{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:10:49Z","timestamp":1771373449711,"version":"3.50.1"},"reference-count":65,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Immunity Inflam &amp;amp; Disease"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Multiple\u00a0myeloma (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\u2010related genes (CRGs) in the prediction of MM diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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\u2010recursive feature elimination (SVM\u2010RFE) algorithms were applied to identify potential signature genes\u00a0for 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\u2010time polymerase chain reaction (qRT\u2010PCR) in MM cell samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The optimal gene signature was identified using LASSO and SVM\u2010RFE algorithms based on the differentially expressed CRGs: <jats:italic>ATP7A<\/jats:italic>, <jats:italic>FDX1<\/jats:italic>, <jats:italic>PDHA1<\/jats:italic>, <jats:italic>PDHB<\/jats:italic>, <jats:italic>MTF1<\/jats:italic>, <jats:italic>CDKN2A<\/jats:italic>, and <jats:italic>DLST<\/jats:italic>. Our gene signature\u2010based 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\u2010related pathways. The expression of the genes was validated in MM cells, indicating the robustness of these findings.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We discovered and validated a novel CRG signature with strong predictive capability for diagnosing MM, potentially implicated in MM pathogenesis and progression through immune\u2010related pathways.<\/jats:p><\/jats:sec>","DOI":"10.1002\/iid3.1058","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T12:55:22Z","timestamp":1699361722000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Identification of a novel cuproptosis\u2010related gene signature for multiple myeloma diagnosis"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8505-8257","authenticated-orcid":false,"given":"Yidong","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Traditional Chinese 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