|
1
|
Kesari S: Understanding glioblastoma tumor
biology: The potential to improve current diagnosis and treatments.
Semin Oncol. 38(Suppl 4): S2–S10. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Aldape K, Zadeh G, Mansouri S,
Reifenberger G and Deimling AV: Glioblastoma: Pathology, molecular
mechanisms and markers. Acta Neuropathol. 129:829–848. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
3
|
McGuire S: World Cancer Report 2014.
Geneva, Switzerland: World Health Organization, International
Agency for Research on Cancer, WHO Press; 2015
Adv Nutr. 7:418–419. 2016. View Article : Google Scholar
|
|
4
|
Bleeker FE, Molenaar RJ and Leenstra S:
Recent advances in the molecular understanding of glioblastoma. J
Neurooncol. 108:11–27. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Bao ZS, Li MY, Wang JY, Zhang CB, Wang HJ,
Yan W, Liu YW, Zhang W, Chen L and Jiang T: Prognostic value of a
nine-gene signature in glioma patients based on mRNA expression
profiling. CNS Neurosci Ther. 20:112–118. 2014. View Article : Google Scholar
|
|
6
|
Kim YW, Koul D, Kim SH, Lucioeterovic AK,
Freire PR, Yao J, Wang J, Almeida JS, Aldape K and Yung WK:
Identification of prognostic gene signatures of glioblastoma: A
study based on TCGA data analysis. Neuro Oncol. 15:829–839. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Sana J, Radova L, Lakomy R, Kren L, Fadrus
P, Smrcka M, Besse A, Nekvindova J, Hermanova M, Jancalek R, et al:
Risk Score based on microRNA expression signature is independent
prognostic classifier of glioblastoma patients. Carcinogenesis.
35:2756–2762. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Ma S, Kosorok MR, Huang J and Dai Y:
Incorporating higher-order representative features improves
prediction in network-based cancer prognosis analysis. BMC Med
Genomics. 4:52011. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Bild AH, Yao G, Chang JT, Wang Q, Potti A,
Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, et al:
Oncogenic pathway signatures in human cancers as a guide to
targeted therapies. Nature. 439:353–357. 2006. View Article : Google Scholar
|
|
10
|
Chin L, Hahn WC, Getz G and Meyerson M:
Making sense of cancer genomic data. Genes Dev. 25:534–555. 2011.
View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Drier Y, Sheffer M and Domany E:
Pathway-based personalized analysis of cancer. Proc Natl Acad Sci
USA. 110:6388–6393. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Huang S, Yee C, Ching T, Yu H and Garmire
LX: A novel model to combine clinical and pathway-based
transcriptomic information for the prognosis prediction of breast
cancer. PLoS Comput Biol. 10:e10038512014. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Yan W, Zhang W, You G, Zhang J, Han L, Bao
Z, Wang Y, Liu Y, Jiang C, Kang C, et al: Molecular classification
of gliomas based on whole genome gene expression: A systematic
report of 225 samples from the Chinese Glioma Cooperative Group.
Neuro Oncol. 14:1432–1440. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Sun Y, Zhang W, Chen D, Lv Y, Zheng J,
Lilljebjörn H, Ran L, Bao Z, Soneson C, Sjögren HO, et al: A glioma
classification scheme based on coexpression modules of EGFR and
PDGFRA. Proc Natl Acad Sci USA. 111:3538–3543. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Parrish RS and Spencer HJ III: Effect of
normalization on significance testing for oligonucleotide
microarrays. J Biopharm Stat. 14:575–589. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW,
Shi W and Smyth GK: limma powers differential expression analyses
for RNA-sequencing and microarray studies. Nucleic Acids Res.
43:e472015. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Wang P, Wang Y, Hang B, Zou X and Mao JH:
A novel gene expression-based prognostic scoring system to predict
survival in gastric cancer. Oncotarget. 7:55343–55351.
2016.PubMed/NCBI
|
|
18
|
Eisen MB, Spellman PT, Brown PO and
Botstein D: Cluster analysis and display of genome-wide expression
patterns. Proc Natl Acad Sci USA. 95:14863–14868. 1998. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Wang L, Cao C, Ma Q, Zeng Q, Wang H, Cheng
Z, Zhu G, Qi J, Ma H, Nian H and Wang Y: RNA-seq analyses of
multiple meri-stems of soybean: Novel and alternative transcripts,
evolutionary and functional implications. BMC Plant Biol.
14:1692014. View Article : Google Scholar
|
|
20
|
Tilford CA and Siemers NO: Gene set
enrichment analysis. Methods Mol Biol. 563:99–121. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Goel MK, Khanna P and Kishore J:
Understanding survival analysis: Kaplan-Meier estimate. Int J
Ayurveda Res. 1:274–278. 2010. View Article : Google Scholar
|
|
22
|
Ohgaki H: Epidemiology of brain tumors.
Methods Mol Biol. 472:323–342. 2009. View Article : Google Scholar
|
|
23
|
Abraham G, Kowalczyk A, Loi S, Haviv I and
Zobel J: Prediction of breast cancer prognosis using gene set
statistics provides signature stability and biological context. BMC
Bioinformatics. 11:2772010. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
van den Akker EB, Passtoors WM, Jansen R,
van Zwet EW, Goeman JJ, Hulsman M, Emilsson V, Perola M, Willemsen
G, Penninx BW, et al: Meta-analysis on blood transcriptomic studies
identifies consistently coexpressed protein-protein interaction
modules as robust markers of human aging. Aging Cell. 13:216–225.
2014. View Article : Google Scholar
|
|
25
|
Lee E, Chuang HY, Kim JW, Ideker T and Lee
D: Inferring pathway activity toward precise disease
classification. PLoS Comput Biol. 4:e10002172008. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Pittman J, Huang E, Dressman H, Horng CF,
Cheng SH, Tsou MH, Chen CM, Bild A, Iversen ES, Huang AT, et al:
Integrated modeling of clinical and gene expression information for
personalized prediction of disease outcomes. Proc Natl Acad Sci
USA. 101:8431–8436. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Cheng F, Prat A, Parker JS, Liu Y, Carey
LA, Troester MA and Perou CM: Building prognostic models for breast
cancer patients using clinical variables and hundreds of gene
expression signatures. BMC Med Genomics. 4:32011. View Article : Google Scholar
|
|
28
|
Wang W, Zhang L, Wang Z, Yang F, Wang H,
Liang T, Wu F, Lan Q, Wang J and Zhao J: A three-gene signature for
prognosis in patients with MGMT promoter-methylated glioblastoma.
Oncotarget. 7:69991–69999. 2016.PubMed/NCBI
|
|
29
|
Mellman I and Yarden Y: Endocytosis and
cancer. Cold Spring Harb Perspect Biol. 5:a0169492013. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Shen H, Decollogne S, Dilda PJ, Hau E,
Chung SA, Luk PP, Hogg PJ and McDonald KL: Dual-targeting of
aberrant glucose metabolism in glioblastoma. J Exp Clin Cancer Res.
34:142015. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Toyonaga T, Yamaguchi S, Hirata K,
Kobayashi K, Manabe O, Watanabe S, Terasaka S, Kobayashi H, Hattori
N, Shiga T, et al: Hypoxic glucose metabolism in glioblastoma as a
potential prognostic factor. Eur J Nucl Med Mol Imaging.
44:611–619. 2017. View Article : Google Scholar
|
|
32
|
Hoeller D and Dikic I: Targeting the
ubiquitin system in cancer therapy. Nature. 458:438–444. 2009.
View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Eke I and Cordes N: Focal adhesion
signaling and therapy resistance in cancer. Semin Cancer Biol.
31:65–75. 2015. View Article : Google Scholar
|
