Obesity is a medical condition in which excess body fat has accumulated to a serious extent. It is a chronic disease that can lead to dyslipidemia, insulin resistance, and type 2 diabetes. In the present study, we investigated the anti-obesity effects of Sicyos angulatus (SA) extract on a high-fat diet (HFD)-induced C57BL/6J obese mice. The mice were divided into vehicle and three SA groups (25, 50, and 100 mg/kg body weight). The mice were fed a HFD with or without SA for 12 weeks. The oral administration of SA reduced body and adipose tissue weight in HFD-fed mice compared to those in the vehicle group (p less then 0.05). Adipocyte size and inflammation significantly decreased in the SA-administered groups in a dose-dependent manner. In particular, adipocytes larger than 5000 µm2 were remarkably reduced by around 50% in the SA-treated groups (p less then 0.05). In addition, SA contributes towards reducing insulin resistance (measured as the HOMA-IR index) and glucose intolerance in HFD-induced obese mice (p less then 0.05; Vehicle 21.5±3.1 vs. SA100 4.7±0.4). These beneficial effects of SA on obesity may be linked to the suppression of lipogenesis and stimulating energy metabolism in white adipose tissue and muscle. In white adipose tissue and muscle, the administration of SA activated AMPK pathway, leading to the inhibition of the development of pathophysiological conditions associated with obesity, including lipogenesis and inflammation. These findings suggest that SA may prevent obesity through inhibiting fat accumulation in HFD-induced obese mice. Therefore, SA is able to exert metabolic benefits in the prevention of obesity and insulin resistance. © The author(s).Background DNA methylation acts as a key component in epigenetic modifications of genomic function and functions as disease-specific prognostic biomarkers for lung squamous cell carcinoma (LUSC). This present study aimed to identify methylation-driven genes as prognostic biomarkers for LUSC using bioinformatics analysis. Materials and Methods Differentially expressed RNAs were obtained using the edge R package from 502 LUSC tissues and 49 adjacent non-LUSC tissues. Differentially methylated genes were obtained using the limma R package from 504 LUSC tissues and 69 adjacent non-LUSC tissues. The methylation-driven genes were obtained using the MethylMix R package from 500 LUSC tissues with matched DNA methylation data and gene expression data and 69 non-LUSC tissues with DNA methylation data. Gene ontology and ConsensusPathDB pathway analysis were performed to analyze the functional enrichment of methylation-driven genes. Univariate and multivariate Cox regression analyses were performed to identify the independent effect of differentially methylated genes for predicting the prognosis of LUSC. Results A total of 44 methylation-driven genes were obtained. Univariate and multivariate Cox regression analyses showed that twelve aberrant methylated genes (ATP6V0CP3, AGGF1P3, RP11-264L1.4, HIST1H4K, LINC01158, CH17-140K24.1, CTC-523E23.14, ADCYAP1, COX11P1, TRIM58, FOXD4L6, CBLN1) were entered into a Cox predictive model associated with overall survival in LUSC patients. Methylation and gene expression combined survival analysis showed that the survival rate of hypermethylation and low-expression of DQX1 and WDR61 were low. The expression of DQX1 had a significantly negatively correlated with the methylation site cg02034222. Conclusion Methylation-driven genes DQX1 and WDR61 might be potential biomarkers for predicting the prognosis of LUSC. selleck chemicals © The author(s).Tumor-infiltrating immune cells are closely related to the prognosis of bladder cancer. Analysis of tumor infiltrating immune cells is usually based on immunohistochemical analysis. Since many immune cell marker proteins are not specific for different immune cells, which may induce misleading or incomplete. CIBERSORT is an algorithm to estimate specific cell types in a mixed cell population using gene expression data. In this study, the CIBERSORT algorithm was used to identify the immune cell infiltration signatures. The gene expression profiles, mutation data, and clinical data were collected from The Cancer Genome Atlas (TCGA) database. Unsupervised consensus clustering was used to acquire the immune cell infiltration subtypes of bladder cancer based on the fractions of 22 immune cell types. Four immune cell clusters with different immune infiltrate and mutation characteristics were identified. In addition, this stratification has a prognostic relevance, with cluster 2 having the best outcome, cluster 1 the worst. These clusters showed distinct mRNA expression patterns. The characteristic genes in subtype cluster 1 were mainly involved in cell division, those in subtype cluster 2 were mainly related in antigen processing and presentation, those in subtype cluster 3 were mainly involved in epidermal cell differentiation, and those in subtype cluster 4 were mainly related in the humoral immune response. These differences may affect the development of the bladder cancer, the sensitivity to treatment as well as the prognosis. Through further validation, this study may contribute to the development of personalized therapy and precision medical treatments. © The author(s).Background Multiple sclerosis (MS) is a demyelinating and disabling inflammatory disease of the central nervous system. MS is triggered by complex environmental factors which mostly affect genetically the susceptible young people. Emerging data has suggested that changes of homocysteine (Hcy), Vitamin B12 and folate serum levels may be associated with MS. However, previous findings are not always consistent. Methods In this study, we aimed to investigate the relationships between MS and Hcy, Vitamin B12 and folate with updated available data (until September, 2019). The diagnosis of MS was performed based on international criteria for the diagnosis of MS, including magnetic resonance imaging and cerebrospinal fluid tests. We searched the databases including PubMed, EMBASE, Cochrane Library and ScienceDirect. After data collection, separate analyses based on random-effect models were used to test for relationships between MS and Hcy, Vitamin B12 or folate blood levels. The effective sizes were estimated by the combined standardized mean difference (SMD) and associated 95% confidence interval (CI).