Hamide Albayrak, Mustafa Ertan Ay, Anıl Tombak, Kenan Çevik, Özlem İzci Ay, Tuba Kabasakal, Mehmet Emin Erdal
{"title":"Circadian Deregulation in Multiple Myeloma: BMAL1/CLOCK Expression Patterns and Diagnostic Performance.","authors":"Hamide Albayrak, Mustafa Ertan Ay, Anıl Tombak, Kenan Çevik, Özlem İzci Ay, Tuba Kabasakal, Mehmet Emin Erdal","doi":"10.1111/ijlh.70154","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Circadian clock disruption has emerged as a relevant axis in cancer; however, the expression patterns and diagnostic relevance of BMAL1 and CLOCK in multiple myeloma (MM) remain insufficiently defined.</p><p><strong>Methods: </strong>BMAL1 and CLOCK mRNA expression was quantified by RT-qPCR in bone marrow samples from 46 newly diagnosed MM patients and 13 healthy controls. Group comparisons, ROC analyses, multivariate logistic regression, partial correlation, and machine-learning models (logistic regression, random forest, and support-vector machine) with stratified 5-fold cross-validation (including class-weighted sensitivity analysis) were performed.</p><p><strong>Results: </strong>BMAL1 expression was significantly higher in MM than in controls (1.12 ± 0.33 vs. 0.85 ± 0.33, p = 0.016; Cohen's d = 0.83), whereas CLOCK showed no significant univariate difference (p > 0.2). BMAL1 demonstrated moderate diagnostic performance (AUC = 0.729), while CLOCK alone showed limited discrimination (AUC = 0.617). Combined analysis modestly improved performance (AUC = 0.756). In multivariate logistic regression, BMAL1 remained a strong independent predictor of MM (OR = 3343.4, p = 0.0046), whereas CLOCK showed an inverse adjusted association (OR = 0.0037, p = 0.019). BMAL1 and CLOCK expression were strongly correlated in MM (ρ ≈ 0.80), persisting after adjustment for age, ISS stage, and β2-microglobulin (partial r = 0.847, p = 2.2 × 10-13). Among classifiers, the random forest achieved the best performance (AUC = 0.832; AUPRC = 0.952). Specificity was limited across classifiers, indicating that these performance estimates are exploratory and require validation in larger cohorts.</p><p><strong>Conclusion: </strong>MM is characterized by BMAL1 upregulation within a preserved but imbalanced BMAL1/CLOCK axis rather than uniform shifts across circadian genes. Although diagnostic performance is moderate, consistent signals across statistical and machine-learning approaches support an MM-associated circadian expression pattern warranting further validation.</p>","PeriodicalId":94050,"journal":{"name":"International journal of laboratory hematology","volume":" ","pages":"1123-1132"},"PeriodicalIF":0.0000,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13555196/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of laboratory hematology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/ijlh.70154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/5/21 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Circadian clock disruption has emerged as a relevant axis in cancer; however, the expression patterns and diagnostic relevance of BMAL1 and CLOCK in multiple myeloma (MM) remain insufficiently defined.
Methods: BMAL1 and CLOCK mRNA expression was quantified by RT-qPCR in bone marrow samples from 46 newly diagnosed MM patients and 13 healthy controls. Group comparisons, ROC analyses, multivariate logistic regression, partial correlation, and machine-learning models (logistic regression, random forest, and support-vector machine) with stratified 5-fold cross-validation (including class-weighted sensitivity analysis) were performed.
Results: BMAL1 expression was significantly higher in MM than in controls (1.12 ± 0.33 vs. 0.85 ± 0.33, p = 0.016; Cohen's d = 0.83), whereas CLOCK showed no significant univariate difference (p > 0.2). BMAL1 demonstrated moderate diagnostic performance (AUC = 0.729), while CLOCK alone showed limited discrimination (AUC = 0.617). Combined analysis modestly improved performance (AUC = 0.756). In multivariate logistic regression, BMAL1 remained a strong independent predictor of MM (OR = 3343.4, p = 0.0046), whereas CLOCK showed an inverse adjusted association (OR = 0.0037, p = 0.019). BMAL1 and CLOCK expression were strongly correlated in MM (ρ ≈ 0.80), persisting after adjustment for age, ISS stage, and β2-microglobulin (partial r = 0.847, p = 2.2 × 10-13). Among classifiers, the random forest achieved the best performance (AUC = 0.832; AUPRC = 0.952). Specificity was limited across classifiers, indicating that these performance estimates are exploratory and require validation in larger cohorts.
Conclusion: MM is characterized by BMAL1 upregulation within a preserved but imbalanced BMAL1/CLOCK axis rather than uniform shifts across circadian genes. Although diagnostic performance is moderate, consistent signals across statistical and machine-learning approaches support an MM-associated circadian expression pattern warranting further validation.