Multi-omics integration and machine learning define robust molecular subtypes and prognostic signatures in hepatocellular carcinoma.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) exhibits high aggressiveness and substantial molecular heterogeneity, yet precise and individualized therapeutic strategies remain limited. Comprehensive multi-omics integration and machine learning-driven modeling hold promise for refining molecular subtyping and improving prognostic prediction in HCC.

Methods

We developed a computational framework integrating multi-omics datasets from HCC patients. Ten clustering algorithms were combined to perform integrative multi-omics clustering and identify molecular subtypes. Ten machine learning algorithms were subsequently applied to construct a consensus prognostic signature. The clinical relevance of subtypes and risk groups was assessed through survival analysis, immunotherapy response p

SUBMITTER: Wang X 

PROVIDER: S-EPMC12903227 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature