Abstract
Blood-based biomarkers for Alzheimer's disease would be very valuable because blood is a more accessible biofluid and is suitable for repeated sampling. However, currently there are no robust and reliable blood-based biomarkers for practical diagnosis. In this study we used a knowledge-based protein feature pool and two novel support vector machine embedded feature selection methods to find panels consisting of two and three biomarkers. We validated these biomarker sets using another serum cohort and an RNA profile cohort from the brain. Our panels included the proteins ECH1, NHLRC2, HOXB7, FN1, ERBB2, and SLC6A13 and demonstrated promising sensitivity (>87%), specificity (>91%), and accuracy (>89%).
MeSH terms
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Alzheimer Disease / blood*
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Biomarkers / blood
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Brain / metabolism
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Carbon-Carbon Double Bond Isomerases / genetics
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Carbon-Carbon Double Bond Isomerases / metabolism
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Fibronectins / genetics
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Fibronectins / metabolism
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GABA Plasma Membrane Transport Proteins / genetics
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GABA Plasma Membrane Transport Proteins / metabolism
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Homeodomain Proteins / genetics
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Homeodomain Proteins / metabolism
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Humans
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Knowledge Bases
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Proteome / analysis*
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RNA, Messenger / genetics
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RNA, Messenger / metabolism
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Receptor, ErbB-2 / genetics
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Receptor, ErbB-2 / metabolism
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Sensitivity and Specificity
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Transcriptome
Substances
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Biomarkers
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FN1 protein, human
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Fibronectins
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GABA Plasma Membrane Transport Proteins
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HOXB7 protein, human
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Homeodomain Proteins
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Proteome
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RNA, Messenger
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SLC6A13 protein, human
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ERBB2 protein, human
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Receptor, ErbB-2
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Carbon-Carbon Double Bond Isomerases
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delta(3,5),delta(2,4)-dienoyl-CoA isomerase