Health authorities have authorized multi-center clinical studies for advanced methylation-based classifiers, marking a significant step towards broader clinical validation. The strong foundation of machine-learning techniques in DNA methylation profiling assures stakeholders of low risks, facilitating advancements in tumor classification and biomarker discovery. Continuous reviews of emerging data will ensure the relevance and accuracy of these assays as they transition into clinical settings.
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“DNA methylation profiling successfully classified gynecologic neuroectodermal tumors as known CNS tumors or sarcoma entities.”
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“Male biological sex was predicted in 54% of methylation-confirmed CNS-like tumors and none of the sarcomas.”
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“Greater declining health anxiety was significantly associated with higher DunedinPACE z-scores (0.07 SD increase, 95 % CI: 0.01, 0.13).”
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“Higher cumulative aging anxiety was significantly associated with a 0.07 SD increase (95 % CI: 0.01, 0.14) in DunedinPACE.”
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“The assay reliably distinguished methylation levels as low as 1 %, with no cross-reactivity or signal interference in the multiplex format.”
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“In the hold-out method, the model achieved a mean absolute error (MAE) of 2.80 years for the test set.”
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“Comparable performance was observed in 5-fold cross-validation, with an average MAE of 3.13 years.”
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“A new commercial assay for detection of MLH1 promoter hyper-methylation (EntroGen), which interrogates regions C and D, was assessed for its performance characteristics.”
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“The assay was 100 % concordant (52/52) for lesional samples with expected MLH1 promoter methylation status.”
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“Finding of MLH1 promoter methylation does not rule out either Lynch syndrome or ultramutated (POLE) carcinoma.”
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“Our findings confirmed a significant upregulation of miR-124-3p in the PFC in MDD.”
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“Furthermore, we identified a significant reduction in DNA methylation in one of the miR-124 promoter regions on chromosome 20 in MDD subjects.”