastric cancer (GC) is a significant global health challenge.
  • Effective biomarkers are crucial for early diagnosis and predicting GC progression.
  • Identifying core genes is essential for understanding GC pathogenesis.

Purpose of the Study:

  • To identify key genes associated with gastric cancer.
  • To develop predictive models for gastric cancer using machine learning.
  • To explore the molecular and immune mechanisms underlying gastric cancer.

Main Methods:

  • Integrated Gene Expression Omnibus (GEO) data.
  • Performed differential expression and enrichment analyses.
  • Utilized machine learning algorithms (LASSO, SVM, RF) for model construction.
  • Applied SHapley Additive exPlanations (SHAP) for gene contribution analysis.
  • Conducted gene set enrichment analysis (GSEA) and immune cell infiltration analysis.

Main Results:

  • Identified 130 differentially expressed genes (DEGs) in gastric cancer.
  • Pinpointed four core genes (INHBA, CLDN1, LY6E, SERPINE1) associated with GC.
  • The Random Forest (RF) model showed superior predictive accuracy.
  • SHAP analysis elucidated the contribution of the identified core genes.
  • GSEA and immune cell infiltration analysis revealed distinct molecular and immune profiles between GC and normal tissues.

Conclusions:

  • Identified potential novel biomarkers for gastric cancer diagnosis and prognosis.
  • The study provides a theoretical foundation for developing new gastric cancer prevention and treatment strategies.
  • Highlights the utility of machine learning in identifying cancer-associated genes.