Integrative Analysis of PPAR and Immune Pathways in Hepatocellular Carcinoma: Constructing a Prognostic Risk Model Using TCGA Data

Abstract

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, with its pathogenesis intricately linked to metabolic and immune dysregulation. This study aims to elucidate the molecular mechanisms underpinning HCC by analyzing metabolic and immune-related pathways and constructing a prognostic risk model.

Methods: We utilized data from The Cancer Genome Atlas (TCGA) to analyze genomic and clinical characteristics of HCC. Techniques such as single-sample gene set enrichment analysis (ssGSEA), weighted gene coexpression network analysis (WGCNA), and gene set variation analysis (GSVA) were employed to explore the interplay between metabolic pathways, immune responses, and HCC progression. In addition, a prognostic risk model was developed using univariate Cox regression and LASSO regression analysis based on PPAR signaling and immune-related genes.

Results: Our ssGSEA results indicated a significant involvement of metabolism-related pathways in HCC. The WGCNA identified key immune-related genes, with particular modules correlating with macrophage activity. The prognostic model, comprising five key genes, effectively stratified patients into low- and high-risk groups, with implications for overall survival (OS). Further analyses revealed the model’s correlation with clinical characteristics and immune-related indexes, suggesting its utility in predicting HCC progression.

Conclusion: This study provides a comprehensive molecular portrait of HCC, emphasizing the role of metabolic reprogramming and immune responses. The prognostic model offers potential for personalized therapeutic strategies and improved clinical outcomes. Future research should focus on validating these findings and exploring the therapeutic potential of targeting metabolic and immune pathways in HCC.