Advertisement

Molecular Genetics and Genomics

, Volume 294, Issue 4, pp 931–940 | Cite as

Disease association of human tumor suppressor genes

  • Asim Bikas DasEmail author
Original Article
  • 158 Downloads

Abstract

The multifactorial disease, cancer, frequently emerges due to perturbations in tumor suppressor genes (TSGs). However, a growing number of noncanonical target genes of TSGs and the highly interconnected nature of the human interactome reveal that the functions of TSGs are not limited to cancer-specific events. The various functions of TSGs lead to the assumption that cancer is linked with other human disorders. Therefore, a disease–gene association network of TSGs (TSDN) was constructed by integrating protein–protein interaction networks of TSGs (TSN) with Morbid Map in Online Mendelian Inheritance in Man. The TSDN revealed links between TSGs and 22 different human disorders including cancer and indicated disease–disease associations. In addition, high-density functional protein clusters in the TSN showed cohesive and overlapping disease–TSG associations, which proved the prevalent role of TSGs in various human diseases beyond cancer. The presence of overlapping disease–gene modules and disease–disease associations via the TSN demonstrated that other diseases can serve as possible roots of the life-threatening disease cancer. Therefore, a disease association map of TSGs could be a promising tool for exploring intricate relationships between cancer and other diseases for the early prediction of cancer and the understanding of disease etiology.

Keywords

Tumor suppressor gene Cancer Protein–protein interaction network Disease–gene association 

Notes

Acknowledgements

I thank Mr.Kuntal Bhusan (Bio-Sciences R&D Division, TCS, Pune, India) for discussion regarding the statistical analysis of networks, Shiv Prasad (Software developer, Param.ai, Hyderabad, India) for assistance in writing the code in R, and Dr Urmila Saxena (NIT Warangal) for the critically reading of the manuscript and her constructive suggestion. I also thank the National Institute of Technology, Warangal for providing computational facilities.

Compliance with ethical standards

Conflict of interest

The author declares he has no competing interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by the author.

Supplementary material

438_2019_1557_MOESM1_ESM.pdf (85.7 mb)
Supplementary material 1 (PDF 87757 kb)

References

  1. Amberger J, Bocchini C, Hamosh A (2011) A new face and new challenges for Online Mendelian inheritance in man (OMIM(R)). Hum Mutat 32:564–567CrossRefPubMedGoogle Scholar
  2. Aylon Y, Oren M (2011) New plays in the p53 theater. Curr Opin Genet Dev 21:86–92CrossRefPubMedGoogle Scholar
  3. Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinf 4:2CrossRefGoogle Scholar
  4. Bader GD, Betel D, Hogue CW (2003) BIND: the biomolecular interaction network database. Nucleic Acids Res 31:248–250CrossRefPubMedPubMedCentralGoogle Scholar
  5. Bandyopadhyay S, Ray S, Mukhopadhyay A, Maulik U (2015) A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions. Brief Bioinform 16:830–851CrossRefPubMedGoogle Scholar
  6. Barabasi AL (2016) The scale-free property. Network Science (Cambridge University Press), pp 113–163Google Scholar
  7. Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512CrossRefPubMedGoogle Scholar
  8. Barabasi AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68CrossRefPubMedPubMedCentralGoogle Scholar
  9. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Edgar R (2009) NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 37:D885–D890CrossRefPubMedGoogle Scholar
  10. Bastian M, Heymann S, Jacomy M (2009) Gephi: An open source software for exploring and manipulating networks. In: International AAAI conference on weblogs and social media: ICWSM, San Jose, CaliforniaGoogle Scholar
  11. Chen L, Guo D (2017) The functions of tumor suppressor PTEN in innate and adaptive immunity. Cell Mol Immunol 14:581–589CrossRefPubMedPubMedCentralGoogle Scholar
  12. Csardi G, Nepusz T (2006) The igraph software package for complex network research. Int J Complex Syst 1695:1–9Google Scholar
  13. Duex JE, Swain KE, Dancik GM, Paucek RD, Owens C, Churchill MEA, Theodorescu D (2018) Functional impact of chromatin remodeling gene mutations and predictive signature for therapeutic response in bladder cancer. Mol Cancer Res 16:69–77CrossRefPubMedGoogle Scholar
  14. Erdős P, Rényi A (1959) On random graphs. Publ Math 6:290–297Google Scholar
  15. Feldman I, Rzhetsky A, Vitkup D (2008) Network properties of genes harboring inherited disease mutations. Proc Natl Acad Sci USA 105:4323–4328CrossRefPubMedGoogle Scholar
  16. Fleming NI, Jorissen RN, Mouradov D, Christie M, Sakthianandeswaren A, Palmieri M, Day F, Li S, Tsui C, Lipton L, Desai J, Jones IT, McLaughlin S, Ward RL, Hawkins NJ, Ruszkiewicz AR, Moore J, Zhu HJ, Mariadason JM, Burgess AW, Busam D, Zhao Q, Strausberg RL, Gibbs P, Sieber OM (2013) SMAD2, SMAD3 and SMAD4 mutations in colorectal cancer. Cancer Res 73:725–735CrossRefPubMedGoogle Scholar
  17. Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, Cole CG, Ward S, Dawson E, Ponting L, Stefancsik R, Harsha B, Kok CY, Jia M, Jubb H, Sondka Z, Thompson S, De T, Campbell PJ (2017) COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res 45:D777–D783CrossRefGoogle Scholar
  18. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL (2007) The human disease network. Proc Natl Acad Sci USA 104:8685–8690CrossRefGoogle Scholar
  19. Hamosh A, Scott AF, Amberger J, Bocchini C, Valle D, McKusick VA (2002) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 30:52–55CrossRefPubMedPubMedCentralGoogle Scholar
  20. Ideker T, Sharan R (2008) Protein networks in disease. Genome Res 18:644–652CrossRefPubMedPubMedCentralGoogle Scholar
  21. Jalili M, Salehzadeh-Yazdi A, Gupta S, Wolkenhauer O, Yaghmaie M, Resendis-Antonio O, Alimoghaddam K (2016) Evolution of centrality measurements for the detection of essential proteins in biological networks. Front Physiol 7:375CrossRefPubMedPubMedCentralGoogle Scholar
  22. Jonsson PF, Bates PA (2006) Global topological features of cancer proteins in the human interactome. Bioinformatics 22:2291–2297CrossRefPubMedPubMedCentralGoogle Scholar
  23. Knudsen ES, Wang JY (2010) Targeting the RB-pathway in cancer therapy. Clin Cancer Res 16:1094–1099CrossRefPubMedPubMedCentralGoogle Scholar
  24. Liu W, Wu A, Pellegrini M, Wang X (2015) Integrative analysis of human protein, function and disease networks. Sci Rep 5:14344CrossRefPubMedPubMedCentralGoogle Scholar
  25. Liyasova MS, Ma K, Lipkowitz S (2015) Molecular pathways: cbl proteins in tumorigenesis and antitumor immunity-opportunities for cancer treatment. Clin Cancer Res 21:1789–1794CrossRefPubMedGoogle Scholar
  26. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabasi AL (2015) Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 347:1257601Google Scholar
  27. Mersch J, Jackson MA, Park M, Nebgen D, Peterson SK, Singletary C, Arun BK, Litton JK (2015) Cancers associated with BRCA1 and BRCA2 mutations other than breast and ovarian. Cancer 121:269–275CrossRefGoogle Scholar
  28. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298:824–827CrossRefPubMedGoogle Scholar
  29. Mora A, Donaldson IM (2011) iRefR: an R package to manipulate the iRefIndex consolidated protein interaction database. BMC Bioinf 12:455CrossRefGoogle Scholar
  30. Muller PA, Vousden KH (2013) p53 mutations in cancer. Nat Cell Biol 15:2–8CrossRefPubMedGoogle Scholar
  31. Nicolas M, Wolfer A, Raj K, Kummer JA, Mill P, van Noort M, Hui CC, Clevers H, Dotto GP, Radtke F (2003) Notch1 functions as a tumor suppressor in mouse skin. Nat Genet 33:416–421CrossRefPubMedGoogle Scholar
  32. Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB (2017) Cancerous perturbations within the ERK, PI3 K/Akt, and Wnt/beta-catenin signaling network constitutively activate inter-pathway positive feedback loops. Mol BioSyst 13:830–840CrossRefPubMedGoogle Scholar
  33. Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TK, Chandrika KN, Deshpande N, Suresh S, Rashmi BP, Shanker K, Padma N, Niranjan V, Harsha HC, Talreja N, Vrushabendra BM, Ramya MA, Yatish AJ, Joy M, Shivashankar HN, Kavitha MP, Menezes M, Choudhury DR, Ghosh N, Saravana R, Chandran S, Mohan S, Jonnalagadda CK, Prasad CK, Kumar-Sinha C, Deshpande KS, Pandey A (2004) Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res 32:D497–D501CrossRefPubMedPubMedCentralGoogle Scholar
  34. Pierson E, Koller D, Battle A, Mostafavi S, Ardlie KG, Getz G, Wright FA, Kellis M, Volpi S, Dermitzakis ET (2015) Sharing and specificity of co-expression networks across 35 human tissues. PLoS Comput Biol 11:e1004220CrossRefPubMedPubMedCentralGoogle Scholar
  35. Raman K, Damaraju N, Joshi GK (2014) The organisational structure of protein networks: revisiting the centrality-lethality hypothesis. Syst Synth Biol 8:73–81CrossRefPubMedGoogle Scholar
  36. Razick S, Magklaras G, Donaldson IM (2008) iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinf 9:405CrossRefGoogle Scholar
  37. Robinson DR, Wu YM, Vats P, Su F, Lonigro RJ, Cao X, Kalyana-Sundaram S, Wang R, Ning Y, Hodges L, Gursky A, Siddiqui J, Tomlins SA, Roychowdhury S, Pienta KJ, Kim SY, Roberts JS, Rae JM, Van Poznak CH, Hayes DF, Chugh R, Kunju LP, Talpaz M, Schott AF, Chinnaiyan AM (2013) Activating ESR1 mutations in hormone-resistant metastatic breast cancer. Nat Genet 45:1446–1451CrossRefPubMedPubMedCentralGoogle Scholar
  38. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504CrossRefPubMedPubMedCentralGoogle Scholar
  39. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539CrossRefPubMedGoogle Scholar
  40. Tan MH, Mester JL, Ngeow J, Rybicki LA, Orloff MS, Eng C (2012) Lifetime cancer risks in individuals with germline PTEN mutations. Clin Cancer Res 18:400–407CrossRefPubMedPubMedCentralGoogle Scholar
  41. Wachi S, Yoneda K, Wu R (2005) Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics 21:4205–4208CrossRefPubMedPubMedCentralGoogle Scholar
  42. Wang JZ, Du Z, Payattakool R, Yu PS, Chen CF (2007) A new method to measure the semantic similarity of GO terms. Bioinformatics 23:1274–1281CrossRefPubMedGoogle Scholar
  43. Wang M, Yang C, Zhang X, Li X (2018) Characterizing genomic differences of human cancer stratified by the TP53 mutation status. Mol Genet Genomics 293:737–746CrossRefPubMedGoogle Scholar
  44. Ward CL, Boggio KJ, Johnson BN, Boyd JB, Douthwright S, Shaffer SA, Landers JE, Glicksman MA, Bosco DA (2014) A loss of FUS/TLS function leads to impaired cellular proliferation. Cell Death Dis 5:e1572CrossRefPubMedPubMedCentralGoogle Scholar
  45. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38:W214–W220CrossRefPubMedPubMedCentralGoogle Scholar
  46. Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D (2000) DIP: the database of interacting proteins. Nucleic Acids Res 28:289–291Google Scholar
  47. Xu J, Li Y (2006) Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics 22:2800–2805CrossRefPubMedGoogle Scholar
  48. Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S (2010) GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 26:976–978CrossRefGoogle Scholar
  49. Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G (2002) MINT: a molecular INTeraction database. FEBS Lett 513:135–140CrossRefPubMedGoogle Scholar
  50. Zhang Y, Fan H, Xu J, Xiao Y, Xu Y, Li Y, Li X (2013) Network analysis reveals functional cross-links between disease and inflammation genes. Sci Rep 3:3426CrossRefPubMedPubMedCentralGoogle Scholar
  51. Zhou X, Menche J, Barabasi AL, Sharma A (2014) Human symptoms-disease network. Nat Commun 5:4212CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of BiotechnologyNational Institute of Technology WarangalWarangalIndia

Personalised recommendations