Our study proposes periostin to be a novel stromal candidate marker of tumor prognosis that may also constitute potential therapeutic target in a broad range of carcinomas. Major depressive disorder is a complex disorder with high prevalence and is the fourth leading cause of CYT 11387 disease burden worldwide. The lifetime prevalence of depression ranges from 9.2 to19.6% worldwide, and heritability is estimated at approximately 37�C43%. Over the last decade, many studies have been devoted to dissecting the genetic influences of depression using a variety of experimental designs and technological approaches, including genomic-wide linkage scans, genetic association studies, and microarray gene expression. Several hypotheses have been proposed for the biological mechanisms of developing depression based on prior evidence, including monoamine-deficiency hypothesis, hypothalamic-pituitary- cortisol hypothesis and other possible pathophysiological mechanisms. Most recently, genome-wide association studies have been applied to search for common susceptible variants and genes in several thousands of samples, in turn generating new hypotheses for the biological mechanisms of depression. Massive amounts of genetic data from numerous studies and Tasocitinib sources have been accumulated rapidly. Moreover, combining genetic information in the regulatory pathway takes advantage of additional biological knowledge that is not directly available from traditional genetic studies. Results from each study are influenced by different study designs, analytic strategies, ethnic populations, and sample sizes. Thus, integrating depression genetic data and information from individual studies, literature review, and biological pathways in multiple resources may provide us list of evidence-based candidate genes for future experimental validation. Such effort has recently been shown in the study of other complex diseases but has not been applied to depression yet. One common statistical method to combine results in several studies is meta-analysis, which usually requires data generated by the same design. Findings from various study designs and data sources made it impractical to combine data directly using rigorous statistical testing. Therefore, an alternative powerful integration strategy is needed to combine genetic data from different study settings and across species. Specifically, in neuropsychiatric genetics, several approaches have been developed and applied to integrate genetic data for schizophrenia and Alzheimer��s disease. Ma et al. prioritized genes by combining gene expression and protein-protein interaction data for Alzheimer��s disease. Sun et al. integrated multi-source genetic data for schizophrenia by a data integration and weighting framework in which the strength of evidence in different data categories is considered and combined by appropriate weights.