DOI: 10.57760/sciencedb.20626
title: Construction and evaluation of prognostic signature for hepatocellular carcinoma based on cuproptosis- and ferroptosis-related lncRNA
title: 基于铜死亡及铁死亡相关lncRNA的肝细胞癌预后模型的构建和评估

description: Objective: Cuproptosis and ferroptosis can regulate the biological behaviors of tumors. Therefore, biomarkers that combined cuproptosis, ferroptosis, and long non-coding RNA (lncRNA) can be a promising candidate bioindicator in clinical therapy of cancers. This study screens the expression of lncRNA related to cuproptosis and ferroptosis on The Cancer Genome Atlas (TCGA) database, and construct a risk prediction model of prognosis of hepatocellular carcinoma (HCC). Methods: Many bioinformatics methods, including Pearson correlation analysis, univariate Cox proportional hazard regression analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis were applied to construct a predictive signature, and the patients were divided into a high-risk group and a low-risk group. We employed Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) analysis to evaluate the signature’s clinical efficacy. In addition, functional enrichment analysis, gene mutation analysis, immune related analysis, and drug sensitivity analysis were also carried out based on high- and low risk groups distinguished by the signature. Results: A total of 2 074 differentially expressed cuproptosis- and ferroptosis-related lncRNAs were obtained, which could integrate 10 prognosis-related lncRNAs (LINC02561, AC006372.2, AL603839.2, AC009974.2, POLH-AS1, AP006222.2, AP001631.2, AC073573.1, AC244034.2, and AC007998.3). The K-M survival curve indicated significant differences in survival rates between the different risk groups (P&lt; 0.001). ROC curve analysis showed that the area under curve (AUC) of the training set was 0.805, and the AUC of the test set was 0.742 in the prediction model, which was better than that of other published signatures. Multivariate Cox regression analysis showed that the prediction model as a risk factor was an independent factor affecting survival (HR=1.005, 95% CI 1.001 to 1.010). In addition, patients in the high-risk group had higher frequency of gene mutations, worse prognosis, and were more likely to benefit from immune checkpoint inhibitors (ICIs) therapy. Finally, the half maximal inhibitory concentration for the majority of chemotherapy drugs (such as doxorubicin, paclitaxel and sunitinib) was lower in the high-risk group. Conclusion: The risk prediction model based on cuproptosis- and ferroptosis-related lncRNA expression can independently predict the survival period of HCC patients, providing reference for patients receiving immunotherapy and chemotherapy.
description: Objective: Cuproptosis and ferroptosis can regulate the biological behaviors of tumors. Therefore, biomarkers that combined cuproptosis, ferroptosis, and long non-coding RNA (lncRNA) can be a promising candidate bioindicator in clinical therapy of cancers. This study screens the expression of lncRNA related to cuproptosis and ferroptosis on The Cancer Genome Atlas (TCGA) database, and construct a risk prediction model of prognosis of hepatocellular carcinoma (HCC). Methods: Many bioinformatics methods, including Pearson correlation analysis, univariate Cox proportional hazard regression analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis were applied to construct a predictive signature, and the patients were divided into a high-risk group and a low-risk group. We employed Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) analysis to evaluate the signature’s clinical efficacy. In addition, functional enrichment analysis, gene mutation analysis, immune related analysis, and drug sensitivity analysis were also carried out based on high- and low risk groups distinguished by the signature. Results: A total of 2 074 differentially expressed cuproptosis- and ferroptosis-related lncRNAs were obtained, which could integrate 10 prognosis-related lncRNAs (LINC02561, AC006372.2, AL603839.2, AC009974.2, POLH-AS1, AP006222.2, AP001631.2, AC073573.1, AC244034.2, and AC007998.3). The K-M survival curve indicated significant differences in survival rates between the different risk groups (P&lt; 0.001). ROC curve analysis showed that the area under curve (AUC) of the training set was 0.805, and the AUC of the test set was 0.742 in the prediction model, which was better than that of other published signatures. Multivariate Cox regression analysis showed that the prediction model as a risk factor was an independent factor affecting survival (HR=1.005, 95% CI 1.001 to 1.010). In addition, patients in the high-risk group had higher frequency of gene mutations, worse prognosis, and were more likely to benefit from immune checkpoint inhibitors (ICIs) therapy. Finally, the half maximal inhibitory concentration for the majority of chemotherapy drugs (such as doxorubicin, paclitaxel and sunitinib) was lower in the high-risk group. Conclusion: The risk prediction model based on cuproptosis- and ferroptosis-related lncRNA expression can independently predict the survival period of HCC patients, providing reference for patients receiving immunotherapy and chemotherapy.