Construction and analysis of a hepatocellular carcinoma prognostic model based on cuproptosis-related lncRNAs

Abstract

Abstract Objective To screen cuproptosis-relatedlncRNAs and establish a prognostic model of cuproptosis-related lncRNAs in hepatocellular carcinoma (HCC). Methods Perl and R languages were used to collate the HCC transcriptome and clinical data downloaded from the TCGA database. Cuproptosis-related lncRNAs were obtained through co-expression analysis, and the total samples were randomly divided into the Train group and the Test group. Univariate Cox regression analysis, LASSO regression and multivariate Cox analysis were used to analyze the data of Train group to establish the prognostic model of cuproptosis-related lncRNAs. The accuracy of the model was verified by principal component analysis (PCA), ROC curve, and independent prognostic analysis. GO and KEGG enrichment analysis, tumor mutation burden correlation analysis, immunocorrelation analysis and drug sensitivity analysis were conducted in depth. Results Co-expression analysis showed 258 cuproptosis-related lncRNAs, univariate Cox regression analysis showed 31 lncRNAs were correlated with HCC prognosis, and multivariate Cox analysis showed 5 lncRNAs involved in model construction. They are LINC00426, AL590705.3, SPRY4-AS1, AL031985.3, and AC002091.2. Validation analysis showed that the survival of patients in the low-risk group was significantly better than that in the high-risk group, and the model could be used as an independent prognostic factor for HCC. Immunocorrelation analysis showed that patients in the high and low-risk groups significantly differed in immune-related functions and immune-related therapy, which was consistent with the GO and KEGG enrichment analysis results. Tumor mutation burden correlation and drug sensitivity analysis can further guide the diagnosis and treatment of HCC patients. Conclusion The HCC prognostic model constructed by 5 cuproptosis-related lncRNAs has high accuracy and has a certain reference value for clinical diagnosis and treatment of HCC patients.