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BackgroundAberrant expression of Nicotinamide N-methyltransferase (NNMT) has been reported in pancreatic cancer. However, the role of NNMT in pancreatic cancer development remains elusive. Therefore, the present study was to investigate the impact of NNMT on pancreatic cancer cell proliferation, metastatic potential and survival under metabolic stress.MethodsPancreatic cancer cell line PANC-1 was transfected with NNMT expression plasmid or small interfering RNA of NNMT to overexpress or knockdown intracellular NNMT expression, respectively. Rate of cell proliferation was monitored. Transwell migration and matrigel invasion assays were conducted to assess cell migration and invasion capacity. Resistance to glucose deprivation, sensitivity to glycolytic inhibition, mitochondrial inhibtion and resistance to rapamycin were examined to evaluate cell survival under metabolic stress.ResultsNNMT silencing markedly reduced cell proliferation, whereas NNMT overexpression promoted cell growth moderately. Knocking down NNMT also significantly suppressed the migration and invasion capacities of PANC-1 cells. Conversely, NNMT upregulation enhanced cell migration and invasion capacities. In addition, NNMT knockdown cells were much less resistant to glucose deprivation and rapamycin as well as glycolytic inhibitor 2-deoxyglucose whereas NNMT-expressing cells showed opposite effects although the effects were not so striking.ConclusionsThese data sugguest that NNMT plays an important role in PANC-1 cell proliferation, metastatic potential and survival under metabolic stress.

The cell metabolome comprises abundant information that may be predictive of cell functions in response to epigenetic or genetic changes at different stages of cell proliferation and metastasis. An unbiased ultra-performance liquid chromatography-mass spectrometry-based metabolomics study revealed a significantly altered metabolome for human pancreatic carcinoma PANC-1 cells with gain-of-function non-coding microRNA-1291 (miR-1291), which led to a lower migration and invasion capacity as well as suppressed tumorigenesis in a xenograft tumor mouse model. A number of metabolites, including N-methylnicotinamide, involved in nicotinamide metabolism, and l-carnitine, isobutyryl-carnitine and isovaleryl-carnitine, involved in fatty acid metabolism, were elevated in miR-1291-expressing PANC-1. Notably, N-methylnicotinamide was elevated to the greatest extent, and this was associated with a sharp increase in nicotinamide N-methyltransferase (NNMT) mRNA level in miR-1291-expressing PANC-1 cells. In addition, expression of NNMT mRNA was inversely correlated with pancreatic tumor size in the xenograft mouse model. These results indicate that miR-1291-altered PANC-1 cell function is associated with the increase in N-methylnicotinamide level and NNMT expression, and in turn NNMT may be indicative of the extent of pancreatic carcinogenesis.

Background: Muscular dystrophies and congenital myopathies encompass various inherited muscular disorders that present diagnostic challenges due to clinical complexity and genetic heterogeneity. Methods: This study aimed to investigate the use of whole exome sequencing (WES) in diagnosing muscular disorders in pediatric patients in Taiwan. Out of 161 pediatric patients suspected to have genetic/inherited myopathies, 115 received a molecular diagnosis through conventional tests, single gene testing, and gene panels. The remaining 46 patients were divided into three groups: Group 1 (multiplex ligation-dependent probe amplification-negative Duchenne muscular dystrophy) with three patients (6.5%), Group 2 (various forms of muscular dystrophies) with 21 patients (45.7%), and Group 3 (congenital myopathies) with 22 patients (47.8%). Results: WES analysis of these groups found pathogenic variants in 100.0% (3/3), 57.1% (12/21), and 68.2% (15/22) of patients in Groups 1 to 3, respectively. WES had a diagnostic yield of 65.2% (30 patients out of 46), detecting 30 pathogenic or potentially pathogenic variants across 28 genes. Conclusion: WES enables the diagnosis of rare diseases with symptoms and characteristics similar to congenital myopathies and muscular dystrophies, such as muscle weakness. Consequently, this approach facilitates targeted therapy implementation and appropriate genetic counseling. [ABSTRACT FROM AUTHOR]

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data. Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS). Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies. Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC. [ABSTRACT FROM AUTHOR]