Abstract: The purpose of this article is to use drug repositioning (DR) strategies to predict candidate compounds for multiple myeloma, and to provide new ideas and directions for the treatment of multiple myeloma. The GEO database was used to retrieve and screen the multiple myeloma expression profile chip data, GEO2R software was used to extract differentially expressed genes (DEGs); DAVID and Panther databases were used to enrich and differentially analyze differentially expressed genes; and the STRING database was used to construct a protein-protein interaction network analysis; the differentially expressed genes were introduced into the CMAP connection map, and candidate compounds for MM were calculated and obtained, and the candidate compounds were further searched and analyzed. It was found that a total of 53 differentially expressed genes were obtained, including 10 differentially up-regulated genes and 43 differentially down-regulated genes; GO analysis showed that biological processes involved 22 functional clusters, molecular functions involved 5 functional clusters, and cellular components involved 5 functional clusters; KEGG analysis involved 10 pathways; analysis of protein-protein interaction network showed that VCAM1, CCR2, CCL3 and CXCL12 genes were the core enriched genes with the highest enrichment; the top 20 were calculated by CMAP connection map candidate compounds, of which LY-294002 and Tanespimycin might have potential therapeutic effects on MM, but further experimental research and clinical verification were needed.

Key words: drug repositioning, multiple myeloma, differentially expressed genes, candidate compounds, bioinformatics