Prognostic and immune predictive roles of a novel tricarboxylic acid cycle-based model in hepatocellular carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer. Since the tricarboxylic acid cycle is widely involved in tumor metabolic reprogramming and cuproptosis, investigating related genes may help to identify prognostic signature of patients with HCC. Data on patients with HCC were sourced from public datasets, and were divided into train, test, and single-cell cohorts. A variety of machine learning algorithms were used to identify different molecular subtypes and determine the prognostic risk model. Our findings revealed that the risk score (TRscore), based on the genes OGDHL, CFHR4, and SPP1, showed excellent predictive performance in different datasets. Pathways related to cell cycle and immune inflammation were enriched in the high-risk group, whereas metabolism-rela

SUBMITTER: Zeng Y 

PROVIDER: S-EPMC10822853 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature