Integrated bioinformatic and machine learning analysis identifies MCM7 and ADAM17 as potential biomarkers for early stage gastric cancer.

ABSTRACT:

Background

Early detection of gastric cancer is crucial for improving prognosis, yet current diagnostic biomarkers remain insufficient for identifying early gastric cancer (EGC, stage I-II). While previous studies have proposed molecular markers, few have systematically validated them across multiple cohorts, and their diagnostic accuracy and immune relevance remain unclear. This study aimed to identify and validate potential early diagnostic biomarkers for EGC using an integrated bioinformatic and machine learning framework.

Methods

The transcriptome data from four Gene Expression Omnibus (GEO) datasets comprising 434 tumor and 100 normal samples were integrated. Only stage I-II gastric cancer samples, defined by pathological criteria according to the American Joint Committ

SUBMITTER: Li F 

PROVIDER: S-EPMC12598333 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature