An Oxidative Stress Response Gene Model for the Prediction of Prognosis and Therapeutic Responses in Hepatocellular Carcinoma

BackgroundOxidative stress response genes are critical for the development and progression of hepatocellular carcinoma (HCC). Still, the predictive value for prognosis and treatment response of oxidative stress response genes needs further elucidation. MethodsWe obtained the transcriptomic data and corresponding clinicopathological information of HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Oxidative stress response genes (OSRGs) were retrieved from the MSigDB database. LASSO Cox regression analysis was utilized to establish an integrated multi-gene signature in the TCGA cohort, and its prediction performance was validated in the ICGC cohort. The risk score of each patient ware determined by the multi-gene signature. The CIBERSORT algorithm was employed to evaluate the immune cell infiltration. Response rate to immune checkpoint inhibition (ICI) therapy was assessed using a TIDE platform. Tumor mutation burden was estimated using VarScan processed somatic mutation data. The drug activity data from the Cancer Genome Project and NCI-60 human cancer cell lines were used to predict sensitivity to chemotherapy. ResultsThe gene signature comprises G6PD, MT3, CBX2, CDKN2B, CCNA2, MAPT, EZH2, and SLC7A11. Patients with high risk scores had shorter overall survival. The risk score was identified as an independent prognostic marker. The immune cell infiltration patterns, response rates to immune checkpoint inhibition (ICI) therapy, and the estimated sensitivity of 89 chemotherapeutic drugs were associated with risk scores. Individual prognostic gene was also associated with the susceptibility of various FDA-approved drugs. ConclusionOur study indicates that an integrated transcriptomic analysis may provide a reliable molecular model that better predicts diagnosis and forecasts the response of ICI therapy and chemotherapy.