Asthma-derived genetic signature predicts lung cancer prognosis: a multi-scale omics investigation

Asthma and lung cancer are prevalent respiratory diseases with unclear interrelationships. This study aimed to investigate their association using multi-omics data and develop an asthma-related prognostic model for lung cancer.

Global incidence trends and correlations were analyzed using Global Burden of Disease data from 1990 to 2019. Two-sample Mendelian randomization (MR) was performed using genome-wide association study summary statistics to assess the causal relationship. Bulk RNA sequencing data from public databases were analyzed to identify differentially expressed genes (DEGs) common to both diseases. A prognostic model was constructed using asthma-related DEGs and validated through immune cell correlation, checkpoint gene analysis, and single-cell RNA sequencing (scRNA-seq).

Epidemiological data showed a positive correlation between asthma and lung cancer, while MR supported a potential causal relationship. A prognostic model incorporating four asthma-related genes (CD69, KIF18A, HN1L, and KLF10) effectively stratified lung cancer patients and was associated with immune cell populations and checkpoint genes, highlighting its potential to identify patients who may benefit from immunotherapy. scRNA-seq validated cell-type-specific expression patterns and revealed potential cell-cell interactions in the tumor microenvironment.

This multi-omics study provides insights into the asthma-lung cancer link and presents a promising prognostic model for lung cancer based on asthma-related genes. The model's association with immune features suggests its potential utility in immunotherapy patient selection.