Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

ABSTRACT:

Background

Stomach adenocarcinoma (STAD) is one of most common cancers with high invasiveness and poor prognosis. Obesity and aging are correlated with higher risk for cancer development and worse prognosis in certain types of malignancies.

Methods

An integrative approach incorporating 10 machine learning methods was employed to develop an obesity and aging-related signature (ORS) using data from the TCGA, GSE15459, GSE26253, GSE62254, and GSE84437 datasets. To assess the predictive value of ORS for immunotherapy benefits, we utilized several indicating scores and three immunotherapy datasets (GSE91061, GSE78220, and IMvigor210).

Results

The predictive model developed using the LASSO method achieved the highest average C-index and was identified as the optimal ORS. Th

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12214133 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature