Study of peripheral biomarkers in patients with obsessive-compulsive disorder and controls

Abstract

Background: The obsessive compulsive disorder (OCD) is a chronic and disabling psychiatric disorder characterized by obsessions and compulsions with a prevalence between 1% and 3%. It Is a miscellaneous disorder, with complex and hard diagnose and treatment. The selective serotonin reuptake inhibitor (SSRI) is more often prescribed substance to OCD, but more than 40% of patients are not good responders. There are no biological markers to OCD diagnostic, prognosis or treatment response. Although there are great efforts to the search for neurobiological understanding of psychiatric disorders, has no biological markers for OCD. These biomarkers could be used to identify groups of patients or even subgroups of patients or non-responders to treatment, and can help you choose the best treatment strategy.Objectives: search for diagnostic biomarkers; search for genes differentially expressed in peripheral blood of match controls; analysis of patterns of gene expression between peripheral and control subjects; Network analysis of peripheral gene coexpression between subjects and controls. Material and Methods: Will be collected blood samples of 40 patients of OCD, in agree of DSM-IV and 40 healthy controls recruited by the PROTOC of IPq-HC-FMUSP. We will do RNA extraction and hybridization using microarrays and PCR in Real Time. Further the extraction and data normalization, the analysis will be made as follows: the analysis of genes differentially expressed and of the analysis of clustering using software MultiExperiment Viewer, performing the "significance of microarray analysis" by SAM-tool with 500 permutations and the FDR of 1%. We will apply a hypergemometric test to identification of biological pathways and functional ontology hyperepresented, using the Pearson correlation and Euclidian distance to clustering and network corrected by boostraping. The network will be made by Pearson correlation coefficient and will be visualized by Cytoscape. We will use machine learning techniques to build classifiers. Waited outcomes: We are waiting to find four outcomes that can be applied by diagnostic biomarkers to OCD: genes differentially expressed, clusters of genes with specific patterns, differential co-expression networks and polygenic classifiers comparing cases and controls. We also hope that the research of biochemistry pathways and/or biological process associated with this clusters of genes can contribute to the understanding of the OCD physiolopathology.