Aging Associated Transcriptomic Signatures in Tumor and Tumor Adjacent Lung Tissues Associated with Recurrence Following Resection of Stage I Lung Adenocarcinoma

Background : Age is an independent prognostic factor in early-stage non-small cell lung cancer (NSCLC), yet the molecular differences between old and young patients and their contribution to disease progression remain unclear. We investigated age-related transcriptomic differences in early-stage lung adenocarcinoma (LUAD) and their association with recurrence. Methods : Tumor and adjacent normal lung tissue (NAT) from 126 stage I LUAD patients underwent bulk RNA sequencing to characterize age-related transcriptomic profiles. Differential expression and multiscale embedded gene co-expression network analysis (MEGENA) were used to identify age- and recurrence-associated modules. Pathways were annotated using Ingenuity Pathway Analysis. External confirmation was performed using TCGA (n=256) and TRACERx (n=83) cohorts. Results : Based on the cohort’s median age, 60 patients were classified as old (>70 years) and 66 as young (≤70 years). In tumors, older patients with recurrence showed marked upregulation of cancer-associated, inflammatory, and extracellular matrix pathways compared with older patients without recurrence. In NAT samples, older patients with recurrence demonstrated upregulation of inflammatory and cancer-associated pathways—including phagosome formation, IL-17, IL-6, and Th2 signaling—that were absent or downregulated in young patients. MEGENA revealed a larger number of recurrence-associated co-expression modules in old versus young patients. These age-related patterns were highly conserved in both external cohorts across tumor and NAT samples. Conclusion : Aging in LUAD is associated with distinct cancer- and inflammation-related transcriptomic alterations that contribute to recurrence. Aging-related molecular signatures may improve risk stratification for early-stage lung cancer. Impact : Aging shapes tumor microenvironment transcriptomes in stage I LUAD, enabling improved relapse risk stratification after surgery