Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.

ABSTRACT: To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular typing and prognosis prediction in lung adenocarcinoma (LUAD). LRG mRNA and long non-coding RNA transcriptomes, epigenetic methylation data, and somatic mutation data from The Cancer Genome Atlas LUAD cohort were analyzed to identify lactylation cancer subtypes (CSs) using 10 multiomics ensemble clustering techniques. The findings were then validated using the GSE31210 and GSE13213 LUAD cohorts. A prognosis model for LUAD was developed using the identified hub LRGs to divide patients into high- and low-risk groups. The effectiveness of this model was validated. We identified two lactylation CSs, which were validated in the GSE31210 and GSE13213 LUAD cohorts. Nine hub LRGs, namely HNRNPC, PPIA

SUBMITTER: Hua M 

PROVIDER: S-EPMC11760357 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature