Development and validation of robust metabolism‐related gene signature in the prognostic prediction of hepatocellular carcinoma

Abstract

AbstractHepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism‐related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism‐related genes associated with prognosis, a risk score based on metabolism‐related genes was formulated via the LASSO‐Cox and LASSO‐pcvl algorithms. According to the risk score, patients were stratified into low‐ and high‐risk groups, and further analysis and validation were accordingly conducted. The results revealed that high‐risk patients had a significantly worse 5‐year overall survival (OS) than low‐risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302–0.651; p < 0.001) This observation was confirmed in the external TCGA‐LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299–0.681; p < 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism‐based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.