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Six Metabolism Related mRNAs Predict the Prognosis of Patients With Hepatocellular Carcinoma.

ABSTRACT: Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive solid malignant tumors and current research regards HCC as a type 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. A high-risk group and low-risk group were separated for further study depending on their median risk score. 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 there were randomly divided into the training cohort (n = 186) and the validation cohort (n = 187). In the training cohort, six metabolism-related mRNAs were used to construct a prognostic model with a LASSO regression model. Based on the risk model, the overall survival rate of the high-risk cohort was significantly lower than that of the low-risk cohort. The results of a time-ROC curve proved that the risk score (AUC = 0.849) had a higher prognostic value than the pathological grade, clinical stage, age or gender. Conclusion: The model constructed by the six metabolism-related mRNAs has a significant value for survival prediction and can be applied to guide the evaluation of HCC and the designation of clinical therapy.

SUBMITTER: Wu X

PROVIDER: S-EPMC8045485 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Globally, hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors. Studies have shown that alterations in the tumor immune microenvironment (TIME) play a significant role in the pathogenesis and progression of HCC, and notably, lipid metabolism has been shown to regulate TIME. Therefore, in predicting the prognosis and efficacy of immunotherapy in patients with HCC, lipid metabolism-related prognostic factors are highly relevant. mRNA expression data of HCC were obtained from the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and gene expression omnibus (GEO) databases. and lipid metabolism-related genes were also obtained from the GSEA databases. Least absolute shrinkage and selection operator regression analysis, univariate and multivariate Cox proportional hazards analysis were used to explore lipid metabolism-related prognostic genes and further construct a prognostic signature in the training set, ICGC and GSE54236 were used to validate the accuracy of the signature. qRT-PCR was used to detect the mRNA levels of lipid metabolism-related prognostic genes in HCC tissues and their paired adjacent tissues. Nile red staining was used to demonstrate lipid content in HCC tissues. Immunofluores-cence and ELISA were used to detect immune cells and immune responses in HCC tissues and serum. Six lipid metabolism-related genes (ADH1C, APEX1, ME1, S100A10, ACACA and CYP2C9) were identified as independent prognostic factors, which were used for risk model construction for HCC patients. The areas under the 1-, 2-, and 3-year ROC curves for the TCGA cohort were 0.758, 0.701 and 0.671, respectively. Compared with paired paracancerous tissues, qRT-PCR revealed that APEX1, ME1, S100A10 and ACACA were up-regulated in HCC tissues, whereas ADH1C and CYP2C9 were down-regulated in HCC tissues. Nile red staining indicated that this study showed that both the HCC tissue and serum of patients in the high-risk group exhibited lipid accumulation. Our identified prognostic model comprising six lipid metabolism-related genes could provide survival prediction. Moreover, HCC drug therapy target selection and molecular marker research can be guided by our predictive model.

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most serious malignant tumors threatening human life with a high mortality rate. The liver regenerative capacity after hepatectomy in early-stage HCC patients is influenced by various factors, including surgical methods and energy metabolism. This study aims to provide a prognostic model based on genes related to liver regeneration that can predict the prognosis of non-tumor tissues in HCC patients.Patients and methodsA total of 584 non-tumor tissues from HCC patients were collected from three independent databases. Kaplan-Meier survival curves were used to identify prognostic liver-regeneration genes. Subsequently, a prognostic indicator, designated as the Liver Regeneration score (LR score), was determined using single-sample gene set enrichment analysis (ssGSEA). Independent cohorts were used to verify the relationship between LR score and prognosis in non-tumor tissues of HCC patients. Furthermore, a liver regeneration-related model was established to validate key genes identified through LASSO Cox regression analysis.ResultsWe constructed a gene set comprising 24 liver regeneration-related genes, and the LR score was utilized to predict the prognosis of HCC patients based on its levels in non-tumor tissues. In non-tumor tissues of HCC patients, higher LR scores were associated with improved prognosis. Higher LR scores in non-tumor tissues indicate improved liver metabolism in HCC patients, revealed by Enrichment analysis. LASSO Cox regression analysis identified two key genes, DHTKD1 (dehydrogenase E1 and transketolase domain containing 1) and PHYH (phytanoyl-CoA 2-hydroxylase), and higher expression levels of these genes in non-tumor tissues were correlated with better prognosis. The expression levels of these two genes also changed corresponding to the progression of liver regeneration.ConclusionIn summary, our study has introduced a novel LR gene signature for HCC patients, providing a predictive model for estimating clinical prognosis from non-tumor tissues. The LR score demonstrates promise as a reliable indicator for predicting overall survival in HCC.

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Six Metabolism Related mRNAs Predict the Prognosis of Patients With Hepatocellular Carcinoma.

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