Comprehensive bioinformatics analysis reveals the hub genes and pathways associated with multiple myeloma

Abstract

Purpose While the prognosis of multiple myeloma (MM) has significantly improved over the last decade because of new treatment options, it remains incurable. Aetiological explanations and biological targets based on genomics may provide additional help for rational disease intervention. Materials and Methods Three microarray datasets associated with MM were downloaded from the Gene Expression Omnibus (GEO) database. GSE125364 and GSE39754 were used as the training set, and GSE13591 was used as the verification set. The differentially expressed genes (DEGs) were obtained from the training set, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate their functions. The hub genes were derived from the combined results of a protein–protein interaction (PPI) network and weighted gene coexpression network analysis (WGCNA). The receiver operating characteristic (ROC) curves of hub genes were plotted to evaluate their clinical diagnostic value. Biological processes and signaling pathways associated with hub genes were explained by gene set enrichment analysis (GSEA). Results A total of 1759 DEGs were identified. GO and KEGG pathway analyses suggested that the DEGs were related to the process of protein metabolism. RPN1, SEC61A1, SPCS1, SRPR, SRPRB, SSR1 and TRAM1 were proven to have clinical diagnostic value for MM. The GSEA results suggested that the hub genes were widely involved in the N-glycan biosynthesis pathway. Conclusion The hub genes identified in this study can partially explain the potential molecular mechanisms of MM and serve as candidate biomarkers for disease diagnosis.

Keywords

• multiple myeloma
• bioinformatics
• weighted gene coexpression network analysis
• gene set enrichment analysis
• aetiology
• biomarker