Multiple Myeloma: Bioinformatic Analysis for Identification of Key Genes and Pathways

Abstract

Multiple myeloma (MM) is a hematological malignancy in which monoclonal plasma cells multiply in the bone marrow and monoclonal immunoglobulins are overproduced in older people. Several molecular and cytogenetic advances allow scientists to identify several genetic and chromosomal abnormalities that cause the disease. The comprehension of the pathophysiology of MM requires an understanding of the characteristics of malignant clones and the changes in the bone marrow microenvironment. This study aims to identify the central genes and to determine the key signaling pathways in MM by in silico approaches. A list of 114 differentially expressed genes (DEGs) is important in the prognosis of MM. The DEGs are collected from scientific publications and databases (https://www.ncbi.nlm.nih.gov/). These data are analyzed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software (https://string-db.org/) through the construction of protein-protein interaction (PPI) networks and enrichment analysis of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, by CytoHubba, AutoAnnotate, Bingo Apps plugins in Cytoscape software (https://cytoscape.org/) and by DAVID database (https://david.ncifcrf.gov/). The analysis of the results shows that there are 7 core genes, including TP53; MYC; CDND1; IL6; UBA52; EZH2, and MDM2. These top genes appear to play a role in the promotion and progression of MM. According to functional enrichment analysis, these genes are mainly involved in the following signaling pathways: Epstein-Barr virus infection, microRNA pathway, PI3K-Akt signaling pathway, and p53 signaling pathway. Several crucial genes, including TP53, MYC, CDND1, IL6, UBA52, EZH2, and MDM2, are significantly correlated with MM, which may exert their role in the onset and evolution of MM.

Keywords: Multiple myeloma; bioinformatics; gene expression; genetic predisposition; heterogeneity; mutational profiles.

Figure 1.

Pipeline chart of all study analysis steps. DEG indicates differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction.

Figure 2.

Network of protein-protein interaction. The network view (evidence view) summarizes the set of predicted associations for a group of 110 genes. The nodes of the network are the gene product, and the edges represent the predicted functional associations. The edges are represented by lines of different colors that indicate the type of interaction to predict the associations. Clicking on a node will give detailed information about the protein and clicking on an edge will display a detailed breakdown of the evidence.

Figure 3.

Gene Ontology enrichment. The diagram represents the most significant GO terms according to the number of genes involved in the network, which are indicated in parenthesis in the diagram. GO indicates Gene Ontology.

Figure 4.

Network of genes interaction degree. Proteins with a higher degree of importance are more likely to be essential.

Figure 5.

Clustered protein association network. The cluster network provides 13 groups (clusters) of all 114 myeloma gene sets. Each node presents a gene or gene product, the color of the node indicates the 3-dimensional structure of the protein. Each gene cluster indicates a biological function.