Novel quasi-mesenchymal state of extravillous trophoblasts and its regulation during pregnancy

doi:10.21203/rs.3.rs-2364868/v1

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Novel quasi-mesenchymal state of extravillous trophoblasts and its regulation during pregnancy

https://doi.org/10.21203/rs.3.rs-2364868/v1

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An appropriately functional placenta is the key to a successful and healthy pregnancy. During human placentation, the cytotrophoblast cells (CTBs) differentiate into the extravillous trophoblast cells (EVTs) that invade the maternal endometrium. The invaded EVTs take part in maternal-fetal crosstalk, modulate the local immune response, and remodel the spiral arteries. Shallow invasion is often associated with preeclampsia (PE) or other placenta-related pathologies. Earlier studies have indicated that CTB to EVT differentiation has features of epithelial to mesenchymal transition (EMT). However, the pathways that control this metastable transition are not fully clear yet. We screened publicly available database GEO for microarray expression datasets containing transcriptomic profile of CTBs and EVTs isolated from first trimester placenta. Three independent datasets were chosen for the study and the dataset having the highest number of differentially expressed genes (DEGs) was chosen as the primary dataset. The DEGs of the primary dataset were used for molecular signature hallmark analysis which showed that EMT hallmark was positively enriched. Further, the series matrix files of all datasets were used to compute the relative signal intensity of EMT associated genes. The results identified a unique pattern of EMT-associated gene expression in EVTs. Further, protein-protein interaction (PPI) network analysis of DEGs identified HIF1A, NOTCH1, ERBB2, and CTNNB1 as hub genes which may be the key regulators of the EMT process during EVT differentiation. Thus, this study documented the existence of a novel quasi-mesenchymal state of EVTs and identified possible upstream regulators involved in placenta-specific EMT.

Biological sciences/Biochemistry

Biological sciences/Cell biology

Biological sciences/Computational biology and bioinformatics

Biological sciences/Developmental biology

Quasi-mesenchymal

epithelial to mesenchymal transition

trophoblast

placenta

pregnancy disorders

NOTCH1

HIF1A

ERBB2

CTNNB1

In eutherians, appropriate placentation is essential for normal fetal development and successful pregnancy ¹. During placental development, the trophoblast cells, derived from the trophectoderm layer of the blastocyst, provide the functional backbone for the placenta. In humans, villous cytotrophoblast (CTB) cells differentiate to generate morphologically and functionally diverse trophoblast lineages like syncytiotrophoblasts (STB), extravillous trophoblasts (EVT), etc. Extravillous trophoblasts originate at the tips of the chorionic villi, dissociate and invade the maternal tissue and finally remodel the maternal spiral arteries by replacing some of the endothelial cells ^2–4. This ensures higher blood flow in the maternal-fetal interface to accommodate the need of the growing fetus ^5–7. Inappropriate trophoblast invasion eventually leads to a cohort of feto-maternal pathological complications including intra-uterine growth restriction (IUGR) and preeclampsia (PE) ^8–13. PE is one of the major causes of maternal-fetal morbidity and mortality ¹⁴. Apart from its role in prenatal development, placental function influences adult health as well ^15,16.

The invasion of maternal tissues by extravillous trophoblasts is critical for developing a functional placenta ^8,17−19. Different environmental cues induce tightly attached epithelial CTBs to differentiate into EVTs, which then acquire the ability to migrate and invade the maternal tissues ^3,4. The ability to migrate is a well-known mesenchymal characteristic ²⁰. Therefore, the differentiation process from epithelial villous cytotrophoblast cells to invasive extravillous trophoblast cells suggests the vital involvement of epithelial to mesenchymal transition (EMT).

Epithelial to mesenchymal transition (EMT) is a developmentally conserved process where epithelial cells lose their apico-basal polarity and acquire mesenchymal characteristics. EMT is marked by distinct changes in the expression pattern of specific genes. However, EMT is a complex process and a diverse cellular signalling network along with various other functional molecules are associated with it ^20–23.

Different stages of embryonic development and pathological conditions exhibit the involvement of EMT. During embryonic development, primary epithelial cells undergo EMT to migrate towards their appropriate position and generate secondary epithelial cells. During wound healing and fibrosis, EMT is required for mending the injured tissue for which some epithelial cells become terminally differentiated mesenchymal nature fibroblasts. Apart from these two physiological conditions, cancer metastasis also demonstrates EMT ^20,21,24,25. Though some previous studies have documented the occurrence of EMT during trophoblast differentiation ^26–28, comprehensive and exhaustive investigations are lacking.

Since, inefficient trans-differentiation along the invasive pathway (CTB to EVT) may induce placental pathologies like preeclampsia ^8,29, it is important to study the EMT process during EVT differentiation. A few microarray-based studies have profiled the gene expression pattern in CTBs and EVTs isolated from the first-trimester placenta ^30–32. Using this publicly available data, we strived to identify the epithelial to mesenchymal associated genes in trophoblast cells to analyse the similarity of the transition with other known EMT contexts. Further, we examined the protein-protein interaction network and identified four key molecules that may act as upstream regulators of the EVT differentiation.

Microarray data screening and selection

The publicly available gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo) was screened using “Trophoblast” as the keyword. The results were filtered by specifying “Homo sapiens” as the organism followed by choosing the study type as “Expression profiling by array”. The obtained datasets were manually scrutinized for sample type. Microarray datasets containing gene expression profiles of CTBs and EVTs isolated from first-trimester placenta were chosen. Three datasets (GSE57834, GSE80996, and GSE106852) were selected and downloaded for further analysis ^30,32,33. The series matrix TXT files were downloaded and the gene expression of cytotrophoblast (EGFR+) and extravillous trophoblast cells (HLA-G+) were compared selectively for each dataset.

Principal component analysis and Heatmap generation

The web application Integrated Differential Expression and Pathway analysis (iDEP) version v.0.94 (Ge et al., 2018) was used to create the heatmap with hierarchical clustering. The default parameters of iDEP and raw microarray data series matrix file extracted from the GEO database were used to generate clustered heatmap. Further, this data was used for principal component analysis (PCA) for multidimensional reduction to assess variability between the comparators and within the samples.

Identification of differentially expressed genes (DEGs)

The differential gene expression analysis was performed by Geo2R, an R-based analysis method incorporated in the GEO database. Benjamini Hochberg’s false discovery rate (FDR) correction method (adjusted p-value) was applied ^34,35. An integrated limma package was applied for weighted gene expression analysis. The volcano plot was generated using the adjusted significance level as a filter. Genes with an adjusted p-value < 0.05 for Benjamini Hochberg FDR and log2 fold-change (FC) ≥ 1 were considered as differentially expressed genes (DEGs). The differential gene expression profile data was extracted in form of a TXT file and used for further analysis.

Hallmark enrichment analysis of DEGs

The software Gene Set Enrichment Analysis (GSEA) from the Broad Institute (Cambridge, USA) was used to identify the molecular signature hallmark enrichment of the DEGs identified in the GSE57834 dataset. The hallmark sets were filtered with false discovery rate set at < 25%. The pre-ranked DEG list was generated using the log2 FC value (highest to lowest) for this analysis. The hallmark predefined gene sets in the Molecular signature database (MsigDB) were used in assessing enrichment ^36–39. The GSEA analysis was set with 1000 permutations and the gene set size was filtered between 5 (lower) and 500 (higher). The rest of the parameters were set at default settings.

Analysis of gene expression pattern in different datasets

The comparative gene expression level was depicted through the relative signal intensity of the relevant probes in the microarray. The average intensity of a probe at the same locus for each sample was computed from the raw series matrix file for each dataset. The signal intensities were averaged in each sample. The relative signal intensity was expressed as the ratio of signal of CTB and EVT in datasets GSE57834, GSE80996, and GSE106852 taking CTB expression as control. The relative signal intensity was plotted as Mean ± SEM.

PPI network and hub gene analysis

Cytoscape software, version 3.9.00, ⁴⁰ was used to import the protein-protein interaction network (PPI network) from the latest STRING database ⁴¹. The interactions that show combined score higher than 0.5 were considered as significant. The hub genes were identified by topology analysis using the cytoscape application Cytohubba ⁴². 11 different global and local centrality scores were calculated using this tool. The top 30 genes were filtered from each centrality score calculation. The genes present in at least 5 centrality measurements were chosen as hub genes. The PPI network of the identified hub genes was generated using STRING db ⁴¹. The node size indicated degree of interaction with other hub genes while color of the nodes denoted their expression levels in GSE57834 dataset. The width of the edges indicated their combined score value in continuous pattern.

Statistical Analysis

The probe signal intensity data was analysed using two stage step-up procedure of Benjamini, Krieger and Yekutieli for controlling false discovery rate within 5% for significance during the identification of the DEGs with their log2 fold change value ³⁵. The data of the relative signal intensities for each gene was analysed using unpaired two-tailed Welch’s t-test assuming unequal variances between the samples. Data was analysed and all the graphs were plotted using Graphpad version 8.0 (PRISM, San Diego, CA, USA). The difference was considered significant when p-value ≤ 0.05.

Selection of microarray expression dataset to compare CTB and EVT

To evaluate the expression pattern of EMT related genes in cytotrophoblasts and extravillous trophoblasts isolated from 1st -trimester placenta, publicly available database GEO was screened for microarray expression datasets. Three independent datasets, namely GSE57834, GSE80996, and GSE106852, were found to contain the desired transcriptomic profile of primary trophoblast cells.

Dataset GSE57834 was based on Affymetrix human gene expression array (GPL15207). It contained 15 trophoblast cell samples obtained from first trimester placenta (n = 5). The cells were sorted into three different trophoblast cell types namely, EVTs (HLA-G+), CTBs (EGFR + and Hoescht negative) and Hoescht positive side population ³⁰. The datasets GSE80996 and GSE106852 used Illumina Human HT-12 V4.0 expression beadchip platform (GPL10558) on different populations of trophoblast cells. GSE80996 contained 7 EGFR + trophoblast cell samples and 4 HLA-G + trophoblast cell samples while GSE106852 contained ITGA5+, EGFR + and HLA-G + trophoblast subtypes isolated from 4 different placenta.

Among these three independent datasets scrutinized, the trophoblast subpopulations, EGFR-positive CTBs and HLA-G-positive EVTs, were most distinctly separated in the dataset GSE57834, both in PCA and UMAP analysis (Fig. 1A & B) compared to the other two datasets (Fig. 1C – F). Based on these multi-dimension reduction analyses, the GSE57834 dataset was selected as the primary dataset for further analysis.

PC1 and PC2 analysis of dataset GSE57834 demonstrated that the CTB and EVT are distinct populations of trophoblast cells based on their gene expression profiles. Further, in this particular dataset, the EVTs show higher variability with the PC2 component compared to their CTB counterpart (Fig. 1A). Another multi-dimension reduction analysis, UMAP showed that the CTBs and EVTs are very tightly clustered among themselves (Fig. 1B), unlike the other two datasets (Fig. 1C-F).

Differential expression of genes in EVT compared to CTB

The gene expression profile between CTB and EVT cells were compared in the selected datasets (Fig. 2A). Genes with an adj. p value < 0.05 for Benjamini Hotchberg false discovery rate and log₂ fold-change ≥ 1 were considered as differentially expressed genes (DEGs). This stringent filter identified 4705 genes differentially expressed, of which 2109 genes were upregulated and 2596 genes were downregulated in EVTs compared to CTBs in the primary dataset GSE57834. Also, compared to the other two datasets available, GSE57834 showed the maximum number of DEGs (4705 in GSE57834 versus 3251 in GSE80996 and 1236 in GSE106852). This indicates that the distinct behaviour of the EVTs can be attributed to differential gene expression.

The top 1000 probes showing the highest variation in the raw microarray data (GSE57834) were used to generate the heatmap with a hierarchical cladogram. The heatmap showed a clear separation of the CTBs and EVTs by gene expression profile (Fig. 2B). This corroborates the PCA and UMAP analysis data (Fig. 1A & B). The top 20 up- and down-regulated genes in the primary dataset have been listed in supplementaryTable 1.

Enriched hallmarks in the identified DEGs

The DEGs identified in the primary dataset were used for molecular signature hallmark analysis using the gene set enrichment analysis (GSEA) tool from Broad Institute, USA. Epithelial to mesenchymal transition was found positively enriched for the defined DEGs with an adjusted p-value < 0.001 (Fig. 3) and normalized enrichment score of 2.5463204.

Expression of EMT-associated genes in CTBs and EVTs

Epithelial to mesenchymal transition is manifested by specific changes in the expression level of some pre-defined genes. This particular gene set may be defined as EMT associated gene set. This set contains EMT core marker genes, as well as transcription factors and tissue remodelling genes. The expression pattern of these genes was examined in all the three microarray datasets. Using a series matrix file of the microarray dataset, the relative signal intensity (expression level) for the individual genes was calculated and compared between the primary cytotrophoblast and extravillous trophoblast cells isolated from the first-trimester placenta.

E-Cadherin was significantly reduced in EVTs compared to CTBs in all three datasets while the mesenchymal cadherin CDH2 did not show any significant alteration in expression levels (Fig. 4). The expression of the other two mesenchymal marker genes, Fibronectin1 (FN1) and Vimentin (VIM), was significantly higher in the extravillous trophoblast cells (Fig. 4).

Several transcription factors family are known to induce epithelial to mesenchymal transition. Therefore, the expression pattern of these transcription factors was also analysed in the microarray datasets. The expression of zinc-finger family transcription factors Snail, Slug, and Snail3 expression was different between the datasets. Similarly, the basic helix-loop-helix transcription factors Twist1 and Twist2 also showed a different pattern in different datasets. The other zinc finger transcription factor family protein Zeb1 and Zeb2 also did not show any alteration between trophoblast subtypes (Fig. 5).

The epithelial to mesenchymal transition is associated with a higher ability of migration and invasion. This ability is associated with altered expression of matrix metalloproteinase (MMP) enzymes. Expression of two such enzymes, MMP2 and MMP9, was examined in the primary trophoblast expression data. It was found that MMP2 showed higher expression in EVTs compared to CTBs (Fig. 6). The expression of tissue inhibitor of metalloproteinase molecules was also tested. While TIMP1 and TIMP2 demonstrated higher expression in EVTs, TIMP3 expression was higher in CTBs (Fig. 6).

Protein-protein interaction network analysis and identification of hub genes

The protein interaction (PPI) network of the DEGs was constructed and visualized by Cytoscape software ⁴⁰ using STRING database of protein interaction ⁴¹. After filtering for combined score ≥ 0.5, the constructed network contained 4420 nodes and 45946 edges showing that the network was extremely dense, and the component nodes have high interaction among themselves (Fig. 7A).

The topological analysis of the whole network was performed to get a functional view. The cytoscape application Cytohubba ⁴² was used to calculate topological feature centrality score and 33 hub genes were identified (supplementary Table 2). These hub genes were used to construct a PPI network which had 33 nodes and 453 edges (Fig. 7B). Since the ratio of nodes to edges was similar between this hub gene network and the full PPI network, therefore, it may serve as an appropriate functional representation of the overly interacting full network (supplementaryTable 3).

Expression of hub genes in CTBs and EVTs

Previous literature reported that four of the hub genes, namely, HIF1A, NOTCH1, ERBB2 (HER2) and CTNNB1 (β – catenin), are possible upstream regulators of EMT ^43–48. Hence, their expression pattern was examined in all the three microarray datasets. The relative signal intensity was calculated and the expression pattern was plotted (Fig. 8). The expression of HIF1A, NOTCH1, and ERBB2 was significantly upregulated in EVTs in all the individual datasets examined, although with varied level of significance. On the contrary, the expression of CTNNB1 was lower in EVTs compared to CTBs (Fig. 8).

The epithelial CTBs need to alter their expression profile to achieve their invasiveness while differentiating into EVTs. In this study, we strived to unravel the occurrence of EMT during CTB to EVT differentiation and analyze its pattern. We observed that EVTs possess a novel quasi-mesenchymal state which is different from other known biological contexts of EMT. We also found three transcription factors and one receptor tyrosine kinase molecule that may be regulating this novel state of EVTs.

Three individual datasets containing gene expression profile of CTBs and EVTs isolated from first trimester placenta were analyzed. We chose dataset GSE57834 as the primary dataset since the PCA and UMAP analysis showed clear distinction between CTBs and EVTs while cells within a sub-population had a high degree of correlation. Also, compared to the other two datasets available, GSE57834 showed the maximum number of DEGs of which 44.82% were upregulated and 55.18% genes were downregulated in EVTs compared to CTBs. This indicates that the distinct behaviour of the EVTs can be attributed to differential gene expression.

The hallmark enrichment analysis identified EMT phenomenon being positively enriched in the DEGs. EMT is a trans-differentiation process, where the epithelial cells lose their polarity and acquire the ability of invasion, a mesenchymal trait ²⁰. This is manifested by the change in expression of some defined set of genes called EMT-associated genes ^22,23,49. EMT is vital for CTB to EVT differentiation since EVTs need to detach from the anchoring villi tip and invade the endometrial tissue. Previous study, using PCR profiler array with 84 EMT associated genes, provided evidence of EMT signature during this differentiation and its regulation through one of the EMT master regulator transcription factor ZEB2 ^26,50.

In all the datasets analysed to compare expression of EMT associated genes in CTBs and EVTs, epithelial marker CDH1 reduced while mesenchymal markers, FN1 and VIM increased. However, there was no change found in the expression of the mesenchymal marker, CDH2. This suggests that although EMT is taking place during EVT differentiation, EVTs have a novel quasi mesenchymal state with the process being different from other well-known biological contexts of EMT ²⁰. Expression pattern of the EMT master regulator transcription factors varied between the different datasets. The primary dataset GSE57834 documented increased expression of ZEB2 in EVTs which is supported by an earlier study indicating the role of ZEB2 in EVT differentiation ⁵⁰. Further, the higher expression of the TIMP1 and TIMP2 genes may indicate that trophoblast invasion is more tightly regulated than the metastatic cell migration / invasion ⁵¹.

Although, epigenetic regulation of EMT associated gene expression pattern during placental development has been suggested earlier ⁵² but the upstream regulators of this unique mesenchymal state of EVTs remain unknown. It is therefore intriguing to identify the key molecules that may have the potential to regulate this quasi-mesenchymal state. Through the topological analysis of the PPI network, four known upstream regulators of EMT were identified as HIF1A, ERBB2, NOTCH1, and CTNNB1 (Fig. 7B).

Hypoxia has been shown to be critical for placental development and trophoblast lineage differentiation ^53–57. HIF1A is one of the key proteins involved in the molecular response to low oxygen tension ^58,59. HIF1A is also involved in regulating EMT in various physiological and pathological states including cancers ^44,60−62. Interestingly, HIF1A can influence trophoblast invasion both positively and negatively by modulating different proteins ^63,64. This study identified higher expression of HIF1A in EVTs compared to CTBs in all the individual datasets suggesting that the quasi-mesenchymal nature of the EVTs might be regulated by HIF1A. Increased HIF1A may also help in preventing anoikis of EVTs under normal physiological conditions, wherein EVTs need to detach from the chorionic villi and invade the endometrium ⁶⁵.

Receptor tyrosine kinase ERBB2 has also been shown to regulate anoikis and apoptosis ^66,67. Our study has identified ERBB2 as a hub gene that showed significantly increased expression in EVTs (Fig. 8), which may be due to copy number variation of the gene ⁶⁸. ERBB2 stabilizes HIF1A in cancer cells which also aids in drug resistance ⁶⁹. Hence, it is possible that higher HIF1A in EVTs is attributable to upregulated expression of ERBB2. Additionally, ERBB2 is also known to promote EMT in various cancers ^45,46. Therefore, higher ERBB2 in EVTs may contribute to the mesenchymal features in these cells either directly or through HIF1A.

NOTCH1 has also been identified as a potent regulator of EMT ^47,48,70. Higher expression of NOTCH1 contributes to drug resistance in cancer, another mesenchymal trait ⁷¹. Interestingly, NOTCH1 was identified as another hub gene that demonstrated increased expression in EVTs (Figs. 7B and 8). A previous study also showed NOTCH1 as one of the critical regulators for the EVT fate specification during placentation ⁷². It has also been demonstrated that NOTCH1 can control trophoblast migration and invasion by upregulating MMPs in trophoblast cells ⁷³.

In addition, many studies point towards the role of HIF1A/NOTCH1 crosstalk in EMT induction in various physio-pathological contexts ^74–76. Hypoxia can induce angiogenic mimicry by trophoblast cells through NOTCH1 protein ⁷⁷. Study using trophoblast cell lines identified that HIF1A regulates cell migration and invasion in NOTCH1 dependent manner ⁷⁸. Increase in both HIF1A and NOTCH1 therefore may regulate the EMT phenomenon in the trophoblast cells.

Another known upstream promoter of EMT in multiple type of malignancies is CTNNB1, which is an adherens junction protein as well as a key mediator of the canonical Wnt signaling pathway ^79–85. Interestingly, this study identified CTNNB1 as one of the hub genes, however, it showed decreased expression of CTNNB1 in EVTs (Figs. 7B and 8). A recent study also identified CTNNB1 as one of the regulators of the trophoblast lineage specification with reduced expression in EVTs ⁸⁶. Hence, the novel quasi-mesenchymal nature of the EVTs may be attributed to the down-regulated expression of CTNNB1 that may function as a checkpoint for acquisition of full mesenchymal state.

It was also reported that HIF1A is involved in tumour progression and induces EMT in various cancers by enhancing CTNNB1 expression or activity ^{43,60,87−89}. In contrast, some other studies suggest that HIF1A can decrease CTNNB1 gene expression which corroborates our observations ^90,91. It has also been reported that EMT is regulated by the HIF1A/CTNNB1/NOTCH1 crosstalk in cancer ⁹². Taken together, we can postulate that the balanced expression of these four hub genes, HIF1A, NOTCH1, CTNNB1, and ERBB2, may be responsible for regulating this unique mesenchymal state of EVTs.

Few studies have examined the role of these four hub genes in different pregnancy associated pathological conditions. HIF1A shows higher expression in preeclamptic placenta compared to the normal placental tissue ^93,94. This indicates prolonged hypoxia exposure to the trophoblast cells during the gestation period may be detrimental for both the mother and the fetus. Similarly, elevated levels of ERBB2 are found in the serum of preeclamptic patients which may lead to lower activity of ERBB2 in the trophoblast cells ^95,96. On the contrary, NOTCH1 is downregulated in preeclampsia ^97–100. The expression level of CTNNB1 in pregnancy-associated disorders is ambiguous with some of the studies reporting higher expression of CTNNB1 in preeclamptic tissue while another demonstrating lowered CTNNB1 expression in preeclampsia ^101–103.

Understanding trophoblast differentiation may provide valuable information to combat a cohort of placenta-related pathologies. Through this study, we report that EMT is involved in CTB to EVT differentiation, however, the mesenchymal nature of EVTs differs from the general pattern of EMT in other well-known contexts. The unique expression pattern of EMT associated genes in the EVTs suggests a novel quasi-mesenchymal state of EVTs. The PPI network analysis using the differentially expressed genes was also performed to identify the genes that may regulate the EMT phenomenon during CTB to EVT differentiation. Our data, along with previous studies on EMT, trophoblast differentiation and pregnancy related pathologies, suggests that balanced expression of the hub genes HIF1A, NOTCH1, ERBB2, and CTNNB1 is pivotal for the induction and maintenance of the novel quasi-mesenchymal state of EVTs, failing which several pregnancy-associated pathologies may arise. However, further dissection of the intricate molecular details of the interaction of these key genes would be needed for full comprehension of placental biology.

Data Availability

The datasets used in this study are publicly available in GEO-NCBI (https://www.ncbi.nlm.nih.gov/geo) (GSE57834, GSE80996, and GSE106852). The reanalysis data may be made available upon reasonable request to the corresponding author.

Acknowledgments

J.C. would like to thank Dr. Devanjan Dey, Department of Biochemistry, AIIMS, New Delhi for help in understanding the methods of GSEA analysis.

Funding

This research was funded by Indian Council of Medical Research, Government of India (5/10/FR/65/2020-RBMCH). J.C. received fellowship from Indian Council of Medical Research.

Authors’ contribution

J.C. and S.G. conceived the idea. J.C. and K. did data mining and analysis. All authors contributed to interpretation of results, critical discussions and the writing of the manuscript. All authors have approved the final version.

Conflict of interest

The authors have no conflicts of interest to disclose.

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No competing interests reported.

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Novel quasi-mesenchymal state of extravillous trophoblasts and its regulation during pregnancy

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Abstract

Figures

Introduction

Materials And Methods

Microarray data screening and selection

Principal component analysis and Heatmap generation

Identification of differentially expressed genes (DEGs)

Hallmark enrichment analysis of DEGs

Analysis of gene expression pattern in different datasets

PPI network and hub gene analysis

Statistical Analysis

Results

Selection of microarray expression dataset to compare CTB and EVT

Differential expression of genes in EVT compared to CTB

Enriched hallmarks in the identified DEGs

Expression of EMT-associated genes in CTBs and EVTs

Protein-protein interaction network analysis and identification of hub genes

Expression of hub genes in CTBs and EVTs

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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