![Rank of the published DEG lists in our fold change data.
A. Rank of Zhou's genes in our data set ordered by fold change expression, with Enrichment plots for the up-regulated and downregulated genes data below. B. As A but for Yao's study. C. As A but for Gudjonsson's study. A detailed explanation of Enrichment plots can be found in Figure 1 of original Subramanian et al [10].](https://www.researchgate.net/profile/Mayte-Suarez-Farinas/publication/43346227/figure/fig1/AS:339862986215442@1458041058814/Rank-of-the-published-DEG-lists-in-our-fold-change-data-A-Rank-of-Zhous-genes-in-our_Q320.jpg){kind=icon}
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Evaluation of the Psoriasis Transcriptome across
Different Studies by Gene Set Enrichment Analysis
(GSEA)
Mayte Sua
´rez-Farin
˜as
1,2
, Michelle A. Lowes
1
, Lisa C. Zaba
1
, James G. Krueger
1
*
1Laboratory for Investigative Dermatology, The Rockefeller University, New York, New York, United States of America, 2Center for Clinical and Translational Science, The
Rockefeller University, New York, New York, United States of America
Abstract
Background:
Our objective was to develop a consistent molecular definition of psoriasis. There have been several published
microarray studies of psoriasis, and we compared disease-related genes identified across these different studies of psoriasis
with our own in order to establish a consensus.
Methodology/Principal Findings:
We present a psoriasis transcriptome from a group of 15 patients enrolled in a clinical
study, and assessed its biological validity using a set of important pathways known to be involved in psoriasis. We also
identified a key set of cytokines that are now strongly implicated in driving disease-related pathology, but which are not
detected well on gene array platforms and require more sensitive methods to measure mRNA levels in skin tissues.
Comparison of our transcriptome with three other published lists of psoriasis genes showed apparent inconsistencies based
on the number of overlapping genes. We extended the well-established approach of Gene Set Enrichment Analysis (GSEA)
to compare a new study with these other published list of differentially expressed genes (DEG) in a more comprehensive
manner. We applied our method to these three published psoriasis transcriptomes and found them to be in good
agreement with our study.
Conclusions/Significance:
Due to wide variability in clinical protocols, platform and sample handling, and subtle disease-
related signals, intersection of published DEG lists was unable to establish consensus between studies. In order to leverage
the power of multiple transcriptomes reported by several laboratories using different patients and protocols, more
sophisticated methods like the extension of GSEA presented here, should be used in order to overcome the shortcomings of
overlapping individual DEG approach.
Citation: Sua
´rez-Farin
˜as M, Lowes MA, Zaba LC, Krueger JG (2010) Evaluation of the Psoriasis Transcriptome across Different Studies by Gene Set Enrichment
Analysis (GSEA). PLoS ONE 5(4): e10247. doi:10.1371/journal.pone.0010247
Editor: H. Peter Soyer, The University of Queensland, Australia
Received January 8, 2010; Accepted March 27, 2010; Published April 20, 2010
Copyright: ß2010 Sua
´rez-Farin
˜as et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by a Clinical and Translational Science Award grant UL1RR024143; MSF is supported by the Milstein Program in Medical
Research; LZ is supported by National Institutes of Health (NIH) Medical Scientist Training Program grant GM07739; and ML by K23 AR052404-01A1 and the Doris
Duke Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: jgk@rockefeller.edu
Introduction
The study of human diseases such as psoriasis has benefited
significantly from analysis of the transcriptome, the global gene
expression of a diseased tissue compared to its healthy counterpart.
However, as more studies are carried out independently in
multiple laboratories, effective methodology to leverage these
multiple studies becomes necessary. Such methodologies have
significant hurdles to overcome: first, multiple studies are likely to
use different platforms, different sample dissection, handling and
preparation, and, especially, different definition of the non-
diseased counterpart, resulting in different physical samples being
hybridized against different platforms [1,2,3]. Second, computa-
tional analysis and statistical treatment required to assess the
transcriptome are just as likely to be considerably different.
In many instances, all that is available from published studies
are lists of differentially expressed genes (DEG). It is tempting to
evaluate the agreement between studies simply by evaluating the
intersection between the published lists, the ‘‘Venn diagram
approach’’. However, such an approach suffers serious method-
ological shortcomings [4,5,6]. Use of the original raw data of the
studies has shown that studies which are apparently discordant in
terms of their overlapping individual DEG lists are, in fact, both
concordant and predictive [4,5]. However, most of the time the
original raw data is unavailable, and furthermore a complete
reanalysis of all data is needlessly laborious. In such cases use of
the published lists of DEG is a necessity. Here we present an
extension to the widely used Gene Set Enrichment Analysis
(GSEA) method, where it suffices to have full access to the
complete list of gene expression values for a single study, while the
remaining studies only require the DEG list.
In the last few years, the use of Gene-Sets approach had
emerged as a powerful tool to identify sets of functionally related
genes or pathways that are associated with a disease phenotype
[7,8]. Gene-Sets based methods were designed to address
limitations of conventional single gene methods [6] by evaluating
PLoS ONE | www.plosone.org 1 April 2010 | Volume 5 | Issue 4 | e10247
differential expression patterns of gene groups instead of individual
genes. GSEA, introduced by Mootha et al [9] and further
developed by Subramanian et al [10], was one of the first method
using the Gene-Sets approach, and is arguably the most widely
used of such methods. Here we use GSEA as a conventional
approach to identify pathways related to the psoriatic phenotype.
Furthermore, we propose to extend the use of GSEA as a tool to
easily cross-compare prior lists of DEG genes.
We developed this method specifically to compare several high-
quality studies that defined the psoriasis transcriptome by
identifying DEG between psoriatic lesions and non-lesional tissue
from the same patients [11,12,13,14,15]. Those studies had
identified key genes involved in psoriasis pathogenesis, using a
non-biased approach. Because the genomic data for more recent
studies is more comprehensive than in the earlier studies due to the
larger number of genes represented in the latest Affymetrix chips,
we chose to compare the transcriptomes for studies published since
2003 [12,14,15].
We recently conducted a clinical trial of 15 psoriasis patients
with the TNF inhibitor etanercept [16], and performed a time-
course experiment using HGU 133 2.0 microarray chips [17]. By
analyzing the baseline data from this experiment, we generated
our psoriasis transcriptome comparing baseline-paired values of
lesional versus non-lesional skin. We have compared our
transcriptome with the three others described above, and
introduce the concept of using GSEA as a more robust way of
comparing genomic data.
Results
Disease-modulated genes
The analysis of our data identified a psoriasis transcriptome
composed of 732 up-regulated probesets (representing 579 genes
with unique ENTREZ identifier) and 890 down-regulated
probesets (703 genes) with fold change (FCH) greater than 2 and
false discovery rate (FDR) less than 0.05 (Table 1, and Table S1).
Certain genes with low expression on the Affymetrix chip were
confirmed by RT-PCR, and will be discussed in the next section.
To further consider the biological significance of our data, we
used GSEA in the classical manner, to identify pathways that
correlate with the psoriatic phenotype [10,18]. GSEA evaluates
how genes in queried pathways are distributed in the fold change
(lesional versus non-lesional) ordered list generated by our data (all
probesets included). This is quantified by using the Enrichment
Score (ES), a weighted Kolmogorov-Smirnov-like statistic that
evaluates if the members of the pathway are randomly distributed
or found at the extremes (top or bottom) of the list. If genes in a
pathway rank at the top of the new fold change list, ie. they are
overrepresented at the top, then the enrichment score (ES) will be
close to 1. Conversely if the ES = 21, then genes are overrepre-
sented at the bottom of our fold change data. A perfect agreement
is reached if ES = 1 for the up-regulated genes and ES = 21 for the
down-regulated genes. A normalized enrichment score (NES) takes
into account the number of genes in the pathway. A positive NES
indicates that the list of genes is enriched at the ‘‘top’’ of the
ordered fold change list, and a negative NES indicates that the list
in question is enriched at the ‘‘bottom’’ of the list.
GSEA may be used with well known ‘‘canonical’’ pathways and
Gene ontology categories, but also with sets that contain genes
sharing the same transcription factor binding site, the same
microRNA binding motif or the same cis-regulatory motif. It can
also be used with curated collections such as GeneSigDb (http://
compbio.dfci.harvard.edu/genesigdb), which contain gene signa-
tures of cancer, viral and stem cell biology, and the CGP collection
of the MSigDb (http://www.broadinstitute.org/gsea/msigdb/)
that contains gene expression signatures of genetic and chemical
perturbations, or computational derived sets such as cancer
modules (presented in [19]).
We queried our psoriasis transcriptome with a set of cytokine-
treated keratinocyte pathways for IL-17, TNF, IL-17+TNF, IFNc,
and IL-22, reported in [17,20] and IL-1a[21]. GSEA showed that
those pathways were enriched in lesional skin of psoriasis patients
(Table 2). We used a list (‘‘IL-17 Gaffen’’) considered to be IL-17-
gene targets defined by Shen et al [22]. This was also significantly
enriched in our psoriasis transcriptome. An IFNa-keratinocyte
pathway [14] was also significantly enriched in lesional skin,
supporting the potential role for IFNadiscussed by Yao et al [14].
A list of genes representing the transcriptome of inflammatory
myeloid DCs [23] was also significantly enriched in lesional skin.
In addition, the cell cycle and TLR signaling from the collection of
canonical pathways (C2 CP) available at the Molecular Signature
Database (MSigDb) were enriched in psoriasis lesional skin.
Gudjonsson et al reported lack of evidence of enrichment of the
Hedghog Signalling Pathways in psoriasis [24]. GSEA analysis did
not detect any significant enrichment of this pathway with the
psoriasis phenotype. (ES = 0.37, NES = 1.05 and p = 0.39).
Comparison with other published studies
We next performed a comparative analysis of the DEG lists of
these three studies [12,14,15] with our transcriptome. Table 1
summarizes the main characteristics of the four studies. Zhou’s
study which uses early hgu95 (a,b,c,d,e) chips, reported 397 up-
regulated and 613 down-regulated probesets representing 270 and
397 unique genes respectively [15]. Yao et al conducted a study on
Table 1. Description of studies.
Zhou Yao Gudhjonsson Sua
´rez-Farin
˜as
Platform/chips hgu95 a,b,c,d,e hgu133plus2 hgu133plus2 hgu133a2
Sample size 16 26 58 15
Expression Algorithm dChip GCRMA RMA GCRMA
Statistical test t-test Sam (paired) t-test Paired t-test Moderated paired t-test
Multiple Hypothesis correction none FDR through permutations FDR through permutations FDR Benjamini-Hochberg
Cut-off FCH.2, p,0.05 FCH.2, q-value,0.05 FCH.2, FDR,0.05 FCH.2, FDR,0.05
#Up-regulated probesets (genes) 397 ps (270 genes) 1408 ps. (974 genes) 721 ps (508 genes) 732 ps (579 genes)
#of Down-regulated probesets (genes) 613 ps (397 genes) 1465 ps (853 genes) 364 ps (248 genes) 890 ps (703 genes)
doi:10.1371/journal.pone.0010247.t001
Psoriasis Transcriptomes
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hgu133plus2 chips and reported 1408 up-regulated and 1465
down-regulated probesets (974 and 853 genes respectively) [14].
More recently, Gudjonsson et al also using hgu133plus2 chips with
a large sample size [12] reported a set of 721 up-regulated and 364
down-regulated probesets (508 genes and 248 genes).
Figure 1 shows a Venn diagram illustrating the intersection
between the four studies. There were approximately 11,000
ENTREZ identifiers common to the 4 chip series, but only 126
genes were up-regulated (Figure 1A) and 38 down-regulated
(Figure 1B) in all four studies. The numbers of upregulated genes
in our transcriptome were similar to the Gudhjonsson group, but
less than Yao’s. The number of down-regulated genes were greater
than the Gudhjonsson group, and again, less than Yao’s.
The list of the DEG in common in the 4 studies (Table S2)
contains many genes known to be upregulated in psoriasis, such as
IFNc-regulated genes STAT-1, STAT-3 and MX1; antimicrobial
peptides (beta defensin 4, lipocalin 2, S100A7); and pro-
inflammatory proteins such as IL-8, CXCL1, IL-1F9,
TNFSF10/TRAIL. However, some genes known to be upregu-
lated in psoriasis were only identified in one study. For example
STAT2 was only identified in our study, JAK3 only in Yao’s, and
IL-12RB1 only in Zhou’s study. The set of genes that were down-
regulated in all 4 studies include CCL27, also called Cutaneous T
cell –attracting chemokine (CTACK), which has a role in memory
T cell homing to the skin [25], and Aquaporin 9, a member of a
family of proteins that form water channels across membranes
[26].
Some well-recognized inflammatory genes involved in psoriasis
were not detected by most of the four studies, for example IFNc,
IL-17, iNOS. This is due to the fact that the expression of these
genes is usually low on the Affymetrix gene array platform (0–4
range of expression in log
2
-scale) and hence fold change is not
accurately measured. Most analysis pipelines filter out low
abundance genes so they may be excluded from the statistical
analysis, or the resultant fold change is very low, albeit significant.
This is a major limitation of the use of these arrays for the study of
these genes.
To confirm the role of these genes in psoriasis, we analyzed RT-
PCR data of many of these inflammatory genes. We used data
from the same clinical trial of etanercept treatment of psoriasis
[16], however, we compared only baseline lesional skin and non-
lesional skin. The fold change and p-value for each gene by RT-
PCR is presented in Table 3, and all of these genes except LTA
and p35 were significantly different in lesional skin (p,0.05). We
also present the fold change of any of these genes that were
detectable in any of the microarray studies. It can be seen that IL-
23p19, IL-12/23p40, IL-22, IFNc, IL-6 were not found to be
differentially expressed by any of the four studies, but the fold
change by RT-PCR was greater than 4. Furthermore, IL-17, IL-
20, CCL4, iNOS and CCL3 were detected in only one study, and
with a lower fold change than detected by RT-PCR (which was
greater than 5.5). We also included the percentage of samples in
our study with low intensity, as defined as expression values less
than 4. For example, p19 has expression of less than 4. It can be
seen that the genes that were not detected by any or only one
microarray study had low abundance in more than 87% of the
samples.
Correlation of other published psoriasis transcriptomes
compared to our transcriptome using Gene Set
Enrichment Analysis (GSEA)
A more robust approach beyond a Venn diagram was required
to overcome these limitations. We propose to use a Gene-Sets
approach to analyze how published DEG rank in our fold change
data. This reduces the bias due to preprocessing steps, statistical
protocols and stringency of cut-offs. This approach is successfully
used in the Connectivity Map [27], a pattern recognition instance
that correlates disease signatures (based on gene expression of any
platform) with drugs. The idea is to use the GSEA framework [10]
by considering the published DEG as a pathway or gene set, and
quantify how well the up (and down) regulated genes rank in the
ordered fold change for all genes in our data. This will generate an
ES for up-regulated genes and one for the down-regulated genes.
The connectivity score (CS) can be used to give a measure of
agreement between studies, by combining the two ES into one
final value, as used in the connectivity map to rank drugs that
better correlate with disease. A value of CS near 1 would indicate
perfect agreement between a study list and our analysis, whereas 0
would indicate no agreement, and 21 a negative correlation.
GSEA showed that there was highly significant enrichment of
psoriasis DEGs, both up- and down-regulated genes, from the
three studies compared to our data (Table 4). The GSEA plot for
Table 2. Pathways enriched in Psoriasis lesions by using GSEA.
PATHWAYS No. of genes in pathway ES NES FDR
IFNaUp in KC (Yao) 28 0.91 2.74 ,10
24
IL17 and TNF Up in KC 30 0.89 2.69 ,10
24
IL17 Up in KC 46 0.87 2.86 ,10
24
IL1 Up in KC 34 0.85 2.66 ,10
24
IL17 GAFFEN 27 0.81 2.43 ,10
24
IFNcUp in KC 872 0.47 2.31 ,10
24
TNF Up in KC 472 0.46 2.14 ,10
24
IL22 Up in KC 10 0.89 2.07 ,10
24
Terminal Differentiation 33 0.69 2.10 ,10
24
Inflammatory myeloid DCs (psoriasis) 121 0.42 1.68 ,10
24
Cell Cycle (KEGG) 64 0.58 2.06 ,10
24
TLR signaling Pathway (KEGG) 58 0.59 2.05 ,10
24
Cytokine-Cytokine receptor interaction (KEGG) 111 0.44 1.71 0.02
doi:10.1371/journal.pone.0010247.t002
Psoriasis Transcriptomes
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up and down-regulated genes in Zhou’s, Yao’s and Gudjonsson’s
lists is shown in Figure 2. P-values for ES and CS were calculated
using 10,000 simulations. For Zhou’s list, the ES for the up-
regulated genes was 0.86 (p,0.0001) and 20.63 (p,0.0001) for
the down-regulated genes (Figure 2A). The CS = 0.75 (p,0.0001)
indicates a positive significant agreement between Zhou’s
signature and our data. For Yao’s transcriptome (Figure 2B), the
ES = 0.90 (p,0.0001) for the up-regulated genes and ES = 20.86
(p,0.0001) for the down–regulated genes, for CS = 0.88
(p,0.0001), which also indicates a positive significant agreement.
For Gudjonsson’s transcriptome (Figure 2C), the ES = 0.93
(p,0.0001) for the up-regulated genes and ES = 20.91
(p,0.0001) for the down–regulated genes, for CS = 0.92
(p,0.0001), which also indicates a positive significant agreement.
In general, a better agreement was observed among up-regulated
genes for all studies. In addition, Yao’s transcriptome correlated
better with our study than Zhou’s, which is not surprising since the
array series and the statistical protocols used in Yao’s and ours
were more similar than those in Zhou’s.
We used the same approach to compare two published DEGs of
other skin diseases produced by own group: squamous cell
carcinoma (SCC) [28] and basal cell carcinoma (BCC) [29]. The
CS for SCC was 0.69 and the CS of BCC was 0.42, considerably
lower than in psoriasis (Table 4). This degree of enrichment is
most likely reflects the origin of this data from our own lab, and
the cutaneous nature of the specimens, as well as epidermal
hyperproliferation and inflammation in all these three diseases.
Discussion
Investigators might be surprised at the lack of overlap between
DEG lists, as shown in the Venn diagram (Figure 1). However, if
one considers all the variables involved in the four studies,
summarized in Table 1, it is not that surprising. Although all the
studies were conducted using Affymetrix platform, they used
different array series, which may contribute to variability in results.
Besides the obvious laboratory effect due to sample preparation,
technician experience, equipment calibration, and the use of
different preprocessing algorithms [30], alternative statistical tests
and stringent cut-offs also contribute to different results [3,31].
Measuring agreement of microarray studies by overlap of DEG
lists generated by individual studies has been largely criticized [5]
because it is highly inconsistent, even in the presence of small
variation in the data as in the case of technical replicates [32,33].
A low overlap between DEG does not directly indicate low
agreement between studies [3,31,33].
Here we present our new data on the psoriasis transcriptome
from our patients, as well as a comparison of our data with three
published DEG lists for psoriasis. We find only 164 genes in
common for the four lists. However by changing the focus of the
single-gene approach behind the intersection of DEG involved in
psoriasis and using a gene set approach, a closer biological
similarity between the studies is revealed. The extension of GSEA
presented here enables us to see that cellular processes and
molecular signature involved in psoriasis is very robust across the
studies. In this paper, we extended the use of GSEA to compare
new expression data with previously published DEG lists in order
to validate psoriasis disease-related gene profiles. It is worth
noting that this approach is applicable to expression data
obtained through deep sequencing (potentially improving sensi-
tivity for low abundance genes and cross-hybridization problems
of current microarray technology). Moreover, this approach is
easily extendable to other omics applications and more complex
phenotypes. Since the method is based on ranking a list according
to a phenotype, the ranked list can be derived from other
measures besides gene expression fold changes from microarray
chips or deep sequencing; such phenotype measures may include
odd ratios of single nucleotide polymorphisms (SNPs), or a
microRNA profile assay derived from microarray technologies or
deep sequencing.
The use of GSEA as a gene set approach is not unique:
extensions to GSEA [34] and other Gene Set methods and
Figure 1. Comparison of four psoriasis transcriptomes. A. Venn
diagram showing the comparison of the number of up-regulated genes
in common and distinct for the four studies. B. Venn diagram showing
the comparison of the number of down-regulated genes in common
and distinct for the four studies.
doi:10.1371/journal.pone.0010247.g001
Psoriasis Transcriptomes
PLoS ONE | www.plosone.org 4 April 2010 | Volume 5 | Issue 4 | e10247
statistics have also been proposed, and could also be used to
compare transcriptomes. Efron and Tibshirani [35] proposed the
MaxMean statistic instead of the weighted Kolmogorov Smirnov
statistics used in the classical GSEA. Dinu et al [36] extended the
single gene analysis SAM and proposed SAM-GS. See [7,37] for a
comparative study of different gene set enrichment methods.
Extensions other than the classical difference between two
phenotypes have also been reported. For example, we used the
time slope of a mixed effect model as a phenotype to evaluate the
time-response of cytokines pathways to psoriasis treatment with
TNF inhibitor [17].
In this report, we show an excellent and simple method for
researchers seeking validation of their own expression data with
published lists from different studies.
Materials and Methods
Patients
Twenty adult patients with moderate to severe psoriasis were
treated with etanercept 50 mg subcutaneously twice weekly for 12
weeks (clinical trial no. NCT00116181). The clinical and
histological response of patients in this trial was previously
published [16]. The gene array was performed on samples from
15 sequential patients [17]. RT-PCR was performed on samples
from all 20 patients.
Ethics Statement
The clinical trial (no. NCT00116181) was conducted according
to the principles expressed in the Declaration of Helsinki and
informed consent for their information to be stored in the hospital
database and used for research was obtained from all patients in
written form. This research was conducted under protocol JKR-
0542 approved by the Rockefeller University Institutional Review
Board.
Table 3. RT-PCR validation.
RT-PCR Microarray FCH (log2)
1
Gene Symbol FCH (log2) FCH p.value Suarez-Farinas Zou Yao Gudjonsson % Low Int
2
p19 IL23A 2.66 6.34 0.011 100
p40 IL12B 4.03 16.32 1.8610
205
97
LTA1 LTA 0.83 1.77 0.302 100
IL22 IL22 3.96 15.53 1.5610
24
100
IFNcIFNg 2.28 4.85 2.8610
24
100
IL4 IL4 21.45 0.36 0.034 100
IL6 IL6 2.66 6.33 7.1610
24
100
IL17 IL17A 6.17 71.87 3.8610
25
1.15 93
IL20 IL20 3.95 15.48 1.4610
25
1.03
CCL4 CCL4 2.83 7.12 9.2610
25
1.38 87
iNOS NOS2 6.37 82.91 1.7610
29
1.11 100
p35 IL12A 21.58 0.34 0.186 100
CCL3 CCL3 2.46 5.52 6.2610
23
1.07
AREG AREG 2.05 4.15 1.2610
23
2.54 2.15 1.35 7
CCL20 CCL20 3.08 8.45 4.7610
25
2.79 3.64 2.90 67
IL19 IL19 5.45 43.57 2.0610
25
1.43 2.21 1.88 8
IL1bIL1B 3.66 12.66 6.8610
26
2.61 1.97 1.11 83
K16 KRT16 5.10 34.34 8.3610
29
3.68 1.99 4.57 4.11 0
MX1 MX1 3.56 11.83 4.1610
25
3.23 3.24 3.24 2.32 0
IL8 IL8 6.19 72.82 1.0610
27
5.85 2.96 4.67 4.03 47
bDefensin DEFB4 4.12 17.42 6.5610
28
5.96 1.96 7.34 7.07 1
1
The largest fold change (FCH) was reported when there were several probesets representing the same gene.
2
Percentage of samples with low intensity as defined by having expression smaller that 4.
doi:10.1371/journal.pone.0010247.t003
Table 4. GESA analysis of published transcriptomes with our
data.
Gene Set
No. of genes
in pathway ES NES FDR CS
Gudjonsson - UP 386 0.93 4.25 ,10
24
0.92
Gudjonsson - Down 153 20.91 23.77 ,10
24
Yao - UP 670 0.90 4.31 ,10
24
0.88
Yao – Down 487 20.86 24.06 ,10
24
Zhou - UP 199 0.86 3.69 ,10
24
0.75
Zhou - Down 227 20.63 22.71 ,10
24
SCC LSvsNL UP 859 0.69 3.37 ,10
24
0.69
SCC LSvsNL Down 655 20.70 23.41 ,10
24
BCC LS vs Normal UP 191 0.38 1.61 ,10
24
0.42
BCC LS vs Normal Down 326 20.46 22.09 ,10
24
ES: Enrichment Score; NES: Normalized Enrichment Score; FDR: False Discovery
Rate.
doi:10.1371/journal.pone.0010247.t004
Psoriasis Transcriptomes
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Psoriasis Transcriptomes
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Sample Preparation and Hybridization
RNA was extracted from skin biopsies and hybridized to
Affymetrix hgu133a2 chips as described in [17].
RTPCR
Primers and Probes for TaqMan RT-PCR assays had been
previously described [16]. New primers used in this study were
CCL3 (Hs00234142_m1), CCL4 (Hs99999148_m1), AREG
(Hs00950669_m1), IL-19 (Hs00604657_m1). All assays were
obtained from Applied Biosystems. Data was normalized using
human acidic ribosomal protein as a housekeeping gene [16].
Statistical Analysis
Expression values were obtained using gcrma algorithm. Samples
were filtered for unreliable low expression value and low variability.
To compare lesional with non-lesional values, the moderated t-test
available at limma package was used. P-values were adjusted for
multiple hypothesis correction using the Benjamini-Hochberg
approach, which controls the false discovery rate (FDR). Probesets
with FDR,0.05 and more than 2 fold change (FCH) were
considered differentially expressed. All analysis was carried out
using R programming language (www.R-project.org) and Biocon-
ductor packages (www.bioconductor.org).
Annotations were obtained by using Bioconductor hgu133a2.db
and hgu133plus2.db packages version 2.3.5. Mappings were based
on ENTREZ identifiers, provided by Entrez Gene fftp://
ftp.ncbi.nlm.nih.gov/gene/DATA with a data stamp of Sept 1,
2009.
Gene Set Enrichment Analysis (GSEA) was conducted using
GSEA software [18].
Data Repository
This data has been deposited at the public repository Gene
Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/)
with accession number GSE11903.
Supporting Information
Table S1 DEG genes identified in this study (FDR,0.05,
FCH.2).
Found at: doi:10.1371/journal.pone.0010247.s001 (0.52 MB
PDF)
Table S2 Genes consistently identified by the four studies.
Found at: doi:10.1371/journal.pone.0010247.s002 (0.08 MB
PDF)
Acknowledgments
We thank Dr J. Mee for sharing his genomic data with our group. We
thank Irma Cardinale and RU Genomic facility for the hybridization of
Affymetrix chips. We thank Dr. Marcelo O. Magnasco and Dr. Leanne M.
Huang-Johnson for valuable comments and suggestions.
Author Contributions
Conceived and designed the experiments: MSF JGK. Performed the
experiments: LCZ. Analyzed the data: MSF MAL JGK. Contributed
reagents/materials/analysis tools: JGK. Wrote the paper: MSF MAL JGK.
Interpreted data analysis: JGK. Interpreted results: MAL LCZ.
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