Screening of Energy Metabolism Genes Related to the Prognosis of Hepatocellular Carcinoma and Construction of Prognosis Prediction Model

Abstract

Abstract: Objective To construct a prognostic prediction model for hepatocellular carcinoma (HCC) based on energy metabolism-related genes and evaluate its predictive ability for HCC patient outcomes. Methods We obtained expression data and clinical information for HCC and normal liver tissues from the TCGA database. Prognosis-related differentially expressed genes were identified using the R limma package and univariate analysis. These data were used as the training set to construct a LASSO Cox regression prognostic model, and the ICGC database was used for model validation. GO and KEGG methods were applied to analyze differentially expressed genes in the high- and low-risk groups. We assessed the predictive ability of the model using Receiver Operating Characteristic (ROC) curves, survival analysis, and multivariate Cox regression. The ssGSEA method was used to perform risk analysis on differential genes between the two groups. Results Analysis of the TCGA database identified five risk genes (IVD, CYB5R3, LDHA, NQO1, and UGDH) for constructing the prognostic model. The risk rating formula was: -0.021IVD + 0.005CYB5R3 + 0.005LDHA + 0.001NQO1 + 0.002*UGDH. ROC curves for 1-, 2-, and 3-year survival rates in both TCGA and ICGC databases had areas under the curve (AUC) exceeding 0.65. Survival analysis indicated that the low-risk group had significantly better outcomes than the high-risk group. Risk scores were confirmed as independent prognostic indicators in both univariate and multivariate Cox analyses. Differential genes between high- and low-risk groups were mainly involved in retinal metabolism and cytochrome P450 pathways. Additionally, there is a statistically significant difference in immune function between the two groups（P<0.05). Conclusion The HCC prognostic model based on energy metabolism genes demonstrates good predictive performance. These energy metabolism genes provide new targets for HCC targeted therapy.

Key words: hepatocellular carcinoma, energy metabolism genes, prognostic model, risk score

CLC Number:

R735.7

WU Yi-jiang, PENG Liang, XIE Bin-hui. Screening of Energy Metabolism Genes Related to the Prognosis of Hepatocellular Carcinoma and Construction of Prognosis Prediction Model[J]. ZHONGHUA YANGSHENG BAOJIAN, 2024, 42(17): 1-4.

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