Identification of a novel nucleotide metabolism-related signature for predicting lung adenocarcinoma prognosis

Background: Lung adenocarcinoma (LUAD) accounts for 50% of lung cancer and has high mortality rate. Nucleotide metabolism exhibit crosstalk in various cancer types, which are closely associated with the progression of LUAD. The in-depth study of genes and metabolites related to nucleotide metabolism will provide new ideas for predicting the prognosis and therapeutic effect of LUAD. Methods: This study integrated transcriptomeand single-cell transcriptome datato explore the characteristics and significance of nucleotide metabolism-related genes in LUAD. We will construct a novel LUAD classifier and prognostic signature via analysis of RNA sequencing and clinical data from the TCGA and GEO databases using Cox and LASSO regression. Subsequently, we performed t-distributed Stochastic Neighbor Embedding (tSNE), estimating relative subsets of RNA transcripts (CIBERSORT), gene set enrichment analysis and other bioinformatics analyses to demonstrate correlations with clinical features, gene mutations, drug sensitivity, immune cell infiltration and the expression of immune-related factors between the stratified groups based on risk scores. Results: A total of 152 nucleotide metabolism-related genes were identified, and a prognostic signature containing 9 hub molecules was constructed. The novel signature can accurately predict LUAD prognosis and can stratify patients into high-risk and low-risk groups. Multivariate analysis indicated that the risk score is an independent prognostic factor. Functional enrichment analysis revealed that the biological functions of signature moleculeswere associated with the cellular metabolic microenvironment. Our results revealed that patients in the high-risk group had a worse prognosis, less sensitivity to chemotherapy and greater proportion of TP53 gene mutations. Then, 22 cell clusters falling within 7 cellular categories were identified from LUAD tissue. Macrophages and immune-related factor scores of cytokines and failure factors were discerned to be significantly greater in the high-risk group than low-risk group. Conclusion: This study indicated that nucleotide metabolism was correlated with LUAD progression, immunosuppression and treatment sensitivity. The developed signature can serve as a potent tailored prognostic prediction model for patients.