Development and Validation of a Novel Nomogram Integrated with Hypoxic and Lactate Metabolic Characteristics for Prognosis Prediction in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Accumulating evidence indicates that hypoxia and lactate metabolism play critical roles in tumor progression and therapeutic efficacy. This study aimed to construct a hypoxia- and lactate metabolism-related prognostic model (HLPM) to evaluate survival and treatment responses for HCC patients and develop a nomogram integrated with HLPM and clinical characteristics for prognosis prediction in HCC. Methods: Expression profile and clinical data of HCC were obtained from TCGA and ICGC databases. The univariate, LASSO and stepwise multivariate Cox analyses were used to identify the hypoxia- and lactate metabolism-related biomarkers, whose expression levels were then validated in 14 pairs tissue samples and single-cell RNA sequencing dataset. Kaplan-Meier survival curves were utilized to assess the prognostic values of biomarkers or models. Analyses of ImmuCellAI, TIDE and drug sensitivity were conducted to evaluate the therapeutic responses of patients. Furthermore, the nomogram integrated with hypoxic and lactate metabolic characteristics was established through univariate and multivariate Cox analyses. ROC curves, C-index, and calibration curves were depicted to evaluate the performance of the nomogram. Results: Five hypoxia- and lactate metabolism-related biomarkers (KIF20A, IRAK1, ADM, PPARGC1A and EPO) were used to construct HLPM. The expression of five prognostic biomarkers was validated in 14 pairs tissue samples and single-cell RNA sequencing dataset. Analyses of ImmuCellAI, TIDE and drug sensitivity implied that patients with low-risk score were more sensitive to immunotherapy and major chemotherapeutic agents. The nomogram that contained age, histological grade and risk score of HLPM was developed and exhibited a better capacity in prognosis prediction than HLPM only. Conclusion: A novel nomogram integrated with hypoxic and lactate metabolic characteristics was developed and validated for prognosis prediction in HCC, providing insight into personalized decision-making in clinical management.