Genome-Wide Identification of Potential Biomarkers in Multiple Myeloma Using Meta-Analysis of mRNA and miRNA Expression Data

Abstract Multiple myeloma (MM) is a plasma cell malignancy with diverse clinical phenotypes and molecular heterogeneity that is not completely understood. Recent studies have identified differentially expressed patterns of genes (DEGs) or miRNAs (DEMs) in MM. But these signatures overlap partially, plausibly due to complexity of myeloma genome, diversity in cell lines studied, molecular technologies and analytical tools utilized in these studies. This warrants further investigations since DEGs/DEMs can impact clinical outcomes and guide personalized therapy. We conducted the genome-wide meta-analysis of expression datasets on DEGs/DEMs in MM and derive net putative signatures and potential biomarkers for MM. A set of 110 DEMs and 3,817 DEGs were identified to be differentially expressed. Among these, 86 DEMs (60 downregulated; 26 upregulated) correlated with 1,970 target DEGs (1373 downregulated; 597 upregulated). Signatures of 23 DEMs (‘Union 23’) and 196 DEGs (‘Union 196’) were deduced that shared 10 DEMs and 13 DEGs with published signatures, respectively. The study has identified five topmost nodal genes (APP, KIAA0101, CDK2, ESR1 and FN1) derived from functional modules in PPI networks and has paved the way for further studies to establish their prognostic potential and role in therapeutics for MM. The integrated bioinformatics methods and expression profiling techniques may lead to the identification of putative hub genes and expression signatures that can serve as predictive biomarkers of MM progression.