|
1
|
Ohira T and Iso H: Cardiovascular disease
epidemiology in Asia: An overview. Circ J. 77:1646–1652. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Sayols-Baixeras S, Lluís-Ganella C, Lucas
G and Elosua R: Pathogenesis of coronary artery disease: Focus on
genetic risk factors and identification of genetic variants. Appl
Clin Genet. 7:15–32. 2014.PubMed/NCBI
|
|
3
|
Torpy JM, Burke AE and Glass RM: Coronary
heart disease risk factors. JAMA. 302:23882009. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Hanson MA, Fareed MT, Argenio SL,
Agunwamba AO and Hanson TR: Coronary artery disease. Prim Care.
40:1–16. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Callaway JL, Shaffer LG, Chitty LS,
Rosenfeld JA and Crolla JA: The clinical utility of microarray
technologies applied to prenatal cytogenetics in the presence of a
normal conventional karyotype: A review of the literature. Prenat
Diagn. 33:1119–1133. 2013. View
Article : Google Scholar : PubMed/NCBI
|
|
6
|
Abulwerdi FA and Schneekloth JS Jr:
Microarray-based technologies for the discovery of selective,
RNA-binding molecules. Methods. 103:188–195. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Ren J, Zhang J, Xu N, Han G, Geng Q, Song
J, Li S, Zhao J and Chen H: Signature of circulating microRNAs as
potential biomarkers in vulnerable coronary artery disease. PLoS
One. 8:e807382013. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Sharma P, Garg G, Kumar A, Mohammad F,
Kumar SR, Tanwar VS, Sati S, Sharma A, Karthikeyan G, Brahmachari V
and Sengupta S: Genome wide DNA methylation profiling for
epigenetic alteration in coronary artery disease patients. Gene.
541:31–40. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Liu J, Jing L and Tu X: Weighted gene
co-expression network analysis identifies specific modules and hub
genes related to coronary artery disease. BMC Cardiovasc Disord.
16:542016. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Wang X, Kang DD, Shen K, Song C, Lu S,
Chang LC, Liao SG, Huo Z, Tang S, Ding Y, et al: An R package suite
for microarray meta-analysis in quality control, differentially
expressed gene analysis and pathway enrichment detection.
Bioinformatics. 28:2534–2536. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Cai H, Xu J, Han Y, Lu Z, Han T, Ding Y
and Ma L: Integrated miRNA-risk gene-pathway pair network analysis
provides prognostic biomarkers for gastric cancer. Onco Targets
Ther. 9:2975–2986. 2016.PubMed/NCBI
|
|
12
|
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
|
|
13
|
Sinnaeve PR, Donahue MP, Grass P, Seo D,
Vonderscher J, Chibout SD, Kraus WE, Sketch M Jr, Nelson C,
Ginsburg GS, et al: Gene expression patterns in peripheral blood
correlate with the extent of coronary artery disease. PLoS One.
4:e70372009. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Elashoff MR, Wingrove JA, Beineke P,
Daniels SE, Tingley WG, Rosenberg S, Voros S, Kraus WE, Ginsburg
GS, Schwartz RS, et al: Development of a blood-based gene
expression algorithm for assessment of obstructive coronary artery
disease in non-diabetic patients. BMC Med Genomics. 4:262011.
View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Beineke P, Fitch K, Tao H, Elashoff MR,
Rosenberg S, Kraus WE and Wingrove JA: PREDICT Investigators: A
whole blood gene expression-based signature for smoking status. BMC
Med Genomics. 5:582012. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
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
|
|
17
|
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
|
|
18
|
Langfelder P and Horvath S: WGCNA: An R
package for weighted correlation network analysis. BMC
Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Zhang B and Horvath S: A general framework
for weighted gene co-expression network analysis. Stat Appl Genet
Mol Biol. 4:Article17. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Zhai X, Xue Q, Liu Q, Guo Y and Chen Z:
Colon cancer recurrence-associated genes revealed by WGCNA
co-expression network analysis. Mol Med Rep. 16:6499–6505. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Vilne B, Skogsberg J, Asl Foroughi H,
Talukdar HA, Kessler T, Björkegren JLM and Schunkert H: Network
analysis reveals a causal role of mitochondrial gene activity in
atherosclerotic lesion formation. Atherosclerosis. 267:39–48. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Min S, Sun T, He Z and Xiong B:
Identification of two novel biomarkers of rectal carcinoma
progression and prognosis via co-expression network analysis.
Oncotarget. 8:69594–69609. 2017.PubMed/NCBI
|
|
23
|
Yip AM and Horvath S: Gene network
interconnectedness and the generalized topological overlap measure.
BMC Bioinformatics. 8:222007. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Hui L, Zhang J, Ding X, Guo X and Jang X:
Identification of potentially critical differentially methylated
genes in nasopharyngeal carcinoma: A comprehensive analysis of
methylation profiling and gene expression profiling. Oncol Lett.
14:7171–7178. 2017.PubMed/NCBI
|
|
25
|
Chatraryamontri A, Breitkreutz BJ,
Oughtred R, Boucher L, Heinicke S, Chen D, Stark C, Breitkreutz A,
Kolas N, O'Donnell L, et al: The BioGRID interaction database: 2015
update. Nucleic Acids Res. 43:(Database Issue). D470–D478. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Prasad Keshava 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
|
|
27
|
Szklarczyk D, Franceschini A, Kuhn M,
Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork
P, et al: The STRING database in 2011: Functional interaction
networks of proteins, globally integrated and scored. Nucleic Acids
Res. 39:(Database Issue). D561–D568. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Lopes CT, Franz M, Kazi F, Donaldson SL,
Morris Q and Bader GD: Cytoscape Web: An interactive web-based
network browser. Bioinformatics. 26:2347–2348. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Gene Ontology Consortium: Gene ontology
consortium: Going forward. Nucleic Acids Res. 43:(Database Issue).
D1049–D1056. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Kanehisa M, Sato Y, Kawashima M, Furumichi
M and Tanabe M: KEGG as a reference resource for gene and protein
annotation. Nucleic Acids Res. 44(D1): D457–D462. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
da Huang W, Sherman BT and Lempicki RA:
Systematic and integrative analysis of large gene lists using DAVID
bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Hanson MA, Fareed MT, Argenio SL,
Agunwamba AO and Hanson TR: Coronary artery disease. Prim Care.
40:1–16. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Patil DP and Kundu GC: LCK
(lymphocyte-specific protein tyrosine kinase). Atlas Genet
Cytogenet Oncol Haematol. 9:229–230. 2005.
|
|
34
|
Pryshchep S, Goronzy JJ, Parashar S and
Weyand CM: Insufficient deactivation of the protein tyrosine kinase
lck amplifies T-cell responsiveness in acute coronary syndrome.
Circ Res. 106:769–778. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Pelosi M, Di Bartolo V, Mounier V, Mège D,
Pascussi JM, Dufour E, Blondel A and Acuto O: Tyrosine 319 in the
interdomain B of ZAP-70 is a binding site for the Src homology 2
domain of Lck. J Biol Chem. 274:14229–14237. 1999. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Staudt LM, Rosenwald A, Wilson W, Barry TS
and Wiestner A: ZAP-70 expression as a marker for chronic
lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL). US
Patent 7,981,610 B2. Filed December 12, 2007; issued July 19.
2011.
|
|
37
|
Flego D, Liuzzo G, Weyand CM and Crea F:
Adaptive immunity dysregulation in acute coronary syndromes: From
cellular and molecular basis to clinical implications. J Am Coll
Cardiol. 68:2107–2117. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Li H, Zuo X, Ouyang P, Lin M, Zhao Z,
Liang Y, Zhong S and Rao S: Identifying functional modules for
coronary artery disease by a prior knowledge-based approach. Gene.
537:260–268. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Tachibana M, Sugimoto K, Fukushima T and
Shinkai Y: Set domain-containing protein, G9a, is a novel
lysine-preferring mammalian histone methyltransferase with
hyperactivity nd specific selectivity to lysines 9 and 27 of
histone H3. J Biol Chem. 276:25309–25317. 2001. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Zhang QJ and Liu ZP: Histone methylations
in heart development, congenital and adult heart diseases.
Epigenomics. 7:321–330. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Papait R, Serio S, Pagiatakis C, Rusconi
F, Carullo P, Mazzola M, Salvarani N, Miragoli M and Condorelli G:
Histone methyltransferase G9a is required for cardiomyocyte
homeostasis and hypertrophy. Circulation. 136:1233–1246. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Thienpont B, Aronsen JM, Robinson EL,
Okkenhaug H, Loche E, Ferrini A, Brien P, Alkass K, Tomasso A,
Agrawal A, et al: The H3K9 dimethyltransferases EHMT1/2 protect
against pathological cardiac hypertrophy. J Clin Invest.
127:335–348. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Bremer S, Rootwelt H and Bergan S:
Real-time PCR determination of IMPDH1 and IMPDH2 expression in
blood cells. Clin Chem. 53:1023–1029. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Xu Y, Zheng Z, Gao Y, Duan S, Chen C, Rong
J, Wang K, Yun M, Weng H, Ye S and Zhang J: High expression of
IMPDH2 is associated with aggressive features and poor prognosis of
primary nasopharyngeal carcinoma. Sci Rep. 7:7452017. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Zhou L, Xia D, Zhu J, Chen Y, Chen G, Mo
R, Zeng Y, Dai Q, He H, Liang Y, et al: Enhanced expression of
IMPDH2 promotes metastasis and advanced tumor progression in
patients with prostate cancer. Clin Transl Oncol. 16:906–913. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Gingras AC, Caballero M, Zarske M, Sanchez
A, Hazbun TR, Fields S, Sonenberg N, Hafen E, Raught B and
Aebersold R: A novel, evolutionarily conserved protein phosphatase
complex involved in cisplatin sensitivity. Mol Cell Proteomics.
4:1725–1740. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Eleftheriadou O, Longman MR, Boguslavskyi
A, Ryan A, Wadzinski BE, Shattock MJ and Snabaitis AK: Expression
of type 2a protein phosphatases in cardiac health and disease.
Heart. 100:A162014. View Article : Google Scholar
|
|
48
|
Eleftheriadou O, Boguslavskyi A, Longman
MR, Cowan J, Francois A, Heads RJ, Wadzinski BE, Ryan A, Shattock
MJ and Snabaitis AK: Expression and regulation of type 2A protein
phosphatases and alpha4 signalling in cardiac health and
hypertrophy. Basic Res Cardiol. 112:372017. View Article : Google Scholar : PubMed/NCBI
|
