Comprehensive bioinformatics analysis reveals key hub genes linked to prognosis in multiple myeloma with drug resistance

Multiple myeloma (MM) is an incurable hematologic malignancy, with chemotherapy being the primary treatment. However, the development of drug resistance remains a major challenge. This study aimed to identify therapeutic targets associated with drug resistance in MM and assess their prognostic significance. Gene expression data from GSE82307, GSE146649, and GSE136725 were analyzed to identify differentially expressed genes (DEGs) using the "limma" and "RobustRankAggreg" R packages. Functional enrichment analysis and protein-protein interaction (PPI) network analysis were performed, with key network modules identified using Cytoscape. The expression and prognostic relevance of DEGs were validated using MM patient samples from the GSE136725 and MMRF CoMMpass databases. A total of 4623 DEGs were identified, and robust rank aggregation analysis revealed the top 20 upregulated genes. Among them, AURKA, DLGAP5, BUB1B, and KIF20A were highly expressed in drug-resistant patients and were associated with poor prognosis. The findings suggest that AURKA, DLGAP5, BUB1B, and KIF20A are potential biomarkers linked to drug resistance and recurrence in MM. Further studies are required to elucidate the underlying molecular mechanisms and explore their potential as therapeutic targets.