Hum. Reprod. Advance Access published online on October 3, 2008
Human Reproduction, doi:10.1093/humrep/den360
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Identification of new biomarkers of human endometrial receptivity in the natural cycle
1 CHU Montpellier, Institut de Recherche en Biothérapie, Hôpital Saint-Eloi, Montpellier F-34000, France 2 CHU Montpellier, Département de Médecine et Biologie de la Reproduction, Hôpital Arnaud de Villeneuve, Montpellier F-34295, France 3 INSERM, U847 ‘Développement embryonnaire précoce et cellules souches embryonnaires humaines’, Montpellier F-34000, France 4 Centre de FIV, Clinique les Jardins, Tunis, Tunisia 5 Merck-Serono, Genève, Switzerland 6 Département de Médecine de la Reproduction, hôpital Jean de Flandre, Lille, France
7 Correspondence address. ART/PGD Division, Département de Médecine et Biologie de la Reproduction, Hôpital Arnaud de Villeneuve, 34295 Montpellier, France. Tel: +33-4-67-33-64-04; Fax: +33-4-67-33-62-90; E-mail: s-hamamah{at}chu-montpellier.fr
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Abstract |
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BACKGROUND: Identification of new markers assessing endometrial receptivity may help in improving the clinical outcome of IVF. This study aimed at identifying genes expressed in human endometrium during the implantation window that could be used as such markers.
METHODS: A series of normoresponder patients (n = 31) underwent endometrial biopsies (n = 62, 2 per patient) during the early secretory phase, 2 days after the LH surge (LH + 2) and the mid-secretory phase (LH + 7) of the same natural cycle that preceded a new ICSI attempt for male infertility factor. Samples were analyzed using DNA microarrays and gene expression profiles at the time of the implantation window were computed. Systems biology analysis allowed the identification of biological pathways that were over-represented in this signature. A new approach for class prediction applied to microarray experiments was then used to identify biomarkers putatively involved in endometrial receptiveness.
RESULTS: Five genes expressed during the implantation window were all up-regulated in the LH + 7 samples compared with LH + 2 [laminin β3 (P = 0.002), microfibril-associated protein 5 (P = 0.009), angiopoietin-like 1 (P = 0.005), endocrine gland-derived vascular endothelial growth factor (P = 0.049) and nuclear localized factor 2 (P = 0.007)]. Increased expression was validated by quantitative RT–PCR.
CONCLUSIONS: Five genes have been identified for the first time as being up-regulated during the implantation window and are proposed as new biomarkers for exploration of endometrial receptiveness. As the endometrial biopsy procedure can be performed during a natural cycle, it would be worth testing this approach as a novel strategy in patients with poor implantation after IVF or ICSI.
Key words: human endometrium/microarray/implantation window/biomarkers
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Introduction |
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Despite many advances in assisted reproduction techniques, implantation rates are still low after controlled ovarian stimulation and IVF (Donaghay and Lessey, 2007
). Implantation failure is thought to result from impairment of embryo development and/or from abnormal uterine receptivity. The endometrium is receptive to blastocyst implantation during a spatially and temporally restricted window, called ‘the implantation window’ (Paria et al., 2000). In humans, this period begins 6–10 days after the LH surge and lasts 
48 h (Wilcox et al., 1999; Martin et al., 2002).
Successful implantation depends on synchronization between the developmental stages of the embryo itself and the complex series of molecular and cellular events induced in the endometrium by paracrine and autocrine regulators during the implantation window (Davis and Senger, 2005; Bischof and Campana, 2000). This suggests that many molecular markers will be candidates to determine the ‘ideal’ endometrial receptive period (Abate et al., 1987; van der Gaast et al., 2003; Horcajadas et al., 2004; Edwards, 2006; Aghajanova et al., 2008). Several approaches have been used to identify these biomarkers, such as endometrium biopsy examination, endometrium ultrasonography, and blood and uterine fluid analysis (Abate et al., 1987; van der Gaast et al., 2003; Aghajanova et al., 2008; Dechaud et al., 2008). More recently, transcriptomic approaches have been used to identify biomarkers of the human implantation window. Using microarray technology in human biopsy samples, several authors have observed modifications in gene expression profile associated with the transition of the human endometrium from a pre-receptive (early secretory phase) to a receptive (mid-secretory phase) state (Carson et al., 2002; Riesewijk et al., 2003; Mirkin et al., 2005; Talbi et al., 2006). However, among the various regulated genes, very few were in common between all these studies. Such variability in the results with the same approach may have several explanations: varying mRNA quantities from endometrium cells, different patient profiles and/or limited patient samples (n 
11). In addition, only one study compared the early and the mid-secretory phase in the same patient (Riesewijk et al., 2003), which seems to us a necessary condition to minimize the impact of inter-patient variability.
Therefore, in this study, we revisited the global gene expression profile of human endometrial biopsies by using a larger series of patients (n = 31) and samples (n = 62) that were collected during the early secretory phase, 2 days after the LH surge (LH + 2) and the mid-secretory phase (LH + 7) of a natural menstrual cycle.
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Materials and Methods |
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Patient characteristics and endometrial biopsies
This project has received institutional review board approval. The study population included 31 patients (mean ± SEM age: 30.4 ± 3.2 years), recruited after written informed consent. All patients had normal serum FSH, LH, estradiol (E2) and anti-Müllerian hormone levels on Day 3 and were normoresponders during a previous first ICSI attempt. They were referred for ICSI for male infertility factor. Endometrial pathologies, such as endometriosis or tubal disease, were not explored by histological analysis. During the same natural cycle that preceded a second ICSI attempt, two endometrial biopsies were obtained in all women at Day 2 (LH + 2) and Day 7 (LH + 7) after the LH peak. The LH surge was estimated by patient herself according to the first day of their menstruation. Histologic analysis was not performed to verify that the LH timing was accurate. Therefore, the possibility for a delay of 1 day from the first day of menstruation cannot be excluded. Each biopsy sample was washed in phosphate-buffered saline and frozen at –80°C in RLT RNA extraction buffer (RNeasy kit, Qiagen, Valencia, CA, USA).
Complementary RNA preparation and microarray hybridization
Total RNA (100 ng) was used to prepare twice amplified labeled complementary RNA (cRNA) for hybridization to HG-U133 plus 2.0 GeneChip pangenomic oligonucleotide arrays (Affymetrix, Santa Clara, CA, USA) as described in Assou et al. (2007) and Gasca et al. (2007).
Microarray data analyses
HG-U133 plus 2.0 arrays contain 54 675 oligonucleotide probe sets, which correspond to 
30 000 unique human genes or predicted genes. Array analysis was performed with the GeneChip Operating Software 1.2 (Affymetrix) to measure significant RNA detection (detection call ‘present’ or ‘absent’) and to evaluate the signal intensity for each probe set (Assou et al., 2007).
Bioinformatics and in silico analyses
The significant analysis of microarrays (SAM, Stanford University, USA, Tusher et al., 2001) was used to identify genes whose expression varied significantly between the two sample groups, LH + 2 (n = 31) and LH + 7 (n = 31). SAM provides mean or median fold change values and a false discovery rate (FDR) confidence percentage based on data permutation.
To compare profile expression of endometrial samples (n = 62) from the LH + 2 and LH + 7 groups, we performed an unsupervised classification with both principal component analysis (PCA) and hierarchical clustering (Eisen et al., 1998; de Hoon et al., 2004). The PCA involved original scripts based on the R-statistics software through the RAGE web interface (http://rage.montp.inserm.fr) (Rème et al., 2008). Hierarchical clustering analyses based on the expression levels of varying probes were performed with the CLUSTER and TREEVIEW software packages. Genetic expression profiles were analyzed with the RAGE supervised analysis module, using a non-parametric Mann–Whitney U-test with multiple testing corrections and were confirmed with the SAM software. Selected gene lists (mean fold change >2 and FDR corrected P < 0.05) were submitted to Ingenuity software (http://www.ingenuity.com) to identify the biological mechanisms altered by these gene expression variations.
Predictor construction
This three-step process extensively described elsewhere (Rème et al., 2008) has been modified for paired samples. Affymatrix detection calls were used throughout with only two levels of expression, ‘Present’ as 1 and ‘Else’ as 0. As recommended by others (McClintick and Edenberg, 2006), probe sets were filtered by selecting half the size of a sample class as the minimal number of present calls across all samples. Probe sets with poorly informative signals were further eliminated using a minimal variation coefficient of 40%, leading to a final 16 130 probe sets out of a 54 675 probe sets U133P chip.
Reduction of the data dimensionality was achieved by comparing each probe set distribution in sample groups considered as multiple drawings of a two-stage criterion (presence, 1; else, 0). Briefly, to account for the paired situations (LH2 and LH7) for one patient, a probe set vector was constructed whose values are either 0 if both situations lead to the same call or 1 if they differ. This vector is then compared with the null vector using a 
2 test, with a multiple testing corrected P-value resulting in a 200–500 probe set list, subsequently used for supervised analysis. The capacity of such a list to separate sample classes is evaluated as described previously by maximizing the significance of sample-to-sample comparisons using a 2 test with the Bonferroni correction for multiple testing and the Yates correction for small sample numbers in two class comparisons. If the significance threshold is reached, the samples are not in the same class. This is repeated for comparison of each class sample paired to any sample of the other class and the initial number and strength of non-significant comparisons can be determined. Reducing the list is achieved by minimizing the number of non-significant comparisons by successive deletions of the probe set giving the best improvement. The process stops when no criterion can be further improved by probe set removal, the remaining list being the predictor.
For leave-one-out cross-validation, each sample pair in turn is removed, and the whole process of dimensionality reduction and predictor building is run with the Bonferroni correction on the remaining samples as described for initial classes. Each predictor build in this way is tested for its capacity to generate misclassification errors when the omitted sample is returned to its class, where the number of non-significant comparisons should be 0.
Quantitative PCR analyses
Labeled cRNA (1 µg) from the patient was used to generate first-strand cDNA. These cDNAs (5 µl of a 1/10 dilution) were used for quantitative PCR reactions according to the manufacturer's recommendations (Applied Biosystems). The 20 µl reaction mixture consisted of cDNA (5 µl), 1 µM of primers and 10 µl of Taqman Universal PCR Master Mix (Applied Biosystem). The amplification was measured during 40 cycles with an annealing temperature at 60°C. Phosphoglycerate kinase 1 was used to normalize expression levels between the samples.
Statistical analyses
Statistical analyses for quantitative PCR values were performed with the Microsoft Office Excel software. A difference of mean ± SEM between sample groups was considered significant when Student's t-test gave a P-value of <0.05.
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Results |
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Gene expression profile as a function of endometrial receptivity
Selection using a variation coefficient (
40%) and the absent/present ‘detection call’ (presence in at least 15 samples) between LH + 2 and LH + 7 samples was first performed, delineating 16 200 probe sets, and then subjected to an unsupervised analysis with PCA (3000 probe sets) (Fig. 1A). In this PCA, 80% of LH + 7 samples were separated from the others using the first two dimensions, representing 16% of the data information (Fig. 1A). A hierarchical clustering was performed with the same data (3000 probe sets) and confirmed the PCA analysis (Fig. 1B). Then, we performed a SAM analysis between the LH + 2 and the LH + 7 sample groups (LH + 2 versus LH + 7, paired sample analysis). One thousand and twelve genes were significantly modulated between these two groups, including 945 up-regulated genes and 67 down-regulated genes in the LH + 7 sample group (fold change 2 and P < 0.05). To identify new markers of endometrial receptivity, we have compared our gene list significantly modulated between the LH + 2 and LH + 7 sample groups with four other studies which compared the same natural endometrium cycle phases (Table I) (Carson et al., 2002; Riesewijk et al., 2003; Mirkin et al., 2005; Talbi et al., 2006). There were only two genes in common with all these studies, 12 genes in four, 59 genes in three and 142 genes in two out of the five studies (including our study) (Fig. 2).
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Determination of genes specifically modulated during the implantation window
We identified a list of genes specifically modulated during the implantation window and exclusive to the current study (Fig. 2). We performed a hierarchical clustering with the same data (797 genes). This list of genes allowed the separation of the two endometrium sample groups, and Fig. 3 illustrates this separation. Interestingly, the majority of these genes were up-regulated during the implantation window (746 up-regulated genes and 51 down-regulated genes).
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Validation of the predictor list
We first performed a PCA analysis of our LH + 2 and LH + 7 samples with the predictor list (comprising 60 probe sets, as described in Materials and Methods) with a 6% leave-one-out cross-validation error for P
0.01. Seventy-five percentage of LH + 7 samples were separated from the other samples in this PCA using the first two dimensions, representing 52% of the data information (Fig. 4A). We then tested our predictor list on the Talbi et al. (2006) samples, which consisted of three early secretory samples and eight mid-secretory samples. As shown in Fig. 4B, the PCA analysis of these independent samples with our predictor list allows a distinct separation using the first two dimensions between the two sample groups (LH + 2 and LH + 7), representing 53% of the data information.
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Candidate gene selection of the implantation window
To select candidate genes of the implantation window, we have compared our list of genes specifically modulated during the implantation window and exclusive to our study with the list of predictor. We have selected four new genes predicting endometrial receptivity, not listed in previous reports of microarray analysis. These new markers are microfibril-associated protein 5 (MFAP5), angiopoietin-like 1 (ANGPTL1), endocrine gland-derived vascular endothelial growth factor (EG-VEGF) and nuclear localized factor 2 (NLF2), and our microarray data show they are all over-expressed during the implantation window by a factor of 37, 12.6, 10.2 and 22.5, respectively. In addition, a gene found in two other microarray analyses (Riesewijk et al., 2003
; Talbi et al., 2006) and in our study was chosen as a positive control of human endometrial receptivity. This gene is laminin β3 (LAMB3) and is over-expressed by a factor of 20.4 in our study, and a factor of 15 and 6.6 in the chosen articles (Table II). To quantify expression of these genes, five LH + 2 samples and five LH + 7 samples were randomly selected from the original samples. Mean transcript levels were significantly higher in LH + 7 samples for all selected genes when compared with LH + 2 samples and were 0.8 ± 0.2 versus 0.05 ± 0.02 for LAMB3 (P = 0.002), 7.3 ± 2.9 versus 1.4 ± 0.6 for ANGPTL1 (P = 0.005), 67.4 ± 28.1 versus 3.5 ± 1.9 for MFAP5 (P = 0.009), 3.9 ± 2.6 versus 0.45 ± 41 for EG-VEGF (P = 0.049) and 0.9 ± 0.4 versus 0.05 ± 0.02 for NLF2 (P = 0.007) (Fig. 5).
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ICSI outcome and marker genes
After the second ICSI attempt, an ongoing pregnancy was obtained in 11 out of the 31 patients who were studied during the preceding natural cycle. The expression of our five genes did not differ between the pregnant and the non-pregnant patients, either in the LH + 2 or LH + 7 subgroups.
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Discussion |
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A recent meta-analysis from Assou et al. (2007)
found that among 38 original studies reporting the transcriptome of human embryonic stem cells, only one gene was identified in all studies. Therefore, we expected that, using a similar approach, there would be very few genes significantly and similarly regulated during the implantation window in all studies of the endometrium. Indeed, when adding our data to the previous studies (Carson et al., 2002; Riesewijk et al., 2003; Mirkin et al., 2005; Talbi et al., 2006), only two up-regulated genes were found in common: secreted phosphoprotein 1 (SPP1) and interleukin 15 (IL15). Briefly, SPP1, also called osteopontin, is a glycoprotein involved in cellular adhesion and migration during embryo implantation. SPP1 is a ligand for 
vβ3 integrin and its maximal expression has been observed in endothelial cells during the implantation window (von Wolff et al., 2001). On the other hand, IL15 is a progesterone-regulated gene in endometrial stroma cells. This cytokine seems to be involved in stages immediately before, during and after the apposition step and it permits adequate proliferation of the stroma (Lédée et al., 2007).
In addition, our microarray analysis identified a series of markers among which we selected five candidates—LAMB3, MFAP5, ANGPTL1, EG-VEGF and NLF2—that we validated by quantitative PCR. Interestingly, in the baboon endometrium, laminin expression is increased at the implantation site and throughout the endometrium, suggesting a role of this extracellular matrix (ECM) component in endometrial receptivity (Fazleabas et al., 1997). On the other hand, MFAP5, also called MAGP2, was also over-expressed in our LH + 7 sample group. This gene encodes a microfibril-associated glycoprotein which is a component of microfibrils, an important structural component of elastic tissues, such as vasculature. By interacting with both ECM, such as collagen, and cell-associated proteins, such as integrins, this protein is therefore positioned to potentially modulate cell–matrix interactions and to participate in cell signaling pathways (Lemaire et al., 2005; Miyamoto et al., 2006). Moreover, it has been recently suggested that MFAP5 has a role in Notch signaling activation, a pathway involved in vascularization during embryogenesis, development and normal homeostasis (Shawber et al., 2003).
We also found that two members of the VEGF family, EG-VEGF and ANGPTL1, were over-expressed in human endometrium during the implantation window. EG-VEGF, also called PROK1, is a newly identified angiogenic and permeability enhancing factor which is predominantly expressed in steroidogenic tissues. EG-VEGF is also expressed in the normal peri-implantation endometrial samples from patients of reproductive age, and rarely detected in the endometrial samples from post-menopausal patients and patients with endometrial carcinoma (Ngan et al., 2006). EG-VEGF is predominantly expressed in the glandular epithelial cells, with a peak protein expression at the mid-luteal phase of the menstrual cycle (Torry et al., 1996; Kisliouk et al., 2003; Battersby et al., 2004; Ngan et al., 2006). The coexistence of EG-VEGF and its receptor, PROKR1 (data not shown), in human endometrium supports the idea that EG-VEGF may regulate proliferation, angiogenesis and permeability and induce the formation of endothelial fenestration. ANGPTL1 is a member of the angiopoietin-related protein family. In in vitro studies, ANGPTL1 has anti-apoptotic activities through the phosphatidylinositol 3-kinase/Akt pathway and regulates angiogenesis (Johnson et al., 2006). In the ovariectomized ewe model, ANGPTL1 mRNA is increased after E2 treatment (Johnson et al., 2006). In our study, we observed an over-expression of ANGPTL1 gene during the implantation window of endometrium, which was confirmed by quantitative PCR.
In our microarray data and quantitative PCR analysis, the NLF2 gene was strongly expressed in the LH + 7 sample group, suggesting that it has a role in endometrium remodeling during the implantation window. Invasion into the endometrial stroma is facilitated by inflammation. NLF2 was recently identified as a nuclear factor and as a member of a family of regulatory genes that play a role in endothelial cell inflammation (Warton et al., 2004). Initially identified in a genome-wide array screen of human microvascular endothelial cells treated with IL 1β, NLF2 is probably part of the signaling pathway causing changes in cell architecture and adhesion in endothelial cell inflammation (Warton et al., 2004).
Disparities with other findings (Carson et al., 2002; Riesewijk et al., 2003; Mirkin et al., 2005; Talbi et al., 2006) may be explained by several factors: type of microarrays used, data analysis, statistical methodologies, size of patient samples, age of subjects, patient fertility/infertility and sampling time during the cycle (Table I). One possibility to explain why our five candidate genes were not identified by others is our choice to use new microarrays containing more genomic information (>30 000 genes for the Hu133P oligonucleotide microarrays versus 12 000 genes for the Hu95A used by others). Moreover, only Riesewijk et al. (2003) analyzed samples from the same patient in the early and the mid-secretory phase as was done in the present study, which is an important point to minimize inter-patient variability. Indeed, performing microarray analysis with paired samples—each patient was biopsied at LH + 2 and LH + 7—allowed us to minimize the impact of inter-patient variability on the data analysis. However, one of the most important differences from all reported studies was the number of patient samples (n 11 versus 62 in the present study). This seems to us important, knowing the changing behavior of the human menstrual cycle that causes significant inter- and intra-individual variability among a patient group.
Finally, we cannot exclude the possibility that the modulation of gene expression observed during the implantation window was the consequence of local injury caused by the first endometrial biopsy. Indeed, Barash et al. (2003) showed that multiple endometrium biopsies during the spontaneous menstrual cycle increased implantation and pregnancy rates in the following cycle of treatment, and more recently Kalma et al. (2008) reported that the first endometrial biopsy modulated a wide variety of genes in the same cycle as well as in the following cycle. Therefore, further investigations should be conducted to validate our biomarkers. In addition, for practical reasons, endometrial biopsies were only performed during a spontaneous cycle and therefore extrapolation of our data to cycles with IVF treatment still requires some caution.
In conclusion, our data using analysis of transcriptomic pattern of endometrium could open new perspectives, especially in patients with multiple implantation failures. Analysis of our list of genes could reveal an alteration of the endometrial expression profile for some normoresponder patients. The information given by these biomarkers during a natural cycle could then be used subsequently to adapt the IVF protocol in patients with poor implantation.
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We wish to thank the Merck-Serono Pharmaceutical Company for their partial financial support of this study.
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Acknowledgements |
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We thank the University-Hospital of Montpellier for support and the ART team for their assistance during this study.
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References |
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Abate V, De Corato R, Cali A, Stinchi A. Endometrial biopsy at the time of embryo transfer: correlation of histological diagnosis with therapy and pregnancy rate. J In Vitro Fert Embryo Transf (1987) 4:173–176.[CrossRef][Web of Science][Medline]
Aghajanova L, Hamilton AE, Giudice LC. Uterine receptivity to human embryonic implantation: histology, biomarkers, and transcriptomics. Semin Cell Dev Biol (2008) 19:204–211.[CrossRef][Web of Science][Medline]
Assou S, Le Carrour T, Tondeur S, Ström S, Gabelle A, Marty S, Nadal L, Pantesco V, Réme T, Hugnot JP, et al. A meta-analysis of human embryonic stem cells transcriptome integrated into a web-based expression atlas. Stem Cells (2007) 25:961–973.[CrossRef][Web of Science][Medline]
Barash A, Dekel N, Fieldust S, Segal I, Schechtman E, Granot I. Local injury of the endometrium doubles the incidence of successful pregnancies in patients undergoing in-vitro fertilization. Fertil Steril (2003) 79:1317–1322.[CrossRef][Web of Science][Medline]
Battersby S, Critchley HO, Morgan K, Millar RP, Jabbour HN. Expression and regulation of the prokineticins (endocrine gland-derived vascular endothelial growth factor and Bv8) and their receptors in the human endometrium across the menstrual cycle. J Clin Endocrinol Metab (2004) 89:2463–2469.
Bischof P, Campana A. Molecular mediators of implantation. Baillieres Best Pract Res Clin Obstet Gynaecol (2000) 14:801–814.[CrossRef][Medline]
Carson DD, Lagow E, Thathiah A, Al-Shami R, Farach-Carson MC, Vernon M, Yuan L, Fritz MA, Lessey B. Changes in gene expression during the early to mid-luteal (receptive phase) transition in human endometrium detected by high-density microarray screening. Mol Hum Reprod (2002) 8:871–879.
Davis GE, Senger DR. Endothelial extracellular matrix: biosynthesis, remodeling, and functions during vascular morphogenesis and neovessel stabilization. Circ Res (2005) 97:1093–1107.
de Hoon MJ, Imoto S, Nolan J, Miyano S. Open source clustering software. Bioinformatics (2004) 20:1453–1454.
Dechaud H, Bessueille E, Bousquet PJ, Reyftmann L, Hamamah S, Hedon B. Optimal timing of ultrasonographic and Doppler evaluation of uterine receptivity to implantation. Reprod Biomed Online (2008) 16:368–375.[Web of Science][Medline]
Donaghay M, Lessey BA. Uterine receptivity: alterations associated with benign gynecological disease. Semin Reprod Med (2007) 25:461–475.[CrossRef][Web of Science][Medline]
Edwards RG. Human implantation: the last barrier in assisted reproduction technologies? Reprod Biomed Online (2006) 13:887–904.[Web of Science][Medline]
Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genomewide expression patterns. Proc Natl Acad Sci USA (1998) 95:14863–14868.
Fazleabas AT, Bell SC, Fleming S, Sun J, Lessey BA. Distribution of integrins and the extracellular matrix proteins in the baboon endometrium during the menstrual cycle and early pregnancy. Biol Reprod (1997) 56:348–356.[Abstract]
Gasca S, Pellestor F, Assou S, Loup V, Anahory T, Dechaud H, De Vos J, Hamamah S. Identifying new human oocyte marker genes: a microarray approach. Reprod Biomed Online (2007) 14:175–183.[Web of Science][Medline]
Horcajadas JA, Riesewijk A, Dominguez F, Cervero A, Pellicer A, Simon C. Determinants of endometrial receptivity. Ann NY Acad Sci (2004) 1034:166–175.[CrossRef][Web of Science][Medline]
Johnson ML, Grazul-Bilska AT, Redmer DA, Reynolds LP. Effects of estradiol-17beta on expression of mRNA for seven angiogenic factors and their receptors in the endometrium of ovariectomized (OVX) ewes. Endocrine (2006) 30:333–342.[CrossRef][Web of Science][Medline]
Kalma Y, Granot I, Gnainsky Y, Or Y, Czernobilsky Bn, Dekel N, Barash A. Endometrial biopsy-induced gene modulation: First evidence for the expression of bladder-transmembranal uroplakin Ib in human endometrium. Fertil Steril (2008).
Kisliouk T, Levy N, Hurwitz A, Meidan R. Presence and regulation of endocrine gland vascular endothelial growth factor/prokineticin-1 and its receptors in ovarian cells. J Clin Endocrinol Metab (2003) 88:3700–3707.
Lemaire R, Korn JH, Shipley JM, Lafyatis R. Increased expression of type I collagen induced by microfibril-associated glycoprotein 2: novel mechanistic insights into the molecular basis of dermal fibrosis in scleroderma. Arthritis Rheum (2005) 52:1812–1823.[CrossRef][Web of Science][Medline]
Lédée N, Dubanchet S, Oger P, Meynant C, Lombroso R, Ville Y, Chaouat G. Uterine receptivity and cytokines: new concepts and new applications. Gynecol Obstet Invest (2007) 64:138–143.[CrossRef][Web of Science][Medline]
Martin J, Dominguez F, Avila S, Castrillo JL, Remohi J, Pellicer A, Simón C. Human endometrial receptivity: gene regulation. J Reprod Immunol (2002) 55:131–139.[CrossRef][Web of Science][Medline]
McClintick J, Edenberg H. Effects of filtering by present call on analysis of microarray experiments. BMC Bioinformatics (2006) 7:49–64.[CrossRef][Medline]
Mirkin S, Arslan M, Churikov D, Corica A, Diaz JI, Williams S, Bocca S, Oehninger S. In search of candidate genes critically expressed in the human endometrium during the window of implantation. Hum Reprod (2005) 20:2104–2117.
Miyamoto A, Lau R, Hein PW, Shipley JM, Weinmaster G. Microfibrillar proteins MAGP-1 and MAGP-2 induce Notch1 extracellular domain dissociation and receptor activation. J Biol Chem (2006) 281:10089–10097.
Ngan ES, Lee KY, Yeung WS, Ngan HY, Ng EH, Ho PC. Endocrine gland-derived vascular endothelial growth factor is expressed in human peri-implantation endometrium, but not in endometrial carcinoma. Endocrinology (2006) 147:88–95.
Paria BC, Lim H, Das SK, Reese J, Dey SK. Molecular signaling in uterine receptivity for implantation. Semin Cell Dev Biol (2000) 11:67–76.[CrossRef][Web of Science][Medline]
Rème T, Hose D, De Vos J, Vassal A, Poulain PO, Pantesco V, Goldschmidt H, Klein B. A new method for class prediction based on signed-rank algorithms applied to Affymetrix microarray experiments. BMC Bioinformatics (2008) 11:16.
Riesewijk A, Martín J, van Os R, Horcajadas JA, Polman J, Pellicer A, Mosselman S, Simón C. Gene expression profiling of human endometrial receptivity on days LH+2 versus LH+7 by microarray technology. Mol Hum Reprod (2003) 9:253–264.
Shawber CJ, Das I, Francisco E, Kitajewski J. Notch signaling in primary endothelial cells. Ann N Y Acad Sci (2003) 995:162–170.[CrossRef][Web of Science][Medline]
Talbi S, Hamilton AE, Vo KC, Tulac S, Overgaard MT, Dosiou C, Le Shay N, Nezhat CN, Kempson R, Lessey BA, et al. Molecular phenotyping of human endometrium distinguishes menstrual cycle phases and underlying biological processes in normo-ovulatory women. Endocrinology (2006) 147:1097–1121.[CrossRef][Web of Science][Medline]
Torry DS, Holt VJ, Keenan JA, Harris G, Caudle MR, Torry RJ. Vascular endothelial growth factor expression in cycling human endometrium. Fertil Steril (1996) 66:72–80.[Web of Science][Medline]
Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA (2001) 98:5116–5121.
van der Gaast MH, Beier-Hellwig K, Fauser BC, Beier HM, Macklon NS. Endometrial secretion aspiration prior to embryo transfer does not reduce implantation rates. Reprod Biomed Online (2003) 7:105–109.[Medline]
von Wolff M, Strowitzki T, Becker V, Zepf C, Tabibzadeh S, Thaler CJ. Endometrial osteopontin, a ligand of beta3-integrin, is maximally expressed around the time of the ‘implantation window. Fertil Steril (2001) 76:775–781.[CrossRef][Web of Science][Medline]
Warton K, Foster NC, Gold WA, Stanley KK. A novel gene family induced by acute inflammation in endothelial cells. Gene (2004) 342:85–95.[CrossRef][Web of Science][Medline]
Wilcox AJ, Baird DD, Weinberg CR. Time of implantation of the conceptus and loss of pregnancy. N Engl J Med (1999) 340:1796–1799.
Submitted on August 5, 2008; resubmitted on September 5, 2008; accepted on September 10, 2008.
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  A. Allegra, A. Marino, F. Coffaro, A. Lama, G. Rizza, P. Scaglione, F. Sammartano, A. Santoro, and A. Volpes Is there a uniform basal endometrial gene expression profile during the implantation window in women who became pregnant in a subsequent ICSI cycle? Hum. Reprod., October 1, 2009; 24(10): 2549 - 2557. [Abstract] [Full Text] [PDF]
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D. Haouzi, S. Assou, K. Mahmoud, S. Tondeur, T. Reme, B. Hedon, J. De Vos, and S. Hamamah Gene expression profile of human endometrial receptivity: comparison between natural and stimulated cycles for the same patients Hum. Reprod., June 1, 2009; 24(6): 1436 - 1445. [Abstract] [Full Text] [PDF]
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