Lung cancer frequently coexists with pulmonary diseases such as emphysema, pulmonary fibrosis, interstitial lung disease, and bronchiectasis, creating complex clinical challenges and significantly worsening patient outcomes. The interplay between malignant transformation and underlying chronic lung inflammation remains poorly understood, particularly regarding shared genetic susceptibility, metabolic reprogramming, and proteomic signatures that drive disease progression. This study aims to elucidate the multi-omics landscape of lung cancer developing within compromised pulmonary environments. Using UK Biobank’s comprehensive data, we will first characterize genomic variants associated with both lung cancer risk and pre-existing pulmonary conditions, focusing on immune regulation, DNA repair, and oxidative stress pathways. Second, we will analyze plasma metabolomic and proteomic profiles to identify circulating biomarkers reflective of the tumor-microenvironment interaction, including metabolites related to hypoxia, inflammation, and tissue remodeling. Third, we will integrate quantitative imaging features from chest CT scans with molecular data to develop predictive models for cancer risk stratification and progression monitoring in patients with underlying lung diseases. Our approach will bridge molecular-level alterations with radiological manifestations, ultimately aiming to improve early detection accuracy and enable personalized surveillance strategies for high-risk populations.
