A Multi-Gene Myofibroblast Framework Stratifies Prognostic and Immune Landscapes in HCC

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Myofibroblasts are crucial in hepatocellular carcinoma (HCC) progression.
• Understanding their role is key to developing new therapies for primary liver cancer.

Purpose of the Study:

• To identify core genes and intercellular communication networks involving myofibroblasts in HCC.
• To develop a predictive model for HCC using myofibroblast-associated genes.

Main Methods:

• Analysis of bulk and single-cell RNA sequencing (scRNA-seq) data from HCC.
• Characterization of myofibroblast communication networks using CellChat.
• Identification of hub genes via weighted gene co-expression network analysis (hdWGCNA).
• Machine learning for key gene selection and nomogram construction.

Main Results:

• Six HCC cell subpopulations identified, with significant myofibroblast-hepatocyte crosstalk.
• Four key genes (PLVAP, RBP7, NDUFA4L2, TPM2) identified as crucial myofibroblast-associated genes in HCC.
• A nomogram integrating imaging features and PLVAP showed strong predictive ability.
• Gene expression correlated with immune cell infiltration, including regulatory T cells and macrophages.

Conclusions:

• Myofibroblasts engage in extensive crosstalk within the HCC microenvironment.
• Myofibroblast-related biomarkers hold promise for HCC therapeutic strategies.
• Hub genes identified offer potential targets for HCC treatment and diagnosis.