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您的检索式：作者名="Simone Gurlit"
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| 1 | Machine Learning to Detect Alzheimer’s Disease from Circulating Non-coding RNAs显示文摘Blood-borne small non-coding(snc RNAs)are among the prominent candidates for blood-based diagnostic tests.Often,high-throughput approaches are applied to discover biomarker signatures.These have to be validated in larger cohorts and evaluated by adequate statistical learning approaches.Previously,we published high-throughput sequencing based microRNA(miRNA)signatures in Alzheimer’s disease(AD)patients in the United States(US)and Germany.Here,we determined abundance levels of 21 known circulating miRNAs in 465 individuals encompassing AD patients and controls by RT-qPCR.We computed models to assess the relation between miRNA expression and phenotypes,gender,age,or disease severity(Mini-Mental State Examination;MMSE).Of the 21 miRNAs,expression levels of 20 miRNAs were consistently de-regulated in the US and German cohorts.18 miRNAs were significantly correlated with neurodegeneration(Benjamini-Hochberg adjusted P<0.05)with highest significance for miR-532-5 p(BenjaminiHochberg adjusted P=4.8×10^-30).Machine learning models reached an area under the curve(AUC)value of 87.6%in differentiating AD patients from controls.Further,ten miRNAs were significantly correlated with MMSE,in particular miR-26a/26b-5p(adjusted P=0.0002).Interestingly,the miRNAs with lower abundance in AD were enriched in monocytes and T-helper cells,while those up-regulated in AD were enriched in serum,exosomes,cytotoxic t-cells,and B-cells.Our study represents the next important step in translational research for a miRNA-based AD test. | Nicole Ludwig Tobias Fehlmann Fabian Kern Manfred Gogol Walter Maetzler Stephanie Deutscher Simone Gurlit Claudia Schulte Anna-Katharina von Thaler Christian Deuschle Florian Metzger Daniela Berg Ulrike Suenkel Verena Keller Christina Backes Hans-Peter Lenhof Eckart Meese Andreas Keller | 2019 | Genomics, Proteomics & Bioinformatics2019，17，4： | 2 |
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