X-G Wang1, Y Peng, X-L Song, J-P Lan. 1. Hematology Department, Zhejiang Provincial People's Hospital, Hangzhou City, China. jianpinglanlll@hotmail.com.
Abstract
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.
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.