Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Bioinformatics Research and Applications

ISBRA 2022: Bioinformatics Research and Applications pp 148–158Cite as

A Network-Based Voting Method for Identification and Prioritization of Personalized Cancer Driver Genes

  • Conference paper
  • First Online:

  • 665 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13760))

Abstract

Identifying cancer driver genes is an important task in cancer research. Numerous methods that identify these driver genes have been proposed, most of which identify driver genes in entire patient cohorts. However, research has shown that cancers in different patients may be caused by different driver genes. Therefore, if we pay more attention to the individual level to identify personalized driver genes, patients can receive precise treatment and achieve better treatment results. Among the methods for identifying personalized cancer drivers, most of these methods only identify the coding driver genes, however, non-coding cancer drivers are also crucial for the initialization and development of cancer. Therefore, we develop an approach to identify coding drivers and non-coding drivers for individual patients named PerVote. In PerVote, the personalized network is firstly constructed based on expression data of mRNAs and miRNAs, then identifies cancer drivers by a voting approach in the network. To verify the performance of our method, we use five cancer datasets of TCGA and compare it with the state-of-the-art methods. The results show that PerVote outperforms other methods. Our method also predicts and prioritizes miRNA drivers, most of which are confirmed by OncomiR to be related to tumorigenesis. Therefore, PerVote can bring further help to the personalized treatment of cancer patients and is an effective method for the identification of cancer drivers.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 71(3), 209–249 (2021). https://doi.org/10.3322/caac.21660

  2. Waks, Z., et al.: Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins. Sci. Rep. 6(1), 1–12 (2016). https://doi.org/10.1038/srep38988

    Article  CAS  Google Scholar 

  3. Xi, J., Li, A., Wang, M.: A novel unsupervised learning model for detecting driver genes from pan-cancer data through matrix tri-factorization framework with pairwise similarities constraints. Neurocomputing 296, 64–73 (2018). https://doi.org/10.1016/j.neucom.2018.03.026

    Article  Google Scholar 

  4. Weinstein, J.N., et al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113–1120 (2013). https://doi.org/10.1038/ng.2764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Consortium, ICG: International network of cancer genome projects. Nature 464(7291), 993 (2010). https://doi.org/10.1038/nature08987

  6. Xi, J., Li, A., Wang, M.: HetRCNA: a novel method to identify recurrent copy number alternations from heterogeneous tumor samples based on matrix decomposition framework. IEEE/ACM Trans. Comput. Biol. Bioinf. 17(2), 422–434 (2018). https://doi.org/10.1109/TCBB.2018.2846599

    Article  CAS  Google Scholar 

  7. Xi, J., Li, A.: Discovering recurrent copy number aberrations in complex patterns via non-negative sparse singular value decomposition. IEEE/ACM Trans. Comput. Biol. Bioinf. 13(4), 656–668 (2015). https://doi.org/10.1109/TCBB.2015.2474404

    Article  Google Scholar 

  8. Lawrence, M.S., et al.: Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499(7457), 214–218 (2013). https://doi.org/10.1038/nature12213

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gonzalez-Perez, A., Lopez-Bigas, N.: Functional impact bias reveals cancer drivers. Nucleic Acids Res. 40(21), e169–e169 (2012). https://doi.org/10.1093/nar/gks743

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Han, Y., et al.: DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies. Nucleic Acids Res. 47(8), e45–e45 (2019). https://doi.org/10.1093/nar/gkz096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Xi, J., et al.: Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication. Bioinformatics 36(6), 1855–1863 (2020). https://doi.org/10.1093/bioinformatics/btz793

    Article  CAS  PubMed  Google Scholar 

  12. Bashashati, A., et al.: DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biol. 13(12), 1–14 (2012). https://doi.org/10.1186/gb-2012-13-12-r124

    Article  CAS  Google Scholar 

  13. Pham, V.V., et al.: CBNA: a control theory based method for identifying coding and non-coding cancer drivers. PLoS Comput Biol. 15(12), e1007538 (2019). https://doi.org/10.1371/journal.pcbi.1007538

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hou, J.P., Ma, J.: DawnRank: discovering personalized driver genes in cancer. Genome Med. 6(7), 1–16 (2014). https://doi.org/10.1186/s13073-014-0056-8

    Article  Google Scholar 

  15. Pham, V.V., et al.: pDriver: a novel method for unravelling personalized coding and miRNA cancer drivers. Bioinformatics 37(19), 3285–3292 (2021). https://doi.org/10.1093/bioinformatics/btab262

    Article  CAS  PubMed  Google Scholar 

  16. Reimand, J., Bader, G.D.: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol Syst Biol. 9(1), 637 (2013). https://doi.org/10.1038/msb.2012.68

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Guo, W.-F., et al.: Discovering personalized driver mutation profiles of single samples in cancer by network control strategy. Bioinformatics 34(11), 1893–1903 (2018). https://doi.org/10.1093/bioinformatics/bty006

    Article  CAS  PubMed  Google Scholar 

  18. Guo, W.-F., et al.: A novel network control model for identifying personalized driver genes in cancer. PLoS Comput. Biol. 15(11), e1007520 (2019). https://doi.org/10.1371/journal.pcbi.1007520

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wong, N.W., Chen, Y., Chen, S., Wang, X.: OncomiR: an online resource for exploring pan-cancer microRNA dysregulation. Bioinformatics 34(4), 713–715 (2018). https://doi.org/10.1093/bioinformatics/btx627

    Article  CAS  PubMed  Google Scholar 

  20. Lizio, M., et al.: Update of the FANTOM web resource: high resolution transcriptome of diverse cell types in mammals. Nucleic Acids Res. 45, D737–D743 (2017). https://doi.org/10.1093/nar/gkw995

    Article  CAS  PubMed  Google Scholar 

  21. Vinayagam, A., et al.: A directed protein interaction network for investigating intracellular signal transduction. Sci. Signal. 4(189), rs8–rs8 (2011). https://doi.org/10.1126/scisignal.2001699

  22. Vlachos, I.S., et al.: DIANA-TarBase v7. 0: indexing more than half a million experimentally supported miRNA: mRNA interactions. Nucleic Acids Res. 43(D1), D153–D159 (2015). https://doi.org/10.1093/nar/gku1215

  23. Agarwal, V., Bell, G.W., Nam, J.-W., Bartel, D.P.: Predicting effective microRNA target sites in mammalian mRNAs. elife 4, e05005 (2015). https://doi.org/10.7554/eLife.05005

  24. Dweep, H., Gretz, N.: miRWalk2. 0: a comprehensive atlas of microRNA-target interactions. Nat. Methods. 12(8), 697–697 (2015). https://doi.org/10.1038/nmeth.3485

  25. Wang, J., Lu, M., Qiu, C., Cui, Q.: TransmiR: a transcription factor–microRNA regulation database. Nucleic Acids Res. 38(suppl_1), D119–D122 (2010). https://doi.org/10.1093/nar/gkp803

  26. Chou, C.-H., et al.: miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res. 44(D1), D239–D247 (2016). https://doi.org/10.1093/nar/gkv1258

    Article  CAS  PubMed  Google Scholar 

  27. Forbes, S.A., et al.: COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43(D1), D805–D811 (2015). https://doi.org/10.1093/nar/gku1075

    Article  CAS  PubMed  Google Scholar 

  28. Van Allen, E.M., et al.: Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat. Med. 20(6), 682–688 (2014). https://doi.org/10.1038/nm.3559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cotto, KC., et al.: DGIdb 3.0: a redesign and expansion of the drug–gene interaction database. Nucleic Acids Res. 46(D1), D1068–D1073 (2018). https://doi.org/10.1093/nar/gkx1143

  30. Kuijjer, M.L., Tung, M.G., Yuan, G., Quackenbush, J., Glass, K.: Estimating sample-specific regulatory networks. Iscience. 14, 226–240 (2019). https://doi.org/10.1016/j.isci.2019.03.021

  31. Yu, D., Yu, Z.: HWVoteRank: a network-based voting approach for identifying coding and non-coding cancer drivers. Mathematics 10(5), 801 (2022). https://doi.org/10.3390/math10050801

    Article  Google Scholar 

  32. Liu, P., Li, L., Fang, S., Yao, Y.: Identifying influential nodes in social networks: a voting approach. Chaos, Solitons Fractals 152, 111309 (2021). https://doi.org/10.1016/j.chaos.2021.111309

  33. Sun, H.-L., Chen, D.-B., He, J.-L., Ch’ng, E.: A voting approach to uncover multiple influential spreaders on weighted networks. Phys. A 519, 303–312 (2019). https://doi.org/10.1016/j.physa.2018.12.001

Download references

Acknowledgment

This work has been supported by the National Natural Science Foundation of China (61902216, 61972236 and 61972226), and Natural Science Foundation of Shan-dong Province (No. ZR2018MF013).

Author information

Authors and Affiliations

  1. School of Computer Science, Qufu Normal University, Rizhao, 276826, China

    Han Li, Feng Li & Junliang Shang

  2. Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan, 250031, Shandong, China

    Xikui Liu & Yan Li

Authors
  1. Han Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junliang Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xikui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Li .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Mukul S. Bansal

  2. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  3. University of Southern California, Los Angeles, CA, USA

    Serghei Mangul

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, H., Li, F., Shang, J., Liu, X., Li, Y. (2022). A Network-Based Voting Method for Identification and Prioritization of Personalized Cancer Driver Genes. In: Bansal, M.S., Cai, Z., Mangul, S. (eds) Bioinformatics Research and Applications. ISBRA 2022. Lecture Notes in Computer Science(), vol 13760. Springer, Cham. https://doi.org/10.1007/978-3-031-23198-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23198-8_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23197-1

  • Online ISBN: 978-3-031-23198-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation