Identification of Neutrophil Extracellular Traps-Related Biomarkers in Multiple Myeloma Using Machine Learning and Experimental Validation

Background: Multiple Myeloma (MM) is a complex hematological malignancy characterized by the uncontrolled proliferation of malignant plasma cells. Neutrophil Extracellular Traps (NETs) have recently been implicated in cancer, but the role in MM remains unclear. This study aimed to identify NETs-related biomarkers in MM and explore the underlying mechanisms using machine learning and experimental validation. Methods and Methods: Three MM-related microarray datasets were downloaded from the Gene Expression Omnibus database. Differential expression and weighted gene co-- expression network analyses were applied to identify NETs-related hub genes for MM. Machine learning algorithms were used to refine the biomarkers. The core genes were validated using receiver operating characteristic analysis, nomogram construction, and an external dataset. Functional analyses were performed to explore the underlying mechanisms. Drug-gene interaction analysis was performed to predict potential therapeutic drugs. Finally, gene expression was validated by quantitative real-time PCR in peripheral blood mononuclear cells from three MM patients and three healthy controls. Results: A total of 24 NETs-related hub genes were identified. Machine learning algorithms identified three NETs-related core biomarkers for MM: KIT proto-oncogene (KIT), Hepatocyte Growth Factor (HGF), and F2R-like trypsin receptor 1 (F2RL1). The predictive model showed good predictive performance in both the training and external cohorts. Functional analyses showed significant associations between the biomarker genes and immune-related biological processes. Additionally, potential therapeutic drugs targeting these biomarkers were identified, including diphenylpyraline, Crizotinib, 1,4-chrysenequinone, and histamine. The expression patterns of the biomarker genes were confirmed in clinical MM samples. Discussion: This study combined multiple machine learning models to identify key NETs-related biomarkers for MM. Clinical validation confirmed the relevance of the biomarkers in clinical practice. These findings provide a foundation for further research into the immune microenvironment and therapeutic strategies for MM. Conclusion: This study successfully identified and validated NETs-related biomarkers for MM through machine learning and experimental validation. Understanding the role of NETs-related biomarkers in MM could not only deepen our understanding of the disease but also offer information for clinical applications.