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An integrative pan-cancer analysis of biological and clinical impacts underlying ubiquitin-specific-processing proteases

Oncogene volume 39, pages 587–602 (2020)Cite this article

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A Correction to this article was published on 18 November 2022

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Abstract

Ubiquitin-specific-processing proteases (USPs), the largest deubiquitinating enzyme (DUB) subfamily, play critical roles in cancer. However, clinical utility of USPs is hindered by limited knowledge about their varied and substrate-dependent actions. Here, we performed a comprehensive investigation on pan-cancer impacts of USPs by integrating multi-omics data and annotated data resources, especially a deubiquitination network. Meaningful insights into the roles of 54 USPs in 29 types of cancers were generated. Although rare mutations were observed, a majority of USPs exhibited significant expressional alterations, prognostic impacts and strong correlations with cancer hallmark pathways. Notably, from our DUB-substrate interaction prediction model, additional USP-substrate interactions (USIs) were recognized to complement knowledge gap about cancer-relevant USIs. Intriguingly, expression signatures of the USIs revealed clinically meaningful cancer subtypes, where key USPs and substrates cooperatively contributed to significant prognosis differences among subtypes. Overall, this investigation provides a valuable resource to assist mechanism research and clinical utility about USPs.

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References

  1. Scheffner M, Nuber U, Huibregtse JM. PRotein Ubiquitination Involving an E1-E2-E3 enzyme ubiquitin thioester cascade. Nature. 1995;373:81–3.

    Article  CAS  PubMed  Google Scholar 

  2. Nijman SMB, Luna-Vargas MPA, Velds A, Brummelkamp TR, Dirac AMG, Sixma TK, et al. A Genomic and functional inventory of deubiquitinating enzymes. Cell. 2005;123:773–86.

    Article  CAS  PubMed  Google Scholar 

  3. Li M, Chen D, Shiloh A, Luo J, Nikolaev AY, Qin J, et al. Deubiquitination of p53 by HAUSP is an important pathway for p53 stabilization. Nature. 2002;416:648–53.

    Article  CAS  PubMed  Google Scholar 

  4. Cummins JM, Rago C, Kohli M, Kinzler KW, Lengauer C, Vogelstein B. Tumour suppression: disruption of HAUSP gene stabilizes p53. Nature. 2004;428:486.

    Article  Google Scholar 

  5. Li M, Brooks CL, Kon N, Gu W. A dynamic role of HAUSP in the p53-Mdm2 pathway. Mol Cell. 2004;13:879–86.

    Article  CAS  PubMed  Google Scholar 

  6. Zhu C, Ji X, Zhang H, Zhou Q, Cao X, Tang M, et al. Deubiquitylase USP9X suppresses tumorigenesis by stabilizing large tumor suppressor kinase 2 (LATS2) in the Hippo pathway. J Biol Chem. 2018;293:1178–91.

    Article  CAS  PubMed  Google Scholar 

  7. Toloczko A, Guo F, Yuen HF, Wen Q, Wood SA, Ong YS, et al. Deubiquitinating enzyme USP9X suppresses tumor growth via LATS kinase and core components of the hippo pathway. Cancer Res. 2017;77:4921–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yang B, Zhang S, Wang Z, Yang C, Ouyang W, Zhou F, et al. Deubiquitinase USP9X deubiquitinates beta-catenin and promotes high grade glioma cell growth. Oncotarget. 2016;7:79515–25.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Weisberg EL, Schauer NJ, Yang J, Lamberto I, Doherty L, Bhatt S, et al. Inhibition of USP10 induces degradation of oncogenic FLT3. Nat Chem Biol. 2017;13:1207–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sun J, Li T, Zhao Y, Huang L, Sun H, Wu H, et al. USP10 inhibits lung cancer cell growth and invasion by upregulating PTEN. Mol Cell Biochem. 2018;441:1–7.

    Article  CAS  PubMed  Google Scholar 

  11. Lu C, Ning Z, Wang A, Chen D, Liu X, Xia T, et al. USP10 suppresses tumor progression by inhibiting mTOR activation in hepatocellular carcinoma. Cancer Lett. 2018;436:139–48.

    Article  CAS  PubMed  Google Scholar 

  12. D’Arcy P, Brnjic S, Olofsson MH, Fryknas M, Lindsten K, De Cesare M, et al. Inhibition of proteasome deubiquitinating activity as a new cancer therapy. Nat Med. 2011;17:1636–40.

    Article  PubMed  Google Scholar 

  13. Huang X, Dixit VM. Drugging the undruggables: exploring the ubiquitin system for drug development. Cell Res. 2016;26:484–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chauhan D, Tian Z, Nicholson B, Kumar KG, Zhou B, Carrasco R, et al. A small molecule inhibitor of ubiquitin-specific protease-7 induces apoptosis in multiple myeloma cells and overcomes bortezomib resistance. Cancer Cell. 2012;22:345–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Turnbull AP, Ioannidis S, Krajewski WW, Pinto-Fernandez A, Heride C, Martin ACL, et al. Molecular basis of USP7 inhibition by selective small-molecule inhibitors. Nature. 2017;550:481–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Davis MI, Pragani R, Fox JT, Shen M, Parmar K, Gaudiano EF, et al. Small molecule inhibition of the ubiquitin-specific protease USP2 accelerates cyclin D1 degradation and leads to cell cycle arrest in colorectal cancer and mantle cell lymphoma models. J Biol Chem. 2016;291:24628–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Yuan T, Yan FJ, Ying MD, Cao J, He QJ, Zhu H, et al. Inhibition of ubiquitin-specific proteases as a novel anticancer therapeutic strategy. Front Pharmacol. 2018;9:1080.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen D, Liu X, Xia T, Tekcham DS, Wang W, Chen H. et al. A Multidimensional Characterization of E3 Ubiquitin Ligase and Substrate Interaction Network. iScience. 2019;16:177–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Li Y, Xie P, Lu L, Wang J, Diao LH, Liu ZY, et al. An integrated bioinformatics platform for investigating the human E3 ubiquitin ligase-substrate interaction network. Nat Commun. 2017;8:347.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Cancer Genome Atlas Research N, Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, et al. The cancer genome atlas pan-cancer analysis project. Nat Genet. 2013;45:1113–20.

    Article  Google Scholar 

  21. Wang Y, Xu X, Maglic D, Dill MT, Mojumdar K, Ng PK, et al. Comprehensive molecular characterization of the hippo signaling pathway in cancer. Cell Rep. 2018;25:1304–.e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, et al. Comprehensive characterization of cancer driver genes and mutations. Cell. 2018;174:1034–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ge Z, Leighton JS, Wang Y, Peng X, Chen Z, Chen H, et al. Integrated genomic analysis of the ubiquitin pathway across cancer types. Cell Rep. 2018;23:213–26 e3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, et al. COSMIC: somatic cancer genetics at high-resolution. Nucl Acids Res. 2017;45:D777–83.

    Article  CAS  PubMed  Google Scholar 

  25. Edwards NJ, Oberti M, Thangudu RR, Cai S, McGarvey PB, Jacob S, et al. The CPTAC data portal: a resource for cancer proteomics research. J Proteome Res. 2015;14:2707–13.

    Article  CAS  PubMed  Google Scholar 

  26. Trompouki E, Hatzivassiliou E, Tsichritzis T, Farmer H, Ashworth A, Mosialos G. CYLD is a deubiquitinating enzyme that negatively regulates NF-kappa B activation by TNFR family members. Nature. 2003;424:793–6.

    Article  CAS  PubMed  Google Scholar 

  27. Meuwissen ME, Schot R, Buta S, Oudesluijs G, Tinschert S, Speer SD, et al. Human USP18 deficiency underlies type 1 interferonopathy leading to severe pseudo-TORCH syndrome. J Exp Med. 2016;213:1163–74.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Nijman SM, Huang TT, Dirac AM, Brummelkamp TR, Kerkhoven RM, D’Andrea AD, et al. The deubiquitinating enzyme USP1 regulates the Fanconi anemia pathway. Mol cell. 2005;17:331–9.

    Article  CAS  PubMed  Google Scholar 

  29. Barabasi AL. Scale-free networks: a decade and beyond. Science. 2009;325:412–3.

    Article  CAS  PubMed  Google Scholar 

  30. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Yu DH, Hung MC. Overexpression of ErbB2 in cancer and ErbB2-targeting strategies. Oncogene. 2000;19:6115–21.

    Article  CAS  PubMed  Google Scholar 

  32. Kapoor A, Yao W, Ying H, Hua S, Liewen A, Wang Q, et al. Yap1 activation enables bypass of oncogenic Kras addiction in pancreatic cancer. Cell. 2014;158:185–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Landi S, Moreno V, Gioia-Patricola L, Guino E, Navarro M, de Oca J, et al. Association of common polymorphisms in inflammatory genes interleukin (IL)6, IL8, tumor necrosis factor alpha, NFKB1, and peroxisome proliferator-activated receptor gamma with colorectal cancer. Cancer Res. 2003;63:3560–6.

    CAS  PubMed  Google Scholar 

  34. Wade M, Li YC, Wahl GM. MDM2, MDMX and p53 in oncogenesis and cancer therapy. Nat Rev Cancer. 2013;13:83–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ishizawar R, Parsons SJ. c-Src and cooperating partners in human cancer. Cancer Cell. 2004;6:209–14.

    Article  CAS  PubMed  Google Scholar 

  36. Chan GKT, Jablonski SA, Sudakin V, Hittle JC, Yen TJ. Human BUBR1 is a mitotic checkpoint kinase that monitors CENP-E functions at kinetochores and binds the cyclosome/APC. J Cell Biol. 1999;146:941–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Brown NR, Lowe ED, Petri E, Skamnaki V, Antrobus R, Johnson LN. Cyclin B and cyclin A confer different substrate recognition properties on CDK2. Cell Cycle. 2007;6:1350–9.

    Article  CAS  PubMed  Google Scholar 

  38. Ronnstrand L. Signal transduction via the stem cell factor receptor/c-Kit. Cell Mol life Sci: CMLS. 2004;61:2535–48.

    Article  CAS  PubMed  Google Scholar 

  39. Sorrentino A, Thakur N, Grimsby S, Marcusson A, von Bulow V, Schuster N, et al. The type I TGF-beta receptor engages TRAF6 to activate TAK1 in a receptor kinase-independent manner. Nat Cell Biol. 2008;10:1199–207.

    Article  CAS  PubMed  Google Scholar 

  40. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Srinivas NR. Clinical pharmacokinetics of panobinostat, a novel histone deacetylase (HDAC) inhibitor: review and perspectives. Xenobiotica. 2017;47:354–68.

    Article  CAS  PubMed  Google Scholar 

  42. Luise C, Capra M, Donzelli M, Mazzarol G, Jodice MG, Nuciforo P, et al. An atlas of altered expression of deubiquitinating enzymes in human cancer. PLoS ONE. 2011;6:e15891.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chen M, Gutierrez GJ, Ronai ZA. Ubiquitin-recognition protein Ufd1 couples the endoplasmic reticulum (ER) stress response to cell cycle control. Proc Natl Acad Sci. 2011;108:9119–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Cotto-Rios XM, Jones MJK, Huang TT. Insights into phosphorylation-dependent mechanisms regulating USP1 protein stability during the cell cycle. Cell Cycle. 2011;10:4009–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucl Acids Res. 2016;44:e71.

    Article  PubMed  Google Scholar 

  46. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 2008;9:559.

    Article  Google Scholar 

  47. Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim JS, et al. A novel signaling pathway impact analysis. Bioinformatics. 2009;25:75–82.

    Article  CAS  PubMed  Google Scholar 

  48. Rani J, Shah AR, Ramachandran S. pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts. J Biosci. 2015;40:671–82.

    Article  PubMed  Google Scholar 

  49. Hanpude P, Bhattacharya S, Dey AK, Maiti TK. Deubiquitinating enzymes in cellular signaling and disease regulation. IUBMB life. 2015;67:544–55.

    Article  CAS  PubMed  Google Scholar 

  50. Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, et al. A draft map of the human proteome. Nature. 2014;509:575–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Matsumoto M, Nishimura T. Mersenne Twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Simul. 1998;8:28.

    Article  Google Scholar 

  52. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57.

    Article  Google Scholar 

  53. Torgo L. Data Mining with R, learning with case studies. Chapman and Hall/CRC, Boca Raton, 2010.

  54. Zhang JD, Wiemann S. KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor. Bioinformatics. 2009;25:1470–1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26:1572–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Li J, Zhao W, Akbani R, Liu W, Ju Z, Ling S, et al. Characterization of human cancer cell lines by reverse-phase protein arrays. Cancer Cell. 2017;31:225–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Breiman L. Random forests. Mach Learn. 2001;45:5–32.

    Article  Google Scholar 

  58. Liaw AWM. Classification and regression by randomForest. R News. 2002;2:18–22.

    Google Scholar 

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Acknowledgements

Dedicated to the 70th anniversary of Dalian Institute of Chemical Physics, CAS. We thank Guowang Xu, Tongming Li, and all members of the Dr. Piao laboratory for helpful discussions and suggestions. This study is supported by the National Natural Science Foundation of China (Grant Nos. 81672440, 31701156, 81502024, and 81572881), Project funded by China Postdoctoral Science Foundation (No. 2017M611281), Innovation program of science and research from the DICP, CAS (DICP TMSR201601, DICP ZZBS201803).

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Author notes

  1. These authors contributed equally: Di Chen, Zhen Ning, Huan Chen, Chang Lu

Authors and Affiliations

  1. CAS Key Laboratory of Separation Science for Analytical Chemistry, Scientific Research Center for Translational Medicine, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China

    Di Chen, Zhen Ning, Huan Chen, Chang Lu, Xiaolong Liu, Tian Xia, Huan Qi, Wen Wang, Ting Ling, Xin Guo, Dinesh Singh Tekcham, Xiumei Liu, Jing Liu & Hai-long Piao

  2. The First Affiliated Hospital of Dalian Medical University, Dalian Medical University, Dalian, 116000, China

    Zhen Ning, Chang Lu, Xin Guo, Aman Wang, Qiu Yan, Ji-Wei Liu & Guang Tan

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Huan Chen & Hai-long Piao

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Chen, D., Ning, Z., Chen, H. et al. An integrative pan-cancer analysis of biological and clinical impacts underlying ubiquitin-specific-processing proteases. Oncogene 39, 587–602 (2020). https://doi.org/10.1038/s41388-019-1002-4

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