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

Abstract

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. 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 assessed matrix stiffness activity scores across different cell subgroups and examined PPARG spatial localization within tissues. Experimental studies and bioinformatics analyses further explored the role of PPARG in HCC carcinogenesis and the immune microenvironment. The matrix stiffness-related signature demonstrated superior prognostic prediction performance in both training and validation cohorts compared to other existing HCC signatures. Distinct immune and mutation landscape characteristics were observed between patients categorized into high and low matrix stiffness groups. PPARG functioned in tumorigenesis through HSC activation and immune suppression. Furthermore, increased matrix stiffness was found to upregulate PPARG expression, promoting cell proliferation, activating lipid metabolism, and enhancing the stemness of HCC cells through the MAPK signaling pathway. Targeting PPARG with trametinib displayed an enhanced therapy response. The matrix stiffness-related signature not only serves as a robust prognostic tool but also aids in identifying immune characteristics and optimizing therapeutic strategies, thus advancing personalized medicine for patients with HCC. The 10 clustering algorithms based on the multi-omics data and 101 machine learning algorithm combinations were used to construct the matrix stiffness-related signature to predict the prognosis and the immune landscape of HCC. Single-cell RNA sequencing and spatial transcriptomics analyses were used to validate the activity scores of our signature and the function of the key gene PPARG. Functional experiments and the xenograft model were established to reveal the role of PPARG comprehensively. Molecular experiments proved that high matrix stiffness-induced PPARG expression promoted cell proliferation, lipogenesis, and cancer stem-like properties of HCC cells through the MAPK signaling pathway.

Topics & Concepts