A gene expression signature based on cell-death related gene expression in multiple myeloma

Abstract

Abstract Background Multiple myeloma (MM) is a complex hematological malignancy characterized by the uncontrolled monoclonal expansion of malignant plasma cells within the bone marrow. The identification of reliable prognostic biomarkers is paramount in the precise risk stratification of MM patients and the tailoring of personalized therapeutic interventions. Methods This comprehensive study harnessed gene expression profiles and clinical data obtained from a cohort of 2080 Multiple myeloma patients drawn from five distinct datasets. These resources were leveraged to construct a prognostic model centered around the intricate phenomenon of cell death. We meticulously integrated microarray gene expression data from the GEO database and the MMRF-CoMMpass dataset sourced from the TCGA website. Our approach for formulating the cell death signature encompassed genes associated with a diverse array of cell death mechanisms, including apoptosis, autophagy, pyroptosis, and necroptosis. Utilizing Lasso regression, we meticulously selected variables and assigned weights, ultimately culminating in the selection of 40 genes for the development of the cell death risk score model. In addition, we conducted a thorough gene set enrichment analysis to probe the biological pathways that underwent aberrant activation within the high-risk patient cohort. Results Our cell death prognosis model exhibited exceptional proficiency in predicting overall survival. When integrated with the International Staging System (ISS), our model further refined the precision of prognosis prediction. Furthermore, our gene set enrichment analysis unveiled the abnormal activation of multiple pivotal biological pathways within the high-risk patient subset. Conclusion The prognosis model founded upon cell death-associated genes not only offers outstanding predictive performance but also facilitates the enhanced identification of high-risk MM patients. It stands as a robust tool for customizing treatment strategies and refining risk stratification. This groundbreaking research holds substantial promise in advancing our understanding of MM pathogenesis, thereby bolstering the development of more efficacious therapeutic approaches.