Login Register Cart 0
Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe Translate in Chinese
Research Article

Identification of WHO II/III Gliomas by 16 Prognostic-related Gene Signatures using Machine Learning Methods

Author(s): Ya Meng Wu, Yu Sa, Yu Guo, Qi Feng Li and Ning Zhang*

Volume 29, Issue 9, 2022

Published on: 27 August, 2021

Page: [1622 - 1639] Pages: 18

DOI: 10.2174/0929867328666210827103049

Price: $65

Abstract

Background: It is found that the prognosis of gliomas of the same grade has large differences among World Health Organization (WHO) grade II and III in clinical observation. Therefore, a better understanding of the genetics and molecular mechanisms underlying WHO grade II and III gliomas is required, with the aim of developing a classification scheme at the molecular level rather than the conventional pathological morphology level.

Methods: We performed survival analysis combined with machine learning methods of Least Absolute Shrinkage and Selection Operator using expression datasets downloaded from the Chinese Glioma Genome Atlas as well as The Cancer Genome Atlas. Risk scores were calculated by the product of expression level of overall survival-related genes and their multivariate Cox proportional hazards regression coefficients. WHO grade II and III gliomas were categorized into the low-risk subgroup, medium-risk subgroup, and high-risk subgroup. We used the 16 prognostic-related genes as input features to build a classification model based on prognosis using a fully connected neural network. Gene function annotations were also performed.

Results: The 16 genes (AKNAD1, C7orf13, CDK20, CHRFAM7A, CHRNA1, EFNB1, GAS1, HIST2H2BE, KCNK3, KLHL4, LRRK2, NXPH3, PIGZ, SAMD5, ERINC2, and SIX6) related to the glioma prognosis were screened. The 16 selected genes were associated with the development of gliomas and carcinogenesis. The accuracy of an external validation data set of the fully connected neural network model from the two cohorts reached 95.5%. Our method has good potential capability in classifying WHO grade II and III gliomas into low-risk, medium-risk, and high-risk subgroups. The subgroups showed significant (P<0.01) differences in overall survival.

Conclusion: This resulted in the identification of 16 genes that were related to the prognosis of gliomas. Here we developed a computational method to discriminate WHO grade II and III gliomas into three subgroups with distinct prognoses. The gene expressionbased method provides a reliable alternative to determine the prognosis of gliomas.

Keywords: Gliomas, survival analysis, gene, neural network, LASSO, prognostic-related gene.

« Previous Next »
[1]
Molinaro, A.M.; Taylor, J.W.; Wiencke, J.K.; Wrensch, M.R. Genetic and molecular epidemiology of adult diffuse glioma. Nat. Rev. Neurol., 2019, 15(7), 405-417.
[http://dx.doi.org/10.1038/s41582-019-0220-2] [PMID: 31227792]
[2]
Rasmussen, B.K.; Hansen, S.; Laursen, R.J.; Kosteljanetz, M.; Schultz, H.; Nørgård, B.M.; Guldberg, R.; Gradel, K.O. Epidemiology of glioma: Clinical characteristics, symptoms, and predictors of glioma patients grade I-IV in the the Danish Neuro-Oncology Registry. J. Neurooncol., 2017, 135(3), 571-579.
[http://dx.doi.org/10.1007/s11060-017-2607-5] [PMID: 28861666]
[3]
Louis, D.N.; Perry, A.; Reifenberger, G.; von Deimling, A.; Figarella-Branger, D.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 world health organization classification of tumors of the central nervous system: A summary. Acta Neuropathol., 2016, 131(6), 803-820.
[http://dx.doi.org/10.1007/s00401-016-1545-1] [PMID: 27157931]
[4]
Ostrom, Q.T.; Gittleman, H.; Stetson, L.; Virk, S.M.; Barnholtz-Sloan, J.S. Epidemiology of Gliomas. .Cancer treatment and research; Raizer, J.; Parsa, A., Eds.; Springer International Publishing: Cham,, 2015, 163, pp. 1-14.
[5]
Jiang, T.; Nam, D.H.; Ram, Z.; Poon, W.S.; Wang, J.; Boldbaatar, D.; Mao, Y.; Ma, W.; Mao, Q.; You, Y.; Jiang, C.; Yang, X.; Kang, C.; Qiu, X.; Li, W.; Li, S.; Chen, L.; Li, X.; Liu, Z.; Wang, W.; Bai, H.; Yao, Y.; Li, S.; Wu, A.; Sai, K.; Li, G.; Yao, K.; Wei, X.; Liu, X.; Zhang, Z.; Dai, Y.; Lv, S.; Wang, L.; Lin, Z.; Dong, J.; Xu, G.; Ma, X.; Zhang, W.; Zhang, C.; Chen, B.; You, G.; Wang, Y.; Wang, Y.; Bao, Z.; Yang, P.; Fan, X.; Liu, X.; Zhao, Z.; Wang, Z.; Li, Y.; Wang, Z.; Li, G.; Fang, S.; Li, L.; Liu, Y.; Liu, S.; Shan, X.; Liu, Y.; Chai, R.; Hu, H.; Chen, J.; Yan, W.; Cai, J.; Wang, H.; Chen, L.; Yang, Y.; Wang, Y.; Han, L.; Wang, Q. Clinical practice guidelines for the management of adult diffuse gliomas. Cancer Lett., 2021, 499, 60-72.
[http://dx.doi.org/10.1016/j.canlet.2020.10.050] [PMID: 33166616]
[6]
Yang, P.; Cai, J.; Yan, W.; Zhang, W.; Wang, Y.; Chen, B.; Li, G.; Li, S.; Wu, C.; Yao, K.; Li, W.; Peng, X.; You, Y.; Chen, L.; Jiang, C.; Qiu, X.; Jiang, T. Classification based on mutations of TERT promoter and IDH characterizes subtypes in grade II/III gliomas. Neuro-oncol., 2016, 18(8), 1099-1108.
[http://dx.doi.org/10.1093/neuonc/now021] [PMID: 26957363]
[7]
Colman, H.; Zhang, L.; Sulman, E.P.; McDonald, J.M.; Shooshtari, N.L.; Rivera, A.; Popoff, S.; Nutt, C.L.; Louis, D.N.; Cairncross, J.G.; Gilbert, M.R.; Phillips, H.S.; Mehta, M.P.; Chakravarti, A.; Pelloski, C.E.; Bhat, K.; Feuerstein, B.G.; Jenkins, R.B.; Aldape, K. A multigene predictor of outcome in glioblastoma. Neuro-oncol., 2010, 12(1), 49-57.
[http://dx.doi.org/10.1093/neuonc/nop007] [PMID: 20150367]
[8]
Zhao, J.; Wang, L.; Hu, G.; Wei, B.A. 6-Gene Risk Signature Predicts Survival of Glioblastoma Multiforme. BioMed Res. Int., 2019, 2019, 1649423.
[http://dx.doi.org/10.1155/2019/1649423] [PMID: 31531345]
[9]
Li, C.; Zou, H.; Xiong, Z.; Xiong, Y.; Miyagishima, D.F.; Wanggou, S.; Li, X. Construction and validation of a 13-gene signature for prognosis prediction in medulloblastoma. Front. Genet., 2020, 11, 429.
[http://dx.doi.org/10.3389/fgene.2020.00429] [PMID: 32508873]
[10]
Cao, H.; Zhang, Y.; Zhao, J.; Zhu, L.; Wang, Y.; Li, J.; Feng, Y-M.; Zhang, N. Prediction of the ebola virus infection related human genes using protein-protein interaction network. Comb. Chem. High Throughput Screen., 2017, 20(7), 638-646.
[http://dx.doi.org/10.2174/1386207320666170310114816] [PMID: 28294056]
[11]
Zhang, N.; Jiang, M.; Huang, T.; Cai, Y.D. Identification of influenza A/H7N9 virus infection-related human genes based on shortest paths in a virus-human protein interaction network. Biomed Res. Int., 2014, 2014, 239462.
[http://dx.doi.org/10.1155/2014/239462] [PMID: 24955349]
[12]
Li, M.; Guo, Y.; Feng, Y-M.; Zhang, N. Identification of triple-negative breast cancer genes and a novel high-risk breast cancer prediction model development based on ppi data and support vector machines. Front. Genet., 2019, 10(MAR), 180.
[http://dx.doi.org/10.3389/fgene.2019.00180] [PMID: 30930932]
[13]
Liu, Q.; Wang, W.; Yang, X.; Zhao, D.; Li, F.; Wang, H. MicroRNA-146a inhibits cell migration and invasion by targeting RhoA in breast cancer. Oncol. Rep., 2016, 36(1), 189-196.
[http://dx.doi.org/10.3892/or.2016.4788] [PMID: 27175941]
[14]
Li, B.Q.; You, J.; Chen, L.; Zhang, J.; Zhang, N.; Li, H.P.; Huang, T.; Kong, X.Y.; Cai, Y.D. Identification of lung-cancer-related genes with the shortest path approach in a protein-protein interaction network. BioMed Res. Int., 2013, 2013, 267375.
[http://dx.doi.org/10.1155/2013/267375] [PMID: 23762832]
[15]
Li, B.Q.; Huang, T.; Zhang, J.; Zhang, N.; Huang, G.H.; Liu, L.; Cai, Y.D. An ensemble prognostic model for colorectal cancer. PLoS One, 2013, 8(5), e63494.
[http://dx.doi.org/10.1371/journal.pone.0063494] [PMID: 23658834]
[16]
van Vliet, M.H.; Horlings, H.M.; van de Vijver, M.J.; Reinders, M.J.T.; Wessels, L.F.A. Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome. PLoS One, 2012, 7(7), e40358.
[http://dx.doi.org/10.1371/journal.pone.0040358] [PMID: 22808140]
[17]
Ceccarelli, M.; Barthel, F.P.; Malta, T.M.; Sabedot, T.S.; Salama, S.R.; Murray, B.A.; Morozova, O.; Newton, Y.; Radenbaugh, A.; Pagnotta, S.M.; Anjum, S.; Wang, J.; Manyam, G.; Zoppoli, P.; Ling, S.; Rao, A.A.; Grifford, M.; Cherniack, A.D.; Zhang, H.; Poisson, L.; Carlotti, C.G., Jr; Tirapelli, D.P.D.C.; Rao, A.; Mikkelsen, T.; Lau, C.C.; Yung, W.K.A.; Rabadan, R.; Huse, J.; Brat, D.J.; Lehman, N.L.; Barnholtz-Sloan, J.S.; Zheng, S.; Hess, K.; Rao, G.; Meyerson, M.; Beroukhim, R.; Cooper, L.; Akbani, R.; Wrensch, M.; Haussler, D.; Aldape, K.D.; Laird, P.W.; Gutmann, D.H.; Noushmehr, H.; Iavarone, A.; Verhaak, R.G.; Balasundaram, M.; Balu, S.; Barnett, G.; Baylin, S.; Bell, S.; Benz, C.; Bir, N.; Black, K.L.; Bodenheimer, T.; Boice, L.; Bootwalla, M.S.; Bowen, J.; Bristow, C.A.; Butterfield, Y.S.N.; Chen, Q.R.; Chin, L.; Cho, J.; Chuah, E.; Chudamani, S.; Coetzee, S.G.; Cohen, M.L.; Colman, H.; Couce, M.; D’Angelo, F.; Davidsen, T.; Davis, A.; Demchok, J.A.; Devine, K.; Ding, L.; Duell, R.; Elder, J.B.; Eschbacher, J.M.; Fehrenbach, A.; Ferguson, M.; Frazer, S.; Fuller, G.; Fulop, J.; Gabriel, S.B.; Garofano, L.; Gastier-Foster, J.M.; Gehlenborg, N.; Gerken, M.; Getz, G.; Giannini, C.; Gibson, W.J.; Hadjipanayis, A.; Hayes, D.N.; Heiman, D.I.; Hermes, B.; Hilty, J.; Hoadley, K.A.; Hoyle, A.P.; Huang, M.; Jefferys, S.R.; Jones, C.D.; Jones, S.J.M.; Ju, Z.; Kastl, A.; Kendler, A.; Kim, J.; Kucherlapati, R.; Lai, P.H.; Lawrence, M.S.; Lee, S.; Leraas, K.M.; Lichtenberg, T.M.; Lin, P.; Liu, Y.; Liu, J.; Ljubimova, J.Y.; Lu, Y.; Ma, Y.; Maglinte, D.T.; Mahadeshwar, H.S.; Marra, M.A.; McGraw, M.; McPherson, C.; Meng, S.; Mieczkowski, P.A.; Miller, C.R.; Mills, G.B.; Moore, R.A.; Mose, L.E.; Mungall, A.J.; Naresh, R.; Naska, T.; Neder, L.; Noble, M.S.; Noss, A.; O’Neill, B.P.; Ostrom, Q.T.; Palmer, C.; Pantazi, A.; Parfenov, M.; Park, P.J.; Parker, J.S.; Perou, C.M.; Pierson, C.R.; Pihl, T.; Protopopov, A.; Radenbaugh, A.; Ramirez, N.C.; Rathmell, W.K.; Ren, X.; Roach, J.; Robertson, A.G.; Saksena, G.; Schein, J.E.; Schumacher, S.E.; Seidman, J.; Senecal, K.; Seth, S.; Shen, H.; Shi, Y.; Shih, J.; Shimmel, K.; Sicotte, H.; Sifri, S.; Silva, T.; Simons, J.V.; Singh, R.; Skelly, T.; Sloan, A.E.; Sofia, H.J.; Soloway, M.G.; Song, X.; Sougnez, C.; Souza, C.; Staugaitis, S.M.; Sun, H.; Sun, C.; Tan, D.; Tang, J.; Tang, Y.; Thorne, L.; Trevisan, F.A.; Triche, T.; Van Den Berg, D.J.; Veluvolu, U.; Voet, D.; Wan, Y.; Wang, Z.; Warnick, R.; Weinstein, J.N.; Weisenberger, D.J.; Wilkerson, M.D.; Williams, F.; Wise, L.; Wolinsky, Y.; Wu, J.; Xu, A.W.; Yang, L.; Yang, L.; Zack, T.I.; Zenklusen, J.C.; Zhang, J.; Zhang, W.; Zhang, J.; Zmuda, E.; Noushmehr, H.; Iavarone, A.; Verhaak, R.G.W. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell, 2016, 164(3), 550-563.
[http://dx.doi.org/10.1016/j.cell.2015.12.028] [PMID: 26824661]
[18]
Zhao, Z.; Zhang, K-N.; Wang, Q.; Li, G.; Zeng, F.; Zhang, Y.; Wu, F.; Chai, R.; Wang, Z.; Zhang, C.; Zhang, W.; Bao, Z.; Jiang, T. Chinese glioma genome atlas (CGGA): A comprehensive resource with functional genomic data from chinese gliomas. Genomics Proteomics Bioinformatics, 2021. S1672-0229(21)00045-0
[PMID: 33662628]
[19]
Sun, Y.; Zhang, W.; Chen, D.; Lv, Y.; Zheng, J.; Lilljebjörn, H.; Ran, L.; Bao, Z.; Soneson, C.; Sjögren, H.O.; Salford, L.G.; Ji, J.; French, P.J.; Fioretos, T.; Jiang, T.; Fan, X. A glioma classification scheme based on coexpression modules of EGFR and PDGFRA. Proc. Natl. Acad. Sci. USA, 2014, 111(9), 3538-3543.
[http://dx.doi.org/10.1073/pnas.1313814111] [PMID: 24550449]
[20]
Ahmad, F.K.; Deris, S.; Othman, N.H. Toward integrated clinical and gene- expression profiles for breast cancer prognosis: A review paper. Int. J. Biometrics Bioinforma., 2009, 3(4), 31-47.
[21]
Spina, R.; Voss, D.M.; Asnaghi, L.; Sloan, A.; Bar, E.E. Atracurium Besylate and other neuromuscular blocking agents promote astroglial differentiation and deplete glioblastoma stem cells. Oncotarget, 2016, 7(1), 459-472.
[http://dx.doi.org/10.18632/oncotarget.6314] [PMID: 26575950]
[22]
Zamorano, A.; Lamas, M.; Vergara, P.; Naranjo, J.R.; Segovia, J. Transcriptionally mediated gene targeting of gas1 to glioma cells elicits growth arrest and apoptosis. J. Neurosci. Res., 2003, 71(2), 256-263.
[http://dx.doi.org/10.1002/jnr.10461] [PMID: 12503088]
[23]
Wijethilake, N.; Islam, M.; Ren, H. Radiogenomics model for overall survival prediction of glioblastoma. Med. Biol. Eng. Comput., 2020, 58(8), 1767-1777.
[http://dx.doi.org/10.1007/s11517-020-02179-9] [PMID: 32488372]
[24]
Qi, C.; Lei, L.; Hu, J.; Wang, G.; Liu, J.; Ou, S. Serine incorporator 2 (serinc2) expression predicts an unfavorable prognosis of low-grade glioma (LGG): Evidence from bioinformatics analysis. J. Mol. Neurosci., 2020, 70(10), 1521-1532.
[http://dx.doi.org/10.1007/s12031-020-01620-w] [PMID: 32642801]
[25]
Bao, Z.S.; Chen, H.M.; Yang, M.Y.; Zhang, C.B.; Yu, K.; Ye, W.L.; Hu, B.Q.; Yan, W.; Zhang, W.; Akers, J.; Ramakrishnan, V.; Li, J.; Carter, B.; Liu, Y.W.; Hu, H.M.; Wang, Z.; Li, M.Y.; Yao, K.; Qiu, X.G.; Kang, C.S.; You, Y.P.; Fan, X.L.; Song, W.S.; Li, R.Q.; Su, X.D.; Chen, C.C.; Jiang, T. RNA-seq of 272 gliomas revealed a novel, recurrent PTPRZ1-MET fusion transcript in secondary glioblastomas. Genome Res., 2014, 24(11), 1765-1773.
[http://dx.doi.org/10.1101/gr.165126.113] [PMID: 25135958]
[26]
Zhao, Z.; Meng, F.; Wang, W.; Wang, Z.; Zhang, C.; Jiang, T. Comprehensive RNA-seq transcriptomic profiling in the malignant progression of gliomas. Sci. Data, 2017, 4, 170024.
[http://dx.doi.org/10.1038/sdata.2017.24] [PMID: 28291232]
[27]
Liu, X.; Li, Y.; Qian, Z.; Sun, Z.; Xu, K.; Wang, K.; Liu, S.; Fan, X.; Li, S.; Zhang, Z.; Jiang, T.; Wang, Y. A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. Neuroimage Clin., 2018, 20, 1070-1077.
[http://dx.doi.org/10.1016/j.nicl.2018.10.014] [PMID: 30366279]
[28]
Wang, Y.; Qian, T.; You, G.; Peng, X.; Chen, C.; You, Y.; Yao, K.; Wu, C.; Ma, J.; Sha, Z.; Wang, S.; Jiang, T. Localizing seizure-susceptible brain regions associated with low-grade gliomas using voxel-based lesion-symptom mapping. Neuro-oncol., 2015, 17(2), 282-288.
[http://dx.doi.org/10.1093/neuonc/nou130] [PMID: 25031032]
[29]
Leek, J.T.; Scharpf, R.B.; Bravo, H.C.; Simcha, D.; Langmead, B.; Johnson, W.E.; Geman, D.; Baggerly, K.; Irizarry, R.A. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet., 2010, 11(10), 733-739.
[http://dx.doi.org/10.1038/nrg2825] [PMID: 20838408]
[30]
Leek, J.T.; Johnson, W.E.; Parker, H.S.; Fertig, E.J.; Jaffe, A.E.; Zhang, Y.; Storey, J.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics, 2012, 28(6), 882-3.
[31]
Lin, H.; Zelterman, D. Modeling survival data: Extending the cox model. Technometrics, 2002, 44(1), 85-86.
[http://dx.doi.org/10.1198/tech.2002.s656]
[32]
Kosinski, M.; Kassambara, A.; Biecek, P. Drawing survival curves using “Ggplot2.” R package version 0.4.7., 2020. Available from: https://CRAN.R-project.org/package=survminer
[33]
Simon, N.; Friedman, J.; Hastie, T.; Tibshirani, R. Regularization paths for cox’s proportional hazards model via coordinate descent. J. Stat. Softw., 2011, 39(5), 1-13.
[http://dx.doi.org/10.18637/jss.v039.i05] [PMID: 27065756]
[34]
Therneau, T.M.; Grambsch, P.M. The cox model.Statistics for biology and health; John Wiley & Sons. Ltd, 2000, 20, 39-77.
[35]
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, É. Scikit-learn: Machine learning in python. J. Mach. Learn. Res., 2011, 12, 2825-2830.
[36]
Van Der Walt, S.; Colbert, S.C.; Varoquaux, G. The Numpy array: A structure for efficient numerical computation. Comput. Sci. Eng., 2011, 13(2), 22-30.
[http://dx.doi.org/10.1109/MCSE.2011.37]
[37]
Li, J.; Pu, Y.; Tang, J.; Zou, Q.; Guo, F. DeepAVP: A dual-channel deep neural network for identifying variable-length antiviral peptides. IEEE J. Biomed. Health Inform., 2020, 24(10), 3012-3019.
[http://dx.doi.org/10.1109/JBHI.2020.2977091] [PMID: 32142462]
[38]
Lv, Z.; Ding, H.; Wang, L.; Zou, Q. A convolutional neural network using dinucleotide one-hot encoder for identifying DNA N6-methyladenine sites in the rice genome. Neurocomputing, 2021, 422(4), 214-221.
[http://dx.doi.org/10.1016/j.neucom.2020.09.056]
[39]
Mostavi, M.; Chiu, Y.C.; Huang, Y.; Chen, Y. Convolutional neural network models for cancer type prediction based on gene expression. BMC Med. Genomics, 2020, 13(S5), 44.
[http://dx.doi.org/10.1186/s12920-020-0677-2] [PMID: 32241303]
[40]
Chen, L.; Pan, X.; Zhang, Y.H.; Liu, M.; Huang, T.; Cai, Y.D. Classification of widely and rarely expressed genes with recurrent neural network. Comput. Struct. Biotechnol. J., 2018, 17, 49-60.
[http://dx.doi.org/10.1016/j.csbj.2018.12.002] [PMID: 30595815]
[41]
Bergstra, J.; Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 2012, 13(null), 281-305.
[42]
Friedman, J.; Hastie, T.; Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw., 2010, 33(1), 1-22.
[http://dx.doi.org/10.18637/jss.v033.i01] [PMID: 20808728]
[43]
Kemp, F. Modern applied statistics with S, 4th. Springer, 2003, 52.
[44]
Kingma, D.P.; Ba, J.L. Adam: A method for stochastic optimization. 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., 2015.
[45]
Yu, G.; Wang, L.G.; Han, Y.; He, Q.Y. Cluster profiler: an R package for comparing biological themes among gene clusters. Omi. A J. Integr. Biol., 2012, 16(5), 284-287.
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[46]
Carlson, M. Genome wide annotation for human; Bioconducter, 2019.
[http://dx.doi.org/10.18129/B9.bioc.org.Hs.eg.db]
[47]
Eisen, M.B.; Spellman, P.T.; Brown, P.O.; Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA, 1998, 95(25), 14863-14868.
[http://dx.doi.org/10.1073/pnas.95.25.14863] [PMID: 9843981]
[48]
Durinck, S.; Spellman, P.T.; Birney, E.; Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc., 2009, 4(8), 1184-1191.
[http://dx.doi.org/10.1038/nprot.2009.97] [PMID: 19617889]
[49]
Arai, E.; Gotoh, M.; Tian, Y.; Sakamoto, H.; Ono, M.; Matsuda, A.; Takahashi, Y.; Miyata, S.; Totsuka, H.; Chiku, S.; Komiyama, M.; Fujimoto, H.; Matsumoto, K.; Yamada, T.; Yoshida, T.; Kanai, Y. Alterations of the spindle checkpoint pathway in clinicopathologically aggressive CpG island methylator phenotype clear cell renal cell carcinomas. Int. J. Cancer, 2015, 137(11), 2589-2606.
[http://dx.doi.org/10.1002/ijc.29630] [PMID: 26061684]
[50]
Etcheverry, A.; Aubry, M.; de Tayrac, M.; Vauleon, E.; Boniface, R.; Guenot, F.; Saikali, S.; Hamlat, A.; Riffaud, L.; Menei, P.; Quillien, V.; Mosser, J. DNA methylation in glioblastoma: Impact on gene expression and clinical outcome. BMC Genomics, 2010, 11(1), 701.
[http://dx.doi.org/10.1186/1471-2164-11-701] [PMID: 21156036]
[51]
Bruyère, C.; Meijer, L. Targeting cyclin-dependent kinases in anti-neoplastic therapy. Curr. Opin. Cell Biol., 2013, 25(6), 772-779.
[http://dx.doi.org/10.1016/j.ceb.2013.08.004] [PMID: 24011867]
[52]
Biedler, J.L.; Roffler-Tarlov, S.; Schachner, M.; Freedman, L.S. Multiple neurotransmitter synthesis by human neuroblastoma cell lines and clones. Cancer Res.,, 1978. 38(11 Pt1), 3751-3757.
[PMID: 29704]
[53]
Sinkus, M. L.; Graw, S.; Freedman, R.; Ross, R. G.; Lester, H. A.; Leonard, S. The human chrna7 and chrfam7a genes: A review of the genetics, regulation, and function. Neuropharmacology,, 2015, 96(PB), 274-288.
[54]
Tsai, Y-S.; Lin, C-T.; Tseng, G.C.; Chung, I-F.; Pal, N.R. Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems. BMC Bioinformatics, 2008, 9(1), 425.
[http://dx.doi.org/10.1186/1471-2105-9-425] [PMID: 18842155]
[55]
Wang, J.; Lin, Z.J.; Liu, L.; Xu, H.Q.; Shi, Y.W.; Yi, Y.H.; He, N.; Liao, W.P. Epilepsy-associated genes. Seizure, 2017, 44, 11-20.
[http://dx.doi.org/10.1016/j.seizure.2016.11.030] [PMID: 28007376]
[56]
Guo, W.; Zhu, L.; Yu, M.; Zhu, R.; Chen, Q.; Wang, Q. A five-DNA methylation signature act as a novel prognostic biomarker in patients with ovarian serous cystadenocarcinoma. Clin. Epigenetics, 2018, 10(1), 142.
[http://dx.doi.org/10.1186/s13148-018-0574-0] [PMID: 30446011]
[57]
Wakabayashi, T.; Natsume, A.; Hashizume, Y.; Fujii, M.; Mizuno, M.; Yoshida, J. A phase I clinical trial of interferon-beta gene therapy for high-grade glioma: Novel findings from gene expression profiling and autopsy. J. Gene Med., 2008, 10(4), 329-339.
[http://dx.doi.org/10.1002/jgm.1160] [PMID: 18220319]
[58]
Liu, W.; Xu, Z.; Zhou, J.; Xing, S.; Li, Z.; Gao, X.; Feng, S.; Xiao, Y. High levels of hist1h2bk in low-grade glioma predicts poor prognosis: A study using cgga and tcga data. Front. Oncol., 2020, 10, 627.
[http://dx.doi.org/10.3389/fonc.2020.00627] [PMID: 32457836]
[59]
MacLeod, D.; Dowman, J.; Hammond, R.; Leete, T.; Inoue, K.; Abeliovich, A. The familial Parkinsonism gene LRRK2 regulates neurite process morphology. Neuron, 2006, 52(4), 587-593.
[http://dx.doi.org/10.1016/j.neuron.2006.10.008] [PMID: 17114044]
[60]
Fig. Method of analysing a blood sample of a subject for the presence of a disease marker. Google Patents, 2019.
[61]
Zhou, Y.; Fu, X.; Zheng, Z.; Ren, Y.; Zheng, Z.; Zhang, B.; Yuan, M.; Duan, J.; Li, M.; Hong, T.; Lu, G.; Zhou, D. Identification of gene co-expression modules and hub genes associated with the invasiveness of pituitary adenoma. Endocrine, 2020, 68(2), 377-389.
[http://dx.doi.org/10.1007/s12020-020-02316-2] [PMID: 32342269]
[62]
Aguilar-Morante, D.; Morales-Garcia, J.A.; Santos, A.; Perez-Castillo, A. CCAAT/enhancer binding protein β induces motility and invasion of glioblastoma cells through transcriptional regulation of the calcium binding protein S100A4. Oncotarget, 2015, 6(6), 4369-4384.
[http://dx.doi.org/10.18632/oncotarget.2976] [PMID: 25738360]
[63]
Du, C.; Pan, P.; Jiang, Y.; Zhang, Q.; Bao, J.; Liu, C. Microarray data analysis to identify crucial genes regulated by CEBPB in human SNB19 glioma cells. World J. Surg. Oncol., 2016, 14(1), 258.
[http://dx.doi.org/10.1186/s12957-016-0997-z] [PMID: 27716259]
[64]
Hnoonual, A.; Thammachote, W.; Tim-Aroon, T.; Rojnueangnit, K.; Hansakunachai, T.; Sombuntham, T.; Roongpraiwan, R.; Worachotekamjorn, J.; Chuthapisith, J.; Fucharoen, S.; Wattanasirichaigoon, D.; Ruangdaraganon, N.; Limprasert, P.; Jinawath, N. Chromosomal microarray analysis in a cohort of underrepresented population identifies SERINC2 as a novel candidate gene for autism spectrum disorder. Sci. Rep., 2017, 7(1), 12096.
[http://dx.doi.org/10.1038/s41598-017-12317-3] [PMID: 28935972]
[65]
Cortez, M.A.; Anfossi, S.; Ramapriyan, R.; Menon, H.; Atalar, S.C.; Aliru, M.; Welsh, J.; Calin, G.A. Role of miRNAs in immune responses and immunotherapy in cancer. Genes Chromosomes Cancer, 2019, 58(4), 244-253.
[http://dx.doi.org/10.1002/gcc.22725] [PMID: 30578699]
[66]
Topalian, S.L.; Drake, C.G.; Pardoll, D.M. Immune checkpoint blockade: A common denominator approach to cancer therapy. Cancer Cell, 2015, 27(4), 450-461.
[http://dx.doi.org/10.1016/j.ccell.2015.03.001] [PMID: 25858804]
[67]
Zhou, J.; Liu, M.; Sun, H.; Feng, Y.; Xu, L.; Chan, A.W.H.; Tong, J.H.; Wong, J.; Chong, C.C.N.; Lai, P.B.S.; Wang, H.K.S.; Tsang, S.W.; Goodwin, T.; Liu, R.; Huang, L.; Chen, Z.; Sung, J.J.Y.; Chow, K.L.; To, K.F.; Cheng, A.S.L. Hepatoma-intrinsic CCRK inhibition diminishes myeloid-derived suppressor cell immunosuppression and enhances immune-checkpoint blockade efficacy. Gut, 2018, 67(5), 931-944.
[http://dx.doi.org/10.1136/gutjnl-2017-314032] [PMID: 28939663]

Rights & Permissions Print Cite
Article Metrics
43
2
World Chagas Disease
© 2024 Bentham Science Publishers | Privacy Policy