Skip to main content
SpringerLink
Log in

Identification of cancer driver genes based on hierarchical weak consensus model

  • Research
  • Published:
Health Information Science and Systems Aims and scope Submit manuscript

Abstract

Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The source code can be obtained at https://github.com/Mrhuhappy/HWC.git.

References

  1. Vandin F, Upfal E, Raphael BJ. De novo discovery of mutated driver pathways in cancer. Genome Res. 2011.

  2. Mclendon R, et al. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061–8.

    Article  ADS  CAS  Google Scholar 

  3. Bobrow M, Zhao S. International network of cancer genome projects. Nature. 2010;464(7291):993–8.

    Article  ADS  PubMed  Google Scholar 

  4. Peng J, Xue H, Shao Y, Shang X, Wang Y, Chen J. A novel method to measure the semantic similarity of hpo terms. Int J Data Min Bioinform. 2017;17(2):173–88.

    Article  Google Scholar 

  5. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458(7239):719–24.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bashashati A, et al. DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biol. 2012;13(12):1–14.

    Article  Google Scholar 

  7. Shi K, Gao L, Wang B. Discovering potential cancer driver genes by an integrated network-based approach. Mol BioSyst. 2016;12(9):2921–31.

    Article  CAS  PubMed  Google Scholar 

  8. Tian R, Basu MK, Capriotti E. ContrastRank: a new method for ranking putative cancer driver genes and classification of tumor samples. Bioinformatics. 2014;30(17):i572–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Dees ND, et al. MuSiC: identifying mutational significance in cancer genomes. Genome Res. 2012;22(8):1589–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lawrence MS, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013;499(7457):214–8.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ding L, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008;455(7216):1069–75.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pon JR, Marra MA. Driver and passenger mutations in cancer. Annu Rev Pathol. 2015;10:25–50.

    Article  CAS  PubMed  Google Scholar 

  13. Wendl MC, et al. PathScan: a tool for discerning mutational significance in groups of putative cancer genes. Bioinformatics. 2011;27(12):1595–602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Youn A, Simon R. Identifying cancer driver genes in tumor genome sequencing studies. Bioinformatics. 2011;27(2):175–81.

    Article  CAS  PubMed  Google Scholar 

  15. Gatza ML, Silva GO, Parker JS, Fan C, Perou CM. An integrated genomics approach identifies drivers of proliferation in luminal-subtype human breast cancer. Nat Genet. 2014;46(10):1051–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dimitrakopoulos CM, Beerenwinkel N. Computational approaches for the identification of cancer genes and pathways. Wiley Interdiscip Rev. 2017;9(1): e1364.

    Google Scholar 

  17. Martincorena I, et al. Universal patterns of selection in cancer and somatic tissues. Cell. 2017;171(5):1029–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Torti D, Trusolino L. Oncogene addiction as a foundational rationale for targeted anti-cancer therapy: promises and perils. EMBO Mol Med. 2011;3(11):623–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hahn WC, Weinberg RA. Modelling the molecular circuitry of cancer. Nat Rev Cancer. 2002;2(5):331–41.

    Article  CAS  PubMed  Google Scholar 

  20. Hahn WC, Counter CM, Lundberg AS, Beijersbergen RL, Brooks MW, Weinberg RA. Creation of human tumour cells with defined genetic elements. Nature. 1999;400(6743):464–8.

    Article  ADS  CAS  PubMed  Google Scholar 

  21. Hou P, Ma J. DawnRank: discovering personalized driver genes in cancer. Genome Med. 2014;6:1–16.

    Article  Google Scholar 

  22. Xi J, Wang M, Li A. Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network. BMC Bioinform. 2018;19(1):1–14.

    Article  Google Scholar 

  23. Xi J, Wang M, Li A. Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information. Mol BioSyst. 2017;13(10):2135–44.

    Article  CAS  PubMed  Google Scholar 

  24. Dimitrakopoulos C, et al. Network-based integration of multi-omics data for prioritizing cancer genes. Bioinformatics. 2018;34(14):2441–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Song J, Peng W, Wang F. A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph. BMC Bioinform. 2019;20(1):1–17.

    Article  Google Scholar 

  26. Song J, Peng W, Wang F. An entropy-based method for identifying mutual exclusive driver genes in cancer. IEEE/ACM Trans Comput Biol Bioinform. 2019;17(3):758–68.

    Article  PubMed  Google Scholar 

  27. Wei T, Fa B, Luo C, Johnston L, Zhang Y, Yu Z. An efficient and easy-to-use network-based integrative method of multi-omics data for cancer genes discovery. Front Genet. 2021;11: 613033.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wang C, Shi J, Cai J, Zhang Y, Zheng X, Zhang N. DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph. BMC Bioinform. 2022;23(1):1–19.

    Article  Google Scholar 

  29. Choudhury Y, et al. Attenuated adenosine-to-inosine editing of microRNA-376a* promotes invasiveness of glioblastoma cells. J Clin Investig. 2012;122(11):4059–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Stahlhut C, Slack FJ. MicroRNAs and the cancer phenotype: profiling, signatures and clinical implications. Genome Med. 2013;5:1–12.

    Article  Google Scholar 

  31. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D. Defining and identifying communities in networks. Proc Natl Acad Sci. 2004;101(9):2658–63.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li M, Zhang H, Wang J-X, Pan Y. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. BMC Syst Biol. 2012;6:1–9.

    Article  Google Scholar 

  33. Xiao Q, Wang J, Peng X, Wu F-X. Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles. Proteome Sci. 2013;11(1):1–8.

    Google Scholar 

  34. Bhattacharyya A. On a measure of divergence between two statistical populations defined by their probability distribution. Bull Calcutta Math Soc. 1943;35:99–110.

    MathSciNet  Google Scholar 

  35. Tomczak K, Czerwińska P, Wiznerowicz M. Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol/Współczesna Onkologia. 2015;2015(1):68–77.

    Article  Google Scholar 

  36. Patil A, Nakamura H. HINT: a database of annotated protein-protein interactions and their homologs. Biophysics. 2005;1:21–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Huang H-Y, et al. miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database. Nucleic Acids Res. 2020;48(D1):D148–54.

    ADS  CAS  PubMed  Google Scholar 

  38. Tate JG, et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 2019;47(D1):D941–7.

    Article  CAS  PubMed  Google Scholar 

  39. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man(OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33(Suppl 1):D514–7.

    CAS  PubMed  Google Scholar 

  40. Ashburner M, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353–61.

    Article  CAS  PubMed  Google Scholar 

  42. Fabregat A, et al. Reactome graph database: efficient access to complex pathway data. PLoS Comput Biol. 2018;14(1): e1005968.

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  43. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Yu G, He Q-Y. ReactomePA: an R/bioconductor package for reactome pathway analysis and visualization. Mol BioSyst. 2016;12(2):477–9.

    Article  CAS  PubMed  Google Scholar 

  45. Kuchenbaecker KB, et al. Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. JAMA. 2017;317(23):2402–16.

    Article  CAS  PubMed  Google Scholar 

  46. Wang J, Rouse C, Jasper JS, Pendergast AM. ABL kinases promote breast cancer osteolytic metastasis by modulating tumor-bone interactions through TAZ and STAT5 signaling. Sci Signal. 2016;9(413):ra12.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Moore-Smith L, Pasche B. TGFBR1 signaling and breast cancer. J Mammary Gland Biol Neoplasia. 2011;16:89–95.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Sugano T, et al. Inhibition of ABCB1 overcomes cancer stem cell–like properties and acquired resistance to MET inhibitors in non-small cell lung cancer ABCB1 inhibition overcomes resistance to MET inhibitors. Mol Cancer Ther. 2015;14(11):2433–40.

    Article  CAS  PubMed  Google Scholar 

  49. Gao X, et al. Estrogen receptors promote NSCLC progression by modulating the membrane receptor signaling network: a systems biology perspective. J Transl Med. 2019;17:1–15.

    Article  Google Scholar 

  50. Gorgisen G, et al. Identification of novel mutations of Insulin Receptor Substrate 1 (IRS1) in tumor samples of non-small cell lung cancer (NSCLC): implications for aberrant insulin signaling in development of cancer. Genet Mol Biol. 2019;42:15–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wei B, et al. TRAF2 is a valuable prognostic biomarker in patients with prostate cancer. Med Sci Monit. 2017;23:4192.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Rochester MA, Riedemann J, Hellawell GO, Brewster SF, Macaulay VM. Silencing of the IGF1R gene enhances sensitivity to DNA-damaging agents in both PTEN wild-type and mutant human prostate cancer. Cancer Gene Ther. 2005;12(1):90–100.

    Article  CAS  PubMed  Google Scholar 

  53. Sunkel B, et al. Integrative analysis identifies targetable CREB1/FoxA1 transcriptional co-regulation as a predictor of prostate cancer recurrence. Nucleic Acids Res. 2016;44(9):4105–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This research is supported by National Natural Science Foundation of China (No. 61972185, No. 62141207, No. 62302107, No. 62366007), Guangxi Natural Science Foundation (No. 2022GXNSFAA035625), Natural Science Foundation of Yunnan Province of China (No. 2019FA024), Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (No. 20-A-01-03,19-A-03-01), Guangxi Normal University Science Research Project (Natural Science) (No. 2021JC008), Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing, Innovation Project of Guangxi Graduate Education (YCSW2023180).

Author information

Authors and Affiliations

  1. Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China

    Gaoshi Li, Zhipeng Hu, Xinlong Luo, Jiafei Liu, Jingli Wu & Xiaoshu Zhu

  2. Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Gaoshi Li, Zhipeng Hu, Xinlong Luo, Jiafei Liu, Jingli Wu & Xiaoshu Zhu

  3. College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Gaoshi Li, Zhipeng Hu, Xinlong Luo, Jiafei Liu, Jingli Wu & Xiaoshu Zhu

  4. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China

    Wei Peng

  5. School of Computer and Information Security & School of Software Engineering, Guilin University of Electronic Science and Technology, Guilin, China

    Xiaoshu Zhu

Authors
  1. Gaoshi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhipeng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinlong Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiafei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jingli Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wei Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaoshu Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jingli Wu or Wei Peng.

Ethics declarations

Conflict of interest

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 140 KB)

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

Li, G., Hu, Z., Luo, X. et al. Identification of cancer driver genes based on hierarchical weak consensus model. Health Inf Sci Syst 12, 21 (2024). https://doi.org/10.1007/s13755-024-00279-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13755-024-00279-6

Keywords

Search

Navigation