Polymorphisms of TGF-ß1 and TGF-ß3 in Chinese women with gestational diabetes mellitus.

BACKGROUND: Gestational diabetes mellitus (GDM) is a pregnancy-specific carbohydrate intolerance Which can cause a large number of perinatal and postpartum complications. The members of Transforming growth factor-ß (TGF-ß) superfamily play key roles in the homeostasis of pancreatic ß-cell and may involve in the development of GDM. This study aimed to explore the association between the polymorphisms of TGF-ß1, TGF-ß3 and the risk to GDM in Chinese women. METHODS: This study included 919 GDM patients (464 with preeclampsia and 455 without preeclampsia) and 1177 healthy pregnant women. TaqMan allelic discrimination real-Time PCR was used to genotype the TGF-ß1 (rs4803455) and TGF-ß3 (rs2284792 and rs3917201), The Hardy-Weinberg equilibrium (HWE) was evaluated by chi-square test. RESULTS: An increased frequency of TGF-ß3 rs2284792 AA and AG genotype carriers was founded in GDM patients (AA vs. AG + GG: χ2 = 6.314, P = 0.012, OR = 1.270, 95%CI 1.054-1.530; AG vs. GG + AA: χ2 = 8.545, P = 0.003, OR = 0.773, 95%CI 0.650-0.919). But there were no significant differences in the distribution of TGF-ß1 rs4803455 and TGF-ß3 rs3917201 between GDM and healthy women. In addition, no significant differences were found in allele and genotype frequencies among GDM patients with preeclampsia (PE). CONCLUSIONS: The AA and AG genotype of TGF-ß3 rs2284792 polymorphism may be significantly associated with increased risk of GDM in Chinese population.

Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Differentially Expressed Genes.

Background/Aims: The incidence of gastric cancer (GC) ranks fifth among common tumors and GC is the third leading cause of cancer-related death worldwide. The aim of this study was to develop and validate a nomogram for predicting the overall survival (OS) of patients with GC. Methods: DNA methylation (DNAm)-driven genes were identified by integrating DNAm and gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort. Then, a risk score model was built based on Kaplan-Meier (K-M), least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. Another cohort (GSE62254) was used for external validation. Results: Thirteen differentially expressed DNAm-driven genes were narrowed down to a six-gene signature (PODN, NPY, MICU3, TUBB6 and RHOJ were hypermethylated, and MYO1A was hypomethylated), which was associated with OS (P < 0.05) after survival and LASSO regression analyses. These differentially expressed genes (DEGs) with altered DNAm statuses were included in the prognostic risk score model. The univariate Cox regression analysis indicated that risk score, age, and number of positive lymph nodes were significantly associated with survival time in GC patients. The multivariate Cox regression analysis also indicated that these variables were significant prognostic factors for GC. A nomogram including these variables was constructed, and its performance in predicting the 1-, 3- and 5-year survival outcomes of GC patients was estimated through time-dependent receiver operating characteristic (ROC) curves. In addition, the clinical benefit of this model was revealed by decision curve analysis (DCA). Pathway enrichment analysis suggested that these DNAm-driven genes might impact tumor progression by affecting signaling pathways such as the "ECM RECEPTOR INTERACTION" and "DNA REPLICATION" pathways. Conclusions: The altered status of the DNAm-driven gene signature (PODN, MYO1A, NPY, MICU3, TUBB6 and RHOJ) was significantly associated with the OS of GC patients. A nomogram incorporating risk score, age and number of positive lymph nodes can be conveniently used to facilitate the individualized prediction of OS in patients with GC.