Construction of a model for predicting the prognosis of liver cancer patients based on CuProtosis-related LncRNA

Abstract

Introduction: Liver cancer is one of the most common malignant tumors in the world, and patients with liver cancer are often in the middle and late stages of cancer when they are diagnosed. Copper death is a newly discovered new cell death method. It is a copper-dependent and regulated cell death method. At the same time, Long noncoding RNAs (LncRNAs) also play an important regulatory role in the pathological process of tumors such as liver cancer. Materials and methods: First, the expression levels of CuProtosis-related genes in liver cancer samples were extracted, and a CuProtosis- related LncRNA prognostic model was constructed. C-index curve and ROC curve were drawn by survival analysis, PFS analysis, and independent prognosis analysis. The model was also validated by clinical grouping and PCA principal component analysis. To ensure its accuracy, enrichment analysis, immune analysis and tumor mutational burden analysis further explored the potential function of this model, and finally discussed potential drugs targeting this model. Results: A prognostic model for predicting survival was constructed and its high predictive ability in liver cancer patients was validated. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment showed that the differential genes were mainly enriched in 5 pathways. Meanwhile, six differentially expressed immune functions were found in the high-risk and low-risk groups. The survival rate of patients in the high mutation group was significantly lower than that of the patients with liver cancer in the low mutation group. Twelve drugs with significant differences in drug sensitivity between high- and low-risk groups were explored. Conclusions: The risk-prognosis model based on CuProtosis LncRNA established in this study is expected to be used to predict the prognosis and immunotherapy response of liver cancer patients. It provides new clues and methods for predicting the survival time of liver cancer patients, and also provides new ideas for guiding individualized immunotherapy strategies for liver cancer patients in the future.