DOI QR코드

DOI QR Code

OrCanome: a Comprehensive Resource for Oral Cancer

  • Bhartiya, Deeksha (Biomedical Informatics Center, Institute of Cytology and Preventive Oncology) ;
  • Kumar, Amit (Biomedical Informatics Center, Institute of Cytology and Preventive Oncology) ;
  • Singh, Harpreet (Bioinformatics Center, Indian Council of Medical Research) ;
  • Sharma, Amitesh (Bioinformatics Center, Indian Council of Medical Research) ;
  • Kaushik, Anita (Centre for Bioinformatics, Maharshi Dayanand University) ;
  • Kumari, Suchitra (Biomedical Informatics Center, Institute of Cytology and Preventive Oncology) ;
  • Mehrotra, Ravi (Institute of Cytology and Preventive Oncology)
  • Published : 2016.04.11

Abstract

Oral cancer is one of the most prevalent cancers in India but the underlying mechanisms are minimally unraveled. Cancer research has immensely benefited from genome scale high throughput studies which have contributed to expanding the volume of data. Such datasets also exist for oral cancer genes but there has been no consolidated approach to integrate the data to reveal meaningful biological information. OrCanome is one of the largest and comprehensive, user-friendly databases of oral cancer. It features a compilation of over 900 genes dysregulated in oral cancer and provides detailed annotations of the genes, transcripts and proteins along with additional information encompassing expression, inhibitors, epitopes and pathways. The resource has been envisioned as a one-stop solution for genomic, transcriptomic and proteomic annotation of these genes and the integrated approach will facilitate the identification of potential biomarkers and therapeutic targets.

Keywords

References

  1. Barrett T, Wilhite SE, Ledoux P, et al (2013). NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res, 41, D991-5. https://doi.org/10.1093/nar/gks1193
  2. Bento AP, Gaulton A, Hersey A, et al (2014). The ChEMBL bioactivity database: an update. Nucleic Acids Res, 42, 1083-90. https://doi.org/10.1093/nar/gkt1031
  3. Berglund L, Bjorling E, Oksvold P, et al (2008). A genecentric Human Protein Atlas for expression profiles based on antibodies. Mol Cell Proteomics, 7, 2019-27. https://doi.org/10.1074/mcp.R800013-MCP200
  4. Berman HM, Battistuz T, Bhat TN, et al (2002). The Protein Data Bank. Acta Crystallogr D Biol Crystallogr, 58, 899-907. https://doi.org/10.1107/S0907444902003451
  5. Burger MC (2015). ChemDoodle Web Components: HTML5 toolkit for chemical graphics, interfaces, and informatics. J Cheminform, 7, 35. https://doi.org/10.1186/s13321-015-0085-3
  6. Coelho KR (2012). Challenges of the oral cancer burden in India. J Cancer Epidemiol, 2012, 701932.
  7. Ferlay J, Soerjomataram I, Dikshit R, et al (2015). Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer, 136, 359-86. https://doi.org/10.1002/ijc.29210
  8. Flicek P, Amode MR, Barrell D, et al (2014). Ensembl 2014. Nucleic Acids Res, 42, 749-55. https://doi.org/10.1093/nar/gkt1196
  9. Gadewal NS, Zingde SM (2011). Database and interaction network of genes involved in oral cancer: Version II. Bioinformation, 6, 169-70. https://doi.org/10.6026/97320630006169
  10. Hamosh A, Scott AF, Amberger JS, et al (2005). Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res, 33, 514-7.
  11. Harrow J, Frankish A, Gonzalez JM, et al (2012). GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res, 22, 1760-74. https://doi.org/10.1101/gr.135350.111
  12. Huang da W, Sherman BT, Lempicki RA (2009a). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 37, 1-13. https://doi.org/10.1093/nar/gkn923
  13. Huang da W, Sherman BT, Lempicki RA (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4, 44-57. https://doi.org/10.1038/nprot.2008.211
  14. Hunter S, Apweiler R, Attwood TK, et al (2009). InterPro: the integrative protein signature database. Nucleic Acids Res, 37, 211-5. https://doi.org/10.1093/nar/gkn785
  15. India Project Team of the International Cancer Genome Consortium (2013). Mutational landscape of gingivo-buccal oral squamous cell carcinoma reveals new recurrentlymutated genes and molecular subgroups. Nat Commun, 4, 2873. https://doi.org/10.1038/ncomms3873
  16. Kanehisa M, Goto S (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 28, 27-30. https://doi.org/10.1093/nar/28.1.27
  17. Krogh A, Larsson B, von Heijne G, et al (2001). Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol, 305, 567-80. https://doi.org/10.1006/jmbi.2000.4315
  18. Lian Y, Ge M, Pan XM (2014). EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression. BMC Bioinformatics, 15, 414. https://doi.org/10.1186/s12859-014-0414-y
  19. Liu T, Lin Y, Wen X, et al (2007). BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res, 35, 198-201. https://doi.org/10.1093/nar/gkl999
  20. Mehrotra R, Gupta DK (2011). Exciting new advances in oral cancer diagnosis: avenues to early detection. Head Neck Oncol, 3, 33. https://doi.org/10.1186/1758-3284-3-33
  21. Mehrotra R, Yadav S (2006). Oral squamous cell carcinoma: etiology, pathogenesis and prognostic value of genomic alterations. Indian J Cancer, 43, 60-6. https://doi.org/10.4103/0019-509X.25886
  22. Mitra S, Das S, Das S, et al (2012). HNOCDB: a comprehensive database of genes and miRNAs relevant to head and neck and oral cancer. Oral Oncol, 48, 117-9. https://doi.org/10.1016/j.oraloncology.2011.09.014
  23. Nakagawa H, Wardell CP, Furuta M, et al (2015). Cancer wholegenome sequencing: present and future. Oncogene.
  24. O'Boyle NM, Banck M, James CA, et al (2011). Open Babel: An open chemical toolbox. J Cheminform, 3, 33. https://doi.org/10.1186/1758-2946-3-33
  25. Petryszak R, Burdett T, Fiorelli B, et al (2014). Expression Atlas update--a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments. Nucleic Acids Res, 42, 926-32. https://doi.org/10.1093/nar/gkt944
  26. Rajaraman P, Anderson BO, Basu P, et al (2015). Recommendations for screening and early detection of common cancers in India. Lancet Oncol, 16, 352-61. https://doi.org/10.1016/S1470-2045(15)00078-9
  27. Reis PP, Waldron L, Perez-Ordonez B, et al (2011). A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence. BMC Cancer, 11, 437. https://doi.org/10.1186/1471-2407-11-437
  28. Reuter JA, Spacek DV, Snyder MP (2015). High-throughput sequencing technologies. Mol Cell, 58, 586-97. https://doi.org/10.1016/j.molcel.2015.05.004
  29. Saeed AA, Sims AH, Prime SS, et al (2015). Gene expression profiling reveals biological pathways responsible for phenotypic heterogeneity between UK and Sri Lankan oral squamous cell carcinomas. Oral Oncol, 51, 237-46. https://doi.org/10.1016/j.oraloncology.2014.12.004
  30. Thariat J, Vignot S, Lapierre A, et al (2015). Integrating genomics in head and neck cancer treatment: promises and pitfalls. Crit Rev Oncol Hematol, 95, 397-406. https://doi.org/10.1016/j.critrevonc.2015.03.005
  31. Uhlen M, Fagerberg L, Hallstrom BM, et al (2015). Proteomics. Tissue-based map of the human proteome. Science, 347, 1260419. https://doi.org/10.1126/science.1260419
  32. Uhlen M, Oksvold P, Fagerberg L, et al (2010). Towards a knowledge-based Human Protein Atlas. Nat Biotechnol, 28, 1248-50. https://doi.org/10.1038/nbt1210-1248
  33. UniProt Consortium (2015). UniProt: a hub for protein information. Nucleic Acids Res, 43, 204-12. https://doi.org/10.1093/nar/gku989

Cited by

  1. CXC chemokine-7 inhibits growth and migration of oral tongue squamous cell carcinoma cells, mediated by the epithelial-mesenchymal transition signaling pathway vol.16, pp.5, 2017, https://doi.org/10.3892/mmr.2017.7441