Six Metabolism Related mRNAs Predict the Prognosis of Patients With Hepatocellular Carcinoma - Wu, Xiwen; Lan, Tian; Li, Muqi; Liu, Junfeng; Wu, Xukun; Shen, Shunli; Chen, Wei; Peng, Baogang (Frontiers in Molecular Biosciences, 2021)
Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive solid malignant tumors and current research regards HCC as a kind of metabolic disease. This study aims to establish a metabolism-related mRNA signature model for risk assessment and prognosis prediction in HCC patients. Methods: HCC data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) was used to screen out the candidate mRNAs and calculate the risk coefficient to establish the prognosis model. The high-risk group and low-risk group were separated depending on the median risk score for further study. The reliability of the prediction was evaluated in the validation cohort and the whole cohort. Results: A total of 548 differential mRNAs were identified from HCC samples (n=374) and normal controls (n=50), 45 of which were correlated with prognosis. A total of 373 samples met the screening criteria and were randomly divided into the training cohort(n=186) and the validation cohort(n=187). In the training cohort, 6 metabolism-related mRNAs were used to construct prognostic model by LASSO regression model. According to the risk model, the overall survival rate of high-risk cohort was significantly lower than that of the low-risk cohort. The results of time‐ROC curve proved that the risk score (AUC=0.849) had the higher prognostic value than the pathological grade, clinical stage, age and gender. Conclusions: The model constructed by the 6 metabolism-related mRNAs have a significant value in survival prediction, and can be applied to guide the evaluation of HCC and the designation of clinical therapy. Key words: Metabolism, hepatocellular carcinoma, The Cancer Genome Atlas, mRNAs, prognostic model.