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

ABSTRACT:

Background

Gastric cancer (GC) is a prevalent malignancy worldwide, necessitating the discovery of biomarkers for early diagnosis and progression prediction. This study aimed to identify core genes associated with GC.

Methods

This study integrated data from the Gene Expression Omnibus (GEO) database, encompassing multiple datasets. Differential expression and enrichment analyses identified genes linked to GC. Using machine learning algorithms-least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), and random forest (RF)-predictive models were constructed, with the optimal one selected for further investigation. The SHapley Additive exPlanations (SHAP) method was applied to assess the contribution of core genes. Additionally, gene set

SUBMITTER: Yuan J 

PROVIDER: S-EPMC12554486 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature