Disease similarity study provides new insights into disease taxonomy, pathogenesis, which plays a guiding role in diagnosis and treatment. The early studies were limited to estimate disease similarities based on clinical manifestations, disease-related genes, medical vocabulary concepts or registry data, which were inevitably biased to well-studied diseases and offered small chance of discovering novel findings in disease relationships. In other words, genome-scale expression data give us another angle to address this problem since simultaneous measurement of the expression of thousands of genes allows for the exploration of gene transcriptional regulation, which is believed to be crucial to biological functions. Although differential expression analysis based methods have the potential to explore new disease relationships, it is difficult to unravel the upstream dysregulation mechanisms of diseases. We therefore estimated disease similarities based on gene expression data by using differential coexpression analysis, a recently emerging method, which has been proved to be more potential to capture dysfunctional regulation mechanisms than differential expression analysis. A total of 1,326 disease relationships among 108 diseases were identified, and the relevant information constituted the human disease network database (DNetDB). Benefiting from the use of differential coexpression analysis, the potential common dysfunctional regulation mechanisms shared by disease pairs (i.e. disease relationships) were extracted and presented. Statistical indicators, common disease-related genes and drugs shared by disease pairs were also included in DNetDB. In total, 1,326 disease relationships among 108 diseases, 5,598 pathways, 7,357 disease-related genes and 342 disease drugs are recorded in DNetDB, among which 3,762 genes and 148 drugs are shared by at least two diseases. DNetDB is the first database focusing on disease similarity from the viewpoint of gene regulation mechanism. It provides an easy-to-use web interface to search and browse the disease relationships and thus helps to systematically investigate etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions.Database URL: http://app.scbit.org/DNetDB/#.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0280-5) contains supplementary material, which is available to authorized users.
Background: Ovarian serous cystadenocarcinoma (OSC) is the most common and lethal gynecological cancer in women worldwide; however, biomarkers to diagnose and predict prognosis of OSC remain limited. Therefore, the present study aimed to investigate whether sodium/potassium adenosine triphosphate (Na + /K +-ATP)ase α-subunits (ATP1As) are helpful diagnostic and prognostic markers of OSC. Methods: Gene expression data (RNA-Seq) of 376 patients with OSC were downloaded from The Cancer Genome Atlas (TCGA) program database. Additional databases used in our analysis included the Gene Expression Omnibus, International Cancer Genome Consortium, Genotype-Tissue Expression, the Human Protein Atlas, cBioPortal for Cancer Genomics, and Cancer Cell Line Encyclopedia. Results: The expression levels of ATP1A1 and ATP1A3 were higher in OSC tissues than in normal ovarian tissues, whereas the expression levels of ATP1A2 and ATP1A4 were lower in OSC tissues than in normal ovarian tissues. Overexpression of ATP1A2 was significantly associated with a higher Federation of Gynecology and Obstetrics (FIGO) stage and histological grade. Increased mRNA expression of ATP1A3 was significantly associated with shorter overall survival (OS) and disease-specific survival (DSS) in patients with OSC, whereas higher expression of ATP1A4 was associated with favorable OS and DSS. Multivariate analysis showed that primary therapy outcome, residual tumor, and mRNA expressions of ATP1A3 and ATP1A4 were independent prognostic factors for both OS and DSS in patients with OSC. Moreover, ATP1A1 staining was abundant in tumor tissues. A high expression of ATP1A3 was significantly correlated with poor OS and DSS in the subgroup of patients aged ≥ 60 years and with FIGO stage III, histological grade G3, and TP53 mutation. Mutation frequencies of the ATP1As were 3-5%. Conclusions: These results indicate that the ATP1A gene family could be potential diagnostic or prognostic markers of OSC. In addition, ATP1As may be effective therapeutic targets in the treatment of OSC.
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