Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer’s disease created by integrative analysis of multi-omics data

• Published in: Alzheimer's research & therapy Vol. 12; no. 1; pp. 145 - 12
• Main Authors: Shigemizu, Daichi, Akiyama, Shintaro, Higaki, Sayuri, Sugimoto, Taiki, Sakurai, Takashi, Boroevich, Keith A., Sharma, Alok, Tsunoda, Tatsuhiko, Ochiya, Takahiro, Niida, Shumpei, Ozaki, Kouichi
• Format: Journal Article
• Language: English
• Published: London BioMed Central 10.11.2020; BioMed Central Ltd; BMC
• Subjects: Age; Alzheimer's disease; Biomarkers; Biomarkers for early diagnosis; Biomedical and Life Sciences; Biomedicine; Cognitive ability; Cognitive disorders; Conversion; Dementia; Development and progression; Diagnosis; Disease; eQTL effect; Gene expression; Genetic aspects; Genomes; Geriatric Psychiatry; Geriatrics; Geriatrics/Gerontology; Health aspects; Health risk assessment; Medical diagnosis; Medical prognosis; MicroRNA; MicroRNAs; Neurology; Neurosciences; Pathogenesis; Prognosis; Japan; Alzheimer’s disease; eQTL effect; Biomarkers for early diagnosis
• ISSN: 1758-9193; 1758-9193
• DOI: 10.1186/s13195-020-00716-0

Background 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.