{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T15:56:18Z","timestamp":1792511778701,"version":"4.5.0"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T00:00:00Z","timestamp":1729728000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["iubmb.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioFactors"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Mitochondrial quality regulation plays an important role in affecting the treatment sensitivity of multiple myeloma (MM). We aimed to develop a mitochondrial quality regulation genes (MQRGs)\u2010related prognostic model for MM patients. The Genomic Data Commons\u2010MM of bulk RNA\u2010seq, mutation, and single\u2010cell RNA\u2010seq (scRNA\u2010seq) dataset were downloaded, and the MQRGs gene set was collected previous study. \u201cmaftools\u201d and CIBERSORT were used for mutation and immune\u2010infiltration analysis. Subsequently, the \u201cConsensusClusterPlus\u201d was used to perform the unsupervised clustering analysis, \u201csurvminer\u201d and \u201cssGSEA\u201d R package was used for the Kaplan\u2013Meier survival and enrichment analysis, \u201climma\u201d R, univariate and Least Absolute Shrinkage and Selection Operator Cox were used for RiskScore model. The \u201ctimeROC\u201d R package was used for Receiver Operating Characteristic Curve analysis. Finally, the \u201cSeurat\u201d R package was used for scRNA\u2010seq analysis. These MQRGs are mainly located on chromosome\u20101,2,3,7, and 22 and had significant expression differences among age, gender, and stage groups, in which PPARGC1A and PPARG are the high mutation genes. Most MQRGs expression are closely associated with the plasma cells infiltration and can divide the patients into 2 different prognostic clusters (C1, C2). Then, 8 risk models were screened from 60 DEGs for RiskScore, which is an independent prognostic factor and effectively divided the patients into high and low risk groups with significant difference of immune checkpoint expression. Nomogram containing RiskScore can accurately predict patient prognosis, and a series of specific transcription factor PRDM1 and IRF1 were identified. We described the based molecular features and developed a high effective MQRGs\u2010related prognostic model in MM.<\/jats:p>","DOI":"10.1002\/biof.2135","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T09:31:23Z","timestamp":1729762283000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Construction of mitochondrial quality regulation genes\u2010related prognostic model based on bulk\u2010\n                    <scp>RNA<\/scp>\n                    \u2010seq analysis in multiple myeloma"],"prefix":"10.1002","volume":"51","author":[{"given":"Xiaohui","family":"Li","sequence":"first","affiliation":[{"name":"Hematology Department Third Affiliated Hospital of Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hematology Department Third Affiliated 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