Postmenopausal osteoporosis (PMOP) is a major global public health concern and older women are more susceptible to experiencing fragility fractures. Our study investigated the associations between circulating proteins with bone mineral density (BMD) in postmenopausal women with or without low BMD (osteoporosis and osteopenia) using a tandem mass tag (TMT) labeling proteomic experiment and parallel reaction monitoring testing. Across all plasma samples, we quantitatively measured 1,092 proteins, and the OP and normal control (NC) samples were differentiated by principal component analysis and a partial least squares-discrimination analysis model based on the protein profiling data. The differentially abundant proteins between the low BMD and NC samples mostly exhibited binding, molecular function regulator, transporter and molecular transducer activity, and were involved in metabolic and cellular processes, stimulus response, biological regulation, immune system processes and so forth. TMT analysis and RRM validation indicated that the expression of protein Lysozyme C (P61626) was negatively related to BMD, while the expression of proteins Glucosidase (A0A024R592) and Protein disulfideisomerase A5 (Q14554) was positively related to BMD values. Collectively, our results suggest that postmenopausal women with low BMD have a different proteomic profile or signature. Protein alterations may play an important role in the pathogenesis of PMOP, and they may act as novel biomarkers and targets of therapeutic agents for this disease.

The aim of the present study was to investigate the effects of 2,3,4',5-tetrahydroxystilbene-2-O-beta-D-glucoside, an active component extracted from Polygonum multiflorum, on pressure overload-induced cardiac remodeling in rats. A rat model with cardiac remodeling was induced by abdominal aortic banding. 2,3,4',5-Tetrahydroxystilbene-2-O-beta-D-glucoside (30, 60, 120 mg/kg/day) was administered 3 days after abdominal aortic banding and continued for 30 days. The abdominal aortic banding-treated rats had significantly elevated blood pressure, left ventricular hypertrophy, and myocardial fibrosis. Left ventricular hypertrophy was characterized by an increase in the ratios of the heart and left ventricular weights to body weight, and increased myocyte cross-sectional areas, hypertrophic ventricular septum, and left ventricular posterior wall. The accumulation of myocardial interstitial perivascular collagen and elevated cardiac hydroxyproline content indicated myocardial fibrosis. The pathological changes above were attenuated by 2,3,4',5-tetrahydroxystilbene-2-O-beta-D-glucoside. Additionally, it markedly reduced collagen I and III expressions and regulated matrix metalloproteinase-2,9 and inhibitors of metalloproteinase expressions, as markers of myocardial fibrosis. Furthermore, we explored the underlying mechanisms for such effects of 2,3,4',5-tetrahydroxystilbene-2-O-beta-D-glucoside. The results showed that it significantly reduced myocardium angiotensin II, enhanced the activities of superoxide dismutase and glutathione peroxidase in serum and myocardial tissue, as well as inhibited protein expression of transforming growth factor-β1 and phosphorylation of extracellular signal-regulated kinase 1/2 and p38 mitogen-activated protein kinase in the myocardial tissue. Our results suggest that 2,3,4',5-tetrahydroxystilbene-2-O-beta-D-glucoside could prevent cardiac remodeling induced by pressure overload in rats. The underlying mechanisms may be related to a decreasing angiotensin II level, an antioxidant effect of the tested compound, suppression of transforming growth factor-β1 expression, and inhibition of extracellular signal-regulated kinase 1/2 and p38 mitogen-activated protein kinase activation.

Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer death worldwide. Discovery of diagnostic biomarkers prompts the early detection of GC. In this study, we used limma method combined with joint mutual information (JMI), a machine learning algorithm, to identify a signature of 11 genes that performed well in distinguishing tumor and normal samples in a stomach adenocarcinoma cohort. Other two GC datasets were used to validate the classifying performances. Several of the candidate genes were correlated with GC tumor progression and survival. Overall, we highlight the application of feature selection approaches in the analysis of high-dimensional biological data, which will improve study accuracies and reduce workloads for the researchers when identifying potential tumor biomarkers.