Heliyon (Nov 2024)
Establishment of prognosis model of hepatocellular carcinoma based on prognosis related gene analysis and study on gene regulation mechanism of model
Abstract
Objective: To analyze the expression and mechanism of prognosis related differentially expressed genes (DEGs) in hepatocellular carcinoma (HCC), and establish a prognosis risk model of prognosis related DEGs. Methods: Transcriptome data and clinical information of 374 HCC samples were downloaded from TCGA data. Kaplan-Meier (KM) survival analysis screened prognostic genes in DEGs. Protein-protein interaction (PPI) network was constructed based on prognostic genes and key genes were screened. Cox regression was used to analyze the key genes and construct the prognostic risk model, calculate the risk score of each patient, divide the patients into low-risk group and high-risk group according to the median risk value, use KM analysis method for survival analysis and draw the survival curve, and use receiver operating characteristic (ROC) curve to evaluate the prognostic risk model, The relationship between risk score and clinicopathological features of HCC patients was analyzed. GEPIA and the human protein atlas (HPA) databases were used for expression verification of model genes. The mirDIP database is used to analyze the regulatory network of model genes. GSCAlite platform is used to analyze the mechanism and drug sensitivity of model genes. Results: A total of 1987 DEGs were extracted from the transcriptome data of HCC and normal samples, of which 258 were related to the prognosis of HCC (P < 0.01). Six key genes (CDK1, CCNA2, BUB1, CDC20, CCNB1 and TOP2A) were screened from the PPI network based on prognostic related genes, and the prognostic risk model was established. Survival analysis showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.01). The AUC values of 1, 3 and 5 years in the prognostic risk model were 0.716, 0.678 and 0.633. Multivariate Cox regression analysis showed that patient age and patient risk score were independent risk factors for the prognosis of HCC. The model gene is highly expressed in HCC and can promote apoptosis, cell cycle and EMT pathway. In addition, the high expression of model gene produced drug resistance to trametinib, selumetinib and RDEA119 (refametinib). Conclusion: The prognostic risk model based on six prognostic related DEGs is an independent prognostic factor of HCC, which can effectively predict the survival and prognosis of HCC patients.
