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Volume 20, Issue 1
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

In cancer genomics research, identifying driver genes is a challenging task. Detecting cancer-driver genes can further our understanding of cancer risk factors and promote the development of personalized treatments. Gene mutations show mutual exclusivity and co-occur, and most of the existing methods focus on identifying driver pathways or driver gene sets through the study of mutual exclusivity, that is functionally redundant gene sets. Moreover, less research on cooperation genes with co-occurring mutations has been conducted.

Objective

We propose an effective method that combines the two characteristics of genes, co-occurring mutations and the coordinated regulation of proliferation genes, to explore cooperation driver genes.

Methods

This study is divided into three stages: (1) constructing a binary gene mutation matrix; (2) combining mutation co-occurrence characteristics to identify the candidate cooperation gene sets; and (3) constructing a gene regulation network to screen the cooperation gene sets that perform synergistically regulating proliferation.

Results

The method performance is evaluated on three TCGA cancer datasets, and the experiments showed that it can detect effective cooperation driver gene sets. In further investigations, it was determined that the discovered set of co-driver genes could be used to generate prognostic classifications, which could be biologically significant and provide complementary information to the cancer genome.

Conclusion

Our approach is effective in identifying sets of cancer cooperation driver genes, and the results can be used as clinical markers to stratify patients.

© 2025 Bentham Science Publishers
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2024-04-04
2025-01-31

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References

  1. VandinF. UpfalE. RaphaelB.J. De novo discovery of mutated driver pathways in cancer.Genome Res.201222237538510.1101/gr.120477.111 21653252
     [Google Scholar]
  2. ZhaoJ. ZhangS. WuL.Y. ZhangX.S. Efficient methods for identifying mutated driver pathways in cancer.Bioinformatics201228222940294710.1093/bioinformatics/bts564 22982574
     [Google Scholar]
  3. LeisersonM.D.M. BlokhD. SharanR. RaphaelB.J. Simultaneous identification of multiple driver pathways in cancer.PLOS Comput. Biol.201395e100305410.1371/journal.pcbi.1003054 23717195
     [Google Scholar]
  4. ZhangJ. ZhangS. Discovery of cancer common and specific driver gene sets.Nucleic Acids Res.20174510e8610.1093/nar/gkx089 28168295
     [Google Scholar]
  5. VandinF. UpfalE. RaphaelB.J. Algorithms for detecting significantly mutated pathways in cancer.J. Comput. Biol.201118350752210.1089/cmb.2010.0265 21385051
     [Google Scholar]
  6. LeisersonM.D.M. VandinF. WuH.T. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes.Nat. Genet.201547210611410.1038/ng.3168 25501392
     [Google Scholar]
  7. ReynaM.A. LeisersonM.D.M. RaphaelB.J. Hierarchical HotNet: Identifying hierarchies of altered subnetworks.Bioinformatics20183417i972i98010.1093/bioinformatics/bty613 30423088
     [Google Scholar]
  8. BaburÖ. GönenM. AksoyB.A. Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations.Genome Biol.20151614510.1186/s13059‑015‑0612‑6 25887147
     [Google Scholar]
  9. Van DaeleD. WeytjensB. De RaedtL. MarchalK. OMEN: Network-based driver gene identification using mutual exclusivity.Bioinformatics202238123245325110.1093/bioinformatics/btac312 35552634
     [Google Scholar]
  10. DaoP. KimY.A. WojtowiczD. MadanS. SharanR. PrzytyckaT.M. BeWith: A between-within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions.PLOS Comput. Biol.20171310e100569510.1371/journal.pcbi.1005695 29023534
     [Google Scholar]
  11. KimY.A. ChoD.Y. DaoP. PrzytyckaT.M. MEMCover: Integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types.Bioinformatics20153112i284i29210.1093/bioinformatics/btv247 26072494
     [Google Scholar]
  12. ElsumI.A. YatesL.L. PearsonH.B. Scrib heterozygosity predisposes to lung cancer and cooperates with KRas hyperactivation to accelerate lung cancer progression in vivo.Oncogene201433485523553310.1038/onc.2013.498 24276238
     [Google Scholar]
  13. LucasM.W. VersluisJ.M. RozemanE.A. BlankC.U. Personalizing neoadjuvant immune-checkpoint inhibition in patients with melanoma.Nat. Rev. Clin. Oncol.202320640842210.1038/s41571‑023‑00760‑3 37147419
     [Google Scholar]
  14. WatsonD.C. BayikD. StorevikS. GAP43-dependent mitochondria transfer from astrocytes enhances glioblastoma tumorigenicity.Nat. Can.20234564866410.1038/s43018‑023‑00556‑5 37169842
     [Google Scholar]
  15. ChenY.C. PengW.C. LeeS.Y. Efficient algorithms for influence maximization in social networks.Knowl. Inf. Syst.201233357760110.1007/s10115‑012‑0540‑7
     [Google Scholar]
  16. CirielloG. CeramiE. SanderC. SchultzN. Mutual exclusivity analysis identifies oncogenic network modules.Genome Res.201222239840610.1101/gr.125567.111 21908773
     [Google Scholar]
  17. UlzP. HeitzerE. SpeicherM.R. Co-occurrence of MYC amplification and TP53 mutations in human cancer.Nat. Genet.201648210410610.1038/ng.3468 26813759
     [Google Scholar]
  18. BeroukhimR. GetzG. NghiemphuL. Assessing the significance of chromosomal aberrations in cancer: Methodology and application to glioma.Proc. Natl. Acad. Sci. USA200710450200072001210.1073/pnas.0710052104 18077431
     [Google Scholar]
  19. SunP. JiaoB. YangY. WGDI: A user-friendly toolkit for evolutionary analyses of whole-genome duplications and ancestral karyotypes.Mol. Plant202215121841185110.1016/j.molp.2022.10.018 36307977
     [Google Scholar]
  20. HuC. WuJ. SunC. ChenX. YanR. Mutual information-based feature disentangled network for anomaly detection under variable working conditions.Mech. Syst. Signal Process.202320411080410.1016/j.ymssp.2023.110804
     [Google Scholar]
  21. KramerO. K-nearest neighbors.Dimensionality Reduction with Unsupervised Nearest Neighbors. KramerO. Berlin, HeidelbergSpringer Berlin Heidelberg2013132310.1007/978‑3‑642‑38652‑7_2
     [Google Scholar]
  22. PearlJ. Bayesian networks2011
     [Google Scholar]
  23. NovovičováJ. SomolP. HaindlM. PudilP. Conditional mutual information based feature selection for classification task. 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, November 13-16, 2007 pp.417426
     [Google Scholar]
  24. ForbesS.A. BeareD. BoutselakisH. COSMIC: Somatic cancer genetics at high-resolution.Nucleic Acids Res.201745D1D777D78310.1093/nar/gkw1121 27899578
     [Google Scholar]
  25. KashyapD. PalD. SharmaR. Global increase in breast cancer incidence: Risk factors and preventive measures.BioMed Res. Int.2022202211610.1155/2022/9605439 35480139
     [Google Scholar]
  26. YeF. DewanjeeS. LiY. Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer.Mol. Cancer202322110510.1186/s12943‑023‑01805‑y 37415164
     [Google Scholar]
  27. BarriosC.H. Global challenges in breast cancer detection and treatment.Breast202262Suppl. 1S3S610.1016/j.breast.2022.02.003 35219542
     [Google Scholar]
  28. YauC. OsdoitM. van der NoordaaM. Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: A multicentre pooled analysis of 5161 patients.Lancet Oncol.202223114916010.1016/S1470‑2045(21)00589‑1 34902335
     [Google Scholar]
  29. din NM, Dar RA, Rasool M, Assad A. Breast cancer detection using deep learning: Datasets, methods, and challenges ahead.Comput. Biol. Med.202214910607310.1016/j.compbiomed.2022.106073 36103745
     [Google Scholar]
  30. AlhasaniA.T. AlkattanH. SubhiA.A. El-KenawyE-S.M. EidM.M. A comparative analysis of methods for detecting and diagnosing breast cancer based on data mining.Methods2023817
     [Google Scholar]
  31. AlifierisC. TrafalisD.T. Glioblastoma multiforme: Pathogenesis and treatment.Pharmacol. Ther.2015152638210.1016/j.pharmthera.2015.05.005 25944528
     [Google Scholar]
  32. ParsonsD.W. JonesS. ZhangX. An integrated genomic analysis of human glioblastoma multiforme.Science200832158971807181210.1126/science.1164382 18772396
     [Google Scholar]
  33. KhabibovM. GarifullinA. BoumberY. Signaling pathways and therapeutic approaches in glioblastoma multiforme. (Review)Int. J. Oncol.20226066910.3892/ijo.2022.5359 35445737
     [Google Scholar]
  34. YangL. YuJ. TaoL. Cuproptosis-related lncRNAs are biomarkers of prognosis and immune microenvironment in head and neck squamous cell carcinoma.Front. Genet.20221394755110.3389/fgene.2022.947551 35938003
     [Google Scholar]
  35. FlachS. HowarthK. HackingerS. Liquid biopsy for minimal residual disease detection in head and neck squamous cell carcinoma (LIONESS)—A personalised circulating tumour DNA analysis in head and neck squamous cell carcinoma.Br. J. Cancer202212681186119510.1038/s41416‑022‑01716‑7 35132238
     [Google Scholar]
  36. GoelB. TiwariA.K. PandeyR.K. Therapeutic approaches for the treatment of head and neck squamous cell carcinoma–An update on clinical trials.Transl. Oncol.20222110142610.1016/j.tranon.2022.101426 35460943
     [Google Scholar]
  37. YangT. PasquierN. PreciosoF. Semi-supervised consensus clustering based on closed patterns.Knowl. Base. Syst.202223510759910.1016/j.knosys.2021.107599
     [Google Scholar]
  38. XuJ. ChenY. OlopadeO.I. MYC and breast cancer.Genes Cancer20101662964010.1177/1947601910378691 21779462
     [Google Scholar]
  39. MahmoodS.F. GruelN. ChapeaublancE. A siRNA screen identifies RAD21, EIF3H, CHRAC1 and TANC2 as driver genes within the 8q23, 8q24.3 and 17q23 amplicons in breast cancer with effects on cell growth, survival and transformation.Carcinogenesis201435367068210.1093/carcin/bgt351 24148822
     [Google Scholar]
  40. GaoQ. CuiY. ShenY. Identifying mutually exclusive gene sets with prognostic value and novel potential driver genes in patients with glioblastoma.BioMed Res. Int.201920191710.1155/2019/4860367 31815141
     [Google Scholar]
  41. SeamanJ EndresL. Putative Role for METTL1 in Human Gliobastoma2017
     [Google Scholar]
  42. EskilssonE. RøslandG.V. SoleckiG. EGFR heterogeneity and implications for therapeutic intervention in glioblastoma.Neuro-oncol.201820674375210.1093/neuonc/nox191 29040782
     [Google Scholar]
  43. PearsonJ.R.D. RegadT. Targeting cellular pathways in glioblastoma multiforme.Signal Transduct. Target. Ther.2017211704010.1038/sigtrans.2017.40 29263927
     [Google Scholar]
/content/journals/cbio/10.2174/0115748936293238240313081211
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An Effective Method to Identify Cooperation Driver Gene Sets
Curr Bioinform 20, 59 (2025); https://doi.org/10.2174/0115748936293238240313081211
/content/journals/cbio/10.2174/0115748936293238240313081211
/content/journals/cbio/10.2174/0115748936293238240313081211
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  • Article Type:     Research Article
Keyword(s): binary gene mutation matrix; Cancer progression; co-occurrence mutations; coordinated regulation; driver gene; stratify patients

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