In silico bioinformatics analysis for identification of differentially expressed genes and therapeutic drug molecules in Glucocorticoid-resistant Multiple myeloma

Multiple myeloma (MM), second most common hematological malignancy, still remains irremediable because of acquisition of drug resistance. Glucocorticoid (GC) therapy, which is used as one of the key therapies against MM, is hindered by the incidence of GC resistance. The underlying mechanism of this acquired GC resistance in MM is not fully elucidated. Therefore, the present study was aimed to identify the differentially expressed genes (DEGs), associated micro RNAs (miRNAs), and transcription factors (TFs) from the microarray datasets of GC-resistant and GC-sensitive MM cell lines, obtained from Gene Expression Omnibus (GEO) database. DEGs were identified using GEO2R tool from two datasets and common DEGs were obtained by constructing Venn diagram. Then the Gene ontology (GO) and pathway enrichment analysis were performed using DAVID database. Genetic alterations in DEGs were examined using COSMIC database. Protein–protein interaction (PPI) network of DEGs was constructed using STRING database and Cytoscape tool. Network of interaction of DEGs and miRNAs as well as TFs were obtained and constructed using mirDIP, TRRUST, and miRNet tools. Drug gene interaction was studied to identify potential drug molecules by DGIdb tool. Six common DEGs, CDKN1A, CDKN2A, NLRP11, BTK, CD52, and RELN, were found to be significantly upregulated in GC-resistant MM and selected for further analysis. miRNA analysis detected hsa-mir-34a-5p that could interact with maximum target DEGs. Two TFs, Sp1 and Sp3, were found to regulate the expression of selected DEGs. The entire study may provide a new understanding about the GC resistance in MM.