Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma.

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

SUBMITTER: Zhang K 

PROVIDER: S-EPMC11750784 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature