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Translational Oncogenomics 2008:3 137–149 137
ORIGINAL RESEARCH
Correspondence: Merav Bar, M.D., Fred Hutchinson Cancer Research
Center, 1100 Fairview Ave. N, D1-100, Seattle WA. U.S.A. 98109.
Tel: (206)667-4545; Email: [email protected]
Copyright in this article, its metadata, and any supplementary
data is held by its author or authors. It is published under the
Creative Commons Attribution By licence. For further information go
to: http://creativecommons.org/licenses/by/3.0/.
Gene Expression Patterns in Myelodyplasia Underline the Role of
Apoptosis and Differentiation in Disease Initiation and
ProgressionMerav Bar1, Derek Stirewalt1, Era Pogosova-Agadjanyan1,
Vitas Wagner1, Ted Gooley1, Nissa Abbasi1, Ravi Bhatia2, H. Joachim
Deeg1 and Jerald Radich11Clinical Research Division, Fred
Hutchinson Cancer Research Center, Seattle, Washington. 2City of
Hope National Medical Center, Duarte, California.
Abstract: The myelodysplastic syndromes (MDS) are clonal stem
cell disorders, characterized by ineffective and dysplastic
hematopoiesis. The genetic and epigenetic pathways that determine
disease stage and progression are largely unknown. In the current
study we used gene expression microarray methodology to examine the
gene expression differences between normal hematopoietic cells and
hematopoietic cells from patients with MDS at different disease
stages, using both unselected and CD34+ selected cells. Signifi
cant differences between normal and MDS hematopoietic cells were
observed for several genes and pathways. Several genes promoting or
opposing apoptosis were dysregulated in MDS cases, most notably
MCL1 and EPOR. Progression from RA to RAEB(T) was associated with
increased expression of several histone genes. In addi-tion, the
RAR-RXR pathway, critical for maintaining a balance between
self-renewal and differentiation of hematopoietic stem cells, was
found to be deregulated in hematopoietic cells from patients with
advanced MDS compared to patients with refractory anemia. These fi
ndings provide new insights into the understanding of the
pathophysiology and progression of MDS, and may guide to new
targets for therapy. Taken together with previous published data,
the present results also underscore the considerable complexity of
the regulation of gene expression in MDS.
Keywords: myelodysplasia, gene expression, apoptosis,
differentiation
IntroductionThe myelodysplastic syndromes (MDS) are a
heterogeneous group of clonal disorders of the hemato-poietic stem
cells [1, 2]. The natural history of MDS is that of progressive
cytopenia with increasing transfusion needs, infectious and
bleeding complications or alternatively, evolution to secondary AML
[1, 2]. While relatively simple clinical and pathologic scoring
systems with prognostic relevance have been developed, the
molecular mechanisms involved in evolution of the disease are
largely unknown [3, 4]. Identifying molecular markers of MDS may
allow for a more accurate assessment of the prognosis and
potentially identify new targets for therapy.
The genetic lesions so far identifi ed in MDS incompletely
describe the biology and heterogeneity of the disease. Clonal
karyotypic abnormalities are observed in approximately 40–50% of
patients with primary MDS, and 90% of therapy-related MDS [2, 5].
Mutations important in denovo AML, for example mutations in RAS
proto-oncogenes and FLT3 internal tandem duplications (ITD), have
been described in 5%–20% of MDS patients and are variably
associated with disease progression [3, 6–9]. Beside overt genetic
lesions, epigenetic lesions may also play a roll in the development
of MDS. Hyper-methylation has been described in many malignancies
including MDS and may be associated with disease development,
progression and prognosis [10–14]. For example, p15 promoter
hypermethylation has been shown to be associated with MDS
progression to AML in some studies [3, 15]. However, none of these
alterations are specifi c for MDS and the underlying molecular
causes of MDS have remained poorly understood.
The vast number of genetic and epigenetic disturbances in MDS
makes investigations to identify potential common pathways that may
involved in disease development and progression challenging.
Oligonucleotide microarrays have been found to be an excellent tool
to study biology and identify potential prognostic factors in many
forms of malignancies, including MDS. This platform allows
http://creativecommons.org/licenses/by/3.0/http://creativecommons.org/licenses/by/3.0/
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Bar et al
Translational Oncogenomics 2008:3
examination of thousands of genes using a single sample. In the
current study we used oligonucle-otide microarrays to determine how
the genetic expression profi le differs between healthy
hema-topoietic cells and hematopoietic cells from patients with
MDS, and to identify genes and pathways that might be relevant for
MDS evolution and progression.
Materials and Methods
PatientsMononuclear cells (N = 35) or purifi ed CD34+ cells (N =
8) from the marrow of 43 MDS patients were studied. Patients were
sub-grouped according to the French-American-British (FAB) classifi
ca-tion [16] into refractory anemia (RA, N = 18), refractory anemia
with ringed sideroblasts (RARS, N = 11), refractory anemia with
excess blasts (RAEB, N = 8), and refractory anemia with excess
blasts in transformation (RAEB-t, N = 1). In addi-tion, we included
one patient with unclassifi ed MDS, one patient with a mixed
MDS/myelopro-liferative picture, and three patients with AML that
had evolved from MDS. Three patients had 5q deletion, however not
as an isolated lesion, but as part of complex cytogenetic
abnormalities. Mono-nuclear (N = 10) or purifi ed CD34+ bone marrow
cells (N = 14) from 24 healthy subjects were used as controls.
Samples from fi ve normal bone mar-rows, four patients with low
grade MDS (RARS or RA), and seven patents with high grade MDS
(RAEB-1 or RAEB-2) not used in the microarray studies were used for
PCR validation studies of MCL1 expression. Some of the patients had
received treatment in the past, including chemo-therapy,
erythropoietin or thalidomide, but no treatment was given within 4
weeks of sample acquisition. All patients and healthy donors had
given informed consent according to the require-ments of the
Institutional Review Board.
Sample preparationHeparinized bone marrow samples were obtained
by aspiration from the posterior iliac crest. Mono-nuclear cells
were separated by density gradient centrifugation through
Ficoll-Hypaque. CD34+ cells were purifi ed by two rounds of
high-gradient magnetic cell separation using autoMACS (Milt-enyi
Biotec Inc, Auburn, CA) with superparamagnetic
microbead labeling of CD34+ cells. Total RNA was extracted using
TRIzol (Invitrogen, Carlsbad, CA.) according to the manufacturer’s
protocol. All RNA samples were analyzed on an HP 2100 bio-analizer
(Aglient Technologyies, Palo Alto, CA USA) to ensure the integrity
of total RNA prior to use in microarray assays [17].
Oligonucleotide microarray gene expressionRNA obtained from
mononuclear cells was pre-pared according to the standard
Affymetrix proto-col (GeneChip Expression Analyses Technical Manual
(http;
//www.affymetrix.com/support/tech-nical/manual/expression_manual.affx).
For CD34+ cells, RNA was prepared using a single stranded linear
amplifi cation protocol (SLAP) prior to RNA labeling and
hybridization [17]. Fragmented, bio-tinylated cDNA was hybridized
to an Affymetrix HG-U133 microarray according to the
manufac-turer’s protocol.
Data analysisDAT fi les for individual samples were generated
using Affymetrix MAS 5.0 software. Target signals for probe sets
were scaled to 500 for analyses. The detection algorithm was based
upon default set-tings per Affymetrix recommendations
(https://www.affymetrix.com/support/downloads/manu-als/data_analysis_fundamentals_manual.pdf).
Signals were transformed into log2 intensity. Normalization was
performed using the R 2.2.1 software [18]. The expression data was
then ana-lyzed using SAM 2.2.1 (Signifi cance Analysis of
Microarrays (SAM), Stanford, CA)[19] or Gene-Plus 1.2
(http://www.enodar.com/technology6.htm) for specifi c gene
expression analysis and Gene Set Enrichment Analysis software (GSEA
v1.0, Broad Institute, MIT, Cambridge, MA) for pathway analysis
[20]. We selected a false discov-ery rate (FDR) of 5% to determine
statistically signifi cant up or down regulated genes in SAM [21].
Hierarchical clustering was performed using the dChip software
available at http://biosun1.harvard.edu/complab/dchip/.
For the analysis of MCL1 levels and transcript ratio in
different MDS disease stages, a global p-value was derived using
linear regression and tested in the null hypothesis that the mean
tran-script ratios were the same across normal, RA, and
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139
Gene expression profi les of CD34+ and unselected mononuclear
cells in myelodysplasia
Translational Oncogenomics 2008:3
advanced phase MDS. The signifi cant test for trend used linear
regression of the mean of the transcript across each group, where
each group was assigned values 1, 2, and 3 for normal, low, and
advanced MDS, respectively.
PCR validation of MCL1To validate the gene expression of MCL1 in
normal and MDS hematopoesis, we developed quantitative RT-PCR
assays. Samples from fi ve normal bone marrows, four patients with
low grade MDS (RARS or RA), and seven patents with high grade MDS
(RAEB-1 or RAEB-2) not used in the micro-array studies were
analyzed. For MCL1, the two alternative splice variants were
amplifi ed sepa-rately, producing a full-length transcript (T×1)
associated with anti-apoptosis, and the alterna-tively spliced,
smaller transcript (T×2) that is pro-apoptotic. Quantitative PCR
validation for MCL1 splice variants was performed on an ABI 7900 HT
Fast Real-Time PCR System. Thermocycler condi-tions were set at: 50
°C for 2 min, 95 °C for 10 min, and 40 cycles at 95 °C for 15 sec
and 60 °C for 1 min. A common primer sequence was used for the
forward MCL1 primer (E×1): 5 ’ -GAAGGCGCTGGAGACCTTAC-3’ ) .
MCL1-T×1 (MCL1-T×1R) and MCL1-T×2 (MCL1-T×1R) reverse primer
sequences were 5’-TTTCCGAAGCATGCCTTGG-3’ and
5’-ACTCCACAAACCCATCCTTGG-3’, respec-tively. All probes were
FAM-TAMRA; the MCL-1 probe sequence was
5’-ATGGCGTGCAGCG-CAACCAC-3’. Controls genes were obtained from
cloned 2.1 TOPO vector plasmids. The size of the MCL1-T×1 and
MCL1-T×2 inserts was 517 bp, and 269 bp respectively.
ResultsTo explore the changes in gene expression that occur with
the evolution of MDS from normal hematopoietic progenitor cells and
during disease progression from RA to RAEB and transformation into
secondary AML, we used two general strate-gies. First, we identifi
ed expression changes com-mon to all MDS cases compared to normal
bone marrow, and secondly, we examined gene expres-sion in MDS
cases that correlated with disease subtypes. Since it is not clear
to what extent the biology of MDS is determined only by the
malig-nant “stem cell” and what the entire cellular envi-ronment
contributes, separate analyses were
performed in unselected and CD34+ selected populations of both
MDS and normal hematopoi-etic cells.
Gene expression in MDS compared to normal bone marrowWe fi rst
compared unselected marrow mononuclear cells from 35 patients with
MDS and 10 healthy donors. Unsupervised hierarchical clustering
showed complete segregation between normal bone marrow and MDS
marrow (Fig. 1A). How-ever, there was no segregation between the
differ-ent morphologic subtypes of MDS (Fig. 1A), underscoring the
diffi culty of describing MDS biology by morphology alone.
In comparing all MDS cases to normal bone marrow, 516 genes were
up-regulated and 2107 were down-regulated in the MDS samples
(Supple-mentary Table 1). The top up-regulated genes in MDS
included: the HSPA1A (heat shock 70 kDa protein), CEACAM6
(carcinoembryonic antigen-related cell adhesion), DEFA1/4 (defensin
alpha 1 and 4), GFI1 (growth factor independent 1) and TCN1
(transcobalamin 1). Among the top down-regulated genes were CREM (a
cAMP responsive element modulator), SC5D (sterol-C5-desaturase
(ERG3 delta-5-desaturase homolog, fungal)-like), PIK3R1
(phosphoinositide-3-kinase, regulatory subunit 1 (p85 alpha) and
IRF4 (interferon regula-tory factor 4). GSEA analysis was then used
to discover annotated biological pathways over-represented by genes
that were up or down-regulated in MDS compared to normal bone
marrow. The pathways most signifi cantly enriched in abnormally
regulated genes in MDS were ves-icle transport, glycogen
metabolism, chaperone modulated interferon signaling, and the
pentose phosphate pathway.
We next compared gene expression in CD34+ selected cells from
MDS and normal bone marrow to examine potential changes occurring
primarily in the putative MDS “stem cell”. We identifi ed 704 genes
that were up-regulated and 826 genes that were down-regulated in
the CD34+ cells from MDS patients compared to CD34+ cells from
healthy donors (Supplementary Table 2). In com-paring the genes
that differed signifi cantly between unsorted samples and purifi ed
CD34+ cells, we observed an overlap of 12 genes that were
consis-tently up-regulated in both the unselected and CD34+
selected cells, and 95 genes that were
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140
Bar et al
Translational Oncogenomics 2008:3
Tabl
e 1.
Top
gen
es d
iffer
entia
lly e
xpre
ssed
in M
DS
com
pare
d to
nor
mal
bon
e m
arro
w.
Gen
e sy
mbo
leD
escr
iptio
nC
D34
+ ce
lls:
Mon
onuc
lear
cel
ls:
SAM
sc
ore1
Fold
ch
ange
q-va
lue(
%)2
SAM
sc
ore
Fold
ch
ange
q-va
lue(
%)
Up
regu
late
d ge
nes:
MA
XM
AX
pro
tein
5.56
1.67
0.00
2.84
3.28
2.61
PM
Lpr
omye
locy
tic le
ukem
ia4.
652.
670.
213.
614.
391.
52P
NU
TL1
sept
in 5
3.57
4.22
1.63
3.94
3.35
0.75
SH
AP
Yca
lciu
m a
ctiv
ated
nuc
leot
idas
e3.
185.
591.
954.
463.
050.
21M
HC
2TA
MH
C c
lass
II tr
ansa
ctiv
ator
2.90
4.03
2.21
3.77
3.09
1.14
ALD
H3B
1al
dehy
de d
ehyd
roge
nase
3 fa
mily
, m
embe
r B1
2.67
2.15
3.03
4.63
1.85
0.21
CC
L18
chem
okin
e (C
-C m
otif)
liga
nd 1
8 (p
ulm
onar
y an
d ac
tivat
ion-
regu
late
d)2.
6212
.50
3.03
3.87
5.61
0.94
SPA
G8
sper
m a
ssoc
iate
d an
tigen
82.
532.
663.
374.
412.
000.
21Q
PR
Tqu
inol
inat
e ph
osph
orib
osyl
trans
fera
se
(nic
otin
ate-
nucl
eotid
e py
roph
osph
oryl
ase
(car
boxy
latin
g))
2.46
3.59
3.37
3.32
3.01
3.11
LAIR
1le
ukoc
yte-
asso
ciat
ed Ig
-like
rece
ptor
12.
211.
844.
764.
161.
630.
34D
own
regu
late
d ge
nes:
VIP
R1
vaso
activ
e in
test
inal
pep
tide
rece
ptor
1−4
.97
0.18
0.00
−2.6
60.
693.
11S
LC2A
14so
lute
car
rier f
amily
2 (f
acili
tate
d gl
ucos
e tra
nspo
rter)
, mem
ber 1
4−4
.12
0.27
0.00
−2.7
10.
653.
11
MC
L1m
yelo
id c
ell l
euke
mia
seq
uenc
e 1
(BC
L2-r
elat
ed)
−4.0
30.
210.
00−3
.32
0.61
1.14
TNFA
IP3
tum
or n
ecro
sis
fact
or, a
lpha
-indu
ced
prot
ein
3−3
.78
0.18
1.31
−4.5
00.
440.
32
DTN
Ady
stro
brev
in, a
lpha
−3.7
70.
241.
31−2
.47
0.78
4.63
NE
LL2
NE
L-lik
e 2
(chi
cken
) ///
NE
L-lik
e 2
(chi
cken
)−3
.72
0.33
1.31
−3.1
70.
631.
43
EP
OR
eryt
hrop
oiet
in re
cept
or−3
.71
0.35
1.31
−3.1
90.
671.
43K
RTH
B6
kera
tin, h
air,
basi
c, 6
(mon
ileth
rix)
−3.6
60.
371.
31−3
.40
0.79
0.94
CX
CR
4ch
emok
ine
(C-X
-C m
otif)
rece
ptor
4−3
.61
0.31
1.31
−3.8
00.
560.
56ZN
F297
Bzi
nc fi
nger
pro
tein
297
B−3
.56
0.31
1.31
−3.8
10.
500.
561 S
core
—Th
e T—
stat
istic
Val
ue.
2 q-v
alue
—Th
e lo
wes
t fal
se d
isco
very
rate
at w
hich
the
gene
is c
alle
d si
gnifi
cant
(Lik
e th
e “p
val
ue” a
dapt
ed to
ana
lysi
s of
a la
rge
num
ber o
f gen
es).
-
141
Gene expression profi les of CD34+ and unselected mononuclear
cells in myelodysplasia
Translational Oncogenomics 2008:3
consistently down-regulated in both the unselected and CD34+
selected cells (Table 1, full list in Supplementary Table 3). These
genes are likely to be highly relevant as markers of biological
activity, as well as targets for diagnostic and therapeutic tools.
The top overlap genes and their expression in all samples are shown
in Figure 1B, 1C and Figure 2. The overlap genes fall into several
rel-evant biological categories. Interestingly, many genes were
deregulated in favor of increased apop-tosis: decreased expression
in the anti-apoptotic regulator MCL1, the erythropoetin receptor
EPOR, and TNF anti-apoptotic modulator TNFAIP3, and an increased
expression in Ca+2 activated nucleo-tidase CANT1, and the
inhibitory receptor LAIR1. There was no association of EPOR level
and past use of erythropoetin. Dysregulated immune func-tion and
cytokine expression have been implicated
in MDS, and in this analysis are highlighted by the increased
expression of the CD4/CD8 cytokine CCL18, the decreased expression
of the “master” control gene for class II MHC expression CIITA, and
the decreased expression of CXCR4, the recep-tor for stroma derived
factor 1.
Gene expression in RA compared to advanced MDSThe signals that
lead to progression from RA to more advanced disease are poorly
understood. We compared unselected mononuclear cells from 16
patients with RA to unselected mononuclear cells from 11 patients
with advanced MDS, again using normal bone marrow as reference.
Several genes were expressed differently in normal bone marrow, RA
and advanced disease (Table 2, Supplementary
NB
M 1
NB
M 3
NB
M 7
NB
M 4
NB
M 9
NB
M 2
NB
M 5
NB
M 6
NB
M 8
NB
M 1
0R
AEB
2R
A 7
RA
11R
A 3
RA
4R
AEB
6R
AEB
7R
AEB
1R
AEB
TR
A 2
AM
L 2
RA
12R
A 10
RA
RS
3R
AR
S 4
RA
EB 5
MD
Sun
clas
sifie
dR
A 15
RA
1R
A 8
RA
5A
ML
3R
A 14
RA
RS
5R
A 13
RA
EB 4
RA
RS
2R
AR
S 6
RA
16R
AR
S 7
RA
6R
A 9
RA
EB 3
RA
RS
1A
ML
1
NBM MDS
d Genes
ea b c d f Genesg a b c ed Genes
A
CB
Figure 1. A. Hierarchical clustering of 1,000 differentially
expressed genes in normal marrow and marrow from MDS patients. Each
row represents a single probe set and each column a separated
normal or MDS marrow sample. Blue coloring represents
down-regulated genes in MDS compared to normal, while red
represents up-regulated genes in MDS. Unsupervised hierarchical
clustering showed complete segregation between normal bone marrow
(n = 10) and MDS (n = 35), but no segregation between the different
stages of MDS.B, C. Heat map of up- and down-regulated genes in
unselected mononuclear and CD34+ selected cells from MDS marrow and
normal marrow samples. B) Comparison of gene expression in
unselected mononuclear cells in MDS and normal bone marrow:
“a”—nor-mal bone marrow samples; “b”—RA samples; “c”—RARS;
“d”—RAEB; “e”—RAEB-t; “f”—unclassifi ed MDS; and “g”—AML cases
arising from antecedent MDS. C) Comparison of gene expression in
CD34+ selected cells: “a”—normal marrow; “b”—RA; “c”—RARS;
“d”—RAEB; and “e”—a case of MDS/MPS (a mixed MDS/myeloproliferative
picture).
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Bar et al
Translational Oncogenomics 2008:3
Table 4). Among these genes are MAX (MYC associated factor X),
HIST2H2BE (histone 2, H2be), HIST2H2AA (histone 2, H2aa), HIST1H2BG
(histone 1, H2bg) and TNFRSF1A (tumor necrosis factor receptor
superfamily, mem-ber 1A) which were increasingly expressed with the
evolution of RA from normal bone marrow and with progression from
RA to more advanced stages of MDS (Fig. 3). ASGR2
(asialoglycoprotein receptor 2), TGFB1 (transforming growth factor,
beta 1) IDH3B (isocitrate dehydrogenase 3 NAD+ beta) and EPB41L3
(erythrocyte membrane pro-tein band 4.1-like 3) were down regulated
in RA compared to normal bone marrow, and were further
down-regulated in advanced MDS (Fig. 3). We next searched for
changes in biological pathways asso-ciated with MDS disease
progression. Using the GSEA software we identifi ed statistically
signifi -cant enrichment in genes involved in the Rac 1 cell
motility signaling pathway and the RAR-RXR pathway with advanced
disease.
Validation studies of MCL1In validation studies (Fig. 4), the
expression of both the longer anti-apoptotic transcript (T×1) and
the shorter pro-apoptotic transcript (T×2) variants of MCL1
decreased signifi cantly from normal bone marrow to low grade, and
high grade MDS (global signifi cance levels of T×1 and T×2 when
compar-ing normal, low, and high grade MDS were p = 0.03 and p =
0.007, respectively). Moreover, there was a shift of the ratio of
the anti-apoptotic/pro-apoptotic mRNA level in these three states,
with a T×1/T×2 of 8.4 for normal bone marrow, 4.8 for low grade
MDS, and 2.6 for high grade MDS (global signifi cance p = 0.0008,
test of trend p = 0.0001). Thus, MDS and progression of MDS
Expr
essi
on –
Log
2
PML
NBM MDSNBM MDS
QPRT
NBM MDSNBM MDSMononuclear cells CD 34+ cells
Expr
essi
on –
Log
2
EPOR
NBM MDSNBM MDSMononuclear cells CD 34+ cells
Expr
essi
on –
Log
2
MCL1
NBM MDSNBM MDSMononuclear cells CD 34+ cells
Expr
essi
on –
Log
2
CD 34+ cellsMononuclear cells
Figure 2. Expression of genes that were up- or down-regulated in
MDS compared to normal bone marrow.Log-2 expression level is shown
on the Y axis; samples and cell type are indicated on the X axis.
For each one of the four genes shown in this fi gure, the
expression of the individual samples (for both unselected
mononuclear cells and CD34+ selected cells) are shown. NBM: normal
bone marrow.
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143
Gene expression profi les of CD34+ and unselected mononuclear
cells in myelodysplasia
Translational Oncogenomics 2008:3
were associated with not only a global decline in MCL1 level,
but a shift in transcript towards a pro-apoptotic bias.
DiscussionMDS comprises a heterogeneous group of clonal
disorders that are characterized by aberrant dif-ferentiation in
multiple hematopoietic cell lineages and are thought to involve
hematopoietic stem cells [1, 2]. However, there is mounting
evidence that the disease process is not entirely stem cell-
autonomous and that signals derived from more differentiated
cells, in particular monocytes and T lymphocytes, and from the
marrow stroma affect the disease process [22–30]. We, therefore,
per-formed an analysis of gene expression in both unselected
mononuclear cells and in selected CD34+ cells from MDS marrow in
comparison to the analogous cell populations from normal mar-row.
Our results identifi ed 2623 genes with expres-sion differences
between unselected marrow mononuclear cells from healthy donors and
MDS patients and 1530 genes with expression differences
Table 2. Top genes differentially expressed in Advanced Disease
(RAEB and RAEBT) compared to RA compared to normal bone marrow.
Gene symbole Gene description Linearslope1
P (linearslope)
Up regulated genes:HIST2H2BE histone 2, H2be 1.16 0.00MAX MAX
protein 0.47 0.00HIST2H2AA histone 2, H2aa 1.52 0.00TNFRSF1A tumor
necrosis factor receptor superfamily,
member 1A0.83 0.00
HIST1H2BG histone 1, H2bg 1.08 0.00ARHGEF2 rho/rac guanine
nucleotide exchange factor
(GEF) 20.37 0.00
SNX19 sorting nexin 19 0.29 0.00C1RL complement component 1,
r subcomponent-like0.87 0.00
H2BFS H2B histone family, member S 1.37 0.01HIST1H2BF histone 1,
H2bf 0.97 0.01Down regulated genes:ASGR2 asialoglycoprotein
receptor 2 −1.75 0.00RNASE4 ribonuclease, RNase A family, 4 −1.11
0.00IDH3B isocitrate dehydrogenase 3 (NAD+) beta −0.36 0.00EPB41L3
erythrocyte membrane protein band
4.1-like 3−1.70 0.00
TGFBI transforming growth factor, beta-induced, 68 kDa
−1.80 0.00
CSPG2 Chondroitin sulfate proteoglycan 2 (versican)
−1.39 0.00
KIAA0399 (ZZEF1) zinc fi nger, ZZ-type with EF-hand domain 1
−1.17 0.00FLJ22222 hypothetical protein FLJ22222 −0.98 0.00IL15
interleukin 15 −0.67 0.01CLIC3 chloride intracellular channel 3
−1.86 0.011Linear Slope—Change of value in the dependent variable
(gene expression change) per unit of independent variable (disease
stage);The changes in gene expression in the different stages of
MDS.
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between CD34+ cells from healthy donors and MDS patients.
Compared to normal marrow, MDS was associated with an aberrant
expression of genes involved in apoptosis, including a decreased
expression of MCL1 and EPOR1, and these changes were present both
in non-selected and CD34+ selected cell populations. Moreover, the
PML gene and genes of the RAR-RXR pathway were found to be
associated with the diagnosis of MDS and with advanced disease,
respectively, suggesting disruptions of the normal differentiation
pathway.
Several genes associated with the promotion of a pro-apoptotic
state were identifi ed, including anti-apoptotic regulator MCL1,
the erythropoetin receptor EPOR, and TNF anti-apoptotic modulator
TNFAIP3. Down regulation of MCL1 is consistent with the increased
rate of apoptosis observed in MDS [26, 31–35]. The protein encoded
by the MCL1 gene belongs to the Bcl-2 family, known to be regulator
of programmed cell death. MCL1 has been shown to be essential in
the survival of
hematopoetic stem cells, as inducible deletions of MCL1 in
murine models results in a profound loss of bone marrow function,
including a loss of hema-topoietic stem cells [36]. MCL1 activity
appears to be required for neutrophil, but not for macro-phage
survival, [37] suggesting the possibility of lineage specific or
differentiation dependent activity. Alternative splicing of the
MCL1 gene results in two transcript variants encoding distinct
isoforms. The longer gene product (isoform 1; T×1) enhances cell
survival by inhibiting apoptosis, while the alternatively spliced
shorter gene product (isoform 2; T×2) promotes apoptosis and is
death-inducing [38]. These fi ndings are reminiscent of those
described for the long and short splice vari-ants of the death
signal inhibitory protein FLIP in MDS, [39] suggesting that
regulation of splice variants at the transcriptional level is
involved in the determination of cell death in MDS. Our data show
that not only was MCL1 expression decreased in MDS compared to
normal hematopoetic cells, but ratio of long/short transcripts
shifted in favor
MAX
NBM RA Advanced Disease
Expr
essi
on –
Log
2
HIST2H2BE
NBM RA Advanced Disease
Expr
essi
on –
Log
2
NBM RA Advanced Disease
CD36 Receptor
Expr
essi
on –
Log
2
TGFB1
NBM RA Advanced Disease
Expr
essi
on –
Log
2
Figure 3. Expression of genes that correlated with advanced
disease.Log-2 gene expression of MAX, HIST2H2BE, CD36 Receptor and
TGFB1 in normal bone marrow (NBM), RA, and advanced disease (RAEB,
RAEB-t, AML).
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Gene expression profi les of CD34+ and unselected mononuclear
cells in myelodysplasia
Translational Oncogenomics 2008:3
of a more pro-apoptotic state. Would such a pattern be
compatible with the general observation that apoptosis in marrow
cells overall tends to decrease as MDS progressed to more advanced
stages? [40] The results appear counterintuitive. However, we have
previously shown that the rate of apoptosis differs between clonal
and non-clonal hematopoi-etic cells, and the relative proportions
of those cell populations change with progression of MDS [41].
Further we observed that expression of the short splice variant of
FLIP, characterized anti-apoptotic protein, showed a positive
correlation with the extent of apoptosis [39]. Taken together with
the lineage specificity of MCL1 as described by Dzhagalove et al.
[37] it is conceivable that a pro-apoptotic effect of MCL1 in
advanced MDS is expressed only in subset of cells, but does not
interfere with increasing proliferation of the mali-gnant clone.
Such a model would also be consistent with the observed overall
decline in expression of this gene as MDS progresses (see
results).
The erythropoietin receptor (EPOR) is a member of the cytokine
receptor family. Upon
erythropoietin binding, the erythropoietin receptor activates
the Jak2 tyrosine kinase, which in turn activates various
intracellular signaling pathways, including, Ras/MAP kinase,
phosphatidylinositol 3-kinase and STAT transcription factors [42].
EPOR has an anti-apoptotic function via the Akt-pathway, and
signaling via the erythropoietin receptor promotes erythroid cell
survival, particu-larly in patients with MDS [43–45]. Thus, the
down-regulation of both MCL1 and EPOR may play a role in the
dysregulation of apoptosis in hematopoetic cells leading to
ineffective hemato-poiesis in MDS. It is clear, however, that the
pattern of expression of MCL1 and EPOR by themselves can not
explain satisfactorily the extent of apopto-sis and proliferation
dysregulations at different stages of MDS. Other factors are
involved, and studies of purifi ed cell populations, simultaneously
analyzing the impact of various signals will be necessary [46].
Of special biological interest are the MAX (MYC associated
factor X) and PML (promyelo-cytic leukemia) genes, which were found
to be up-regulated in both unselected mononuclear cells and CD34+
selected cells in MDS compared to normal bone marrow and which
showed a correla-tion with progression to advanced disease. The
protein encoded by the MAX gene is a transcription factor that
interacts with the MYC oncoprotein to form homodimers and
heterodimers. Rearrange-ment among these dimer forms provides a
complex system of transcriptional regulation [47]. There-fore,
alteration in transcription regulation resulted by up regulation of
the MAX gene might play a role in the pathophysiology of MDS. In
addition, the correlation with progression to advanced dis-ease and
the fact that the MAX gene was found to be up regulated in both
CD34+ selected cells and unselected marrow cells make this gene a
candidate marker for disease progression. The protein encoded by
the PML gene is a member of the tri-partite motif (TRIM) family.
This phosphoprotein localizes to nuclear bodies where it functions
not only as a transcription factor but also as a tumor suppressor.
Its expression is cell-cycle related and regulates the p53 response
to oncogenic signals [48]. The gene is also involved in the
translocation of the retinoic acid receptor alpha gene associated
with acute promyelocytic leukemia (APL) [49]. A Further suggestion
of the importance of PML and the RARA pathway in MDS disease
progression is the deregulation of the RAR-RXR pathway in
0
5
10
15
20
25
MCL1-tx2MCL1-tx1
NBM RA ADVANCED DISEASE
MCL1 Gene Expression
MCL1 tx1/tx2
0
2
4
6
8
10
12
NBM RA ADVANCED DISEASE
MCL1 tx1/tx2
A
B
Figure 4. Quantitative RT-PCR assay of MCL1.A. Expression of
MCL1 anti-apoptotic transcript (t×1) and pro-apoptotic transcript
(t×2). Values on the y axis represent MCL1 expression relative to
endogenous control (beta-2-microglobulin) in normal bone marrow (n
= 5), RA patients (n = 4) and MDS patients with advanced disease (n
= 7). Both normal and MDS marrows were obtained from individuals
who were not used for the gene expression array.B. Relative
expression of MCL1 anti-apoptotic transcript (t×1) versus
pro-apoptotic transcript (t×2). Values on the y axis repre-sent the
relative expression of MCL1 t×1 versus t×2 for each of the
individuals shown in panel A.
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Translational Oncogenomics 2008:3
the advanced MDS noted in our analysis. RXR and RAR are nuclear
receptors that bind either all-trans retinoic, 9-cis retinoic acid,
or other retinoid ligands [50]. Ligand binding induces a
conforma-tional change in the receptors which results in
dissociation of the co-repressors and binding of co-activators with
histone acetylase activity [50]. The retinoic acid pathway is
critical for maintain-ing a balance between self-renewal and
differen-tiation of hematopoetic stem cells, [51, 52] and
deregulation of the RAR-RXR pathway, as was shown in our study, may
affect differentiation and self-renewal, and thereby allow RA to
progress to acute leukemia.
Three histone genes (HIST2H2BE, HIST-2H2AA, HIST1H2BG) were
found to be up-regulated in MDS compared to normal bone marrow, and
moreover found to be correlated with advanced disease. The
expression of specifi c his-tone classes dictate changes in
chromatin structure and gene expression, and infl uences various
path-ways, including cell cycle progression [53–55]. In addition,
there is considerable evidence that histone H1 also functions as a
non-specifi c repressor of transcription [56]. Moreover,
post-translational modifi cations of histones by histone
acetylation and deacetylation play a role in tumorgenesis. Histone
deacetylases are promising targets in drug development for cancer
therapy [57]. Given that our data suggest that aberrations in
histone biology are involved in MDS progression, their may be a
rationale for using these agents to stem progression of early MDS,
especially in combination with agents that block apoptosis, as
discussed above.
Several studies have examined gene expression in MDS, using
either purifi ed CD34+ or unselected mononuclear cells [58–62].
There is little overlap between the genes identifi ed across those
studies or our current results. Such discrepancies are not uncommon
in gene expression studies, and likely result from differences in
cell types studied (CD34+ versus mononuclear cells), analytic
tech-niques to defi ne signifi cant genes, and composi-tion of the
cohorts of patients studied. For example, the study by Pellagatti
et al. [60] exam-ined CD34+ cells from 55 MDS patients obtained
from multiple centers, and showed that MDS cells had gene
signatures enriched in interferon response genes. Of the 55
patents, 20 had the 5q- chromosomal aberration. Patients with 5q-
MDS are unusually sensitive to the drug lenalidomide, and have
recently been shown to have a unique
gene expression signature using unselected mononuclear cells
[63].
Since it is unknown which cell population, CD34+ selected or
unselected mononuclear cells, best describes the pathology of MDS,
and given that various studies have used both types of sam-ples, we
performed both types of arrays, and focused on genes found to be
dysregulated in both cell populations. Thus, we consider our gene
selec-tion to be fairly robust. We are comforted by the fact that
the most relevant genes identifi ed in the present analysis seem
biologically relevant: alter-ations of differentiation and
proliferation pathways (PML, RAR-RXR, MAX), involvement in
apop-tosis (MCL1, EPOR) and regulation of hemato-poiesis (TNFAIP3,
CXCR4, CCL18).
Since the patient population included in this study was
relatively small, we were unable to study specific correlations
between gene expression, cytogenetics, and clinical presentation.
However there did not appear to be an association of previous
treatment to the molecular signature, and specifi -cally there was
no effect of erythropoietin treatment on EPOR gene expression.
These fi ndings strengthen our interpretation that low EPOR levels
might be related to the pathophysiology of MDS, and might explain
the heterogeneity of response to treatment with erythropoietin
among MDS patients.
In conclusion, this study provides new data on gene expression
in the different phases of MDS. Although no single gene can likely
explain the pathophysiology of the disease, some of the
dif-ferences delineated in this study may prove relevant in our
efforts to identify prognostic markers and therapeutic targets.
AcknowledgmentsWe thank all the patients and healthy donors who
agreed to donate marrows for these investigations.
Author contributionsSupported by NHI/NCI grants: HL36444,
HL082941, CA18021. Merav Bar: Conception and design, Collection
and/or assembly of data, Data analysis and inter-pretation,
Manuscript writing.Derek Stirewalt: Conception and design,
Collec-tion and/or assembly of data, Data analysis and
interpretation.Era Pogosova-Agadjanyan: Collection and/or assembly
of data.
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Gene expression profi les of CD34+ and unselected mononuclear
cells in myelodysplasia
Translational Oncogenomics 2008:3
Vitas Wagner: Collection and/or assembly of data.Ted Gooley:
Data analysis and interpretation.Nissa abbasi: Collection and/or
assembly of data.Ravi Bhatia: Provision of study material or
patients, Data analysis and interpretation.Joachim Deeg: Conception
and design, Financial support, Data analysis and interpretation,
Manuscript writing.Jeral Radich: Conception and design, Financial
support, Data analysis and interpretation, Manuscript writing.
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149
Gene expression profi les of CD34+ and unselected mononuclear
cells in myelodysplasia
Translational Oncogenomics 2008:3
Gene Expression Patterns in Myelodyplasia Underline the Role of
Apoptosis and Differentiation in Disease Initiation and
ProgressionMerav Bar, Derek Stirewalt, Era Pogosova-Agadjanyan,
Vitas Wagner, Ted Gooley, Nissa Abbasi, Ravi Bhatia, H. Joachim
Deeg and Jerald Radich
Supplementary TablesTable S1. MDS (multiple groups) vs NBM
mononuclear cells.
Table S2. MDS (multiple groups) vs NBM CD34+ cells.
Table S3. OVELAPPED GENES (up/down regulated) in MDS multiple
groups vs NBM (Mononuclear cells and CD 34+ cells).
Table S4. Advanced Disease (RAEB and RAEBT) versus RA versus
normal bone marrow.
Sheet1
MDS (multiple groups) vs NBM mononuclear cells:
Positive genes
Gene ID (Affymetrix probe #):Gene
NameScore(d)Numerator(r)Denominator(s+s0)Fold Changeq-value(%)
214290_s_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=9.71934019432.55753067620.26313830216.56750663670
218280_x_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=8.83024728792.40993030990.27291764676.21667079490
200799_atHSPA1A8.00331393122.06638547620.2581912314.61933609890
213603_s_atRAC27.97699271460.68065246710.08532695111.624039690
205897_atNFATC47.46332510721.51507518940.20300270562.75347124440
214281_s_atZNF3637.02670409640.94955972910.1351358641.94129222810
211657_atCEACAM67.01955670544.46028849820.635408856335.24434952840
200800_s_atHSPA1A6.83579359962.39413114980.3502345586.44047921190
212749_s_atZNF3636.8056063961.01643368370.14935240512.00717006650
205033_s_atDEFA16.78563794635.22288872850.769697524447.83030966330
206589_atGFI16.76554840132.76478129510.40865590366.51991532070
205513_atTCN16.71355301343.73791844750.556772016220.53436889980
222315_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=6.66372056433.17325404050.476198545510.61217438990
205633_s_atALAS16.6306579372.0330481530.30661333654.95282700570
207269_atDEFA46.5627897335.31987987810.81061257473.35330837680
203757_s_atCEACAM66.37451557614.87243724740.764361964346.18743307970
208864_s_atTXN6.25692507360.8890495510.14209049021.90110391020
219281_atMSRA6.00312627641.13167464890.18851421692.3266603660
209893_s_atFUT45.90227890581.41833173080.24030239062.96071584160
218332_atBEX15.89617302752.72307896530.46183837439.6727083550
203897_atLOC571495.83964392050.55413143070.09489130471.48452696730
207384_atPGLYRP5.79786115856.36694154091.098153503136.90852266430
202708_s_atHIST2H2BE5.78757218471.60325294570.27701649233.55960698950
212408_atLAP1B5.77502977930.85304970570.14771347311.82404921940
203964_atNMI5.77144331260.71030424170.12307220281.66329019230
206207_atCLC5.74417028073.66629606090.638263819120.5782626710
218983_atC1RL5.71881136011.46028454060.25534756243.01031735160
222067_x_atHIST1H2BD5.71529633921.58664449260.27761368763.06144954360
208490_x_atHIST1H2BF5.70247731241.351762530.2370482962.5979267660
202252_atRAB135.65841965091.37991579970.24386946972.85652207160
206772_atPTHR25.6101208121.81181791510.32295524033.59726605350
220005_atGPR865.58506649693.12619834210.55974236729.27665444060
203986_atGENX-34145.56273086632.36162840760.424544795.56223776220
209332_s_atMAX5.56055666370.70472204840.12673588111.67437727210
208496_x_atHIST1H3G5.55935841312.25788357320.40614103386.20642324090
203021_atSLPI5.51490161012.87544067760.521394737610.68975575940
208546_x_atHIST1H2BH5.48791124951.73508284470.31616452343.14140069810
204430_s_atSLC2A55.4804769122.48806827310.45398754766.23959360730
204351_atS100P5.47440964823.56102307110.650485312617.69746118180
202442_atAP3S15.46182354450.90234681920.1652098081.96757102480
212062_atATP9A5.43845318772.18326598710.40144980785.43575724440
210244_atCAMP5.40199488875.0514528350.935108777248.44608084610
207802_atCRISP35.3896274765.76645817271.06991776385.48798256410
205480_s_atUGP25.36963779210.75084709680.13983198231.73026181330
209331_s_atMAX5.34950002230.63757546250.11918412191.55316320440
205041_s_atORM15.33382802124.80247380470.900380324563.47409139980
204804_atSSA15.30732089510.69140987010.13027474381.64561731130
213545_x_atSNX35.30396602270.65866374930.1241832521.61208938470
202875_s_atPBX25.28581835470.70593200510.13355207421.62676486410
200961_atSPS25.25633173530.71309978120.13566491181.68791270050
221523_s_atRRAGD5.24540846971.58548901150.30226225863.4993448940
204220_atGMFG5.21928228080.72611319770.13912127351.70814078920
204185_x_atPPID5.20660513790.63265979250.12151099911.59401402960
220811_atPRG35.1831175913.77464828790.728258277331.4096317280
203041_s_atLAMP25.16864794471.24050930710.24000653952.58736927070
214627_atEPX5.16729368392.71150604960.524743940510.30797400160
211275_s_atGYG5.14983327751.3764379670.26727816082.92201506250
209806_atHIST1H2BK5.14147069941.37807819550.26803190683.01264598680
220615_s_atFLJ104625.08931536552.19471093750.43123893495.9748930180
209357_atCITED25.07390737861.27793583020.25186424092.68961217010
202794_atINPP15.05982024140.79277497760.15668046291.80326587660
221864_atMGC130245.05333754690.88535044550.17520112941.95151469290
207643_s_atTNFRSF1A5.03990350961.0863232540.21554445482.26223113610
217794_atDKFZP564J1575.03836776920.68569699350.13609506591.63839027820
212268_atSERPINB15.0347567321.27983087740.25419914912.69374986010
212082_s_atMYL65.03052087520.55209132310.10974834151.47694320
200734_s_atARF35.0016784320.4199902470.08396986191.34967601170
211743_s_atPRG24.992967653.9768256120.796485355230.13265853020
201315_x_atIFITM24.99031035981.28204083550.25690603252.69391969960
220421_atFLJ214584.98086405592.83254008580.5686844813.15638788120
218643_s_atCRIPT4.94501803751.06957997220.21629445312.10792237810
202529_atPRPSAP14.93940176920.63055605050.12765838451.5865202510
214040_s_atGSN4.92402575932.45787046210.49915873367.13873956310
218870_atARHGAP154.92091416530.51730406230.10512356951.44004155560
219271_atGalNac-T104.919522292.29442635350.46639210446.07280392810
200067_x_atSNX34.91327099740.54918252870.11177533851.48235697460
202349_atDYT14.89850251560.59531527910.12153005481.5410601040
206640_x_atGAGE44.89003954581.0424301760.21317418121.7971110330
200942_s_atHSBP14.8557245050.53778106350.11075197181.45871188250
212203_x_atIFITM34.8501434691.00208992850.20661036832.12566666180
204436_atpp90994.85004013890.84401333150.17402192711.92288932020
208636_atACTN14.82764511461.41694058410.29350553953.08471239660
206157_atPTX34.82695225691.86544686770.38646474394.83780819690
212700_x_atKIAA03564.8249060760.96090874430.19915594821.99566254610
201318_s_atMRCL34.79546800760.60846585920.12688351971.55699437230
202030_atBCKDK4.78697789660.51081784060.10670988081.42444595870
214522_x_atHIST1H3D4.748321342.20817631430.46504357145.55172565790
200011_s_atARF34.73698504720.43716858010.09228835971.37128234840
208579_x_atH2BFS4.73180843651.61700807550.34173151713.74102153510
204636_atCOL17A14.7247852442.74192661630.58032830599.49831039720.212424178
203936_s_atMMP94.72145907213.33413336230.706165893117.59444780510.212424178
219191_s_atBIN24.72050388750.72880004020.15439030611.68311397120.212424178
213102_atACTR34.68222372920.67072219320.14324864251.63707754370.212424178
78047_s_atMMP244.67774499670.37574283180.08032563381.31177235480.212424178
221524_s_atRRAGD4.67412629741.32698919920.28390101482.81085376360.212424178
205040_atORM14.67293723034.86861042371.041873704680.24258583590.212424178
208583_x_atHIST1H2AJ4.65555984880.73093214720.15700198711.72727372120.212424178
217918_atDNCL2A4.64933651850.57781181370.12427833761.53087220180.212424178
206503_x_atPML4.64647828241.37419306010.29574937762.67079699250.212424178
208626_s_atVAT14.6459007840.91607388370.19717895972.03285217770.212424178
208527_x_atHIST1H2BE4.63786908980.94249924160.20321816412.08588464440.212424178
219014_atPLAC84.63518191390.9706082480.20940024922.06991955450.212424178
211004_s_atALDH3B14.63488685450.8416442550.18158895381.85006127060.212424178
212892_atZNF2824.62674219170.79869922250.1726266971.73913358450.212424178
200663_atCD634.61617580230.77309882180.16747603531.80828670390.212424178
214455_atHIST1H2BC4.61533687272.09373152460.4536465234.83857231570.212424178
200696_s_atGSN4.61383583091.46657387350.31786433833.30174288720.212424178
202811_atAMSH4.61270446150.51530961650.111715291.45045799260.212424178
206676_atCEACAM84.61061031074.12443583450.894553119128.62332472110.212424178
210648_x_atSNX34.60942290240.49450542080.1072814171.43653057310.212424178
212768_s_atGW1124.60366824824.70084002541.021107467336.93014597830.212424178
205147_x_atNCF44.60060225841.30221074440.28305223342.76117708350.212424178
205780_atBIK4.5701105182.20958368870.48348583266.75727513230.212424178
203282_atGBE14.55636532111.16479495740.25564125682.53013781830.212424178
200944_s_atHMGN14.53732033310.39400420340.08683632071.32281100530.212424178
206208_atCA44.53723336392.88064052370.634889213912.02071276270.212424178
202100_atRALB4.53518732720.7747510650.17083110561.81282164880.212424178
218145_atC20orf974.53231477950.83600108690.18445344761.91220443730.212424178
51192_atSSH-34.52168085440.79114286270.17496654191.72029263820.212424178
211509_s_atRTN44.52113644130.57795774770.12783461751.54836755280.212424178
207519_atSLC6A44.52067705870.94481611870.20899880851.72137473080.212424178
203765_atGCA4.49274180631.5203744310.33840681183.52053853420.212424178
213572_s_atSERPINB14.47664012831.03334271780.23082997252.2232985660.212424178
203274_atF8A4.47569926640.39708270040.08871970091.33730878210.212424178
215779_s_atHIST1H2BG4.47075465261.7005829930.38037940463.67967606460.212424178
209515_s_atRAB27A4.46927749891.11285325820.24900070732.42984850420.212424178
211883_x_atCEACAM14.4595311022.00785358930.45023872325.22951414070.212424178
221732_atSHAPY4.45859960981.50911193130.33847218043.04664254120.212424178
203042_atLAMP24.45836963281.03355716340.23182401832.17431923950.212424178
203802_x_atWBSCR20A4.45418713450.8326404450.18693432041.76477157240.212424178
205769_atSLC27A24.44956974891.15008444970.25847093422.41867129310.212424178
62212_atMGC9554.4480529450.3893122570.08752419581.31392902870.212424178
210484_s_atMGC319574.44634022162.05273151210.46166766594.05663775440.212424178
215102_atLOC892314.44076747762.34792372370.52872025746.25452634040.212424178
216210_x_atHRIHFB21224.44040353940.7149475240.16100958341.72101588510.212424178
209155_s_atNT5C24.43633579790.80152664110.18067312251.80157233470.212424178
200625_s_atCAP14.42200816460.72041874290.16291664691.74108195980.212424178
201487_atCTSC4.41340481350.76507141590.17335174281.64064591920.212424178
206816_s_atSPAG84.40596622981.16302292970.26396546622.00318581250.212424178
203960_s_atLOC516684.40342230830.62393889780.14169408571.49968000510.212424178
211535_s_atFGFR14.39898015541.18621391390.26965657312.28620752750.212424178
215884_s_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=4.39805143840.66647158020.1515379231.64083785960.212424178
221485_atB4GALT54.39645623841.40340837930.31921354453.11331411190.212424178
209911_x_atHIST1H2BD4.37814042721.07759647540.246131092.42147981140.212424178
215343_atKIAA15094.36591044370.99867849990.22874461422.02449245670.212424178
202990_atPYGL4.3656846131.52023650180.34822407863.53642241940.212424178
209089_atRAB5A4.36353150760.52812271570.12103103071.47814011020.212424178
210789_x_atCEACAM34.35899627850.94709200310.21727295522.0739596160.212424178
213773_x_atWBSCR20A4.33875897160.88445966360.20385084061.94777628560.3384027849
214138_atZNF794.33548931311.35706871450.31301396832.48903088390.3384027849
217837_s_atNEDF4.32808407480.61645096260.142430451.56826777610.3384027849
31845_atELF44.32186448880.59356484470.13733999441.54674858110.3384027849
201301_s_atANXA44.31583418590.75554435870.17506334261.74570688840.3384027849
204039_atCEBPA4.29068865281.09570372630.25536780112.41511935610.3384027849
217780_atPTD0084.28967123320.3053015110.07117130761.24727026070.3384027849
219933_atGLRX24.27192727020.57044513570.13353343811.52130613430.3384027849
211163_s_atTNFRSF10C4.26837883172.57732023090.60381712418.07072280360.3384027849
218773_s_atPILB4.26695027930.56416910060.13221834421.51788408070.3384027849
212230_atPPAP2B4.26588711641.1877898220.27843911232.58812704010.3384027849
205899_atCCNA14.25594105590.86054714930.20219902911.80564309810.3384027849
209215_atTETRAN4.25032407850.99744096010.23467409582.12679723990.3384027849
219295_s_atPCOLCE24.23802711492.30934455770.54491028376.2439988290.3384027849
209944_atAPA14.23706109430.49735742910.11738264281.44360343830.3384027849
209369_atANXA34.23473784652.94128122740.694560403512.76677409640.3384027849
209094_atDDAH14.2270290911.44675917410.34226383193.0412564020.3384027849
207008_atIL8RB4.22555240052.11347874430.50016626095.64121611580.3384027849
208637_x_atACTN14.21275287191.20508272880.28605587972.5859053670.3384027849
214606_atTSPAN-24.20992705941.89902174190.45108186324.77143664930.3384027849
204186_s_atPPID4.19344018410.50236089270.11979684241.43147211540.3384027849
216592_atMAGEC34.18477154161.34578211080.32159034193.1037514210.3384027849
215088_s_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=4.18420175350.3493134690.08348389721.28213771610.3384027849
210788_s_atretSDR44.17903784670.62426625640.14938037881.59200660530.3384027849
220945_x_atFLJ102984.16164524361.69646573070.40764304294.24839511750.3384027849
221766_s_atC6orf374.15881879871.17039455860.28142475432.50801863140.3384027849
221149_atGPR774.15721570251.75782166450.422836294.6760461760.3384027849
210644_s_atLAIR14.15682581350.66169813830.15918351361.63234680220.3384027849
209397_atME24.14615616730.66078273720.15937237061.65937157650.5589193537
212531_atLCN24.14565463143.64743184880.879820480323.02988890750.5589193537
206200_s_atANXA114.14036528580.71969473270.17382397041.73571119590.5589193537
218910_atFLJ103754.13640443061.32071799460.31929131132.77954088410.5589193537
202200_s_atSRPK14.13356158660.74883248420.18115914531.77409639560.5589193537
207677_s_atNCF44.13314005261.57967984960.38219848093.55301378750.5589193537
201605_x_atCNN24.13122236090.73306441580.17744491871.76172612810.5589193537
208749_x_atFLOT14.11758084140.66433848520.1613419411.66313003710.5589193537
65517_atAP1M24.1149612321.12001938010.27218224352.39720231410.5589193537
206851_atRNASE34.1073703412.88068598120.701345567112.63584503950.5589193537
211013_x_atPML4.10476750441.43454660750.34948303553.18221826630.5589193537
214554_atHIST1H2AL4.10207714811.21515831760.2962300012.31924033460.5589193537
205569_atLAMP34.09682261071.60744783440.39236451933.25746149760.5589193537
202286_s_atTACSTD24.09668870352.47946941410.60523744757.10696877180.5589193537
209892_atFUT44.07728206851.41476104540.34698630653.0695167190.5589193537
206039_atRAB33A4.06586580780.68488997930.16844874171.6543179550.5589193537
201023_atTAF74.05605109210.55670919990.13725399091.49288049990.6267294224
209355_s_atPPAP2B4.04626241161.78278971050.44060160444.17731277530.6267294224
205557_atBPI4.04562674573.27334363270.80910668216.99663382090.6267294224
216605_s_atR29124_14.03673422272.03450654330.50399814084.32243393340.6267294224
203725_atGADD45A4.02928203711.10289222380.27371929132.41472995510.6267294224
213589_s_atLOC2842084.02826035961.65600030270.4110956484.9193429310.6267294224
200618_atLASP14.01776868430.69086304080.17195192031.70944092210.6267294224
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