Hum. Reprod. Advance Access published online on June 11, 2007
Human Reproduction, doi:10.1093/humrep/dem129
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A system-wide analysis of differentially expressed genes in ectopic and eutopic endometrium
1 Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Room W313, Oklahoma City, OK 73104-5005, USA 2 Department of Pediatrics, Medical College of Wisconsin, 8701 Watertown Plank Road, PO Box 26509, Milwaukee, WI 53226, USA
3 Correspondence address. Tel: +1-405-271-6989; Fax: +1-405-271-4110; E-mail: jdwren{at}gmail.com
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Abstract |
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BACKGROUND: Decades of research suggest that endometriosis is a complex disorder, with varying severity, onset and progression. Many genes have been associated with endometriosis through a number of studies and now microarray analyses have added to the list of perturbed or differentially regulated genes. Thus, it is difficult to see ‘the big picture’ without first integrating these multiple, heterogeneous sources of high-quality information for analysis.
METHODS: The goal of this study was to infer correlative and/or causal trends by combining empirical microarray analysis with a historical knowledge base of genetic relationships in endometriosis via a program called IRIDESCENT.
RESULTS: Importantly, we found a number of genes, which may have a central role in endometriosis, despite the fact that few or no past studies have reported these associations.
CONCLUSIONS: Several genes listed as non-responders on the microarray were found to be regulated post-transcriptionally, illustrating the importance of integrating multiple data sources.
Key words: data mining/endometriosis/gene expression/microarray
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Introduction |
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In the last five years, gene expression microarrays have become synonymous with genome-wide or near genome-wide analysis of gene expression patterns and changes in many diseases. In endometriosis, close to a dozen gene expression profiling studies have been published so far (Eyster et al., 2002
; Lebovic et al., 2002; Arimoto et al., 2003; Kao et al., 2003; Matsuzaki et al., 2004; Smith, 2004; Fan et al., 2005; Matsuzaki et al., 2005a; Matsuzaki et al., 2005b; Kato et al., 2006; Wu et al., 2006a), differing by focus, study design, microarray platform, quality control measure, data analysis, cell type used for RNA extraction, and patients. The number of differentially expressed genes identified in these studies varies from a few to hundreds. Typically, for previously identified genes, their roles in the pathogenesis of endometriosis are further discussed and for genes that have not been identified, their roles are speculated. Only one study went one step further to classify subtypes of endometriosis based on expression patterns and to identify possible pathways involved in endometriosis (Wu et al., 2006a). It is the sheer size of these gene datasets that makes it difficult to conduct a thorough and complete analysis in the initial report of the study. Even linking dataset genes to gene ontology (GO) categories is only useful to get a general idea of the processes involved, as GO annotation and GO development are both far from complete. Furthermore, GO analysis of microarray responders only includes associations with GO categories, and does not identify associations with diseases, chemical compounds, metabolites or phenotypic information, all of which are highly relevant to disease etiology. In this paper, using the list of 904 differentially expressed genes identified by comparing eutopic and ectopic endometrium of women with endometriosis, we re-analysed this dataset to identify associations with genes, diseases, phenotypes, chemical compounds and pharmaceuticals based upon what has been reported in the published literature. After discarding 337 expressed sequence tags (ESTs) from this set that were not reported in the published literature, we performed a shared relationship analysis of the remaining 567 genes/ESTs. We found a number of genes experimentally implicated as having a central role in the progression of endometriosis, despite the fact that few or no past studies have reported these associations. Several genes listed as non-responders on the microarray, yet linked to many of the responding genes, have been reported as post-transcriptionally regulated, such as STAT1, ERK2 and IGF2, illustrating the importance of integrating multiple data sources.
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Materials and Methods |
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In the experiments previously described (Wu et al., 2006a
), 904 genes were identified to be differentially expressed between ectopic and eutopic endometrium of 12 women with endometriosis, using cDNA microarrays containing 9600 genes/ESTs. Since the microarrays used in the study were house-made and contained a substantial number of ESTs, not all gene symbols/names appear in MEDLINE. Therefore, after removing clone names and accession numbers that would not be found in the literature (e.g. LOC0101023, FLJ1dkad19327, etc.), the list contained 567 gene symbols. A software package called IRIDESCENT (Wren, 2004; Wren et al., 2004) was used to identify commonalities among the genes in the list (Wren and Garner, 2004). In brief, IRIDESCENT works by searching every published MEDLINE abstract for associations between objects. Objects encompass genes, diseases, phenotypes, chemical compounds, drugs and ontology categories. The more frequently two objects mentioned together and whether they are mentioned in the same sentence or abstract determines their relative strength of association. Integrating microarray data with published associations is a useful way to examine microarray data (Mizumoto et al., 2005; Xu et al., 2005) beyond a simple analysis of ontology categories.
At the time of this analysis, MEDLINE contained approximately 16 million records, which highlights the need for a computational means of assimilating this information. Objects co-mentioned in the same sentence are weighted higher (0.8 per co-mention) than objects co-mentioned in the same abstract (0.5 per co-mention). These values were obtained from previous studies on the probability of significance for an association of co-mentioned objects (Wren et al., 2004) and reflect strength of association. Once a network of associations has been constructed, then given a list of objects such as microarray responders, IRIDESCENT can identify what objects they have in common based upon previous publications. Fig. 1 depicts this process — a set of responding genes is analysed for what they have in common. Each of these commonalities is then weighted for its potential significance based upon how frequently they are mentioned within the literature (i.e. if something is mentioned frequently in the literature, then the fact that several genes have been mentioned with it is unexceptional) (Somech et al., 2004). Each association is weighted by the ratio of how frequently it is observed (Obs) with a set of genes to how many times we would expect (Exp) to observe it by chance alone (i.e. based upon how frequently it is mentioned with any gene).
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Results |
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For the 567 gene symbols analysed, the average Obs/Exp for the entire list of associations was high (1.34) compared to what would be expected of a randomly assembled set of genes (0.67 ± 0.06), indicating that the microarray data contains a great deal of previously documented and informative interactions.
Table 1 lists the top 40 literature-based commonalities found among the microarray responders, ranked in order of their Obs/Exp ratio. Intriguingly, many of the top hits had little or no documented relationships to endometriosis, suggesting a rich base for knowledge discovery and for generating new hypotheses. Rather than enumerate each of these connections one by one, we have identified three interrelated themes that are contained within this list and will instead summarize what this analysis reveals about differential gene expression in endometriosis. The three themes are: cellular growth and division, nuclear import and export, and cellular adhesion and growth. Each theme has elements already anchored in the endometriosis literature as well as several novel associations revealed by our analysis of this experimental dataset.
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Cellular growth and division
Many genes in the differentially expressed (De and Hardy, 1990
) list have been linked to tumor suppression (131 genes). Besides tumor suppressors, other genes involved in cell growth and division such as the cyclins or CDK cell cycle genes, transcription factors, post-translational modification (94 genes), c-JUN, PPAR
, NF-
B, histone deacetylase and hypermethylation, also have been identified. There are several non-responding genes on this dataset that are linked to many of the responding genes — almost all of these are involved in cell growth, differentiation and/or organ development. Bear in mind that many but not all genes are regulated at the transcriptional level and thus their involvement in some processes will not be observable from microarray experiments alone. One study reported that approximately three-fourths of reported regulatory events took place at the transcriptional level (Wren and Conway, 2006). Taken together, this suggests that genes that ordinarily suppress cell division and growth are being turned off or genes that induce it are turned on.
The second on the list is CDC42, a gene that, as of this writing, has only been identified in the original profiling study as Rac (Wu et al., 2006a) for its role in either endometriosis or endometrium based on our MEDLINE search. CDC42 belongs to the Rho-family of GTPases that regulate various aspects of cellular functions, including morphogenesis (actin cytoskeleton, a theme identified with 83 genes connected), polarity, trafficking regulation, and cell division (Jaffe and Hall, 2005). Interestingly, #26 and #40 on the list are GSTase and RhoA, respectively, where RhoA is a gene in the same family. Members of the Rho family of small GTPases are key regulators of actin reorganization, cell motility, cell–cell and cell–extracellular matrix (ECM) adhesion as well as of cell cycle progression, gene expression and apoptosis (Fritz and Kaina, 2006). The Rho GTPases are thought to be promising cellular targets for novel anticancer drugs (Fritz and Kaina, 2006).
The identification of the theme of transcription factors is also not surprising, since several sex steroid receptor genes such as estrogen receptors and progesterone receptors known to be involved in endometriosis belong to the nuclear receptor gene family of transcription factors. In addition, other genes linked to the dataset, such as c-Jun and NF-B, are also transcription factors.
We also found many cell-cycle genes/themes on the list: CDK2 (60 genes), cyclin A (49 genes), cyclin D1 (77 genes), CDK4 (46 genes), cyclin B1 (48 genes), p21/WAF1 (46 genes), cyclin E (75 genes), cell cycle arrest (80 genes), p53 (137 genes), C-kit (50 genes), cell cycle (197 genes), cyclin-dependent kinase (67 genes), M phase (59 genes), cell cycle control (58 genes), S phase (87 genes), G1 phase (64 genes). This strongly suggests that the cell cycle was disturbed in the endometriotic cells, echoing the finding that these cells have greater anti-apoptotic capability (Beliard et al., 2004), even in the face of apoptosis-inducing drugs (Izawa et al., 2006).
Consistent with the involvement of cell cycle disturbance, several genes/themes on cell growth and apoptosis were identified: IGF2 (41 genes), c-met (39 genes), c-Jun 106 genes), HGF (67 genes), FGFR1 (40 genes), caspase (84 genes), platelet-derived growth factors or PDGFs (86 genes), induction of apoptosis (85 genes), caspase 3 (86 genes), cell growth (197), fibroblast growth factor (68 genes), bcl-xl (45 genes), Ki-67 (54 genes), apoptosis (226 genes), c-fos (84 genes), Bcl-2 (94 genes), mitosis (96 genes), CD95/Fas (43 genes), caspase activation (44 genes), PCNA (65 genes), cell proliferation (192 genes), nerve growth receptor (87 genes), apoptotic (142 genes), cell death (171 genes), Fas ligand (47 genes), growth factors (160 genes), IGF2 (Giudice et al., 1994; Sbracia et al., 1997; Kim et al., 2000), HER-2, c-Met and HGF (Khan et al., 2003; Yoshida et al., 2004; Khan et al., 2005), PCNA (a proliferation index) (Khan et al., 2003), PDGF (Surrey and Halme, 1991; Munson et al., 1995; Garcia-Velasco et al., 1999), Fas and FasL (Garcia-Velasco et al., 2002; Selam et al., 2002; Garcia-Velasco and Arici, 2003; Harada et al., 2004; Eidukaite et al., 2006), Bcl-2 (Nezhat and Kalir, 2002; Harada et al., 2004; Nishida et al., 2005), Bcl-xl (Nishida et al., 2005), FGFs and their receptors (Di Blasio et al., 1995; Mihalich et al., 2003; Wing et al., 2003; Bourlev et al., 2006), caspase (Wang et al., 2005; Izawa et al., 2006), and c-Jun (Shazand et al., 2004). c-fos has been recently reported to be upregulated in a baboon model of induced endometriosis (Hastings et al., 2006). It should be noted that c-Fos and c-Jun are two members of the AP-1 transcription family, which plays a critical role in the life and death of cells (Shaulian and Karin, 2002).
The identification of the AP-1 theme actually ties together several genes that were identified in the original study such as PDGF, PDGFR, Raf1 (which also happened to be a gene identified in this study), ERK, MKP, cPLA2, MNK1/2, and RSK2 (Wu et al., 2006a), which are all in the ERK signalling pathway, leading to activation of c-Fos, which, in turn, is responsible for cellular proliferation and differentiation. Raf1 can be regulated by Ras. Interestingly, GRB2 (related to 44 genes), fourth on the list in Table 1, happens to be the downstream gene for PDGF-PDGFR, FGF-FGFR, and EGF-EGFR (all have been identified in this analysis, see also Fig. 3 in (Wu et al., 2006a)), and the upstream gene for SOS, which activates Ras (Chardin et al, 1995). Ras, in turn, activates Raf1, which, through MEK1/MEK2, activates ERK. This is consistent with the mounting evidence that suggests the involvement of MAPK pathway in general and ERK pathway in particular in endometriosis (Yoshino et al., 2004; Deura et al., 2005; Hirota et al., 2005; Matsuzaki et al., 2005b; Wu et al., 2006a).
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Nuclear import and export
Among the high-ranking associations were found nuclear translocation (91 genes), nuclear export (51 genes), and nuclear import (49 genes), which are relevant since steroid hormone receptor genes including estrogen receptor genes and progesterone receptor genes, as transcription factors, go through nuclear import and export processes as part of the activation or inactivation processes. These transcription factors are, in most cases, nuclear proteins that undergo a continuous nucleocytoplasmic shuttling (Guiochon-Mantel et al., 1991
; Dauvois et al., 1993; Madan and DeFranco, 1993). For example, progesterone receptor activation involves multiple steps, including a conformational change, dissociation from a multiprotein sequestering complex consisting of protein chaperones, translocation into nucleus and dimerization, and binding to specific DNA sequences (Leonhardt and Edwards, 2002). The signal involved in the transport of these receptors from the cytoplasm to the nucleus has been studied extensively, which includes a basic type nuclear localization signal NL1 (LaCasse and Lefebvre, 1995).
Visualizing the entire co-citation network is not easily achieved due to the sheer number of interconnections, thus we opted to use a subset of genes for analysis based on a recent finding that NF-B and AP-1 (c-JUN) transcription factors are involved in endometriosis (Wu and Guo, unpublished data), shown in Fig. 2. As expected, proinflammatory cytokines and chemokines IL-1, IL-6, IL-8 and RANTES have been well-documented to be involved in endometriosis (Garcia-Velasco and Arici, 1999a,b; Hornung et al., 2001; Lebovic et al., 2001; Arici, 2002; Wu and Ho, 2003; Lebovic et al., 2004; Luk et al., 2004; Wieser et al., 2005), as have matrix metalloproteinases (MMPs) (Osteen et al., 2003; Zhou and Nothnick, 2005). The involvement of IL-2 and IL-10 in endometriosis has also been documented (Szyllo et al., 2003; Tabibzadeh et al., 2003; Lee et al., 2005). In fact, IL-1 (Mori et al., 1992; Hiscott et al., 1993), IL-2 (Hoyos et al., 1989; Serfling et al., 1989; Lai et al., 1995), IL-6 (Libermann and Baltimore, 1990; Shimizu et al., 1990), IL-8 (Kunsch and Rosen, 1993), IL-10 (Xu and Shu, 2002), RANTES (Moriuchi et al., 1997; Wickremasinghe et al., 2004), MMP-2 (Yeh et al., 2005), and MMP-3 (Schulze-Tanzil et al., 2004) are all known to be NF-B target genes. The expression of c-Jun has been shown to be increased in endometriosis (Shazand et al., 2004). The upregulation of c-Fos has been recently reported in a baboon model of induced endometriosis (Hastings et al., 2006). It is interesting to note that in c-Fos transformed cells, DNMT1 is upregulated (Bakin and Curran, 1999). Perhaps not so coincidental, DNMT1 and the other two genes coding for DNA methyltransferases that are responsible for DNA methylation have been shown recently to be upregulated in endometriosis (Guo et al., 2005), and promoter hypermethylation of PR-B in endometriosis also has been found recently (Wu et al., 2006b).
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Fig. 3 examines the same data in terms of ‘cliques’ or highly interconnected neighbourhoods (Adamcsek et al., 2006
). As can be seen in the graph, most of the responding genes have not been reported to be related to endometriosis, yet are differentially regulated between normal and endometriotic cells. Especially the green coloured genes, which are largely implicated in cellular growth and tumorigenesis. This integration and visualization of experimental data with literature-based relationships helps us to better understand what processes are implicated in endometriosis by the data, but largely unexplored in the literature
Cellular adhesion and growth
Several genes/themes identified in this study, such as 
-catenin (67 genes), E-cadherin (59 genes), cell migration (94 genes), MMP-9 (60 genes), retinoic acid (107 genes), Matrigel (45 genes), cell differentiation (119 genes), PAI-1 (44 genes), and vimentin (77 genes) strongly suggest that the invasiveness phenotype is involved with endometriosis, which has been reported previously (Gaetje et al., 1995; Zeitvogel et al., 2001). Cadherin is a large family of transmembrane glycoproteins responsible for mediating calcium-dependent cell–cell adhesion in normal cells (Jiang et al., 1998) and also involved in contact inhibition of cell growth by inducing cell-cycle arrest (Feeley and Wells, 2001). Mounting evidence indicates that the modulation of this complex, which acts as an invasion suppressor and as a major growth and proliferation suppressor through a variety of mechanisms, plays an important role in the initiation and progression of human cancer (Boling et al., 1988). E-cadherin expression in endometriosis has been a bit controversial. Some investigators found increased expression of E-cadherin in endometriosis (Beliard et al., 1997; Ueda et al., 2002), but a preponderance of studies found its expression decreased (van der Linden et al., 1994; Gaetje et al., 1997; Scotti et al., 2000; Poncelet et al., 2002). Gaetje et al. (1997) have shown that E-cadherin negative epithelial cells from peritoneal endometriosis biopsies were invasive in vitro. Recent data showed that the promoter region of E-cadherin in the endometriotic cell lines studied in (Gaetje et al., 1997) is hypermethylated and that E-cadherin can be reactivated by a histone deacetylase inhibitor, trichostatin A (Y. Wu and S.-W. Guo, unpublished observations). E-cadherin has been shown to be silenced by aberrant methylation of the promoter in various cancers (Herman et al., 1996; Graff et al., 1997; Corn et al., 2000; Esteller, 2003), and loss of E-cadherin expression has been found to be widespread in sporadically occurring epithelial tumors (Strathdee, 2002).
-Catenin functions in cell adhesion at the plasma membrane, by linking E-cadherin to 
-catenin. Loss of intercellular adhesion mediated by the cadherin/catenin complex plays a key role in the precondition for loss of cell polarity and the onset of cell invasion leading to tumor metastasis (Aberle et al., 1996). -catenin expression has been reported to be reduced in endometriotic cells (Scotti et al., 2000), suggesting some defects in the E-cadherin/-catenin complex in endometriosis.
The plasminogen-activating system, which consists of plasminogen activators (PAs) and their inhibitors (PAIs) and the urokinase plasminogen activator receptor (uPAR), are involved in tissue degradation and remodelling under both physiological and pathological conditions (Dano et al., 1985). In endometriosis, dysregulated uPA and PAI expression was noted a decade ago (Fernandez-Shaw et al., 1995), and has been consistently reported (Bruse et al., 1998; Kobayashi, 2000; Guan et al., 2002; Gilabert-Estelles et al., 2003; Bruse et al., 2004; Bruse et al., 2005). Increased mRNA and protein levels of uPA in peritoneal fluid and the eutopic endometrium of women with endometriosis also have been reported (Edelstam et al., 1998; Sillem et al., 1998; Bruse et al., 2004; Ramon et al., 2005), suggesting that they may facilitate the attachment of endometrial tissue to the peritoneum and ovarian surface, as well as the invasion of the extracellular matrix. Similarly, dysregulated MMP expression in endometriosis has been well-documented (Osteen et al., 2003; Ramon et al., 2005; Zhou and Nothnick, 2005). In particular, upregulation of MMP-9 in endometriotic cells has been reported by several groups (Chung et al., 2001; Liu et al., 2002; Ueda et al., 2002; Collette et al., 2004).
Vimentin is an intermediate filament that cross-links other cytoskeletal proteins and is normally expressed by cells of mesenchymal origin, but is a feature of highly invasive, ER negative, breast cancer cell lines (Thompson et al., 1992). Given the genes/themes such as -catenin, E-cadherin, cell migration, and MMP-9 that are identified, it is perhaps no coincidence that endometriosis has been proposed as a disease of de-differentiation with invasive phenotypes (Starzinski-Powitz et al., 2001). This is further buttressed by the two more themes identified, retinoic acid and Matrigel, the former playing an important role in cell differentiation and transformation (Chambon, 1996) and the latter being an extract from tumors, containing all of the components present in the basement membrane, very biologically active and used to measure the invasive activity of tumor cells (Kleinman and Martin, 2005).
The themes histone deacetylase (63 genes), ubiquitin (98 genes), hyperphosphorylation (47 genes), hypermethylation (48 genes), transcriptional regulation (143 genes), histone (81 genes), post-translational modifications (94 genes), chromatin (130 genes), and acetyltransferase (85 genes) strongly suggest the possibility of epigenetic regulation of gene expression in endometriosis. It has been recently reported by our group that the promoter region of PR-B is hypermethylated in endometriosis (Wu et al., 2006b), which may be responsible for PR-B downregulation (Attia et al., 2000). Interestingly, histone deacetylase inhibitors have been shown to suppress proliferation of endometrial and endometriotic cells (Wu and Guo, 2006).
The themes, post-translational modification and histone deacetylase, may refer to the post-translational modification of histones which is one of two major mechanisms that foster epigenetic changes (the other being DNA methylation at cytosine bases within a CpG dinucleotide) (Issa, 2002). Post-translational modification of histone modifications include methylation, acetylation, phosphorylation, and ubiquitination (Li, 2002; Jaenisch and Bird, 2003). Histone deacetylase is an enzyme involved in the deacetylation of histones.
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Discussion |
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This analysis highlights the importance of several issues that have not been addressed in endometriosis research, yet have now been strongly linked to it by experimental data. First, what role does NF-
B play in endometriosis? Second, what role does nuclear translocation/import/export play in endometriosis? Third, many genes implicated in the growth/development cycle were differentially expressed in the microarray dataset. Some such as RAC1, Raf-1 and FGFR1 also play central roles and are related to many of the themes. RAC1, for example, is a critical upstream signalling molecule that can cause stem cells to divide (Benitah et al., 2005), whereas Raf-1 regulates cellular differentiation, proliferation and apoptosis (Ziogas et al., 2005). Does interfering with these genes induce or prevent endometriosis?
The astounding complexity of the interconnections of the 567 genes underscores several points that have not been appreciated fully in the past. First, piecemeal approaches may be necessary to dissect individual components of the system but, to understand endometriosis as a phenomenon, a system-wide approach is necessary, especially since endometriosis appears to be a system-wide disease affecting many aspects of reproductive health and well-being (Leyendecker, 2000; Vinatier et al., 2000). As in complex systems, "while in many cases properties of individual components can be well characterized in a laboratory, these isolated measurements are typically of relatively little use in predicting the behaviour of large-scale interconnected systems or mitigating the cascading spread of damage due to the seemingly innocuous breakdown of individual parts" (Carlson and Doyle, 1999). The heterogeneity observed in endometriosis is to be expected, since, like tumors, heterogeneity among endometriotic lesions may be a result of "a high level of redundancy, and hence increased chances of survival and growth" (Kitano, 2003) and may be generated by complex genetic and epigenetic changes. Since the female reproductive system itself is very complex, involving several organs, a more fundamental, system or near system-wide approach is needed (Schadt and Lum, 2006).
Second, perhaps as a corollary, any brazen claim that a single gene, or protein, that plays a vital role either in disease initiation or progression and that its activation/inhibition would be effective to treat endometriosis should be carefully scrutinized. This is especially true when, say, a putative novel gene is identified from a gene profiling study or a putative novel protein is identified from a 2D electrophoresis or other high-throughput proteomic technologies. In these cases hundreds, if not thousands, of genes/proteins can be differentially expressed between the uteri and ectopic uterine implants, and the chance of picking up the causal protein is slim to none. As an example, the two proteins identified by this method, so-called ENDO-1 and ENDO-2 (Sharpe et al., 1993), turned out to be two known proteins: one is a variant of hepatic haptoglobin (Piva and Sharpe-Timms, 1999), and the other, tissue inhibitor of metaloproteinase-1 (TIMP-1) (Sharpe-Timms et al., 1995). Not surprisingly, the promise of providing alternative methods of therapeutic management for endometriosis based on these two proteins have so far not been materialized.
Finally, the data-mining of the large data sets arising from gene expression profiling has shown its use here in generating feed-forward hypotheses regarding the pathogenesis of endometriosis. While most, if not all, gene expression profiling studies of endometriosis have discussed and speculated about the roles of differentially expressed genes, none so far has linked these genes to the current knowledge base regarding the disease. This study demonstrates that this type of literature-based data mining not only helps corroborate that the experimental data is consistent with established knowledge, but can also identify new areas of research where future efforts are likely to yield a greater understanding.
It is worth noting several limitations of this study. First, this study is based on a gene expression profiling of the difference between eutopic and ectopic endometrium of women with endometriosis. Due to the cross-sectional nature of the study, many identified changes or differences are likely the consequences, rather than the causes, of endometriosis. Second, the literature-based analysis is based upon the presumption that co-mentioned objects in MEDLINE abstracts represent meaningful relationships. While this has been examined in the original IRIDESCENT studies, and found to be generally true, there is nonetheless a false-positive rate that diminishes with the frequency of co-mentions (i.e. the more two things are mentioned together, the less likely it is a trivial relationship). There are also false-negatives, whereby due to nomenclature difficulties, certain relationships are not detected, such as when the gene name is identical to a common English word (e.g. red, basket, arrow). Third, the nature of some identified relationships can have multiple interpretations. A more in-depth examination of the references will tell, but the number of genes so linked makes this a non-trivial task. Lastly, some discussions on the possible roles of certain genes or themes are, by necessity, mostly speculative, based on circumstantial evidence or data. Speculative as it may be, however, the weight of evidence provided by the general themes identified should yield some educated guess about the pathogenesis of endometriosis and help generate novel hypotheses that can be tested in future studies.
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Submitted on January 9, 2007; resubmitted on March 13, 2007; accepted on April 17, 2007.
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