Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Comparative study on genomic and epigenomic profiles of retinoblastoma or tuberous sclerosis complex via nanopore sequencing and a joint screening framework

Cancer Gene Therapy volume 31, pages 439–453 (2024)Cite this article

Subjects

Abstract

Recurrence and extraocular metastasis in advanced intraocular retinoblastoma (RB) are still major obstacles for successful treatment of Chinese children. Tuberous sclerosis complex (TSC) is a very rare, multisystemic genetic disorder characterized by hamartomatous growth. In this study, we aimed to compare genomic and epigenomic profiles with human RB or TSC using recently developed nanopore sequencing, and to identify disease-associated variations or genes. Peripheral blood samples were collected from either RB or RB/TSC patients plus their normal siblings, followed by nanopore sequencing and identification of disease-specific structural variations (SVs) and differentially methylated regions (DMRs) by a systematic biology strategy named as multiomics-based joint screening framework. In total, 316 RB- and 1295 TSC-unique SVs were identified, as well as 1072 RB- and 1114 TSC-associated DMRs, respectively. We eventually identified 6 key genes for RB for further functional validation. Knockdown of CDK19 with specific siRNAs significantly inhibited Y79 cellular proliferation and increased sensitivity to carboplatin, whereas downregulation of AHNAK2 promoted the cell growth as well as drug resistance. Those two genes might serve as potential diagnostic markers or therapeutic targets of RB. The systematic biology strategy combined with functional validation might be an effective approach for rare pediatric malignances with limited samples and challenging collection process.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A framework for identifying key genes in RB or TSC.
Fig. 2: Profiles of structural variations in RB and TSC.
Fig. 3: Distribution of DMRs identified from the DNA methylation dataset.
Fig. 4: Construct a joint screening framework.
Fig. 5: Effect of knockdown of CDK19 and AHNAK2 on the proliferative ability and chemotherapy response of Y79 cell lines.
Fig. 6: Schematic illustration model of the regulatory mechanism of SVs in Y79 cells.

Similar content being viewed by others

Data availability

The raw data sequencing reads have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences, under accession number HRA005951 (https://ngdc.cncb.ac.cn/gsa-human).The data generated in this study are available within the article and its supplementary data files. All data is available from the corresponding author upon reasonable request.

Code availability

The main code has been uploaded to GitHub (https://github.com/Jtwangbio/RB_TSC).

References

  1. Dimaras H, Corson TW, Cobrinik D, White A, Zhao J, Munier FL, et al. Retinoblastoma. Nat Rev Dis Prim. 2015;1:15021.

    Article  PubMed  Google Scholar 

  2. Shields CL, Mashayekhi A, Au AK, Czyz C, Leahey A, Meadows AT, et al. The International Classification of Retinoblastoma predicts chemoreduction success. Ophthalmology. 2006;113:2276–80.

    Article  PubMed  Google Scholar 

  3. Friend SH, Bernards R, Rogelj S, Weinberg RA, Rapaport JM, Albert DM, et al. A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma. Nature. 1986;323:643–6.

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Tomar AS, Finger PT, Gallie B, Kivela TT, Mallipatna A, Zhang C, et al. Global Retinoblastoma Treatment Outcomes: Association with National Income Level. Ophthalmology. 2021;128:740–53.

    Article  PubMed  Google Scholar 

  5. Lu JE, Francis JH, Dunkel IJ, Shields CL, Yu MD, Berry JL, et al. Metastases and death rates after primary enucleation of unilateral retinoblastoma in the USA 2007-2017. Br J Ophthalmol. 2019;103:1272–7.

    Article  PubMed  Google Scholar 

  6. Zhou C, Wen X, Ding Y, Ding J, Jin M, Liu Z, et al. Eye-Preserving Therapies for Advanced Retinoblastoma: A Multicenter Cohort of 1678 Patients in China. Ophthalmology. 2022;129:209–19.

    Article  PubMed  Google Scholar 

  7. Henske EP, Jozwiak S, Kingswood JC, Sampson JR, Thiele EA. Tuberous sclerosis complex. Nat Rev Dis Prim. 2016;2:16035.

    Article  PubMed  Google Scholar 

  8. Dereure O, Savoy D, Doz F, Junien C, Guilhou JJ. Multiple acral fibromas in a patient with familial retinoblastoma: a cutaneous marker of tumour-suppressor gene germline mutation? Br J Dermatol. 2000;143:856–9.

    Article  CAS  PubMed  Google Scholar 

  9. Shields JA, Eagle RC Jr., Shields CL, Marr BP. Aggressive retinal astrocytomas in four patients with tuberous sclerosis complex. Trans Am Ophthalmol Soc. 2004;102:139–47.

    PubMed  PubMed Central  Google Scholar 

  10. Weischenfeldt J, Symmons O, Spitz F, Korbel JO. Phenotypic impact of genomic structural variation: insights from and for human disease. Nat Rev Genet. 2013;14:125–38.

    Article  CAS  PubMed  Google Scholar 

  11. Wang X, Xu J, Zhang B, Hou Y, Song F, Lyu H, et al. Genome-wide detection of enhancer-hijacking events from chromatin interaction data in rearranged genomes. Nat Methods. 2021;18:661–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Feinberg AP, Koldobskiy MA, Gondor A. Epigenetic modulators, modifiers and mediators in cancer aetiology and progression. Nat Rev Genet. 2016;17:284–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rushlow DE, Mol BM, Kennett JY, Yee S, Pajovic S, Thériault BL, et al. Characterisation of retinoblastomas without RB1 mutations: genomic, gene expression, and clinical studies. Lancet Oncol. 2013;14:327–34.

    Article  CAS  PubMed  Google Scholar 

  14. Berdasco M, Gomez A, Rubio MJ, Catala-Mora J, Zanon-Moreno V, Lopez M, et al. DNA Methylomes Reveal Biological Networks Involved in Human Eye Development, Functions and Associated Disorders. Sci Rep. 2017;7:11762.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  15. Stirzaker C, Millar DS, Paul CL, Warnecke PM, Harrison J, Vincent PC, et al. Extensive DNA methylation spanning the Rb promoter in retinoblastoma tumors. Cancer Res. 1997;57:2229–37.

    CAS  PubMed  Google Scholar 

  16. van Dijk EL, Jaszczyszyn Y, Naquin D, Thermes C. The Third Revolution in Sequencing Technology. Trends Genet. 2018;34:666–81.

    Article  PubMed  Google Scholar 

  17. Wang Y, Zhao Y, Bollas A, Wang Y, Au KF. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol. 2021;39:1348–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhang C, Feng ZX, Li L, Solarte CE, Ma X. Retinoblastoma in a child with tuberous sclerosis complex. Can J Ophthalmol. 2020;55:e199–e201.

    Article  PubMed  Google Scholar 

  19. Liu SH, Shen PC, Chen CY, Hsu AN, Cho YC, Lai YL, et al. DriverDBv3: a multi-omics database for cancer driver gene research. Nucleic Acids Res. 2020;48:D863–70.

    CAS  PubMed  Google Scholar 

  20. Feng H, Wu H. Differential methylation analysis for bisulfite sequencing using DSS. Quant Biol. 2019;7:327–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47:W556–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Spielmann M, Lupianez DG, Mundlos S. Structural variation in the 3D genome. Nat Rev Genet. 2018;19:453–67.

    Article  CAS  PubMed  Google Scholar 

  23. Sedlazeck FJ, Rescheneder P, Smolka M, Fang H, Nattestad M, von Haeseler A, et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat Methods. 2018;15:461–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Geoffroy V, Herenger Y, Kress A, Stoetzel C, Piton A, Dollfus H, et al. AnnotSV: an integrated tool for structural variations annotation. Bioinformatics. 2018;34:3572–4.

    Article  CAS  PubMed  Google Scholar 

  25. Laird PW. Principles and challenges of genomewide DNA methylation analysis. Nat Rev Genet. 2010;11:191–203.

    Article  CAS  PubMed  Google Scholar 

  26. Woo HG, Choi JH, Yoon S, Jee BA, Cho EJ, Lee JH, et al. Integrative analysis of genomic and epigenomic regulation of the transcriptome in liver cancer. Nat Commun. 2017;8:839.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  27. Zhang Y, Yang L, Kucherlapati M, Hadjipanayis A, Pantazi A, Bristow CA, et al. Global impact of somatic structural variation on the DNA methylome of human cancers. Genome Biol. 2019;20:209.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Yang X, Han H, De Carvalho DD, Lay FD, Jones PA, Liang G. Gene body methylation can alter gene expression and is a therapeutic target in cancer. Cancer Cell. 2014;26:577–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Jones PA, Baylin SB. The epigenomics of cancer. Cell. 2007;128:683–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Saleheen D, Natarajan P, Armean IM, Zhao W, Rasheed A, Khetarpal SA, et al. Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature. 2017;544:235–9.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Pietraszkiewicz A, van Asten F, Kwong A, Ratnapriya R, Abecasis G, Swaroop A, et al. Association of Rare Predicted Loss-of-Function Variants in Cellular Pathways with Sub-Phenotypes in Age-Related Macular Degeneration. Ophthalmology. 2018;125:398–406.

    Article  PubMed  Google Scholar 

  32. Cummings BB, Karczewski KJ, Kosmicki JA, Seaby EG, Watts NA, Singer-Berk M, et al. Transcript expression-aware annotation improves rare variant interpretation. Nature. 2020;581:452–8.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Geoffroy V, Guignard T, Kress A, Gaillard JB, Solli-Nowlan T, Schalk A, et al. AnnotSV and knotAnnotSV: a web server for human structural variations annotations, ranking and analysis. Nucleic Acids Res. 2021;49:W21–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Becker F, Joerg V, Hupe MC, Roth D, Krupar R, Lubczyk V, et al. Increased mediator complex subunit CDK19 expression associates with aggressive prostate cancer. Int J Cancer. 2020;146:577–88.

    Article  CAS  PubMed  Google Scholar 

  35. Paulsen FO, Idel C, Ribbat-Idel J, Kuppler P, Klapper L, Rades D, et al. CDK19 as a Potential HPV-Independent Biomarker for Recurrent Disease in HNSCC. Int J Mol Sci. 2020;21:5508.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Battista S, Ambrosio MR, Limarzi F, Gallo G, Saragoni L. Molecular Alterations in Gastric Preneoplastic Lesions and Early Gastric Cancer. Int J Mol Sci. 2021;22:6652.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wang M, Li X, Zhang J, Yang Q, Chen W, Jin W, et al. AHNAK2 is a Novel Prognostic Marker and Oncogenic Protein for Clear Cell Renal Cell Carcinoma. Theranostics. 2017;7:1100–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Roch-Arveiller M, Abbouyi AE, Paul JL, Smets P, Raichvarg D, Giroud JP. Modulation by RU 41740 of oxidative metabolism and migration of rat polymorphonuclear leukocytes collected after induction of immune and nonimmune inflammations. J Biol Response Mod. 1988;7:199–203.

    CAS  PubMed  Google Scholar 

  39. Ramanand SG, Chen Y, Yuan J, Daescu K, Lambros MBK, Houlahan KE, et al. The landscape of RNA polymerase II–associated chromatin interactions in prostate cancer. J Clin Investig. 2020;130:3987–4005.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Peng H, Zheng J, Su Q, Feng X, Peng M, Gong L, et al. VPS53 Suppresses Malignant Properties in Colorectal Cancer by Inducing the Autophagy Signaling Pathway. Onco Targets Ther. 2020;13:10667–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bignone PA, Lee KY, Liu Y, Emilion G, Finch J, Soosay AE, et al. RPS6KA2, a putative tumour suppressor gene at 6q27 in sporadic epithelial ovarian cancer. Oncogene. 2007;26:683–700.

    Article  CAS  PubMed  Google Scholar 

  42. Sun Y, Xu K, He M, Fan G, Lu H. Overexpression of Glypican 5 (GPC5) Inhibits Prostate Cancer Cell Proliferation and Invasion via Suppressing Sp1-Mediated EMT and Activation of Wnt/β-Catenin Signaling. Oncol Res Featur Preclin Clin Cancer Therap. 2018;26:565–72.

    Google Scholar 

  43. Li F, Shi W, Capurro M, Filmus J. Glypican-5 stimulates rhabdomyosarcoma cell proliferation by activating Hedgehog signaling. J Cell Biol. 2011;192:691–704.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Williamson D, Selfe J, Gordon T, Lu Y-J, Pritchard-Jones K, Murai K, et al. Role for Amplification and Expression of Glypican-5 in Rhabdomyosarcoma. Cancer Res. 2007;67:57–65.

    Article  CAS  PubMed  Google Scholar 

  45. Koch A, De Meyer T, Jeschke J, Van Criekinge W. MEXPRESS: visualizing expression, DNA methylation and clinical TCGA data. BMC Genom. 2015;16:636.

    Article  Google Scholar 

  46. Zhao J, Li Q, Wu S, Jin L, Ma X, Jin M, et al. Pars Plana Vitrectomy and Endoresection of Refractory Intraocular Retinoblastoma. Ophthalmology. 2018;125:320–2.

    Article  PubMed  Google Scholar 

  47. Zhao J, Li Q, Feng ZX, Zhang J, Wu S, Jin L, et al. Tylectomy Safety in Salvage of Eyes with Retinoblastoma. Cancers (Basel). 2021;13:5862.

    Article  CAS  PubMed  Google Scholar 

  48. Karcioglu ZA. Fine needle aspiration biopsy (FNAB) for retinoblastoma. Retina. 2002;22:707–10.

    Article  PubMed  Google Scholar 

  49. Munier FL, Beck-Popovic M, Chantada GL, Cobrinik D, Kivela TT, Lohmann D, et al. Conservative management of retinoblastoma: Challenging orthodoxy without compromising the state of metastatic grace. “Alive, with good vision and no comorbidity. Prog Retin Eye Res. 2019;73:100764.

    Article  PubMed  Google Scholar 

  50. Liu C, Xiang X, Han S, Lim H, Li L, Zhang X, et al. Blood-based liquid biopsy: Insights into early detection and clinical management of lung cancer. Cancer Lett. 2022;524:91–102.

    Article  CAS  PubMed  Google Scholar 

  51. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, et al. An integrated map of structural variation in 2,504 human genomes. Nature. 2015;526:75–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Huddleston J, Chaisson MJP, Steinberg KM, Warren W, Hoekzema K, Gordon D, et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 2017;27:677–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Chen M, Liang J, Ji H, Yang Z, Altilia S, Hu B, et al. CDK8/19 Mediator kinases potentiate induction of transcription by NFkappaB. Proc Natl Acad Sci USA. 2017;114:10208–13.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Steinparzer I, Sedlyarov V, Rubin JD, Eislmayr K, Galbraith MD, Levandowski CB, et al. Transcriptional Responses to IFN-gamma Require Mediator Kinase-Dependent Pause Release and Mechanistically Distinct CDK8 and CDK19 Functions. Mol Cell. 2019;76:485–99.e8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. MacArthur DG, Tyler-Smith C. Loss-of-function variants in the genomes of healthy humans. Hum Mol Genet. 2010;19:R125–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Minikel EV, Karczewski KJ, Martin HC, Cummings BB, Whiffin N, Rhodes D, et al. Evaluating drug targets through human loss-of-function genetic variation. Nature. 2020;581:459–64.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  57. Supper E, Rudat S, Iyer V, Droop A, Wong K, Spinella JF, et al. Cut-like homeobox 1 (CUX1) tumor suppressor gene haploinsufficiency induces apoptosis evasion to sustain myeloid leukemia. Nat Commun. 2021;12:2482.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang Y, Huang D, Shi J, Ma J, Zhao J, Li B, et al. Clinical treatment and prognostic observation for diferent pathological infiltrations in 537 patients with unilateral retinoblastoma. Chin Med J (Engl). 2014;127:P3581–6.

    Article  Google Scholar 

  59. Luo X, Ye H, Ding Y, Du Y, Yang H. Analysis of histopathology and prognosis of retinoblastoma after enucleation. Recent Adv Ophthalmol. 2014;34:1147–51.

    Google Scholar 

  60. Song WP, Zheng S, Yao HJ, Zhou XF, Li R, Zhang CY, et al. Different transcriptome profiles between human retinoblastoma Y79 cells and an etoposide-resistant subline reveal a chemoresistance mechanism. BMC Ophthalmol. 2020;20:92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank the technical support from the Beijing Biomarker Technologies Co., Ltd.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC no. 81828010 to LL; no. 62173338 to HC), the CAMS Innovation Fund for Medical Sciences (CIFMS 2021-1-I2M-026 to LL), the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (ZYYCXTD-C-202205 to LL), the “Belt and Road Initiatives” Innovative Talent Exchange Foreign Experts Project (DL2022194002L to LL), and the Beijing Nova Program of Science and Technology (no. 20220484198 to HC).

Author information

Author notes

  1. These authors contributed equally: Junting Wang, Chengyue Zhang, Li Zhang.

Authors and Affiliations

  1. State Key Laboratory of Respiratory Health and Multimorbidity, NHC Key Laboratory of Biotechnology for Microbial Drugs, Department of Oncology, Institute of Medicinal Biotechnology (IMB), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), NO.1 Tiantan Xili, Beijing, 100050, China

    Junting Wang, Li Zhang, Hong-juan Yao & Liang Li

  2. Institute of Health Service and Transfusion Medicine, Beijing, 100850, P.R. China

    Junting Wang, Xiaochen Bo & Hebing Chen

  3. Department of Ophthalmology, Beijing Children’s Hospital affiliated with Capital Medical University, National Center for Children’s Health, Beijing, 100045, China

    Chengyue Zhang & Junyang Zhao

  4. Guang’anmen Hospital, Chinese Academy of Chinese Medical Sciences, No.5 BeiXianGe St., Beijing, 100053, China

    Xiaohong Liu

  5. Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Beijing, 100700, China

    Yuchen Shi

Authors
  1. Junting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengyue Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong-juan Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaohong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuchen Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Junyang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaochen Bo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hebing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Liang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LL, ZCY, CHB, BXC and ZJY contributed to the conception of the research idea and study design. ZCY and WJT performed data analysis and wrote the manuscript. ZL conducted functional validation and wrote the manuscript. YHJ, LXH and SYC performed data verification and analysis. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Chengyue Zhang, Hebing Chen or Liang Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Zhang, C., Zhang, L. et al. Comparative study on genomic and epigenomic profiles of retinoblastoma or tuberous sclerosis complex via nanopore sequencing and a joint screening framework. Cancer Gene Ther 31, 439–453 (2024). https://doi.org/10.1038/s41417-023-00714-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41417-023-00714-y

Search

Advanced search

Quick links