ArticleFrontiers in genetics2024
Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma.
Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Lung adenocarcinoma (LUAD) is a highly aggressive tumor with one of the highest morbidity and mortality rates in the world. Nucleotide metabolic processes are critical for cancer development, progression, and alteration of the tumor microenvironment. However, the effect of nucleotide metabolism on LUAD remains to be thoroughly investigated. Methods: Transcriptomic and clinical data of LUAD were downloaded and organized from TCGA and GEO databases. Genes related to nucleotide metabolism were downloaded from the Msigdb database. Genes associated with LUAD prognosis were identified using univariate COX analysis, and a prognostic risk model was constructed using the machine learning combination of Lasso + Stepcox. The model's predictive validity was evaluated using KM survival and timeROC curves. Based on the prognostic model, LUAD patients were classified into different nucleotide metabolism subtypes, and the differences between patients of different subtypes were explored in terms of genomic mutations, functional enrichment, tumor immune characteristics, and immunotherapy responses. Finally, the key gene SNRPA was screened, and a series of Result: LUAD patients could be accurately categorized into subtypes based on the nucleotide metabolism-related prognostic risk score (NMBRS). There were significant differences in prognosis between patients of different subtypes, and the NMBRS showed high accuracy in predicting the prognosis of LUAD patients. In addition, patients of different subtypes showed significant differences in genomic mutation and functional enrichment and exhibited different anti-tumor immune profiles. Importantly, NMBRS can be used to predict the responsiveness of LUAD patients to immunotherapy. The results of Conclusion: This study comprehensively reveals the prognostic value and clinical application of nucleotide metabolism in LUAD. A prognostic signature constructed based on genes related to nucleotide metabolism accurately predicted the prognosis of LUAD patients, and this signature can be used as a guide for LUAD immunotherapy.
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