Anti-VEGFA monoclonal antibody, clone 27G2

Background: The aim of this study was to find genes with significantly aberrant expression in diabetic nephropathy (DINT) and determine their underlying mechanisms. Methods: GSE30528 and GSE1009 were obtained by querying the Gene Expression Omnibus (GEO) database. The difference in target gene expression between normal renal tissues and kidney tissues in patients with DN was screened by using the GEO2R tool. Using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database, differentially expressed genes (DEGs) were analysed by Gene Ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Then, the protein-protein interactions (PPIs) of DEGs were analyzed by Cytoscape with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the hub genes in this PPI network were recognized by centrality analysis. Results: There were 110 genes with significant expression differences between normal and DN tissues. The differences in gene expression involved many functions and expression pathways, such as the formation of the extracellular matrix and the construction of the extracellular domain. The correlation analysis and subgroup analysis of 14 hub genes and the clinical characteristics of DN showed that CTGF, ALB, PDPN, FLT1, IGF1, WT1, GJA1, IGFBP2, FGF9, BMP2, FGF1, BMP7, VEGFA, and TGFBR3 may be involved in the progression of DN. Conclusions: We confirmed the differentially expressed hub genes and other genes which may be the novel biomarker and target candidates in DNT.

Breast cancer is the most common cause of death in women worldwide. Approximately 5%-10% of instances are attributed to mutations acquired from the parents. Therefore, it is highly recommended to design more potential drugs and drug targets to eradicate such complex diseases. Network-based gene expression profiling is a suggested tool for discovering drug targets by incorporating various factors such as disease states, intensities based on gene expression as well as protein-protein interactions. To find prospective biomarkers in breast cancer, we first identified differentially expressed genes (DEGs) statistical methods p-value and false discovery rate were initially used. Of the total 82 DEGs, 67 were upregulated while the remaining 17 were downregulated. Sub-modules and hub genes include VEGFA with the highest degree, followed by 15 CCND1 and CXCL8 with 12-degree score was found. The survival analysis revealed that all the hub genes have important role in the development and progression of breast cancer. Enrichment analysis revealed that most of these genes are involved in signaling pathways and in the extracellular spaces. We also identified transcription factors and kinases, which regulate proteins in the DEGs PPI. Finally, drugs for each hub genes were identified. These results further expanded the knowledge regarding important biomarkers in breast cancer.