Integrative Genomic and AI Approaches to Lung Cancer and Implications for Disease Prevention in Former Smokers

Tobacco smoking accounts for nearly 90% of lung cancer deaths worldwide, yet the mechanisms underlying persistent cancer risk in former smokers are not fully understood. Epidemiological evidence shows that more than 40% of lung cancers develop over 15 years after cessation, demonstrating that while some smoking-induced molecular alterations resolve rapidly, others remain as long-lasting scars that promote carcinogenesis. This review synthesizes longitudinal and cross-sectional genomic, epigenomic, and transcriptomic studies of airway and lung tissues to distinguish persistent from nonpersistent smoking-induced molecular alterations. Persistent alterations include somatic mutations in <i>TP53</i> and <i>KRAS</i>, DNA methylation at tumor suppressor loci, dysregulated noncoding RNAs, chromosomal instability, and epigenetic age acceleration. Nonpersistent changes, such as acute inflammatory responses and detoxification pathways, generally normalize within months to several years following cessation. Multi-omics profiling reveals coordinated patterns of dysregulation consistent with field cancerization in former smokers. In addition, the integration of multi-omics data with artificial intelligence may enable composite molecular signatures for stratifying high-risk former smokers, link molecular persistence to clinical outcomes, and inform chemoprevention strategies. Collectively, these observations clarify which molecular alterations sustain long-term cancer risk despite smoking cessation and highlight opportunities for precision prevention and earlier detection in high-risk populations.