Drug discovery in cardiovascular disease identified by text mining and data analysis

Abstract

Background: Cardiovascular diseases are currently prevalent in cardiology and vascular surgery in the hospital. The purpose of this study based on text mining and microarray data analysis was designed to find some existing drugs target to gene and expand the potential new drug indications. Methods: Firstly, we used text mining ("Atherosclerosis") and microarray data analysis (GSE28829) to obtain a common set of genes. Secondly, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis performed to these genes, as well as protein-protein interaction (PPI) network. Then, the significant genes clustered in the PPI network were chose to execute gene-drug interaction analysis for potential drug discovery. Results: We got 1,788 text mining genes (TMGs) and 275 differentially expressed genes (DEGs) through text mining and data analysis, respectively. Ninety-three genes were duplicated between TMGs and DEGs, in which 89 genes were up-regulated genes and four genes were down regulated. Twenty-three genes clustered in the significant gene module. Lastly, the eight out of 23 genes can target 20 existing drugs. Conclusions: The findings of these eight genes (VCAM1, CSF1R, C5AR1, CXCR4, CD86, CCR1, ITGB2, TLR8), which were associated with inflammatory response, target to 20 existing drugs may expand drug indications to atherosclerosis-related disease.