Identification and construction of a novel NET-related gene signature for predicting prognosis in multiple myeloma

Abstract

Neutrophil extracellular traps are essential in the development and advancement of multiple myeloma (MM). However, research investigating the prognostic value with NET-related genes (NRGs) in MM has been limited. Patient transcriptomic and clinical information was sourced from the gene expression omnibus database. Cox regression analysis with a univariate approach was employed to explore the link between NRGs and overall survival (OS). Kaplan-Meier methods were applied to assess variations in survival rates. A nomogram integrating clinical data and predictive risk metrics was crafted using multivariate logistic and Cox proportional risk model regression analyses. Additionally, we investigated the disparities in biological pathways, drug sensitivity, and immune cell involvement, and validated differential levels of two key genes through qPCR. We identified 148 differentially expressed NRGs through published articles, of which 14 were associated with prognosis in MM. Least absolute shrinkage and selection operator Cox regression model established a nine-gene NRG signature-comprising ANXA1, ANXA2, ENO1, HIF1A, HSPE1, LYZ, MCOLN3, THBD, and FN1-that demonstrated strong predictive power for patient survival. The Cox regression model with multiple variables demonstrated that the risk score independently predicted OS, showing that those with a high score had worse survival rates. Furthermore, a nomogram incorporating patient age, LDH levels, the International Staging System, and NRGs was developed, demonstrating strong prognostic prediction capabilities. Drug sensitivity correlation analysis also offered valuable guidance for future immuno-oncological therapies and drug selection in MM patients. The NRGs signature was a reliable biomarker for MM, effectively identifying high-risk patients and forecasting clinical outcomes.