Introduction
Welcome to the MenstrualCycleGCN documentation page!
MenstrualCycleGCN is a webtool database containing details about endometrial receptivity array gene behaviour Diaz-Gimeno et al. 2011 in terms of weighted gene co-expression network analysis (WGCNA) (Zhang et al. 2005). WGCNA is an approach to network modeling that improves simple correlation networks by quantifying not only correlations between gene pairs, but also what genes share with their neighbours—which is why this approach was used to establish modules of highly co-expressed genes with common behaviours in endometrial profiles Diaz-Gimeno et al. 2017. Correlation values shown here were estimated using Pearson's correlation coefficient. In addition, this database includes information about functional annotation [Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)] and regulation (transcription factors and miRNAs) for each gene, as well as functional/regulation enrichment for each menstrual profile.
The information is divided in two main branches:
- Endometrial profile includes the co-expression network, correlation matrix, and functional/regulation enrichment for each co-expression module of each menstrual profile. This section also includes a tool to build the co-expression network centered on each gene according to a particular correlation threshold. In addition, information about functional annotation or regulators is provided for a selected gene.
- Menstrual cycle progression compares menstrual profiles from different points of view, including functional/regulation annotation comparison between profiles or comparison between co-expression values from profiles of two specified genes.
Tutorial
Endometrial profile
Six transcriptomic profiles associated with menstrual cycle phases have been defined in previous work Diaz-Gimeno et al. 2017: Proliferative (PF), Early Pre-Receptive (EPR), Receptive (RR), Late Pre-Receptive (LPR), Late Receptive (LR), and Post-Receptive (PS).
Co-expression networks, regulators, and functional annotation of each profile can be accessed by selecting the endometrial phase of interest in the endometrial plot. Once a profile is selected, different options are available, including information about co-expression networks, co-expression modules, and functional/regulation enrichment.
Transcriptional Regulation includes information about co-expression networks in the selected profile as well as detailed information about co-expression and functional/regulation annotation for each co-expression module that forms the network.
- Co-expression network includes a dynamic network associated with the selected gene according a selected threshold. Grey node represents the selected node, and yellow nodes represent genes with an absolute Pearson correlation (R2) values higher than the selected threshold. These nodes are listed in the right panel. Edges represent Pearson correlations, with positive correlations shown in green and negative correlations shown in red. In this section, two main actions can be taken: 1) select a gene and locate it in its module; and 2) select a module. Both actions bring up a new menu including correlation matrix as well as functional/regulator enrichment information about the selected module (in blue):
- Correlation matrix includes a heatmap representing correlation matrix values for the selected module. Gradient colors depend on Pearson correlation values, from green (positive correlation, R2 >0) to white (no correlation, R2 = 0) and from white to red (negative correlation, R2 < 0). This heatmap can be downloaded (.JPG format) using the export button
. - Transcription Factor includes a table showing results for TF enrichment using the Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) webtool (Han et al. 2017) for genes in the considered module. Key TF indicates name of the TF; Description indicates description of the TF; No. genes indicates number of target genes for this TF in the considered module; P-value is the p-value for enrichment analysis; FDR is the false discovery rate (FDR)-corrected p-value; and Genes lists target genes for this TF in the considered module.
- miRNA family includes a table showing results for miRNA family enrichment using the miRNA binding site over-representation (mBISON) (Gebhardt et al. 2014) webtool for genes belonging to the considered module. miRNA family indicates name of the miRNA family; miRNAs indicates miRNAs included in the family; No. genes indicates number of target genes for this miRNA family in the considered module; P-value is the p-value for enrichment analysis; FDR is the FDR-corrected p-value; and Genes lists target genes for this miRNA family in the considered module.
- Biological Process includes a table showing results for functional enrichment considering manually defined functional groups (Sebastian-Leon et al. 2018) based on Biological Process ontology from the Gene Ontology (GO) database (Ashburner et al. 2000). Enrichment was done using Fisher exact test (Fisher, 1922) followed by FDR correction (Benjamini et al. 1995) for multiple testing. Functional Group is name of the functional group; Gene Ratio indicates number of genes in the module that are included in the functional group over total number of genes in the module that are included in at least one functional group; Bg Ratio indicates number of genes included in the functional group over total number of considered genes; P-value is the p-value for enrichment analysis; FDR is the FDR-corrected p-value; and Genes lists target genes for this miRNA family in the considered module.
- Molecular Function includes a table showing results for functional enrichment considering manually defined functional groups (Sebastian-Leon et al. 2018) based on Molecular Function ontology from the Gene Ontology (GO) database (Ashburner et al. 2000). Enrichment was done using Fisher exact test (Fisher, 1922) followed by FDR correction (Benjamini et al. 1995) for multiple testing. Functional Group is name of the functional group; Gene Ratio indicates number of genes in the module that are included in the functional group over total number of genes in the module that are included in at least one functional group; Bg Ratio indicates number of genes included in the functional group over total number of considered genes; P-value is the p-value for enrichment analysis; FDR is the FDR-corrected p-value; and Genes lists target genes for this miRNA family in the considered module.
- Cellular Component includes a table showing results for functional enrichment considering manually defined functional groups (Sebastian-Leon et al. 2018) based on Cellular Component ontology from the Gene Ontology (GO) database (Ashburner et al. 2000). Enrichment was done using Fisher exact test (Fisher, 1922) followed by FDR correction (Benjamini et al. 1995) for multiple testing. Functional Group is name of the functional group; Gene Ratio indicates number of genes in the module that are included in the functional group over total number of genes in the module that are included in at least one functional group; Bg Ratio indicates number of genes included in the functional group over total number of considered genes; P-value is the p-value for enrichment analysis; FDR is the FDR-corrected p-value; and Genes lists target genes for this miRNA family in the considered module.
- KEGG includes a table showing results for functional enrichment considering manually defined functional groups (Sebastian-Leon et al. 2018) based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (Kanehisa et al. 2000). Enrichment was done using Fisher exact test (Fisher, 1922) followed by FDR correction (Benjamini et al. 1995) for multiple testing. Functional Group is name of the functional group; Gene Ratio indicates number of genes in the module that are included in the functional group over total number of genes in the module that are included in at least one functional group; Bg Ratio indicates number of genes included in the functional group over total number of considered genes; P-value is the p-value for enrichment analysis; FDR is the FDR-corrected p-value; and Genes lists target genes for this miRNA family in the considered module.
- Correlation matrix includes a heatmap representing correlation matrix values for the selected module. Gradient colors depend on Pearson correlation values, from green (positive correlation, R2 >0) to white (no correlation, R2 = 0) and from white to red (negative correlation, R2 < 0). This heatmap can be downloaded (.JPG format) using the export button
- Co-expression matrix includes a heatmap representing Pearson correlation values for every gene pair. Gradient colors depend on the Pearson correlation values, from green (positive correlation, R2 >0) to white (no correlation, R2 = 0) and from white to red (negative correlation, R2 < 0). Black rectangles represent co-expression modules, which can be selected to show information about the selected module included in the previous section. In addition, this heatmap can be downloaded (.JPG format) using the export button
. 
- Network regulators includes a dynamic network showing gene co-expression networks for the selected endometrial profile and enriched regulators (TFs and miRNAs) for each module. Grey rounded nodes represent genes, black diamonds represent enriched TFs, and black rectangles represent enriched miRNA families. Edges represent Pearson’s correlation and are colored green (positive correlation, R2 > 0) or red (negative correlation, R2 < 0), with a variable width depending on the value. R2 is filtered to better visualize the network. Edges into modules are filtered if R2 < 0.5 (preserving modules obtained from WGCNA), and edges between modules are shown if R2 > 0.75. Grey dashed edges represent relations between a regulator (TF or miRNA) and its target gene.
Single Gene Co-expression Network includes a drop-down menu that allows selection of a gene of interest and a minimum correlation value threshold. Selection of these two parameters provides information about the selected gene’s co-expression relations and external information:
- A dynamic network showing the co-expression network associated with the selected gene according to the selected threshold. Grey node represents the selected node, and yellow nodes represent genes with an absolute Pearson correlation value higher than the selected threshold. These nodes are listed in the right panel and are clickable. Edges represent Pearson correlations, with positive correlations shown in green and negative correlations shown in red. In addition, the bottom shows degree and betweenness centrality of the selected gene in the resulting network.
- GO - Biological Process includes a table indicating annotation of GO functional terms based on Biological Process ontology for the selected gene. Id is identifier of the functional term in the database; and Description is name of the functional term.
- GO - Molecular Function includes a table indicating annotation of GO functional terms based on Molecular Function ontology for the selected gene. Id is identifier of the functional term in the database; and Description is name of the functional term.
- GO - Cellular Component includes a table indicating annotation of GO functional terms based on Cellular Component ontology for the selected gene. Id is identifier of the functional term in the database; and Description is name of the functional term.
- KEGG pathways includes a table indicating KEGG pathways annotation for the selected gene. Id is the identifier of the functional term in the database; and Description is the name of the functional term.
- Transcription Factors includes a table indicating TFs that target the selected gene based on TRANSFAC annotation (Matys et al. 2006). Transcription Factor is name of the TF; Description is description of the TF; and Type of Regulation is type of regulation (activation, repression, or unknown) indicated in the database.
- miRNAs includes a table indicating miRNA families that target the selected gene based on TargetScan 6.2 annotation (Lewis et al. 2005). miRNA family is name of the miRNA family; and miRNAs is miRNAs included in the family.
- ClinVar links to Clinvar database (Landrum et al. 2013) information about the selected gene.
- Expression Atlas links to Expression Atlas (Kapushesky et al. 2011) information about the selected gene.
- GeneCards links to GeneCards database (Rebhan et al. 1998) information about the selected gene.
Menstrual cycle progression
This section shows comparisons between different endometrial profiles in terms of functional annotation and regulation. This section also includes correlation value comparisons between every gene pair throughout the menstrual cycle.
- Functional progressionincludes functional annotation progression based on the Gene Ontology (GO) database (Ashburner et al. 2000) for Biological Process, Molecular Function, and Cellular Component ontologies; and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Kanehisa et al. 2000). For each functional database, a table indicates which functional terms are annotated in the endometrial profile. Blue cells indicate terms annotated in the endometrial profile, and grey cells indicate terms not annotated in this profile. Terms can be sorted to focus on a particular endometrial profile by using the arrows. In addition, a search function is included to easily query by filtering terms by name or identifier.
- Regulators progression includes information about enriched regulators [transcription factors (TFs) and miRNAs families] in each endometrial profile.
- TF enrichment includes a table indicating which functional terms are enriched in an endometrial profile according to analysis by the Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) webtool (Han et al. 2017). Blue cells indicate TFs enriched in the endometrial profile, and grey cells indicate terms not enriched in this profile. Terms can be sorted to focus on a particular endometrial profile by using the arrows. In addition, a search function is included to easily query by filtering terms by name or identifier.
- Regulators network includes a dynamic network for each endometrial profile (rounded nodes in color), including enriched regulators. Black diamonds represent enriched transcription factors, and black rectangles represent enriched miRNA families. Edges represent enrichment relations between an endometrial profile and regulator. This network can be downloaded (.JPG format) using the export button
.
- Gene pair co-expression progression includes a tool to compare gene pair correlation throughout the menstrual cycle. Once a gene pair is selected, the compare button generates a connected scatter plot that shows correlation values for each endometrial profile. Blue points and line represent correlation between the gene pair, and red dashed line indicates no correlation. Points above red dashed line indicate positive correlations, and points below red dashed line indicate negative correlations. This plot can be downloaded (.JPG format) using the export button
.
Worked examples
Single gene co-expression network for stimulating hypothesis-driven single-molecule studies in endometrium
Introduction
The transcription factor SOX17 has main roles in development, endometrial infertility, and cancer (Igarashi et al. 2018, Hirate et al. 2016, Fu et al. 2015A, Fu et al. 2015B). In addition, SOX17 is involved in endometrial transcriptional control in rabbits (García et al. 2007) and in uterine receptivity and embryo implantation in mice (Hirate et al. 2016). Hence, we could design a molecular experiment of SOX17 transcriptional regulation in the menstrual cycle to discover molecular details about the specific role of SOX17 in endometrial receptivity. Such investigation would enable exploration of SOX17 co-expressed genes at the receptive profile and how this co-expression changes throughout the menstrual cycle.
The working plan
- Is SOX17 included in the endometrial dating genes provided by the menstrualCycleGCN database?
IOn the home page, use the search function at the bottom of the page to search for a gene (1). After clicking the search button (2), a message appears indicating if the gene is included in the MenstrualCycleGCN database (3). In this case, SOX17 is included.
- Which module of the receptive profile co-expresses SOX17?
Go to ENDOMETRIAL PROFILE (1) > Co-expression regulator network (2). In the endometrium image, select the receptive profile (RR) (3). In the search function on the right of the page, enter a gene (4). The gene is highlighted in yellow and the module that includes the gene is highlighted in blue. In this case, SOX17 is included in co-expression module 2 in the receptive profile.
- What functions and regulators are in the module with SOX17?
Information related to the module of interest is still selected from the previous step. From the functional and regulation menu (1), click any subsection or scroll down to see all information. A table for each subsection appears with information about enrichment of genes belonging to the module in the selected database (2). This section also includes a correlation matrix indicating correlation values between every gene pair in the module (3).
- Which genes are correlated with SOX17 in the receptive phase? Are they activated or suppressed?
In the single gene co-expression network section, enter the gene name (1) and select the correlation threshold using the slider (2). Searching (3) will then provide a co-expression network that includes genes within the selected correlation threshold. For SOX17, there are 5 genes with an activation relationship (green edges) at a correlation threshold of 0.8: MAP2K6, MGC11242, MSX1, PECI, and SERPINA5. The menu at the right lists these genes and is clickable. For example, selecting PECI (4) shows a co-expression network that indicates PECI is the only gene with transcriptional co-activation with SOX17 at that threshold.
- What are the specific functions and regulators for SOX17 according to external databases? Does this information match the module’s information?
This information appears in the upper menu that contains external information for the selected gene (1). Number of entries related to the selected gene in the associated database is shown in brackets. Click any subsection or scroll down to see all information. An empty table indicates no annotations are available. ClinVar, Expression Atlas, and GeneCards directly link to the respective gene information.
- How does co-expression behaviour change throughout the menstrual cycle for SOX17 and co-expressed genes (e.g PECI)?
Under MENSTRUAL CYCLE PROGRESSION (1) > Gene pair co-expression progression (2), enter the name of both genes (3) and click compare (4). A connected scatter plot showing correlation values for each endometrial profile appears (5). Blue points and line represent correlation between the gene pair, and red dashed line indicates no correlation. For the example of SOX17 and PECI, all endometrial profiles have positive correlations, with the highest correlation in the receptive profile.
Conclusions
MenstrualCycleGCN provides transcriptional regulation information regarding gene co-expression throughout the menstrual cycle and its role in the receptive endometrium. Using a Pearson’s correlation threshold of 0.8, we prioritized a network of transcriptional activation composed of 5 genes (MAP2K6, MGC11242, MSX1, PECI, and SERPINA5) as potentially regulated by the transcription factor SOX17. From these, MSX1 is a transcription regulator also involved in development (Nassif et al. 2014, Tesfaye et al. 2010) that could act together with SOX17. The highlighted information suggests a hypothesis that SOX17 is molecularly related to these co-expressed genes, especially MSX1. The next step would be to experimentally test the ability of SOX17 to regulate these genes, for example through electrophoretic mobility shift assays (EMSA) (Garner & Revzin, 1981), bacterial two-hybrid selection systems (Joung et al. 2000), and chromatin immunoprecipitation (ChIP) assays (Nie et al. 2009) to examine transcription factor–DNA interactions; or yeast two-hybrid screening (Young, 1998) and affinity purification coupled to mass spectrometry (Dunham et al. 2012) to examine potential protein–protein interactions.
How functions are transcriptionally regulated throughout the Menstrual cycle
Introduction
The menstrual cycle is highly regulated by changes in hormone levels that are also associated with metabolic changes (Davidsen et al. 2007). Therefore, studying how metabolism-related functions and pathways are annotated to specific gene co-expression modules that might change throughout the menstrual cycle can provide insight into which regulatory elements orchestrate these changes. For instance, if gene co-expression modules within the receptive profile are annotated to a certain type of metabolism, this process is likely regulated by genes co-expressed within the modules of this profile.
The working plan
- How are Gene Ontology functions related to metabolism transcriptionally regulated throughout menstrual cycle?
Go to MENSTRUAL CYCLE PROGRESSION (1) > Functional co-expression > GO - Biological Process (2). A table indicates whether biological terms are annotated on each menstrual profile. In the search function, use “metabolic” as a keyword (3) to filter results (4). In this case, regulation of reactive oxygen is related to Proliferative (PF) and Receptive (RR) profiles; positive regulation of nitric oxide is related only with the Proliferative (PF) profile; and cellular and alpha amino acids are related only with the Post-Receptive (PS) profile.
- How are metabolic KEGG pathways transcriptionally regulated throughout the menstrual cycle?
Go to MENSTRUAL CYCLE PROGRESSION (1) > Functional progression > KEGG (2). A table indicates whether pathways are annotated on each menstrual profile. In the search function, use “metabol” as a keyword (to include both metabolism and metabolic) (3) to filter results (4). In this case, the more general KEGG pathway of metabolic pathways is annotated in all menstrual cycles; pyrimidine and purine metabolism are related to all profiles except the late secretory phase (LR and PS); arginine, proline, and arachidonic acid metabolism are included in all menstrual cycles except for the Receptive profile (RR); retinol metabolism is related to Proliferative (PF), Early Pre-Receptive (EPR), and Post-Receptive (PS) profiles; linoleic acid metabolism is related to Late Pre-Receptive (LPR) and Post-Receptive (PS) profiles; histidine metabolism is related to Late Receptive (LR) and Post-Receptive (PS) profiles; and tyrosine, glutathione, nicotinate, nicotinamide, and drug metabolism are related only to the Post-Receptive (PS) profile.
- Pyrimidine and purine metabolism are related to co-expression modules in all profiles except the late secretory phase (LR and PS)—which transcription factors have the same behaviour throughout the menstrual cycle and could be a potential regulator?
Go to MENSTRUAL CYCLE PROGRESSION (1) > Regulators > TF annotation (2). A table indicates whether TFs are annotated on each menstrual profile. Change receptive order in the table using the arrows close to the LR and PS labels (3). In this example, JUN is the only TF with the same behaviour throughout the menstrual cycle.
Conclusions
This example demonstrates how a researcher could identify functional processes associated with a group of genes that are highly co-expressed within modules of a given menstrual cycle profile, and how these functions are regulated according to persistence of this co-expression throughout the menstrual cycle. For instance, oxidative stress was associated with gene co-expression modules in the Proliferative (PF) and Receptive (RR) profiles, but gene co-expression module changes at the Late Receptive (LR) and Post-Receptive (PS) profiles no longer implicated oxidative stress. This agrees with previous findings that increasing progesterone in the secretory endometrium is associated with decreased nitric oxide (Cornelli et al. 2013, Giusti et al. 2002), which could be consistent with menstrualCycleGCN database results showing that transcriptional regulation involving this function is lost in Late Receptive (LR) and Post-Receptive (PR) profiles. Further, gene co-expression modules from the proliferative phase (RR, characterized by endometrial proliferation) to Late Receptive (LR) profile are annotated to pyrimidine and purine metabolism, but the co-expressed genes that define modules of the remaining menstrual profiles were not implicated in this metabolic process. Searching for regulatory elements whose enrichment patterns coincide with changes of a given metabolic process identified a single transcription factor, JUN, which was enriched only in the proliferative (PF) to late receptive (LR) profiles. This agreement between annotation and enrichment profiles of the pyrimidine and purine pathway and JUN, respectively, suggests the hypothesis that JUN may regulate this metabolic process. Therefore, this database can be used to identify and prioritize potential regulators of individual functional processes and pathways, thus helping researchers design new experimental assays.
Frequently Asked Questions (FAQ)
Team
Genomic & Systems Reproductive Medicine Group
The Genomic & Systems Reproductive Medicine Group is part of IVI-RMA Global. The group specializes in applying genomic approaches as transcriptomic predictors (machine learning algorithms) for precision reproductive medicine in assisted reproductive treatments (ARTs) and in understanding the molecular basis of human infertility using functional genomics approaches from a systems biology perspective through network modeling. We specialize in “in silico” reproductive biomedicine research using data-driven hypotheses.
Contact
- To report an error or suggestion, please contact us at: [email protected]
References
- Díaz-Gimeno, P., Horcajadas, J. A., Martínez-Conejero, J. A., Esteban, F. J., Alamá, P., Pellicer, A., & Simón, C. (2011). A genomic diagnostic tool for human endometrial receptivity based on the transcriptomic signature. Fertility and sterility, 95(1), 50-60.
- Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology, 4(1).
- Díaz-Gimeno, P., Ruiz-Alonso, M., Sebastian-Leon, P., Pellicer, A., Valbuena, D., & Simón, C. (2017). Window of implantation transcriptomic stratification reveals different endometrial subsignatures associated with live birth and biochemical pregnancy. Fertility and sterility, 108(4), 703-710.
- Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., ... & Harris, M. A. (2000). Gene Ontology: tool for the unification of biology. Nature genetics, 25(1), 25.
- Kanehisa, M., & Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research, 28(1), 27-30.
- Han, H., Cho, J. W., Lee, S., Yun, A., Kim, H., Bae, D., ... & Lee, S. (2017). TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic acids research, 46(D1), D380-D386.
- Gebhardt, M. L., Mer, A. S., & Andrade-Navarro, M. A. (2015). mBISON: Finding miRNA target over-representation in gene lists from ChIP-sequencing data. BMC research notes, 8(1), 157.
- Matys, V., Kel-Margoulis, O. V., Fricke, E., Liebich, I., Land, S., Barre-Dirrie, A., ... & Voss, N. (2006). TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes. Nucleic acids research, 34(suppl_1), D108-D110.
- Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. cell, 120(1), 15-20.
- Landrum, M. J., Lee, J. M., Riley, G. R., Jang, W., Rubinstein, W. S., Church, D. M., & Maglott, D. R. (2013). ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic acids research, 42(D1), D980-D985.
- Kapushesky, M., Adamusiak, T., Burdett, T., Culhane, A., Farne, A., Filippov, A., ... & Kurnosov, P. (2011). Gene Expression Atlas update—a value-added database of microarray and sequencing-based functional genomics experiments. Nucleic acids research, 40(D1), D1077-D1081
- Rebhan, M., Chalifa-Caspi, V., Prilusky, J., & Lancet, D. (1998). GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics (Oxford, England), 14(8), 656-664.
- Igarashi, H., Uemura, M., Hiramatsu, R., Hiramatsu, R., Segami, S., Pattarapanawan, M., ... & Sumitomo, H. (2018). Sox17 is essential for proper formation of the marginal zone of extraembryonic endoderm adjacent to a developing mouse placental disk. Biology of reproduction
- Hirate, Y., Suzuki, H., Kawasumi, M., Takase, H. M., Igarashi, H., Naquet, P., ... & Kanai-Azuma, M. (2016). Mouse Sox17 haploinsufficiency leads to female subfertility due to impaired implantation. Scientific reports, 6, 24171.
- Fu, D. Y., Tan, H. S., Wei, J. L., Zhu, C. R., Jiang, J. X., Zhu, Y. X., ... & Ren, C. L. (2015). Decreased expression of SOX17 is associated with tumor progression and poor prognosis in breast cancer. Tumor biology, 36(10), 8025-8034.
- Fu, D., Ren, C., Tan, H., Wei, J., Zhu, Y., He, C., ... & Zhang, J. (2015). Sox17 promoter methylation in plasma DNA is associated with poor survival and can be used as a prognostic factor in breast cancer. Medicine, 94(11).
- Garcia, C., Calvo, E., & Nieto, A. (2007). The transcription factor SOX17 is involved in the transcriptional control of the uteroglobin gene in rabbit endometrium. Journal of cellular biochemistry, 102(3), 665-679.
- Nassif, A., Senussi, I., Meary, F., Loiodice, S., Hotton, D., Robert, B., ... & Babajko, S. (2014). Msx1 role in craniofacial bone morphogenesis. Bone, 66, 96-104.
- Tesfaye, D., Regassa, A., Rings, F., Ghanem, N., Phatsara, C., Tholen, E., ... & Hoelker, M. (2010). Suppression of the transcription factor MSX1 gene delays bovine preimplantation embryo development in vitro. Reproduction, 139(5), 857-870.
- Garner, M. M., & Revzin, A. (1981). A gel electrophoresis method for quantifying the binding of proteins to specific DNA regions: application to components of the Escherichia coli lactose operon regulatory system. Nucleic acids research, 9(13), 3047-3060.
- Joung, J. K., Ramm, E. I., & Pabo, C. O. (2000). A bacterial two-hybrid selection system for studying protein–DNA and protein–protein interactions. Proceedings of the National Academy of Sciences, 97(13), 7382-7387.
- Dunham, W. H., Mullin, M., & Gingras, A. C. (2012). Affinity‐purification coupled to mass spectrometry: Basic principles and strategies. Proteomics, 12(10), 1576-1590.
- Davidsen, L., Vistisen, B., & Astrup, A. (2007). Impact of the menstrual cycle on determinants of energy balance: a putative role in weight loss attempts. International journal of obesity, 31(12), 1777.
- Cornelli, U., Belcaro, G., Cesarone, M. R., & Finco, A. (2013). Analysis of oxidative stress during the menstrual cycle. Reproductive biology and endocrinology, 11(1), 74.
- Giusti, M., Fazzuoli, L., Cavallero, D., & Valenti, S. (2002). Circulating nitric oxide changes throughout the menstrual cycle in healthy women and women affected by pathological hyperprolactinemia on dopamine agonist therapy. Gynecological endocrinology, 16(5), 407-412.