Bioinformatics analysis of a disease-specific lncRNA-miRNA-mRNA regulatory network in recurrent spontaneous abortion (RSA)

doi:10.21203/rs.3.rs-3395820/v1

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Research Article

Bioinformatics analysis of a disease-specific lncRNA-miRNA-mRNA regulatory network in recurrent spontaneous abortion (RSA)

https://doi.org/10.21203/rs.3.rs-3395820/v1

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published 04 Feb, 2024

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Background:This study investigated the molecular mechanisms of long non-coding RNAs (lncRNAs) in RSA using the lncRNA-miRNA-mRNA regulatory network.

Methods: The present study obtained expression datasets of long non-coding RNAs (lncRNAs) (GSE179996), messenger RNAs (mRNAs) (GSE201469), and microRNAs (miRNAs) (GSE178619, GSE201442) from blood samples of individuals with RSA and healthy controls. The differentially expressed lncRNAs (DELs), mRNAs (DEMs), and miRNAs (DEmiRs) were revealed. Subsequently, the identification of miRNAs that interact with DELs and exhibit overlap with DEmiRs was conducted. The specific genes were achieved through the overlapping predicted target genes and DEmiRs. A regulatory network comprising lncRNA, miRNA, and mRNA was established, followed by a subsequent analysis of enrichment. Also, the enrichment analysis was performed, and a protein–protein interaction (PPI) network was constructed.

Results: This study identified 57 DELs, 212 DEmiRs, and 301 DEMs regarding RSA. Subsequent analysis revealed a lncRNA-miRNA-mRNA network comprising nine upregulated lncRNAs, 14 downregulated miRNAs, and 65 mRNAs. The ceRNA network's genes were then subjected to functional enrichment and pathway analyses, which showed their association with various processes, such as cortisol and thyroid hormone synthesis and secretion, human cytomegalovirus infection, and parathyroid hormone synthesis. Furthermore, ten hub genes (ITGB3, GNAI2, GNAS, SRC, PLEC, CDC42, RHOA, RAC1, CTNND1, FN1) were identified based on the PPI network results.

Conclusion: In summary, the outcomes of our study offer new understandings towards comprehending the potential pathogenic mechanism in RSA via the lncRNA-miRNA-mRNA network and reveal the possibility of identifying new lncRNAs and miRNAs as promising molecular biomarkers.

Recurrent spontaneous abortion

long non-coding RNAs

Integrated analysis

Regulatory network

Recurrent spontaneous abortion (RSA) is a challenging reproductive disorder that remains unsolved in gynecology. RSA is the spontaneous loss of three or more pregnancies and affects 1-5% of women of reproductive age(Medicine 2012, Guo, Gao et al. 2020). The etiology of RSA is complex and can be caused by various factors, including advanced maternal age, genetic abnormalities, anatomical issues, infections, and endocrine disorders. However, in over 50% of cases, the cause remains unclear, leading to unexplained recurrent spontaneous abortion (URSA) categorization (Baek, Lee et al. 2007, El Hachem, Crepaux et al. 2017). URSA is often diagnosed when genetic tests, anatomical, endocrine, and immunological evaluations show no abnormalities. The pathogenesis of RSA is still not fully understood, making its treatment a significant challenge in reproductive medicine (Sham, Yiu et al. 2010). Ineffective treatment can cause physical and psychological distress to the patient. Thus, a comprehensive understanding of the disease's cause and extensive clinical studies are essential to address this medical issue.

RNA sequencing technologies, gene expression profiling by microarray, and transcriptome analysis have become popular tools for investigating molecular expression patterns, allowing for a greater understanding of the role of non-coding RNAs as essential regulators of gene expression and signal transduction(Heo, Yun et al. 2019). These methods are beneficial for elucidating the molecular characteristics of pathological tissues on a global scale. Non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), play a significant role in regulating gene expression and have been implicated in various disorder. These non-coding RNAs interact with messenger RNA (mRNA) in a multi-layered regulatory network called competing endogenous RNAs (ceRNAs), providing insights into their regulatory mechanisms and role in normal physiology and pathology (Cava, Bertoli et al. 2019, Zhu, Zhang et al. 2019). Such ceRNAs are complicated in important biological processes and may serve as potential diagnostic or prognostic biomarkers or therapeutic targets (Ala 2020). MiRNAs are small RNA molecules that can bind to target mRNAs and inhibit protein-coding gene expression (Li, Peng et al. 2018). When lncRNAs contain miRNA response elements, they can interact with other target RNAs and act as ceRNAs (Salmena, Poliseno et al. 2011). A miRNA can modulated numerous targets that comprise particular miRNA response elements, enabling a ceRNA regulatory network construction (Plotnikova, Baranova et al. 2019). The ceRNA regulatory network has been studied in various diseases but has not been studied in RSA patients and healthy controls. Hence, the current investigation aimed to construct a ceRNA network that involves lncRNA-miRNA-mRNA interactions to analyze the association between mRNA-miRNA and lncRNA-miRNA in RSA. The study conducted Geno Ontology (GO) analysis on mRNAs present in the regulatory network to identify the involved genes and regulatory pathways responsible for recurrent abortion

Data collection and processing

The NCBI Gene Expression Omnibus database was searched to obtain gene expression profiles of datasets for RSA, and inclusion criteria were applied to select relevant data sets. The selected data sets were generated through microarray or RNA-Seq techniques. They consisted of normal controls and RSA women of reproductive age without any evident endocrine disorders, karyotype abnormalities, uterine malformations, fallopian tube blockage, acute infections, or other issues, as verified by the 2022 update ESHRE guidelines (RPL, Bender Atik et al. 2023); Four GSE profiles were chosen and downloaded from the database (Table 1), including GSE179996, GSE178619, GSE201442, and GSE201469. The platforms used for the GSE profiles were GPL21827, GPL25134, GPL21576, and GPL21185, respectively. GPL21827 only included the probe name data (or sequence data) and not the gene symbol. Hence, for probes annotation of GPL21287 sequences according human genome (GRCH-37 release in GENCODE), the R package “Rsubread” was used. The regulatory pathways and genes involved in recurrent abortion were investigated by performing GO analysis on the mRNAs in the regulatory network of lncRNA-miRNA-mRNA constructed in this study (Harrow, Frankish et al. 2012, Liao, Smyth et al. 2019).

Identification of DEGs, DELs and DEmiRs

The study used the Limma package in R software (Ritchie, Phipson et al. 2015) to identify differentially expressed mRNAs, lncRNAs, and miRNAs, where statistical significance was determined based on a p-value <0.05 and |log₂ fold-change|>2.0 threshold for DEGs, DELs, and DEmiRs. The results were presented using volcano plots using R packages "EnhancedVolcano" and "ggplot2 The overlapping miRNA and genes in each group were examined using the Venn diagram web tool. Moreover, the EnhancedVolcano package was used to generate a hierarchical cluster analysis of the volcano plots of DELs, DEmiRs, and DEGs for visualization.

Prediction of target miRNAs of DELs and miRNA-target interactions

This study used three databases, namely lncbasev3 (DIANA Tools), LncSEA, and LncTar, to predict lncRNA (Li, Ma et al. 2015, Karagkouni, Paraskevopoulou et al. 2020, Chen, Zhang et al. 2021). RNA interactions and miRNA target sites on non-coding RNAs for each DEL. Overlapped miRNAs of the between DEmiRs and miRNAs interacting with DELs were used as potential target miRNAs for future analysis. The predicted function of the overlapped miRNAs was assessed using DIANA-miRPath v3.0 (https://www.microrna.gr/miRPathv3), a web server database that evaluated miRNAs regulatory roles and estimated the associated regulation pathways. Finally, the overlapped miRNAs were selected for further target predictions.

The prediction of miRNA-mRNA interactions was performed using two Bioconductor packages in R software, namely "multiMiR" and "SpidermiR"(Ru, Kechris et al. 2014, Cava, Colaprico et al. 2017). "multiMiR" is an R package that predicts miRNA targets by combining data from 8 databases (Miranda, TargetScan, EIMMo, DIANA, MicroCosm, PicTar, PITA, and miRDB) and validated targets from miRTarBase, TarBase, and miRecords. As well as, "SpidermiR" provides miRNA-gene-gene interactions and analyzes miRNA gene regulatory networks to identify miRNA-gene relationships. Overlapping miRNA target genes from the analysis were selected and intersected with the DEGs to obtain candidate target genes.

Construction of the ceRNA network

According to previous research, lncRNAs can act as a sponge for miRNA and regulate mRNA expression by sequestering miRNA (Gupta 2014). This study established a regulatory network of lncRNA-mediated competing endogenous RNA (ceRNA) by constructing a miRNA-mRNA-lncRNA network. The network was created based on the binding of ceRNA to miRNA via miRNA response elements (MREs). The Cytoscape software version 3.9.1 was used to visualize the miRNA-mRNA-lncRNA network (Franz, Lopes et al. 2023). To construct the lncRNA-miRNA-mRNA network, overlapping miRNAs and lncRNAs or mRNAs with inverse relation in lncRNA-miRNA or miRNA-mRNA interactions were selected.

GO and KEGG enrichment analysis

To investigate the roles of differentially expressed genes (DEGs) in the ceRNA network, Fisher's test was used for GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Enrichr and clusterProfiler packages in R software were employed for the enrichment analysis. GO analysis was carried out for molecular function (MF), biological process (BP), and cell component (CC). KEGG enrichment analysis was conducted to identify the signaling pathways related to genes in the ceRNA regulatory network. A pvalue<0.05 was shown statistically significant.

protein–protein interaction (PPI) network construction

The mRNA molecules in the ceRNA network produced a protein-protein interaction (PPI) network, visualized using Cytoscape software version 3.9.1. A combined score > 0.4 was set as the cut-off value for the protein pairs in the PPI network (Kohl, Wiese et al. 2011). The molecular complex detection (MCODE) plugin was applied to detect essential modules in the PPI network, including hub genes and connective modules. The degree cut-off was established at 2, the node score was 0.2, and the maximum depth was set at 100. The STRING database (https://string-db.org/) was employed to construct the PPI network.

Identification of the differentially expressed lncRNAs, miRNAs and mRNAs

By applying the screening data threshold (P < 0.05 and |log2FC|> 2), the study was able to detect 2171 differentially expressed lncRNAs (DELs), consisting of 38 upregulated and 20 downregulated elements. Additionally, by examining two datasets of differentially expressed microRNAs (DEmiRs), the study identified 620 DEmiRs, including 80 upregulated and 132 downregulated components and and 2358 differentially expressed genes (DEGs), with 136 being upregulated and 114 being downregulated. The volcano plot analysis were used to visualize these DELs, DEmiRs and DEGs. The top 20 dysregulated elements of DELs, DEmiRs, and DEGs were presented in Tables 2-4, respectively.

Construction of the lncRNA‑miRNA‑mRNA network in RSA

A regulatory network consisting of lncRNA-miRNA-mRNA interactions was established to examine the involvement of lncRNA in URSA. At the outset, the study employed three databases, namely lncBase, lncTar, and LncSEA, to forecast the interactions between lncRNAs and miRNAs. The analysis revealed nine upregulated lncRNAs with binding sites for 220 miRNAs and ten downregulated lncRNAs that harbor miRNA response elements (MRE) for 51 miRNAs. Subsequently, an analysis was conducted on the miRNAs identified in both MRE and DEmiRs locations to ascertain an inverse relationship between lncRNAs and miRNAs. The study identified 17 miRNAs with reduced expression among the DEmiRs and miRNAs in the MRE locus. Notably, there was no observed overlap among the miRNAs with increased expression. As a result, a total of 17 miRNAs that were down-regulated were subjected to further analysis. These miRNAs were then used to predict target genes within the lncRNA-miRNA interaction network, and their association with DEmiRs was subsequently investigated. The findings indicate that 14 microRNAs interacted with 65 mRNAs, demonstrating elevated expression levels. The ceRNA regulatory network comprised nine lncRNAs, fourteen miRNAs, and sixty-five mRNAs, as depicted in Figure 2A

Functional enrichment analysis

The Enrichr database and R scripts were used to do a GO/KEGG analysis on the genes in the ceRNA network. The GO analysis shows that biological processes that regulate trans-synaptic signaling, modify chemical synaptic transmission, and control GABAergic synaptic transmission are significantly enriched (Figure 2B). Regarding molecular function, some observations such as G protein-coupled receptor binding and DNA-binding transcription activator activity were noticed with the highest significance level (Figure 2C). Also for cell component, cellular components such as dendrite, presynaptic membrane, and microvillus membrane were enriched in GO analysis (Figure 2D).

The KEGG pathway analysis revealed several pathways (Figure 2E and Table 5), including adrenergic signaling in cardiomyocytes, cortisol synthesis and secretion, thyroid hormone production, human cytomegalovirus infection, and cocaine addiction. A GO interaction network was constructed (Figure 3) to show how GO terms communicate internally.

Key lncRNA-miRNA-mRNA subnetwork

To identify the pivotal lncRNAs in the ceRNA regulatory network, node degree analysis was performed. Based on the analysis, three lncRNAs including MIR17HG, PPP4R1L, and MIR22HG were drawn as having the highest score. Three key lncRNAs, along with their related miRNAs and mRNAs, were used to construct the subnetwork. Subnetwork related to MIR17HG includes 1 lncRNA, 5 miRNAs, and 43 mRNAs along with 62 edges (Figure 4A). PPP4R1L-related subnetwork includes 1 lncRNA, 6 miRNAs, and 46 mRNAs along with 74 edges (Figure 4B). Also, the subnetwork related to MIR22HG includes 1 lncRNA, 5 miRNAs, and 58 mRNAs with 64 edges (Figure 4C). To further understand the functions of three key lncRNAs, KEGG pathway analysis was performed for each subnetwork. Results from pathway enrichment analysis regarding subnetworks related to MIR17HG, PPP4R1L, and MIR22HG revealed certain terms such as human cytomegalovirus infection, thyroid hormone synthesis and Rap1 signaling pathway in all three subnetworks (Figure 4D-F).

PPI network and hub genes identification

The string database was first searched to find key genes among the 65 genes that comprise the ceRNA network. The network of protein interactions was shown using the Cytoscape software. The CytoHabba plugin included in the Cytoscape program found hub genes in the ceRNA network. Based on their network scores, the study identified the top 10 hub genes as ITGB3, GNAI2, GNAS, SRC, PLEC, CDC42, RHOA, RAC1, CTNND1, and FN1. Figure 5 is an illustration of the hub genes.

URSA is a complicated pregnancy-related illness with an unknown cause (Cozzolino, Rizzello et al. 2019). It is still entirely unknown why trophoblast adhesion to endothelial cells in the endometrium is typically suppressed in this state. To better understand the functional mechanism of these factors in RSA, a regulatory network of ceRNA was also developed in this study. The expression profiles of lncRNAs, miRNAs, and genes associated with peripheral blood samples were compared in URSA patients and controls. According to the network analysis, nine lncRNAs showed a substantial increase in expression in RSA. Further network analysis revealed the three important lncRNAs with the highest node degrees, namely MIR17HG, PPP4R1L, and MIR22HG. It has been noted that MIR17HG expression is upregulated in colorectal cancer, and this upregulation increases carcinogenesis, cell invasion, and proliferation (Xu, Meng et al. 2019). The MIR17HG gene (miR-17-92 cluster) participates in cell proliferation, invasion, adhesion, apoptosis, and angiogenesis, all crucial for a healthy pregnancy. One potential candidate gene for placental abruption is this lncRNA (van Haaften and Agami 2010). This result was also seen for the genes implicated in the network of cardiomyopathy-related pathways in the analysis of KEGG pathways. It has been established via research on this disorder that MIR17HG may, via related pathways and genes, contribute to the pathophysiology of both cardiomyopathy and recurrent abortion. A study revealed that parents of girls who miscarried twice or more were more likely to have ischemic heart disease. Therefore, repeated abortions and ischemic heart disease may be caused by similar mechanisms and hereditary predispositions (Smith, Wood et al. 2011). The involvement of MIR17HG in preeclampsia, a potentially lethal pregnancy disease that includes abortion, has been supported by numerous studies. Additionally, it has been established how this lncRNA affects embryo implantation and development (Fu, Brkić et al. 2013). To the best of our knowledge, no research has been done to examine the function of PPP4R1L and MIR22HG in pregnancy health and recurrent abortion. But two lncRNAs may contribute to embryo implantation and preservation because of their up-regulation in RSA. In another part of the network, miRNAs interacting with lncRNAs were analyzed and finally 14 miRNAs with decreased expression in RSA were selected. MiRNAs generated from the placenta circulate in the mother's blood throughout pregnancy and can be utilized as non-invasive indicators for pregnancy problems, according to several studies. The two miRNAs with the highest scores in the ceRNA regulatory network study were miR-671-5p and miR-27b-3p. In RSA patients, there has been evidence of a downregulation in miRNA expression. In endometrial samples from individuals who experienced repeated abortions during early pregnancy, miR-671-5p was similarly found to be down-regulated (Gu, Zang et al. 2022). The targets of miR-671 are implicated in the cell cycle, intercellular communication, and cell adhesion pathways, all of which are crucial for the growth and implantation of the embryo (Van Mourik, Macklon et al. 2009). It was discovered that miR-671 was down-regulated in cultured endometrial cells in a study to examine the impact of miRNAs on endometrial function. Progesterone therapy of these cells could boost miRNA production and thus change its target genes effects concerning endometrial function. According to the analysis done for this study, a decrease in miRNA expression can result in an increase in the expression of genes involved in the production and secretion of hormones like parathyroid and growth hormone, as well as cell cycle pathways and cell transporters. Additionally, it was discovered that miR-671 target genes regulate the relaxin signaling pathway. Relaxin is a hormone peptide that relaxes the birth canal before the labor process and has been found to play an important role in maintaining pregnancy at the beginning of this process (Ng, Shen et al. 2019). An abnormal change in this hormone's level during pregnancy can lead to premature birth, issues with glucose metabolism, and insulin resistance (Goldsmith and Weiss 2009). MiR-27b-3p is another significant miRNA in this research. The down-regulation of miR-27b, frequently thought of as a tumor suppressor, has been shown in several malignancies, including colorectal cancer and neuroblastoma (Lee, Drakaki et al. 2012, Ye, Wu et al. 2013). By targeting VEGF, miR-27b can prevent cancer cells from proliferating, migrating, and invading healthy tissue. VEGF stimulates the growth and migration of endothelial cells and the development of new blood vessels (Van Trappen and Pepper 2005). The invasion of cancer cells is quite similar to the implantation process. Therefore, a change in the expression levels of miR-27b may impact the embryo's development or implantation (Reed, Babayev et al. 2018). Increased miR-27b promotes trophoblast invasion and is crucial for the migration and proliferation of umbilical vein endothelial cells (Zhu and Liu 2021). Investigation of the pathways related to the mRNAs involved in the ceRNA network showed that the pathways of cortisol synthesis and secretion, adrenergic signaling in cardiomyocytes, human cytomegalovirus infection, parathyroid hormone production, secretion and action, and thyroid hormone production had a high combined score. In a study, the connection between elevated cortisol levels and abortion was examined. It was discovered that abortion in the first trimester of pregnancy is caused by elevated maternal cortisol levels (Nepomnaschy, Welch et al. 2006). Cortisol is necessary at the physiological level to maintain pregnancy and also increases in response to environmental and immunological stress. Therefore, the increase of cortisol can be a physiological indicator in the body of women regarding the deterioration of reproductive conditions (Rivier and Rivest 1991, Wingfield and Sapolsky 2003). Therefore, cortisol has a direct or indirect effect on miscarriage. Cortisol can cause a decrease in luteal progesterone production, and this decrease in progesterone level may be effective during implantation (Norwitz, Schust et al. 2001). On the other hand, cortisol can change the ratio of Th1/Th2 cells by reducing progesterone, which causes spontaneous abortion of the fetus (Blois, Joachim et al. 2004). In addition, an increase in cortisol levels can cause trophoblast cell decomposition or premature senescence (Wellmann and Volk 1972).

The enrichment analysis also identified infection with cytomegalovirus (CMV) as a pathway. Primary CMV infection can occur during pregnancy, resulting in congenital CMV. If the fetus survives, it can cause as many as 90 percent of the complications, including hearing loss, visual impairment, and varying degrees of mental retardation (Yinon, Farine et al. 2010, Adler 2011). The correlation between CMV infection and abortion has been supported by numerous research, but information on how this virus affects recurrent abortion is discrepancies and inconsistent (Odland, Sergejeva et al. 2001). In a comparison between women with normal pregnancy and recurrent abortion, the results showed that the previous exposure to CMV was greater in women with recurrent abortion than in the control group, so that this exposure can be considered as a risk factor for recurrent abortion (Sherkat, Meidani et al. 2014). Two important genes in PPI are integrin beta 3 (ITGB3) and guanine nucleotide-binding protein alpha stimulating (GNAS). Both of these interact with the CMV infection pathway. The ITGB3 gene promotes endometrial receptivity during embryo implantation and is crucial for cell-cell and cell-matrix adhesions (He, Zeng et al. 2019). Fetal trophoblast attachment, invasion, and migration are all facilitated by the elevated expression of ITGB3 at the beginning of pregnancy, which declines in women who have had recurrent abortions (Germeyer, Savaris et al. 2014). The GNAS gene is a multi-product gene, the main product of which is for intracellular cAMP production and activates signaling pathways dependent on this molecule (Weinstein, Liu et al. 2004). Therefore, it is likely that CMV infection also contributes to recurrent miscarriages by activating these genes' signaling pathways. Key genes involved in synthesis thyroid and parathyroid hormones also have functional roles. According to numerous studies, the increase or reduction in thyroid hormone during pregnancy has been linked to several fetal abnormalities. These conditions include mental retardation in the fetus, abortion, preeclampsia, placental abruption, and premature birth (Alemu, Terefe et al. 2016). It has also been noted that primary hyperparathyroidism during pregnancy raises the chance of abortion by up to three times (DiMarco, Christakis et al. 2018).

Overall, most of the nodes in the network and subnetworks created in the pathological process of RSA are directly or indirectly effective. Nine lncRNA were chosen for this investigation, some of which have not yet been published. By targeting several cellular pathways, miRNAs associated with this lncRNA may increase the risk of URSA. Nevertheless, systematic, accurate tests with the right amount of samples are required to corroborate the study's conclusions.

Author Contributions

K. Ahmadiperformed most of the bioinformatics data analysis. S.Reiisi wrote the manuscript and coordinated the study, participated in intellectual discussions of the data, and revised the manuscript.

Funding: None

Ethics Approval

This article does not contain any studies on human or animals.

Consent to Participate

This article does not contain any individual person’s data in any form

Consent for Publication

This article does not contain any individual person’s data in any form.

Availability of data and materials

The authors declare that all generated and analyzed data are included in the.

Conflict of interest

We certify that there is no conflict of interest with any financial organization.

Competing interests

The authors declare that there are no competing interests.

Data Availability

The data generated during and/or analyses during the current study are available from the corresponding author on reasonable request.

Adler, S. P. (2011). "Screening for cytomegalovirus during pregnancy." Infectious diseases in obstetrics and gynecology 2011.
Ala, U. (2020). "Competing endogenous RNAs, non-coding RNAs and diseases: an intertwined story." Cells 9(7): 1574.
Alemu, A., B. Terefe, M. Abebe and B. Biadgo (2016). "Thyroid hormone dysfunction during pregnancy: A review." International journal of reproductive biomedicine 14(11): 677.
Baek, K.-H., E.-J. Lee and Y.-S. Kim (2007). "Recurrent pregnancy loss: the key potential mechanisms." Trends in molecular medicine 13(7): 310-317.
Blois, S. M., R. Joachim, J. Kandil, R. Margni, M. Tometten, B. F. Klapp and P. C. Arck (2004). "Depletion of CD8+ cells abolishes the pregnancy protective effect of progesterone substitution with dydrogesterone in mice by altering the Th1/Th2 cytokine profile." The Journal of Immunology 172(10): 5893-5899.
Cava, C., G. Bertoli and I. Castiglioni (2019). "Portrait of tissue-specific coexpression networks of noncoding RNAs (miRNA and lncRNA) and mRNAs in normal tissues." Computational and Mathematical Methods in Medicine 2019: 902-9351.
Cava, C., A. Colaprico, G. Bertoli, A. Graudenzi, T. C. Silva, C. Olsen, H. Noushmehr, G. Bontempi, G. Mauri and I. Castiglioni (2017). "SpidermiR: an R/bioconductor package for integrative analysis with miRNA data." International journal of molecular sciences 18(2): 274.
Chen, J., J. Zhang, Y. Gao, Y. Li, C. Feng, C. Song, Z. Ning, X. Zhou, J. Zhao and M. Feng (2021). "LncSEA: a platform for long non-coding RNA related sets and enrichment analysis." Nucleic acids research 49(D1): D969-D980.
Cozzolino, M., F. Rizzello, C. Riviello, C. Romanelli and M. Coccia Elisabetta (2019). "Ongoing pregnancies in patients with unexplained recurrent pregnancy loss: adverse obstetric outcomes." Human Fertility 22(3): 219-225.
DiMarco, A., I. Christakis, V. Constantinides, L. Regan and F. F. Palazzo (2018). "Undiagnosed primary hyperparathyroidism and recurrent miscarriage: the first prospective pilot study." World Journal of Surgery 42: 639-645.
El Hachem, H., V. Crepaux, P. May-Panloup, P. Descamps, G. Legendre and P.-E. Bouet (2017). "Recurrent pregnancy loss: current perspectives." International journal of women's health 9: 331-345.
Franz, M., C. T. Lopes, D. Fong, M. Kucera, M. Cheung, M. C. Siper, G. Huck, Y. Dong, O. Sumer and G. D. Bader (2023). "Cytoscape. js 2023 update: a graph theory library for visualisation and analysis." Bioinformatics.
Fu, G., J. Brkić, H. Hayder and C. Peng (2013). "MicroRNAs in human placental development and pregnancy complications." International journal of molecular sciences 14(3): 5519-5544.
Germeyer, A., R. F. Savaris, J. Jauckus and B. Lessey (2014). "Endometrial beta3 integrin profile reflects endometrial receptivity defects in women with unexplained recurrent pregnancy loss." Reproductive Biology and Endocrinology 12(1): 1-5.
Goldsmith, L. T. and G. Weiss (2009). "Relaxin in human pregnancy." Annals of the New York Academy of Sciences 1160(1): 130-135.
Gu, S., X. Zang, L. Jiang, T. Gu, F. Meng, S. Huang, G. Cai, Z. Li, Z. Wu and L. Hong (2022). "Differential MicroRNA Expression in Porcine Endometrium Related to Spontaneous Embryo Loss during Early Pregnancy." International Journal of Molecular Sciences 23(15): 8157.
Guo, H., H. Gao, J. Li, Y. Cong, Q. Chen, Y. Wang, Q. Zhu, Q. Lyu, L. Wu and W. Chai (2020). "Impacts of medroxyprogesterone acetate on oocytes and embryos: matched case-control study in women with stage III–IV ovarian endometriosis undergoing controlled ovarian hyperstimulation for in vitro fertilization." Annals of Translational Medicine 8(6).
Gupta, P. (2014). "Competing endogenous RNA (ceRNA): a new class of RNA working as miRNA sponges." Current Science: 823-830.
Harrow, J., A. Frankish, J. M. Gonzalez, E. Tapanari, M. Diekhans, F. Kokocinski, B. L. Aken, D. Barrell, A. Zadissa and S. Searle (2012). "GENCODE: the reference human genome annotation for The ENCODE Project." Genome research 22(9): 1760-1774.
He, D., H. Zeng, J. Chen, L. Xiao, Y. Zhao and N. Liu (2019). "H19 regulates trophoblastic spheroid adhesion by competitively binding to let-7." Reproduction (Cambridge, England) 157(5): 423.
Heo, M. J., J. Yun and S. G. Kim (2019). "Role of non-coding RNAs in liver disease progression to hepatocellular carcinoma." Archives of pharmacal research 42: 48-62.
Karagkouni, D., M. D. Paraskevopoulou, S. Tastsoglou, G. Skoufos, A. Karavangeli, V. Pierros, E. Zacharopoulou and A. G. Hatzigeorgiou (2020). "DIANA-LncBase v3: indexing experimentally supported miRNA targets on non-coding transcripts." Nucleic acids research 48(D1): D101-D110.
Kohl, M., S. Wiese and B. Warscheid (2011). "Cytoscape: software for visualization and analysis of biological networks." Data mining in proteomics: from standards to applications: 291-303.
Lee, J.-J., A. Drakaki, D. Iliopoulos and K. Struhl (2012). "MiR-27b targets PPARγ to inhibit growth, tumor progression and the inflammatory response in neuroblastoma cells." Oncogene 31(33): 3818-3825.
Li, J., W. Ma, P. Zeng, J. Wang, B. Geng, J. Yang and Q. Cui (2015). "LncTar: a tool for predicting the RNA targets of long noncoding RNAs." Briefings in bioinformatics 16(5): 806-812.
Li, L., M. Peng, W. Xue, Z. Fan, T. Wang, J. Lian, Y. Zhai, W. Lian, D. Qin and J. Zhao (2018). "Integrated analysis of dysregulated long non-coding RNAs/microRNAs/mRNAs in metastasis of lung adenocarcinoma." Journal of translational medicine 16: 1-14.
Liao, Y., G. K. Smyth and W. Shi (2019). "The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads." Nucleic acids research 47(8): e47-e47.
Medicine, P. C. o. t. A. S. f. R. (2012). "Evaluation and treatment of recurrent pregnancy loss: a committee opinion." Fertility and sterility 98(5): 1103-1111.
Nepomnaschy, P. A., K. B. Welch, D. S. McConnell, B. S. Low, B. I. Strassmann and B. G. England (2006). "Cortisol levels and very early pregnancy loss in humans." Proceedings of the National Academy of Sciences 103(10): 3938-3942.
Ng, H. H., M. Shen, C. S. Samuel, J. Schlossmann and R. G. Bennett (2019). "Relaxin and extracellular matrix remodeling: Mechanisms and signaling pathways." Molecular and cellular endocrinology 487: 59-65.
Norwitz, E. R., D. J. Schust and S. J. Fisher (2001). "Implantation and the survival of early pregnancy." New England Journal of Medicine 345(19): 1400-1408.
Odland, J. Ø., I. V. Sergejeva, M. D. Ivaneev, I. P. Jensen and B. Stray-Pedersen (2001). "Seropositivity of cytomegalovirus, parvovirus and rubella in pregnant women and recurrent aborters in Leningrad County, Russia." Acta obstetricia et gynecologica Scandinavica 80(11): 1025-1029.
Plotnikova, O., A. Baranova and M. Skoblov (2019). "Comprehensive analysis of human microRNA–mRNA interactome." Frontiers in genetics 10: 933.
Reed, B. G., S. N. Babayev, L. X. Chen, B. R. Carr, R. A. Word and P. T. Jimenez (2018). "Estrogen-regulated miRNA-27b is altered by bisphenol A in human endometrial stromal cells." Reproduction (Cambridge, England) 156(6): 559.
Ritchie, M. E., B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi and G. K. Smyth (2015). "limma powers differential expression analyses for RNA-sequencing and microarray studies." Nucleic acids research 43(7): e47-e47.
Rivier, C. and S. Rivest (1991). "Effect of stress on the activity of the hypothalamic-pituitary-gonadal axis: peripheral and central mechanisms." Biology of reproduction 45(4): 523-532.
RPL, E. G. G. o., R. Bender Atik, O. B. Christiansen, J. Elson, A. M. Kolte, S. Lewis, S. Middeldorp, S. Mcheik, B. Peramo and S. Quenby (2023). "ESHRE guideline: recurrent pregnancy loss: an update in 2022." Human Reproduction Open 2023(1): hoad002.
Ru, Y., K. J. Kechris, B. Tabakoff, P. Hoffman, R. A. Radcliffe, R. Bowler, S. Mahaffey, S. Rossi, G. A. Calin and L. Bemis (2014). "The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations." Nucleic acids research 42(17): e133-e133.
Salmena, L., L. Poliseno, Y. Tay, L. Kats and P. P. Pandolfi (2011). "A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language?" cell 146(3): 353-358.
Sham, A. k.-h., M. g.-c. Yiu and W. y.-b. Ho (2010). "Psychiatric morbidity following miscarriage in Hong Kong." General hospital psychiatry 32(3): 284-293.
Sherkat, R., M. Meidani, H. Zarabian, A. Rezaei and A. Gholamrezaei (2014). "Seropositivity of cytomegalovirus in patients with recurrent pregnancy loss." Journal of research in medical sciences: the official journal of Isfahan University of Medical Sciences 19(Suppl 1): S22.
Smith, G., A. Wood, J. Pell and J. Hattie (2011). "Recurrent miscarriage is associated with a family history of ischaemic heart disease: a retrospective cohort study." BJOG: An International Journal of Obstetrics & Gynaecology 118(5): 557-563.
van Haaften, G. and R. Agami (2010). "Tumorigenicity of the miR-17-92 cluster distilled." Genes & development 24(1): 1-4.
Van Mourik, M. S., N. S. Macklon and C. J. Heijnen (2009). "Embryonic implantation: cytokines, adhesion molecules, and immune cells in establishing an implantation environment." Journal of leukocyte biology 85(1): 4-19.
Van Trappen, P. O. and M. S. Pepper (2005). "Lymphangiogenesis in human gynaecological cancers." Angiogenesis 8(2): 137.
Weinstein, L. S., J. Liu, A. Sakamoto, T. Xie and M. Chen (2004). "Minireview: GNAS: normal and abnormal functions." Endocrinology 145(12): 5459-5464.
Wellmann, K. and B. Volk (1972). "Fine structural changes in the rabbit placenta induced by cortisone." Archives of Pathology 94(2): 147-157.
Wingfield, J. and R. Sapolsky (2003). "Reproduction and resistance to stress: when and how." Journal of neuroendocrinology 15(8): 711-724.
Xu, J., Q. Meng, X. Li, H. Yang, J. Xu, N. Gao, H. Sun, S. Wu, G. Familiari and M. Relucenti (2019). "Long Noncoding RNA MIR17HG Promotes Colorectal Cancer Progression via miR-17-5pMIR17HG Role in Colorectal Cancer." Cancer research 79(19): 4882-4895.
Ye, J., X. Wu, D. Wu, P. Wu, C. Ni, Z. Zhang, Z. Chen, F. Qiu, J. Xu and J. Huang (2013). "miRNA-27b targets vascular endothelial growth factor C to inhibit tumor progression and angiogenesis in colorectal cancer." PloS one 8(4): e60687.
Yinon, Y., D. Farine and M. H. Yudin (2010). "Screening, diagnosis, and management of cytomegalovirus infection in pregnancy." Obstetrical & gynecological survey 65(11): 736-743.
Zhu, K.-P., C.-L. Zhang, X.-L. Ma, J.-P. Hu, T. Cai and L. Zhang (2019). "Analyzing the interactions of mRNAs and ncRNAs to predict competing endogenous RNA networks in osteosarcoma chemo-resistance." Molecular Therapy 27(3): 518-530.
Zhu, L. and Z. Liu (2021). "Serum from patients with hypertension promotes endothelial dysfunction to induce trophoblast invasion through the miR‑27b‑3p/ATPase plasma membrane Ca 2+ transporting 1 axis." Molecular Medicine Reports 23(5): 1-10.

Table 1- Expression datasets in the study.

GEO accession number	Platform	Type	Tissue	Samples of RSA/control
GSE179996	GPL21827 (Agilent 079487 Arraystar Human LncRNA microarray V4)	lncRNA	blood	5/5
GSE178619	GPL25134 (Agilent 070156 Human miRNA V21.0 Microarray 046064)	miRNA	blood	3/3
GSE201442	GPL21576 (Agilent 070156 Human miRNA V21.0 Microarray 046064)	miRNA	blood	3/2
GSE201469	GPL21185 (Agilent 072363 SurePrint G3 Human GE v3 8x60K Microarray 039494)	mRNA	blood	3/2

Table 2-Top20 downregulated and upregulated differentially expressed lncRNAs

Down-regulated DELs			Up- regulated DELs
lncRNA name	Log₂FC	P.Value	lncRNA name	Log₂FC	P.Value
*LOC100288911*	-3.29	0.001498	HSD11B1-AS1	4.75	0.0026363
*AC253536.6*	-2.93	0.0233614	Lnc-MT1G	4.01	0.0141782
*AC100803.3*	-2.69	0.0147399	PACERR	3.76	0.0023719
*AP006621.1*	-2.67	0.0289738	Lnc-BTG1	3.32	0.0001481
*CCL23*	-2.43	0.0024064	SLED1	3.29	0.0002449
*FOLH1*	-2.42	0.0194664	AC105046.1	2.99	0.0258191
*AC091153.3*	-2.34	0.0210908	DIP2A-IT1	2.94	0.0045235
*ELAC2*	-2.22	0.0006152	CCIN	2.86	0.0002204
*RNF182*	-2.21	0.0117257	BABAM2-AS1	2.84	0.0061297
*RBMS3*	-2.18	0.0208703	AC123912.4	2.82	0.0298153
*S100A8*	-2.13	0.0447050	Lnc-IRS2	2.78	0.0039986
*CR381670.1*	-2.11	0.0010294	BABAM2	2.75	0.0194278
*lnc-MINA-3*	-2.1	0.0029372	Lnc-CFAP45	2.73	0.0012457
*LINC01563*	-2.09	0.0127886	Lnc-NR4A2	2.72	0.0029753
*EFHB*	-2.06	0.0379794	Lnc-RFPL4A	2.70	0.0035983
*CEBPE*	-2.05	0.0000746	Lnc-CXCL8	2.69	0.0210302
*AC010149.1*	-2.04	0.0398308	MIR17HG	2.56	0.0349744
*LOC100272217*	-2.02	0.0003619	LINC00944	2.55	0.0048491
*MIX23*	-2.02	0.0019109	RFPL4AL1	2.54	0.0006436
*C6orf120*	-2.01	0.0007515	CSNK1D	2.37	0.0060747

Table 3-Top20 downregulated and upregulated differentially expressed miRNAs

Down-regulated DEmiRs			UP-regulated DEmiRs
miRNA name	Log₂FC	P-Value	miRNA name	Log₂FC	P-Value
*hsa-miR-126-5p*	-6.15	8.61E-05	hsa-miR-6875-5p	5.09	1.45E-03
*hsa-miR-744-5p*	-5.97	0.000404	hsa-miR-6780b-5p	4.44	8.71E-04
*hsa-miR-99b-5p*	-5.80	0.001022	hsa-miR-4443	4.32	3.19E-04
*hsa-miR-4455*	-5.72	1.19E-06	hsa-miR-1246	3.57	1.40E-03
*hsa-miR-335-5p*	-5.7	0.00091	hsa-miR-6717-5p	3.46	1.23E-03
*hsa-miR-10a-5p*	-5.67	0.001588	hsa-miR-3934-5p	3.45	1.14E-05
*hsa-miR-181c-5p*	-5.59	0.000457	hsa-miR-6131	3.41	8.14E-04
*hsa-miR-152-3p*	-5.51	0.003295	hsa-miR-371b-5p	3.25	1.25E-03
*hsa-miR-4440*	-5.49	1.67E-08	hsa-miR-575	3.22	3.80E-03
*hsa-miR-4455*	-5.33	7.89E-06	hsa-miR-4485-5p	3.18	1.03E-03
*hsa-miR-126-3p*	-5.26	0.008107	hsa-miR-4713-3p	3.15	5.33E-03
*hsa-miR-330-3p*	-5.09	0.011882	hsa-miR-6131	3.14	2.54E-03
*hsa-miR-574-5p*	-4.87	4.01E-05	hsa-miR-6717-5p	3.08	8.70E-03
*hsa-miR-32-3p*	-4.76	3.94E-06	hsa-miR-7704	3.08	9.51E-03
*hsa-miR-32-3p*	-4.71	5.09E-06	hsa-miR-6780b-5p	3.06	2.01E-02
*hsa-miR-376c-3p*	-4.71	0.027687	hsa-miR-371b-5p	2.99	3.92E-03
*hsa-miR-3149*	-4.69	5.53E-06	hsa-miR-4713-3p	2.95	4.21E-02
*hsa-miR-3663-3p*	-4.69	3.54E-06	hsa-miR-4443	2.93	5.30E-02
*hsa-miR-32-3p*	-4.66	6.80E-06	hsa-miR-6772-5p	2.86	1.05E-02
*hsa-miR-130a-3p*	-4.63	0.016209	hsa-miR-3652	2.86	7.60E-05

Table 4-Top20 downregulated and upregulated differentially expressed mRNAs

Down-regulated DEMs			Up- regulated DEMs
mRNA name	Log₂FC	P-Value	mRNA name	Log₂FC	P-Value
*HBB*	-7.91	0.001153	CCN2	5.08	0.001419
*HBD*	-7.51	0.000141	IGFBP7	5.07	0.003714
*HBA2*	-7.40	0.000863	PAX5	4.66	0.004627
*HBA2*	-7.19	0.000801	SEMA6C	4.58	0.003053
*CTSE*	-6.48	0.010929	TMEM250	4.46	0.017763
*DEFA3*	-6.45	0.00089	lnc-SCRN3-3	4.46	0.010667
*GP1BB*	-5.90	0.000694	CREB3L4	4.43	0.002017
*GPX1*	-5.79	0.00203	ADORA2A	4.41	0.008577
*GP1BB*	-5.28	0.001951	ZC3HAV1L	4.38	0.005103
*GPX1*	-4.66	0.003353	SLC22A7	4.37	0.048001
*OAZ1*	-4.65	0.001638	HLX	4.00	0.010471
*ADAMTS7*	-4.62	0.026248	TPT1-AS1	3.98	0.000848
*ACTB*	-4.58	0.014397	CARMIL2	3.98	0.041954
*UBB*	-4.55	0.007135	MED19	3.95	0.003223
*PPBP*	-4.36	0.012115	PLEC	3.80	0.005774
*PF4*	-4.32	0.011598	ZDHHC21	3.79	0.012363
*ACTB*	-4.26	0.003496	TFDP3	3.79	0.010504
*FTH1*	-4.14	0.005461	MEF2B	3.78	0.006448
*KLF1*	-4.14	0.040818	CPN1	3.77	0.018591
*HBA2*	-4.07	0.010639	TEF	3.73	0.032894

Table 5- Top 20 enriched KEGG pathways of genes in ceRNA networks.

Term	P-value	Adjusted P-value	Combined Score
Cocaine addiction	1.19E-04	0.008082701	326.1066005
Cortisol synthesis and secretion	2.75E-04	0.008082701	219.2440083
Amphetamine addiction	3.29E-04	0.008082701	201.4810119
Thyroid hormone synthesis	4.20E-04	0.008082701	178.9809145
Adrenergic signaling in cardiomyocytes	2.04E-04	0.008082701	131.8142728
Alcoholism	4.62E-04	0.008082701	95.38918124
Insulin secretion	6.27E-04	0.009409130	147.1715419
Aldosterone synthesis and secretion	9.17E-04	0.010989082	121.8805214
Human cytomegalovirus infection	9.42E-04	0.010989082	71.12615598
Parathyroid hormone synthesis, secretion and action	0.001151099	0.011178035	108.7215542
Glucagon signaling pathway	0.001182714	0.011178035	107.2396338
Cholinergic synapse	0.001383947	0.011178035	98.99657021
Parkinson disease	0.001369849	0.011178035	60.63611387
Growth hormone synthesis, secretion and action	0.001605519	0.012041391	91.73049491
Platelet activation	0.001806225	0.012643574	86.30832233
Relaxin signaling pathway	0.002021967	0.013269156	81.38233106
Dopaminergic synapse	0.002158792	0.013333716	78.63883101
Estrogen signaling pathway	0.002399397	0.013996484	74.38254042
Vasopressin-regulated water reabsorption	0.003328402	0.017853808	146.258861
Cushing syndrome	0.003400725	0.017853808	61.72682635

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Bioinformatics analysis of a disease-specific lncRNA-miRNA-mRNA regulatory network in recurrent spontaneous abortion (RSA)

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