Interpretable artificial intelligence based on immunoregulation-related genes predicts prognosis and immunotherapy response in lung adenocarcinoma.

ABSTRACT:

Introduction

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer, and its benefit from immune checkpoint inhibitors (ICIs) is controversial, especially for patients without driver gene mutations. The potential of immunoregulation-related genes (IRGs) in predicting the prognosis of LUAD and the efficacy of immunotherapy becomes emerging. There is an urgent need to establish a reliable IRGs-based predictive model of ICI response.

Methods

Extract and merge LUAD RNA sequencing data and clinical data from GEO database. The differences in genomic and tumor microenvironment (TME) cell infiltration landscape between normal lung tissue and tumor tissue were comprehensively analyzed. Unsupervised consistent cluster analysis based on genes related to imm

SUBMITTER: Wang M 

PROVIDER: S-EPMC12491262 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature