1.
Hodson, R . Alzheimer’s disease. Nature. 2018;559(7715):S1.
Google Scholar |
Crossref |
Medline2.
Dubois, B, Hampel, H, Feldman, HH, et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12(3):292–323.
Google Scholar |
Crossref |
Medline3.
Kroh, EM, Parkin, RK, Mitchell, PS, Tewari, M. Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods. 2010;50(4):298–301.
Google Scholar |
Crossref |
Medline |
ISI4.
Silvestro, S, Bramanti, P, Mazzon, E. Role of miRNAs in Alzheimer’s disease and possible fields of application. Int J Mol Sci. 2019;20(16):3979.
Google Scholar |
Crossref5.
Wu, HZY, Thalamuthu, A, Cheng, L, et al. Differential blood miRNA expression in brain amyloid imaging-defined Alzheimer’s disease and controls. Alzheimers Res Ther. 2020;12(1):59.
Google Scholar |
Crossref |
Medline6.
Serpente, M, Fenoglio, C, D’Anca, M, et al. MiRNA Profiling in plasma neural-derived small extracellular vesicles from patients with Alzheimer’s disease. Cells. 2020;9(6):1443.
Google Scholar |
Crossref7.
Maffioletti, E, Milanesi, E, Ansari, A, et al. miR-146a plasma levels are not altered in Alzheimer’s disease but correlate with age and illness severity. Front Aging Neurosci. 2019;11366.
Google Scholar8.
De Felice, B, Montanino, C, Oliva, M, Bonavita, S, Di Onofrio, V, Coppola, C. MicroRNA Expression signature in mild cognitive impairment due to Alzheimer’s disease. Mol Neurobiol. 2020;57(11):4408–4416.
Google Scholar |
Crossref |
Medline9.
McKeever, PM, Schneider, R, Taghdiri, F, et al. MicroRNA Expression levels are altered in the cerebrospinal fluid of patients with young-onset Alzheimer’s disease. Mol Neurobiol. 2018;55(12):8826–8841.
Google Scholar |
Crossref |
Medline10.
Lusardi, TA, Phillips, JI, Wiedrick, JT, et al. MicroRNAs in human cerebrospinal fluid as biomarkers for Alzheimer’s disease. J Alzheimers Dis. 2017;55(3):1223–1233.
Google Scholar |
Crossref |
Medline11.
Cieślik, M, Czapski, GA, Wójtowicz, S, et al. Alterations of transcription of genes coding anti-oxidative and mitochondria-related proteins in amyloid beta toxicity: relevance to Alzheimer’s disease. Mol Neurobiol. 2020;57(3):1374–1388.
Google Scholar |
Crossref |
Medline12.
Zolochevska, O, Taglialatela, G. Selected microRNAs increase synaptic resilience to the damaging binding of the Alzheimer’s disease amyloid beta oligomers. Mol Neurobiol. 2020;57(5):2232–2243.
Google Scholar |
Crossref |
Medline13.
Brito, LM, Ribeiro-Dos-Santos, A, Vidal, AF, de Araújo, GS. Differential expression and miRNA-gene interactions in early and late mild cognitive impairment. Biology (Basel). 2020;9(9):251.
Google Scholar |
Crossref14.
Wang, WX, Rajeev, BW, Stromberg, AJ, et al. The expression of microRNA miR-107 decreases early in Alzheimer’s disease and may accelerate disease progression through regulation of beta-site amyloid precursor protein-cleaving enzyme 1. J Neurosci. 2008;28(5):1213–1223.
Google Scholar |
Crossref |
Medline |
ISI15.
Nelson, PT, Wang, WX. MiR-107 is reduced in Alzheimer’s disease brain neocortex: validation study. J Alzheimers Dis. 2010;21(1):75–79.
Google Scholar |
Crossref |
Medline16.
Yang, G, Song, Y, Zhou, X, et al. MicroRNA-29c targets beta-site amyloid precursor protein-cleaving enzyme 1 and has a neuroprotective role in vitro and in vivo. Mol Med Rep. 2015;12(2):3081–3088.
Google Scholar |
Crossref |
Medline17.
Jian, C, Lu, M, Zhang, Z, et al. miR-34a knockout attenuates cognitive deficits in APP/PS1 mice through inhibition of the amyloidogenic processing of APP. Life Sci. 2017;182:104–111.
Google Scholar |
Crossref |
Medline18.
Smith, PY, Hernandez-Rapp, J, Jolivette, F, et al. miR-132/212 deficiency impairs tau metabolism and promotes pathological aggregation in vivo. Hum Mol Genet. 2015;24(23):6721–6735.
Google Scholar |
Crossref |
Medline19.
Dickson, JR, Kruse, C, Montagna, DR, Finsen, B, Wolfe, MS. Alternative polyadenylation and miR-34 family members regulate tau expression. J Neurochem. 2013;127(6):739–749.
Google Scholar |
Crossref |
Medline20.
Dickson, JR, Kruse, C, Montagna, DR, Finsen, B, Wolfe, MS. Circulating microRNAs as biomarkers of Alzheimer’s disease: a systematic review. J Alzheimers Dis. 2016;49(3):755–766.
Google Scholar |
Medline21.
Shigemizu, D, Akiyama, S, Asanomi, Y, et al. Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data. Commun Biol. 2019;2:77.
Google Scholar |
Crossref |
Medline22.
Shigemizu, D, Akiyama, S, Asanomi, Y, et al. A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data. BMC Med Genomics. 2019;12(1):150.
Google Scholar |
Crossref |
Medline23.
McKhann, GM, Knopman, DS, Chertkow, H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):263–269.
Google Scholar |
Crossref |
Medline |
ISI24.
Rawat, C, Kushwaha, S, Srivastava, AK, Kukreti, R. Peripheral blood gene expression signatures associated with epilepsy and its etiologic classification. Genomics. 2020;112(1):218–224.
Google Scholar |
Crossref |
Medline25.
Dweep, H, Gretz, N. miRWalk2.0: a comprehensive atlas of microRNA-target interactions. Nat Methods. 2015;12(8):697.
Google Scholar |
Crossref |
Medline |
ISI26.
Yu, G, Wang, LG, Han, Y, He, QY, ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–287.
Google Scholar |
Crossref |
Medline27.
Kanehisa, M, Furumichi, M, Tanabe, M, Sato, Y, Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):353–361.
Google Scholar |
Crossref |
Medline28.
Ashburner, M, Ball, CA, Blake, JA, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25(1):25–29.
Google Scholar |
Crossref |
Medline |
ISI29.
Davis, AP, Grondin, CJ, Johnson, RJ, et al. The comparative toxicogenomics database: update 2019. Nucleic Acids Res. 2019;47(D1):948–954.
Google Scholar |
Crossref |
Medline30.
Wang, J, Duncan, D, Shi, Z, Zhang, B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41(Web Server issue):77–83.
Google Scholar |
Crossref |
Medline |
ISI31.
Shannon, P, Markiel, A, Ozier, O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–2504.
Google Scholar |
Crossref |
Medline |
ISI32.
Paraskevopoulou, MD, Vlachos, IS, Karagkouni, D, et al. DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts. Nucleic Acids Res. 2016;44(D1):231–238.
Google Scholar |
Crossref |
Medline33.
Cotto, KC, Wagner, AH, Feng, YY, et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res. 2018;46(D1):1068–1073.
Google Scholar |
Crossref34.
Zaidi, SH, Malter, JS. Nucleolin and heterogeneous nuclear ribonucleoprotein C proteins specifically interact with the 3’-untranslated region of amyloid protein precursor mRNA. J Biol Chem. 1995;270(29):17292–17298.
Google Scholar |
Crossref |
Medline35.
Lee, EK, Kim, HH, Kuwano, Y, et al. hnRNP C promotes APP translation by competing with FMRP for APP mRNA recruitment to P bodies. Nat Struct Mol Biol. 2010;17(6):732–739.
Google Scholar |
Crossref |
Medline36.
Matsunaga, MC, Yamauchi, PS. IL-4 and IL-13 inhibition in atopic dermatitis. J Drugs Dermatol. 2016;15(8):925–929.
Google Scholar |
Medline37.
Andersson, CH, Hansson, O, Minthon, L, et al. A Genetic variant of the sortilin 1 gene is associated with reduced risk of Alzheimer’s disease. J Alzheimers Dis. 2016;53(4):1353–1363.
Google Scholar |
Crossref |
Medline38.
Chang, XL, Tan, L, Tan, MS, et al. Association of HMGCR polymorphism with late-onset Alzheimer’s disease in Han Chinese. Oncotarget. 2016;7(16):22746–22751.
Google Scholar |
Crossref |
Medline39.
Zhang, YY, Mei, ZQ, Wu, ZW, Wang, ZX. Enzymatic activity and substrate specificity of mitogen-activated protein kinase p38alpha in different phosphorylation states. J Biol Chem. 2008;283(39):26591–26601.
Google Scholar |
Crossref |
Medline40.
Lee, JK, Kim, NJ. Recent advances in the inhibition of p38 MAPK as a potential strategy for the treatment of Alzheimer’s disease. Molecules. 2017;22(8):1287.
Google Scholar |
Crossref |
Medline41.
Kheiri, G, Dolatshahi, M, Rahmani, F, Rezaei, N. Role of p38/MAPKs in Alzheimer’s disease: implications for amyloid beta toxicity targeted therapy. Rev Neurosci. 2018;30(1):9–30.
Google Scholar |
Crossref |
Medline42.
Muraleva, NA, Kolosova, NG, Stefanova, NA. p38 MAPK-dependent alphaB-crystallin phosphorylation in Alzheimer’s disease-like pathology in OXYS rats. Exp Gerontol. 2019;119:45–52.
Google Scholar |
Crossref |
Medline43.
Schnöder, L, Gasparoni, G, Nordström, K, et al. Neuronal deficiency of p38alpha-MAPK ameliorates symptoms and pathology of APP or Tau-transgenic Alzheimer’s mouse models. FASEB J. 2020;34(7):9628–9649.
Google Scholar |
Crossref |
Medline44.
Zhou, X, Xu, J. Identification of Alzheimer’s disease-associated long noncoding RNAs. Neurobiol Aging. 2015;36(11):2925–2931.
Google Scholar |
Crossref |
Medline45.
Mercer, TR, Qureshi, IA, Gokhan, S, et al. Long noncoding RNAs in neuronal-glial fate specification and oligodendrocyte lineage maturation. BMC Neurosci. 2010;11:14.
Google Scholar |
Crossref |
Medline |
ISI46.
Earls, LR, Westmoreland, JJ, Zakharenko, SS. Non-coding RNA regulation of synaptic plasticity and memory: implications for aging. Ageing Res Rev. 2014;17:34–42.
Google Scholar |
Crossref |
Medline47.
Dhakal, S, Subhan, M, Fraser, JM, Gardiner, K, Macreadie, I. Simvastatin efficiently reduces levels of Alzheimer’s amyloid beta in yeast. Int J Mol Sci. 2019;20(14):3531.
Google Scholar |
Crossref48.
Huang, W, Li, W, Zhao, L, Zhao, W. Simvastatin ameliorate memory deficits and inflammation in clinical and mouse model of Alzheimer’s disease via modulating the expression of miR-106b. Biomed Pharmacother. 2017;92:46–57.
Google Scholar |
Crossref |
Medline49.
Lorenzoni, R, Davies, S, Cordenonsi, LM, et al. Lipid-core nanocapsules containing simvastatin improve the cognitive impairment induced by obesity and hypercholesterolemia in adult rats. Eur J Pharm Sci. 2020;151:105397.
Google Scholar |
Crossref |
Medline
Comments (0)