Identification of Biomarker and Biological Risk Genes to Drive Drug Repurposing in Malaria Using Transcriptomics Database

This study applied the transcriptomic-based bioinformatics analysis to systematically integrate data on risk loci for malaria biology and drug discovery from various databases. it was hypothesized that genomic-driven drug repurposing can be utilized as an alternative approach for malaria drug repurposing. herein, transcriptomic profiles were extracted and retrieved from the ncbi-geo website by using the keywords malaria and homo sapiens. in sum, the data mining analysis for malaria drug targets was conducted by integrating the three datasets, including gse33811, gse7586, and gse5418. limma package was used to detect differentially expressed genes (degs). the following cut-off criteria were used for screening degs: [log fold change (logfc)] > 1 and p-value < 0.05. this study employed a scoring system with seven criteria called functional annotations to prioritize the risk gene candidates for malaria. following the scoring system, a score of> 2 was identified as a malaria biological risk gene. overlapping analyses between gene target candidates and drug candidates were conducted using the drugbank database to obtain new drug targets for malaria. eighty drug-target genes were identified in this study, but only five genes exhibited druggability and overlapped with the targets of existing drugs, thereby presenting a potential avenue for malaria drug repurposing. these genes, namely psmb2, cxcr4, itga4, raf1, and ptger3, hold promising prospects for malaria therapy. interestingly, five genes were overlapped with 12 drug candidates (sorafenib, regorafenib, dabrafenib, natalizumab, vedolizumab, bimatoprost, dinoprostone, misoprostol, gemeprost, castor oil, carfilzomib, and plerixafor). in conclusion, an in-silico drug screening system not only can identify the biological risk genes but also potential to drives malaria drug repurposing.