|
1
|
Rooth C: Ovarian cancer: Risk factors,
treatment and management. Br J Nurs. 22 Suppl:23–30. 2013.
View Article : Google Scholar
|
|
2
|
Omura GA, Brady MF, Homesley HD, Yordan E,
Major FJ, Buchsbaum HJ and Park RC: Long-term follow-up and
prognostic factor analysis in advanced ovarian carcinoma: The
gynecologic oncology group experience. J Clin Oncol. 9:1138–1150.
1991. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Goetsch CM: Genetic tumor profiling and
genetically targeted cancer therapy. Semin Oncol Nurs. 27:34–44.
2011. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Djebali S, Davis CA, Merkel A, Dobin A,
Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F,
et al: Landscape of transcription in human cells. Nature.
489:101–108. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Kornienko AE, Guenzl PM, Barlow DP and
Pauler FM: Gene regulation by the act of long non-coding RNA
transcription. BMC Biol. 11:592013. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Derrien T, Johnson R, Bussotti G, Tanzer
A, Djebali S, Tilgner H, Guernec G, Martin D, Merkel A, Knowles DG,
et al: The GENCODE v7 catalog of human long noncoding RNAs:
Analysis of their gene structure, evolution, and expression. Genome
Res. 22:1775–1789. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Yang G, Lu X and Yuan L: LncRNA: A link
between RNA and cancer. Biochim Biophys Acta. 1839:1097–1109. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Zhang X, Sun S, Pu JK, Tsang AC, Lee D,
Man VO, Lui WM, Wong ST and Leung GK: Long non-coding RNA
expression profiles predict clinical phenotypes in glioma.
Neurobiol Dis. 48:1–8. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Zhou Y, Zhong Y, Wang Y, Zhang X, Batista
DL, Gejman R, Ansell PJ, Zhao J, Weng C and Klibanski A: Activation
of p53 by MEG3 non-coding RNA. J Biol Chem. 282:24731–24742. 2007.
View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Elgaaen BV, Olstad OK, Sandvik L, Odegaard
E, Sauer T, Staff AC and Gautvik KM: ZNF385B and VEGFA are strongly
differentially expressed in serous ovarian carcinomas and correlate
with survival. PLoS One. 7:e463172012. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Mok SC, Bonome T, Vathipadiekal V, Bell A,
Johnson ME, Wong KK, Park DC, Hao K, Yip DK, Donninger H, et al: A
gene signature predictive for outcome in advanced ovarian cancer
identifies a survival factor: Microfibril-associated glycoprotein
2. Cancer Cell. 16:521–532. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Michiels S, Koscielny S and Hill C:
Prediction of cancer outcome with microarrays: A multiple random
validation strategy. Lancet. 365:488–492. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Yu F, Jiang Q, Zhou Y, Yang Z, Yu X, Wang
H, Liu Z, Wang L, Fang W and Guo S: Abnormal expression of matrix
metalloproteinase-9 (MMP9) correlates with clinical course in
Chinese patients with endometrial cancer. Dis Markers. 32:321–327.
2012. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Shen X, Yue M, Meng F, Zhu J, Zhu X and
Jiang Y: Microarray analysis of differentially-expressed genes and
linker genes associated with the molecular mechanism of colorectal
cancer. Oncol Lett. 12:3250–3258. 2016.PubMed/NCBI
|
|
15
|
R Development Core Team, . R: A language
and environment for statistical computing. R Foundation for
Statistical Computing; Vienna, Austria: 2015
|
|
16
|
Zhi H, Ning S and Li X, Li Y, Wu W and Li
X: A novel reannotation strategy for dissecting DNA methylation
patterns of human long intergenic non-coding RNAs in cancers.
Nucleic Acids Res. 42:8258–8270. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Camacho C, Coulouris G, Avagyan V, Ma N,
Papadopoulos J, Bealer K and Madden TL: BLAST+: Architecture and
applications. BMC Bioinformatics. 10:4212009. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Han L and Zhu J: Using matrix of
thresholding partial correlation coefficients to infer regulatory
network. Biosystems. 91:158–165. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Saeed AI, Sharov V, White J, Li J, Liang
W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, et
al: TM4: A free, open-source system for microarray data management
and analysis. Biotechniques. 34:374–378. 2003.PubMed/NCBI
|
|
20
|
Li K, Hüsing A, Fortner RT, Tjønneland A,
Hansen L, Dossus L, Chang-Claude J, Bergmann M, Steffen A, Bamia C,
et al: An epidemiologic risk prediction model for ovarian cancer in
Europe: The EPIC study. Br J Cancer. 112:1257–1265. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Li J, Huang H, Li Y, Li L, Hou W and You
Z: Decreased expression of long non-coding RNA GAS5 promotes cell
proliferation, migration and invasion, and indicates a poor
prognosis in ovarian cancer. Oncol Rep. 36:3241–3250.
2016.PubMed/NCBI
|
|
22
|
Li H, Liu C, Lu Z, Chen L, Wang J, Li Y
and Ma H: Upregulation of the long non-coding RNA SPRY4-IT1
indicates a poor prognosis and promotes tumorigenesis in ovarian
cancer. Biomed Pharmacother. 88:529–534. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Li J, Yu H, Xi M and Lu X: Long noncoding
RNA C17orf91 is a potential prognostic marker and functions as an
oncogene in ovarian cancer. J Ovarian Res. 9:492016. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Huang S, Qing C, Huang Z and Zhu Y: The
long non-coding RNA CCAT2 is up-regulated in ovarian cancer and
associated with poor prognosis. Diagn Pathol. 11:492016. View Article : Google Scholar : PubMed/NCBI
|
