Comprehensive analysis of prognosis of cuproptosis-related oxidative stress genes in multiple myeloma

Abstract

Introduction: Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy. The patients’ survival outcomes vary widely. Establishing a more accurate prognostic model is necessary to improve prognostic precision and guide clinical therapy.Methods: We developed an eight-gene model to assess the prognostic outcome of MM patients. Univariate Cox analysis, Least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses were used to identify the significant genes and construct the model. Other independent databases were used to validate the model.Results: The results showed that the overall survival of patients in the high-risk group was signifificantly shorter compared with that of those in the low-risk group. The eight-gene model demonstrated high accuracy and reliability in predicting the prognosis of MM patients.Discussion: Our study provides a novel prognostic model for MM patients based on cuproptosis and oxidative stress. The eight-gene model can provide valid predictions for prognosis and guide personalized clinical treatment. Further studies are needed to validate the clinical utility of the model and explore potential therapeutic targets.

Identification of a novel cuproptosis‐related gene signature for multiple myeloma diagnosis

“… 23 The results of recent studies have emphasized the potential utility of CRGs in the development of prognostic models for MM. 24 , 25 These findings indicate the existence of an association between CRGs and MM. However, the primary focus of these studies has been placed on the use of CRGs for the prediction of MM prognosis rather than on its diagnosis.…”

“…It is worth noting that previous studies have reported the utilization of CRGs for prognostic prediction in MM 24,25 . However, given the significance of early diagnosis and effective management for improving patients' quality of life and reducing the burden of symptoms and complications, an exploration of the potential impact of CRGs on predicting the diagnosis of MM is imperative.…”

“…It is worth noting that previous studies have reported the utilization of CRGs for prognostic prediction in MM. 24 , 25 However, given the significance of early diagnosis and effective management for improving patients' quality of life and reducing the burden of symptoms and complications, an exploration of the potential impact of CRGs on predicting the diagnosis of MM is imperative. The main discovery in this study is the successful identification and validation of a gene signature associated with CRGs for MM diagnosis, which was achieved by differentially expressed gene analysis, LASSO, and SVM‐RFE algorithms.…”

“…For instance, CRGs were used to predict clinical outcome and immune response in hematological malignancies, including acute myeloid leukemia 21,22 and diffuse large B‐cell lymphoma 23 . The results of recent studies have emphasized the potential utility of CRGs in the development of prognostic models for MM 24,25 . These findings indicate the existence of an association between CRGs and MM.…”