bims-necame 2025-09-21 papers

bims-necame

Biomed News

on Metabolism in small cell neuroendocrine cancers

Issue of 2025–09–21
seven papers selected by
Grigor Varuzhanyan, UCLA

Publisher Correction: Functional synapses between neurons and small cell lung cancer.
HDAC inhibition unlocks tumor plasticity and enhances immunotherapy response in Myc-Driven Small Cell Lung Cancer.
Development and validation of a serum inflammatory biomarker-driven machine-learning model for prognostic stratification in surgical limited-stage small cell lung cancer.
The impact of neuron-related cell adhesion molecule on cellular biological functions and its clinical significance in small-cell lung cancer.
Diagnostic value and therapeutic efficacy of serum levels of pro-gastrin-releasing peptide precursor (ProGRP) and neuron-specific enolase (NSE) in patients with lung cancer.
Molecular mechanisms and clinical insights in transformed small cell lung cancer: a narrative review.
Role of 18F-AlF-NOTATATE PET/CT in selecting pediatric neuroblastoma candidates for 177Lu-DOTATATE peptide receptor radionuclide therapy.

Nature. 2025 Sep 19.

Publisher Correction: Functional synapses between neurons and small cell lung cancer.

Vignesh Sakthivelu, Anna Schmitt, Franka Odenthal, Kristiano Ndoci, Marian Touet, Ali H Shaib, Abdulla Chihab, Gulzar A Wani, Pascal Nieper, Griffin G Hartmann, Isabel Pintelon, Ilmars Kisis, Maike Boecker, Naja M Eckert, Manoela Iannicelli Caiaffa, Olta Ibruli, Julia Weber, Roman Maresch, Christina M Bebber, Ali Chitsaz, Anna Lütz, Mira Kim Alves Carpinteiro, Kaylee M Morris, Camilla A Franchino, Jonas Benz, Laura Pérez-Revuelta, Jorge A Soriano-Campos, Maxim A Huetzen, Jonas Goergens, Milica Jevtic, Hannah M Jahn-Kelleter, Hans Zempel, Aleksandra Placzek, Alexandru A Hennrich, Karl-Klaus Conzelmann, Hannah L Tumbrink, Pascal Hunold, Joerg Isensee, Lisa Werr, Felix Gaedke, Astrid Schauss, Marielle Minère, Marie Müller, Henning Fenselau, Yin Liu, Alena Heimsoeth, Gülce S Gülcüler Balta, Henning Walczak, Christian Frezza, Ron D Jachimowicz, Julie George, Marcel Schmiel, Johannes Brägelmann, Tim Hucho, Silvia von Karstedt, Martin Peifer, Alessandro Annibaldi, Robert Hänsel-Hertsch, Thorsten Persigehl, Holger Grüll, Martin L Sos, Guido Reifenberger, Matthias Fischer, Dirk Adriaensen, Reinhard Büttner, Julien Sage, Inge Brouns, Roland Rad, Roman K Thomas, Max Anstötz, Silvio O Rizzoli, Matteo Bergami, Elisa Motori, Hans Christian Reinhardt, Filippo Beleggia.

DOI: https://doi.org/10.1038/s41586-025-09638-z
bioRxiv. 2025 Aug 09. pii: 2025.08.06.668958. [Epub ahead of print]

HDAC inhibition unlocks tumor plasticity and enhances immunotherapy response in Myc-Driven Small Cell Lung Cancer.

Azam Ghafoor, Linying Zhu, Zoe Weaver Ohler, Rajaa El Meskini, Devon Atkinson, Amanda Day, Laura Bassel, Weixin Wang, Haoxuan Ying, Katherine R Calvo, Lorinc Pongor, Yves Pommier, Anish Thomas, Yilun Sun.

Small Cell Lung Cancer (SCLC) is a highly aggressive malignancy, accounting for approximately 15% of all lung cancer cases. Characterized by low immunogenicity, SCLC may utilize epigenetic mechanisms to evade immune detection. Here, we demonstrate that entinostat, a class I histone deacetylase inhibitor (HDACi) upregulates immune-related genes in human SCLC cells. In vivo, we confirmed entinostat treatment increased expression of immunecheckpoint ligands and antigen presentation machinery in Myc-driven tumors in a Rb1/Trp53/Myc T58A (RPM) SCLC mouse model, while shifting tumors from a neuroendocrine(NE)-high to a NE-low phenotype. Notably, combining entinostat with anti-PD-1 immunotherapy significantly enhances T-cell infiltration, suppresses tumor growth, and prolongs survival in RPM allograft models. These findings underscore the potential of entinostat to reprogram the immunological landscape and NE status of SCLC, enhance immune checkpoint blockade efficacy, and improve therapeutic outcomes.

DOI: https://doi.org/10.1101/2025.08.06.668958
Transl Lung Cancer Res. 2025 Aug 31. 14(8): 2983-2995

Development and validation of a serum inflammatory biomarker-driven machine-learning model for prognostic stratification in surgical limited-stage small cell lung cancer.

Zhiyuan Yao, Changlei Li, Fengyi Han, Yi Qin, Xiao Sun, Guohua Wang, Enzheng Yu, Peng Song, Hanqun Liu, Wenjie Jiao.

   Background: Robust prognostic markers for small cell lung cancer (SCLC) are currently lacking, underscoring the need for novel prediction models to optimize individualized treatment and improve patient outcomes. Inflammatory/nutritional indexes have been extensively employed in prognostic investigations of malignant tumors. The study aimed to precisely ascertain the prognosis of SCLC patients undergoing surgery by preoperative serological indexes.
Methods: We included patients with SCLC who underwent surgery at The Affiliated Hospital of Qingdao University. Potential predictors included basic clinical characteristics and preoperative serum inflammatory/nutritional indexes. We employed 10 machine learning algorithms and their 101 combinations to select the superior model and establish a novel nomogram. Follow-up involved regular clinic visits or telephone contact, with imaging and laboratory tests conducted at defined intervals to assess overall survival (OS) and progression-free survival (PFS). The cohort was randomly split into training and validation cohorts in a 7:3 ratio. Harrell's C-index, Kaplan-Meier curves, log-rank tests, and Cox regression analyses were used for model evaluation and prognostic assessment.
Results: A total of 219 patients were included in this study. Prognostic nutritional index (PNI), lymphocyte-to-monocyte ratio (LMR), platelet-to-neutrophil ratio (PNR), neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammatory index (SII), pan-immune-inflammation value (PIV), and systemic inflammatory response index (SIRI) were correlated with the prognosis of SCLC patients. Smoking status and the tumor-node-metastasis (TNM) stage were independent prognostic indicators of OS. The Random Forest model achieved the highest mean concordance index (C-index) (0.784). Patients classified as high-risk based on this model exhibited a higher prevalence of smoking and more advanced pathological N stage and TNM stage. No significant differences were observed between risk groups regarding age, gender, body mass index (BMI), alcohol history, tumor site, pathological T stage, Ki-67 index, or visceral pleural invasion (VPI). Nomograms based on risk grouping, smoking status, and TNM stage demonstrated high precision and considerable clinical value. Multivariate Cox analysis identified PNI and NLR as the most valuable prognostic markers, with optimal cut-off values of 50.6 and 1.99, respectively.
Conclusions: A machine learning model based on serological inflammatory/nutritional indexes can reasonably estimate the long-term prognosis of SCLC patients and is anticipated to serve as a practical instrument for identifying the ideal candidates for thoracic surgery.

Keywords:  Small cell lung cancer (SCLC); inflammatory/nutritional index; machine learning; prognosis

DOI:  https://doi.org/10.21037/tlcr-2025-182
J Thorac Dis. 2025 Aug 31. 17(8): 6112-6126

The impact of neuron-related cell adhesion molecule on cellular biological functions and its clinical significance in small-cell lung cancer.

Tao Li, Peilong Liu, Yan Wang, Hong Xu.

   Background: Neuron-related cell adhesion molecule (NrCAM) has been implicated in tumor progression, but its role in small-cell lung cancer (SCLC) remains unclear. This study examined the clinical significance of NrCAM in SCLC and its impact on cellular processes.
Methods: Differentially expressed genes (DEGs) in SCLC were identified from The Cancer Genome Atlas (TCGA) datasets (GSE6044, GSE111044, and GSE44447) according to a |log fold change (FC)| >2 and a P value <0.05. Clinical and pathological data, along with tumor tissue samples, were obtained from 43 patients with SCLC treated at Nantong Tumor Hospital from 2017 to 2019. NrCAM expression was analyzed via immunohistochemistry. NrCAM knockdown or overexpression models were established in SCLC cell lines to evaluate proliferation, colony formation, migration, and invasion in vitro. A xenograft mouse model was used to assess NrCAM's effects in vivo.
Results: Immunohistochemical analysis revealed higher NrCAM expression in SCLC tissues as compared to adjacent noncancerous tissues. Patients with high NrCAM expression exhibited shorter survival (P=0.04) and significant associations with tumor-node-metastasis (TNM) stage, tumor size, and differentiation grade (P<0.05). Multivariate Cox analysis identified high NrCAM expression as an independent risk factor for poor prognosis (P=0.04). NrCAM knockdown significantly inhibited SCLC cell proliferation, migration, and invasion (P<0.05), while NrCAM overexpression resulted in the opposite effects. In the xenograft model, NrCAM knockdown reduced tumor volume and Ki-67 expression (P<0.05).
Conclusions: NrCAM overexpression in SCLC is associated with poor prognosis. Knockdown of NrCAM expression inhibited the proliferation, migration, and invasion of SCLC cells, suggesting that NrCAM could be a potential biomarker for diagnosis and prognostic evaluation in SCLC.

Keywords:  Neuron-related cell adhesion molecule (NrCAM); migration; proliferation; small-cell lung cancer (SCLC)

DOI:  https://doi.org/10.21037/jtd-2025-1273
J Med Biochem. 2025 Aug 21. 44(5): 945-954

Diagnostic value and therapeutic efficacy of serum levels of pro-gastrin-releasing peptide precursor (ProGRP) and neuron-specific enolase (NSE) in patients with lung cancer.

Ping Wang, Mingjie Yu.

   Background: Primary lung cancer is one of the most prevalent malignant tumours in China. Small cell lung cancer (SCLC) is a highly malignant, undifferentiated tumour prone to metastasis and is usually diagnosed in its middle or late stages. Pro-gastrin-releasing peptide precursor (ProGRP) and neuron-specific enolase (NSE) tumour markers are recommended in the literature for early diagnosis. Objective: The purpose of this research is to probe the diagnostic value and therapeutic efficacy of serum levels of ProGRP and NSE in SCLC to enhance the level of clinical diagnosis.
Methods: A total of 84 SCLC patients who were admitted to our hospital from December 2022 to March 2024 were included in the SCLC group. The NSCLC group consisted of 45 patients diagnosed with NSCLC, while the benign lung disease group consisted of 57 patients diagnosed with non-cancerous lung conditions. Furthermore, the healthy control group comprised 60 healthy individuals. The serum levels of ProGRP and NSE were compared across all four groups.
Results: The SCLC group exhibited considerably elevated serum ProGRP and NSE levels compared to the healthy control group, benign lung disease group, and NSCLC group (P< 0.05). ProGRP and NSE values were higher in limited-stage SCLC than in extensive-stage SCLC (P < 0.05). The ROC curve displayed that the critical value of ProGRP for diagnosing SCLC was 136.49 pg/mL, the area under the curve (AUC) was 0.869, the sensitivity attained 80.00%, and the specificity reached 84.87%, indicating a better diagnostic efficacy than that of NSE (P< 0.05).
Conclusions: The tumour markers ProGRP and NSE levels are of paramount significance for the clinical diagnosis and staging of SCLC patients. ProGRP is a more specific and sensitive tumour marker for SCLC than NSE and can be employed as an auxiliary diagnostic tool for SCLC. Thus, it is worth promoting ProGRP in a clinical setting.

Keywords:  application value; serum NSE; serum ProGRP; small cell lung cancer; tumour marker

DOI:  https://doi.org/10.5937/jomb0-50852
Transl Lung Cancer Res. 2025 Aug 31. 14(8): 3233-3248

Molecular mechanisms and clinical insights in transformed small cell lung cancer: a narrative review.

Xianzi Dai, Yue Hao, Jiaojiao Hong, Jiangxia Yuan, Chengyu Chen, Zhengxing Huo, Jia Zhu, Qian Wang.

   Background and Objective: In recent years, the application of targeted therapies has significantly improved survival rates in patients with driver gene-positive non-small cell lung cancer (NSCLC). However, one mechanism underlying acquired resistance is the histological transformation from NSCLC to small cell lung cancer (SCLC). NSCLC-to-SCLC transformation is thought to occur due to selective pressure from targeted therapies, yet this shift has also been observed in patients receiving non-targeted treatments, raising questions about its underlying mechanisms. This review aims to identify key molecular biomarkers predictive of this transformation to optimize clinical management strategies for transformed SCLC (T-SCLC).
Methods: We systematically searched PubMed, EMBASE, the Cochrane Library, and major international conference proceedings for all English-language articles published up to December 31, 2024. This review synthesizes current evidence on the mechanisms of T-SCLC transformation, its genomic and transcriptomic alterations, and related therapeutic approaches.
Key Content and Findings: T-SCLC is hypothesized to involve tumor heterogeneity and lineage plasticity. Key molecular players include dysregulation of the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway, NOTCH-ASCL1 signaling, mothers against decapentaplegic homolog 4 (SMAD4), SRY-related HMG-box 2 (SOX2), and epigenetic abnormalities such as histone modifications (methylation, acetylation, ubiquitination). Tumor protein p53 (TP53) and retinoblastoma 1 (RB1) inactivation may serve as predictive biomarkers, though causal relationships require validation. Post-transformation, chemotherapy remains the first-line treatment, while combining chemotherapy with epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) improves progression-free survival.
Conclusions: Current T-SCLC research is limited by retrospective designs and small sample sizes, leaving transformation mechanisms incompletely understood. This phenotypic shift highlights lung cancer plasticity as a novel resistance mechanism rooted in lineage plasticity and tumor heterogeneity. Personalized therapies guided by molecular profiling may represent a future direction for improving outcomes.

Keywords:  Transformed small cell lung cancer (T-SCLC); acquired drug resistance profile; plasticity

DOI:  https://doi.org/10.21037/tlcr-2025-165
Front Med (Lausanne). 2025 ;12 1615136

Role of 18F-AlF-NOTATATE PET/CT in selecting pediatric neuroblastoma candidates for 177Lu-DOTATATE peptide receptor radionuclide therapy.

Yuxuan Liu, Yingying Sun, Di Zuo, Han Wang, Fei Zheng, Jingfu Wang, Xiaorong Sun.

   Background: Neuroblastoma is the most common extracranial solid tumor in children. Peptide receptor radionuclide therapy (PRRT) is a treatment modality with great potential, however, the predictive indicators for its efficacy remain unclear. The aim of the study is to evaluate the prognostic utility of quantitative metrics obtained from 18F-AlF-NOTATATE PET/CT at baseline and post-treatment for predicting response in PRRT in pediatric neuroblastoma.
Methods: Patients with high-risk neuroblastoma that was either recurrent or resistant to treatment were prospectively enrolled for one or two cycles of 177Lu-PRRT. 18F-AlF-NOTATATE PET/CT was performed 1 month before and after PRRT; some patients underwent mid-treatment scans (7 weeks post-cycle). Treatment response was evaluated using a modified approach combining principles from European Organization for Research and Treatment of Cancer (EORTC) criteria and Response Evaluation Criteria In Solid Tumors (RECIST version 1.1) criteria. Lesions were delineated semiautomatically to obtain maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), ratio of tumor SUVmax to liver SUVmax (SUVT/L), ratio of tumor SUVmax to spleen SUVmax (SUVT/S), tumor volume, total lesion activity, and heterogeneity values. Data were analyzed using independent t-tests or Mann-Whitney U tests. Receiver operating characteristic curves were used to determine the optimal cut-offs for PET parameters.
Results: Twenty-two patients (13 boys, 9 girls) were included. Baseline PET revealed significantly lower SUVT/S, tumor volume, and total lesion activity in non-progressive lesions (p < 0.05); SUVT/S predicted efficacy (area under the curve [AUC], 0.588). Interim PET showed significantly lower SUVmax, SUVmean, SUVT/L, and SUVT/S in non-progressive lesions (p < 0.05); SUVT/L predicted efficacy (AUC, 0.740). The SUVmax ratio (interim/baseline) had the highest predictive accuracy, with a cut-off of 1.25 (AUC, 0.796; sensitivity, 73.03%; specificity, 76.92%).
Conclusion: Quantitative baseline and mid-treatment 18F-AlF-NOTATATE PET/CT-derived parameters possess value in predicting PRRT response. An interim-to-baseline PET-derived lesion SUVmax ratio of ≤1.25 can effectively predict neuroblastoma response to PRRT.

Keywords:  neuroblastoma; peptide receptor radionuclide therapy; quantitative; radionuclide therapy; therapy response

DOI:  https://doi.org/10.3389/fmed.2025.1615136