ABSTRACT: BACKGROUND: A large amount of liver-related physiological and pathological data exist in publicly available biological and bibliographic databases, which are usually far from comprehensive or integrated. Data collection, integration and mining processes pose a great challenge to scientific researchers and clinicians interested in the liver. METHOD: To address these problems, we constructed LiverAtlas (http://liveratlas.hupo.org.cn), a comprehensive resource of biomedical knowledge related to the liver and various hepatic diseases by incorporating 53 databases. RESULTS: In the present version, LiverAtlas covers data on liver-related genomics, transcriptomics, proteomics, metabolomics and hepatic diseases. Additionally, LiverAtlas provides a wealth of manually curated information, relevant literature citations and cross-references to other databases. Importantly, an expert-confirmed Human Liver Disease Ontology, including relevant information for 227 types of hepatic disease, has been constructed and is used to annotate LiverAtlas data. Furthermore, we have demonstrated two examples of applying LiverAtlas data to identify candidate markers for hepatocellular carcinoma (HCC) at the systems level and to develop a systems biology-based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC differential diagnosis. CONCLUSION: LiverAtlas is the most comprehensive liver and hepatic disease resource, which helps biologists and clinicians to analyse their data at the systems level and will contribute much to the biomarker discovery and diagnostic performance enhancement for liver diseases.
Liver international: official journal of the International Association for the Study of the Liver 03/2013; · 3.82 Impact Factor
ABSTRACT: CD97 as a member of the EGF-TM7 family with adhesive properties plays an important role in tumor aggressiveness by binding its cellular ligand CD55, which is a complement regulatory protein expressed by cells to protect them from bystander complement attack. Previous studies have shown that CD97 and CD55 both play important roles in tumor dedifferentiation, migration, invasiveness, and metastasis. The aim of this study was to investigate CD97 and CD55 expression in primary gallbladder carcinoma (GBC) and their prognostic significance.
Immunohistochemistry was used to investigate the expression of CD97 and CD55 proteins in 138 patients with GBC.
CD97 and CD55 were absent or only weakly expressed in the normal epithelium of the gallbladder but in 69.6% (96/138) and 65.2% (90/138) of GBC, respectively, remarkably at the invasive front of the tumors. In addition, CD97 and CD55 expressions were both significantly associated with high histologic grade (both P = 0.009), advanced pathologic T stage (P = 0.01 and 0.009, resp.) and clinical stage (both P = 0.009), and positive venous/lymphatic invasion (both P = 0.009). Multivariate analyses showed that CD97 (hazard ratio, 3.236; P = 0.02) and CD55 (hazard ratio, 3.209; P = 0.02) expressions and clinical stage (hazard ratio, 3.918; P = 0.01) were independent risk factor for overall survival.
Our results provide convincing evidence for the first time that the expressions of CD97 and CD55 are both upregulated in human GBC. The expression levels of CD97 and CD55 in GBC were associated with the severity of the tumor. Furthermore, CD97 and CD55 expressions were independent poor prognostic factors for overall survival in patients with GBC.
Journal of Biomedicine and Biotechnology 01/2012; 2012:587672. · 2.44 Impact Factor
ABSTRACT: The diagnosis of hepatocellular carcinoma (HCC) in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis.
In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71%) and area under ROC curve (approximating 1.0), and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers.
Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier.
PLoS ONE 01/2011; 6(7):e22426. · 4.09 Impact Factor