A metabolism-associated gene signature for prognosis prediction of hepatocellular carcinoma - Tian, Yilin; Lu, Jing; Qiao, Yongxia (Frontiers in Molecular Biosciences, 2022)
Hepatocellular carcinoma (HCC), the most frequently occurring type of cancer, is strongly associated with metabolic disorders. Here we aimed to characterize the metabolic features of HCC and normal tissue adjacent to the tumor (NAT). By using samples from The Cancer Genome Atlas (TCGA) liver cancer cohort and comparing 85 well-defined metabolic pathways obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG), 70 pathways were found significantly down-regulated in HCC and 7 pathways were up-regulated, revealing that tumor tissue was lack of ability to maintain normal metabolic levels. Through unsupervised hierarchical clustering of metabolic pathways, we found metabolic heterogeneity was correlated with prognosis in HCC samples. Thus, using the least absolute shrinkage and selection operator (LASSO) and filtering independent prognostic genes by Cox Proportional-Hazards model, a six-gene based metabolic score model was constructed to enable HCC classification. This model showed that high expression of LDHA and CHAC2 were associated with unfavorable prognosis, whereas high ADPGK, GOT2, MTHFS and FTCD were associated with favorable prognosis. Patients with higher metabolic-score harbored poor prognosis (P-value=2.19e-11, Hazard Ratio=3.767, 95% CI=2.555-5.555). Through associating score level with clinical features and genomic alterations, it was found that NAT had lowest metabolic-score and HCC with tumor stage III/IV harbored the highest score. Results from the experimental qRT-PCR of HCC patients also revealed tumor samples had higher score-level than NAT. For genetic alternations, patients with higher metabolic-score were accompanied by more TP53 gene mutations than that with lower metabolic-score (P-value=8.383e-05). Validation of this metabolic-score model was performed using another two independent HCC cohorts from Gene Expression Omnibus (GEO) repository and other TCGA datasets and achieved good performance, suggesting this model could be used as a reliable tool for prediction prognosis of HCC patients.