Screening air pollutants-related genes to construct a prognostic risk model for lung adenocarcinoma and analyzing its
Zhimiao Tang1, Jia Ye1, Dong Chen1
1Department of Cardiothoracic Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
Background:
As the most common histological type of lung cancer, lung adenocarcinoma (LUAD) remains a major global health concern. The acceleration of worldwide industrialization has led to deteriorating air quality, which is recognized as a contributing factor in the development and advancement of numerous malignancies. This study aims to investigate the value of air pollutants-related genes (APRGs) as potential biomarkers for LUAD.
Methods:
Drawing on data from The Cancer Genome Atlas-LUAD and the GSE42127 cohort, this study identified key prognostic genes for LUAD through an integrated approach combining differential expression analysis with univariate/multivariate Cox regression and least absolute shrinkage and selection operator regression. Patients were stratified into high- and low-risk groups based on the median risk score derived from these prognostic genes. Subsequently, the patterns of immune cell infiltration were evaluated between the two groups. Drug sensitivity analysis was also performed to predict patient responses to conventional chemotherapy drugs. Furthermore, consensus clustering of LUAD samples was conducted to identify molecular subtypes with distinct biological characteristics.
Results:
Prognostic modeling based on eight air pollutants-related genes (APRGs) revealed that LUAD patients in the low-risk group experienced significantly superior overall survival. This survival benefit was accompanied by a notably enriched tumor microenvironment, characterized by elevated infiltration of B cells and resting memory CD4+ T cells. Furthermore, patients in the low-risk group may demonstrate greater sensitivity to crizotinib while exhibiting reduced responsiveness to gefitinib. Two robust molecular subtypes of LUAD were identified through consensus clustering.
Conclusions:
By constructing prognostic models centered on APRGs, this investigation systematically elucidated the immune microenvironment and molecular underpinnings of LUAD, contributing fresh perspectives on disease mechanisms and potential treatment avenues.