J Comput Aided Mol Des. 2025 Aug 12;39(1):65. doi: 10.1007/s10822-025-00645-w.

ABSTRACT

Eg5 is a mitotic kinesin motor protein essential for the formation of bipolar spindles during cell division. Its inhibition disrupts mitosis, leading to cell cycle arrest and apoptosis in cancer cells. This makes Eg5 a promising target for chemotherapeutic interventions, especially in cases resistant to traditional treatments. In this study, a drug repurposing strategy was employed to design and synthesise quinoline-based Schiff base derivatives as potential Eg5 inhibitors. These compounds were subjected to in vitro biological evaluations, including cytotoxicity testing against the human breast cancer cell line MDA-MB-231 and the normal mouse fibroblast cell line L929 using the MTT assay. Enzymatic assays targeting Eg5 were also conducted. Among the synthesised molecules, compound (5) demonstrated significant Eg5 inhibition in enzymatic assays, with an IC50 of 2.544 ± 0.810 µM in the Malachite Green assay and 4.03 ± 2.027 µM in the steady-state ATPase assay, and moderate inhibition against triple-negative breast cancer cells (MDA-MB-231). Computational studies, including molecular docking, molecular dynamics simulations, and MM/GBSA free energy calculations, were performed to analyse binding interactions. ADMET properties were predicted using the QikProp module. The findings suggest that targeting mitosis through Eg5 inhibition may offer a strategic approach in chemotherapy, potentially enhancing treatment efficacy.

PMID:40794230 | DOI:10.1007/s10822-025-00645-w

Assay Drug Dev Technol. 2025 Aug 12. doi: 10.1177/1540658X251365257. Online ahead of print.

ABSTRACT

From the steady stream of drug repurposing patent applications published under the Patent Cooperation Treaty (PCT), we have selected fifteen documents that first became available during the first quarter of 2025. As in each installment, some of these claims are truly surprising. Few researchers would have expected that SSRI antidepressants such as sertraline and indatraline could exhibit pronounced anticancer effects. Equally unexpected is the disclosure that sitagliptin, the first antidiabetic agent from the DPP-4 inhibitor class, may be used for the treatment of glioblastoma. Another striking example is the report that artemisinin derivatives, well known for their use against malaria, may induce differentiation in undifferentiated erythroid and myeloid cells in patients with myelodysplastic syndrome. In addition, the compound bucillamine-relatively obscure in Western medicine but long used for the treatment of rheumatoid arthritis in East Asia-has been proposed for potential benefit in organophosphate poisoning. These highlights exemplify the breadth of innovation currently shaping the drug repurposing landscape. The reviewed patent applications originate from a diverse range of jurisdictions, including France, Spain, Greece, Slovenia, South Korea, China, Japan, Canada, and the United States, illustrating the global nature of ongoing research efforts in this field.

PMID:40793958 | DOI:10.1177/1540658X251365257

J Eval Clin Pract. 2025 Aug;31(5):e70242. doi: 10.1111/jep.70242.

ABSTRACT

PURPOSE: Rare diseases (RDs), which are often chronic, degenerative, and life-threatening conditions, pose significant challenges due to their complexity and limited awareness among healthcare professionals. This study assessed the knowledge, awareness, and educational needs related to RDs among 5th- and 6th-year medical students at Atatürk University, Başkent University, and Istanbul University, as well as pediatric and non-pediatric specialists in Türkiye, with a focus on differences between these groups.

MATERIALS AND METHODS: A total of 258 physicians and 132 medical students participated. Data were collected through surveys examining demographics, self-assessed knowledge, awareness, and perceptions of RD-related education. Statistical analyses evaluated differences in knowledge and awareness across the groups.

RESULTS: Pediatric specialists reported significantly higher self-assessed RD knowledge than non-pediatric specialists. However, both groups showed notable gaps in awareness, particularly concerning RD prevalence and diagnostic timelines in Türkiye. Most participants expressed interst in further education but were unaware of available resources. Among students, 65.9% rated their RD knowledge as 'Poor' or 'Very Poor,' with no significant differences observed across institutions despite curriculum variations.

CONCLUSION: The findings highlight a critical lack of competence in RD-related knowledge among healthcare professionals, especially non-pediatric specialists. To address this gap, we recommend integrating integrating RD-specific into medical curricula, promoting continuous professional development through specialized training events, and enhancing the visibility of reliable RD information sources. These measures are crucial for improving early diagnosis and management of RDs, ultimately enhancing patient care and outcomes in Türkiye.

PMID:40793993 | DOI:10.1111/jep.70242

Clin Pharmacokinet. 2025 Aug 12. doi: 10.1007/s40262-025-01529-w. Online ahead of print.

ABSTRACT

BACKGROUND: Tacrolimus is a cornerstone of acute graft-versus-host disease (aGVHD) prophylaxis in allogeneic hematopoietic cell transplant (allo-HCT) recipients. However, a narrow therapeutic index and high interindividual variability in pharmacokinetics (PK) make starting dose selection a major challenge in clinical practice.

METHODS: Data from two PK studies conducted at the University of North Carolina Medical Center (UNCMC) were used to develop an oral tacrolimus population pharmacokinetic (popPK) model specific to adult allo-HCT recipients. Monte Carlo simulations were performed to compare the likelihood of achieving the UNCMC institutional target trough concentration range (ITR) (5-10 ng/mL) on the day of transplant (D0) under the current institutional dosing protocol, dosing recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC), and model-derived dosing recommendations.

RESULTS: In total, 290 allo-HCT recipients contributed a total of 906 PK samples to the final analysis. A two-compartment popPK model adequately described the PK data. Population typical values of apparent clearance (TVCL/F) for 70 kg individuals receiving reduced intensity conditioning were 0.33 L/h/kg for CYP3A5 poor metabolizers (PMs) and 0.70 L/h/kg for intermediate and normal metabolizers (IMs and NMs). The probability of the population-level average D0 trough concentration being within the UNCMC ITR under the current UNCMC weight-based dosing protocol, CPIC-based, and model-derived dosing strategies were estimated to be 37%, 45%, and 76%, respectively. CYP3A5 IMs and NMs were predicted to require a 100% dose increase relative to CYP3A5 PMs.

CONCLUSIONS: We propose a new oral tacrolimus dosing strategy for adult allo-HCT recipients, which suggests the current weight-based dosing paradigm is insufficient. This new strategy includes CYP3A5 metabolizer phenotypes and conditioning regimen intensity, and could increase the percentage of allo-HCT recipients achieving target concentrations on D0.

CLINICAL TRIAL REGISTRATION NUMBER: Clinicaltrials.gov NCT04645667.

PMID:40794300 | DOI:10.1007/s40262-025-01529-w

Discov Oncol. 2025 Aug 12;16(1):1536. doi: 10.1007/s12672-025-03140-8.

ABSTRACT

PURPOSE: Omics technologies, such as genomics, transcriptomics, proteomics, and radiomics, play an increasingly important role in the diagnosis and treatment of non-small cell lung cancer (NSCLC). It is, therefore, essential to unveil the research landscape and future trends of relevant research. This study aims to explore the research fields based on omics technologies in NSCLC, elucidating the research status, hotspots, and trends from a bibliometric perspective.

METHODS: The Web of Science Core Collection was utilized to retrieve relevant publications in omics technologies and their applications in NSCLC. By using the bibliometric methods and tools ("bibliometrix" R package, VOSviewer, and CiteSpace), data and visualized analyses for annual publication outputs, countries, institutions, authors, journals, references, and keywords proceeded.

RESULTS: A total of 5,337 publications were involved in our analysis. These articles, written by 32,286 authors, originated in 5,863 institutions from 82 countries and were published in 797 journals. The Journal of Thoracic Oncology and Clinical Cancer Research were representative journals in omics-based research in NSCLC. "Survival," "adenocarcinoma," "mutation," "epidermal growth factor receptor," "resistance," and "chemotherapy" were the highest-frequency keywords. Liquid biopsy and deep learning were also trending topics in omics-related research, according to keyword clustering, trend topics, and citation burst analysis.

CONCLUSION: Omics technologies, including genomics, transcriptomics, and proteomics, were widely used in the diagnosis, prognosis, and treatment of NSCLC. And innovative methods, including liquid biopsy and deep learning, demonstrate a profound impact on advancing the understanding and treatment strategies for NSCLC and warrant further investigation.

PMID:40794364 | DOI:10.1007/s12672-025-03140-8

J Imaging Inform Med. 2025 Aug 12. doi: 10.1007/s10278-025-01630-3. Online ahead of print.

ABSTRACT

Accurate segmentation and classification of liver tumors are crucial for early diagnosis and effective treatment planning. However, conventional deep learning models such as tumor heterogeneity, class imbalance, and high computational demands face challenges, limiting their clinical deployment. This study introduces a lightweight hybrid framework combining an optimized Swin-UNet for segmentation with a Quantum Convolutional Neural Network (QCNN) for classification. The Swin-UNet is enhanced using a metaheuristic Search and Rescue (SAR) algorithm and a quadratic penalty-based objective function to balance compactness and accuracy. A Focal AUC loss function addresses class imbalance and improves sensitivity to minority regions. The QCNN leverages quantum-inspired mechanisms such as entanglement and superposition to achieve superior performance with reduced parameters. Evaluated on three benchmark datasets (3D-IRCADb, LiTS17, and MSD Task03), the framework achieves Dice scores of 85.8%, 88.7%, and 88.4%, respectively, alongside 96.7% classification accuracy. The model size is reduced to 64.16 MB, enabling real-time inference on edge devices (Jetson Nano). The QCNN classifier outperforms traditional CNNs in all metrics, demonstrating its effectiveness in high-dimensional medical data analysis. This work bridges the gap between diagnostic precision and computational efficiency, presenting a clinically viable AI solution for liver tumor analysis.

PMID:40794345 | DOI:10.1007/s10278-025-01630-3

Theor Appl Genet. 2025 Aug 12;138(9):211. doi: 10.1007/s00122-025-05003-w.

ABSTRACT

Genome-environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limited by missing geographic origin data. To address this limitation, we explored the use of neural networks to predict the geographic origins of barley accessions and integrate imputed environmental data into GEA. Neural networks demonstrated high accuracy in cross-validation but occasionally produced ecologically implausible predictions as models solely considered geographical proximity. For example, some predicted origins were located within non-arable regions, such as the Mediterranean Sea. Using barley flowering time genes as benchmarks, GEA integrating imputed environmental data ( N = 11 , 032 ) displayed partially concordant yet complementary detection of genomic regions near flowering time genes compared to regular GEA ( N = 1 , 626 ), highlighting the potential of GEA with imputed data to complement regular GEA in uncovering novel adaptive loci. Also, contrary to our initial hypothesis anticipating a significant improvement in GEA performance by increasing sample size, our simulations yield unexpected insights. Our study suggests potential limitations in the sensitivity of GEA approaches to the considerable expansion in sample size achieved through predicting missing geographical data. Overall, our study provides insights into leveraging incomplete geographical origin data by integrating deep learning with GEA. Our findings indicate the need for further development of GEA approaches to optimize the use of imputed environmental data, such as incorporating regional GEA patterns instead of solely focusing on global associations between allele frequencies and environmental gradients across large-scale landscapes.

PMID:40794289 | DOI:10.1007/s00122-025-05003-w

EJNMMI Res. 2025 Aug 12;15(1):106. doi: 10.1186/s13550-025-01300-z.

NO ABSTRACT

PMID:40794258 | DOI:10.1186/s13550-025-01300-z

Eur Radiol Exp. 2025 Aug 12;9(1):75. doi: 10.1186/s41747-025-00612-y.

ABSTRACT

BACKGROUND: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension.

METHODS: We assessed retrospectively healthy controls, non-advanced and advanced chronic liver disease (ACLD) patients using a 3D U-Net model for hepatic vessel segmentation on portal venous phase gadoxetic acid-enhanced 3-T MRI. Total (TVVR), hepatic (HVVR), and intrahepatic portal vein-to-volume ratios (PVVR) were compared between groups and correlated with: albumin-bilirubin (ALBI) and "model for end-stage liver disease-sodium" (MELD-Na) score) and fibrosis/portal hypertension (Fibrosis-4 (FIB-4) Score, liver stiffness measurement (LSM), hepatic venous pressure gradient (HVPG), platelet count (PLT), and spleen volume.

RESULTS: We included 197 subjects, aged 54.9 ± 13.8 years (mean ± standard deviation), 111 males (56.3%): 35 healthy controls, 44 non-ACLD, and 118 ACLD patients. TVVR and HVVR were highest in controls (3.9; 2.1), intermediate in non-ACLD (2.8; 1.7), and lowest in ACLD patients (2.3; 1.0) (p ≤ 0.001). PVVR was reduced in both non-ACLD and ACLD patients (both 1.2) compared to controls (1.7) (p ≤ 0.001), but showed no difference between CLD groups (p = 0.999). HVVR significantly correlated indirectly with FIB-4, ALBI, MELD-Na, LSM, and spleen volume (ρ ranging from -0.27 to -0.40), and directly with PLT (ρ = 0.36). TVVR and PVVR showed similar but weaker correlations.

CONCLUSION: Deep learning-based hepatic vessel volumetry demonstrated differences between healthy liver and chronic liver disease stages and shows correlations with established markers of disease severity.

RELEVANCE STATEMENT: Hepatic vessel volumetry demonstrates differences between healthy liver and chronic liver disease stages, potentially serving as a non-invasive imaging biomarker.

KEY POINTS: Deep learning-based vessel analysis can provide automated quantification of hepatic vascular changes across healthy liver and chronic liver disease stages. Automated quantification of hepatic vasculature shows significantly reduced hepatic vascular volume in advanced chronic liver disease compared to non-advanced disease and healthy liver. Decreased hepatic vascular volume, particularly in the hepatic venous system, correlates with markers of liver dysfunction, fibrosis, and portal hypertension.

PMID:40794249 | DOI:10.1186/s41747-025-00612-y

Int J Comput Assist Radiol Surg. 2025 Aug 12. doi: 10.1007/s11548-025-03491-1. Online ahead of print.

ABSTRACT

PURPOSE: Numerous medical procedures, such as pharmaceutical fluid injections and biopsies, require the use of a surgical needle. During such procedures, the localization of the needle is of prime importance, both to ensure that no vital organs will be or have been damaged and to confirm that the target location has been reached. The guidance to a target and its localization is done using different imaging devices, such as MRI machines, CT scans, and US devices. All of them suffer from artifacts, making the accurate localization, especially the tip, of the needle difficult. This implies the necessity for a new needle guidance technique.

METHODS: The movement of a needle through human tissue produces vibroacoustic signals which may be leveraged to retrieve information on the needle's location using data processing and deep learning techniques. We have constructed a specialized phantom with animal tissue submerged in gelatine to gather the data needed to prove this hypothesis.

RESULTS AND CONCLUSION: This paper summarizes our initial experiments, in which we preprocessed the data, converted it into two different spectrogram representations (Mel and continuous wavelet transform spectrograms), and used them as input for two different deep learning models: NeedleNet and ResNet-34. The goal of this work was to chart out an optimal direction for further research.

PMID:40794229 | DOI:10.1007/s11548-025-03491-1

J Assist Reprod Genet. 2025 Aug 12. doi: 10.1007/s10815-025-03570-x. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate whether the use of a fully automated AI-based scoring system (iDAScore V2) for selecting viable embryos using fetal heartbeat (FHB) as an indicator is equivalent to morphology assessment.

METHODS: A retrospective observational cohort study across four fertility centers analyzed embryos selected for single embryo transfer on Day 3 or Day 5 + based on morphology and time-lapse video. All transferred embryos from participating centers were retrospectively scored using a fully automated AI-based embryo scoring algorithm and standardized morphology assessment. The predictive ability of both methods for implantation (FHB rate) was compared for Day 3 and Day 5 + transfer.

RESULTS: A multi-center analysis revealed that AI-based embryo scoring significantly outperformed morphological embryo assessment in predicting FHB for both Day 3 (n = 2965) and Day 5 + (n = 6970) transfers (P < 0.0001). Similarly, the discrimination of low versus high scores regarding FHB resulted in a significantly better area under the curve (AUC) for iDAScore V2 compared to standardized morphology assessment for Day 3 (0.63; 95% CI: 0.61-0.65 versus 0.59; 95% CI: 0.58-0.61) and for Day 5 + (0.59; 95% CI: 0.57-0.60 versus 0.55; 95% CI: 0.54-0.57).

CONCLUSIONS: As a multi-center validation of fully automated embryo assessment, this study confirms that AI-based selection provides outcomes that are either equivalent to or superior to morphological embryo assessment, without compromising clinical outcomes.

PMID:40794157 | DOI:10.1007/s10815-025-03570-x

Neuroradiology. 2025 Aug 12. doi: 10.1007/s00234-025-03733-8. Online ahead of print.

ABSTRACT

PURPOSE: This study compares a novel reconstruction algorithm deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) for CTA in acute ischemic stroke (AIS) patients, emphasizing DLIR's potential to improve diagnostic accuracy and visualization of large vessel occlusion.

METHODS: This study retrospectively assessed 108 consecutive AIS-suspected emergency department patients (mean age 72.3 years +/- 17) who underwent head and neck CTA with DLIR and ASIR-V reconstructions. The analysis compared the impact of DLIR versus ASIR-V on image quality, assessing signal-to-noise (SNR), contrast-to-noise ratios (CNR), and contrast-enhanced arteries homogeneity computed on mean HU values and SD in six regions of interest located in head and neck including three arteries.

RESULTS: The DLIR reconstruction allowed for significant SNR and CNR improvement, with the largest SNR distinction obtained in the common carotid artery (52.29% increased SNR) and white matter of the pons (63.98% increased SNR). Among the three regions subject to CNR evaluation DLIR yielded superiority in the neck and posterior cerebral fossa while ASIR-V accounted for higher CNR in the medial cerebral fossa (MCF). Additionally, DLIR-reconstructed images achieved a 21.10% improvement in arterial homogeneity, enhancing the visualization of potential occlusion.

CONCLUSION: DLIR yields superior image quality of the contrast-enhanced head and neck structures in CTA, providing artery images with increased homogeneity and potentially allowing for more proficient occlusion evaluation specifically in the area of the posterior cerebral fossa. However, this technique faces challenges in the visualization of MCF.

PMID:40794135 | DOI:10.1007/s00234-025-03733-8

Biopreserv Biobank. 2025 Aug 12. doi: 10.1177/19475535251367317. Online ahead of print.

ABSTRACT

Introduction: This study is part of the broader Stem Line project Mito-Cell-UAB073, specifically focusing on "Stem Cell Lines-Quality Control," and aims to innovate in the field of Quality Control (QC) through a unique, artificial intelligence (AI)-powered model known as Life Cell AI UAB. This model utilizes deep learning algorithms and computer vision, allowing it to make accurate viability assessments of cell and stem cell lines based solely on static images captured through standard optical microscopes. Aim: The aim of this study was to develop and validate an AI-driven, image-based model that reliably predicts cell line viability. Methods: Our methodology involved training the Life Cell AI UAB model on single static images of cell lines using advanced computer vision and deep learning techniques. Performance evaluation was conducted on three independent blind test sets sourced from various biotechnology laboratories, allowing for assessment across diverse environments. Results: The Life Cell AI UAB model achieved a sensitivity of 82.1% in identifying viable cell lines and a specificity of 67.5% for non-viable lines across the test sets. Each blind test set exhibited a weighted accuracy above 63%, with a combined accuracy of 64.3%. Notably, predictions showed a clear distinction between correctly and incorrectly classified cells. The model outperformed traditional QC methods by improving accuracy in binary classification tasks by 21.9% (p = 0.042) and demonstrated a 42.0% enhancement over conventional Standard Operation Procedure (SOP) procedures (p = 0.026). Conclusion: The Life Cell AI UAB model represents a notable advancement in biobanking QC, offering a precise, standardized, and non-invasive method for assessing cell line viability. This model has the potential to streamline QC processes across laboratories, minimizing the need for time-lapse imaging and promoting uniformity in QC practices for both cell and stem cells.

PMID:40793964 | DOI:10.1177/19475535251367317

Int J Surg. 2025 Aug 11. doi: 10.1097/JS9.0000000000003182. Online ahead of print.

NO ABSTRACT

PMID:40793836 | DOI:10.1097/JS9.0000000000003182

Metabolomics. 2025 Aug 12;21(5):111. doi: 10.1007/s11306-025-02316-1.

ABSTRACT

INTRODUCTION: Myocardial revascularization (MR) is recommended in acute myocardial infarction. Understanding the physiological disturbances caused by MR may be pertinent for future therapeutic strategies in the postoperative period.

OBJECTIVES: This study aims to analyze the MR impact on plasma metabolites and investigate potential correlations between them with routinely measured biochemical and clinical parameters in MR, and with the cardiopulmonary bypass time (CPB).

METHODS: Twenty-five patients had plasma samples collected before and after MR for metabolomic analysis, performed by liquid chromatography coupled with high-resolution mass spectrometry.

RESULTS: One hundred eleven ions showed statistically significant differences due to MR and thirteen were identified. Only Pregnenolone Sulfate had its abundance in plasma decreased due to MR. Hydrocortisone succinate, Cortisone, and Corticosterone increased in response to the glucocorticoids administered during surgery. LysoPS 16:1, LysoPC 14:0, Phenylvaleric acid, 13-Hydroxyoctadecadienoic acid, N-Linoleoyl Glutamine, and N-Myristoyl Methionine, along with the significant increase in the white blood cell count are associated with inflammation processes, possibly caused by MR. Furthermore, Pregnenolone sulfate, Pentosidine, Phenylvaleric acid, and N-Myristoyl Methionine were correlated with biochemical/clinical parameters and CPB.

CONCLUSION: These results open new horizons in the interpretation of physiological disturbances caused by MR, as well as provide support for future studies. The scientific community is invited to build upon the outcomes obtained to confirm the associations suggested in this study, advancing the realm of metabolomics and MR.

PMID:40794378 | DOI:10.1007/s11306-025-02316-1

Metabolomics. 2025 Aug 12;21(5):112. doi: 10.1007/s11306-025-02310-7.

ABSTRACT

INTRODUCTION: Rheumatoid arthritis (RA) is an auto-immune disease which causes irreversible damage to tissue and cartilage within synovial joints. Rapid diagnosis and treatment with disease-modifying therapies is essential to reduce inflammation and prevent joint destruction. RA is a heterogeneous disease, and many patients do not respond to front-line therapies, requiring escalation of treatment onto biologics, of which TNF inhibitors (TNF-i) are the most common.

OBJECTIVES/METHODS: In this study we determined whether serum metabolomics, using nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) spectroscopy, could discriminate RA blood sera from healthy human controls and whether the technologies could be used to predict response or non-response to TNF inhibitor (TNF-i) therapy.

RESULTS: NMR spectroscopy identified 35 metabolites in RA sera, with acetic acid being significantly lower in RA sera compared to healthy controls (HC, FDR < 0.05). PLS-DA modelling identified 2-hydroxyisovalericacetic acid, acetoacetic acid, mobile lipids, alanine and leucine as important metabolites for discrimination of RA and HC sera by 1H NMR spectroscopy (averaged 83.1% balanced accuracy, VIP score > 1). FTIR spectroscopy identified a significant difference between RA and HC sera in the 1000-1200 cm- 1 spectral area, representing the mixed region of carbohydrates and nucleic acids (FDR < 0.05). Sera from RA patients who responded to TNF-i were significantly different from TNF-i non-responder sera in the 1600-1700 cm- 1 region (FDR < 0.05).

CONCLUSION: We propose that NMR and FTIR serum metabolomics could be used as a diagnostic tool alongside current clinical parameters to diagnose RA and to predict whether someone with severe RA will respond to TNF-i.

PMID:40794325 | DOI:10.1007/s11306-025-02310-7

Transl Cancer Res. 2025 Jul 30;14(7):4179-4193. doi: 10.21037/tcr-2024-2563. Epub 2025 Jul 23.

ABSTRACT

BACKGROUND: Neuroblastoma is a highly heterogeneous pediatric malignancy, with high-risk cases exhibiting poor clinical outcomes. Store-operated calcium entry (SOCE) channels have been implicated in cancer progression, yet their prognostic significance in neuroblastoma remains unclear. This study aimed to investigate the relevance of SOCE-related genes in predicting patient prognosis and guiding therapeutic strategies.

METHODS: We performed unsupervised clustering based on SOCE-related gene expression in multiple neuroblastoma RNA sequencing (RNA-seq) datasets. A prognostic scoring system, the SOCE_Score, was developed using machine learning algorithms. The model's predictive performance was validated across independent datasets. Immune characteristics were assessed using established deconvolution algorithms, and candidate therapeutic compounds were identified via the Connectivity Map (CMap) platform.

RESULTS: Two distinct molecular clusters were identified, differing significantly in survival outcomes, immune infiltration, and stemness signatures. The SOCE_Score stratified patients with high accuracy and outperformed conventional clinical predictors. Lower SOCE_Score groups were associated with favorable immune landscapes and greater responsiveness to immune checkpoint blockade. CMap analysis highlighted MS-275, a histone deacetylase (HDAC) inhibitor, as a promising compound targeting low SOCE_Score phenotypes.

CONCLUSIONS: SOCE-related transcriptional features serve as robust biomarkers for prognosis and immune activity in neuroblastoma. The SOCE_Score holds potential for guiding risk stratification, immunotherapeutic selection, and drug repurposing efforts. These findings underscore the clinical utility of integrating calcium signaling profiles into neuroblastoma management and warrant further experimental validation.

PMID:40792155 | PMC:PMC12335702 | DOI:10.21037/tcr-2024-2563