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Current Genomics

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ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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Research Article

Recursive Feature Elimination-based Biomarker Identification for Open Neural Tube Defects

Author(s): Kadhir Velu Karthik, Aruna Rajalingam, Mallaiah Shivashankar and Anjali Ganjiwale*

Volume 23, Issue 3, 2022

Published on: 10 June, 2022

Page: [195 - 206] Pages: 12

DOI: 10.2174/1389202923666220511162038

Price: $65

Abstract

Background: Open spina bifida (myelomeningocele) is the result of the failure of spinal cord closing completely and is the second most common and severe birth defect. Open neural tube defects are multifactorial, and the exact molecular mechanism of the pathogenesis is not clear due to disease complexity for which prenatal treatment options remain limited worldwide. Artificial intelligence techniques like machine learning tools have been increasingly used in precision diagnosis.

Objective: The primary objective of this study is to identify key genes for open neural tube defects using a machine learning approach that provides additional information about myelomeningocele in order to obtain a more accurate diagnosis.

Materials and Methods: Our study reports differential gene expression analysis from multiple datasets (GSE4182 and GSE101141) of amniotic fluid samples with open neural tube defects. The sample outliers in the datasets were detected using principal component analysis (PCA). We report a combination of the differential gene expression analysis with recursive feature elimination (RFE), a machine learning approach to get 4 key genes for open neural tube defects. The features selected were validated using five binary classifiers for diseased and healthy samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbour (KNN) with 5-fold cross-validation.

Results: Growth Associated Protein 43 (GAP43), Glial fibrillary acidic protein (GFAP), Repetin (RPTN), and CD44 are the important genes identified in the study. These genes are known to be involved in axon growth, astrocyte differentiation in the central nervous system, post-traumatic brain repair, neuroinflammation, and inflammation-linked neuronal injuries. These key genes represent a promising tool for further studies in the diagnosis and early detection of open neural tube defects.

Conclusion: These key biomarkers help in the diagnosis and early detection of open neural tube defects, thus evaluating the progress and seriousness in diseases condition. This study strengthens previous literature sources of confirming these biomarkers linked with open NTD’s. Thus, among other prenatal treatment options present until now, these biomarkers help in the early detection of open neural tube defects, which provides success in both treatment and prevention of these defects in the advanced stage.

Keywords: Spina bifida, myelomeningocele, PCA, recursive feature elimination (RFE), machine learning-based classification, NTD’s.

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[1]
Juriloff, D.M.; Harris, M.J. Hypothesis: The female excess in cranial neural tube defects reflects an epigenetic drag of the inactivating X chromosome on the molecular mechanisms of neural fold elevation. Birth Defects Res. A Clin. Mol. Teratol., 2012, 94(10), 849-855.
[http://dx.doi.org/10.1002/bdra.23036] [PMID: 22753363]
[2]
Wu, Y.; Peng, S.; Finnell, R.H.; Zheng, Y. Organoids as a new model system to study neural tube defects. FASEB J., 2021, 35(4), e21545.
[http://dx.doi.org/10.1096/fj.202002348R] [PMID: 33729606]
[3]
Sadler, T.W. Embryology of neural tube development. Am. J. Med. Genet. C. Semin. Med. Genet., 2005, 135C(1), 2-8.
[http://dx.doi.org/10.1002/ajmg.c.30049] [PMID: 15806586]
[4]
Copp, A.J.; Adzick, N.S.; Chitty, L.S.; Fletcher, J.M.; Holmbeck, G.N.; Shaw, G.M. Spina bifida. Nat. Rev. Dis. Primers, 2015, 1(1), 15007.
[http://dx.doi.org/10.1038/nrdp.2015.7] [PMID: 27189655]
[5]
Greene, N.D.; Copp, A.J. Neural tube defects. Annu. Rev. Neurosci., 2014, 37(1), 221-242.
[http://dx.doi.org/10.1146/annurev-neuro-062012-170354] [PMID: 25032496]
[6]
Copp, A.J.; Greene, N.D. Genetics and development of neural tube defects. J. Pathol., 2010, 220(2), 217-230.
[http://dx.doi.org/10.1002/path.2643] [PMID: 19918803]
[7]
Harris, M.J.; Juriloff, D.M. Mouse mutants with neural tube closure defects and their role in understanding human neural tube defects. Birth Defects Res. A Clin. Mol., 2007, 79(3), 187-210.
[http://dx.doi.org/10.1002/bdra.20333]
[8]
Nagy, G.R.; Gyõrffy, B.; Galamb, O.; Molnár, B.; Nagy, B.; Papp, Z. Use of routinely collected amniotic fluid for whole-genome expression analysis of polygenic disorders. Clin. Chem., 2006, 52(11), 2013-2020.
[http://dx.doi.org/10.1373/clinchem.2006.074971] [PMID: 17008366]
[9]
Tarui, T.; Kim, A.; Flake, A.; McClain, L.; Stratigis, J.D.; Fried, I.; Newman, R.; Slonim, D.K.; Bianchi, D.W. Amniotic fluid transcriptomics reflects novel disease mechanisms in fetuses with myelomeningocele. Am. J. Obstet. Gynecol., 2017, 217(5), 587.e1-587.e10.
[http://dx.doi.org/10.1016/j.ajog.2017.07.022] [PMID: 28735706]
[10]
Li, Z.; Feng, J.; Yuan, Z. Key modules and hub genes identified by coexpression network analysis for revealing novel biomarkers for spina bifida. Front. Genet., 2020, 11, 583316.
[http://dx.doi.org/10.3389/fgene.2020.583316] [PMID: 33343629]
[11]
Sun, Y.; Zhang, J.; Wang, Y.; Wang, L.; Song, M.; Khan, A.; Zhang, L.; Niu, B.; Zhao, H.; Li, M.; Luo, T.; He, Q.; Xie, X.; Liu, Z.; Xie, J. miR-222-3p is involved in neural tube closure by directly targeting Ddit4 in RA induced NTDs mouse model. Cell Cycle, 2021, 20(22), 2372-2386.
[http://dx.doi.org/10.1080/15384101.2021.1982506] [PMID: 34779712]
[12]
Kasemeier-Kulesa, J.C.; Spengler, J.A.; Muolo, C.E.; Morrison, J.A.; Woolley, T.E.; Schnell, S.; Kulesa, P.M. The embryonic trunk neural crest microenvironment regulates the plasticity and invasion of human neuroblastoma via TrkB signaling. Dev. Biol., 2021, 480, 78-90.
[http://dx.doi.org/10.1016/j.ydbio.2021.08.007] [PMID: 34416224]
[13]
Martins, I.J. Nutrition therapy regulates caffeine metabolism with relevance to NAFLD and induction of type 3 diabetes. J. Diabetes Metab. Disord., 2017, 4(1), 1-9.
[14]
Schmidt, R.J.; Romitti, P.A.; Burns, T.L.; Browne, M.L.; Druschel, C.M.; Olney, R.S. Maternal caffeine consumption and risk of neural tube defects. Birth Defects Res. A Clin. Mol. Teratol., 2009, 85(11), 879-889.
[http://dx.doi.org/10.1002/bdra.20624] [PMID: 19711421]
[15]
Emig, D.; Salomonis, N.; Baumbach, J.; Lengauer, T.; Conklin, B.R.; Albrecht, M. AltAnalyze and DomainGraph: Analyzing and visualizing exon expression data. Nucleic Acids Res., 2010, 38(Suppl. 2), W755-62.
[http://dx.doi.org/10.1093/nar/gkq405] [PMID: 20513647]
[16]
Irizarry, R.A.; Hobbs, B.; Collin, F.; Beazer-Barclay, Y.D.; Antonellis, K.J.; Scherf, U.; Speed, T.P. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003, 4(2), 249-264.
[http://dx.doi.org/10.1093/biostatistics/4.2.249] [PMID: 12925520]
[17]
Lenz, M.; Müller, F.J.; Zenke, M.; Schuppert, A. Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data. Sci. Rep., 2016, 6(1), 25696.
[http://dx.doi.org/10.1038/srep25696] [PMID: 27254731]
[18]
Hassan, C.A.; Khan, M.S.; Shah, M.A. Comparison of machine learning algorithms in data classification. IEEE, 2018, 2018, 8748995.
[http://dx.doi.org/10.23919/IConAC.2018.8748995]
[19]
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 2011, 12, 2825-2830.
[20]
Díaz-Uriarte, R.; Alvarez de Andrés, S. Gene selection and classification of microarray data using random forest. BMC Bioinf, 2006, 7(1), 3.
[http://dx.doi.org/10.1186/1471-2105-7-3] [PMID: 16398926]
[21]
Guyon, I.; Weston, J.; Barnhill, S.; Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn., 2002, 46(1), 389-422.
[http://dx.doi.org/10.1023/A:1012487302797]
[22]
Baldi, P.; Brunak, S.; Chauvin, Y.; Andersen, C.A.; Nielsen, H. Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics, 2000, 16(5), 412-424.
[http://dx.doi.org/10.1093/bioinformatics/16.5.412] [PMID: 10871264]
[23]
Bradley, A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit., 1997, 30(7), 1145-1159.
[http://dx.doi.org/10.1016/S0031-3203(96)00142-2]
[24]
Abbas, M.; El-Manzalawy, Y. Machine learning based refined differential gene expression analysis of pediatric sepsis. BMC Med. Genomics, 2020, 13(1), 122.
[http://dx.doi.org/10.1186/s12920-020-00771-4] [PMID: 32859206]
[25]
Liao, Y.; Wang, J.; Jaehnig, E.J.; Shi, Z.; Zhang, B. WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res., 2019, 47(W1), W199-W205.
[http://dx.doi.org/10.1093/nar/gkz401] [PMID: 31114916]
[26]
Warde-Farley, D.; Donaldson, S.L.; Comes, O.; Zuberi, K.; Badrawi, R.; Chao, P.; Franz, M.; Grouios, C.; Kazi, F.; Lopes, C.T.; Maitland, A.; Mostafavi, S.; Montojo, J.; Shao, Q.; Wright, G.; Bader, G.D.; Morris, Q. The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res., 2010, 38(Suppl. 2), W214-20.
[http://dx.doi.org/10.1093/nar/gkq537] [PMID: 20576703]
[27]
Zhao, J.C.; Zhang, L.X.; Zhang, Y.; Shen, Y.F. The differential regulation of Gap43 gene in the neuronal differentiation of P19 cells. J. Cell. Physiol., 2012, 227(6), 2645-2653.
[http://dx.doi.org/10.1002/jcp.23006] [PMID: 21938722]
[28]
Van Regemorter, N.; Gheuens, J.; Noppe, M.; Vamos, E.; Seller, M.J.; Lowenthal, A. Value of glial fibrillary acidic protein determination in amniotic fluid for prenatal diagnosis of neural tube defects. Clin. Chim. Acta, 1987, 165(1), 83-88.
[http://dx.doi.org/10.1016/0009-8981(87)90221-X] [PMID: 2440621]
[29]
Pinner, E.; Gruper, Y.; Ben Zimra, M.; Kristt, D.; Laudon, M.; Naor, D.; Zisapel, N. CD44 splice variants as potential players in Alzheimer’s disease pathology. J. Alzheimers Dis., 2017, 58(4), 1137-1149.
[http://dx.doi.org/10.3233/JAD-161245] [PMID: 28550248]
[30]
Dzwonek, J.; Wilczynski, G.M. CD44: Molecular interactions, signaling and functions in the nervous system. Front. Cell. Neurosci., 2015, 9, 175.
[http://dx.doi.org/10.3389/fncel.2015.00175] [PMID: 25999819]
[31]
Pastural, E.; Ersoy, F.; Yalman, N.; Wulffraat, N.; Grillo, E.; Ozkinay, F.; Tezcan, I.; Gediköglu, G.; Philippe, N.; Fischer, A.; de Saint Basile, G. Two genes are responsible for Griscelli syndrome at the same 15q21 locus. Genomics, 2000, 63(3), 299-306.
[http://dx.doi.org/10.1006/geno.1999.6081] [PMID: 10704277]
[32]
Tang, F.Y.; Ma, L.; Tam, P.O.S.; Pang, C.P.; Tham, C.C.; Chen, L.J. Genetic association of the PARL-ABCC5-HTR3D-HTR3C locus with primary angle-closure glaucoma in Chinese. Invest. Ophthalmol. Vis. Sci., 2017, 58(10), 4384-4389.
[http://dx.doi.org/10.1167/iovs.17-22304] [PMID: 28813580]
[33]
Scalabrino, G.; Veber, D.; Tredici, G. Relationships between cobalamin, epidermal growth factor, and normal prions in the myelin maintenance of central nervous system. Int. J. Biochem. Cell Biol., 2014, 55, 232-241.
[http://dx.doi.org/10.1016/j.biocel.2014.09.011] [PMID: 25239885]
[34]
Martins, I.J. Anti-aging genes improve appetite regulation and reverse cell senescence and apoptosis in global populations. Adv. Aging Res., 2016, 5(1), 9-26.
[http://dx.doi.org/10.4236/aar.2016.51002]
[35]
Martins, I.J. Single gene inactivation with implications to diabetes and multiple organ dysfunction syndrome. J. Clin. Epigenet., 2017, 3(3), 24.
[http://dx.doi.org/10.21767/2472-1158.100058]
[36]
Li, G.; Jiapaer, Z.; Weng, R.; Hui, Y.; Jia, W.; Xi, J.; Wang, G.; Zhu, S.; Zhang, X.; Feng, D.; Liu, L.; Zhang, X.; Kang, J. Dysregulation of the SIRT1/OCT6 axis contributes to environmental stress-induced neural induction defects. Stem Cell Reports, 2017, 8(5), 1270-1286.
[http://dx.doi.org/10.1016/j.stemcr.2017.03.017] [PMID: 28434941]
[37]
Boulet, S.L.; Yang, Q.; Mai, C.; Kirby, R.S.; Collins, J.S.; Robbins, J.M.; Meyer, R.; Canfield, M.A.; Mulinare, J. Trends in the postfortification prevalence of spina bifida and anencephaly in the United States. Birth Defects Res. A Clin. Mol. Teratol., 2008, 82(7), 527-532.
[http://dx.doi.org/10.1002/bdra.20468] [PMID: 18481813]
[38]
Geisel, J. Folic acid and neural tube defects in pregnancy: A review. J. Perinat. Neonatal Nurs., 2003, 17(4), 268-279.
[http://dx.doi.org/10.1097/00005237-200310000-00005] [PMID: 14655787]
[39]
Salbaum, J.M.; Kappen, C. Neural tube defect genes and maternal diabetes during pregnancy. Birth Defects Res. A Clin. Mol. Teratol., 2010, 88(8), 601-611.
[http://dx.doi.org/10.1002/bdra.20680] [PMID: 20564432]
[40]
Steele, J.W.; Lin, Y.L.; Chen, N.; Wlodarczyk, B.J.; Chen, Q.; Attarwala, N.; Venkatesalu, M.; Cabrera, R.M.; Gross, S.S.; Finnell, R.H. Embryonic hypotaurine levels contribute to strain-dependent susceptibility in mouse models of valproate-induced neural tube defects. Front. Cell Dev. Biol., 2022, 10, 832492.
[http://dx.doi.org/10.3389/fcell.2022.832492] [PMID: 35265619]
[41]
Shen, Y.; Mani, S.; Donovan, S.L.; Schwob, J.E.; Meiri, K.F. Growth-associated protein-43 is required for commissural axon guidance in the developing vertebrate nervous system. J. Neurosci., 2002, 22(1), 239-247.
[http://dx.doi.org/10.1523/JNEUROSCI.22-01-00239.2002] [PMID: 11756507]
[42]
Arstikaitis, P.; Gauthier-Campbell, C.; Huang, K.; El-Husseini, A.; Murphy, T.H. Proteins that promote filopodia stability, but not number, lead to more axonal-dendritic contacts. PLoS One, 2011, 6(3), e16998.
[http://dx.doi.org/10.1371/journal.pone.0016998] [PMID: 21408225]
[43]
Gispen, W.H.; Nielander, H.B.; De Graan, P.N.; Oestreicher, A.B.; Schrama, L.H.; Schotman, P. Role of the growth-associated protein B-50/GAP-43 in neuronal plasticity. Mol. Neurobiol., 1991, 5(2-4), 61-85.
[http://dx.doi.org/10.1007/BF02935540] [PMID: 1840422]
[44]
Strittmatter, S.M.; Vartanian, T.; Fishman, M.C. GAP-43 as a plasticity protein in neuronal form and repair. J. Neurobiol., 1992, 23(5), 507-520.
[http://dx.doi.org/10.1002/neu.480230506] [PMID: 1431834]
[45]
Allegra Mascaro, A.L.; Cesare, P.; Sacconi, L.; Grasselli, G.; Mandolesi, G.; Maco, B.; Knott, G.W.; Huang, L.; De Paola, V.; Strata, P.; Pavone, F.S. In vivo single branch axotomy induces GAP-43-dependent sprouting and synaptic remodeling in cerebellar cortex. Proc. Natl. Acad. Sci. USA, 2013, 110(26), 10824-10829.
[http://dx.doi.org/10.1073/pnas.1219256110] [PMID: 23754371]
[46]
Basi, G.S.; Jacobson, R.D.; Virág, I.; Schilling, J.; Skene, J.H. Primary structure and transcriptional regulation of GAP-43, a protein associated with nerve growth. Cell, 1987, 49(6), 785-791.
[http://dx.doi.org/10.1016/0092-8674(87)90616-7] [PMID: 3581170]
[47]
Nguyen, L.; He, Q.; Meiri, K.F. Regulation of GAP-43 at serine 41 acts as a switch to modulate both intrinsic and extrinsic behaviors of growing neurons, via altered membrane distribution. Mol. Cell. Neurosci., 2009, 41(1), 62-73.
[http://dx.doi.org/10.1016/j.mcn.2009.01.011] [PMID: 19249369]
[48]
Mishra, R.; Gupta, S.K.; Meiri, K.F.; Fong, M.; Thostrup, P.; Juncker, D.; Mani, S. GAP-43 is key to mitotic spindle control and centrosome-based polarization in neurons. Cell Cycle, 2008, 7(3), 348-357.
[http://dx.doi.org/10.4161/cc.7.3.5235] [PMID: 18235238]
[49]
Kawasaki, T.; Nishio, T.; Kawaguchi, S.; Kurosawa, H. Spatiotemporal distribution of GAP-43 in the developing rat spinal cord: A histological and quantitative immunofluorescence study. Neurosci. Res., 2001, 39(3), 347-358.
[http://dx.doi.org/10.1016/S0168-0102(00)00234-0] [PMID: 11248375]
[50]
Huber, M.; Siegenthaler, G.; Mirancea, N.; Marenholz, I.; Nizetic, D.; Breitkreutz, D.; Mischke, D.; Hohl, D. Isolation and characterization of human repetin, a member of the fused gene family of the epidermal differentiation complex. J. Invest. Dermatol., 2005, 124(5), 998-1007.
[http://dx.doi.org/10.1111/j.0022-202X.2005.23675.x] [PMID: 15854042]
[51]
Wang, S.; Ren, H.; Xu, J.; Yu, Y.; Han, S.; Qiao, H.; Cheng, S.; Xu, C.; An, S.; Ju, B.; Yu, C.; Wang, C.; Wang, T.; Yang, Z.; Taylor, E.W.; Zhao, L. Diminished serum repetin levels in patients with schizophrenia and bipolar disorder. Sci. Rep., 2015, 5(1), 7977.
[http://dx.doi.org/10.1038/srep07977] [PMID: 25613293]
[52]
Lopez, J.; Mikaelian, I.; Gonzalo, P. Amniotic fluid glial fibrillary acidic protein (AF-GFAP), a biomarker of open neural tube defects. Prenat. Diagn., 2013, 33(10), 990-995.
[http://dx.doi.org/10.1002/pd.4181] [PMID: 23784867]
[53]
Petzold, A.; Stiefel, D.; Copp, A.J. Amniotic fluid brain-specific proteins are biomarkers for spinal cord injury in experimental myelomeningocele. J. Neurochem., 2005, 95(2), 594-598.
[http://dx.doi.org/10.1111/j.1471-4159.2005.03432.x] [PMID: 16190875]
[54]
Petzold, A.; Eikelenboom, M.J.; Gveric, D.; Keir, G.; Chapman, M.; Lazeron, R.H.; Cuzner, M.L.; Polman, C.H.; Uitdehaag, B.M.; Thompson, E.J.; Giovannoni, G. Markers for different glial cell responses in multiple sclerosis: Clinical and pathological correlations. Brain, 2002, 125(Pt 7), 1462-1473.
[http://dx.doi.org/10.1093/brain/awf165] [PMID: 12076997]
[55]
O’Callaghan, J.P.; Sriram, K. Glial fibrillary acidic protein and related glial proteins as biomarkers of neurotoxicity. Expert Opin. Drug Saf., 2005, 4(3), 433-442.
[http://dx.doi.org/10.1517/14740338.4.3.433] [PMID: 15934851]
[56]
George, T.M.; Cummings, T.J. The immunohistochemical profile of the myelomeningocele placode: Is the placode normal? Pediatr. Neurosurg., 2003, 39(5), 234-239.
[http://dx.doi.org/10.1159/000072867] [PMID: 14512686]
[57]
Yan, Y.; Zuo, X.; Wei, D. Concise review: Emerging role of CD44 in cancer stem cells: A promising biomarker and therapeutic target. Stem Cells Transl. Med., 2015, 4(9), 1033-1043.
[http://dx.doi.org/10.5966/sctm.2015-0048] [PMID: 26136504]
[58]
Haegel, H.; Dierich, A.; Ceredig, R. CD44 in differentiated embryonic stem cells: Surface expression and transcripts encoding multiple variants. Dev. Immunol., 1994, 3(4), 239-246.
[http://dx.doi.org/10.1155/1994/25484] [PMID: 7542511]
[59]
Jackson, R.L.; Busch, S.J.; Cardin, A.D. Glycosaminoglycans: Molecular properties, protein interactions, and role in physiological processes. Physiol. Rev., 1991, 71(2), 481-539.
[http://dx.doi.org/10.1152/physrev.1991.71.2.481] [PMID: 2006221]
[60]
Corbel, C.; Lehmann, A.; Davison, F. Expression of CD44 during early development of the chick embryo. Mech. Dev., 2000, 96(1), 111-114.
[http://dx.doi.org/10.1016/S0925-4773(00)00347-6] [PMID: 10940630]
[61]
Zhu, H.; Mitsuhashi, N.; Klein, A.; Barsky, L.W.; Weinberg, K.; Barr, M.L.; Demetriou, A.; Wu, G.D. The role of the hyaluronan receptor CD44 in mesenchymal stem cell migration in the extracellular matrix. Stem Cells, 2006, 24(4), 928-935.
[http://dx.doi.org/10.1634/stemcells.2005-0186] [PMID: 16306150]
[62]
Wheatley, S.C.; Isacke, C.M.; Crossley, P.H. Restricted expression of the hyaluronan receptor, CD44, during postimplantation mouse embryogenesis suggests key roles in tissue formation and patterning. Development, 1993, 119(2), 295-306.
[http://dx.doi.org/10.1242/dev.119.2.295] [PMID: 7507029]
[63]
Sahin Inan, Z.D.; Unver Saraydin, S. Immunohistochemical profile of CD markers in experimental neural tube defect. Biotech. Histochem., 2019, 94(8), 617-627.
[http://dx.doi.org/10.1080/10520295.2019.1622783] [PMID: 31184499]
[64]
Zöller, M. CD44: Can a cancer-initiating cell profit from an abundantly expressed molecule? Nat. Rev. Cancer, 2011, 11(4), 254-267.
[http://dx.doi.org/10.1038/nrc3023] [PMID: 21390059]

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