Shared diagnostic genes and potential mechanisms between asthma and lung cancer revealed by integrated transcriptomic analysis and machine learning.

Abstract

Lung cancer, a severe malignancy with poor prognosis, poses a formidable public health challenge. Beyond conventional risk factors such as smoking, evidence suggests that chronic respiratory diseases also contribute to its development. Among these, asthma, the second most prevalent chronic respiratory condition, is recognized as a risk factor for lung cancer. Nevertheless, the underlying molecular link between these two diseases remains elusive. Our study, leveraging multi-cohort data integration and employing Weighted Gene Co-expression Network Analysis (WGCNA), identified conserved shared genes between lung cancer and asthma. By constructing the functional landscape of these shared genes, we underscored the pivotal roles of pathways related to lung development and cellular metabolic homeostasis in the pathogenesis of both lung cancer and asthma. Utilizing machine learning-based screening, we identified three hub biomarkers: P2RY14, ANXA3, and SLIT2, which could serve as diagnostic tools for these diseases. In summary, our research provides invaluable insights into the shared mechanisms underlying asthma and lung cancer, and potential diagnostic biomarkers.