Systematic integration of machine learning algorithms to develop immune escape-related signatures to improve clinical outcomes in lung adenocarcinoma patients.

ABSTRACT:

Background

Immune escape has recently emerged as one of the barriers to the efficacy of immunotherapy in lung adenocarcinoma (LUAD). However, the clinical significance and function of immune escape markers in LUAD have largely not been clarified.

Methods

In this study, we constructed a stable and accurate immune escape score (IERS) by systematically integrating 10 machine learning algorithms. We further investigated the clinical significance, functional status, TME interactions, and genomic alterations of different IERS subtypes to explore potential mechanisms. In addition, we validated the most important variable in the model through cellular experiments.

Results

The IERS is an independent risk factor for overall survival, superior to traditional clinical variables a

SUBMITTER: Wang T 

PROVIDER: S-EPMC10018159 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature