Abstract
Hepatocellular carcinoma (HCC) ranks among the most lethal malignancies around the world. However, the current management strategies for predicting prognosis in HCC patients remain unreliable. Our study developed a robust prognostic model based on glutamine metabolism associated-genes (GMAGs), utilizing data from The Cancer Genome Atlas database. The prognostic values of model were validated through the databases of the Gene Expression Omnibus and International Cancer Genome Consortium via Kaplan‒Meier curves and receiver operating characteristic (ROC). The potential biological pathways associated with prognostic risk were investigated through different enrichment analysis, and Gene variation analysis. The correlation between prognostic model and therapeutic responses were analyzed. Quantitative real-time PCR (qRT-PCR) and cellular experiments were measured to analyze the GMAGs. Consequently, a prognostic model was constructed of 4 GMAGs (RRM1, RRM2, G6PD, and GPX7) through least absolute shrinkage and selection operator (LASSO) regression analysis. The Kaplan‒Meier curves and ROC curves showed a reliable predictive capacity of prognosis for HCC patients (p < 0.05). The enrichment analyses revealed a multitude of biological pathways that are significantly associated with cancer. Patients with high prognostic risk might be sensitive to immunotherapy (p < 0.05). The results of qRT-PCR revealed that all 4 GMAGs exhibited significantly higher expression levels in HCC samples compared to normal samples (p < 0.05). Moreover, the knockdown of RRM1 suppresses the progression of HCC cells. In this study, we developed a robust prognostic model for predicting the prognosis of HCC patients based on GMAGs, and identified RRM1 as a potential therapeutic target for HCC.