Development and Validation of a Machine Learning Prognostic Model of m5C Related immune Genes in Lung Adenocarcinoma.

ABSTRACT:

Background

The aim of this retrospective research was to develop an immune-related genes significantly associated with m5C methylation methylation (m5C-IRGs)-related signature associated with lung adenocarainoma (LUAD).

Methods

We introduced transcriptome data to screen out m5C-IRGs in The Cancer Genome Atlas (TCGA)-LUAD dataset. Subsequently, the m5C-IRGs associated with survival were certificated by Kaplan Meier (K-M) analysis. The univariate Cox, least absolute shrinkage and selection operator (LASSO) regression, and xgboost.surv tool were adopted to build a LUAD prognostic signature. We further conducted gene functional enrichment, immune microenvironment and immunotherapy analysis between 2 risk subgroups. Finally, we verified m5C-IRGs-related prognostic gene expression

SUBMITTER: Cao X 

PROVIDER: S-EPMC10976492 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature