Multi-omics and machine learning refine HCC molecular subtypes and prognosis based on liquid-liquid phase separation related genes.

ABSTRACT: Accumulating evidence has demonstrated that biological processes associated with liquid-liquid phase separation (LLPS) play a critical role in cancer development. However, the effect of LLPS on hepatocellular carcinoma (HCC) remains largely unknown. In this study, we integrated consensus clustering with an ensemble machine learning framework to establish robust LLPS-related molecular subtypes and a consensus machine learning-driven LLPS-related signature (CMLLS) for HCC. The consensus clustering robustly identified three fundamental LLPS-driven subtypes (LS1-LS3), and the subsequent machine learning integration, which encompassed 101 algorithm combinations, objectively identified the most generalizable prognostic signature from multiple candidate genes. Our analysis revealed that LS3 exhib

SUBMITTER: Li M 

PROVIDER: S-EPMC12749366 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature