Shin-Ya Watanabe1, Jun-Ichi Iga2, Kazuo Ishii3, Shusuke Numata1, Shinji Shimodera4, Hirokazu Fujita4, Tetsuro Ohmori1. 1. Department of Psychiatry, Course of Integrated Brain Sciences, University of Tokushima School of Medicine, Tokushima 770-8503, Japan. 2. Department of Psychiatry, Course of Integrated Brain Sciences, University of Tokushima School of Medicine, Tokushima 770-8503, Japan. Electronic address: igajunichi@hotmail.com. 3. Department of Applied Biological Science, Faculty of Agriculture, Tokyo University of Agriculture and Technology, Saiwai, Fuchu, Tokyo, 183-8509, Japan. 4. Department of Neuropsychiatry, Kochi Medical School, Kochi University, Kochi, Japan.
Abstract
BACKGROUND: Development of easy-to-use biological diagnostic tests for major depressive disorder (MDD) may facilitate MDD diagnosis and delivery of optimal treatment. Here, we examined leukocyte gene expression to develop a biological diagnostic test for MDD. METHODS: 25 drug-naive MDD patients (MDDs) and 25 age- and sex-matched healthy subjects (Controls) participated in a pilot study. A subsequent replication study involved 20 MDDs and 18 Controls. We used custom-made PCR array plates to examine mRNA levels of 40 candidate genes in leukocyte samples to assess whether any combination of these genes could be used to differentiate MDDs from Controls based on expression profiles. RESULTS: Among 40 candidate genes, we identified a set of seven genes (PDGFC, SLC6A4, PDLIM5, ARHGAP24, PRNP, HDAC5, and IL1R2), each of which had expression levels that differed significantly between MDD and Control samples in the pilot study. To identify genes whose expression best differentiated between MDDs and Controls, a linear discriminant function was developed to discriminate between MDDs and Controls based on the standardized values of gene expression after Z-score transformation. Ultimately, five genes (PDGFC, SLC6A4, ARHGAP24, PRNP, and HDAC5) were selected for a multi-assay diagnostic test. In the pilot study, this diagnostic test demonstrated sensitivity and specificity of 80% and 92%, respectively. The replication study yielded nearly identical results, sensitivity of 85% and specificity of 89%. CONCLUSIONS: Using leukocyte gene expression profiles, we could differentiate MDDs from Controls with adequate sensitivity and specificity. Additional markers not yet identified might further improve the performance of this test.
BACKGROUND: Development of easy-to-use biological diagnostic tests for major depressive disorder (MDD) may facilitate MDD diagnosis and delivery of optimal treatment. Here, we examined leukocyte gene expression to develop a biological diagnostic test for MDD. METHODS: 25 drug-naive MDDpatients (MDDs) and 25 age- and sex-matched healthy subjects (Controls) participated in a pilot study. A subsequent replication study involved 20 MDDs and 18 Controls. We used custom-made PCR array plates to examine mRNA levels of 40 candidate genes in leukocyte samples to assess whether any combination of these genes could be used to differentiate MDDs from Controls based on expression profiles. RESULTS: Among 40 candidate genes, we identified a set of seven genes (PDGFC, SLC6A4, PDLIM5, ARHGAP24, PRNP, HDAC5, and IL1R2), each of which had expression levels that differed significantly between MDD and Control samples in the pilot study. To identify genes whose expression best differentiated between MDDs and Controls, a linear discriminant function was developed to discriminate between MDDs and Controls based on the standardized values of gene expression after Z-score transformation. Ultimately, five genes (PDGFC, SLC6A4, ARHGAP24, PRNP, and HDAC5) were selected for a multi-assay diagnostic test. In the pilot study, this diagnostic test demonstrated sensitivity and specificity of 80% and 92%, respectively. The replication study yielded nearly identical results, sensitivity of 85% and specificity of 89%. CONCLUSIONS: Using leukocyte gene expression profiles, we could differentiate MDDs from Controls with adequate sensitivity and specificity. Additional markers not yet identified might further improve the performance of this test.
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