Abstract
Because of the variety of factors affecting glioma prognosis, prediction of patient survival is particularly difficult. Protein–protein interaction (PPI) networks have been considered with regard to how their spatial characteristics relate to glioma. However, the dynamic nature of PPIs in vivo makes them temporally and spatially complex events. Integration of prognosis-specific co-expression information adds further dynamic features to these networks. Although some biomarkers for glioma prognosis have been identified, none is sufficient for accurate prediction of either prognosis or improved survival. We have established co-expressed protein-interaction networks that integrate protein–protein interactions with glioma gene-expression profiles related to different survival times. Biomarkers related to glioma prognosis were identified by comparative analysis of the dynamic features of the glioma prognosis network, particularly subnetworks. Four significantly differently expressed genes (SDEGs) are upregulated and ten SDEGs downregulated as lifetime is extended. In addition, 97 enhanced differently co-expressed protein interactions (DCPIs) and 99 weakened DCPIs were associated with glioma patient lifetime extension. We propose a method for estimating glioma prognosis on the basis of the construction of a dynamic modular network. We have used this method to identify dynamic genes and interactions related to glioma prognosis. Among these, enhanced MYC expression was related to lifetime extension, as were interactions between E2F1 and RB1 and between EGFR and p38. This method is a novel means of studying the molecular mechanisms determining prognosis in glioma.
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Rainov NG, Dobberstein KU, Bahn H, Holzhausen HJ, Lautenschlager C, Heidecke V, Burkert W (1997) Prognostic factors in malignant glioma: influence of the overexpression of oncogene and tumor-suppressor gene products on survival. J Neurooncol 35:13–28
Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL (2009) Dynamic modularity in protein-interaction networks predicts breast cancer outcome. Nat Biotechnol 27:199–204. doi:10.1038/nbt.1522
Yang J, Liao D, Wang Z, Liu F, Wu G (2011) Mammalian target of rapamycin signaling pathway contributes to glioma progression and patients’ prognosis. J Surg Res 168:97–102. doi:10.1016/j.jss.2009.06.025
Liu X, Tang H, Zhang Z, Li W, Wang Z, Zheng Y, Wu M, Li G (2011) POTEH hypomethylation, a new epigenetic biomarker for glioma prognosis. Brain Res 1391:125–131. doi:10.1016/j.brainres.2011.03.042
Wang C, Cao S, Yan Y, Ying Q, Jiang T, Xu K, Wu A (2010) TLR9 expression in glioma tissues correlated to glioma progression and the prognosis of GBM patients. BMC Cancer 10:415. doi:10.1186/1471-2407-10-415
Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B, Wyrick JJ, Tagne JB, Volkert TL, Fraenkel E, Gifford DK, Young RA (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298:799–804. doi:10.1126/science.1075090298/5594/799[pii]
Watson MA, Perry A, Budhraja V, Hicks C, Shannon WD, Rich KM (2001) Gene expression profiling with oligonucleotide microarrays distinguishes World Health Organization grade of oligodendrogliomas. Cancer Res 61:1825–1829
Fuller GN, Hess KR, Rhee CH, Yung WK, Sawaya RA, Bruner JM, Zhang W (2002) Molecular classification of human diffuse gliomas by multidimensional scaling analysis of gene expression profiles parallels morphology-based classification, correlates with survival, and reveals clinically-relevant novel glioma subsets. Brain Pathol 12:108–116
Masica DL, Karchin R (2011) Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival. Cancer Res 71:4550–4561. doi:10.1158/0008-5472.CAN-11-0180
Boudreau CR, Yang I, Liau LM (2005) Gliomas: advances in molecular analysis and characterization. Surg Neurol 64:286–294; discussion 294
Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD, Misra A, Nigro JM, Colman H, Soroceanu L, Williams PM, Modrusan Z, Feuerstein BG, Aldape K (2006) Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9:157–173. doi:10.1016/j.ccr.2006.02.019
Brown KR, Jurisica I (2005) Online predicted human interaction database. Bioinformatics 21:2076–2082. doi:10.1093/bioinformatics/bti273
Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31:e15
Newman JR, Ghaemmaghami S, Ihmels J, Breslow DK, Noble M, DeRisi JL, Weissman JS (2006) Single-cell proteomic analysis of S. Cerevisiae reveals the architecture of biological noise. Nature 441:840–846. doi:10.1038/nature04785
Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98:5116–5121. doi:10.1073/pnas.091062498
Larsson O, Wahlestedt C, Timmons JA (2005) Considerations when using the significance analysis of microarrays (SAM) algorithm. BMC Bioinformatics 6:129. doi:10.1186/1471-2105-6-129
Jin R, McCallen S, Liu CC, Xiang Y, Almaas E, Zhou XJ (2009) Identifying dynamic network modules with temporal and spatial constraints. Pac Symp Biocomput: 203–214
Suzuki H (2006) Protein–protein interactions in the mammalian brain. J Physiol 575:373–377. doi:10.1113/jphysiol.2006.115717
Yao C, Li H, Zhou C, Zhang L, Zou J, Guo Z (2010) Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis. BMC Syst Biol 4:151. doi:10.1186/1752-0509-4-151
Sano M, Genkai N, Yajima N, Tsuchiya N, Homma J, Tanaka R, Miki T, Yamanaka R (2006) Expression level of ECT2 proto-oncogene correlates with prognosis in glioma patients. Oncol Rep 16:1093–1098
Genkai N, Homma J, Sano M, Tanaka R, Yamanaka R (2006) Increased expression of pituitary tumor-transforming gene (PTTG)-1 is correlated with poor prognosis in glioma patients. Oncol Rep 15:1569–1574
Sun B, Chu D, Li W, Chu X, Li Y, Wei D, Li H (2009) Decreased expression of NDRG1 in glioma is related to tumor progression and survival of patients. J Neurooncol 94:213–219. doi:10.1007/s11060-009-9859-7
Wang Y, Weil BR, Herrmann JL, Abarbanell AM, Tan J, Markel TA, Kelly ML, Meldrum DR (2009) MEK, p38, and PI-3K mediate cross talk between EGFR and TNFR in enhancing hepatocyte growth factor production from human mesenchymal stem cells. Am J Physiol Cell Physiol 297:C1284–C1293. doi:10.1152/ajpcell.00183.2009
De Carli E, Wang X, Puget S (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:2248; author reply 2249. doi:10.1056/NEJMc090593
Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, Kros JM, Hainfellner JA, Mason W, Mariani L, Bromberg JE, Hau P, Mirimanoff RO, Cairncross JG, Janzer RC, Stupp R (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997–1003. doi:10.1056/NEJMoa043331
Durand KS, Guillaudeau A, Weinbreck N, DeArmas R, Robert S, Chaunavel A, Pommepuy I, Bourthoumieu S, Caire F, Sturtz FG, Labrousse FJ (2010) 1p19q LOH patterns and expression of p53 and Olig2 in gliomas: relation with histological types and prognosis. Mod Pathol 23:619–628. doi:10.1038/modpathol.2009.185
Crowe DL, Nguyen DC (2001) Rb and E2F–1 regulate telomerase activity in human cancer cells. Biochim Biophys Acta 1518:1–6. doi:10.1016/S0167-4781(00)00296-7
Xu K, Shu HK (2007) EGFR activation results in enhanced cyclooxygenase-2 expression through p38 mitogen-activated protein kinase-dependent activation of the Sp1/Sp3 transcription factors in human gliomas. Cancer Res 67:6121–6129. doi:10.1158/0008-5472.CAN-07-0141
Ventura JJ, Tenbaum S, Perdiguero E, Huth M, Guerra C, Barbacid M, Pasparakis M, Nebreda AR (2007) p38alpha MAP kinase is essential in lung stem and progenitor cell proliferation and differentiation. Nat Genet 39:750–758. doi:10.1038/ng2037
Bodnar AG, Ouellette M, Frolkis M, Holt SE, Chiu CP, Morin GB, Harley CB, Shay JW, Lichtsteiner S, Wright WE (1998) Extension of life-span by introduction of telomerase into normal human cells. Science 279:349–352
Kanaya T, Kyo S, Hamada K, Takakura M, Kitagawa Y, Harada H, Inoue M (2000) Adenoviral expression of p53 represses telomerase activity through down-regulation of human telomerase reverse transcriptase transcription. Clin Cancer Res 6:1239–1247
Chandra H, Reddy PJ, Srivastava S (2011) Protein microarrays and novel detection platforms. Expert Rev Proteomics 8:61–79. doi:10.1586/epr.10.99
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Xiaoyu Zhang and Hongbin Yang contributed equally to this work.
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Supplementary material 4. Dynamic observation of a protein–protein interaction (PPI) network in relation to survival time prognosis: (a) Nodes represent proteins and edges denote PPIs. This is a large size, small-world network. (b) The power law of the network with decreasing connectivity distribution (R 2 = 0.917). (c) 99.27% of shortest path lengths are ≤6. (TIFF 2434 kb)
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Zhang, X., Yang, H., Gong, B. et al. Combined gene expression and protein interaction analysis of dynamic modularity in glioma prognosis. J Neurooncol 107, 281–288 (2012). https://doi.org/10.1007/s11060-011-0757-4
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DOI: https://doi.org/10.1007/s11060-011-0757-4