Objective To screen the differentially expressed genes (DEGs) between multiple myeloma (MM) patients and the healthy controls, to explore the pathogenesis of MM, and to provide a theoretical basis for gene diagnosis and gene therapy. Methods Gene expression profiles of MM patients were obtained from GEO database. Morpheus Online (https://software.broadinstitute.org/morpheus/) was used to determine the chip-data quality control and DEGs screening; DAVID Online (https://david.ncifcrf.gov/) was used to perform the gene ontology and pathway enrichment analysis; STRING Online (https://string-db.org/) was used to integrate the protein-protein interaction (PPI) network, and Cytoscape was used to screen the modules of PPI. Results A total of 16 211 DEGs (7 586 up-regulated genes and 8 625 down-regulated genes) were identified (P<0.05). The up-regulated DEGs enriched in biological process terms mainly involved 30 functional categories, like glycosphingolipid metabolic process, and the down-regulated mainly involved 163 functional categories, like cell division; the up-regulated DEGs enriched in molecular function terms mainly involved 29 functional categories, like protein binding, and the down-regulated mainly involved 59 functional categories, like histone binding; the up-regulated DEGs enriched in cellular component terms mainly involved 27 functional categories, like cytosol, and the down-regulated mainly involved 78 functional categories, like nucleoplasm. The KEGG analysis showed that the up-regulated DEGs mainly involved 26 pathways, like lysosome related pathway, and the down-regulated were mainly related to 27 pathways, like DNA replication. Ten genes, including CDK1, TOP2A, AURKB, BRCA1, CHEK1, PTEN, RAD51, GMPS, CDC45 and CDKN2A, were the hub DEGs with highest enrichment. Module analysis showed that the three most significant DEGs were mainly related to nuclear division, DNA replication and nucleic acid metabolism process. Conclusion A series of DEGs between MM patients and the healthy controls have been screened with a variety of bioinformatics methods, and gene and expression features of MM pathogenesis have been explained in various perspectives, which may provide the basis for targeted diagnosis therapy of MM.