biostudies-literatureUnknown104(46)Ma YLung adenocarcinoma (LUAD), a common type of non-small cell lung cancer, is associated with low survival rates and challenges in early detection. Therefore, identifying prognostic biomarkers is crucial for improving patient outcomes. This study utilized 2 datasets - the Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) and GSE72094 - along with 182 immune escape-related genes and 597 cancer-associated fibroblast-related genes. Weighted gene co-expression network analysis was used to identify module genes. Differential expression analysis of TCGA-LUAD data revealed LUAD-associated differentially expressed genes, which were then intersected with module genes to identify LUAD-specific differentially expressed immune escape-cancer fibroblast-related genes. To identify potential biomarkers anMedicinee45756https://www.ebi.ac.uk/biostudies/studies/S-EPMC12622665biostudies-literatureIdentification and validation of a diagnostic and prognostic model based on immune escape and cancer-associated fibroblast-related genes in lung adenocarcinoma.PMC12622665Li XGeng RSong BMa YWang XHao YSu WfalseIdentification and validation of a diagnostic and prognostic model based on immune escape and cancer-associated fibroblast-related genes in lung adenocarcinoma.Lung adenocarcinoma (LUAD), a common type of non-small cell lung cancer, is associated with low survival rates and challenges in early detection. Therefore, identifying prognostic biomarkers is crucial for improving patient outcomes. This study utilized 2 datasets - the Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) and GSE72094 - along with 182 immune escape-related genes and 597 cancer-associated fibroblast-related genes. Weighted gene co-expression network analysis was used to identify module genes. Differential expression analysis of TCGA-LUAD data revealed LUAD-associated differentially expressed genes, which were then intersected with module genes to identify LUAD-specific differentially expressed immune escape-cancer fibroblast-related genes. To identify potential biomarkers an2025-01-01T00:00:00Z2025 Nov2026-06-05T16:36:15.585Z2026-05-18T03:13:18.699ZS-EPMC126226654123971610.1097/MD.0000000000045756