Abstract
BACKGROUND: Multiple myeloma (MM) is a B-cell malignancy characterized by clonal plasma cell proliferation in the bone marrow. Although significant advances have been achieved in treatment, it remains largely incurable, and fundamental insights at the molecular level remain to be obtained. OBJECTEVES: This study aimed to identify key genes and microRNA (miRNA) involved in multiple myeloma by re-analyzing transcriptomic datasets. We sought to determine differentially expressed genes and miRNAs, perform pathway and network analyses, and highlight their roles in disease progression, prognosis, and therapeutic resistance. MATERIALS AND METHODS: We identified and characterized the hub genes and miRNAs associated with MM by re-analyzing three microarray datasets, GSE16558, GSE141260, and GSE146649, using high-throughput sequencing. We re-identified DEGs using a strict filtering criterion: |logFC| ≥ 1 and p-value < 0.05. The application of the Venn diagram analysis highlighted 13 common DEGs among the datasets. A total of 3211 differentially expressed genes (DEGs) and 25 differentially expressed microRNAs (DEMs) were screened out, Thereafter, GO and pathway enrichment of the DEGs were analyzed using FunRich software, involving biological processes, cellular components, and molecular functions. The PPI network was constructed using the Cytoscape software to determine the interactions among these DEGs. RESULTS: Our analyses underlined several key biological processes, including the migration of immune cells, lymphocyte activation, and TGF-β signaling pathways, which play crucial roles in the progression of MM. The PPI network identified a number of hub genes; among these, CCND1, ITGB1, and CREB1 were significantly associated with patient survival outcomes. In addition, the interaction predictions indicated an important function of miR-34c-5p and miR-155-5p in governing apoptosis, thereby promoting drug resistance in MM cells. We identified 13 common DEGs across datasets, with key enrichments in immune cell migration, lymphocyte activation, and TGF-β signaling. PPI analysis revealed CCND1, ITGB1, and CREB1 as top hub genes, significantly linked to survival outcomes. MiRNA interactions, particularly miR-34c-5p and miR-155-5p, were implicated in apoptosis and drug resistance. CONCLUSION: These data highlight the complex interplay between genetic alterations and the immune microenvironment in MM, opening new prospects for biomarkers and therapeutic targets that may hopefully improve patient management, treatment strategies, prognosis, and therapeutic resistance.