[474] A. Subramanian, P. Tamayo, V. Mootha, S. Mukherjee, B. Ebert, M. Gillette, A. Paulovich, S. Pomeroy, T. Golub, E. Lander, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression pr<strong>of</strong>iles. Proceedings <strong>of</strong> the National Academy <strong>of</strong> Sciences <strong>of</strong> the United States <strong>of</strong> America, 102(43):15545–15550, 2005. [475] E. Sum, D. Segara, B. Duscio, M. Bath, A. Field, R. Sutherland, G. Lindeman, and J. Visvader. Overexpression <strong>of</strong> lmo4 induces mammary hyperplasia, promotes cell invasion, and is a predictor <strong>of</strong> poor outcome in breast cancer. Proceedings <strong>of</strong> the National Academy <strong>of</strong> Sciences <strong>of</strong> the United States <strong>of</strong> America, 102(21):7659–7664, 2005. [476] L. Sun, W. Yan, Y. Wang, G. Sun, H. Luo, J. Zhang, X. Wang, Y. You, Z. Yang, and N. Liu. Microrna-10b induces glioma cell invasion by modulating mmp-14 and upar expression via hoxd10. Brain research, 1389:9–18, 2011. [477] N. Sun, T. Huiatt, D. Paulin, Z. Li, and R. Robson. Synemin interacts with the lim domain protein zyxin and is essential for cell adhesion and migration. Experimental cell research, 316(3):491–505, 2010. [478] P. Sun, S. Xia, B. Lal, C. Eberhart, A. Quinones-Hinojosa, J. Maciaczyk, W. Matsui, F. DiMeco, S. Piccirillo, A. Vescovi, et al. Dner, an epigenetically modulated gene, regulates glioblastoma-derived neurosphere cell differentiation and tumor propagation. <strong>Stem</strong> <strong>Cells</strong>, 27(7):1473–1486, 2009. [479] T. Sun, X. Wang, S. Xie, D. Zhang, X. Wang, B. Li, W. Ma, and H. Xin. A comparison <strong>of</strong> proliferative capacity and passaging potential between neural stem and progenitor cells in adherent and neurosphere cultures. International Journal <strong>of</strong> Developmental Neuroscience, 2011. [480] Y. Sun, W. Kong, A. Falk, J. Hu, L. Zhou, S. Pollard, and A. Smith. Cd133 (prominin) negative human neural stem cells are clonogenic and tripotent. PloS one, 4(5):e5498, 2009. [481] Y. Sun, S. Pollard, L. Conti, M. Toselli, G. Biella, G. Parkin, L. Willatt, A. Falk, E. Cattaneo, and A. Smith. Long-term tripotent differentiation capacity <strong>of</strong> human neural stem (ns) cells in adherent culture. Molecular and Cellular Neuroscience, 38(2):245–258, 2008. [482] C. Suo, A. Salim, K. S. Chia, Y. Pawitan, and S. Calza. Modified least-variant set normalization for miRNA microarray. RNA, 16(12):2293–303, 2010. [483] C. Svendsen, M. ter Borg, R. Armstrong, A. Rosser, S. Chandran, T. Ostenfeld, and M. Caldwell. A new method for the rapid and long term growth <strong>of</strong> human neural precursor cells. Journal <strong>of</strong> neuroscience methods, 85(2):141–152, 1998. [484] Y. Takamura, H. Ikeda, T. Kanaseki, M. Toyota, T. Tokino, K. Imai, K. Houkin, and N. Sato. Regulation <strong>of</strong> mhc class ii expression in glioma cells by class ii transactivator (ciita). Glia, 45(4):392–405, 2004. [485] O. Tam, A. Aravin, P. Stein, A. Girard, E. Murchison, S. Cheloufi, E. Hodges, M. Anger, R. Sachidanandam, R. Schultz, et al. Pseudogene-derived small interfering rnas regulate gene expression in mouse oocytes. Nature, 453(7194):534, 2008. [486] B. Tan, C. Park, L. Ailles, and I. Weissman. The cancer stem cell hypothesis: a work in progress. Laboratory investigation, 86(12):1203–1207, 2006. [487] N. Taniguchi, H. Taniura, M. Niinobe, C. Takayama, K. Tominaga-Yoshino, A. Ogura, and K. Yoshikawa. The postmitotic growth suppressor necdin interacts with a calcium-binding protein (nefa) in neuronal cytoplasm. Journal <strong>of</strong> Biological Chemistry, 275(41):31674–31681, 2000. [488] M. Taniwaki, Y. Daigo, N. Ishikawa, A. Takano, T. Tsunoda, W. Yasui, K. Inai, N. Kohno, and Y. Nakamura. Gene expression pr<strong>of</strong>iles <strong>of</strong> small-cell lung cancers: molecular signatures <strong>of</strong> lung cancer. International journal <strong>of</strong> oncology, 29(3):567–576, 2006. [489] A. Tarca, S. Draghici, P. Khatri, S. Hassan, P. Mittal, J. Kim, C. Kim, J. Kusanovic, and R. Romero. A novel signaling pathway impact analysis. Bioinformatics, 25(1):75, 2009.
[490] P. A. C. t’Hoen, Y. Ariyurek, H. H. Thygesen, E. Vreugdenhil, R. H. A. M. Vossen, R. X. De Menezes, J. M. Boer, G.-J. B. Van Ommen, and J. T. Den Dunnen. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Research, 36(21):e141–e141, Oct. 2008. [491] C. Thomas, G. Ely, C. D. James, R. Jenkins, M. Kastan, A. Jedlicka, P. Burger, and R. Wharen. Glioblastoma-related gene mutations and over-expression <strong>of</strong> functional epidermal growth factor receptors in SKMG-3 glioma cells. Acta neuropathologica, 101(6):605–615, June 2001. [492] J. Ting and J. Trowsdale. Genetic control <strong>of</strong> mhc class ii expression. Cell, 109(2):S21– S33, 2002. [493] E. Tobias, A. Hurlstone, E. MacKenzie, R. McFarlane, D. Black, et al. The tes gene at 7q31. 1 is methylated in tumours and encodes a novel growth-suppressing lim domain protein. Oncogene, 20(22):2844, 2001. [494] V. Tropepe, M. Sibilia, B. Ciruna, J. Rossant, E. Wagner, and D. Kooy. Distinct neural stem cells proliferate in response to egf and fgf in the developing mouse telencephalon. Developmental biology, 208(1):166–188, 1999. [495] L. Trotman, X. Wang, A. Alimonti, Z. Chen, J. Teruya-Feldstein, H. Yang, N. Pavletich, B. Carver, C. Cordon-Cardo, H. Erdjument-Bromage, et al. Ubiquitination regulates pten nuclear import and tumor suppression. Cell, 128(1):141–156, 2007. [496] A. B. Trovó-Marqui and E. H. Tajara. Neur<strong>of</strong>ibromin: a general outlook. Clinical genetics, 70(1):1–13, July 2006. [497] C. Tso, P. Shintaku, J. Chen, Q. Liu, J. Liu, Z. Chen, K. Yoshimoto, P. Mischel, T. Cloughesy, L. Liau, et al. Primary glioblastomas express mesenchymal stem-like properties. Molecular cancer research, 4(9):607–619, 2006. [498] A. Tsuchida, T. Okajima, K. Furukawa, T. Ando, H. Ishida, A. Yoshida, Y. Nakamura, R. Kannagi, M. Kiso, and K. Furukawa. Synthesis <strong>of</strong> disialyl lewis a (lea) structure in colon cancer cell lines by a sialyltransferase, st6galnac vi, responsible for the synthesis <strong>of</strong> α-series gangliosides. Journal <strong>of</strong> Biological Chemistry, 278(25):22787–22794, 2003. [499] N. Tsuji, K. Kondoh, M. Furuya, D. Kobayashi, A. Yagihashi, Y. Inoue, T. Meguro, S. Horita, H. Takahashi, and N. Watanabe. A novel aspartate protease gene, alp56, is related to morphological features <strong>of</strong> colorectal adenomas. International journal <strong>of</strong> colorectal disease, 19(1):43–48, 2004. [500] V. Turk, B. Turk, G. Guncar, D. Turk, and J. Kos. Lysosomal cathepsins: structure, role in antigen processing and presentation, and cancer. Advances in enzyme regulation, 42:285, 2002. [501] A. Tzschach, A. Bisgaard, M. Kirchh<strong>of</strong>f, L. Graul-Neumann, H. Neitzel, S. Page, A. Ahmed, I. Müller, F. Erdogan, H. Ropers, et al. Chromosome aberrations involving 10q22: report <strong>of</strong> three overlapping interstitial deletions and a balanced translocation disrupting c10orf11. European Journal <strong>of</strong> Human Genetics, 18(3):291–295, 2009. [502] N. Uchida, D. Buck, D. He, M. Reitsma, M. Masek, T. Phan, A. Tsukamoto, F. Gage, and I. Weissman. Direct isolation <strong>of</strong> human central nervous system stem cells. Proceedings <strong>of</strong> the National Academy <strong>of</strong> Sciences, 97(26):14720, 2000. [503] M. Van De Wiel, K. Kim, S. Vosse, W. Van Wieringen, S. Wilting, and B. Ylstra. Cghcall: calling aberrations for array cgh tumor pr<strong>of</strong>iles. Bioinformatics, 23(7):892– 894, 2007. [504] J. Van Den Boom, M. Wolter, R. Kuick, D. Misek, A. Youkilis, D. Wechsler, C. Sommer, G. Reifenberger, and S. Hanash. <strong>Characterization</strong> <strong>of</strong> gene expression pr<strong>of</strong>iles associated with glioma progression using oligonucleotide-based microarray analysis and real-time reverse transcription-polymerase chain reaction. The American journal <strong>of</strong> pathology, 163(3):1033–1043, 2003. [505] A. Van der Krol, L. Mur, M. Beld, J. Mol, and A. Stuitje. Flavonoid genes in petunia: addition <strong>of</strong> a limited number <strong>of</strong> gene copies may lead to a suppression <strong>of</strong> gene expression. The Plant Cell Online, 2(4):291, 1990.
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- Page 325 and 326: [38] G. Bain, D. Kitchens, M. Yao,
- Page 327 and 328: [78] S. Bustin, V. Benes, J. Garson
- Page 329 and 330: [112] M. Czystowska, J. Han, M. Szc
- Page 331 and 332: [153] T. Fujiwara, M. Bandi, M. Nit
- Page 333 and 334: [192] M. Hernandez, M. Nieto, and M
- Page 335 and 336: [231] T.-M. Kim, W. Huang, R. Park,
- Page 337 and 338: [268] H. Lemjabbar-Alaoui, A. van Z
- Page 339 and 340: [305] K. MAEDA, S. MATSUHASHI, K. T
- Page 341 and 342: [342] H. Moon, M. Ahn, J. Park, K.
- Page 343 and 344: [379] D. Park and J. Rich. Biology
- Page 345 and 346: [417] P. Rakic. Guidance of neurons
- Page 347: [456] T. Shima, N. Okumura, T. Taka
- Page 351 and 352: [523] T. Watanabe, A. Takeda, T. Ts