|
1
|
Germing U, Kobbe G, Haas R and Gattermann
N: Myelodysplastic syndromes: Diagnosis, prognosis, and treatment.
Dtsch Arztebl Int. 110:783–790. 2013.PubMed/NCBI
|
|
2
|
Lindsley RC and Ebert BL: Molecular
pathophysiology of myelodysplastic syndromes. Annu Rev Pathol.
8:21–47. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Issa JP: The myelodysplastic syndrome as a
prototypical epigenetic disease. Blood. 121:3811–3817. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Glenthøj A, Ørskov AD, Hansen JW, Hadrup
SR, O'Connell C and Grønbæk K: Immune mechanisms in myelodysplastic
syndrome. Int J Mol Sci. 17:E9442016. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Varney ME, Melgar K, Niederkorn M, Smith
M, Barreyro L and Starczynowski DT: Deconstructing innate immune
signaling in myelodysplastic syndromes. Exp Hematol. 43:587–598.
2015. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Gañán-Gómez I, Wei Y, Starczynowski DT,
Colla S, Yang H, Cabrero-Calvo M, Bohannan ZS, Verma A, Steidl U
and Garcia-Manero G: Deregulation of innate immune and inflammatory
signaling in myelodysplastic syndromes. Leukemia. 29:1458–1469.
2015. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Jiang Y, Dunbar A, Gondek LP, Mohan S,
Rataul M, O'Keefe C, Sekeres M, Saunthararajah Y and Maciejewski
JP: Aberrant DNA methylation is a dominant mechanism in MDS
progression to AML. Blood. 113:1315–1325. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Zhao X, Yang F, Li S, Liu M, Ying S, Jia X
and Wang X: CpG island methylator phenotype of myelodysplastic
syndrome identified through genome-wide profiling of DNA
methylation and gene expression. Br J Haematol. 165:649–658. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Zhao G, Li N, Li S, Wu W, Wang X and Gu J:
The high methylation of 4-aminobutyrate aminotransferase gene
predicts a poor prognosis in patients with myelodysplastic
syndrome. Int J Oncol. 54:491–504. 2019.PubMed/NCBI
|
|
10
|
Medina-Kauwe LK, Tobin AJ, De Meirleir L,
Jaeken J, Jakobs C, Nyhan WL and Gibson KM: 4-Aminobutyrate
aminotransferase (GABA-transaminase) deficiency. J Inherit Metab
Dis. 22:414–427. 1999. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Jaeken J, Casaer P, de Cock P, Corbeel L,
Eeckels R, Eggermont E, Schechter PJ and Brucher JM:
Gamma-aminobutyric acid-transaminase deficiency: A newly recognized
inborn error of neurotransmitter metabolism. Neuropediatrics.
15:165–169. 1084. View Article : Google Scholar
|
|
12
|
Wegerer M, Adena S, Pfennig A, Czamara D,
Sailer U, Bettecken T, Müller-Myhsok B, Modell S and Ising M:
Variants within the GABA transaminase (ABAT) gene region are
associated with somatosensory evoked EEG potentials in families at
high risk for affective disorders. Psychol Med. 43:1207–1217. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Maguire SE, Rhoades S, Chen WF, Sengupta
A, Yue Z, Lim JC, Mitchell CH, Weljie AM and Sehgal A: Independent
effects of γ-Aminobutyric acid transaminase (GABAT) on metabolic
and sleep homeostasis. J Biol Chem. 290:20407–20416. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Barnby G, Abbott A, Sykes N, Morris A,
Weeks DE, Mott R, Lamb J, Bailey AJ and Monaco AP; International
Molecular Genetics Study of Autism Consortium, : Candidate-gene
screening and association analysis at the autism-susceptibility
locus on chromosome 16p: Evidence of association at GRIN2A and
ABAT. Am J Hum Genet. 76:950–966. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Jirholt J, Asling B, Hammond P, Davidson
G, Knutsson M, Walentinsson A, Jensen JM, Lehmann A, Agreus L and
Lagerström-Fermer M: 4-Aminobutyrate aminotransferase (ABAT):
Genetic and pharmacological evidence for an involvement in gastro
esophageal reflux disease. PLos One. 6:E190952011. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Budczies J, Brockmöller SF, Müller BM,
Barupal DK, Richter-Ehrenstein C, Kleine-Tebbe A, Griffin JL,
Orešič M, Dietel M, Denkert C and Fiehn O: Comparative metabolomics
of estrogen receptor positive and estrogen receptor negative breast
cancer: Alterations in glutamine and beta-alanine metabolism. J
Proteomics. 94:279–288. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Jansen MP, Sas L, Sieuwerts AM, Van
Cauwenberghe C, Ramirez-Ardila D, Look M, Ruigrok-Ritstier K,
Finetti P, Bertucci F, Timmermans MM, et al: Decreased expression
of ABAT and STC2 hallmarks ER-positive inflammatory breast cancer
and endocrine therapy resistance in advanced disease. Mol Oncol.
9:1218–1233. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Ma L, Bajic VB and Zhang Z: On the
classification of long non-coding RNAs. RNA Biol. 10:925–933. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Kapranov P, Cheng J, Dike S, Nix DA,
Duttagupta R, Willingham AT, Stadler PF, Hertel J, Hackermuller J,
Hofacker IL, et al: RNA maps reveal new RNA classes and a possible
function for pervasive transcription. Science. 316:1484–1488. 2007.
View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Rodríguez-Malavé NI and Rao DS: Long
noncoding RNAs in hematopoietic malignancies. Brief Funct Genomics.
15:227–238. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Benetatos L, Hatzimichael E, Dasoula A,
Dranitsaris G, Tsiara S, Syrrou M, Georgiou I and Bourantas KL: CpG
methylation analysis of the MEG3 and SNRPN imprinted genes in acute
myeloid leukemia and myelodysplastic syndromes. Leuk Res.
34:148–153. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Vardiman JW, Thiele J, Arber DA, Brunning
RD, Borowitz MJ, Porwit A, Harris NL, Le Beau MM,
Hellstrom-Lindberg E, Tefferi A and Bloomfield CD: The 2008
revision of the World Health Organization (WHO) classification of
myeloid neoplasms and acute leukemia: Rationale and important
changes. Blood. 114:937–951. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Livak KJ and Schmittgen TD: Analysis of
relative gene expression data using real-time quantitative PCR and
the 2(-Delta Delta C (T)) method. Methods. 25:402–408. 2001.
View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Irizarry RA, Hobbs B, Collin F,
Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP:
Exploration, normalization, and summaries of high density
oligonucleotide array probe level data. Biostatistics. 4:249–264.
2003. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Liao Q, Liu C, Yuan X, Kang S, Miao R,
Xiao H, Zhao G, Luo H, Bu D, Zhao H, et al: Large-scale prediction
of long non-coding RNA functions in a coding-non-coding gene
co-expression network. Nucleic Acids Res. 39:3864–3878. 2011.
View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Huang Da W, Sherman BT and Lempicki RA:
Systematic and integrative analysis of large gene lists using DAVID
bioinformatics resources. Nat Protoc. 4:44–57. 2008. View Article : Google Scholar
|
|
27
|
Huang Da W, Sherman BT and Lempicki RA:
Bioinformatics enrichment tools: Paths toward the comprehensive
functional analysis of large gene lists. Nucleic Acids Res.
37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Luo W and Brouwer C: Pathview: An
R/Bioconductor package for pathway-based data integration and
visualization. Bioinformatics. 29:1830–1831. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Lin MF, Jungreis I and Kellis M: PhyloCSF:
A comparative genomics method to distinguish protein coding and
non-coding regions. Bioinformatics. 27:i275–i282. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Wingender E, Chen X, Hehl R, Karas H,
Liebich I, Matys V, Meinhardt T, Prüss M, Reuter I and Schacherer
F: TRANSFAC: An integrated system for gene expression regulation.
Nucleic Acids Res. 28:316–319. 2000. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Geiling B, Vandal G, Posner AR, de Bruyns
A, Dutchak KL, Garnett S and Dankort D: A modular lentiviral and
retroviral construction system to rapidly generate vectors for gene
expression and gene knockdown in vitro and in vivo. PLos One.
8:e762792013. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Will S and Jabbari H: Sparse RNA folding
revisited: Space-efficient minimum free energy structure
prediction. Algorithms Mol Biol. 11:72016. View Article : Google Scholar : PubMed/NCBI
|
