Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis. Issue 12 (18th June 2019)

• Record Type: Journal Article
• Authors: Yan, Haimeng; Zheng, Gaofeng; Qu, Jianwei; Liu, Yang; Huang, Xi; Zhang, Enfan; Cai, Zhen
• Is Part Of: Journal of cellular physiology. Volume 234:Issue 12(2019:Dec.)
• Page Start: 23785
• Page End: 23797
• Publication Date: 2019-06-18
• Subjects: bioinformatics analysis -- immune‐associated genes -- multiple myeloma -- prognosis; Physiology -- Periodicals; Cell physiology -- Periodicals; 571.6
• DOI: 10.1002/jcp.28947
• Languages: English
• ISSNs: 0021-9541

Abstract

Abstract: Multiple myeloma (MM) is a common hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. This study aimed to elucidate key candidate genes and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE6477 and GSE47552 were obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) with p < .05 and [logFC] > 1 were identified. Functional enrichment, protein–protein interaction network construction and survival analyses were then performed. First, 51 upregulated and 78 downregulated DEGs shared between the two GSE datasets were identified. Second, functional enrichment analysis showed that these DEGs are mainly involved in the B cell receptor signaling pathway, hematopoietic cell lineage, and NF‐kappa B pathway. Moreover, interrelation analysis of immune system processes showed enrichment of the downregulated DEGs mainly in B cell differentiation, positive regulation of monocyte chemotaxis and positive regulation of T cell proliferation. Finally, the correlation between DEG expression and survival in MM was evaluated using the PrognoScan database. In conclusion, we identified key candidate genes that affect the outcomes of patients with MM, and these genes might serve as potential therapeutic targets.