P-240: Survival Analysis and Transcriptomic Profiling Unveil Key Genes in Multiple Myeloma: Insights from MMRF CoMMpass Data

Introduction: Multiple myeloma (MM) is a heterogeneous hematological malignancy characterized by clonal proliferation of plasma cells in the bone marrow. Despite advancements in treatment, MM remains incurable in most cases, necessitating a deeper understanding of its molecular mechanisms and prognostic factors. Transcriptomic profiling has emerged as a powerful tool for identifying biomarkers and therapeutic targets in MM. In this study, we conducted a comprehensive analysis of gene expression data from MM patients to elucidate the role of specific genes in MM prognosis and pathogenesis.

Methods: We obtained gene expression data from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, which includes transcriptomic profiles of MM patients. Survival analysis was performed to compare the expression levels of various genes between survived and deceased patients. Differential expression analysis was conducted to identify genes with significant fold changes and p-values. Drug-associated genes were identified from the Drug Gene Interaction Database and examined for potential adverse event (AE) clinical features using OMIM. Additionally, gene-gene interaction analysis was performed using STRING to explore potential pathways and networks underlying MM pathogenesis. Signaling pathways were compared using QIAGEN Ingenuity Pathway Analysis (IPA) to elucidate molecular mechanisms associated with MM progression and treatment response.

Results: Our analysis revealed significant associations between survival outcomes and the expression levels of several genes in MM patients. Notably, BRCA1 (p < 0.0001), ATM (p = 0.00023), CYBA (p = 0.019), IFNG (p = 0.023), NR3C1 (p = 0.00055), PIK3CA (p = 0.00039), PIK3CG (p = 0.049), CTLA4 (p = 0.0049), NFE2L2 (p = 0.035), and RPS19 (p = 0.029) exhibited significant prognostic value. Differential expression analysis identified potential biomarkers associated with MM prognosis, providing insights into disease biology and treatment response. Moreover, gene-gene interaction analysis revealed intricate networks and pathways implicated in MM pathogenesis.

Conclusions: Our study sheds light on the molecular landscape of MM and highlights the prognostic significance of specific genes in disease progression. The integration of transcriptomic data with clinical outcomes offers valuable insights into disease biology and therapeutic targets. These findings have implications for personalized medicine approaches and the development of novel therapies to improve outcomes for MM patients. Further research is warranted to validate the prognostic value of identified genes and explore their therapeutic potential in MM management.