Classification and prediction of frontotemporal dementia based on plasma microRNAs

Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder characterized by frontal and temporal lobe atrophy, typically manifesting with behavioural or language impairment. Because of its heterogeneity and lack of available diagnostic laboratory tests there can be a substantial delay in diagnosis. Cell-free, circulating, microRNAs are increasingly investigated as biomarkers for neurodegeneration, but their value in FTD is not yet established. In this study, we investigate microRNAs as biomarkers for FTD diagnosis. We performed next generation small RNA sequencing on cell-free plasma from 52 FTD cases and 21 controls. The analysis revealed the diagnostic importance of 20 circulating endogenous miRNAs in distinguishing FTD cases from controls. The study was repeated in an independent second cohort of 117 FTD cases and 35 controls. The combinatorial microRNA signature from the first cohort, precisely diagnosed FTD samples in a second cohort. To further increase the generalizability of the prediction, we implemented machine learning techniques in a merged dataset of the two cohorts, which resulted in a comparable or improved classification precision with a smaller panel of miRNA classifiers. In addition, there are intriguing molecular commonalities with cell free miRNA signature in ALS, a motor neuron disease that resides on a pathological continuum with FTD. However, the signature that describes the ALS-FTD spectrum is not shared with blood miRNA profiles of patients with multiple sclerosis. Thus, microRNAs are promising FTD biomarkers that might enable earlier detection of FTD and improve accurate identification of patients for clinical trials