1
|
DeSantis C, Ma J, Bryan L and Jemal A:
Breast cancer statistics, 2013. CA Cancer J Clin. 64:52–62. 2014.
View Article : Google Scholar : PubMed/NCBI
|
2
|
Jemal A, Siegel R, Xu J and Ward E: Cancer
statistics, 2010. CA Cancer J Clin. 60:277–300. 2010. View Article : Google Scholar : PubMed/NCBI
|
3
|
Weigelt B, Peterse JL and van't Veer LJ:
Breast cancer metastasis: Markers and models. Nat Rev Cancer.
5:591–602. 2005. View
Article : Google Scholar : PubMed/NCBI
|
4
|
Sleeman J and Steeg PS: Cancer metastasis
as a therapeutic target. Eur J Cancer. 46:1177–1180. 2010.
View Article : Google Scholar : PubMed/NCBI
|
5
|
Khan S, Shukla S, Sinha S, Lakra AD, Bora
HK and Meeran SM: Centchroman suppresses breast cancer metastasis
by reversing epithelial-mesenchymal transition via downregulation
of HER2/ERK1/2/MMP-9 signaling. Int J Biochem Cell Biol. 58:1–16.
2015. View Article : Google Scholar : PubMed/NCBI
|
6
|
Joyce JA and Pollard JW:
Microenvironmental regulation of metastasis. Nat Rev Cancer.
9:239–252. 2009. View
Article : Google Scholar : PubMed/NCBI
|
7
|
Chen DT, Hernandez JM, Shibata D, McCarthy
SM, Humphries LA, Clark W, Elahi A, Gruidl M, Coppola D and Yeatman
T: Complementary strand microRNAs mediate acquisition of metastatic
potential in colonic adenocarcinoma. J Gastrointest Surg.
16:905–913. 2012. View Article : Google Scholar : PubMed/NCBI
|
8
|
Kang DD, Sibille E, Kaminski N and Tseng
GC: MetaQC: Objective quality control and inclusion/exclusion
criteria for genomic meta-analysis. Nucleic Acids Res. 40:e152012.
View Article : Google Scholar : PubMed/NCBI
|
9
|
Qi C, Hong L, Cheng Z and Yin Q:
Identification of metastasis-associated genes in colorectal cancer
using metaDE and survival analysis. Oncol Lett. 11:568–574. 2016.
View Article : Google Scholar : PubMed/NCBI
|
10
|
Fan ZG, Wang KA and Lu BL: Feature
selection for fast image classification with support vector
machines. International Conference on Neural Information
Processing. Springer. 3316. 1026–1031. 2004. View Article : Google Scholar
|
11
|
Guyon I, Weston J, Barnhill S and Vapnik
V: Gene selection for cancer classification using support vector
machines. Machine learning. 46:pp389–422. 2002. View Article : Google Scholar
|
12
|
Järvinen AK, Hautaniemi S, Edgren H,
Auvinen P, Saarela J, Kallioniemi OP and Monni O: Are data from
different gene expression microarray platforms comparable?
Genomics. 83:1164–1168. 2004. View Article : Google Scholar : PubMed/NCBI
|
13
|
Smyth GK: Limma: Linear models for
microarray dataBioinformatics and computational biology solutions
using R and Bioconductor. Springer; pp. 397–420. 2005, View Article : Google Scholar
|
14
|
Wang X, Li J, Tseng GC and Wang MX:
Package ‘MetaDE’. 2012.
|
15
|
Chatr-Aryamontri A, Oughtred R, Boucher L,
Rust J, Chang C, Kolas NK, O'Donnell L, Oster S, Theesfeld C,
Sellam A, et al: The BioGRID interaction database: 2017 update.
Nucleic Acids Res. 45:D369–D379. 2017. View Article : Google Scholar : PubMed/NCBI
|
16
|
Keshava Prasad TS, Goel R, Kandasamy K,
Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R,
Shafreen B, Venugopal A, et al: Human protein reference
database-2009 update. Nucleic Acids Res. 37(Database Issue):
D767–D772. 2009. View Article : Google Scholar : PubMed/NCBI
|
17
|
Bader GD, Betel D and Hogue CW: BIND: The
biomolecular interaction network database. Nucleic Acids Res.
31:248–250. 2003. View Article : Google Scholar : PubMed/NCBI
|
18
|
Smoot ME, Ono K, Ruscheinski J, Wang PL
and Ideker T: Cytoscape 2.8: New features for data integration and
network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar : PubMed/NCBI
|
19
|
Chang KC, Wang Y, Bodine PV, Nagpal S and
Komm BS: Gene expression profiling studies of three SERMs and their
conjugated estrogen combinations in human breast cancer cells:
Insights into the unique antagonistic effects of bazedoxifene on
conjugated estrogens. J Steroid Biochem Mol Biol. 118:117–124.
2010. View Article : Google Scholar : PubMed/NCBI
|
20
|
Servant N, Gravier E, Gestraud P, Laurent
C, Paccard C, Biton A, Brito I, Mandel J, Asselain B, Barillot E
and Hupé P: EMA-A R package for easy microarray data analysis. BMC
Res Notes. 3:2772010. View Article : Google Scholar : PubMed/NCBI
|
21
|
Chuang HY, Lee E, Liu YT, Lee D and Ideker
T: Network-based classification of breast cancer metastasis. Mol
Syst Biol. 3:1402007. View Article : Google Scholar : PubMed/NCBI
|
22
|
Jonsson PF, Cavanna T, Zicha D and Bates
PA: Cluster analysis of networks generated through homology:
Automatic identification of important protein communities involved
in cancer metastasis. BMC Bioinformatics. 7:22006. View Article : Google Scholar : PubMed/NCBI
|
23
|
Sodek KL, Evangelou AI, Ignatchenko A,
Agochiya M, Brown TJ, Ringuette MJ, Jurisica I and Kislinger T:
Identification of pathways associated with invasive behavior by
ovarian cancer cells using multidimensional protein identification
technology (MudPIT). Mol Biosyst. 4:762–773. 2008. View Article : Google Scholar : PubMed/NCBI
|
24
|
Walsh LA, Alvarez MJ, Sabio EY, Reyngold
M, Makarov V, Mukherjee S, Lee KW, Desrichard A, Turcan Ş, Dalin
MG, et al: An integrated systems biology approach identifies TRIM25
as a key determinant of breast cancer metastasis. Cell Rep.
20:1623–1640. 2017. View Article : Google Scholar : PubMed/NCBI
|
25
|
Kourtellis N, Francisci Morales GD and
Bonchi F: Scalable online betweenness centrality in evolving
graphs. IEEE Transact Know Data Eng. 27:2494–2506. 2015. View Article : Google Scholar
|
26
|
Brandes U: On variants of shortest-path
betweenness centrality and their generic computation. Soc Net.
30:136–145. 2008. View Article : Google Scholar
|
27
|
Lee MH and Yang HY: Regulators of G1
cyclin-dependent kinases and cancers. Cancer Metastasis Rev.
22:435–449. 2003. View Article : Google Scholar : PubMed/NCBI
|
28
|
Kourea H, Koutras A, Scopa C, Marangos MN,
Tzoracoeleftherakis E, Koukouras D and Kalofonos HP: Expression of
the cell cycle regulatory proteins p34cdc2, p21waf1, and p53 in
node negative invasive ductal breast carcinoma. Mol Pathol.
56:328–335. 2003. View Article : Google Scholar : PubMed/NCBI
|
29
|
Kim S, Nakayama S, Miyoshi Y, Taguchi T,
Tamaki Y, Matsushima T, Torikoshi Y, Tanaka S, Yoshida T, Ishihara
H and Noguchi S: Determination of the specific activity of CDK1 and
CDK2 as a novel prognostic indicator for early breast cancer. Ann
Oncol. 19:68–72. 2008. View Article : Google Scholar : PubMed/NCBI
|
30
|
Roesley SNA, Suryadinata R, Morrish E, Tan
AR, Issa SM, Oakhill JS, Bernard O, Welch DR and Šarčević B:
Cyclin-dependent kinase-mediated phosphorylation of breast cancer
metastasis suppressor 1 (BRMS1) affects cell migration. Cell Cycle.
15:137–151. 2016. View Article : Google Scholar : PubMed/NCBI
|
31
|
Pandithage R, Lilischkis R, Harting K,
Wolf A, Jedamzik B, Lüscher-Firzlaff J, Vervoorts J, Lasonder E,
Kremmer E, Knöll B and Lüscher B: The regulation of SIRT2 function
by cyclin-dependent kinases affects cell motility. J Cell Biol.
180:915–929. 2008. View Article : Google Scholar : PubMed/NCBI
|
32
|
Weiss RH, Marshall D, Howard L, Corbacho
AM, Cheung AT and Sawai ET: Suppression of breast cancer growth and
angiogenesis by an antisense oligodeoxynucleotide to
p21(Waf1/Cip1). Cancer Lett. 189:39–48. 2003. View Article : Google Scholar : PubMed/NCBI
|
33
|
Ma H, Jin G, Hu Z, Zhai X, Chen W, Wang S,
Wang X, Qin J, Gao J, Liu J, et al: Variant genotypes of CDKN1A and
CDKN1B are associated with an increased risk of breast cancer in
Chinese women. Int J Cancer. 119:2173–2178. 2006. View Article : Google Scholar : PubMed/NCBI
|
34
|
Bianco S, Jangal M, Garneau D and Gévry N:
LRH-1 controls proliferation in breast tumor cells by regulating
CDKN1A gene expression. Oncogene. 34:4509–4518. 2015. View Article : Google Scholar : PubMed/NCBI
|
35
|
Nagata M, Kurita H, Uematsu K, Ogawa S,
Takahashi K, Hoshina H and Takagi R: Diagnostic value of
cyclin-dependent kinase/cyclin-dependent kinase inhibitor
expression ratios as biomarkers of locoregional and hematogenous
dissemination risks in oral squamous cell carcinoma. Mol Clin
Oncol. 3:1007–1013. 2015. View Article : Google Scholar : PubMed/NCBI
|
36
|
Matsumura I, Tanaka H and Kanakura Y: E2F1
and c-Myc in cell growth and death. Cell Cycle. 2:333–338. 2003.
View Article : Google Scholar : PubMed/NCBI
|
37
|
Vuaroqueaux V, Urban P, Labuhn M,
Delorenzi M, Wirapati P, Benz CC, Flury R, Dieterich H, Spyratos F,
Eppenberger U and Eppenberger-Castori S: Low E2F1 transcript levels
are a strong determinant of favorable breast cancer outcome. Breast
Cancer Res. 9:R332007. View Article : Google Scholar : PubMed/NCBI
|
38
|
Chen XZ, Cao ZY, Chen TS, Zhang YQ, Liu
ZZ, Su YT, Liao LM and Du J: Water extract of Hedyotis Diffusa
Willd suppresses proliferation of human HepG2 cells and potentiates
the anticancer efficacy of low-dose 5-fluorouracil by inhibiting
the CDK2-E2F1 pathway. Oncol Rep. 28:742–748. 2012. View Article : Google Scholar : PubMed/NCBI
|
39
|
Amundadottir LT, Johnson M, Merlino G,
Smith GH and Dickson RB: Synergistic interaction of transforming
growth factor alpha and c-myc in mouse mammary and salivary gland
tumorigenesis. Cell Growth Differ. 6:737–748. 1995.PubMed/NCBI
|
40
|
Singhi AD, Cimino-Mathews A, Jenkins RB,
Lan F, Fink SR, Nassar H, Vang R, Fetting JH, Hicks J, Sukumar S,
et al: MYC gene amplification is often acquired in lethal distant
breast cancer metastases of unamplified primary tumors. Mod Pathol.
25:378–387. 2012. View Article : Google Scholar : PubMed/NCBI
|
41
|
Liu H, Radisky DC, Yang D, Xu R, Radisky
ES, Bissell MJ and Bishop JM: MYC suppresses cancer metastasis by
direct transcriptional silencing of αv and β3 integrin subunits.
Nat Cell Biol. 14:567–574. 2012. View Article : Google Scholar : PubMed/NCBI
|