Zhu Y, Chang S, Liu J, Wang B. Identification of a novel cuproptosis-related gene signature for multiple myeloma diagnosis.
Immun Inflamm Dis 2023;
11:e1058. [PMID:
38018590 PMCID:
PMC10629272 DOI:
10.1002/iid3.1058]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/19/2023] [Accepted: 10/11/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND
Multiple myeloma (MM) ranks second among the most prevalent hematological malignancies. Recent studies have unearthed the promise of cuproptosis as a novel therapeutic intervention for cancer. However, no research has unveiled the particular roles of cuproptosis-related genes (CRGs) in the prediction of MM diagnosis.
METHODS
Microarray data and clinical characteristics of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to identify potential signature genes for MM diagnosis. Predictive performance was further assessed by receiver operating characteristic (ROC) curves, nomogram analysis, and external data sets. Functional enrichment analysis was performed to elucidate the involved mechanisms. Finally, the expression of the identified genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in MM cell samples.
RESULTS
The optimal gene signature was identified using LASSO and SVM-RFE algorithms based on the differentially expressed CRGs: ATP7A, FDX1, PDHA1, PDHB, MTF1, CDKN2A, and DLST. Our gene signature-based nomogram revealed a high degree of accuracy in predicting MM diagnosis. ROC curves showed the signature had dependable predictive ability across all data sets, with area under the curve values exceeding 0.80. Additionally, functional enrichment analysis suggested significant associations between the signature genes and immune-related pathways. The expression of the genes was validated in MM cells, indicating the robustness of these findings.
CONCLUSION
We discovered and validated a novel CRG signature with strong predictive capability for diagnosing MM, potentially implicated in MM pathogenesis and progression through immune-related pathways.
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