Abstract
To obtain insights into similarities and differences in the biological actions of related drugs or toxic agents, their transcriptomal signature profiles (TSPs) have been examined in a large number of studies. However, many such reports did not provide proper justification for the dosage criteria of each agent. Using a well characterized cell culture model of estrogen-dependent proliferation of MCF7 human breast cancer cells, we demonstrate how different approaches to dosage standardization exert critical influences on TSPs, leading to different and even conflicting conclusions. Using quantitative cellular response (QCR)-based dosage criteria, TSPs were determined by Affymetrix microarray when cells were proliferating at comparable rates in the presence of various estrogens. We observed that TSPs of the xenoestrogens (e.g., genistein or bisphenol A) were clearly different from the TSP of 17β-estradiol; namely, the former strongly enhanced expression of genes involved in mitochondrial oxidative phosphorylation, whereas the latter showed minimal effects. In contrast, TSPs for genistein and 17β-estradiol were indistinguishable by using the marker gene expression-based dosage criteria, conditions in which there was comparable expression of the mRNA transcripts for the estrogen-inducible WISP2 gene. Our findings indicate that determination and interpretation of TSPs in pharmacogenomic and toxicogenomic studies that examine the transcriptomal actions of related agents by microarray require a clear rationale for the dosage standardization method to be used. We suggest that future studies involving TSP analyses use quantitative and objective dosage standardization methods, such as those with quantitative cellular response or marker gene expression-based dosage criteria.
Keywords: breast cancer, estrogen, pharmacogenomics, toxicogenomics, transcriptome
Microarray determinations of transcriptomal changes induced by biologically active substances have become increasingly popular in pharmacogenomics research. For example, a large number of studies have reported time- and dose-dependent aspects of transcriptomal changes induced by estrogenic and antiestrogenic agents in cell culture and animal models (1–17). To obtain insights into similarities and differences in the effects of multiple hormonal agents, it has been common practice to determine their transcriptomal signature profiles (TSPs; ref. 18). TSP analyses of antiestrogens such as tamoxifen, raloxifen, or fulvestrant have suggested a molecular basis for their partial estrogen-like activities (1–6). Studies of TSPs for 17β-estradiol (E2) and xenoestrogens have provided insights into a genomics-based classification of estrogens and their mechanisms of action (6–9).
To determine TSPs of hormonally active agents, estrogen target cells and tissues such as estrogen-dependent human breast cancer cell cultures (1–7, 12–14) and the rodent uterus (8, 9, 16, 17) have been used as standards in in vitro and in vivo model systems, respectively. For both systems, the time-dependent aspects of the agent-induced transcriptomal changes have been well characterized (3, 5, 9, 12–14, 16, 17). In contrast, relatively few studies have systematically examined the dosage-dependent aspects of the hormone-induced transcriptomal changes (6, 19). In many studies, TSPs of estrogens and antiestrogens were determined by using a relatively high single dose of each agent. For example, whereas the proliferative effect of E2 on estrogen-target MCF7 human breast cancer cells is saturated at ≈0.1 nM (12, 19), with few exceptions (12, 19) most studies have determined TSPs of this model using E2 at 1–10 nM (1–7, 10, 13, 14). However, we observed that the transcriptomal effects of E2 on MCF7 cells showed a strong dosage dependency, and that high concentrations of E2 (>0.1 nM) induced expression of mRNA transcripts for TGF-α and stromal cell-derived factor-1 (SDF-1), which could not be induced by lower concentrations of E2, yet these lower concentrations induced maximal cell proliferation (19). Thus, interpretation of TSP data using a single saturating concentration of a hormonal agent may have significant limitations.
In the present study, by characterizing the transcriptomal effects of E2 and xenoestrogens in MCF7 cells, we demonstrate the importance of using objective and quantitative criteria to standardize the dosage of hormonal agents in TSP-based pharmacogenomics. We propose two approaches to dosage standardization and discuss their advantages and limitations.
Results
To characterize the dose-dependent aspects of estrogen effects on MCF7 cell proliferation, we measured the increase in cell yield during 120 h of exposure to varying concentrations of E2 and of xenoestrogens (Fig. 1A). E2, the natural animal estrogen, strongly enhanced MCF7 cell proliferation at 3–100 pM. Bisphenol A and p-nonylphenol, representative plastic-related xenoestrogens, enhanced MCF7 cell proliferation at 3–100 nM. Genistein and daidzein, representative isoflavone phytoestrogens that bind preferably to estrogen receptor (ER)β (20), were weak xenoestrogens that stimulated MCF7 cell proliferation at concentrations of ≥0.1 μM. Tris (4-hydroxyphenyl)-4-propyl-1-pyrazole (PPT), a synthetic selective agonist for ERα, increased MCF7 cell proliferation relatively strongly at 0.1–10 nM. Our 120-h assay clearly demonstrated a quantitative difference between 30 pM E2 and 3 μM genistein on MCF7 cell proliferation effects. However, there was no observed difference in the effects of 30 pM E2 and 10 μM genistein. Assays of cell proliferation at shorter time periods of estrogen exposure (e.g., 48 h) were not quantitatively different in distinguishing the effects of 30 pM E2 and 3 μM genistein (data not shown).
Fig. 1.
Determination of xenoestrogen concentrations based on QCR and MGE-based dosage criteria. (A) Estrogen-dependent proliferation of MCF7 cells. Effects of xenoestrogens on cell proliferation were determined by the 120-h E-SCREEN assay, as described (19). The number of cells in the wells supplemented with 100 pM E2 was determined by a Coulter (Fullerton, CA) cell counter and defined as 100% relative cell number, which corresponded to ≈50% confluence. Numbers of cells cultured in the presence of the xenoestrogens were expressed relative to this condition. Each point represents mean ± SEM (n = 5). Xenoestrogen concentrations determined by QCR criteria are indicated by red circles; E2 and genistein concentrations determined by MGE criteria are indicated by blue circles. (B) Effects of 48 h of exposure to E2 (30 pM) and genistein (3 μM) on expression of the WISP2 mRNA transcripts in MCF7 cells. Real-time quantitative PCR determination of amount of WISP2 mRNA by three independent sets of experiments is shown.
Our preliminary experiments on the time-dependent aspects of the transcriptomal effects of E2 in MCF7 cells revealed that many known estrogen-inducible genes reached their maximum levels of expression only after 48 h of exposure. A previous study by us showed consistent reproducibility of the transcriptomal profiling of MCF7 cells when exposed to varying concentrations of E2 for 48 h (19). However, MCF7 cell transcriptomes determined after 120 h of exposure to E2 showed poorer reproducibility, presumably reflecting that cells were no longer synchronized in their transcriptomal responses to E2 (data not shown). Therefore, although the estrogen effects on cell proliferation were assayed after 120 h of exposure, transcriptomal effects were determined at 48 h.
To determine TSPs of xenoestrogens using DNA microarrays, we defined two criteria for dosage standardization. The first, quantitative cellular response (QCR)-based dosage criteria, identified doses that induce an equal QCR. Using the QCR criteria, estrogen concentrations that supported MCF7 cell proliferation with a strength equal to 30 pM E2 were identified, namely, 3 nM PPT, 10 nM bisphenol A, 10 nM p-nonylphenol, 1 μM daidzein, and 10 μM genistein (Fig. 1A, red circles). Interestingly, even though 30 pM E2 and 10 μM genistein equally enhanced MCF7 cell proliferation, the latter induced expression of the mRNA transcripts for WISP2 ≈3-fold more strongly than the former (Fig. 4B, which is published as supporting information on the PNAS web site). Because WISP2 is a representative E2-inducible gene whose expression is strongly enhanced from 1 to 60 pM E2 in MCF7 cells (19), our observation demonstrated a significant discrepancy between the proliferative and the transcriptional effects of estrogens. By exposing MCF7 cells to varying concentrations of genistein, we observed that 30 pM E2 and 3 μM genistein equally induced WISP2 mRNA (48 h of exposure; Fig. 1B). However, at this concentration, genistein stimulated MCF7 cell proliferation only half as much as 30 pM E2 (120 h of exposure; Fig. 1A). Based on these observations, we defined the second criteria of estrogen dosage standardization, namely, marker gene expression (MGE)-based dosage criteria that identified concentrations of E2 (30 pM) and genistein (3 μM) that equally induced mRNA for WISP2 (Fig. 1A, blue circles).
The xenoestrogen TSPs were determined by using Affymetrix (Santa Clara, CA) GeneChip DNA microarrays by exposing MCF7 cells to these agents for 48 h at the dosage chosen by the QCR and MGE criteria. Initial characterization of the xenoestrogen TSPs with 20 representative estrogen-inducible genes (19) revealed that the transcriptomal effects of E2 and genistein at the dosage determined by the MGE criteria were remarkably similar (Fig. 4A). In contrast, transcriptomal effects of E2 and the xenoestrogens (including genistein) on these representative marker genes were clearly different when their dosage was determined by the QCR criteria (Fig. 4 B–D). The expression profiles of these 20 marker genes were similar between genistein and daidzein (Fig. 4B). Profiles for bisphenol A and p-nonylphenol also showed apparent similarity (Fig. 4C). The profile for PPT fit between the phytoestrogen pattern and the plastic estrogen pattern (Fig. 4D). The remarkable differences observed among the TSPs using the QCR criteria suggested that different estrogens have discrete transcriptomal actions that can be used to discriminate the E2, soybean isoflavones, and man-made chemical xenoestrogens even when their pharmacological or toxicological effects are comparable. In contrast, the TSPs of E2 and genistein, which were clearly different using the QCR criteria, were strikingly similar using the MGE criteria, suggesting that the transcriptomal actions of E2 and genistein were essentially identical. These conflicting observations exemplify the significant but often-overlooked risk of interpreting the transcriptomal profiling data beyond the limitations of the experimental conditions.
The TSPs of estrogens were further characterized by hierarchical clustering analysis involving 1,675 informative genes whose expression was strongly affected by at least one of the estrogens (Fig. 2A). This analysis involved 24 TSP sets representing seven different estrogens and control vehicle. The TSPs of each agent were determined by three independently performed cell culture experiments and were grouped into five major classes. Consistent with the initial characterization involving the 20 selected genes, E2 and genistein at the concentrations determined by MGE criteria [shown as genistein(L) in Fig. 2A] formed a single class (TSP names shown in red). The two phytoestrogens (genistein and daidzein) formed a separate class (TSP names in blue), and the two plastic-related chemicals (bisphenol A and p-nonylphenol) formed another class (TSP names in green). PPT and vehicle (0.1% ethanol) showed patterns different from those observed for the above-mentioned estrogens (TSP names in black or purple, respectively).
Fig. 2.
Clustering analysis of xenoestrogen effects on MCF7 cell transcriptome. (A) Heat-map representation of 2D hierarchical clustering analysis of the TSPs of xenoestrogens. Concentrations of the estrogenic agents were determined with QCR criteria except genistein(L) (low concentration), which was determined by using MGE criteria. Bars below the heat map indicate seven subclusters of genes. (B) Statistical significance of similarities between the TSPs of xenoestrogens. Histogram shows the joint null distribution of the PARij. Dots indicate observed PARij between the indicated pairs of groups of xenoestrogen TSPs. The P values indicate how unlikely the observed TSP similarity would be to happen by chance. (C) Details of xenoestrogen effects on expression of representative estrogen-discriminating marker genes. Each column represents a marker gene, and the points indicate fold changes in mRNA expression over vehicle control in the log scale. The pattern of the representative unchanged genes is also shown as control. BPA, bisphenol A; PNP, p-nonylphenol.
To objectively examine the similarities between the TSP classes, pairwise angle ratios (PARs) were calculated as measurements for distances between two TSP groups. To obtain the P values for the TSP similarities, the null distribution of PARs was calculated by using randomly generated PARs, and the locations of the measured PARs were determined (Fig. 2B). As expected from the heat-map pattern (Fig. 2A), a strong similarity between TSPs for E2 and the low-concentration genistein (3 μM; MGE criteria) was confirmed by this approach (P < 0.0002). A strong similarity between TSPs for daidzein and high-concentration genistein (10 μM, QCR criteria) was also shown (P < 0.0002). A marginally significant similarity between TSPs for bisphenol A and p-nonylphenol was demonstrated by the PAR location at the foot of the null distribution curve (P < 0.002).
The clustering analysis identified seven subclusters of class-discriminating marker genes (Fig. 2A, bottom bars, and Table 1, which is published as supporting information on the PNAS web site). Subclusters 1a, 1b, and 1c were similar in their expression patterns, together forming cluster 1; subclusters 3a and 3b formed cluster 3; and subclusters 2 and 4 were unique. Expression profiles of 20 genes representing each subcluster are shown in Fig. 2C (subclusters 1a, 2, 3a, and 4) and Fig. 5, which is published as supporting information on the PNAS web site (all subclusters). Profiles of unchanged genes are also shown as control. Cluster 1 consisted of genes induced by the xenoestrogens but not by E2. The low MGE-criteria concentration of genistein [3 μM, shown as genistein(L)] also did not induce the cluster 1 genes. Cluster 2 involved genes strongly down-regulated by E2 as well as by low and high (10 μM, QCR criteria) concentrations of genistein and daidzein. Expression of these cluster 2 genes was only modestly affected by bisphenol A, p-nonylphenol, or PPT. In contrast, expression of cluster 3 genes was strongly up-regulated by E2, by low and high concentrations of genistein and daidzein, but not by bisphenol A or PPT. Interestingly, the cluster 3 genes were induced by p-nonylphenol, demonstrating the differential effects of the two plastic-related xenoestrogens. Cluster 4 involved genes most strongly induced by daidzein, high-concentration genistein, and p-nonylphenol; they were induced relatively weakly by E2, low-concentration genistein, and bisphenol A. Taken together, these results provide further support for the following concepts: (i) TSPs determined by using the QCR-criteria dosage reflect the divergent transcriptomal effects of the estrogenic agents when their cell proliferation responses are set to be equal; and (ii) the TSPs determined by using the MGE-criteria dosage reflect the maximum degree of similarity in their transcriptomal effects.
To predict the biological outcomes of the differential transcriptomal effects of the xenoestrogens, we performed gene ontology/pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY database (21). As expected from their cell proliferation effects, large numbers of genes involved in cell cycle progression were up-regulated by both E2 and the xenoestrogens (Table 2, which is published as supporting information on the PNAS web site). Genes involved in purine and pyrimidine metabolism that are required for DNA synthesis as well as genes involved in steroid biosynthesis were also significantly activated by the estrogens. Interestingly, of the 103 genes involved in mitochondrial oxidative phosphorylation, 46–83% were up-regulated by the xenoestrogens. Fig. 6, which is published as supporting information on the PNAS web site, shows that the p-nonylphenol up-regulated genes were involved in all five oxidative phosphorylation complexes. However, E2, PPT, and low-concentration genistein did not significantly affect the expression of these genes. Analyses using the Gene Ontology database (22) revealed results similar to the KEGG database (data not shown).
To compare the effects of each xenoestrogen on the expression of oxidative phosphorylation-related genes, amounts of these genes (82 genes, p-nonylphenol-induced) were plotted in Fig. 3A. Although expression of most of these genes was not significantly enhanced by E2 or by low-concentration genistein, they were increased 2-fold or greater by high-concentration genistein and other xenoestrogens. PPT also significantly enhanced expression of these genes but more weakly than other xenoestrogens. Expression of the mRNA transcripts for the uncoupling proteins (UCP-1, -2, and -3) that reflect mitochondrial biogenesis (23) was not affected by E2 or by low-concentration genistein; interestingly, UCP-1 expression was moderately enhanced by genistein (Fig. 3B). In contrast, expression of the UCP genes was strongly enhanced by the xenoestrogens, but expression of GAPDH and β-actin (ACTB) was unaffected by any estrogen. Thus, when the QCR criteria were used for dosage standardization, the xenoestrogens (including genistein) significantly increased expression of the oxidative phosphorylation-related genes, but E2 did not. In contrast, when the MGE criteria were used to determine concentrations of E2 and genistein, neither affected expression of these genes. These findings indicate that the use of different criteria for dosage standardization for pharmacogenomics studies may lead to different conclusions.
Fig. 3.
Xenoestrogen induction of genes involved in mitochondrial oxidative phosphorylation. (A) Xenoestrogen induction of 82 genes involved in oxidative phosphorylation. The low [genistein(L), 3 μM] and high concentrations [genistein, 10 μM] of genistein were determined by using MGE and QCR criteria, respectively. Each point indicates fold increase in mRNA amounts in estrogen-exposed MCF7 cells over vehicle-exposed control (average of three microarray data, log scale). Averages of fold increase of all points for each estrogen are shown on the top of the graph (mean ± SEM, n = 82). (B) Estrogen-induced expression of mRNA for mitochondrial uncoupling protein (UCP1–3) and two housekeeping genes, GAPDH and β-actin (ACTB). Each bar represents fold increase in mRNA amounts in estrogen-exposed MCF7 cells over vehicle-exposed control (average of three microarray data).
Discussion
Microarray technology is increasingly being used in pharmacological and toxicological studies. Several guidelines such as Minimum Information About a Microarray Experiment (MIAME) and its toxicology-adapted version MIAME/Tox (24), as well as the Minimum Information Needed for a Toxicology experiment (MINTox; ref. 25), have been proposed for standardized description of experiments for pharmaco- and toxicogenomic studies. Unfortunately, however, these guidelines do not specify any method for dosage standardization in studying biologically active agents. Although we have emphasized the critical importance of the dosage-dependent aspects of the experimental determination of TSPs by DNA microarray (19), this concern is often overlooked. For example, microarray experiments comparing the TSPs of estrogen-related agents are often performed with doses selected either arbitrarily or empirically, namely, without apparent objective or quantitative criteria for dosage standardization (1–7, 10, 13, 14).
To provide some guidance for dosage standardization of pharmaco- and toxicogenomic experiments, in the present study, we describe two approaches: the QCR criteria, that provide the TSPs representing different agents known to induce an equal QCR; and the MGE criteria, that provide the TSPs including reference marker genes that are expressed equally in the presence of different agents (Fig. 1). The TSPs determined using these two criteria were remarkably different (Fig. 2) and thus led to different and conflicting conclusions (Fig. 3). Experiments using the QCR criteria indicate that, in MCF7 cells, the TSPs of xenoestrogens differed significantly from the TSP of E2 (Fig. 2). TSP pathway analysis showed that the xenoestrogens (including genistein) significantly increased expression of the genes involved in mitochondrial oxidative phosphorylation, whereas E2 showed no effects on expression of these genes (Fig. 3). However, it must be noted that these observations were valid only at estrogen concentrations that enhanced MCF7 cell proliferation to comparable levels (Fig. 1). In contrast, experiments performed using the MGE criteria convincingly demonstrated that the transcriptomal effects of genistein and E2 were practically identical (Fig. 2). In other words, the transcriptomal effects of genistein and E2 in MCF7 cells became indistinguishable at certain concentrations. Thus, the MGE criteria may be more appropriate than the QCR criteria when the objective of a study is to determine the maximum TSP similarities of different agents. In contrast, the QCR criteria may be useful to characterize differences in mechanisms of action as well as side effects of agents showing quantitatively similar biological actions. Table 3, which is published as supporting information on the PNAS web site, summarizes the features of different dosage criteria for the determination of TSPs.
The concept of the QCR and MGE dosage standardization criteria should be applicable not only to studies of estrogenic agents but more generally for other types of pharmaco/toxicogenomic studies involving both in vitro and in vivo models. It is important to mention that the QCR and MGE criteria may be applicable to studies involving human subjects or wildlife animals as long as a specific and quantitative agent-induced phenotype or marker gene is available (see Table 3). For example, applying the MGE criteria, a clinical study may be able to select patient-derived specimens whose drug-inducible MGEs are comparable and ask whether the behavior of other genes is similar when different drugs are used. Similarly, using the QCR criteria, wildlife animals exposed to a group of environmental contaminants showing similar and specific effects on a common target tissue may be stratified before transcriptomal profiling. To facilitate database-based bioinformatics research on genomics data, it would be important to have a description of the dosage standardization criteria (e.g., QCR, MGE, or empirical) when TSPs and information on the doses of agents are incorporated into databases. For this purpose, it may be helpful to extend the MIAME/Tox (Minimum Information About a Microarray Experiment/Tox) and MIN-Tox (Minimum Information Needed for a Toxicology Experiment) guidelines accordingly.
In summary, we have presented evidence that different criteria for dosage standardization of biologically active agents may result in conflicting and/or misleading conclusions of a pharmaco/toxicogenomic study. We have also called attention to the risk of interpreting TSP data where hormonal doses were selected on either an empirical or an arbitrary basis. We have also described applications of two dosage-standardizing criteria, namely, QCR and MGE criteria. To correctly interpret genomics studies generating TSPs, it is critical to specify the methods and criteria used for dosage standardization and to provide the rationale for their use.
Materials and Methods
Cell Culture.
Culture conditions and the 120-h E-SCREEN proliferation assay of MCF7 cells (BUS stock) were previously described (19). To determine the transcriptomal effects of estrogens, cells were washed three times with phenol red-free DMEM and incubated at 37°C for 1 h in the final wash. The medium was then changed to phenol red-free DMEM supplemented with 5% charcoal/dextran-stripped FCS (HyClone, Logan, UT), and cells were cultured for 48 h in the presence of estrogens. All plasticware was carefully selected to minimize xenoestrogen contamination. As reported (26), contamination of labware-derived xenoestrogens exerted critical effects on both MCF7 cell proliferation and gene expression.
Microarray Experiment and Data Analysis.
Total RNA extraction and Affymetrix U133A GeneChip DNA microarray experiments were performed as described in our previous study (19). Twenty-four microarray data sets, which represented eight different estrogen treatments, were obtained from three independently performed cell culture experiments for each treatment. To perform 2D hierarchical clustering analysis, 22,283 genes on the array were filtered by using the following criteria: (i) the scaled intensity value was >600 for at least three microarray data sets; (ii) the difference between the maximum and minimum intensity value was >300; and (iii) the standard deviation of the gene vector was >300. Expression intensities of 1,675 informative genes were log-transformed and analyzed by using Cluster software and visualized with TreeView software (27). Complete linkage clustering analysis was performed with median center for both genes and array data sets using centered correlation as a similarity metric. Gene ontology analysis was performed by using the GeneSifter.net on-line service (vizXlabs, Seattle, WA), which served as a communication tool to the Kyoto Encyclopedia of Genes and Genomes database (28) and the Gene Ontology Consortium Database (22).
To objectively evaluate similarities of transcriptomal effects between two estrogens, we defined the PARij as
 |
where ρij is the sample correlation of gene expression under treatments i and j (1 ≤ i < j ≤ 8). The inverse cosine of the sample correlation measures the angle between the two gene expression vectors, with cos−1 (1) = 0 when gene expression is perfectly positively correlated under the two treatments and cos−1 (−1) = π for perfect negative correlation. cos−1(ρij) can therefore be interpreted as the gene-expression distance between treatments i and j. When PARij, which is the ratio of these distances, is significantly small, the transcriptomal effects of treatments i and j are significantly close to each other. To assess the significance of the observed PARij, the joint null distribution of PARij was computed for a randomly sampled set of 1,675 genes from the list of 22,283 genes. The observed PARij was compared with the distribution of mini,j PARij across 5,000 samples from this null distribution to obtain a P value as:
 |
Real-Time Quantitative PCR (RTQ-PCR).
RTQ-PCR was performed using TaqMan PCR Master Mix and ABI Prism 7700 thermal cycler (Applied Biosystems, Framingham, MA). PCR primers for human WISP2 were 5′-CATGAGAGGCACACCGAAGA (sense) and 5′- GCACCTTTGAGAGGAGGCAG (antisense), and a TaqMan probe for this mRNA transcript was 5′-VIC-CCACCTCCTGGCCTTCTCCCTCC-TAMRA.
Supplementary Material
Acknowledgments
We thank Ben Wittner, Paul Grosu, and Reddy Gali for useful discussions and Kremena Star for initial contributions to microarray data interpretation. We also thank James Signorovitch for his contributions to the statistical evaluation of similarities between transcriptomal profiles. This study was funded in part by Susan Komen Breast Cancer Foundation Grant BCTR0503620 (to T.S.), National Institutes of Health R01 Grants CA82230 (to T.S.) and ES012301 (to A.M.S.), and the Foundation for Research in Cell Biology and Cancer (T.S.).
Abbreviations
- E2
17β-estradiol
- MGE
marker gene expression
- PAR
pairwise angle ratio
- PPT
1,3,5-tris(4-hydroxyphenyl)-4-propyl-1-pyrazole
- QCR
quantitative cellular response
- TSP
transcriptomal signature profile
Footnotes
Conflict of interest: no conflicts declared.
Data deposition: The microarray data presented in this study have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE5200).
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