Integrated analysis of DNA repair and neuroendocrine marker expression in large cell neuroendocrine carcinoma with external small cell lung cancer reference data

Abstract

Lung cancer is one of the leading causes of cancer-related mortality worldwide. Large-cell neuroendocrine carcinoma (LCNEC) represents a rare and highly aggressive subtype that combines features of small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), creating challenges for diagnosis and therapy. Its molecular heterogeneity, particularly regarding DNA repair and neuroendocrine differentiation, remains poorly understood and limits the development of targeted treatment approaches. We performed an integrated analysis of patient-derived LCNEC samples, combining quantitative gene expression profiling with curated pathway databases. Boolean network modeling and rule-based simulations were employed to explore signaling and DNA repair pathway dynamics in LCNEC and SCLC. Clinical and histopathological parameters were correlated with gene expression data to identify potential biomarkers and regulatory mechanisms. Gene expression profiling revealed substantial heterogeneity across LCNEC tumors. Exploratory correlation analysis identified a moderate positive association between RAD51 and ASCL1 expression (Pearson r  = 0.56, n  = 17, P  = 0.021), whereas the association between BRCA1 and RAD51 expression was weak and non-significant ( r  = 0.15, n  = 17, P  = 0.573). The previously reported association between BRCA1 and BRCA2 expression was re-evaluated in the quality-controlled TaqMan subset and is reported separately together with the corresponding sample size and statistical significance. Peritumoral inflammation showed a strong positive correlation with vascular invasion ( r  = 0.68, n  = 16, P  = 0.0035), whereas neither feature was significantly associated with tumor size. Boolean network simulations revealed that SCLC maintained multiple attractor states, consistent with phenotypic plasticity, whereas LCNEC displayed a more constrained attractor profile, in line with its distinct phenotype characteristics. Overall, these exploratory findings highlight potential relationships between neuroendocrine phenotype, DNA repair–associated gene expression, and tumor microenvironment features, while requiring validation in larger cohorts. Our study suggests that LCNEC displays marked transcript-level heterogeneity in selected DNA repair–associated and neuroendocrine lineage–associated genes. External SCLC reference data and in-silico modeling provide additional context for potential differences in regulatory network behavior between LCNEC and SCLC. Rather than demonstrating a causal RAD51–DLL3 regulatory mechanism, these findings generate hypotheses regarding possible interactions between DNA repair and neuroendocrine differentiation programs in LCNEC. Functional validation will be required to determine whether these transcript-level and model-derived associations translate into altered DNA repair capacity, DLL3 regulation, or proliferative behavior. Integrated molecular profiling and systems biology modeling may support future stratification approaches for this rare lung cancer subtype.