Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms.

ABSTRACT:

Background

Gastric cancer (GC) is considered a highly heterogeneous disease, and currently, a comprehensive approach encompassing molecular data from various biological levels is lacking.

Methods

This study conducted different analyses, including the identification of differentially expressed genes (DEGs), weighted correlation networks (WGCNA), single-cell RNA sequencing (scRNA-seq), mRNA expression-based stemness index (mRNAsi), and multiCox analysis, utilizing data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, the machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), combined with multiCox analysis were exploited to identify hub genes. These findings

SUBMITTER: Liu Y 

PROVIDER: S-EPMC11907202 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature