Signature of immune-related metabolic genes predicts the prognosis of hepatocellular carcinoma - Zhuo, Weibin; Xia, Hongmei; Lan, Bin; Chen, Yu; Wang, Xuefeng; Liu, Jingfeng (Frontiers in Immunology, 2024)
IntroductionThe majority of liver cancer cases (90%) are attributed to hepatocellular carcinoma (HCC), which exhibits significant heterogeneity and an unfavorable prognosis. Modulating the immune response and metabolic processes play a crucial role in both the prevention and treatment of HCC. However, there is still a lack of comprehensive understanding regarding the immune-related metabolic genes that can accurately reflect the prognosis of HCC.MethodsIn order to address this issue, we developed a prognostic prediction model based on immune and metabolic genes. To evaluate the accuracy of our model, we performed survival analyses including Kaplan-Meier (K-M) curve and time-dependent receiver operating characteristic (ROC) curve. Furthermore, we compared the predictive performance of our risk model with existing models. Finally, we validated the accuracy of our risk model using mouse models with in situ transplanted liver cancer.ResultsBy conducting lasso regression analysis, we identified four independent prognostic genes: fatty acid binding protein 6 (FABP6), phosphoribosyl pyrophosphate amidotransferase (PPAT), spermine synthase (SMS), and dihydrodiol dehydrogenase (DHDH). Based on these findings, we constructed a prognostic model. Survival analysis revealed that the high-risk group had significantly lower overall survival (OS) rates. Besides that, the ROC curve demonstrated the effective prognostic capability of our risk model for hepatocellular carcinoma (HCC) patients. Furthermore, through animal experiments, we validated the accuracy of our model by showing a correlation between high-risk scores and poor prognosis in tumor development.DiscussionIn conclusion, our prognostic model surpasses those solely based on immune genes or metabolic genes in terms of accuracy. We observed variations in prognosis among different risk groups, accompanied by distinct immune and metabolic characteristics. Therefore, our model provides an original evaluation index for personalized clinical treatment strategies targeting HCC patients.