Abstract

BACKGROUND: Multiple myeloma (MM) is the second most common hematologic cancer with poor prognosis. Novel therapeutic strategies are needed to decrease the high mortality rate. The aim of this study was to identify prospective agents for MM.

MATERIAL AND METHODS: A microarray dataset was mined, which contains the transcriptome profiles of 588 MM patients. Univariate Cox analysis was performed to analyze the relationships between genes and clinical outcome. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were determined. Protective and risky genes were uploaded to Connectivity Map (CMAP) database to identify the potentially unknown effects of existing drugs. An example was selected to be docked on the known molecules.

RESULTS: A total of 1445 genes significantly correlated with the event free survival (EFS) of MM patients were identified and included 676 protective and 769 risky indicators. KEGG pathway analysis revealed that these prognosis-associated genes were enriched in the “cell cycle,” “DNA replication,” and “P53 signaling pathway”. The top t3 most significant potential molecules were vorinostat, trifluoperazine, and thioridazine. CDK1 (cyclin-dependent kinase-1) ranked as the core in the class of prognosis-related genes in MM based on protein-protein interaction (PPI) network analysis. With Sybyl-X 2.0, the majority of the top 10 molecules aforementioned displayed high binding forces with CDK1. Among these molecules, trichostatin A had the greatest ability in combining with CDK1.

CONCLUSIONS: Genes that mainly accumulate in the cell cycle pathway play an essential role in the prognosis of MM, and these prognosis-related genes also have great value in drug development.

Keywords: Drug Repositioning, Multiple Myeloma, Prognosis, transcriptome, Biomarkers, Pharmacological, Computational Biology, Databases, Genetic, Gene Expression Profiling, gene ontology, Gene Regulatory Networks, Molecular Docking Simulation, Precision Medicine, Progression-Free Survival, Proportional Hazards Models, Prospective Studies, Protein Interaction Maps