Risk model of nucleotide metabolism and immunity signature genes in predicting of hepatocellular carcinoma prognosis and immune microenvironment

Background: Given the persistent poor prognosis of hepatocellular carcinoma (HCC), it is imperative to establish a multi-feature prognostic prediction model. Methods: LIHC patient data were obtained from TCGA, and LIHC patients were clustered by NMF and a nucleotide metabolism and immune-related genes (NMIRGs) prognostic risk model was built byby cox regression and Lasso regression, the gene expression was detected by immunohistochemistry and qPCR. The GSE14520, GSE10141 and GSE27150 datasets were used as external validation sets. The prognostic value of the model was evaluated by immune checkpoint genes, immune infiltration, functional enrichment analysis (GSEA) and tumor mutation load, and its potential mechanism was explored. The accuracy of our model was evaluated by the C-index value, compared to the reported prediction model. Results: The HCC patients were grouped into two subtypes based on nucleotide metabolism and immune differentially expressed genes (DEGs), with cluster 1 (C1) having more DEGs and worse prognosis than cluster 2 (C2). A NMIRGs signature gene prognostic model was established, with HSP90AA1, HDAC1, STC1, MAPT and CHGA genes as high-risk genes and GHR genes as low-risk genes. We found that the stage and risk score of HCC patients were independent prognostic factors. The model also assess the prognostic risk of patients in relation to immune checkpoint genes and immune infiltration. Further analysis showed that our model's risk score from our model and tumor mutation burden (TMB) were largely independent in predicting the efficacy of immunotherapy and were closely related to immune-related processes. Finally, the model was found to have a higher C-index and accuracy in predicting the prognosis of HCC patients. Conclusions: Our study provides a helpful predictive model for the prognosis of HCC and its immune microenvironment. In addition, it identifies potential new biomarkers associated with HCC prognosis.