Identification of a Novel PPAR Signature for Predicting Prognosis and Immune Microenvironment in Hepatocellular Carcinoma

Abstract Background Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver, which the 5-year survival rate has not substantially improved in the past few decades. We aimed to explore the potential role of peroxisome proliferator activated receptors (PPARs) in HCC progression and diagnosis. Methods The clinical information and corresponding gene expression matrix of HCC patients were obtained from The Cancer Genome Atlas and International Cancer Genome Consortium databases (ICGC). Consensus cluster analysis was used to identify novel molecular subgroups. ESTMATE, Microenvironment Cell Populations-counter and single sample Gene Set Enrichment Analysis were used to determine the tumor immune microenvironment (TIME) and immune status of the identified molecular subtypes. Functional analyses were used to elucidate the underlying mechanisms, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Gene Set Enrichment Analysis and Gene Set Variation Analysis. The prognostic risk model was constructed by LASSO analysis and multivariate Cox regression analysis, and validated by ICGC datasets. A nomogram was constructed to predict the prognosis of HCC patients by integrating risk scores and clinical characteristics. Results Significant differences in survival for the identified two molecular subgroups. The subgroup with poorer prognosis was associated with higher immune score, higher abundance of immune cell infiltration and correspondingly higher immune status. DEGs between the two molecular subgroups were mainly enriched in the metabolic- and PPAR-pathways. The abnormal expression pattern of PPAR genes (PPARGs) may alter the metabolic activity, affecting the function of the immune system. Moreover, the risk model based on 4 PPARGs (TTC33, TMEM135, TALDO1, and TXNIP) exhibited excellent ability of predictive prognostic. Finally, A nomogram integrating risk scores and clinical features could accurately predict the prognosis of HCC patients. Conclusion We constructed and validated a novel PPAR signature associated with the TIME, which exhibited extremely excellent performance in predicting the prognosis of HCC patients.