Machine learning-based prognostic model of lactylation-related genes for predicting prognosis and immune infiltration in patients with lung adenocarcinoma.

ABSTRACT:

Background

Histone lactylation is a novel epigenetic modification that is involved in a variety of critical biological regulations. However, the role of lactylation-related genes in lung adenocarcinoma has yet to be investigated.

Methods

RNA-seq data and clinical information of LUAD were downloaded from TCGA and GEO datasets. Unsupervised consistent cluster analysis was performed to identify differentially expressed genes (DEGs) between the two clusters, and risk prediction models were constructed by Cox regression analysis and LASSO analysis. Kaplan-Meier (KM) survival analysis, ROC curves and nomograms were used to validate the accuracy of the models. We also explored the differences in risk scores in terms of immune cell infiltration, immune cell function, TMB, TIDE, and

SUBMITTER: Gao M 

PROVIDER: S-EPMC11656871 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature