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.
Related
- MRI-Based Radiomics Model for Pretreatment Prediction of Pathological Lymph Node Metastasis in Neuroendocrine Carcinoma of the Cervix: Dual-Center Development and External Validation.
- Integrated transcriptomic and clinical prediction model for malignant pulmonary nodules : Model development, external dataset evaluation, and single-center expression validation
- Primary adrenal small cell neuroendocrine carcinoma in a patient with sarcoidosis and synchronous colon adenocarcinoma
- Renal Neuroendocrine Tumor Mimicking Upper Tract Urothelial Carcinoma
- Intracranial metastasis following intrahepatic remission in primary hepatic small cell neuroendocrine carcinoma: a case report
BibTeX
@article{put2026integrated,
title = {{Integrated analysis of DNA repair and neuroendocrine marker expression in large cell neuroendocrine carcinoma with external small cell lung cancer reference data}},
author = {Adam Put and Jana Schwarzerová and Hana Krystofova and Petra Švecová and Erdő Gabor Mate and Wolfram Weckwerth and Marcel Mitták and Radoslava Černeková and Zsolt Megyesfalvi and Jozef Skarda},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-74978-3},
url = {https://www.nature.com/articles/s41598-026-74978-3},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrated analysis of DNA repair and neuroendocrine marker expression in large cell neuroendocrine carcinoma with external small cell lung cancer reference data
AU - Adam Put
AU - Jana Schwarzerová
AU - Hana Krystofova
AU - Petra Švecová
AU - Erdő Gabor Mate
AU - Wolfram Weckwerth
AU - Marcel Mitták
AU - Radoslava Černeková
AU - Zsolt Megyesfalvi
AU - Jozef Skarda
PY - 2026
DA - 2026/10/15
JO - Scientific Reports
DO - 10.1038/s41598-026-74978-3
UR - https://www.nature.com/articles/s41598-026-74978-3
AB - 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.
SN - 2045-2322
ER -