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dc.contributor.authorShigemizu, Daichi
dc.contributor.authorAkiyama, Shintaro
dc.contributor.authorHigaki, Sayuri
dc.contributor.authorSugimoto, Taiki
dc.contributor.authorSakurai, Takashi
dc.contributor.authorBoroevich, Keith A
dc.contributor.authorSharma, Alok
dc.contributor.authorTsunoda, Tatsuhiko
dc.contributor.authorOchiya, Takahiro
dc.contributor.authorNiida, Shumpei
dc.contributor.authorOzaki, Kouichi
dc.date.accessioned2021-02-11T23:42:58Z
dc.date.available2021-02-11T23:42:58Z
dc.date.issued2020
dc.identifier.issn1758-9193
dc.identifier.doi10.1186/s13195-020-00716-0
dc.identifier.urihttp://hdl.handle.net/10072/402023
dc.description.abstractBackground: 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. Methods: We 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]). Results: The 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). Conclusions: Our 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherBMC
dc.relation.ispartofpagefrom145
dc.relation.ispartofissue1
dc.relation.ispartofjournalAlzheimer's Research & Therapy
dc.relation.ispartofvolume12
dc.subject.fieldofresearchBiomedical and clinical sciences
dc.subject.fieldofresearchcode32
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsClinical Neurology
dc.subject.keywordsNeurosciences
dc.subject.keywordsNeurosciences & Neurology
dc.titlePrognosis prediction model for conversion from mild cognitive impairment to Alzheimer's disease created by integrative analysis of multi-omics data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationShigemizu, D; Akiyama, S; Higaki, S; Sugimoto, T; Sakurai, T; Boroevich, KA; Sharma, A; Tsunoda, T; Ochiya, T; Niida, S; Ozaki, K, Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer's disease created by integrative analysis of multi-omics data, Alzheimer's Research & Therapy, 2020, 12 (1), pp. 145
dcterms.dateAccepted2020-10-26
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-02-11T23:40:16Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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