Identification and validation of prognostic features of amino acid metabolism-related lncRNAs in hepatocellular carcinoma

Abstract

Abstract Background As an important part of cellular energy metabolism, amino acid metabolism provides energy support for tumor progression. In recent years, it has been found that in addition to traditional proteins, long-stranded non-coding RNAs (lncRNAs) are also involved in amino acid metabolism in tumors. However, potential lncRNA biomarkers that potentially regulate amino acid metabolism and affect the prognosis of HCC patients remain to be further explored. Materials and Methods Genomic and clinical data were obtained from The Cancer Genome Atlas (TCGA) database, and amino acid metabolism-related genes were obtained from the Molecular Signature Database v5.1 (MSigDB). Prognostic features were constructed by co-expression analysis and Cox regression analysis. Patients were divided into high-risk and low-risk groups, and then independent prognostic analysis and ROC curve plotting were performed to assess the prognostic value of the features. Subsequently, immune-related functions of lncRNA and tumor mutational burden (TMB) were analyzed. Finally, we analyzed amino acid metabolism-related lncRNAs using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to determine their sensitivity to potential drugs for hepatocellular carcinoma. Results A total of 6 lncRNAs related to amino acid metabolism were obtained as LINC02870, AL031985.3, AC011476.3, AC012640.1, AL365361.1, LUCAT1, and prognostic features were established. We found that high-risk patients had poorer overall survival (OS) and progression-free survival (PFS) and higher mortality. Independent prognostic analysis, ROC, C-index and column line plot showed that amino acid metabolism-related lncRNAs could accurately predict the prognosis of patients. Column line plots and heat maps showed a significant difference in the distribution of amino acid metabolism-related lncRNAs between high- and low-risk groups. We also found that patients with high TMB had poorer OS, and the TIDE algorithm showed that high-risk patients had a greater likelihood of immune escape and poorer immunotherapy outcomes. Conclusion In conclusion, six lncRNAs associated with amino acid metabolism can accurately predict the prognosis of patients with hepatocellular carcinoma and may provide new insights for clinical application and treatment.