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Supervised pathway analysis of blood gene expression profiles in Alzheimer's disease.
Neurobiol Aging 2019; 84:98-108NA

Abstract

Early identification and treatment of Alzheimer's disease (AD) is hampered by the lack of easily accessible biomarkers. Currently available fluid biomarkers of AD provide indications of the disease stage; however, these are measured in the cerebrospinal fluid, requiring invasive procedures, which are not applicable at the population level. Thus, gene expression profiling of blood provides a viable alternative as a way to screen individuals at risk of AD. Previous studies have shown that despite the limited permeability of the blood-brain barriers, expression profiles of blood genes can be used for the diagnosis and prognosis of several brain disorders. Here, we propose a new approach to pathway analysis of blood gene expression profiles to classify healthy (control [CTL]), mildly cognitively impaired (mild cognitive impairment [MCI]; preclinical stage of AD), and AD subjects. In the pathway analysis, gene expression data are mapped to pathway scores according to a predefined gene set instead of considering each gene separately. The robustness of the analysis enables detection of weak differences between groups owing to the inherent dimension reduction. Our proposed method for pathway analysis takes advantage of linear discriminant analysis for identifying a linear combination of features best separating groups of subjects within each gene set. The gene expression data were retrieved from Gene Expression Omnibus (batch 1: GSE63060; batch 2: GSE63061). Predefined gene sets for pathway analysis were obtained from the Broad Institute Collection of Curated Pathways. The method achieved a 10-fold cross-validated area under receiver operating characteristic curve of 0.84 for classification of AD versus CTL and 0.80 for classification of mild cognitive impairment versus CTL. These results reveal the good potential of blood-based biomarkers for assisting early diagnosis and disease monitoring of AD.

Authors+Show Affiliations

Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland. Electronic address: elaheh.moradi@tuni.fi.Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31522136

Citation

Moradi, Elaheh, et al. "Supervised Pathway Analysis of Blood Gene Expression Profiles in Alzheimer's Disease." Neurobiology of Aging, vol. 84, 2019, pp. 98-108.
Moradi E, Marttinen M, Häkkinen T, et al. Supervised pathway analysis of blood gene expression profiles in Alzheimer's disease. Neurobiol Aging. 2019;84:98-108.
Moradi, E., Marttinen, M., Häkkinen, T., Hiltunen, M., & Nykter, M. (2019). Supervised pathway analysis of blood gene expression profiles in Alzheimer's disease. Neurobiology of Aging, 84, pp. 98-108. doi:10.1016/j.neurobiolaging.2019.07.004.
Moradi E, et al. Supervised Pathway Analysis of Blood Gene Expression Profiles in Alzheimer's Disease. Neurobiol Aging. 2019 Jul 16;84:98-108. PubMed PMID: 31522136.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Supervised pathway analysis of blood gene expression profiles in Alzheimer's disease. AU - Moradi,Elaheh, AU - Marttinen,Mikael, AU - Häkkinen,Tomi, AU - Hiltunen,Mikko, AU - Nykter,Matti, Y1 - 2019/07/16/ PY - 2018/06/27/received PY - 2019/06/15/revised PY - 2019/07/08/accepted PY - 2019/9/16/pubmed PY - 2019/9/16/medline PY - 2019/9/16/entrez KW - Alzheimer's disease KW - Gene expression KW - Linear discriminant analysis KW - Mild cognitive impairment KW - Pathway analysis SP - 98 EP - 108 JF - Neurobiology of aging JO - Neurobiol. Aging VL - 84 N2 - Early identification and treatment of Alzheimer's disease (AD) is hampered by the lack of easily accessible biomarkers. Currently available fluid biomarkers of AD provide indications of the disease stage; however, these are measured in the cerebrospinal fluid, requiring invasive procedures, which are not applicable at the population level. Thus, gene expression profiling of blood provides a viable alternative as a way to screen individuals at risk of AD. Previous studies have shown that despite the limited permeability of the blood-brain barriers, expression profiles of blood genes can be used for the diagnosis and prognosis of several brain disorders. Here, we propose a new approach to pathway analysis of blood gene expression profiles to classify healthy (control [CTL]), mildly cognitively impaired (mild cognitive impairment [MCI]; preclinical stage of AD), and AD subjects. In the pathway analysis, gene expression data are mapped to pathway scores according to a predefined gene set instead of considering each gene separately. The robustness of the analysis enables detection of weak differences between groups owing to the inherent dimension reduction. Our proposed method for pathway analysis takes advantage of linear discriminant analysis for identifying a linear combination of features best separating groups of subjects within each gene set. The gene expression data were retrieved from Gene Expression Omnibus (batch 1: GSE63060; batch 2: GSE63061). Predefined gene sets for pathway analysis were obtained from the Broad Institute Collection of Curated Pathways. The method achieved a 10-fold cross-validated area under receiver operating characteristic curve of 0.84 for classification of AD versus CTL and 0.80 for classification of mild cognitive impairment versus CTL. These results reveal the good potential of blood-based biomarkers for assisting early diagnosis and disease monitoring of AD. SN - 1558-1497 UR - https://www.unboundmedicine.com/medline/citation/31522136/Supervised_pathway_analysis_of_blood_gene_expression_profiles_in_Alzheimer's_disease L2 - https://linkinghub.elsevier.com/retrieve/pii/S0197-4580(19)30206-4 DB - PRIME DP - Unbound Medicine ER -