Machine learning developed a programmed cell death signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma.

ABSTRACT:

Background

Lung cancer is the leading cause of cancer-related deaths worldwide with poor prognosis. Programmed cell death (PCD) plays a crucial function in tumor progression and immunotherapy response in lung adenocarcinoma (LUAD).

Methods

Integrative machine learning procedure including 10 methods was performed to develop a prognostic cell death signature (CDS) using TCGA, GSE30129, GSE31210, GSE37745, GSE42127, GSE50081, GSE68467, GSE68571, and GSE72094 dataset. The correlation between CDS and tumor immune microenvironment was evaluated using various methods and single cell analysis. qRT-PCR and CCK-8 assay were conducted to explore the biological functions of hub gene.

Results

The prognostic CDS developed by Lasso + survivalSVM method was regarded as the optimal pr

SUBMITTER: Ding D 

PROVIDER: S-EPMC10511492 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature