Cancerome: A hidden informative subnetwork of the diseasome

Neoplastic disorders are a leading cause of mortality and morbidity worldwide. Studying the relationships between different cancers using high throughput-generated data may elucidate undisclosed aspects of cancer etiology, diagnosis, and treatment. Several studies have described relationships between different diseases based on genes, proteins, pathways, gene ontology, comorbidity, symptoms, and other features. In this study, we first constructed an integrated human disease network based on nine different biological aspects, including molecular, functional, and clinical features. Next, we extracted the cancerome as a cancer-related subnetwork. Further investigation of cancerome could reveal hidden mechanisms of cancer and could be useful in developing new diagnostic tests and effective new drugs.

[1]  G Y H Lip,et al.  Hypertension and breast cancer: an association revisited? , 2006, Journal of Human Hypertension.

[2]  M. Tewari,et al.  The Limits of Reductionism in Medicine: Could Systems Biology Offer an Alternative? , 2006, PLoS medicine.

[3]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database's 10th year anniversary: update 2015 , 2014, Nucleic Acids Res..

[4]  Minoru Kanehisa,et al.  KEGG as a reference resource for gene and protein annotation , 2015, Nucleic Acids Res..

[5]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2004, Nucleic Acids Res..

[6]  Natasa Przulj,et al.  Predicting disease associations via biological network analysis , 2014, BMC Bioinformatics.

[7]  Jan Ramon,et al.  A new ensemble coevolution system for detecting HIV-1 protein coevolution , 2015, Biology Direct.

[8]  Pankaj Agarwal,et al.  A Pathway-Based View of Human Diseases and Disease Relationships , 2009, PloS one.

[9]  Elaine Nsoesie,et al.  Prediction of Disease and Phenotype Associations from Genome-Wide Association Studies , 2011, PloS one.

[10]  Olivier Bodenreider,et al.  The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..

[11]  K. Plaimas,et al.  DDA: A Novel Network-Based Scoring Method to Identify Disease–Disease Associations , 2015, Bioinformatics and biology insights.

[12]  O. Elemento,et al.  Cancer systems biology: embracing complexity to develop better anticancer therapeutic strategies , 2014, Oncogene.

[13]  S. Strittmatter,et al.  Fyn inhibition rescues established memory and synapse loss in Alzheimer mice , 2015, Annals of neurology.

[14]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[15]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[16]  C. Sabatti,et al.  The Human Phenome Project , 2003, Nature Genetics.

[17]  M. Morange,et al.  The field of cancer research: an indicator of present transformations in biology , 2007, Oncogene.

[18]  María Martín,et al.  UniProt: A hub for protein information , 2015 .

[19]  G. Mills,et al.  Future of personalized medicine in oncology: a systems biology approach. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[20]  Pall I. Olason,et al.  A human phenome-interactome network of protein complexes implicated in genetic disorders , 2007, Nature Biotechnology.

[21]  Jing Yang,et al.  The human disease network in terms of dysfunctional regulatory mechanisms , 2015, Biology Direct.

[22]  A. Jemal,et al.  Cancer statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[23]  G. Gkoutos,et al.  Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases , 2014, Scientific Reports.

[24]  B. Zupan,et al.  Discovering disease-disease associations by fusing systems-level molecular data , 2013, Scientific Reports.

[25]  Albert-László Barabási,et al.  A Dynamic Network Approach for the Study of Human Phenotypes , 2009, PLoS Comput. Biol..

[26]  Peggy Hall,et al.  The NHGRI GWAS Catalog, a curated resource of SNP-trait associations , 2013, Nucleic Acids Res..

[27]  Deanna M. Church,et al.  ClinVar: public archive of relationships among sequence variation and human phenotype , 2013, Nucleic Acids Res..

[28]  C. Myers,et al.  Using networks to measure similarity between genes: association index selection , 2013, Nature Methods.

[29]  Andrey Rzhetsky,et al.  DiseaseConnect: a comprehensive web server for mechanism-based disease–disease connections , 2014, Nucleic Acids Res..

[30]  Xing-Ming Zhao,et al.  Identifying dysregulated pathways in cancers from pathway interaction networks , 2012, BMC Bioinformatics.

[31]  Changhua Wang,et al.  Association between diabetes mellitus and breast cancer risk: a meta-analysis of the literature. , 2011, Asian Pacific journal of cancer prevention : APJCP.

[32]  K. Goh,et al.  Exploring the human diseasome: the human disease network. , 2012, Briefings in functional genomics.

[33]  Joel Dudley,et al.  Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets , 2010, PLoS Comput. Biol..

[34]  Deendayal Dinakarpandian,et al.  Finding disease similarity based on implicit semantic similarity , 2012, J. Biomed. Informatics.

[35]  Fidel Ramírez,et al.  Computing topological parameters of biological networks , 2008, Bioinform..

[36]  P. Jaccard.  Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines , 1901 .

[37]  Hsien-Da Huang,et al.  miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database , 2015, Nucleic Acids Res..

[38]  G. Vriend,et al.  A text-mining analysis of the human phenome , 2006, European Journal of Human Genetics.

[39]  Rebecca L. Siegel Mph,et al.  Cancer statistics, 2016 , 2016 .

[40]  E. Snitkin,et al.  Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network , 2009, Genome Biology.

[41]  Zhen Liu,et al.  Identifying disease associations via genome-wide association studies , 2009, BMC Bioinformatics.

[42]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[43]  Sara Gandini,et al.  Use of beta‐blockers, angiotensin‐converting enzyme inhibitors and angiotensin receptor blockers and breast cancer survival: Systematic review and meta‐analysis , 2016, International journal of cancer.

[44]  A. Barabasi,et al.  Human symptoms–disease network , 2014, Nature Communications.