Identification potential biomarkers and therapeutic agents in multiple myeloma based on bioinformatics analysis.

OBJECTIVE

The study aimed to identify potential therapeutic biomarkers and agents in multiple myeloma (MM) based on bioinformatics analysis.

MATERIALS AND METHODS

The microarray data of GSE36474 were downloaded from Gene Expression Omnibus database. A total of 4 MM and 3 normal bone marrow mesenchymal stromal cells (BM-MSCs) samples were used to identify the differentially expressed genes (DEGs). The hierarchical clustering analysis and functional enrichment analysis of DEGs were performed. Furthermore, co-expression network was constructed by Cytoscape software. The potential small molecular agents were identified with Connectivity Map (cMap) database.

RESULTS

A total of 573 DEGs were identified in MM samples comparing with normal samples, including 322 down- and 251 up-regulated genes. The DEGs were separated into two clusters. Down-regulated genes were mainly enriched in cell cycle function, while up-regulated genes were related to immune response. Down-regulated genes such as checkpoint kinase 1 (CHEK1), MAD2 mitotic arrest deficient-like 1 (MAD2L1) and DBF4 zinc finger (DBF4) were identified in cell cycle-related co-expression network. Up-regulated gene of guanylate binding protein 1, interferon-inducible (GBP1) was a hub node in immune response-related co-expression network. Additionally, the small molecular agent vinblastine was identified in this study.

CONCLUSIONS

The genes such as CHEK1, MAD2L1, DBF4 and GBP1 may be potential therapeutic biomarkers in MM. Vinblastine may be a potential therapeutic agent in MM.