Identification of key genes and immune infiltration in multiple myeloma by bioinformatics analysis

Multiple Myeloma (MM) is a hematologic malignant disease with unclear molecular mechanisms. This integrated bioinformatic study aimed to identify key genes, pathways and immune cell infiltration pattern in MM.Differentially expressed genes (DEGs) from GSE6477 and GSE16558 dataset were filtrated with R package 'limma', whose function were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The key genes were selected from Protein-protein interaction network (PPI) and logistic regression model. The correlation between key genes and survival in MM was evaluated using the survival and survminer package. Additionally, immune filtration analysis was accomplished by CIBERSORT tools.118 DEGs (92 up-regulated and 26 down-regulated) from two GSE datasets were identified, which were closely related with B cell receptor signaling pathway and Epstein-Barr virus infection. Furthermore, CD24 and PTPRC of five hub genes identified in PPI network were further screened out by the logistic regression model. Besides, CD24 and PTPRC expression were significantly correlated to the survival time in MM patients. Finally, MM might cause different infiltrating immune cell compositions, including increased infiltrations of B cells memory, Plasma cells, T cells CD4 memory resting, T cells follicular helper, Tregs, NK cells resting, Macrophages(M0/M1), Dendritic cells resting and Mast cells activating, and lower proportions of B cells naïve, T cells CD4 naïve, Macrophages M2 and Neutrophils.Targeting CD24 and PTPRC as molecular markers of MM is valuable to MM therapy. Moreover, the immune cell infiltration will provide new insights into MM immunopathology.