Abstract
BackgroundMild cognitive impairment (MCI) is a precursor to Alzheimer’s disease (AD), but not all MCI patients develop AD. Biomarkers for early detection of individuals at high risk for MCI-to-AD conversion are urgently required.MethodsWe used blood-based microRNA expression profiles and genomic data of 197 Japanese MCI patients to construct a prognosis prediction model based on a Cox proportional hazard model. We examined the biological significance of our findings with single nucleotide polymorphism-microRNA pairs (miR-eQTLs) by focusing on the target genes of the miRNAs. We investigated functional modules from the target genes with the occurrence of hub genes though a large-scale protein-protein interaction network analysis. We further examined the expression of the genes in 610 blood samples (271 ADs, 248 MCIs, and 91 cognitively normal elderly subjects [CNs]).ResultsThe final prediction model, composed of 24 miR-eQTLs and three clinical factors (age, sex, and APOE4 alleles), successfully classified MCI patients into low and high risk of MCI-to-AD conversion (log-rank test P = 3.44 × 10−4 and achieved a concordance index of 0.702 on an independent test set. Four important hub genes associated with AD pathogenesis (SHC1, FOXO1, GSK3B, and PTEN) were identified in a network-based meta-analysis of miR-eQTL target genes. RNA-seq data from 610 blood samples showed statistically significant differences in PTEN expression between MCI and AD and in SHC1 expression between CN and AD (PTEN, P = 0.023; SHC1, P = 0.049).ConclusionsOur proposed model was demonstrated to be effective in MCI-to-AD conversion prediction. A network-based meta-analysis of miR-eQTL target genes identified important hub genes associated with AD pathogenesis. Accurate prediction of MCI-to-AD conversion would enable earlier intervention for MCI patients at high risk, potentially reducing conversion to AD.
Highlights
Mild cognitive impairment (MCI) is a precursor to Alzheimer’s disease (AD), but not all MCI patients develop AD
The adjusted model was evaluated on the validation cohort, which was completely independent of the discovery cohort
Functional gene annotations We examined the biological significance of our findings with miR-eQTLs by focusing on the target genes of the miRNAs. miRNAs regulate the expression of thousands of mRNAs from protein-coding genes at both value from the log-rank test and comparing the differences in survival without MCI-to-AD conversion as determined by Kaplan-Meier curves. b The adjusted model was evaluated on the validation cohort. c, d Prediction models constructed using only clinical factors in the discovery cohort (c) and the validation cohort (d) the post-transcriptional and translational levels [42,43,44]
Summary
Mild cognitive impairment (MCI) is a precursor to Alzheimer’s disease (AD), but not all MCI patients develop AD. Biomarkers for early detection of individuals at high risk for MCI-to-AD conversion are urgently required. Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia, and its presence is associated with a higher risk of progression to clinically probable Alzheimer’s disease (AD) [1,2,3]. Biomarkers for early detection of MCI-C and prognosis prediction models are both desperately required. These will allow early treatment of patients with MCI before they convert to AD, which could reduce the number of patients with AD
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