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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Prediction of Cancer Driver Genes through Integrated Analysis of Differentially Expressed Genes at the Individual Level

Author(s): Bo Gao, Yue Zhao and Guojun Li*

Volume 18, Issue 10, 2023

Published on: 27 September, 2023

Page: [792 - 804] Pages: 13

DOI: 10.2174/1574893618666230524142013

Price: $65

Abstract

Introduction: It is expected that certain driver mutations may alter the gene expression of their associated or interacting partners, including cognate proteins.

Methods: We introduced DEGdriver, a novel method that can discriminate between mutations in drivers and passengers by utilizing gene differential expression at the individual level.

Results: After being tested on eleven TCGA cancer datasets, DEGdriver substantially outperformed cutting-edge approaches in distinguishing driver genes from passengers and exhibited robustness to varying parameters and protein-protein interaction networks.

Conclusion: Through enrichment analysis, we prove that DEGdriver can identify functional modules or pathways in addition to novel driver genes.

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[1]
Cancer Genome Atlas Research Network Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008; 455(7216): 1061-8.
[http://dx.doi.org/10.1038/nature07385] [PMID: 18772890]
[2]
Greenman C, Stephens P, Smith R, et al. Patterns of somatic mutation in human cancer genomes. Nature 2007; 446(7132): 153-8.
[http://dx.doi.org/10.1038/nature05610] [PMID: 17344846]
[3]
Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature 2009; 458(7239): 719-24.
[http://dx.doi.org/10.1038/nature07943] [PMID: 19360079]
[4]
Greenman C, Wooster R, Futreal PA, Stratton MR, Easton DF. Statistical analysis of pathogenicity of somatic mutations in cancer. Genetics 2006; 173(4): 2187-98.
[http://dx.doi.org/10.1534/genetics.105.044677] [PMID: 16783027]
[5]
Beroukhim R, Getz G, Nghiemphu L, et al. Assessing the significance of chromosomal aberrations in cancer: Methodology and application to glioma. Proc Natl Acad Sci USA 2007; 104(50): 20007-12.
[http://dx.doi.org/10.1073/pnas.0710052104] [PMID: 18077431]
[6]
Youn A, Simon R. Identifying cancer driver genes in tumor genome sequencing studies. Bioinformatics 2011; 27(2): 175-81.
[http://dx.doi.org/10.1093/bioinformatics/btq630] [PMID: 21169372]
[7]
Wood LD, Parsons DW, Jones S, et al. The genomic landscapes of human breast and colorectal cancers. Science 2007; 318(5853): 1108-13.
[http://dx.doi.org/10.1126/science.1145720] [PMID: 17932254]
[8]
Torkamani A, Schork NJ. Identification of rare cancer driver mutations by network reconstruction. Genome Res 2009; 19(9): 1570-8.
[http://dx.doi.org/10.1101/gr.092833.109] [PMID: 19574499]
[9]
Shi X, Teng H, Shi L, et al. Comprehensive evaluation of computational methods for predicting cancer driver genes. Brief Bioinform 2022; 23(2): bbab548.
[http://dx.doi.org/10.1093/bib/bbab548] [PMID: 35037014]
[10]
Lawrence MS, Stojanov P, Mermel CH, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 2014; 505(7484): 495-501.
[http://dx.doi.org/10.1038/nature12912] [PMID: 24390350]
[11]
Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013; 499(7457): 214-8.
[http://dx.doi.org/10.1038/nature12213] [PMID: 23770567]
[12]
Hodis E, Watson IR, Kryukov GV, et al. A landscape of driver mutations in melanoma. Cell 2012; 150(2): 251-63.
[http://dx.doi.org/10.1016/j.cell.2012.06.024] [PMID: 22817889]
[13]
Dees ND, Zhang Q, Kandoth C, et al. MuSiC: Identifying mutational significance in cancer genomes. Genome Res 2012; 22(8): 1589-98.
[http://dx.doi.org/10.1101/gr.134635.111] [PMID: 22759861]
[14]
Zhao S, Liu J, Nanga P, et al. Detailed modeling of positive selection improves detection of cancer driver genes. Nat Commun 2019; 10(1): 3399.
[http://dx.doi.org/10.1038/s41467-019-11284-9] [PMID: 31363082]
[15]
Jiang L, Zheng J, Kwan JSH, et al. WITER: A powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts. Nucleic Acids Res 2019; 47(16): e96.
[http://dx.doi.org/10.1093/nar/gkz566] [PMID: 31287869]
[16]
Han Y, Yang J, Qian X, et al. DriverML: A machine learning algorithm for identifying driver genes in cancer sequencing studies. Nucleic Acids Res 2019; 47(8): e45.
[http://dx.doi.org/10.1093/nar/gkz096] [PMID: 30773592]
[17]
Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: Application to cancer genomics. Nucleic Acids Res 2011; 39(17): e118.
[http://dx.doi.org/10.1093/nar/gkr407] [PMID: 21727090]
[18]
Carter H, Chen S, Isik L, et al. Cancer-specific high-throughput annotation of somatic mutations: Computational prediction of driver missense mutations. Cancer Res 2009; 69(16): 6660-7.
[http://dx.doi.org/10.1158/0008-5472.CAN-09-1133] [PMID: 19654296]
[19]
Gonzalez-Perez A, Deu-Pons J, Lopez-Bigas N. Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation. Genome Med 2012; 4(11): 89.
[http://dx.doi.org/10.1186/gm390] [PMID: 23181723]
[20]
Shihab HA, Gough J, Cooper DN, et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat 2013; 34(1): 57-65.
[http://dx.doi.org/10.1002/humu.22225] [PMID: 23033316]
[21]
Dietlein F, Weghorn D, Taylor-Weiner A, et al. Identification of cancer driver genes based on nucleotide context. Nat Genet 2020; 52(2): 208-18.
[http://dx.doi.org/10.1038/s41588-019-0572-y] [PMID: 32015527]
[22]
Cho A, Shim JE, Kim E, Supek F, Lehner B, Lee I. MUFFINN: Cancer gene discovery via network analysis of somatic mutation data. Genome Biol 2016; 17(1): 129.
[http://dx.doi.org/10.1186/s13059-016-0989-x] [PMID: 27333808]
[23]
Hou Y, Gao B, Li G, Su Z. MaxMIF: A new method for identifying cancer driver genes through effective data integration. Adv Sci 2018; 5(9): 1800640.
[http://dx.doi.org/10.1002/advs.201800640] [PMID: 30250803]
[24]
Boca SM, Kinzler KW, Velculescu VE, Vogelstein B, Parmigiani G. Patient-oriented gene set analysis for cancer mutation data. Genome Biol 2010; 11(11): R112.
[http://dx.doi.org/10.1186/gb-2010-11-11-r112] [PMID: 21092299]
[25]
Efroni S, Ben-Hamo R, Edmonson M, Greenblum S, Schaefer CF, Buetow KH. Detecting cancer gene networks characterized by recurrent genomic alterations in a population. PLoS One 2011; 6(1): e14437.
[http://dx.doi.org/10.1371/journal.pone.0014437] [PMID: 21283511]
[26]
Raphael BJ, Dobson JR, Oesper L, Vandin F. Identifying driver mutations in sequenced cancer genomes: Computational approaches to enable precision medicine. Genome Med 2014; 6(1): 5.
[http://dx.doi.org/10.1186/gm524] [PMID: 24479672]
[27]
Ding L, Raphael BJ, Chen F, Wendl MC. Advances for studying clonal evolution in cancer. Cancer Lett 2013; 340(2): 212-9.
[http://dx.doi.org/10.1016/j.canlet.2012.12.028] [PMID: 23353056]
[28]
Vandin F, Upfal E, Raphael BJ. De novo discovery of mutated driver pathways in cancer. Genome Res 2012; 22(2): 375-85.
[http://dx.doi.org/10.1101/gr.120477.111] [PMID: 21653252]
[29]
Zhao J, Zhang S, Wu LY, Zhang XS. Efficient methods for identifying mutated driver pathways in cancer. Bioinformatics 2012; 28(22): 2940-7.
[http://dx.doi.org/10.1093/bioinformatics/bts564] [PMID: 22982574]
[30]
Li HT, Zhang YL, Zheng CH, Wang HQ. Simulated annealing based algorithm for identifying mutated driver pathways in cancer. BioMed Res Int 2014; 2014: 375980.
[http://dx.doi.org/10.1155/2014/375980] [PMID: 24982873]
[31]
Leiserson MDM, Blokh D, Sharan R, Raphael BJ. Simultaneous identification of multiple driver pathways in cancer. PLOS Comput Biol 2013; 9(5): e1003054.
[http://dx.doi.org/10.1371/journal.pcbi.1003054] [PMID: 23717195]
[32]
Zhang J, Wu LY, Zhang XS, Zhang S. Discovery of co-occurring driver pathways in cancer. BMC Bioinformatics 2014; 15(1): 271.
[http://dx.doi.org/10.1186/1471-2105-15-271] [PMID: 25106096]
[33]
Vandin F, Upfal E, Raphael BJ. Algorithms for detecting significantly mutated pathways in cancer. J Comput Biol 2011; 18(3): 507-22.
[http://dx.doi.org/10.1089/cmb.2010.0265] [PMID: 21385051]
[34]
Leiserson MDM, Vandin F, Wu HT, et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat Genet 2015; 47(2): 106-14.
[http://dx.doi.org/10.1038/ng.3168] [PMID: 25501392]
[35]
Ciriello G, Cerami E, Sander C, Schultz N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res 2012; 22(2): 398-406.
[http://dx.doi.org/10.1101/gr.125567.111] [PMID: 21908773]
[36]
Kim YA, Cho DY, Dao P, Przytycka TM. MEMCover: Integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types. Bioinformatics 2015; 31(12): i284-92.
[http://dx.doi.org/10.1093/bioinformatics/btv247] [PMID: 26072494]
[37]
Gao B, Li G, Liu J, Li Y, Huang X. Identification of driver modules in pan-cancer via coordinating coverage and exclusivity. Oncotarget 2017; 8(22): 36115-26.
[http://dx.doi.org/10.18632/oncotarget.16433] [PMID: 28415609]
[38]
Gao B, Zhao Y, Li Y, et al. Prediction of driver modules via balancing exclusive coverages of mutations in cancer samples. Adv Sci 2019; 6(4): 1801384.
[http://dx.doi.org/10.1002/advs.201801384] [PMID: 30828525]
[39]
Gao B, Zhao Y, Gao Y, Li G, Wu LY. Identification of common driver gene modules and associations between cancers through integrated network analysis. Glob Chall 2021; 5(9): 2100006.
[http://dx.doi.org/10.1002/gch2.202100006] [PMID: 34504716]
[40]
Bashashati A, Haffari G, Ding J, et al. DriverNet: Uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biol 2012; 13(12): R124.
[http://dx.doi.org/10.1186/gb-2012-13-12-r124] [PMID: 23383675]
[41]
Hou JP, Ma J. DawnRank: Discovering personalized driver genes in cancer. Genome Med 2014; 6(7): 56.
[http://dx.doi.org/10.1186/s13073-014-0056-8] [PMID: 25177370]
[42]
Wang H, Sun Q, Zhao W, et al. Individual-level analysis of differential expression of genes and pathways for personalized medicine. Bioinformatics 2015; 31(1): 62-8.
[http://dx.doi.org/10.1093/bioinformatics/btu522] [PMID: 25165092]
[43]
Tate JG, Bamford S, Jubb HC, et al. COSMIC: The catalogue of somatic mutations in cancer. Nucleic Acids Res 2019; 47(D1): D941-7.
[http://dx.doi.org/10.1093/nar/gky1015] [PMID: 30371878]
[44]
Dressler L, Bortolomeazzi M, Keddar MR, et al. Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: An update of the Network of Cancer Genes (NCG) resource. Genome Biol 2022; 23(1): 35.
[http://dx.doi.org/10.1186/s13059-022-02607-z] [PMID: 35078504]
[45]
Davis J, Goadrich M. The relationship between precision-recall and ROC curves. ICML ’06: Proceedings of the 23rd international conference on Machine learning. Pittsburgh, Pennsylvania. 233-40.
[http://dx.doi.org/10.1145/1143844.1143874]
[46]
Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019; 10(1): 1523.
[http://dx.doi.org/10.1038/s41467-019-09234-6] [PMID: 30944313]
[47]
Piñero J, Bravo À, Queralt-Rosinach N, et al. DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 2017; 45(D1): D833-9.
[http://dx.doi.org/10.1093/nar/gkw943] [PMID: 27924018]
[48]
Hwang S, Kim CY, Yang S, et al. HumanNet v2: Human gene networks for disease research. Nucleic Acids Res 2019; 47(D1): D573-80.
[http://dx.doi.org/10.1093/nar/gky1126] [PMID: 30418591]

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