Genetic analysis of potential biomarkers in cuproptosis from multiple myeloma

Abstract

Abstract Background Multiple myeloma (MM) accounts for 1% of neoplastic diseases. Cuproptosis, a copper-triggered modality of mitochondrial cell death, might be a promising therapeutic target for cancer treatment. However, the role of cuproptosis-related genes (CRGs) in MM is not well characterized. Thus, we aimed to explore the diagnostic value of CRGs in MM and further illustrate the potential mechanism. Methods The differential expression of CRGs between MM and control samples was identified and validated in the GSE6477 and GSE47552 datasets downloaded from the Gene Expression Omnibus database. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to identify potential CRGs as diagnostic biomarkers for MM. Receiver operating characteristic (ROC) curve analysis was applied to determine the diagnostic performance of the biomarkers. Functional enrichment and correlation analyses were then conducted to figure out the underlying mechanisms. Results Based on the differentially expressed CRGs by the gene expression difference of samples, LASSO and SVM-RFE algorithms were used to identify a final number of two CRGs as potential biomarkers for MM: CDKN2A and GLS. The all area under the curve (AUC) values of the 2 marker gene-based logistic regression model were 0.933 and 0.886 in the training and validation cohort, respectively, indicating a good performance in predicting MM diagnosis. Functional enrichment and correlation analyses suggested that the biomarkers may promote MM cell tumorigenesis and survival by modulating the immune cells through its immune-related pathways. Conclusion Two CRGs (CDKN2A and GLS) were identified and validated as possible MM biomarkers, which developed a diagnostic potency and provided an insight for exploring the mechanism for MM.