Clinical overall survival prediction and disease characteristics of locally advanced non-small cell lung cancer: an integrated analysis based on the SEER and TCGA databases - Tian, Yuanhui; Deng, Yan; Liao, Xiaoli; Yuan, Shutao; Li, Ke (Translational Cancer Research, 2026)
Background: Lung cancer is the second most commonly diagnosed malignancy worldwide and remains the leading cause of cancer-related death. Non-small cell lung cancer (NSCLC) accounts for approximately 80–85% of lung cancers. Given the low rate of early detection and substantial interpatient heterogeneity, management of NSCLC, particularly locally advanced disease, remains challenging. This study aimed to characterize clinical heterogeneity and progression-associated molecular features of locally advanced NSCLC and to identify candidate biomarkers and therapeutic targets.Methods: Patients with locally advanced NSCLC diagnosed between 2010 and 2017 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. Eligible stage III NSCLC cases were randomly divided in a 7:3 ratio into a training cohort (n=11,459) and an internal validation cohort (n=4,911). Univariable and multivariable Cox proportional hazards models were used to identify independent prognostic factors, which were integrated into a nomogram to predict 1-, 3-, and 5-year overall survival (OS). Model performance was evaluated using the concordance index (C-index), time-dependent area under the curve (AUC), and calibration plots. In parallel, transcriptomic ribonucleic acid sequencing (RNA-seq) and clinical data were retrieved from The Cancer Genome Atlas (TCGA). Differential expression was found using DESeq2 by comparing stage III tumors with normal tissues and with early-stage tumors; the intersection of differentially expressed genes (DEGs) was used to capture progression-associated signals. Functional enrichment was conducted with clusterProfiler. Candidate genes were further selected using least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), followed by immune infiltration analysis (CIBERSORT).Results: In total, 16,370 patients with stage III NSCLC were included from SEER. A nomogram incorporating age, sex, race, marital status, histologic subtype, grade, T stage, N stage, tumor size, and treatment modalities demonstrated stable discrimination and good calibration in both cohorts for predicting 1-, 3-, and 5-year OS. TCGA analysis yielded 130 intersecting DEGs. Enrichment analyses highlighted extracellular matrix remodeling, immune response, and metabolic reprogramming as key processes associated with locally advanced progression. The dual-method feature-selection strategy identified three core genes (SFTPC, GKN2, and CLDN18), its expression levels of which were associated with differences in immune cell infiltration, including macrophages, dendritic cells, and mast cells.Conclusions: This study integrates SEER-based prognostic modeling with TCGA-based transcriptomic analyses to characterize heterogeneity in stage III NSCLC. The nomogram may support individualized OS estimation, and the identified genes warrant further external validation and functional investigation.