Paeoniflorin alleviates high glucose-induced endothelial cell apoptosis in diabetes mellitus by inhibiting HRAS-activated RAS pathway.

Paeoniflorin (Pae) can improve diabetes mellitus (DM), especially endothelial dysfunction induced by high glucose (HG). Molecularly, the mechanism pertinent to Pae and DM lacks further in-depth research. Hence, this study determined the molecular mechanism of Pae in treating DM through network pharmacology. The target of Pae was analyzed by TCMSP database, and DM-related genes were dissected by Genecards database and Omim database. PPI network was constructed for cross targets through Cytoscape 3.9.1 and STRING platform. GO and KEGG analyses were carried out on the cross targets. Protein molecular docking verification was completed by AutoDockTools and Pymol programs. Human umbilical vein endothelial cells (HUVECs) were separately treated with HG, Pae (5, 10, 20 µM) and/or HRAS overexpression plasmids (oe-HRAS). The cell viability, apoptosis and the protein expressions of HRAS and Ras-GTP were evaluated. There were 50 cross targets between Pae and DM, and VEGFA, EGFR, HRAS, SRC and HSP90AA1 were the key genes identified by PPI network analysis. GO and KEGG analyses revealed signal paths such as Rap1 and Ras. Molecular docking results confirmed that Pae had a good binding ability with key genes. In HG-treated HUVECs, Pae dose-dependently facilitated cell viability, attenuated cell apoptosis, and dwindled the expressions of HRAS and Ras-GTP, but these effects of Pae were reversed by oe-HRAS. In conclusion, Pae regulates the viability and apoptosis of HG-treated HUVECs by inhibiting the expression of HRAS.

Tumor promoting effects of glucagon receptor: a promising biomarker of papillary thyroid carcinoma via regulating EMT and P38/ERK pathways.

Glucagon is a crucial hormone involved in the maintenance of glucose homeostasis. Large efforts to define the role of glucagon receptor (GCGR) have been continuously made in recent years, but it is still incomplete about its function and mechanism. We performed this study to verify its potential impacts on papillary thyroid carcinoma (PTC) progression. Correlation between GCGR expression and PTC was elaborated using The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier method was used to analyze the connection between GCGR expression and prognosis of PTC patients. GCGR expression was measured by quantitative real-time polymerase chain reaction (qRT-PCR) and western blot analysis; simultaneously, cell viability was elucidated using cell proliferation and colony formation assays following siRNAs strategy. Transwell analyses were conducted to measure the invasion and migration of PTC cells. Flow cytometry analysis was conducted to examine apoptotic ability. The cAMP ELISA kit was employed to measure the cAMP level in PTC cells. Our data determined that the expression level of GCGR was increased in PTC tissues and cells in contrast to normal tissues and Nthy-ori 3-1, respectively. Up-regulated GCGR expression was linked with the lower survival rate in patients with PTC. Functional analysis in vitro suggested that GCGR knockdown attenuated PTC cell proliferation, colony formation, invasion, and migration whilst intensified apoptosis. Down-regulated GCGR was able to increase cAMP level. Furthermore, reduction of GCGR could result in the inactivation of epithelial-mesenchymal transition (EMT) and P38/ERK pathways. In conclusion, the findings of this study disclosed that GCGR promoted PTC cell behaviors by mediating the EMT and P38/ERK pathways, serving as a potential diagnostic and prognostic biomarker as well as therapeutic target for PTC.

Comparison of 128-Slice Low-Dose Prospective ECG-Gated CT Scanning and Trans-Thoracic Echocardiography for the Diagnosis of Complex Congenital Heart Disease.

OBJECTIVE: To compare prospective ECG-gated multi-slice computed tomography (MSCT) and trans-thoracic echocardiography (TTE) in the diagnosis of complex congenital heart disease (CHD). METHODS: This was a prospective study of consecutive patients with complex CHD (age <7 years) treated at a tertiary hospital between May 2013 and May 2015. All patients were imaged with TTE and prospective ECG-gated 128-slice spiral CT in the week before surgery. Effective radiation dose (ED) was calculated from volume CT dose index (CTDIvol) and dose length product (DLP). Image quality (5-point scale) was assessed independently by two radiologists. Using surgical findings as the reference, the diagnostic capabilities of MSCT and TTE were compared. RESULTS: Thirty-five patients (19 males) aged 1.59±1.58 years (range, 3 days to 74 months) were included. CTDIvol, DLP and ED were 0.90±0.24 mGy, 12.9±4.7 mGy∙cm and 0.64±0.21 mSv (range, 0.358-1.196 mSv), respectively. Image quality score was 4.3±0.5, and all images met the diagnostic requirements. The sensitivity, specificity, positive predictive value, and negative predictive value for diagnosing CHD were 97.2%, 99.8%, 99.0%, and 99.5%, respectively, for MSCT, and 90.6%, 99.8%, 99.0%, and 98.4%, respectively, for TTE. MSCT not only had a higher sensitivity than TTE overall (97.2% vs. 90.6%; P<0.05), but was much more sensitive for the diagnosis of extracardiac vascular abnormalities (92.0% vs. 68.0%; P<0.05). CONCLUSION: 128-slice low-dose prospective ECG-gated CT scanning has important clinical value in the diagnosis of complex CHD in children, complementing and extending the findings of TTE.

Integration of gene expression data and genetic variations involved in breast cancer.

PURPOSE: The studies of transcriptome and genome involved in breast cancer are effectively promote the understanding of biological processes and the development of novel targeted therapies. Here we performed an integrated analysis of gene expression and genetic variation to disclose the molecular pathogenesis in breast cancer. METHODS: Gene expression profiles were applied to identify differential expression levels of genes between breast cancer and normal subjects. DNA sequencing data were extracted to analyze gene mutational information including number of mutations, number of mutated genes and their chromosomal distributions. Correlation analysis of gene mutations and differential expression was performed. Network-based approach was applied to compare the topological properties between the differentially expressed (DE) genes prone to mutation and those that(were not. Two-tailed p<0.05 was considered as statistically significant. RESULTS: Statistical analysis showed that DE genes presented significantly positive correlation with the number of mutations (p=1.267E(-05)), mutated genes (p=0.00001) and total genes in the genome (p=2.489E(-06)). There were 81 genes, both DE and mutant, and they were distributed on chromosome 4 (N=51), chromosome 15 (N=29), and chromosome 18 (N=1). These 81 genes showed an increase in the number of genes interacting with in the protein-protein network. CONCLUSION: Analysis of the integration of transcriptome and genome in breast cancer disclosed distinctive topology between the DE genes prone to mutation and those that were not.