Search Advanced

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 12: 21643-21669. doi: 10.3934/mbe.2023958
Previous Article Next Article
Review Special Issues

Advances in computational methods for identifying cancer driver genes

  • 1.
    School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
  • 2.
    School of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China
  • 3.
    School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China

  • Academic Editor: Jinshan Tang
  • Received: 27 August 2023 Revised: 19 November 2023 Accepted: 22 November 2023 Published: 06 December 2023

  • Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

    Citation: Ying Wang, Bohao Zhou, Jidong Ru, Xianglian Meng, Yundong Wang, Wenjie Liu. Advances in computational methods for identifying cancer driver genes[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 21643-21669. doi: 10.3934/mbe.2023958

    Related Papers:

  • Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.


    加载中


    [1] B. Vogelstein, N. Papadopoulos, V. E. Velculescu, S. Zhou, L. A. Diaz, K. W. Kinzler, Cancer genome landscapes, Science, 339 (2013), 1546–1558. https://doi.org/10.1126/science.1235122 doi: 10.1126/science.1235122
    [2] J. Gao, B. A. Aksoy, U. Dogrusoz, G. Dresdner, B. Gross, S. O. Sumer, et al., Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal, Sci. Signal, 6 (2013), l1. https://doi.org/10.1126/scisignal.2004088 doi: 10.1126/scisignal.2004088
    [3] S. Agajanian, O. Oluyemi, G. M. Verkhivker, Integration of random forest classifiers and deep convolutional neural networks for classification and biomolecular modeling of cancer driver mutations, Front. Mol. Biosci., 6 (2019), 44. https://doi.org/10.3389/fmolb.2019.00044 doi: 10.3389/fmolb.2019.00044
    [4] M. I. Klein, V. L. Cannataro, J. P. Townsend, D. F. Stern, H. Zhao, Identifying combinations of cancer drivers in individual patients, bioRxiv, (2019), 674234. https://doi.org/10.1101/674234 doi: 10.1101/674234
    [5] F. Cheng, J. Zhao, Z. Zhao, Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes, Briefings Bioinf., 17 (2016), 642–656. https://doi.org/10.1093/bib/bbv068 doi: 10.1093/bib/bbv068
    [6] W. F. Guo, S. W. Zhang, T. Zeng, T. Akutsu, L. Chen, Network control principles for identifying personalized driver genes in cancer, Briefings Bioinf., 21 (2020), 1641–1662. https://doi.org/10.1093/bib/bbz089 doi: 10.1093/bib/bbz089
    [7] M. Sinkala, Mutational landscape of cancer-driver genes across human cancers, Sci. Rep., 13 (2023), 12742. https://doi.org/ARTN 1274210.1038/s41598-023-39608-2
    [8] M. S. Lawrence, P. Stojanov, C. H. Mermel, J. T. Robinson, L. A. Garraway, T. R. Golub, et al., Discovery and saturation analysis of cancer genes across 21 tumour types, Nature, 505 (2014), 495–501. https://doi.org/10.1038/nature12912 doi: 10.1038/nature12912
    [9] N. D. Dees, Q. Zhang, C. Kandoth, M. C. Wendl, W. Schierding, D. C. Koboldt, et al., MuSiC: identifying mutational significance in cancer genomes, Genome Res., 22 (2012), 1589–1598. https://doi.org/10.1101/gr.134635.111 doi: 10.1101/gr.134635.111
    [10] D. Tamborero, A. Gonzalez-Perez, N. Lopez-Bigas, OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes, Bioinformatics, 29 (2013), 2238–2244. https://doi.org/10.1093/bioinformatics/btt395 doi: 10.1093/bioinformatics/btt395
    [11] J. P. Hou, J. Ma, DawnRank: discovering personalized driver genes in cancer, Genome Med., 6 (2014), 56. https://doi.org/10.1186/s13073-014-0056-8 doi: 10.1186/s13073-014-0056-8
    [12] F. Vandin, E. Upfal, B. J. Raphael, De novo discovery of mutated driver pathways in cancer, Genome Res., 22 (2012), 375–385. https://doi.org/10.1101/gr.120477.111 doi: 10.1101/gr.120477.111
    [13] S. Zhao, J. Liu, P. Nanga, Y. Liu, A. E. Cicek, N. Knoblauch, et al., Detailed modeling of positive selection improves detection of cancer driver genes, Nat. Commun., 10 (2019), 3399. https://doi.org/10.1038/s41467-019-11284-9 doi: 10.1038/s41467-019-11284-9
    [14] A. Bashashati, G. Haffari, J. Ding, G. Ha, K. Lui, J. Rosner, et al., DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer, Genome Biol., 13 (2012), R124. https://doi.org/10.1186/gb-2012-13-12-r124 doi: 10.1186/gb-2012-13-12-r124
    [15] E. O. Paull, D. E. Carlin, M. Niepel, P. K. Sorger, D. Haussler, J. M. Stuart, Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE), Bioinformatics, 29 (2013), 2757–2764. https://doi.org/10.1093/bioinformatics/btt471 doi: 10.1093/bioinformatics/btt471
    [16] M. D. Leiserson, F. Vandin, H. T. Wu, J. R. Dobson, J. V. Eldridge, J. L. Thomas, et al., Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes, Nat. Genet., 47 (2015), 106–114. https://doi.org/10.1038/ng.3168 doi: 10.1038/ng.3168
    [17] A. Cho, J. E. Shim, E. Kim, F. Supek, B. Lehner, I. Lee, MUFFINN: cancer gene discovery via network analysis of somatic mutation data, Genome Biol., 17 (2016), 129. https://doi.org/10.1186/s13059-016-0989-x doi: 10.1186/s13059-016-0989-x
    [18] Y. Hou, B. Gao, G. Li, Z. Su, MaxMIF: A new method for identifying cancer driver genes through effective data integration, Adv. Sci., 5 (2018), 1800640. https://doi.org/10.1002/advs.201800640 doi: 10.1002/advs.201800640
    [19] P. Jia, Z. Zhao, VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data, PLoS Comput. Biol., 10 (2014), e1003460. https://doi.org/10.1371/journal.pcbi.1003460 doi: 10.1371/journal.pcbi.1003460
    [20] J. Song, W. Peng, F. Wang, J. Wang, Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network, BMC Med. Genomics, 12 (2019), 168. https://doi.org/10.1186/s12920-019-0619-z doi: 10.1186/s12920-019-0619-z
    [21] D. Bertrand, K. R. Chng, F. G. Sherbaf, A. Kiesel, B. K. Chia, Y. Y. Sia, et al., Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles, Nucleic Acids Res., 43 (2015), e44. https://doi.org/10.1093/nar/gku1393 doi: 10.1093/nar/gku1393
    [22] C. A. Miller, S. H. Settle, E. P. Sulman, K. D. Aldape, A. Milosavljevic, Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors, BMC Med. Genomics, 4 (2011), 34. https://doi.org/10.1186/1755-8794-4-34 doi: 10.1186/1755-8794-4-34
    [23] M. D. Leiserson, H. T. Wu, F. Vandin, B. J. Raphael, CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer, Genome Biol., 16 (2015), 160. https://doi.org/10.1186/s13059-015-0700-7 doi: 10.1186/s13059-015-0700-7
    [24] M. D. Leiserson, D. Blokh, R. Sharan, B. J. Raphael, Simultaneous identification of multiple driver pathways in cancer, PLoS Comput. Biol., 9 (2013), e1003054. https://doi.org/10.1371/journal.pcbi.1003054 doi: 10.1371/journal.pcbi.1003054
    [25] S. Cristea, J. Kuipers, N. Beerenwinkel, pathTiMEx: Joint inference of mutually exclusive cancer pathways and their progression dynamics, J. Comput. Biol., 24 (2017), 603–615. https://doi.org/10.1089/cmb.2016.0171 doi: 10.1089/cmb.2016.0171
    [26] S. Constantinescu, E. Szczurek, P. Mohammadi, J. Rahnenfuhrer, N. Beerenwinkel, TiMEx: a waiting time model for mutually exclusive cancer alterations, Bioinformatics, 32 (2016), 968–975. https://doi.org/10.1093/bioinformatics/btv400 doi: 10.1093/bioinformatics/btv400
    [27] G. Ciriello, E. Cerami, C. Sander, N. Schultz, Mutual exclusivity analysis identifies oncogenic network modules, Genome Res., 22 (2012), 398–406. https://doi.org/10.1101/gr.125567.111 doi: 10.1101/gr.125567.111
    [28] B. H. Hristov, M. Singh, Network-based coverage of mutational profiles reveals cancer genes, Cell Syst., 5 (2017), 221–229. https://doi.org/10.1016/j.cels.2017.09.003 doi: 10.1016/j.cels.2017.09.003
    [29] J. Song, W. Peng, F. Wang, An entropy-based method for identifying mutual exclusive driver genes in cancer, IEEE/ACM Trans. Comput. Biol. Bioinf., 17 (2020), 758–768. https://doi.org/10.1109/TCBB.2019.2897931 doi: 10.1109/TCBB.2019.2897931
    [30] C. J. Tokheim, N. Papadopoulos, K. W. Kinzler, B. Vogelstein, R. Karchin, Evaluating the evaluation of cancer driver genes, Proc. Natl. Acad. Sci. U.S.A., 113 (2016), 14330–14335. https://doi.org/10.1073/pnas.1616440113 doi: 10.1073/pnas.1616440113
    [31] Y. Han, J. Yang, X. Qian, W. C. Cheng, S. H. Liu, X. Hua, et al., DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies, Nucleic Acids Res., 47 (2019), e45. https://doi.org/10.1093/nar/gkz096 doi: 10.1093/nar/gkz096
    [32] A. Colaprico, C. Olsen, M. H. Bailey, G. J. Odom, T. Terkelsen, T. C. Silva, et al., Interpreting pathways to discover cancer driver genes with moonlight, Nat. Commun., 11 (2020), 69. https://doi.org/10.1038/s41467-019-13803-0 doi: 10.1038/s41467-019-13803-0
    [33] P. Luo, Y. Ding, X. Lei, F. X. Wu, deepDriver: Predicting cancer driver genes based on somatic mutations using deep convolutional neural networks, Front. Genet., 10 (2019), 13. https://doi.org/10.3389/fgene.2019.00013 doi: 10.3389/fgene.2019.00013
    [34] P. Chandrashekar, N. Ahmadinejad, J. Wang, A. Sekulic, J. B. Egan, Y. W. Asmann, et al., Somatic selection distinguishes oncogenes and tumor suppressor genes, Bioinformatics, 36 (2020), 1712–1717. https://doi.org/10.1093/bioinformatics/btz851 doi: 10.1093/bioinformatics/btz851
    [35] J. Lyu, J. J. Li, J. Su, F. Peng, Y. E. Chen, X. Ge, et al., DORGE: Discovery of oncogenes and tumor suppressor genes using genetic and epigenetic features, Sci. Adv., 6 (2020). https://doi.org/10.1126/sciadv.aba6784 doi: 10.1126/sciadv.aba6784
    [36] M. Sudhakar, R. Rengaswamy, K. Raman, Novel ratio-metric features enable the identification of new driver genes across cancer types, Sci. Rep., 12 (2022), 5. https://doi.org/10.1038/s41598-021-04015-y doi: 10.1038/s41598-021-04015-y
    [37] J. Lever, E. Y. Zhao, J. Grewal, M. R. Jones, S. J. M. Jones, CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer, Nat. Methods, 16 (2019), 505–507. https://doi.org/10.1038/s41592-019-0422-y doi: 10.1038/s41592-019-0422-y
    [38] O. Collier, V. Stoven, J. P. Vert, LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes, PLoS Comput. Biol., 15 (2019), e1007381. https://doi.org/10.1371/journal.pcbi.1007381 doi: 10.1371/journal.pcbi.1007381
    [39] J. Reimand, G. D. Bader, Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers, Mol. Syst. Biol., 9 (2013), 637. https://doi.org/10.1038/msb.2012.68 doi: 10.1038/msb.2012.68
    [40] L. Qu, Z. Wang, H. Zhang, Z. Wang, C. Liu, W. Qian, et al., The analysis of relevant gene networks based on driver genes in breast cancer, Diagnostics, 12 (2022), 2882. https://doi.org/10.3390/diagnostics12112882 doi: 10.3390/diagnostics12112882
    [41] X. Shi, H. Teng, L. Shi, W. Bi, W. Wei, F. Mao, et al., Comprehensive evaluation of computational methods for predicting cancer driver genes, Briefings Bioinf., 23 (2022), bbab548. https://doi.org/10.1093/bib/bbab548 doi: 10.1093/bib/bbab548
    [42] A. C. Gumpinger, K. Lage, H. Horn, K. Borgwardt, Prediction of cancer driver genes through network-based moment propagation of mutation scores, Bioinformatics, 36 (2020), i508–i515. https://doi.org/10.1093/bioinformatics/btaa452 doi: 10.1093/bioinformatics/btaa452
    [43] S. Ng, E. A. Collisson, A. Sokolov, T. Goldstein, A. Gonzalez-Perez, N. Lopez-Bigas, et al., PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis, Bioinformatics, 28 (2012), i640–i646. https://doi.org/10.1093/bioinformatics/bts402 doi: 10.1093/bioinformatics/bts402
    [44] K. Shi, L. Gao, B. Wang, Discovering potential cancer driver genes by an integrated network-based approach, Mol. Biosyst., 12 (2016), 2921–2931. https://doi.org/10.1039/c6mb00274a doi: 10.1039/c6mb00274a
    [45] C. Suo, O. Hrydziuszko, D. Lee, S. Pramana, D. Saputra, H. Joshi, et al., Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival, Bioinformatics, 31 (2015), 2607–2613. https://doi.org/10.1093/bioinformatics/btv164 doi: 10.1093/bioinformatics/btv164
    [46] E. Hodzic, R. Shrestha, K. Zhu, K. Cheng, C. C. Collins, S. Cenk Sahinalp, Combinatorial detection of conserved alteration patterns for identifying cancer subnetworks, Gigascience, 8 (2019), giz024. https://doi.org/10.1093/gigascience/giz024 doi: 10.1093/gigascience/giz024
    [47] E. Lusito, B. Felice, G. D'Ario, A. Ogier, F. Montani, P. P. Di Fiore, et al., Unraveling the role of low-frequency mutated genes in breast cancer, Bioinformatics, 35 (2018), 36–46. https://doi.org/10.1093/bioinformatics/bty520 doi: 10.1093/bioinformatics/bty520
    [48] F. Li, L. Gao, X. Ma, X. Yang, Detection of driver pathways using mutated gene network in cancer, Mol. Biosyst., 12 (2016), 2135–2141. https://doi.org/10.1039/C6MB00084C doi: 10.1039/C6MB00084C
    [49] B. Gao, G. Li, J. Liu, Y. Li, X. Huang, Identification of driver modules in pan-cancer via coordinating coverage and exclusivity, Oncotarget, 8 (2017), 36115–36126. https://doi.org/10.18632/oncotarget.16433 doi: 10.18632/oncotarget.16433
    [50] D. Silverbush, S. Cristea, G. Yanovich-Arad, T. Geiger, N. Beerenwinkel, R. Sharan, Simultaneous integration of multi-omics data improves the identification of cancer driver modules, Cell Syst., 8 (2019), 456–466 e5. https://doi.org/10.1016/j.cels.2019.04.005 doi: 10.1016/j.cels.2019.04.005
    [51] A. Garavand, C. Salehnasab, A. Behmanesh, N. Aslani, A. H. Zadeh, M. Ghaderzadeh, Efficient model for coronary artery disease diagnosis: a comparative study of several machine learning algorithms, J. Healthcare Eng., 2022 (2022), 5359540. https://doi.org/10.1155/2022/5359540 doi: 10.1155/2022/5359540
    [52] S. J. Malebary, Y. D. Khan, Evaluating machine learning methodologies for identification of cancer driver genes, Sci. Rep., 11 (2021), 12281. https://doi.org/10.1038/s41598-021-91656-8 doi: 10.1038/s41598-021-91656-8
    [53] S. W. Zhang, Z. N. Wang, Y. Li, W. F. Guo, Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network, BMC Bioinf., 23 (2022), 341. https://doi.org/10.1186/s12859-022-04802-y doi: 10.1186/s12859-022-04802-y
    [54] P. H. Acosta, V. Panwar, V. Jarmale, A. Christie, J. Jasti, V. Margulis, et al., Intratumoral resolution of driver gene mutation heterogeneity in renal cancer using deep learning, Cancer Res., 82 (2022), 2792–2806. https://doi.org/10.1158/0008-5472.CAN-21-2318 doi: 10.1158/0008-5472.CAN-21-2318
    [55] F. Sadoughi, M. Ghaderzadeh, A hybrid particle swarm and neural network approach for detection of prostate cancer from benign hyperplasia of prostate, Stud. Health Technol. Inf., 205 (2014), 481–485.
    [56] A. J. Moshayedi, A. S. Roy, A. Kolahdooz, S. Yang, Deep learning application pros and cons over algorithm, EAI Endorsed Trans. AI Rob., 1 (2022), 1–13
    [57] M. Gheisari, G. Wang, M. Z. A. Bhuiyan, A survey on deep learning in big data, in 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), (2017), 173–180.
    [58] U. D. Akavia, O. Litvin, J. Kim, F. Sanchez-Garcia, D. Kotliar, H. C. Causton, et al., An integrated approach to uncover drivers of cancer, Cell, 143 (2010), 1005–1017. https://doi.org/10.1016/j.cell.2010.11.013 doi: 10.1016/j.cell.2010.11.013
    [59] Y. Chen, J. Hao, W. Jiang, T. He, X. Zhang, T. Jiang, et al., Identifying potential cancer driver genes by genomic data integration, Sci. Rep., 3 (2013), 3538. https://doi.org/10.1038/srep03538 doi: 10.1038/srep03538
    [60] K. M. Jagodnik, Y. Shvili, A. Bartal, HetIG-PreDiG: A heterogeneous integrated graph model for predicting human disease genes based on gene expression, PLoS One, 18 (2023), e0280839. https://doi.org/10.1371/journal.pone.0280839 doi: 10.1371/journal.pone.0280839
    [61] Y. Chen, X. Wu, R. Jiang, Integrating human omics data to prioritize candidate genes, BMC Med. Genomics, 6 (2013), 57. https://doi.org/10.1186/1755-8794-6-57 doi: 10.1186/1755-8794-6-57
    [62] Z. Tian, M. Guo, C. Wang, L. Xing, L. Wang, Y. Zhang, Constructing an integrated gene similarity network for the identification of disease genes, J. Biomed. Semant., 8 (2017), 32. https://doi.org/10.1186/s13326-017-0141-1 doi: 10.1186/s13326-017-0141-1
    [63] L. Chin, J. N. Andersen, P. A. Futreal, Cancer genomics: from discovery science to personalized medicine, Nat. Med., 17 (2011), 297–303. https://doi.org/10.1038/nm.2323 doi: 10.1038/nm.2323
    [64] R. Edgar, M. Domrachev, A. E. Lash, Gene Expression Omnibus: NCBI gene expression and hybridization array data repository, Nucleic Acids Res., 30 (2002), 207–210. https://doi.org/10.1093/nar/30.1.207 doi: 10.1093/nar/30.1.207
    [65] J. Zhang, R. Bajari, D. Andric, F. Gerthoffert, A. Lepsa, H. Nahal-Bose, et al., The international cancer genome consortium data portal, Nat. Biotechnol., 37 (2019), 367–369. https://doi.org/10.1038/s41587-019-0055-9 doi: 10.1038/s41587-019-0055-9
    [66] Cancer Cell Line Encyclopedia Consortium, Genomics of Drug Sensitivity in Cancer Consortium, Pharmacogenomic agreement between two cancer cell line data sets, Nature, 528 (2015), 84–87. https://doi.org/10.1038/nature15736
    [67] J. Pinero, J. M. Ramirez-Anguita, J. Sauch-Pitarch, F. Ronzano, E. Centeno, F. Sanz, et al., The DisGeNET knowledge platform for disease genomics: 2019 update, Nucleic Acids Res., 48 (2020), D845–D855. https://doi.org/10.1093/nar/gkz1021 doi: 10.1093/nar/gkz1021
    [68] D. Repana, J. Nulsen, L. Dressler, M. Bortolomeazzi, S. K. Venkata, A. Tourna, et al., The Network of Cancer Genes (NCG), a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens, Genome Biol., 20 (2019), 1. https://doi.org/10.1186/s13059-018-1612-0 doi: 10.1186/s13059-018-1612-0
    [69] M. Sedova, M. Iyer, Z. Li, L. Jaroszewski, K. W. Post, T. Hrabe, et al., Cancer3D 2.0: interactive analysis of 3D patterns of cancer mutations in cancer subsets, Nucleic Acids Res., 47 (2019), D895–D899. https://doi.org/10.1093/nar/gky1098 doi: 10.1093/nar/gky1098
    [70] R. Mosca, J. Tenorio-Laranga, R. Olivella, V. Alcalde, A. Ceol, M. Soler-Lopez, et al., dSysMap: exploring the edgetic role of disease mutations, Nat. Methods, 12 (2015), 167–168. https://doi.org/10.1038/nmeth.3289 doi: 10.1038/nmeth.3289
    [71] E. P. Consortium, An integrated encyclopedia of DNA elements in the human genome, Nature, 489 (2012), 57–74. https://doi.org/10.1038/nature11247 doi: 10.1038/nature11247
    [72] E. C. Roadmap, A. Kundaje, W. Meuleman, J. Ernst, M. Bilenky, A. Yen, et al., Integrative analysis of 111 reference human epigenomes, Nature, 518 (2015), 317–330. https://doi.org/10.1038/nature14248 doi: 10.1038/nature14248
    [73] R. Andersson, C. Gebhard, I. Miguel-Escalada, I. Hoof, J. Bornholdt, M. Boyd, et al., An atlas of active enhancers across human cell types and tissues, Nature, 507 (2014), 455–461. https://doi.org/10.1038/nature12787 doi: 10.1038/nature12787
    [74] G. T. Consortium, The Genotype-Tissue Expression (GTEx) project, Nat Genet., 45 (2013), 580–585. https://doi.org/10.1038/ng.2653 doi: 10.1038/ng.2653
    [75] S. A. Forbes, D. Beare, P. Gunasekaran, K. Leung, N. Bindal, H. Boutselakis, et al., COSMIC: exploring the world's knowledge of somatic mutations in human cancer, Nucleic Acids Res., 43 (2015), D805–D811. https://doi.org/10.1093/nar/gku1075 doi: 10.1093/nar/gku1075
    [76] T. S. Keshava Prasad, R. Goel, K. Kandasamy, S. Keerthikumar, S. Kumar, S. Mathivanan, et al., Human protein reference database—2009 update, Nucleic Acids Res., 37 (2009), D767–D772. https://doi.org/10.1093/nar/gkn892 doi: 10.1093/nar/gkn892
    [77] A. Chatr-Aryamontri, B. J. Breitkreutz, S. Heinicke, L. Boucher, A. Winter, C. Stark, et al., The BioGRID interaction database: 2013 update, Nucleic Acids Res., 41 (2013), D816–D823. https://doi.org/10.1093/nar/gks1158 doi: 10.1093/nar/gks1158
    [78] D. Szklarczyk, A. L. Gable, K. C. Nastou, D. Lyon, R. Kirsch, S. Pyysalo, et al., The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets, Nucleic Acids Res., 49 (2021), D605–D612. https://doi.org/10.1093/nar/gkaa1074 doi: 10.1093/nar/gkaa1074
    [79] B. Turner, S. Razick, A. L. Turinsky, J. Vlasblom, E. K. Crowdy, E. Cho, et al., iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence, Database, 2010 (2010), baq023. https://doi.org/10.1093/database/baq023 doi: 10.1093/database/baq023
    [80] L. Licata, L. Briganti, D. Peluso, L. Perfetto, M. Iannuccelli, E. Galeota, et al., MINT, the molecular interaction database: 2012 update, Nucleic Acids Res., 40 (2012), D857–D861. https://doi.org/10.1093/nar/gkr930 doi: 10.1093/nar/gkr930
    [81] S. Kerrien, B. Aranda, L. Breuza, A. Bridge, F. Broackes-Carter, C. Chen, et al., The IntAct molecular interaction database in 2012, Nucleic Acids Res., 40 (2012), D841–D846. https://doi.org/10.1093/nar/gkr1088 doi: 10.1093/nar/gkr1088
    [82] M. J. Cowley, M. Pinese, K. S. Kassahn, N. Waddell, J. V. Pearson, S. M. Grimmond, et al., PINA v2.0: mining interactome modules, Nucleic Acids Res., 40 (2012), D862–D865. https://doi.org/10.1093/nar/gkr967 doi: 10.1093/nar/gkr967
    [83] P. V. Hornbeck, J. M. Kornhauser, S. Tkachev, B. Zhang, E. Skrzypek, B. Murray, et al., PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse, Nucleic Acids Res., 40 (2012), D261–D270. https://doi.org/10.1093/nar/gkr1122 doi: 10.1093/nar/gkr1122
    [84] F. Diella, S. Cameron, C. Gemund, R. Linding, A. Via, B. Kuster, et al., Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins, BMC Bioinf., 5 (2004), 79. https://doi.org/10.1186/1471-2105-5-79 doi: 10.1186/1471-2105-5-79
    [85] P. Minguez, I. Letunic, L. Parca, L. Garcia-Alonso, J. Dopazo, J. Huerta-Cepas, et al., PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins, Nucleic Acids Res., 43 (2015), D494–D502. https://doi.org/10.1093/nar/gku1081 doi: 10.1093/nar/gku1081
    [86] R. Mosca, A. Ceol, P. Aloy, Interactome3D: adding structural details to protein networks, Nat. Methods, 10 (2013), 47–53. https://doi.org/10.1038/nmeth.2289 doi: 10.1038/nmeth.2289
    [87] R. Mosca, A. Ceol, A. Stein, R. Olivella, P. Aloy, 3did: a catalog of domain-based interactions of known three-dimensional structure, Nucleic Acids Res., 42 (2014), D374–D379. https://doi.org/10.1093/nar/gkt887 doi: 10.1093/nar/gkt887
    [88] M. J. Meyer, J. Das, X. Wang, H. Yu, INstruct: a database of high-quality 3D structurally resolved protein interactome networks, Bioinformatics, 29 (2013), 1577–1579. https://doi.org/10.1093/bioinformatics/btt181 doi: 10.1093/bioinformatics/btt181
    [89] M. Kanehisa, S. Goto, Y. Sato, M. Kawashima, M. Furumichi, M. Tanabe, Data, information, knowledge and principle: back to metabolism in KEGG, Nucleic Acids Res., 42 (2014), D199–D205. https://doi.org/10.1093/nar/gkt1076 doi: 10.1093/nar/gkt1076
    [90] T. Kelder, M. P. van Iersel, K. Hanspers, M. Kutmon, B. R. Conklin, C. T. Evelo, et al., WikiPathways: building research communities on biological pathways, Nucleic Acids Res., 40 (2012), D1301–D1307. https://doi.org/10.1093/nar/gkr1074 doi: 10.1093/nar/gkr1074
    [91] D. Croft, A. F. Mundo, R. Haw, M. Milacic, J. Weiser, G. Wu, et al., The Reactome pathway knowledgebase, Nucleic Acids Res., 42 (2014), D472–D477. https://doi.org/10.1093/nar/gkt1102 doi: 10.1093/nar/gkt1102
    [92] C. F. Schaefer, K. Anthony, S. Krupa, J. Buchoff, M. Day, T. Hannay, et al., PID: the pathway interaction database, Nucleic Acids Res., 37 (2009), D674–D679. https://doi.org/10.1093/nar/gkn653 doi: 10.1093/nar/gkn653
    [93] E. G. Cerami, B. E. Gross, E. Demir, I. Rodchenkov, O. Babur, N. Anwar, et al., Pathway Commons, a web resource for biological pathway data, Nucleic Acids Res., 39 (2011), D685–D690. https://doi.org/10.1093/nar/gkq1039 doi: 10.1093/nar/gkq1039
    [94] H. Mi, A. Muruganujan, D. Ebert, X. Huang, P. D. Thomas, PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools, Nucleic Acids Res., 47 (2019), D419–D426. https://doi.org/10.1093/nar/gky1038 doi: 10.1093/nar/gky1038
    [95] A. Franceschini, D. Szklarczyk, S. Frankild, M. Kuhn, M. Simonovic, A. Roth, et al., STRING v9.1: protein-protein interaction networks, with increased coverage and integration, Nucleic Acids Res., 41 (2013), D808–D815. https://doi.org/10.1093/nar/gks1094 doi: 10.1093/nar/gks1094
    [96] M. Imielinski, A. H. Berger, P. S. Hammerman, B. Hernandez, T. J. Pugh, E. Hodis, et al., Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing, Cell, 150 (2012), 1107–1120. https://doi.org/10.1016/j.cell.2012.08.029 doi: 10.1016/j.cell.2012.08.029
    [97] E. Hodis, I. R. Watson, G. V. Kryukov, S. T. Arold, M. Imielinski, J. P. Theurillat, et al., A landscape of driver mutations in melanoma, Cell, 150 (2012), 251–263. https://doi.org/10.1016/j.cell.2012.06.024 doi: 10.1016/j.cell.2012.06.024
    [98] G. Wu, X. Feng, L. Stein, A human functional protein interaction network and its application to cancer data analysis, Genome Biol., 11(2010), R53. https://doi.org/10.1186/gb-2010-11-5-r53 doi: 10.1186/gb-2010-11-5-r53
    [99] The Cancer Genome Atlas Network, Comprehensive molecular portraits of human breast tumours, Nature, 490 (2012), 61–70. https://doi.org/10.1038/nature11412
    [100] L. B. Alexandrov, S. Nik-Zainal, D. C. Wedge, S. A. Aparicio, S. Behjati, A. V. Biankin, et al., Signatures of mutational processes in human cancer, Nature, 500 (2013), 415–421. https://doi.org/10.1038/nature12477 doi: 10.1038/nature12477
    [101] T. Davoli, A. W. Xu, K. E. Mengwasser, L. M. Sack, J. C. Yoon, P. J. Park, et al., Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome, Cell, 155 (2013), 948–962. https://doi.org/10.1016/j.cell.2013.10.011 doi: 10.1016/j.cell.2013.10.011
    [102] H. Rizvi, F. Sanchez-Vega, K. La, W. Chatila, P. Jonsson, D. Halpenny, et al., Molecular determinants of response to anti-programmed cell death (PD)-1 and anti-programmed death-ligand 1 (PD-L1) blockade in patients with non-small-cell lung cancer profiled with targeted next-generation sequencing, J. Clin. Oncol., 36 (2018), 633–641. https://doi.org/10.1200/jco.2017.75.3384 doi: 10.1200/jco.2017.75.3384
    [103] R. D. Kumar, A. C. Searleman, S. J. Swamidass, O. L. Griffith, R. Bose, Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data, Bioinformatics, 31 (2015), 3561–3568. https://doi.org/10.1093/bioinformatics/btv430 doi: 10.1093/bioinformatics/btv430
    [104] Y. Mao, H. Chen, H. Liang, F. Meric-Bernstam, G. B. Mills, K. Chen, CanDrA: cancer-specific driver missense mutation annotation with optimized features, PLoS One, 8 (2013), e77945. https://doi.org/10.1371/journal.pone.0077945 doi: 10.1371/journal.pone.0077945
    [105] L. G. Martelotto, C. K. Ng, M. R. De Filippo, Y. Zhang, S. Piscuoglio, R. S. Lim, et al., Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations, Genome Biol., 15 (2014), 484. https://doi.org/10.1186/s13059-014-0484-1 doi: 10.1186/s13059-014-0484-1
    [106] M. H. Bailey, C. Tokheim, E. Porta-Pardo, S. Sengupta, D. Bertrand, A. Weerasinghe, et al., Comprehensive characterization of cancer driver genes and mutations, Cell, 173 (2018), 371–385.e18. https://doi.org/10.1016/j.cell.2018.02.060 doi: 10.1016/j.cell.2018.02.060
    [107] I. Martincorena, K. M. Raine, M. Gerstung, K. J. Dawson, K. Haase, P. Van Loo, et al., Universal patterns of selection in cancer and somatic tissues, Cell, 173 (2018), 1823. https://doi.org/10.1016/j.cell.2018.06.001 doi: 10.1016/j.cell.2018.06.001
    [108] R. Andrades, M. Recamonde-Mendoza, Machine learning methods for prediction of cancer driver genes: a survey paper, Briefings Bioinf., 23 (2022). https://doi.org/10.1093/bib/bbac062 doi: 10.1093/bib/bbac062
    [109] S. Parvandeh, L. A. Donehower, K. Panagiotis, T. K. Hsu, J. K. Asmussen, K. Lee, et al., EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants, Nucleic Acids Res., 50 (2022), e70. https://doi.org/10.1093/nar/gkac215 doi: 10.1093/nar/gkac215
    [110] K. Wong, T. M. Keane, J. Stalker, D. J. Adams, Enhanced structural variant and breakpoint detection using SVMerge by integration of multiple detection methods and local assembly, Genome Biol., 11 (2010), R128. https://doi.org/10.1186/gb-2010-11-12-r128 doi: 10.1186/gb-2010-11-12-r128
    [111] H. Carter, S. Chen, L. Isik, S. Tyekucheva, V. E. Velculescu, K. W. Kinzler, et al., Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations, Cancer Res., 69 (2009), 6660–6667. https://doi.org/10.1158/0008-5472.CAN-09-1133 doi: 10.1158/0008-5472.CAN-09-1133
    [112] H. A. Shihab, J. Gough, D. N. Cooper, I. N. Day, T. R. Gaunt, Predicting the functional consequences of cancer-associated amino acid substitutions, Bioinformatics, 29 (2013), 1504–1510. https://doi.org/10.1093/bioinformatics/btt182 doi: 10.1093/bioinformatics/btt182
    [113] X. Lu, X. Li, P. Liu, X. Qian, Q. Miao, S. Peng, The integrative method based on the module-network for identifying driver genes in cancer subtypes, Molecules, 23 (2018), 183. https://doi.org/10.3390/molecules23020183 doi: 10.3390/molecules23020183
    [114] F. Yuan, X. Cao, Y. H. Zhang, L. Chen, T. Huang, Z. Li, et al., Identification of novel lung cancer driver genes connecting different omics levels with a heat diffusion algorithm, Front. Cell Dev. Biol., 10 (2022), 825272. https://doi.org/10.3389/fcell.2022.825272 doi: 10.3389/fcell.2022.825272
    [115] M. Tsuchiya, M. Tomita, M. Hashimoto, Robust global regulations of gene expression in biological processes: a major driver of cell fate decision revealed, in 2012 ICME International Conference on Complex Medical Engineering (CME), (2012), 744–749. https://doi.org/10.1109/ICCME.2012.6275649
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Mathematical Biosciences and Engineering
3.9

Metrics

Article views(816) PDF downloads(53) Cited by(0)

Preview PDF
Download XML
Export Citation
Article outline

Figures and Tables

Figures(2)  /  Tables(6)

Other Articles By Authors

Related pages

Tools

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog