Anti-DOK5 (aa 293-306) polyclonal antibody

Retinal neovascularization occurs in various ocular disorders including proliferative diabetic retinopathy and secondary neovascular glaucoma, resulting in blindness. This paper aims to investigate the effect of microRNA-141-3p (miR-141-3p) on retinal neovascularization and retinal ganglion cells (RGCs) in glaucoma mice through the Docking protein 5 (DOK5)-mediated mitogen-activated protein kinase (MAPK) signaling pathway. Chip retrieval and difference analysis were used for the potential mechanism of miR-141-3p on glaucoma. All modeled mice were transfected with different expression of mimic or inhibitor. The expressions of miR-141-3p, DOK5, and related genes and proteins of the MAPK signaling pathway were detected by Reverse transcription quantitative polymerase chain reaction and western blot analysis. Cell proliferation, lumen formation, and apoptosis in the retinal vascular epithelial cells and RGCs were detected using Matrigel angiogenesis and terminal deoxynucleotidyl transferase mediated dUTP nick-end labeling assays. Moreover, a total of 63 and 294 differentially expressed genes were obtained in GSE2378 and GSE9944 chips, and 4 genes were within the intersection of the chips. In addition, the results showed that miR-141-3p was found to inhibit the DOK5 gene and activate the MAPK pathway. The number of RGCs, the expression of p38, extracellular-signal-regulated kinases (ERK), Jun N-terminal kinase (JNK), IGF-1, VEGF, HIF1-alpha, Bax, caspase-3, and the extent of p38, ERK, and JNK phosphorylated were decreased with miR-141-3p upregulation. Lastly, the results obtained showed that miR-141-3p inhibited the proliferation of retinal vascular epithelial cells and inhibited angiogenesis, as well as promoted apoptosis of RGCs. The study suggests that miR-141-3p inhibits retinal neovascularization in glaucoma mice by impeding the activation of the DOK5-mediated MAPK signaling pathway.

Breast cancer metastasis is a demanding problem in clinical treatment of patients with breast cancer. It is necessary to examine the mechanisms of metastasis for developing therapies. Classification of the aggressiveness of breast cancer is an important issue in biological study and for clinical decisions. Although aggressive and non-aggressive breast cancer cells can be easily distinguished among different cell lines, it is very difficult to distinguish in clinical practice. The aim of the current study was to use the gene expression analysis from breast cancer cell lines to predict clinical outcomes of patients with breast cancer. Weighted gene co-expression network analysis (WGCNA) is a powerful method to account for correlations between genes and extract co-expressed modules of genes from large expression datasets. Therefore, WGCNA was applied to explore the differences in sub-networks between aggressive and non-aggressive breast cancer cell lines. The greatest difference topological overlap networks in both groups include potential information to understand the mechanisms of aggressiveness. The results show that the blue and red modules were significantly associated with the biological processes of aggressiveness. The sub-network, which consisted of TMEM47, GJC1, ANXA3, TWIST1 and C19orf33 in the blue module, was associated with an aggressive phenotype. The sub-network of LOC100653217, CXCL12, SULF1, DOK5 and DKK3 in the red module was associated with a non-aggressive phenotype. In order to validate the hazard ratio of these genes, the prognostic index was constructed to integrate them and examined using data from the Cancer Genomic Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients with breast cancer from TCGA in the high-risk group had a significantly shorter overall survival time compared with patients in the low-risk group (hazard ratio=1.231, 95% confidence interval=1.058-1.433, P=0.0071, by the Wald test). A similar result was produced from the GEO database. The findings may provide a novel strategy for measuring cancer aggressiveness in patients with breast cancer.