Machine learning based immune evasion signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

ABSTRACT:

Background

Stomach adenocarcinoma (STAD) remains a major contributor to cancer-related mortality worldwide. Despite advances in immunotherapy, only a subset of STAD patients benefits from immune checkpoint inhibitors, largely due to tumor-intrinsic immune evasion mechanisms. Therefore, robust predictive biomarkers are urgently needed to guide prognosis assessment and therapeutic decision-making.

Methods

An integrative machine learning framework incorporating 10 algorithms was applied to construct an immune evasion signature (IES) using 101 model combinations. The optimal model was selected based on concordance index (C-index) across validation datasets. The prognostic and immunological relevance of the IES was assessed via survival analyses, immune infiltration deconvolution

SUBMITTER: Xue W 

PROVIDER: S-EPMC12507746 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature