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Departments of Reproduction and Development (P.H.-M., L.J.B.), Obstetrics and Gynecology (S.C.J.P.G., F.H.v.W., C.W.B.), Urology (M.S.), and Bioinformatics (M.M.), Erasmus MC, 3000 CA Rotterdam, The Netherlands; and Research and Development Laboratories, N.V. Organon (H.J.K., M.E.D.G., A.M.S., A.J.v.G.), 5340 BH Oss, The Netherlands
Address all correspondence and requests for reprints to: Dr. P. Hanifi-Moghaddam, Department of Reproduction and Development, Erasmus Medical Center, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. E-mail: p.hanifi_moghaddam{at}erasmusmc.nl.
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Abstract |
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), and the progestagenic action was studied in the progesterone-responsive cell line Ishikawa PRAB-36 (expressing PRA and PRB). The data showed that the progestagenic and estrogenic effects of tibolone produce different expression profiles with a narrow overlap in genes; however, both properties modulate the same biological processes. The final genetic network analysis indicated that the estrogenic effect of tibolone is potentially counterbalanced by the progestagenic metabolite of tibolone via differential regulation of similar cellular processes. For example, both progestagenic and estrogenic properties stimulate proliferation, but they exert the opposite effect on apoptosis. The apoptosis network was stimulated by the progestagenic properties of tibolone; in contrast, the estrogenic effect of tibolone suppressed the apoptosis network. The current results indicate that this differential regulation is realized through modulation of a different group of genes and rarely via contraregulation of the same set of genes. |
Introduction |
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-reduced derivative (3-OH-tibolone), its 3ß-reduced derivative (3ß-OH-tibolone), and its 
4-isomer (4-tibolone). The 3- and 3ß-hydroxy metabolites bind exclusively to the estrogen receptor (ER), whereas the 4-isomer and tibolone activate the progesterone (PR) and androgen (AR) receptors (1, 2).
The 3- and 3ß-OH metabolites of tibolone have a half-life of about 7 h. These metabolites are also present in large amounts as sulfated metabolites and serve as a source of estrogenic activity in tissues where sulfatases are active. Surprisingly, we and others have found that the 3ß-OH metabolite possesses progestagenic activity (3, 4), which may be due to the conversion of the 3ß-OH metabolite back into tibolone (which has progestagenic properties), whereas subsequent metabolism of tibolone into the 4-isomer is also possible. The 4-isomer is found in low concentration and is only detectable for 6 h. In the endometrium, tibolone is locally converted into its progestagenic 4-isomer (5). Progestagens can induce sulfotransferase (3, 6, 7) in the endometrium, and because the 3-OH metabolites are good substrates for at least three isoforms of sulfotransferase, this may also contribute to abrogation of the estrogenic response. It appears that the progestagenic response in the endometrium is dominant, which is confirmed by the induction of many progesterone-sensitive parameters in endometrial cells, as indicated below.
In animal and human studies, it has been shown that tibolone has tissue-specific effects; it acts as an estrogenic compound on bone and on the central nervous system, whereas no stimulation is found on the breast or endometrium (1, 8, 9, 10, 11, 12, 13). In the endometrium, under physiological circumstances, estrogenic cell growth stimulation is counterbalanced by progesterone-induced cell differentiation (3). During tibolone treatment, it is thought that local formation of the 4-isomer, which has progestagenic and androgenic activities, and sulfation of the estrogenic metabolites (1, 14) effectively counterbalance any putative adverse estrogenic effects of the 3- and 3ß-reduced derivatives of tibolone (5, 14, 15, 16). Knowledge about the biological processes through which tibolone exerts its counterbalancing actions on the endometrium, however, is lacking (10, 14).
The main objective of the present study was to characterize the transcription profile that reflects the endometrial response to the estrogenic (growth-inducing) and progestagenic (growth-inhibiting) metabolites of tibolone. Using this information, it was our goal to gain insight into the counteracting effect of the different activities of tibolone. The study of estrogenic and progestagenic actions of tibolone separately in one cell line that expresses both ER and PR would be very difficult due to cross-talk and interactions between the receptors. Therefore, we used two endometrial cell lines, which respond only to progesterone or estrogen: PRAB-36 and ECC1. The Ishikawa PRAB-36 cell line is progesterone responsive, and the ECC1 cell line is estrogen responsive. Like progesterone, tibolone inhibits the growth of PRAB-36 cells, and both estrogen and tibolone are able to stimulate the growth of ECC1 cells. After testing of the cell lines for their ability to convert tibolone into its metabolites, the cell lines were treated with tibolone for 6, 24, and 48 h. The expression profiles of the cell lines at different time points were generated using a 9600 cDNA microarray. The differentially expressed genes were analyzed to determine which biological processes were (significantly) regulated in each cell line and at each time point. Finally, to gain insight into the counterbalancing effect of tibolone, we generated a genetic network, which indicated the behavior of cellular processes in response to the progestagenic and estrogenic activities of tibolone.
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Materials and Methods |
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Ishikawa (17) and ECC1 cells (18) were derived from two different well differentiated human endometrial adenocarcinomas. The parental Ishikawa cell line (17) does not express detectable levels of PR, ER, or AR (19, 20). Using stable transfection techniques we have generated modified Ishikawa cell lines, which express PRA and PRB (19). The human endometrial carcinoma ECC1 cell line contains ERs (mostly ER), and in our laboratory these cells are estrogen responsive in growth. Furthermore, this cell line contains low amounts of PR and AR, but no growth response to progesterone or androgen administration was observed (20). Both cell lines were maintained in DMEM/Ham’s F-12 medium (Invitrogen Life Technologies, Gaithersburg, MD), containing 5% fetal bovine serum (Perbio Science, Helsingborg, Sweden) and antibiotics, in a humid atmosphere in the presence of 5% CO2. Growth studies were performed and conversion of tibolone into its metabolites was measured as described by Blok et al. (4).
RNA isolation and gene expression profiling
Total RNA was isolated according to the method described by Blok et al. (4). Microarray hybridizations were performed with the Human Unigene 1 microarray (Incyte Genomics, Inc., St. Louis, MO), containing 9.600 cDNAs. Both labeling and hybridizations were performed according to previously described protocols (21).
Statistical analysis
Because most subsequent analyses depend on the accuracy of assessment of the expression ratios between the treatment and control groups, one of the most critical steps in microarray profiling experiments is the accurate assessment of these ratios. Spots were selected for additional analysis when the signal to background ratio was greater than 2.5 and the area occupied by the signal or control on the cDNA spot on the array was greater than 40%. After signal quantification, a signal correction algorithm was used to correct for systematic differences between the Cy3 and Cy5 labels. This algorithm applied a second order polynomial regression model to the data by fitting a parabola through log-transformed Cy3 vs. Cy5 intensities. The resulting new data from the regression model were taken as the new gene expression ratios. Genes that were significantly differentially expressed in the treated vs. the control sample were selected by a statistical method derived from box plots, which are widely used to visualize the overall shape of a dataset (22). In our experiments the expression of the majority of genes is not expected to differ between the treatment and control samples, and their ratios will be in the center of the distribution of expression ratios. Therefore, we computed the first (Q1) and third (Q3) quartiles of the distribution of the residuals of the regression model and the interquartile range (IQR) of the distribution as a measure for the variation in the expression ratios of nondifferentially expressed genes. Then, in analogy with box plots, an inner fence was set at Q1 – (1.58 x IQR) and Q3 + (1.58 x IQR). Using these criteria, genes within the inner fence have a probability of P = 0.995 to be nondifferentially expressed, whereas the outlier group will harbor the differentially expressed genes. Genes were selected for additional study if they fell outside the inner fence (fold change, 
1.3; P 
0.005). The complete list of differentially regulated genes is available on our website (http://www.eur.nl/fgg/rede/hanifi_moghaddam/differentaly_regulated_genelist.xls).
Overlap of differentially expressed genes between two cell lines and different times points
To identify co-occurrence (or overlap) of differentially expressed genes at different time points in both cell lines, Venn Mapper was used (23). Venn Mapper is a program that compares heterologous microarray datasets, based on the number of common, differentially expressed genes. The application loads microarray data (gene expression ratios) and determines which genes are up- or down-regulated by a user-defined ratio cut-off level. For each experiment, lists of differentially expressed genes are computed. Every list will be compared with every other list, and the number of co-occurring genes will be calculated.
Analysis of functional gene categories
For functional classification of genes and identification of enrichment and depletion of gene categories, four programs were used: GO-Mapper (24), Ease (25), GoMiner (26), and FatiGo (27). This analysis was performed for all genes regulated in three time points and at each time point separately.
A custom visualization system was developed to display the GO classification of genes (using the GO-Mapper output) found to be of interest. By means of hierarchical tree, the context of each GO term is maintained relative to both its parents and children (if any). The ratios of the numbers of genes between the two cell lines at each of the three different time points is presented graphically. The absolute numbers of up- and down-regulated genes are included in the graphic: grouped in six pairs, split as two per cell line, three per time point.
Clustering and genetic network building
The set of genes significantly differentially expressed (P < 0.005; fold change, 1.3) was clustered by the similarity of their expression profiles using a Self-Organizing Three Algorithm (28). For the construction of a genetic network, the Pathway Assist version 2.5 (Ariadne Genomics, Inc., Rockville, MD) was used. The database (updated April 2004) of the Pathway Assist contains biological knowledge represented in a formalized form with the focus on how proteins, cellular processes, and small molecules interact, modify, and regulate each other. The Pathway Assist provides a method for searching objects individually by keyword, string, or attributes. These include, for example, type, effect (positive, negative, or unknown), mechanism (transcription or phosphorylation), tissue type, biological process, belonging to cell structure, and others. In addition, the program provides a search method for objects such as proteins, small molecules, and enzymes. The complete databases of Kyoto Encyclopedia of Genes and Genomes (www.genome.ad.jp/kegg), Database of Interacting Proteins (dip.doe-mbi.ucla.edu), Bimolecular Interaction Network Database (bind.mshri.on.ca), and Gene Ontology (www.geneontology.org) were imported into the pathway Assist database.
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Results |
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Responsiveness to the hormones: cell growth study.
Tibolone is a compound that, through metabolic conversion, can act on ER-, AR-, and PR-positive cells. In previous studies we have shown that PRAB-36 cells express PRA and PRB at high levels, and no expression of ER and AR could be detected. In ECC1 cells, high expression of ER was observed, whereas expression of PR and AR was very low. Furthermore, in the currently used cell lines, effects of androgens on growth were never observed (data not shown). Therefore, addition of tibolone to the progesterone-responsive Ishikawa cell line (PRAB-36) was expected to result in 4-isomer-induced growth inhibition, whereas in the estrogen-responsive ECC1 cell line, tibolone treatment was expected to result in 3-OH- and/or 3ß-OH-tibolone-induced growth. It was observed that the progestagenic growth-inhibiting effect of tibolone administration was similar to the effect of medroxyprogesterone acetate (MPA) in the PRAB-36 cell line (Fig. 1
). Furthermore, in ECC1 cells, a comparable growth-inducing effect was measured for estradiol and tibolone (Fig. 1).
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show that after 3 d of culture, tibolone is converted into the progestagenic/androgenic 4-isomer (80% in PRAB-36 and 70% in ECC1 cells, respectively) and the estrogenic 3ß-OH metabolite (19% in PRAB-36 and 27% in ECC1 cells, respectively). No 3-OH-tibolone was detected.
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Analyses of gene expression profiles
Identification of up- and down-regulated genes.
In Fig. 2, all raw data obtained are depicted in so-called box plots to indicate the quality of the array data. Because microarray experiments are prone to systematic variation, the high correlation between logG and logR shows that measured fluorescent intensities arise from biological differences between the RNA samples rather than from systematic biases.
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1.3; P < 0.005). In both cell lines, the majority of genes were down-regulated by tibolone (Table 2), and the number of genes regulated in ECC1 cells was higher than that in PRAB-36 cells [ECC1, 385 (309 + 76); PRAB-36, 261 (185 + 76)].
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, it is shown that in the PRAB-36 cell line, about 54 (14 + 40) of 112 (19 + 93) genes up- and down-regulated at 6 h were also regulated after 24 h, and 58 (13 + 45) of these 112 genes were also regulated after 48 h. The overlap in regulated genes between 24 and 48 h is high (109 genes). In the ECC1 cell line, about 45% of the regulated genes showed regulation at more than one time point. The large overlap in the number of genes between different time points in a cell line suggests that a number of biological processes are activated over a longer period of time. In Table 3B, it is shown that only a small number of genes are regulated by both progestagenic and estrogenic properties of tibolone. Interestingly, only 13% (76 genes; Table 2) of the significantly regulated genes were coregulated by both progestagenic and estrogenic properties of tibolone. Opposite regulation was found in only 3.9% of the total of regulated genes (22 genes; Table 2
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, of these 229 overlapping genes, 58 were regulated in PRAB-36 cells, 131 were regulated in ECC1 cells, and 40 were regulated in both cell lines. The results in Table 4 show that the 4-isomer-mediated effect (progestagenic effect on PRAB-36 cells) of tibolone predominantly affects known PR-regulated genes, whereas the 3ß-OH-tibolone-mediated effect (estrogenic effect on ECC1 cells) of tibolone predominantly affects known ER-regulated genes. Furthermore, in the current experiments, androgenic effects of the 4-isomer of tibolone are scarcely apparent; only four AR-responsive genes were identified. This indicates that, as observed in growth experiments, the androgenic effect of tibolone is negligible in our cell lines.
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Progestagenic and estrogenic effects of tibolone on cellular processes: analysis of gene categories, clustering, and building genetic networks.
As shown in Tables 2 and 3B, only 13% of the genes are coregulated, and only 3.9% of the genes are contraregulated between the two cell lines. This indicates that the progestagenic and estrogenic properties of tibolone regulate very distinct sets of genes. The question was then raised of whether the distinct expression profiles generated by the progestagenic and estrogenic properties of tibolone act at the same biological processes or at different biological processes.
To answer this question, for each cell line the regulated genes at three time points were compiled. Subsequently, the genes were categorized into their associated biological processes using four different programs. Table 5 data show an example of (GO-Mapper) GO categories and the number and percentage of genes in each category. The percentage of genes that fell into each category of biological processes was compared between the two cell lines to find over- or underrepresentation of gene numbers in different categories. If the progestagenic and estrogenic properties of tibolone affect different biological processes, then one of the cell lines should show depletion or enrichment in a certain category. Significant enrichment or depletion of a category suggests the activation or repression of a biological process. However, no significant enrichment or depletion in categories was found between the two cell lines using the four programs (the complete data of GO-Mapper analysis and the visualization of functional classification are available on our website: www.eur.nl/fgg/rede/hanifi_moghaddam/functional_classification.xls, and www.eur.nl/fgg/rede/hanifi_moghaddam/main.html. In addition, repeating this analysis to compare time points within and between cell lines showed no significant differences between both cell lines or between different time points regarding over- or underrepresentation of gene numbers in different categories. Taking into account the narrow overlap of regulated genes in both cell lines, these results indicate that the progestagenic and estrogenic properties of tibolone modulate similar biological processes and do so through modulation of a different set of genes.
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, which shows cluster analyses of the genes involved in apoptosis (Fig. 3A), proliferation and differentiation (Fig. 3B), and transcription (Fig. 3C). The drawback of clustering, however, is that the cluster program groups together genes regardless of their function and without reference to the valuable biological information available in public databases. To elucidate how the counterbalancing effect of the estrogenic and progestagenic activities of tibolone modulates cellular processes, a genetic network analysis was performed. Genetic network models are typically constructed to describe which genes regulate each other and how environmental inputs could affect gene expression.
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represents transcription, apoptosis, differentiation, and proliferation (in total, 134 genes could be annotated to these cellular processes). The genetic network approach depicted in Fig. 4 also confirmed that the PR- and ER-mediated effects of tibolone on cellular processes indeed occur predominantly via modulation of a different set of genes and rarely via contraregulation of the same set of genes.
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). In this analysis we have taken into account that genes can either induce or suppress a cellular process. The results in Table 6 suggest that both progestagenic and estrogenic properties of tibolone stimulate proliferation, but they have an opposite effect on apoptosis. The progestagenic effect of tibolone stimulates apoptosis, whereas the estrogenic effect of tibolone inhibits apoptosis. It should be noted, however, that a certain caution when interpreting these data is needed, because the gene regulatory information used is not based on endometrium-specific data. Furthermore, all genes have been annotated a similar weight, but we did not take into account that some genes are key regulators of a process while others are not. Results from the network analysis will be evaluated in Discussion.
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Discussion |
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For the present study, we used two well differentiated endometrial adenocarcinoma cell lines, PRAB-36 and ECC1. The PRAB-36 cell line expresses PR and is inhibited in growth by progestagenic compounds, whereas the ECC1 cell line expresses ER and is stimulated in growth by estrogenic compounds. Furthermore, both cell lines were shown to convert tibolone partly into its progestagenic 4-metabolite and its estrogenic 3ß-OH-metabolite. Because the cell lines are derived from different patients, their genetic backgrounds could possibly interfere with their hormone-stimulated gene expression profiles. Therefore, the basal gene expression profiles of the two cell lines were compared. A very high similarity between the basal expression profiles of the cell lines suggested that the generated expression portrait under effect of hormones is probably hardly affected by differences in the genetic constellation of the cell lines. Overall, these results indicated that the PRAB-36 and ECC1 cell lines are suitable models to study the separated progestagenic and estrogenic properties of tibolone administration to endometrial cells.
Analysis of the gene expression profiles generated in the tibolone-stimulated cell lines revealed that one group of regulated genes had already been recognized in the literature to be regulated via ER or PR (as indicated in Table 4), whereas the other group of genes was not known to be regulated by these hormone receptors. One explanation could be that tibolone regulates a tibolone-specific set of genes; however, it is also possible that the information in the public domain is not complete in this respect.
The most striking finding in the current investigations was that despite distinct expression profiles, both estrogenic and progestagenic properties of tibolone regulate similar biological processes. This suggests that the balance between the estrogenic, growth-inducing activities and the progestagenic, differentiative, and consequently growth-inhibiting activities of tibolone in the current model cannot be explained by opposed regulation of the same genes or by modulation of completely different biological processes. Most interestingly, the analysis of genetic networks and overlying the expression data on these networks (as shown in Fig. 4) revealed that the counterbalancing effect of tibolone on cellular processes is realized through regulation of a different set of genes, not via contraregulation of the same set of genes. Going one step further, it was shown that the actual modulation of a process is realized by differential regulation of genes with inducing or suppressing effects. Therefore, the number of up- or down-regulated genes may not be enough to analyze whether a pathway is activated or inhibited.
As an example, we discuss below the differential regulation of apoptosis and transcription by PR- and ER-mediated signaling of tibolone. The selection of the discussed genes is based on the number of connections with other genes in the network, and the consistency of their expression. Genes with a high connectivity play a vital role in the stability of a network or any other complex system (30, 31).
Differential modulation of apoptosis
Many physiological growth control mechanisms that govern cell proliferation and tissue homeostasis are linked to apoptosis. The genetic network, as depicted in Fig. 4, suggests a central role for apoptosis in the regulation of proliferation and differentiation by tibolone. By calculating the net effect of each gene in the network on apoptosis and proliferation, we observed that both progestagenic and estrogenic properties of tibolone stimulate proliferation, but they have opposite effects on apoptosis. The progestagenic effect of tibolone stimulates apoptosis, whereas the estrogenic effect of tibolone inhibits the apoptosis. In fact, Smid-Koopman (Smid-Koopman, E., L. C. M. Kuhne, E. E. Hanekamp, S. C. J. P. Gielen, P. E. De Ruiter, J. A. Grootegoed, T. J. M. Helmerhorst, C. W. Burger, A. O. Brinkmann, F. J. Huikeshoven, L. J. Blok, submitted for publication) showed that long-term culture (28 d) in the presence of MPA did indeed induce apoptosis and brought cell growth to a halt. A similar experiment in PRAB-36 cells with tibolone also induced apoptosis (not shown). In contrast to this, recently it was shown that tibolone did not affect the rate of apoptosis in the postmenopausal endometrium (32). These results seem to contradict each other, but may be explained when we consider that progestagen-induced (4-tibolone) apoptosis may in vivo be counterbalanced by the estrogenic metabolite of tibolone.
This counterbalancing effect may be seen in the regulation of IGF-binding proteins (IGFBPs), which are differently regulated in each cell line. Down-regulation of IGFBP3 was observed only in PRAB-36 cells, whereas in ECC1 cells, IGFBP-4 and IGFBP-5 were up-regulated. The IGFs, their receptors, and their binding proteins play key roles in regulating cell proliferation and apoptosis. Noteworthy, in the cell lines investigated in this study, tibolone did not show any effect on the expression of either IGFs or their receptors. This suggests that any effect resulting from regulation of IGFBPs by tibolone occurs independently of IGFs or their receptors. A series of studies performed over the past years has established that certain actions of the IGFBPs are indeed IGF independent. IGFBP-3 and IGFBP-5, in particular, have been shown to have effects on proliferation, migration, and sensitivity to apoptosis that are independent of IGF signaling per se (33, 34, 35). These data combined with the present findings suggest that tibolone may differentially regulate apoptotic signaling pathways, partly through differential regulation of several IGFBPs (17).
Differential regulation of the transcription machinery
Tibolone metabolites can bind to PR (4-isomer-mediated effect) and ER (3ß-OH-tibolone-mediated effect) and modulate PR- and ER-mediated transcription. Both steroid receptors are nuclear transcription factors and can regulate target gene transcription directly by binding to the promoters of these genes or indirectly through regulation of the expression of one or more transcription factors that will interact with the promoter regions of other target genes.
Binding of a receptor to a hormone response element is associated with the recruitment of coregulators and subsequent histone acetylation (36, 37). From the list of known steroid receptor coregulators, nuclear receptor interacting protein 1 (NRIP1 or RIP140) and thymine-guanine-interacting factor (TGIF) were regulated by tibolone (Fig. 4, Transcription network). NRIP1 is a transcription coactivator that specifically interacts with the hormone-dependent activation domain AF2 of nuclear receptors, whereas TGIF is a transcription corepressor that interacts with HDAC1 and CBP/P300, thereby inhibiting nuclear hormone receptor-mediated transcription.
Tibolone-induced up-regulation of NRIP1 (in ECC1 cells, at all three time points) and down-regulation of TGIF (in PRAB-36 cells, at all three time points) may point to the fact that these genes are necessary determinants for the estrogenic and progestagenic actions of tibolone. A similar finding was reported in a recent paper showing that the estrogen-like activity of tamoxifen in the uterus and in endometrial cells requires a high level of steroid receptor coactivator 1 (38).
This observation shows that in the endometrium, the specific coregulator requirements for estrogenic and progestagenic properties of tibolone are distinct. Because coregulators are themselves involved in multiple signal transduction pathways, the ability of each tibolone metabolite to modulate the expression of a specific coregulator could be a key factor underlying the counterbalancing character of tibolone. It has to be mentioned that besides regulation of the expression of coregulators, other factors, such as differential regulation of coregulator activity, also play an important role in the determination of the biocharacter of a (synthetic) hormone. This, however, could not be studied using the current experimental set-up.
As mentioned above, PR and ER can indirectly regulate target gene transcription through a mechanism involving other transcription factors (39). The transcription factors Specificity Protein 3 and B-cell lymphoma 6 protein, for example, are both down-regulated in PRAB-36 cells, whereas myeloblastosis protooncogene protein (MYB) was up-regulated in the ECC1 cell line. SP3 is a bifunctional transcription factor (transcription activator or repressor) (39, 40), which is involved in recruitment of other transcription factors to promoter regions (41); BCL6 is a zinc finger transcription factor with cell cycle regulatory functions that can inhibit apoptosis (42). MYB is a transcriptional activator, and recently it was shown that the expression of IGFBP-5 is regulated by MYB (43).
Concluding remarks
The biological effect the tibolone treatment exerts on an endometrial cell is orchestrated by the selective expression of genes in response to tibolone’s estrogenic and progestagenic properties. It seems logical that the counterbalancing effect can be realized through regulation of the same biological processes, but it was intriguing to find that different genes are involved in this process and not counterregulation of the same genes.
The study of the expression dynamics of many genes simultaneously enabled us to dissect the complex genetic network that controls the patterns and rhythms of gene expression in the cells in response to tibolone treatment. However, we are aware that constructing a genetic network, based on experimental data and literature-driven knowledge, is a simplification of the cellular system, and that the processes selected in this study (transcription, apoptosis, differentiation, and proliferation) are only part of the total biochemical network in a cell. Furthermore, not taking into account the progestagenic and estrogenic effects on the expression of each others receptors introduces an additional simplification of tibolone working mechanism. In contrast, we do think that our work helps in understanding at least partly the behavior of an endometrial cellular system in response to the progestogenic and estrogenic activities of tibolone.
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Footnotes |
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First Published Online November 30, 2004
Abbreviations: AR, Androgen receptor; ER, estrogen receptor; IGFBP, IGF-binding protein; IQR, interquartile range; MPA, medroxyprogesterone acetate; NRIP, nuclear receptor interacting protein; 3-OH-tibolone, 3-reduced derivative of tibolone; 3ß-OH-tibolone, 3ß-reduced derivative of tibolone; PR, progesterone receptor; PRA, progesterone receptor A; PRB, progesterone receptor B; TGIF, thymine-guanine-interacting factor.
Received July 20, 2004.
Accepted November 16, 2004.
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