Literature DB >> 25708651

Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy.

Eugene Lin1, Shih-Jen Tsai2.   

Abstract

Major depressive disorder (MDD) is a serious health concern worldwide. Currently there are no predictive tests for the effectiveness of any particular antidepressant in an individual patient. Thus, doctors must prescribe antidepressants based on educated guesses. With the recent advent of scientific research, genome-wide gene expression microarray studies are widely utilized to analyze hundreds of thousands of biomarkers by high-throughput technologies. In addition to the candidate-gene approach, the genome-wide approach has recently been employed to investigate the determinants of MDD as well as antidepressant response to therapy. In this review, we mainly focused on gene expression studies with genome-wide approaches using RNA derived from peripheral blood cells. Furthermore, we reviewed their limitations and future directions with respect to the genome-wide gene expression profiling in MDD pathogenesis as well as in antidepressant therapy.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Antidepressants; Genome-wide gene expression profiling; Genome-wide microarray analysis; Major depressive disorder; Transcriptional profiling

Mesh:

Substances:

Year:  2015        PMID: 25708651     DOI: 10.1016/j.pnpbp.2015.02.008

Source DB:  PubMed          Journal:  Prog Neuropsychopharmacol Biol Psychiatry        ISSN: 0278-5846            Impact factor:   5.067


  19 in total

Review 1.  The promise of biomarkers in diagnosing major depression in primary care: the present and future.

Authors:  Eva E Redei; Neha S Mehta
Journal:  Curr Psychiatry Rep       Date:  2015-08       Impact factor: 5.285

2.  Blood-based gene expression signatures of medication-free outpatients with major depressive disorder: integrative genome-wide and candidate gene analyses.

Authors:  Hiroaki Hori; Daimei Sasayama; Toshiya Teraishi; Noriko Yamamoto; Seiji Nakamura; Miho Ota; Kotaro Hattori; Yoshiharu Kim; Teruhiko Higuchi; Hiroshi Kunugi
Journal:  Sci Rep       Date:  2016-01-05       Impact factor: 4.379

3.  Identifying a gene expression signature of cluster headache in blood.

Authors:  Else Eising; Nadine Pelzer; Lisanne S Vijfhuizen; Boukje de Vries; Michel D Ferrari; Peter A C 't Hoen; Gisela M Terwindt; Arn M J M van den Maagdenberg
Journal:  Sci Rep       Date:  2017-01-11       Impact factor: 4.379

4.  Replicable and Coupled Changes in Innate and Adaptive Immune Gene Expression in Two Case-Control Studies of Blood Microarrays in Major Depressive Disorder.

Authors:  Gwenaël G R Leday; Petra E Vértes; Sylvia Richardson; Jonathan R Greene; Tim Regan; Shahid Khan; Robbie Henderson; Tom C Freeman; Carmine M Pariante; Neil A Harrison; V Hugh Perry; Wayne C Drevets; Gayle M Wittenberg; Edward T Bullmore
Journal:  Biol Psychiatry       Date:  2017-07-06       Impact factor: 13.382

Review 5.  Genetic Biomarkers on Age-Related Cognitive Decline.

Authors:  Chieh-Hsin Lin; Eugene Lin; Hsien-Yuan Lane
Journal:  Front Psychiatry       Date:  2017-11-21       Impact factor: 4.157

Review 6.  Machine learning and systems genomics approaches for multi-omics data.

Authors:  Eugene Lin; Hsien-Yuan Lane
Journal:  Biomark Res       Date:  2017-01-20

7.  Ernst Rüdin's Unpublished 1922-1925 Study "Inheritance of Manic-Depressive Insanity": Genetic Research Findings Subordinated to Eugenic Ideology.

Authors:  Gundula Kösters; Holger Steinberg; Kenneth Clifford Kirkby; Hubertus Himmerich
Journal:  PLoS Genet       Date:  2015-11-06       Impact factor: 5.917

8.  Molecular Mechanism for Stress-Induced Depression Assessed by Sequencing miRNA and mRNA in Medial Prefrontal Cortex.

Authors:  Ke Ma; Li Guo; Aiping Xu; Shan Cui; Jin-Hui Wang
Journal:  PLoS One       Date:  2016-07-18       Impact factor: 3.240

9.  A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers.

Authors:  Eugene Lin; Po-Hsiu Kuo; Yu-Li Liu; Younger W-Y Yu; Albert C Yang; Shih-Jen Tsai
Journal:  Front Psychiatry       Date:  2018-07-06       Impact factor: 4.157

10.  Differentially expressed genes related to major depressive disorder and antidepressant response: genome-wide gene expression analysis.

Authors:  Doh Kwan Kim; Soo-Youn Lee; Hye In Woo; Shinn-Won Lim; Woojae Myung
Journal:  Exp Mol Med       Date:  2018-08-03       Impact factor: 8.718

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