Prioritising risk pathways of complex human diseases based on functional profiling

European Journal of Human Genetics

Volumn 21, Issue 6, 2013, Pages 666-672

  Li, Yan ^a   Huang, Teng ^a   Xiao, Yun ^a   Ning, Shangwei ^a   Wang, Peng ^a   Wang, Qianghu ^a   Chen, Xin ^a   Chaohan, Xu ^a   Sun, Donglin ^a   Li, Xia ^a   Li, Yixue ^b  

^a HARBIN MEDICAL UNIVERSITY   (China)

^b Chinese Academy of Sciences   (China)

Author keywords

complex human diseases; concordance score; functional profiling; prioritising risk pathways

Indexed keywords

BIOLOGICAL FACTOR; MITOGEN ACTIVATED PROTEIN KINASE; TRANSFORMING GROWTH FACTOR BETA;

ACTIN FILAMENT; ACUTE LYMPHOBLASTIC LEUKEMIA; ACUTE MYELOBLASTIC LEUKEMIA; ARTICLE; CONGESTIVE CARDIOMYOPATHY; DISEASE ASSOCIATION; DISEASE RISK PATHWAY; FUNCTIONAL PROFILING; FUNCTIONAL PROFILING BASED PATHWAY PRIORITIZATION; GAP JUNCTION; GENE EXPRESSION; GENE ONTOLOGY; GENETIC PROCEDURES; GENETIC RISK; HEART RIGHT VENTRICLE DYSPLASIA; HUMAN; HYPERTROPHIC CARDIOMYOPATHY; OVERLAPPING GENE; PATHOGENESIS; PRIORITY JOURNAL; RANDOMIZATION; RECEIVER OPERATING CHARACTERISTIC; RISK FACTOR; SIGNAL TRANSDUCTION; ALGORITHM; CLUSTER ANALYSIS; DISEASES; GENE EXPRESSION PROFILING; GENETIC ASSOCIATION; GENETIC PREDISPOSITION; GENETICS; REPRODUCIBILITY;

ALGORITHMS; CARDIOMYOPATHY, DILATED; CLUSTER ANALYSIS; DISEASE; GENE EXPRESSION PROFILING; GENETIC ASSOCIATION STUDIES; GENETIC PREDISPOSITION TO DISEASE; HUMANS; REPRODUCIBILITY OF RESULTS; RISK FACTORS; SIGNAL TRANSDUCTION;

EID: 84885899850     PISSN: 10184813     EISSN: 14765438     Source Type: Journal    
DOI: 10.1038/ejhg.2012.218     Document Type: Article

References (38)

1. Kelley R, Ideker T. Systematic interpretation of genetic interactions using protein networks. Nat Biotechnol. 2005; 23: 561-566.
2. Kim T, Yoon J, Cho H, et al. Downregulation of lipopolysaccharide response in Drosophila by negative crosstalk between the AP1 and NF-kappaB signaling modules. Nat Immunol. 2005; 6: 211-218.
3. Taylor BS, Schultz N, Hieronymus H, et al. Integrative genomic profiling of human prostate cancer. Cancer Cell. 2010; 18: 11-22.
4. Iliopoulos D, Jaeger SA, Hirsch HA, Bulyk ML, Struhl K. STAT3 activation of miR-21 and miR-181b-1 via PTEN and CYLD are part of the epigenetic switch linking inflammation to cancer. Mol Cell. 2010; 39: 493-506.
5. Efferson CL, Winkelmann CT, Ware C, et al. Downregulation of Notch pathway by a gamma-secretase inhibitor attenuates AKT/mammalian target of rapamycin signaling and glucose uptake in an ERBB2 transgenic breast cancer model. Cancer Res. 2010; 70: 2476-2484.
6. Goeman JJ, Buhlmann P. Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics. 2007; 23: 980-987.
7. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009; 37: 1-13.
8. Zhou X, Su Z. EasyGO: Gene Ontology-based annotation and functional enrichment analysis tool for agronomical species. BMC Genomics. 2007; 8: 246.
9. Zheng Q, Wang XJ. GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis. Nucleic Acids Res. 2008; 36: W358-W363.
10. Mlecnik B, Scheideler M, Hackl H, Hartler J, Sanchez-Cabo F, Trajanoski Z. PathwayExplorer: web service for visualizing high-Throughput expression data on biological pathways. Nucleic Acids Res. 2005; 33: W633-W637.
11. Dennis Jr. G, Sherman BT, Hosack DA, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003; 4: P3.
12. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4: 44-57.
13. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005; 102: 15545-15550.
14. Kanehisa M. The KEGG database. Novartis Found Symp. 2002; 247: 91-101, discussion 101-103, 119-128, 244-152.
15. Vastrik I, D'Eustachio P, Schmidt E, et al. Reactome: a knowledge base of biologic pathways and processes. Genome Biol. 2007; 8: R39.
16. Becker KG, Barnes KC, Bright TJ, Wang SA. The genetic association database. Nat Genet. 2004; 36: 431-432.
17. Li X, Li C, Shang D, et al. The implications of relationships between human diseases and metabolic subpathways. PLoS One. 2011; 6: e21131.
18. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25: 25-29.
19. Pesquita C, Faria D, Falcao AO, Lord P, Couto FM. Semantic similarity in biomedical ontologies. PLoS Comput Biol. 2009; 5: e1000443.
20. Resnik P. Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res, 11: 1999; 95-130.
21. Lin D. An information-Theoretic definition of similarity. In Proc of the 15th Int Conf on Machine Learning. 1998; 296-304.
22. Schlicker A, Domingues FS, Rahnenfuhrer J, Lengauer T. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics. 2006; 7: 302.
23. Lv S, Li Y, Wang Q, et al. A novel method to quantify gene set functional association based on gene ontology. J R Soc Interface. 2011; 9: 1063-1072.
24. Barth AS, Kuner R, Buness A, et al. Identification of a common gene expression signature in dilated cardiomyopathy across independent microarray studies. J AmColl Cardiol. 2006; 48: 1610-1617.
25. Melendez J, Welch S, Schaefer E, et al. Activation of pyk2/related focal adhesion tyrosine kinase and focal adhesion kinase in cardiac remodeling. J Biol Chem. 2002; 277: 45203-45210.
26. Zhang H, Zhang A, Guo C, et al. S-diclofenac protects against doxorubicin-induced cardiomyopathy in mice via ameliorating cardiac gap junction remodeling. PLoS One. 2011; 6: e26441.
27. Sheikh F, Chen Y, Liang X, et al. alpha-E-catenin inactivation disrupts the cardiomyocyte adherens junction, resulting in cardiomyopathy and susceptibility to wall rupture. Circulation. 2006; 114: 1046-1055.
28. Connelly KA, Kelly DJ, Zhang Y, et al. Inhibition of protein kinase C-beta by ruboxistaurin preserves cardiac function and reduces extracellular matrix production in diabetic cardiomyopathy. Circ Heart Fail. 2009; 2: 129-137.
29. Eigenthaler M, Engelhardt S, Schinke B, et al. Disruption of cardiac Ena-VASP protein localization in intercalated disks causes dilated cardiomyopathy. Am J Physiol Heart Circ Physiol. 2003; 285: H2471-H2481.
30. Hammer E, Goritzka M, Ameling S, et al. Characterization of the human myocardial proteome in inflammatory dilated cardiomyopathy by label-free quantitative shotgun proteomics of heart biopsies. J Proteome Res. 2011; 10: 2161-2171.
31. Sun YM, Zhang Y, Zeng LQ, et al. Broad profiling of DNA-binding transcription factor activities improves regulatory network construction in adult mouse tissues. J Proteome Res. 2008; 7: 4455-4464.
32. Molkentin JD. Calcineurin-NFAT signaling regulates the cardiac hypertrophic response in coordination with the MAPKs. Cardiovasc Res. 2004; 63: 467-475.
33. Miao Y, Zhang W, Zhong M, Ma X, Qi TG, Sun H. Activation of transforming growth factor-beta1/Smads signal pathway in diabetic cardiomyopathy and effects of valsartan thereon: experiment with rats. Zhonghua Yi Xue Za Zhi. 2007; 87: 366-370.
34. Heineke J, Molkentin JD. Regulation of cardiac hypertrophy by intracellular signalling pathways. Nat Rev Mol Cell Biol. 2006; 7: 589-600.
35. Leong HS, Kipling D. Text-based over-representation analysis of microarray gene lists with annotation bias. Nucleic Acids Res. 2009; 37: e79.
36. Minguez P, Al-Shahrour F, Montaner D, Dopazo J. Functional profiling of microarray experiments using text-mining derived bioentities. Bioinformatics. 2007; 23: 3098-3099.
37. Jelier R, Goeman JJ, Hettne KM, Schuemie MJ, den Dunnen JT, t Hoen PA. Literatureaided interpretation of gene expression data with the weighted global test. Brief Bioinform. 2011; 12: 518-529.
38. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999; 286: 531-537.