Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms.

ABSTRACT:

Background

Matrix stiffness is strongly associated with hepatocarcinogenesis and significantly influences the properties of hepatocellular carcinoma (HCC). Investigating matrix stiffness-related signatures provides crucial insights into HCC prognosis and therapeutic response.

Methods

Multi-omics data from liver hepatocellular carcinoma (LIHC) were integrated using 10 clustering algorithms, identifying three subgroups with distinct survival outcomes and treatment responses. A matrix stiffness-related signature comprising 57 genes was constructed by evaluating 101 machine learning algorithm combinations. PPARG, the key gene with the greatest contribution to the model, was selected for validation. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) analyses

SUBMITTER: Li H 

PROVIDER: S-EPMC12329936 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature