Tea consumption and risk of lower respiratory tract infections: a two-sample mendelian randomization study.

BACKGROUND: Observational studies have reported the association between tea consumption and the risk of lower respiratory tract infections (LRTIs). However, a consensus has yet to be reached, and whether the observed association is driven by confounding factors or reverse causality remains unclear. METHOD: A two-sample Mendelian randomization (MR) analysis was conducted to determine whether genetically predicted tea intake is causally associated with the risk of common LRTI subtypes. Genome-wide association study (GWAS) from UK Biobank was used to identify single-nucleotide polymorphisms (SNPs) associated with an extra cup of tea intake each day. The summary statistics for acute bronchitis, acute bronchiolitis, bronchiectasis, pneumonia, and influenza and pneumonia were derived from the FinnGen project. RESULTS: We found that genetically predicted an extra daily cup of tea intake was causally associated with the decreased risk of bronchiectasis [odds ratio (OR) = 0.61, 95% confidence interval (CI) = 0.47-0.78, P < 0.001], pneumonia (OR = 0.90, 95% CI = 0.85-0.96, P = 0.002), influenza and pneumonia (OR = 0.91, 95% CI = 0.85-0.97, P = 0.002), but not with acute bronchitis (OR = 0.91, 95% CI = 0.82-1.01, P = 0.067) and acute bronchiolitis (OR = 0.79, 95% CI = 0.60-1.05, P = 0.100). Sensitivity analyses showed that no heterogeneity and pleiotropy could bias the results. CONCLUSIONS: Our findings provided new evidence that genetically predicted an extra daily cup of tea intake may causally associated with a decreased risk of bronchiectasis, pneumonia, and influenza and pneumonia.

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer.

Objective: Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods: Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan-Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results: Univariate Cox regression and Kaplan-Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan-Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa (p < 0.05). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions: The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.

Suppression of synuclein gamma inhibits the movability of endometrial carcinoma cells by PI3K/AKT/ERK signaling pathway.

BACKGROUND: Although overexpression of synuclein gamma (SNCG) has been reported in several cancers, few studies have been performed onSNCG in endometrial carcinomas. OBJECTIVE: This study aimed to investigate the role of SNCG in the progression of endometrial carcinoma. METHODS: The expression pattern and function ofSNCG gene were analyzed using the Gene Expression Omnibus (GEO) and Gene Set Enrichment Analysis (GSEA) datasets. Two vector types, containing either SNCG or negative control shRNAs, were used to evaluate cell proliferation, apoptosis, and metastasis using Cell Counting Kit 8, colony formation, flow cytometry, wound-healing, transwell, and invasion assays. The relative protein levels of N-cadherin, E-cadherin, vimentin, p-PI3K, PI3K, p-AKT, AKT, p-ERK, and ERK were determined by western bloting. RESULTS: Our results revealed thatSNCG mRNA expression and SNCG protein levels in shRNA-treated SPEC2 cells were lower than in the negative control cells. Furthermore, cell proliferation, migration, and invasion were significantly inhibited in SNCG shRNA-treated cells, but apoptosis was increased. The results of western blot analysis indicated that SNCG silencing reduced the protein levels of N-cadherin, vimentin, p-PI3K, p-AKT, and p-ERK, but not those of total PI3K, AKT, and ERK. CONCLUSIONS: Therefore, shRNA-mediated suppression of SNCG inhibited SPEC2 cell proliferation, migration, and invasion, and promoted SPEC2 cell apoptosis, which was presumably accomplished via regulation of the PI3K/AKT/ERK signaling pathway.

Cytotoxic 9,19-cycloartane triterpenes from the aerial parts of Cimicifuga yunnanensis.

Six new 9,19-cycloartane triterpenes (1-6) were isolated from the aerial parts of Cimicifuga yunnanensis. The new chemical structures were determined by extensive analyses of 1D and 2D NMR spectroscopy. Compounds 1 and 2 are the first 9,19-cycloartane triterpenes characterized by CH3-18 shifting from C-13 to C-12 in the Cimicifuga spp. The evaluation of inhibition activity against human HL-60, SMMC-7721, A-549, MCF-7, and SW480 cell lines indicated that compounds 1-6 showed different levels of cytotoxic activities with IC50 values ranging from 1.2 to 27.8 µm.