Collagen XI Alpha 1 (COL11A1) Expression in the Tumor Microenvironment Drives Neuroblastoma Dissemination.

BACKGROUND: Neuroblastoma (NB) is among the most common cancers in children. A highly aggressive form of cancer, NB relies on cells in the microenvironment for dissemination particularly cancer associated fibroblast (CAFs). CAFs synthesise the extracellular matrix to create a scaffold for tumor growth thus enabling the carcinogenesis of NB, Collagen, an abundant scaffold protein produced by CAFs, has been implicated in the creation of an optimal tumor microenvironment, however, the expression profile of collagen within NB is not yet known. METHODS: We characterised collagen expression within the tumor-stroma boundary by microarray and confirmed by qRT-PCR and immunohistochemistry. RESULTS: The collagen marker, COL11A1, was also upregulated in NB CD45+ cells and SMA+ CAFs. Furthermore, SMA+ CAFs led to neuroblastoma cell invasion in an in vitro co-culture system which was subsequently attenuated by gene silencing COL11A1. Immunohistochemical staining of clinical tumor samples revealed that high COL11A1 expression in the stroma adjacent to tumour site, significantly associated with advanced cancer stages, age ≥18 months, undifferentiated tumor status, relapse and poor overall survival. CONCLUSION: Collectively, these results suggest that a COL11A1 signature in the NB microenvironment could represent a novel target for therapeutic intervention.

The Distribution of Autoantibodies by Age Group in Children with Type 1 Diabetes versus Type 2 Diabetes in Southern Vietnam.

Asian children are increasingly being diagnosed with type 1 diabetes (T1D) or type 2 diabetes (T2D), and the presence of coexisting islet autoimmune antibodies complicate diagnosis. Here, we aimed to determine the prevalence of islet cell autoantibodies (ICAs) and glutamic acid decarboxylase 65 autoantibodies (GADAs) in children with T1D versus T2D living in Vietnam. This cross-sectional study included 145 pediatric patients aged 10.3 ± 3.6 years, with 53.1% and 46.9% having T1D and T2D, respectively. ICAs were reported in only 3.9% of pediatric T1Ds, which was not significantly different from the 1.5% of those with T2D. Older children with T1D were positive for either ICAs, or ICAs and GADAs (5-9 and 10-15 years), whereas only a small proportion of children aged 0-4 years were positive for GADAs (18%). Notably, 27.9% of children with T2D aged 10-15 were positive for GADAs, and all were classified as overweight (n = 9) or obese (n = 10). GADAs were more commonly observed in T1D patients younger than four years than ICAs, which were more prevalent in older children (5-15 years). Even though few children with T2D carried ICAs and GADAs, finding a better biomarker or an appropriate time to confirm diabetes type may require further investigation.

Pathological brain lesions in girls with central precocious puberty at initial diagnosis in Southern Vietnam.

PURPOSE: Cranial magnetic resonance imaging (MRI) is recommended to identify intracranial lesions in girls with central precocious puberty (CPP). Yet, the use of routine MRI scans in girls with CPP is still debatable, as pathological findings in girls 6 years of age or older with CPP are limited. Therefore, we aimed to identify the prevalence of brain lessons in CPP patients stratified by age group (0-2, 2-6, and 6-8 years). METHODS: This retrospective cross-sectional study recruited 257 girls diagnosed with CPP for 6 years (2010-2016). MRI was used to detect brain abnormalities. Levels of luteinizing hormone, follicle-stimulating hormone, and sex hormones in blood samples were measured. RESULTS: Most girls had no brain lesions (82.9%, n=213), and of the minor proportion of girls with CPP that exhibited brain lesions (17.1%, n=44), 32 girls had organic CPP. Pathological findings were detected in 33.3% (2 of 6) of girls aged 0-2 years, 15.6% (5 of 32) of girls aged 2-6 years, and 3.6% (8 of 219) of girls aged 6-8 years. Hypothalamic hamartoma and tumors in the pituitary stalk were the most common pathological findings. The likelihood of brain lesions decreased with age. Girls with organic CPP were more likely to be younger (6.1±2.4 vs. 7.3±1.3 years, p<0.01) than girls with idiopathic CPP. CONCLUSION: Older girls appeared to have a lower prevalence of organic CPP. Clinicians should cautiously use cranial MRI for girls aged 6-8 years with CPP.

Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches.

BACKGROUND: A brief gonadotropin-releasing hormone analogues (GnRHa) stimulation test which solely focused on LH 30-minute post-stimulation was considered to identify girls with central precocious puberty (CPP). However, it was tested using traditional statistical methods. With advanced computer science, we aimed to develop a machine learning-based diagnostic model that processed baseline CPP-related variables and a brief GnRHa stimulation test for CPP diagnosis. METHODS: We recruited girls suspected of precocious puberty and underwent a GnRHa stimulation test at Children Hospital 2, Vietnam, and Cathay General Hospital, Taiwan. Clinical data, bone age measurement, and 30-min post-stimulation blood test were used to build up the predictive model. The candidate model was developed by different machine learning algorithms that were mainly evaluated by sensitivity, specificity, the area under the receiver operator characteristic curve (AUC), and F1-score in internal and external validation data to classify girls as CPP and non-CPP at different time-points (0-min, 30-min, 60-min, and 120-min post-stimulation). RESULTS: Among the 614 girls diagnosed with PP, 524 (85.3%) had CPP. The random forest algorithm yielded the highest value of F1-score (0.976), specificity (0.893), positive predicted value (0.987), and relatively high value of AUC (0.972) that contributed to high probability to identify CPP. The performance metrics of the 30-min post-stimulation diagnostic model including sensitivity and specificity surpassed those of the 0-minute model (0-min) and were equivalent to those of the model obtained 60-min and 120-min post-stimulation. Hence, our machine learning-based model helps shorten the stimulation test to 30 minutes after GnRHa injection, in general, it requires 120 minutes for a completed GnRHa stimulation test. CONCLUSIONS: We developed a diagnostic model based on clinical features and a single sample 30-minute post-stimulation to identify CPP in girls that can reduce distress for children caused by multiple blood samplings.