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Development and validation of a skin fibroblast biomarker profile for schizophrenic patients
Neuropsychiatric Research Laboratory, Faculty of Medicine and Health, School of Health and Medical Sciences, Örebro University, Örebro, Sweden; Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
Örebro University, School of Health Sciences. (Experimentell neuropsykiatri)ORCID iD: 0000-0001-8102-1804
Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece.
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2016 (English)In: AIMS Bioengineering, ISSN 2375-1487, Vol. 3, no 4, 552-565 p.Article in journal (Refereed) Published
Abstract [en]

Gene expression profiles of non-neural tissues through microarray technology could be used in schizophrenia studies, adding more information to the results from similar studies on postmortem brain tissue. The ultimate goal of such studies is to develop accessible biomarkers. Supervised machine learning methodologies were used, in order to examine if the gene expression from skin fibroblast cells could be exploited for the classification of schizophrenic subjects. A dataset of skin fibroblasts gene expression of schizophrenia patients was obtained from Gene Expression Omnibus database. After applying statistical criteria, we concluded to genes that present a differential expression between the schizophrenic patients and the healthy controls. Based on those genes, functional profiling was performed with the BioInfoMiner web tool. After the statistical analysis, 63 genes were identified as differentially expressed. The functional profiling revealed interesting terms and pathways, such as mitogen activated protein kinase and cyclic adenosine monophosphate signaling pathways, as well as immune-related mechanisms. A subset of 16 differentially expressed genes from fibroblast gene expression profiling that occurred after Support Vector Machines Recursive Feature Elimination could efficiently separate schizophrenic from healthy controls subjects. These findings suggest that through the analysis of fibroblast based gene 553 expression signature and with the application of machine learning methodologies we might conclude to a diagnostic classification model in schizophrenia.

Place, publisher, year, edition, pages
Springfield, USA: AIMS press , 2016. Vol. 3, no 4, 552-565 p.
Keyword [en]
schizophrenia; peripheral biomarker; fibroblasts; machine learning; classification
National Category
Medical and Health Sciences Basic Medicine Psychiatry
Research subject
Psychiatry; Biomedicine; Molecular Cellbiology
Identifiers
URN: urn:nbn:se:oru:diva-55679DOI: 10.3934/bioeng.2016.4.552ISI: 000396609100010OAI: oai:DiVA.org:oru-55679DiVA: diva2:1074063
Funder
Magnus Bergvall FoundationSwedish Research Council
Available from: 2017-02-14 Created: 2017-02-14 Last updated: 2017-06-14Bibliographically approved

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