Next Article in Journal
Early Routine Biomarkers of SARS-CoV-2 Morbidity and Mortality: Outcomes from an Emergency Section
Next Article in Special Issue
The Eutopic Endometrium Proteome in Endometriosis Reveals Candidate Markers and Molecular Mechanisms of Physiopathology
Previous Article in Journal
Nanoparticles for Diagnosis and Target Therapy in Pediatric Brain Cancers
Previous Article in Special Issue
Ruptured Hemorrhagic Ectopic Pregnancy Implanted in the Diaphragm: A Rare Case Report and Brief Literature Review
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Clues for Improving the Pathophysiology Knowledge for Endometriosis Using Serum Micro-RNA Expression

Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, Sorbonne University, 4 Rue de la Chine, 75020 Paris, France
Clinical Research Group (GRC) Paris 6, Centre Expert Endométriose (C3E), Sorbonne University (GRC6 C3E SU), 4 Rue de la Chine, 75020 Paris, France
Cancer Biology and Therapeutics, Centre de Recherche Saint-Antoine (CRSA), Sorbonne University, INSERM UMR_S_938, 75020 Paris, France
Ziwig Health, 19 Rue Reboud, 69003 Lyon, France
Paris Brain Institute—Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP—Hôpital Pitié-Salpêtrière, Sorbonne University, 4 Rue de la Chine, 75020 Paris, France
Gentoyping and Sequencing Core Facility, iGenSeq, Institut du Cerveau et de la Moelle Épinière, ICM, Hôpital Pitié-Salpêtrière, 47-83 Boulevard de l’Hôpital, 75013 Paris, France
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(1), 175;
Received: 3 December 2021 / Revised: 6 January 2022 / Accepted: 9 January 2022 / Published: 12 January 2022
(This article belongs to the Special Issue Advances in the Diagnostics of Endometriosis)


The pathophysiology of endometriosis remains poorly understood. The aim of the present study was to investigate functions and pathways associated with the various miRNAs differentially expressed in patients with endometriosis. Plasma samples of the 200 patients from the prospective “ENDO-miRNA” study were analyzed and all known human miRNAs were sequenced. For each miRNA, sensitivity, specificity, and ROC AUC values were calculated for the diagnosis of endometriosis. miRNAs with an AUC ≥ 0.6 were selected for further analysis. A comprehensive review of recent articles from the PubMed, Clinical, Cochrane Library, and Web of Science databases was performed to identify functions and pathways associated with the selected miRNAs. In total, 2633 miRNAs were found in the patients with endometriosis. Among the 57 miRNAs with an AUC ≥ 0.6: 20 had never been reported before; one (miR-124-3p) had previously been observed in endometriosis; and the remaining 36 had been reported in benign and malignant disorders. miR-124-3p is involved in ectopic endometrial cell proliferation and invasion and plays a role in the following pathways: mTOR, STAT3, PI3K/Akt, NF-κB, ERK, PLGF-ROS, FGF2-FGFR, MAPK, GSK3B/β–catenin. Most of the remaining 36 miRNAs are involved in carcinogenesis through cell proliferation, apoptosis, and invasion. The three main pathways involved are Wnt/β–catenin, PI3K/Akt, and NF–KB. Our results provide evidence of the relation between the miRNA profiles of patients with endometriosis and various signaling pathways implicated in its pathophysiology.

1. Introduction

Endometriosis, defined by the presence of endometrial-like tissue outside the uterus, affects 5–10% of women of reproductive age, but is also diagnosed in menopausal patients with an incidence estimated at 2–5% [1,2]. In the premenopausal period, diagnosis is mainly based on symptoms including severe chronic pelvic pain, dysmenorrhea, dyspareunia, dyschezia, and infertility. However, no single sign is sufficiently characteristic to make a diagnosis. In postmenopausal patients, endometriosis can be symptomatic but is also diagnosed in a context associated with, or mimicking, a cancer. From the pathophysiology point of view, endometriosis is considered a multifactorial disease with genetic and epigenetic controls involving multiple pathways such as cell proliferation, cell differentiation, cell adhesion, apoptosis, angiogenesis, steroidogenesis, inflammatory and immune responses, oncogenic suppressors, as well as exposome factors, particularly persistent organic pollutants (POP) [3]. However, despite numerous investigations of these various pathways, the pathophysiology of endometriosis remains an enigma.
Human miRNAs are single-stranded highly conserved non-coding RNAs composed of 21–25 nucleotides binding to their complementary mRNAs regulating their degradation and translation [4,5]. It is estimated that about 60% of genes are regulated by miRNAs [6,7]. Cumulative evidence suggests that miRNA dysregulation plays a pivotal role in many benign and malignant disorders, as well as in endometriosis which shares features of both pathologies. More than 2600 miRNAs have been identified in humans to date. Evaluation of these miRNAs in patients with endometriosis has shown that more than 200 are differentially expressed in patients with and without endometriosis [8,9,10] with some literature on their relevance in endometriosis’ diagnostic [11,12,13]. Among these 200 miRNAs, only a few have been analyzed with a view to better understanding the pathophysiology of endometriosis [14,15].
Therefore, using data from the prospective “ENDOmiRNA” study [16], the aim of the present work was to investigate functions and pathways associated with the various miRNAs differentially expressed in patients with and without endometriosis to highlight new potential fields for research and treatments.

2. Materials and Methods

2.1. Study Population

We used data from the prospective “ENDOmiARN” study ( Identifier: NCT04728152). Data collection and analysis were carried out under the Research Protocol n° ID RCB: 2020-A03297-32. The ENDOmiARN study included 200 plasma samples obtained from patients with chronic pelvic pain suggestive of endometriosis. All had undergone a laparoscopic procedure (either therapeutic or diagnostic laparoscopic) and/or MRI imaging evidencing endometriosis by the presence of endometrioma and/or deep endometriosis [17,18,19], as stated in the trial registration. The laparoscopy procedures were systematically recorded and the video analyzed by two operators (CT, YD), who were blind to the symptoms and imaging findings, to confirm the presence or absence of endometriosis. All patients undergoing diagnostic or operative laparoscopy underwent a systematic histological confirmation of endometriosis when potential lesions were present. For the patients in the endometriosis group without laparoscopic evaluation, all had MRI features of deep endometriosis with colorectal involvement and/or endometrioma have been revised in the multidisciplinary endometriosis committee. All the plasma samples were collected between January 2021 and June 2021. All samples were collected at the first consultation, prior to laparoscopy (in patients that underwent surgery). Analysis was performed blinded to the surgical and imaging findings. If endometriosis was detected, the subjects were stratified according to the revised American Society of Reproductive Medicine (rASRM) classification [20].

2.2. Plasma Sample Collection

Blood samples (4 mL) were collected in EDTA tubes (BD, Franklin Lakes, NJ, USA). The plasma was then isolated from whole blood within a maximum of 2 h by two successive centrifugations at 4 °C (first at 1900× g (3000 rpm) for 10 min, followed by 13,000–14,000× g for 10 min to remove all cell debris), and aliquoted, labeled, and stored at −80 °C until analysis, as previously published [21,22,23].

2.3. RNA Sample Extraction, Preparation, and Quality Control

The RNA was extracted from 500 μL of plasma on a Maxwell 48® RSC automat using the Maxwell® RSC miRNA Plasma and Serum Kit (ref AS1680, Promega, Madison, WI, USA) according to the manufacturer’s protocol. Libraries for small RNA sequencing were prepared using the QIAseq miRNA Library Kit for Illumina (Qiagen, Hilden, Germany). The resulting small RNA libraries were concentrated by ethanol precipitation and quantified using a Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) prior to sequencing on a Novaseq 6000 sequencer (Illumina, San Diego, CA, USA) with read lengths of 100 bases and 17 million single-end reads per sample, on average [24,25].

3. Bioinformatics

3.1. Raw Data Preprocessing (Raw, Filtered, Aligned Reads) and Quality Control

Sequencing reads were processed using the data processing pipeline. FastQ files were trimmed to remove adapter sequences using Cutadapt version v.1.18 and were aligned using Bowtie version 1.1.1 to the following transcriptome databases: the human reference genome available from NCBI (, accessed on 28 August 2021), and miRBase22 (miRNAs) using the MirDeep2 v0.1.0 package. The raw sequencing data quality was assessed using FastQC software v0.11.7 [25,26,27,28,29]. The bioinformatic process used was previously described by Potla et al. [30].

3.2. Differential Expression Analysis of the miRNAs

miRNA expression was quantified by miRDeep2 [31]. Differential expression tests were then conducted in DESeq2 for miRNAs with read counts in ≥1 of the samples. DESeq2 integrates methodological advances with several novel features to facilitate more quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change [32,33]. miRNAs were considered as differentially expressed if the absolute value of log2-fold change was >1.5 (upregulated) and <0.5 (downregulated). The p-value adjusted for multiple testing was <0.05 [32].

3.3. Study of the miRNA Accuracy

To evaluate the diagnostic accuracy of each miRNA biomarker, sensitivity, specificity, and ROC analysis was performed, and the ROC AUC was calculated [34,35].
Additional statistical analysis was based on the Chi2 test as appropriate for categorical variables. Values of p < 0.05 were considered to denote significant differences. Data were managed with an Excel database (Microsoft, Redmond, WA, USA) and analyzed using R 2.15 software, available online (, accessed on 28 August 2021).

3.4. Sources and Search Strategy

The PubMed,, Cochrane Library, and Web of Science databases were queried for relevant studies published before 1 July 2021 using the miRNAs names exclusively as search terms. All English results were screened to perform a comprehensive evaluation of relevant articles.

4. Results

4.1. Demographic Characteristics of the Population

The ENDOmiARN study included 200 patients, 76.5% (n = 153) and 23.5% (n = 47) have been diagnose with and without endometriosis, respectively. Clinical characteristics of the endometriosis and controls patients are displayed in Table 1. None of the patients had a history of ovarian cancer in the cohort. Within the group of patients diagnosed with endometriosis, a similar proportion had minimal to mild and moderate to severe endometriosis. A total of 14.4% of the patients with endometriosis were smokers. Patients with endometriosis were equally diagnosed by either surgery or MRI (only when stage III–IV).

4.2. Comparison of miRNAs Expressed in Patients with and without Endometriosis

A total of 2633 miRNAs were found to be expressed in patients with endometriosis. The distribution of the miRNAs according to the AUC values is given in Table S1. None had an AUC ≥ 0.70, and 2077 miRNAs had an AUC between ≥0.5 and <0.60.
For the 57 miRNAs with an AUC ≥ 0.60, the sensitivity, specificity, accuracy, up/down regulation and AUC values are given in Table S2. Of note, 5 miRNA were up-regulated in endometriosis patients (miR-6502-5p; miR-515-5p; miR-548j-5p; miR-29b-1-5p; miR-4748) and 2 miRNA were down regulated (miR-3137 and miR-3168). Eight members of the miRNA-548 family were identified (miR–548j–5p; miR–548p; miR–548ah–3p; miR–548l; miR–548q; miR–548f–5p; miR–548ay–3p; miR–548b–3p).
Among the 57 miRNAs with an AUC ≥0.6, 20 had not been reported before (miR–6502–5p; miR–548j–5p; miR–4748; miR–5697; miR–3124–5p; miR–4511; miR–3940–3p; miR–5009–5p; miR–10399; miR–3942–5p; miR–92b-5p; miR–4732–3p; miR–6789–5p; miR–6773–5p; miR–4466; miR–6802–5p; miR–4655–5p; miR–1343–5p; miR–8089; miR–3137), one had previously been reported in endometriosis (miR–124–3p), and the remaining 36 had previously been reported in benign and malignant disorders (miR–515–5p; miR–29b–1–5p; miR–548p; miR4999–5p; miR–6501–5p; miR–1270; miR–433–3p; miR–548ah–3p; miR–1278; miR548l; miR–1292–5p; miR–144–5p; miR–362–5p; miR–1285–3p; miR–3913–5p; miR–548q; miR–30e–3p; miR–151a–3p; miR–421; miR–27b–5p; miR–1910–3p; miR–542–5p; miR–548f–5p; miR–1250–5p; miR–1972; miR–548ay–3p; miR–6785–5p; miR–6777–5p; miR–4514; miR–4658; miR–1266–5p; miR–548b–3p; miR–6509–5p; miR–7107–5p; miR–6813–5p; miR–3168).

4.3. Relation between miRNA Expression and Signaling Pathways Known in Endometriosis

As mentioned above, the only miRNA previously reported in endometriosis was miR–124–3p (Table S3). This miRNA is involved in ectopic endometrial cell proliferation and invasion by targeting ITGB3 and is downregulated by LncRNA-H19. It has also been described in other benign (peripheral arterial disease, hypertension, acute respiratory distress syndrome, Parkinson’s disease) and malignant (ovarian cancer, hepatocellular carcinoma, gastric cancer, glioma, breast cancer) disorders. The signaling pathways identified were mTOR, STAT3, PI3K/Akt, NF-κB, ERK, PLGF-ROS, FGF2-FGFR, MAPK, GSK3B/β–catenin. Besides its role in endometriosis, this miRNA is involved in cell proliferation, invasion, apoptosis, angiogenesis, inflammation, metastasis, and neurogenic functions. In addition, it has been associated with epithelial to mesenchymal transition, promoting trophoblast cell pyroptosis, chemosensitivity, and bone formation (Table S3).

4.4. Relation between miRNA Expression and Signaling Pathways Involved in Disorders Other Than Endometriosis

The large majority of miRNAs differentially expressed in patients with and without endometriosis have not previously been identified as being involved in the pathophysiology of endometriosis. Most of them are known to be involved in numerous benign (atherosclerosis, diabetic nephropathy and retinopathy, renal and myocardial injury, vitiligo development, retinal degeneration, sickle cell disease, depressive disorders, epilepsy, early-onset preeclampsia, atrial fibrillation, hepatic steatosis, intracerebral hemorrhage, neurodegenerative disorders, bone formation, ovarian failure, nicotine initiation and addiction, endometrial receptivity in PCOS patients, response to estradiol, glaucoma), and malignant (hepatocellular carcinoma, retinoblastoma, prostate cancer, breast cancer, lung cancer, bladder cancer, thyroid cancer, osteosarcoma, ovarian cancer, gastric cancer, colorectal cancer, laryngeal squamous cell carcinoma, cholangiocarcinoma, chemo- and radiosensitivity) disorders, mainly in signaling pathways involved in key functions in carcinogenesis. The miRNA-associated disorders of the 10 miRNAs with the highest AUC and those differentially expressed in patients with and without endometriosis (up or down-regulated) are displayed Table 2, and in Table S4 for the others.
The main functions regulated by the miRNAs were: cell proliferation (22 miRNAs), apoptosis (16 miRNAs), adhesion/invasion (16 miRNAs), therapeutic (chemo- or radiotherapy), sensitivity (10 miRNAs), angiogenesis (5 miRNAs), immune response (5 miRNAs), inflammation (4 miRNAs), neurogenic function (2 miRNAs), related to pollutants (2 miRNAs), extracellular matrix remodeling or fibrosis (2 miRNAs), steroidogenesis or hormonal influence (1 miRNA), and other (18 miRNAs) (Table 3). The signaling pathways involved (Table 3) were: JAK/STAT (4 miRNAs), Notch1 (1 miRNA), FoxC1/Snail (2 miRNAs), Hippo (1 miRNA), NF–KB (4 miRNAs), YAP/TAZ (2 miRNAs), PIK3/Akt (7 miRNAs), HIF–1 alpha (2 miRNAs), JNK, Rap1b (1 miRNA), VEGF (1 miRNA), ERK (1 miRNA), PTH signaling (1 miRNA), Wnt/β-catenin (8 miRNAs), endogenous glucocorticoids (1 miRNA), insulin signaling pathway (1 miRNA), HBXIP (1 miRNA), GSK3B (1 miRNA), PTEN (1 miRNA), FOXO (3 miRNAs), MAPK (3 miRNAs), p53 (2 miRNAs), mTOR (2 miRNAs), TGF–ß (2 miRNAs). The main functions regulated by the 10 miRNAs with the highest AUC and those differentially expressed in patients with and without endometriosis (up or down-regulated) are displayed in Table 2 and Table 3, and in Table S5 for the others.

4.5. miRNA Expression Level According to Patient’s Characteristics

Among the 2633 reported miRNA, the top six according (miR-548j-5p, miR-29b-1-5p, miR-548p, miR-548l, miR-3913-5p, miR-124-3p) to their AUC value have been studied for their respective expression level based on the variation of rASRM stage, BMI, age, fertility status, smoking habit and hormonal treatment use. Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6 display miRNA expression level. For those miRNA, we noticed in majority no significant variation of their expression level according to condition, excepted for miR-548l according to age factor (p = 0.03), miR-548p according to fertile status (p = 0.01), miR-29b-1-5p and miR-124-3p according to tobacco (p = 0.01 and p = 0.03 respectively) and miR-548p and miR-548l (p = 0.01 and p = 0.04) according to hormonal treatment use.

5. Discussion

Using miRNAome analysis, the present study contributes to establishing relations between miRNA expression in patients with endometriosis and various signaling pathways (often common to carcinogenesis) as well as identifying new potential pathways involved in the pathophysiology of endometriosis.
This is the first study to evaluate the expression of the currently known 2633 human miRNAs in endometriosis. However, it is important to note that all the human miRNAs were detected in the blood of patients with endometriosis. This poses the challenge of which miRNAs deserve to be analyzed to better understand the pathophysiology of endometriosis. Previous studies have demonstrated the pivotal role of miRNAs to improve diagnosis, prediction and forecasting for numerous diseases mainly based on micro-array of miRNAs or NGS sequencing using bioinformatics platforms with a limited number of miRNA, imposing a validation by qRT-PCR [26]. Another crucial issue is to evaluate whether results of NGS require qRT-PCR validation. Previous studies have shown that absolute NGS reads correlated modestly with qRT-PCR but fold changes, as used in the current study, correlated highly supporting that NGS is robust at relative but not absolute quantification of miRNA [85,86,87,88]. Moreover, as previously demonstrated, the number of microRNAs detected in biofluids by NGS and qRT-PCR was similar after filtering the data and applying thresholds supporting our results. In addition, recent studies validated the use of NGS technology to improve the diagnosing of using saliva RNA or to predict concussion duration and detect symptom recovery after mild traumatic brain injury. In this setting, in accordance with ’t Hoen et al. [89], bioinformatics allows the exhaustive analysis of all ARN fragments that are aligned and mapped, and their expression levels quantified, thus eliminating the need for sequence specific hybridization probes or qRT-PCR which are required in a microarray [26,90,91]. Moreover, NGS has the advantages of high sensitivity and resolution and excellent reproducibility but imposes considerable computational support [26,89,91]. So far, very few miRNAs (<300) have been used to determine the various biological mechanisms involved in the poorly understood and multifactorial pathophysiology of endometriosis. In the specific context of endometriosis, data from our prospective ENDOmiARN study [16] identified 57 miRNAs (2.2%) significantly expressed in patients with endometriosis with a diagnostic AUC of ≥0.60, suggesting a potential contribution in the pathophysiology of the disease. One crucial result of the current study was the identification of 20 miRNAs significantly expressed in patients with endometriosis but not previously reported in either benign or malignant disorders. This highlights the need for more fundamental research investigating their role in the pathophysiologic process of endometriosis.
Among the 57 miRNAs differentially expressed in patients with endometriosis, only miR–124–3p has previously been reported in endometriosis. Interestingly, none of the miRNAs previously reported as potentially involved in endometriosis [10,92,93] were found to be a predictive marker of endometriosis in our cohort (i.e., with an AUC ≥0.6). Another crucial result is the high number of miRNAs not previously reported in the pathophysiology of endometriosis. Among these 57 miRNAs significantly expressed in patients with endometriosis, the vast majority have previously been detected in various cancers confirming that endometriosis shares several signaling pathways with carcinogenesis [94]. Overall, the miRNA–548 family seems to play a determinant role in endometriosis. Of the eight identified members of the miRNA-548 family, one has not been reported before (miR–548j–5p), three have been reported in carcinogenesis (miR-548p; miR-548l; miR-548b-3p) [66,67,68,69,70,71,72,95,96], and the remaining four (miR-548ah-3p; miR-548q; miR-548f-5p; miR-548ay-3p) are known to be involved in diabetic nephropathy, weight loss, circadian rhythm and smooth muscle contraction [97,98,99,100]. When analyzing the potential contribution of each miRNA in the pathophysiology of endometriosis, it is important to note that a single miRNA could be involved in multiple signaling pathways. The translational regulation by miRNAs involves intricately regulated composite interactions in which a single miRNA regulates the transcription of many mRNAs, and a single mRNA can be influenced by multiple miRNAs. The expression of miRNA in an individual is dynamic and is influenced by an array of factors, including age, ethnicity, the physiological stage of the body, the presence of various diseases, smoking, and various other external factors [93,94].
As mentioned above, the pathophysiology of endometriosis involves numerous signaling pathways which we cannot develop in a single report. In the present study, we did not perform experimental confirmation of the pathways potentially regulated by the miRNAs identified. Instead, we performed extensive literature research to set the basis for further well—designed works that will confirm the roles of the main miRNAs in the physiopathology of endometriosis. However, our work highlights the similar pathophysiological pathways involved in endometriosis and cancer genesis, with most miRNAs regulating cell proliferation (22 miRNAs), apoptosis (16 miRNAs), and adhesion/invasion (16 miRNAs). These three pathways could be determinant to promote endometriosis confirming preliminary analysis by Panir et al. [8]. These pathways compete with inflammation to promote endometriosis, although only four miRNAs have been directly linked to inflammation. Moreover, the contribution of angiogenesis and immune response has been underlined (5 miRNAs each). All these data reinforce the concept that endometriosis shares several signaling pathways of carcinogenesis.
Among the various pathways implicated in endometriosis, hypoxia plays a specific role in early phases of ectopic endometrial tissue survival induced by factor 1-α (HIF-1α) gene expression that is upregulated in endometriotic tissues [101]. Aberrant immune surveillance is thought to reduce the clearance of endometrial cells within the peritoneal cavity, permitting attachment, progression, and subsequent disease persistence [102,103]. The inflammatory mediators interleukin-1β (IL-1B) [104], TNF [105,106], and cyclooxygenase (COX)-2 [107] can be targeted by miRNAs in endometrial tissue. Lagana et al. reported the variation of the balance through the course of the disease between macrophages type 1 (pro-inflammatory) and 2 (pro fibrosis) that could be involved in the pathogenesis [108]. In our cohort, two identified miRNAs were associated with macrophages expression: miR-144 and miR-421. This latter was reported in the inflammatory process [109]. Aberrant estrogen and progesterone biosynthesis and metabolism contribute to the development of endometriosis [109] by increasing local estrogen production and promoting endometriosis development. Increased miR-142-3p seems to reduce steroid sulfatase and IL-6 activity, suggesting a dual effect on steroidogenic and inflammatory pathways in endometriosis [110]. Previous studies have shown a high expression of miR-210 increasing proliferation, angiogenesis, and resistance to apoptosis [111], whereas upregulation of miR-196 increases proliferation and anti-apoptotic mechanisms [112]. There are evidence that matrix metalloproteases (MMPs) also play a crucial role. Indeed, miR-520g acts on MMP2 synthesis that could act to enhance the degradation of the extracellular matrix and facilitate the anchoring of endometrial fragments in ectopic sites [113,114].
Although previous epidemiologic studies using Artificial Intelligence and Machine Learning have demonstrated the involvement of POPs in endometriosis, little data are available about their impact on miRNA expression in endometriosis. In the current study, we found two miRNAs significantly associated with pollutants: miR–421 (previously described in light pollution) and miR–542–5p (previously described in pulmonary fibrosis secondary to silicosis). Moreover, one miRNA (miR-548ay-3p) was previously found to regulate the circadian rhythm [100]. All these data underline the need to investigate other potential factors involved in the pathophysiology of endometriosis besides the classic POPs (octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77, or trans-nonachlor) [3].
We focused our analysis on a few frequently observed specific pathways which may have a therapeutic impact, such as the Wnt/β-catenin, PI3K/AKT/mTOR, HOXA11, and Hippo pathways. To date, the main therapeutic options for patients with endometriosis are based on gonadotropin-releasing hormone agonists (as endometriosis is a well-known hormone-dependent pathology) or angiogenesis inhibitors but with inconsistent results [115,116,117,118,119,120]. The Wnt/β-catenin pathway: It has been demonstrated that Wnt signals are crucial for the activity of epithelial stem cells. The loss of the APC tumor suppressor gene function may lead to the deregulation of β-catenin stability [121]. Various targets activating or inhibiting Wnt signaling have been published (Porcupine, vacuolar ATPase, tankyrase Axin, PP2A, ARFGAP1 and GSK3) [122]. Moreover, soluble Wnt protein agonists have been shown to activate Wnt signaling in vivo [123] and several small molecule compounds (L807mts, Bio, CHIR, and SB-216763) [124] interfere with GSK3 and thus induce Wnt target gene expression. This could be of interest in the development of treatments for neurodegenerative disorders, including Alzheimer’s disease [125]. The Hippo pathway: The Hippo pathway has been shown to play a role in organ development, epithelial homeostasis, tissue regeneration, wound healing, immune modulation, as well as fibrosis that characterizes deep endometriosis [126]. Many of these roles are mediated by the transcriptional effectors YAP and TAZ [127,128]. The YAP/TAZ complex regulates pro-fibrotic factors and interferes with small-molecule inhibitors of PAI-1 and converges with pro-fibrotic signaling pathways such as TGFβ previously described in endometriosis [129]. Recently, verteporfin and VGLL4 mimetic peptides have been shown to inhibit YAP/TAZ-dependent transcription as well as suppress tumor growth. Thus, therapies targeting this transcription could potentially result in treatments for various diseases [130,131]. HOXA11: Long non-coding RNA HOXA11-AS has been shown to regulate target genes by epigenetic methylation and has been found to inhibit the Wnt signaling pathway via the upregulation of HOXA11, thus inhibiting proliferation and invasion properties [132]. Moreover, it has been found that overexpression of HOXA11-AS increases the membrane levels of CD44 [133] and decreases the expression of matrix metalloproteinase-2 [134], MMP-9, and vascular endothelial growth factors that are dysregulated in endometriosis. The PI3K/AKT/mTOR pathway: Previous studies have demonstrated that this pathway can modulate proliferation and angiogenesis in endometriosis [135], and that two rapamycin-analogues (temsirolimus and everolimus), already used in various cancers, inhibit mTOR signaling and reduce the growth of endometriosis implants [136].
Some limits of our study deserve to be underlined. First, in the present miRNAs analysis, we adopted as normalization thresholds read >1.5-fold as up-regulation and <0.5 downregulation which could be debated. However, this is in line which previous publications in the endometriosis fields [8,10,92]. Second, a surprising result of our work was the absence in the highly valuable miRNA of some miRNA previously reported as significant before [11,12,137,138,139,140,141]. This could be explained by the size of our cohort being much larger, which necessarily had an impact on the individual value of diagnostic performance for a single miRNA. Third, we focused on miRNAs with an accuracy ≥0.60, but it is not possible to rule out that some miRNAs with an accuracy between 0.50 and 0.59 might play a role in the pathophysiology of endometriosis. However, among the 2633 miRNAs detected in patients with endometriosis, 2077 had an AUC >b0.50 and <0.60. We would not have been able to comprehensively investigate all the signaling pathways potentially involved in endometriosis in a single report. Fourth, we only focused on miRNA expression, although several studies have demonstrated that other non-coding RNAs may play a role in the pathophysiology of endometriosis. Fifth, several reports underlined the potential impact of the menstrual cycle and the hormonal treatment on the miRNA expression, especially when endometrium samples have been analyzed [142,143]. In our cohort, a correlation was noted for two miRNAs (miR-548p and miR-548l) with the use of hormonal therapy which is conflicting with the results reported by the two aforementioned studies by Vanhie et al. and Moustafa et al. that found no impact of either treatment or menstrual phase on miRNAs levels. In addition, numerous other conditions may impact the expression level on miRNA, such as the endometriosis stage, age, BMI, tobacco and hormonal treatment use. Here, we reported the expression level for the six most accurate miRNA and demonstrated a low impact of such characteristics [10,92]. Sixth, we have not performed subgroup analysis to investigate the potential influence of miRNA expression in minimal cases to evolve toward severe endometriosis. This could be relevant, especially when further bench work will have deeply studied the miRNA identified as highly expressed in our cohort. Finally, due to the numerous pathways involved in the pathophysiology of endometriosis, we only focused on the most frequently observed, representing a true limit. However, all these data underline the need of further in vivo and in vitro analysis to confirm the pivotal role of miRNAs in endometriosis.
Our results provide evidence of the relation between miRNA profiles in patients with endometriosis and various signaling pathways implicated in its pathophysiology. In addition, the analysis of the miRNAome opens up new perspectives of investigation in the understanding of the underlying biological mechanisms involved not only in endometriosis but also in other pathologies qualified as multifactorial.

Supplementary Materials

The following supporting information can be downloaded at:, Table S1. Distribution of miRNAs according to the AUC values. Table S2. Accuracy metrics of the top 57 miRNAs. Table S3. Specific analysis of hsa-miR-124-3p. Table S4. miRNA-associated benign and malignant disorders. Table S5. miRNA-associated pathophysiologic pathways.

Author Contributions

Methodology and Design: S.B. and E.D. Data collection: S.B., Y.D., A.P., C.T. and E.D. Biologic data collection: Y.D., L.J., D.B. Analysis: S.S., S.B., E.D. Data Interpretation: S.B., S.S., Y.D., A.P., C.T. et E.D. Manuscript writing: Y.D., S.B., E.D. All authors reviewed the manuscript for critical intellectual content. All authors have read and agreed to the published version of the manuscript.


Part of this work was funded by a grant from the Conseil Régional d’Ile de France (grant number EX024087) and from Ziwig, Inc.

Institutional Review Board Statement

The authors state that the data used are from the prospective ENDO-miRNA study ( Identifier: NCT04728152). Data collection and analysis were carried out under Research Protocol n° ID RCB: 2020-A03297-32.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article and its Supplementary Materials.

Conflicts of Interest

S. Suisse is a former employee of Ziwig, Inc. The remaining authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.


ITGB3Integrin Subunit Beta 3
PDE4BPhosphodiesterase 4B
EGR2Early Growth Response 2
CRKLCRK Like Proto-Oncogene, Adaptor Protein
ABCA2ATP Binding Cassette Subfamily A Member 2
MGAT5Alpha-1,6-Mannosylglycoprotein 6-Beta-N-Acetylglucosaminyltransferase
Fra-2Fos-related antigen 2
ANXA7Annexin A7
CD1/CDK6Cell division protein kinase 6
EZH2Enhancer of zeste homolog 2
EDNRBEndothelin Receptor Type B
DAPK1Death Associated Protein Kinase 1
FIP200FAK family kinase-interacting protein of 200 kDa
TRIM14Tripartite Motif Containing 14
mTormammalian target of rapamycin
STAT3Signal transducer and activator of transcription 3
PI3K/AktPhosphoinositide 3-kinases/Protein kinase B
NF–KBnuclear factor-kappa B
ERKextracellular signal-regulated kinase
PLGF/ROSPlacental Growth Factor/Reactive oxygen species
FGF2–EGFRfibroblast growth factor/Epidermal Growth Factor
MAPKMitogen-activated protein kinases
GSK3B/Beta catenineGlycogen synthase kinase 3 beta/β-catenine


  1. Secosan, C.; Balulescu, L.; Brasoveanu, S.; Balint, O.; Pirtea, P.; Dorin, G.; Pirtea, L. Endometriosis in Menopause-Renewed Attention on a Controversial Disease. Diagnostics 2020, 10, 134. [Google Scholar] [CrossRef] [PubMed][Green Version]
  2. Haas, D.; Chvatal, R.; Reichert, B.; Renner, S.; Shebl, O.; Binder, H.; Wurm, P.; Oppelt, P. Endometriosis: A premenopausal disease? Age pattern in 42,079 patients with endometriosis. Arch. Gynecol. Obstet. 2012, 286, 667–670. [Google Scholar] [CrossRef]
  3. Matta, K.; Vigneau, E.; Cariou, V.; Mouret, D.; Ploteau, S.; Le Bizec, B.; Antignac, J.-P.; Cano-Sancho, G. Associations between persistent organic pollutants and endometriosis: A multipollutant assessment using machine learning algorithms. Environ. Pollut. 2020, 260, 114066. [Google Scholar] [CrossRef]
  4. Wu, J.; Fang, X.; Huang, H.; Huang, W.; Wang, L.; Xia, X. Construction and topological analysis of an endometriosis-related exosomal circRNA-miRNA-mRNA regulatory network. Aging 2021, 13, 12607–12630. [Google Scholar] [CrossRef]
  5. Bartel, D.P. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 2004, 116, 281–297. [Google Scholar] [CrossRef][Green Version]
  6. Bartel, D.P. MicroRNAs: Target recognition and regulatory functions. Cell 2009, 136, 215–233. [Google Scholar] [CrossRef] [PubMed][Green Version]
  7. Hammond, S.M. RNAi, microRNAs, and human disease. Cancer Chemother. Pharmacol. 2006, 58 (Suppl. 1), s63–s68. [Google Scholar] [CrossRef]
  8. Panir, K.; Schjenken, J.E.; Robertson, S.A.; Hull, M.L. Non-coding RNAs in endometriosis: A narrative review. Hum. Reprod. Update 2018, 24, 497–515. [Google Scholar] [CrossRef] [PubMed][Green Version]
  9. Monnaka, V.U.; Hernandes, C.; Heller, D.; Podgaec, S. Overview of miRNAs for the non-invasive diagnosis of endometriosis: Evidence, challenges and strategies. A systematic review. Einstein Sao Paulo Braz. 2021, 19, eRW5704. [Google Scholar] [CrossRef]
  10. Vanhie, A.; O, D.; Peterse, D.; Beckers, A.; Cuéllar, A.; Fassbender, A.; Meuleman, C.; Mestdagh, P.; D’Hooghe, T. Plasma miRNAs as biomarkers for endometriosis. Hum. Reprod. Oxf. Engl. 2019, 34, 1650–1660. [Google Scholar] [CrossRef] [PubMed]
  11. Maged, A.M.; Deeb, W.S.; El Amir, A.; Zaki, S.S.; El Sawah, H.; Al Mohamady, M.; Metwally, A.A.; Katta, M.A. Diagnostic accuracy of serum miR-122 and miR-199a in women with endometriosis. Int. J. Gynaecol. Obstet. Off. Organ. Int. Fed. Gynaecol. Obstet. 2018, 141, 14–19. [Google Scholar] [CrossRef] [PubMed]
  12. Misir, S.; Hepokur, C.; Oksasoglu, B.; Yildiz, C.; Yanik, A.; Aliyazicioglu, Y. Circulating serum miR-200c and miR-34a-5p as diagnostic biomarkers for endometriosis. J. Gynecol. Obstet. Hum. Reprod. 2021, 50, 102092. [Google Scholar] [CrossRef] [PubMed]
  13. Cosar, E.; Mamillapalli, R.; Ersoy, G.S.; Cho, S.; Seifer, B.; Taylor, H.S. Serum microRNAs as diagnostic markers of endometriosis: A comprehensive array-based analysis. Fertil. Steril. 2016, 106, 402–409. [Google Scholar] [CrossRef] [PubMed][Green Version]
  14. Javadi, M.; Rad, J.S.; Farashah, M.S.G.; Roshangar, L. An Insight on the Role of Altered Function and Expression of Exosomes and MicroRNAs in Female Reproductive Diseases. Reprod. Sci. Thousand Oaks Calif 2021. [Google Scholar] [CrossRef]
  15. Mu, P.; Zhou, J.; Ma, X.; Zhang, G.; Li, Y. Expression, regulation and function of MicroRNAs in endometriosis. Die Pharm.-Int. J. Pharm. Sci. 2016, 71, 434–438. [Google Scholar] [CrossRef]
  16. Bendifallah, S. Evaluation of miRNAs in Endometriosis. Available online: (accessed on 28 August 2021).
  17. Ito, T.E.; Abi Khalil, E.D.; Taffel, M.; Moawad, G.N. Magnetic resonance imaging correlation to intraoperative findings of deeply infiltrative endometriosis. Fertil. Steril. 2017, 107, e11–e12. [Google Scholar] [CrossRef][Green Version]
  18. Bazot, M.; Daraï, E. Diagnosis of deep endometriosis: Clinical examination, ultrasonography, magnetic resonance imaging, and other techniques. Fertil. Steril. 2017, 108, 886–894. [Google Scholar] [CrossRef] [PubMed][Green Version]
  19. Bazot, M.; Darai, E.; Hourani, R.; Thomassin, I.; Cortez, A.; Uzan, S.; Buy, J.-N. Deep pelvic endometriosis: MR imaging for diagnosis and prediction of extension of disease. Radiology 2004, 232, 379–389. [Google Scholar] [CrossRef][Green Version]
  20. Revised American Society for Reproductive Medicine classification of endometriosis: 1996. Fertil. Steril. 1997, 67, 817–821. [CrossRef]
  21. de Foucher, T.; Sbeih, M.; Uzan, J.; Bendifallah, S.; Lefevre, M.; Chabbert-Buffet, N.; Aractingi, S.; Uzan, C.; Abd Alsalam, I.; Mitri, R.; et al. Identification of micro-RNA expression profile related to recurrence in women with ESMO low-risk endometrial cancer. J. Transl. Med. 2018, 16, 131. [Google Scholar] [CrossRef]
  22. Canlorbe, G.; Wang, Z.; Laas, E.; Bendifallah, S.; Castela, M.; Lefevre, M.; Chabbert-Buffet, N.; Daraï, E.; Aractingi, S.; Méhats, C.; et al. Identification of microRNA expression profile related to lymph node status in women with early-stage grade 1-2 endometrial cancer. Mod. Pathol. Off. J. U. S. Can. Acad. Pathol. Inc. 2016, 29, 391–401. [Google Scholar] [CrossRef] [PubMed][Green Version]
  23. Canlorbe, G.; Castela, M.; Bendifallah, S.; Wang, Z.; Lefevre, M.; Chabbert-Buffet, N.; Aractingi, S.; DaraÏ, E.; Méhats, C.; Ballester, M. Micro-RNA signature of lymphovascular space involvement in type 1 endometrial cancer. Histol. Histopathol. 2017, 32, 941–950. [Google Scholar] [CrossRef]
  24. Gyvyte, U.; Kupcinskas, J.; Juzenas, S.; Inciuraite, R.; Poskiene, L.; Salteniene, V.; Link, A.; Fassan, M.; Franke, A.; Kupcinskas, L.; et al. Identification of long intergenic non-coding RNAs (lincRNAs) deregulated in gastrointestinal stromal tumors (GISTs). PLoS ONE 2018, 13, e0209342. [Google Scholar] [CrossRef] [PubMed]
  25. Gyvyte, U.; Juzenas, S.; Salteniene, V.; Kupcinskas, J.; Poskiene, L.; Kucinskas, L.; Jarmalaite, S.; Stuopelyte, K.; Steponaitiene, R.; Hemmrich-Stanisak, G.; et al. MiRNA profiling of gastrointestinal stromal tumors by next-generation sequencing. Oncotarget 2017, 8, 37225–37238. [Google Scholar] [CrossRef]
  26. Lopez-Rincon, A.; Mendoza-Maldonado, L.; Martinez-Archundia, M.; Schönhuth, A.; Kraneveld, A.D.; Garssen, J.; Tonda, A. Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification. Cancers 2020, 12, 1785. [Google Scholar] [CrossRef]
  27. Langmead, B.; Trapnell, C.; Pop, M.; Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10, R25. [Google Scholar] [CrossRef][Green Version]
  28. Griffiths-Jones, S.; Saini, H.K.; van Dongen, S.; Enright, A.J. miRBase: Tools for microRNA genomics. Nucleic Acids Res. 2008, 36, D154–D158. [Google Scholar] [CrossRef] [PubMed][Green Version]
  29. Gao, L.; Zhang, L. Construction and comprehensive analysis of a ceRNA network to reveal potential prognostic biomarkers for lung adenocarcinoma. BMC Cancer 2021, 21, 849. [Google Scholar] [CrossRef]
  30. Potla, P.; Ali, S.A.; Kapoor, M. A bioinformatics approach to microRNA-sequencing analysis. Osteoarthr. Cartil. Open 2021, 3, 100131. [Google Scholar] [CrossRef]
  31. Li, Q.; Liu, G.; Bao, Y.; Wu, Y.; You, Q. Evaluation and application of tools for the identification of known microRNAs in plants. Appl. Plant Sci. 2021, 9, e11414. [Google Scholar] [CrossRef]
  32. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef][Green Version]
  33. Bargaje, R.; Hariharan, M.; Scaria, V.; Pillai, B. Consensus miRNA expression profiles derived from interplatform normalization of microarray data. RNA 2010, 16, 16–25. [Google Scholar] [CrossRef] [PubMed][Green Version]
  34. Harrell, F.E.; Lee, K.L.; Mark, D.B. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 1996, 15, 361–387. [Google Scholar] [CrossRef]
  35. Steyerberg, E.W.; Eijkemans, M.J.; Harrell, F.E.; Habbema, J.D. Prognostic modelling with logistic regression analysis: A comparison of selection and estimation methods in small data sets. Stat. Med. 2000, 19, 1059–1079. [Google Scholar] [CrossRef]
  36. Li, N.; Zhu, D. Circ_0017956 promotes the proliferation and metastasis of non-small cell lung cancer through regulating miR-515-5p/ITGB8 axis. Cell Cycle Georget. Tex 2021, 1–13. [Google Scholar] [CrossRef]
  37. Zhang, Y.; Shi, Z.; Li, Z.; Wang, X.; Zheng, P.; Li, H. Circ_0057553/miR-515-5p Regulates Prostate Cancer Cell Proliferation, Apoptosis, Migration, Invasion and Aerobic Glycolysis by Targeting YES1. OncoTargets Ther. 2020, 13, 11289–11299. [Google Scholar] [CrossRef] [PubMed]
  38. Ma, H.; Lu, L.; Xia, H.; Xiang, Q.; Sun, J.; Xue, J.; Xiao, T.; Cheng, C.; Liu, Q.; Shi, A. Circ0061052 regulation of FoxC1/Snail pathway via miR-515-5p is involved in the epithelial-mesenchymal transition of epithelial cells during cigarette smoke-induced airway remodeling. Sci. Total Environ. 2020, 746, 141181. [Google Scholar] [CrossRef]
  39. Liu, G.-X.; Zheng, T.; Zhang, Y.; Hao, P. CircFOXM1 silencing represses cell proliferation, migration and invasion by regulating miR-515-5p/ADAM10 axis in prostate cancer. Anticancer Drugs 2021, 33, e573–e583. [Google Scholar] [CrossRef]
  40. Liu, J.; Liu, H.; Zeng, Q.; Xu, P.; Liu, M.; Yang, N. Circular RNA circ-MAT2B facilitates glycolysis and growth of gastric cancer through regulating the miR-515-5p/HIF-1α axis. Cancer Cell Int. 2020, 20, 171. [Google Scholar] [CrossRef] [PubMed]
  41. Yuan, X.W.; Yan, T.Q.; Tong, H. Effect of miR-515-5p on Proliferation and Drug Sensitivity of Retinoblastoma Cells. Cancer Manag. Res. 2020, 12, 12087–12098. [Google Scholar] [CrossRef] [PubMed]
  42. Ye, P.; Lv, X.; Aizemaiti, R.; Cheng, J.; Xia, P.; Di, M. H3K27ac-activated LINC00519 promotes lung squamous cell carcinoma progression by targeting miR-450b-5p/miR-515-5p/YAP1 axis. Cell Prolif. 2020, 53, e12797. [Google Scholar] [CrossRef][Green Version]
  43. Gilam, A.; Edry, L.; Mamluk-Morag, E.; Bar-Ilan, D.; Avivi, C.; Golan, D.; Laitman, Y.; Barshack, I.; Friedman, E.; Shomron, N. Involvement of IGF-1R regulation by miR-515-5p modifies breast cancer risk among BRCA1 carriers. Breast Cancer Res. Treat. 2013, 138, 753–760. [Google Scholar] [CrossRef] [PubMed]
  44. Qiao, K.; Ning, S.; Wan, L.; Wu, H.; Wang, Q.; Zhang, X.; Xu, S.; Pang, D. LINC00673 is activated by YY1 and promotes the proliferation of breast cancer cells via the miR-515-5p/MARK4/Hippo signaling pathway. J. Exp. Clin. Cancer Res. CR 2019, 38, 418. [Google Scholar] [CrossRef] [PubMed][Green Version]
  45. Dai, G.; Huang, C.; Yang, J.; Jin, L.; Fu, K.; Yuan, F.; Zhu, J.; Xue, B. LncRNA SNHG3 promotes bladder cancer proliferation and metastasis through miR-515-5p/GINS2 axis. J. Cell. Mol. Med. 2020, 24, 9231–9243. [Google Scholar] [CrossRef]
  46. Li, Y.; Gao, L.; Zhang, C.; Meng, J. LncRNA SNHG3 Promotes Proliferation and Metastasis of Non-Small-Cell Lung Cancer Cells Through miR-515-5p/SUMO2 Axis. Technol. Cancer Res. Treat. 2021, 20, 15330338211019376. [Google Scholar] [CrossRef] [PubMed]
  47. Xie, Q.; Li, F.; Shen, K.; Luo, C.; Song, G. LOXL1-AS1/miR-515-5p/STAT3 Positive Feedback Loop Facilitates Cell Proliferation and Migration in Atherosclerosis. J. Cardiovasc. Pharmacol. 2020, 76, 151–158. [Google Scholar] [CrossRef] [PubMed]
  48. Rong, F.; Liu, L.; Zou, C.; Zeng, J.; Xu, Y. MALAT1 Promotes Cell Tumorigenicity Through Regulating miR-515-5p/EEF2 Axis in Non-Small Cell Lung Cancer. Cancer Manag. Res. 2020, 12, 7691–7701. [Google Scholar] [CrossRef] [PubMed]
  49. Zhang, X.; Zhou, J.; Xue, D.; Li, Z.; Liu, Y.; Dong, L. MiR-515-5p acts as a tumor suppressor via targeting TRIP13 in prostate cancer. Int. J. Biol. Macromol. 2019, 129, 227–232. [Google Scholar] [CrossRef]
  50. Ni, J.-S.; Zheng, H.; Ou, Y.-L.; Tao, Y.-P.; Wang, Z.-G.; Song, L.-H.; Yan, H.-L.; Zhou, W.-P. miR-515-5p suppresses HCC migration and invasion via targeting IL6/JAK/STAT3 pathway. Surg. Oncol. 2020, 34, 113–120. [Google Scholar] [CrossRef]
  51. Han, Y.; Li, F.; Xie, J.; Wang, Y.; Zhang, H. PVT1 Mediates Cell Proliferation, Apoptosis and Radioresistance in Nasopharyngeal Carcinoma Through Regulating miR-515-5p/PIK3CA Axis. Cancer Manag. Res. 2020, 12, 10077–10090. [Google Scholar] [CrossRef]
  52. Zhang, L.; Wan, Q.; Zhou, H. Targeted-regulating of miR-515-5p by LncRNA LOXL1-AS1 on the proliferation and migration of trophoblast cells. Exp. Mol. Pathol. 2021, 118, 104588. [Google Scholar] [CrossRef]
  53. Cai, D.; Hong, S.; Yang, J.; San, P. The Effects of microRNA-515-5p on the Toll-Like Receptor 4 (TLR4)/JNK Signaling Pathway and WNT1-Inducible-Signaling Pathway Protein 1 (WISP-1) Expression in Rheumatoid Arthritis Fibroblast-Like Synovial (RAFLS) Cells Following Treatment with Receptor Activator of Nuclear Factor-kappa-B Ligand (RANKL). Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 2020, 26, e920611. [Google Scholar] [CrossRef]
  54. Ren, R.; Du, Y.; Niu, X.; Zang, R. ZFPM2-AS1 transcriptionally mediated by STAT1 regulates thyroid cancer cell growth, migration and invasion via miR-515-5p/TUSC3. J. Cancer 2021, 12, 3393–3406. [Google Scholar] [CrossRef]
  55. De Blasio, A.; Di Fiore, R.; Pratelli, G.; Drago-Ferrante, R.; Saliba, C.; Baldacchino, S.; Grech, G.; Scerri, C.; Vento, R.; Tesoriere, G. A loop involving NRF2, miR-29b-1-5p and AKT, regulates cell fate of MDA-MB-231 triple-negative breast cancer cells. J. Cell. Physiol. 2020, 235, 629–637. [Google Scholar] [CrossRef]
  56. Inoue, A.; Mizushima, T.; Wu, X.; Okuzaki, D.; Kambara, N.; Ishikawa, S.; Wang, J.; Qian, Y.; Hirose, H.; Yokoyama, Y.; et al. A miR-29b Byproduct Sequence Exhibits Potent Tumor-Suppressive Activities via Inhibition of NF-κB Signaling in KRAS-Mutant Colon Cancer Cells. Mol. Cancer Ther. 2018, 17, 977–987. [Google Scholar] [CrossRef] [PubMed][Green Version]
  57. Chen, X.; Ouyang, H.; Wang, Z.; Chen, B.; Nie, Q. A Novel Circular RNA Generated by FGFR2 Gene Promotes Myoblast Proliferation and Differentiation by Sponging miR-133a-5p and miR-29b-1-5p. Cells 2018, 7, 199. [Google Scholar] [CrossRef][Green Version]
  58. Elmansi, A.M.; Hussein, K.A.; Herrero, S.M.; Periyasamy-Thandavan, S.; Aguilar-Pérez, A.; Kondrikova, G.; Kondrikov, D.; Eisa, N.H.; Pierce, J.L.; Kaiser, H.; et al. Age-related increase of kynurenine enhances miR29b-1-5p to decrease both CXCL12 signaling and the epigenetic enzyme Hdac3 in bone marrow stromal cells. Bone Rep. 2020, 12, 100270. [Google Scholar] [CrossRef] [PubMed]
  59. Shi, Q.; Sun, B.; Wang, D.; Zhu, Y.; Zhao, X.; Yang, X.; Zhang, Y. Circ6401, a novel circular RNA, is implicated in repair of the damaged endometrium by Wharton’s jelly-derived mesenchymal stem cells through regulation of the miR-29b-1-5p/RAP1B axis. Stem Cell Res. Ther. 2020, 11, 520. [Google Scholar] [CrossRef] [PubMed]
  60. Jia, H.; Li, Z.; Chang, Y.; Fang, B.; Zhou, Y.; Ma, H. Downregulation of Long Noncoding RNA TUG1 Attenuates MTDH-Mediated Inflammatory Damage via Targeting miR-29b-1-5p After Spinal Cord Ischemia Reperfusion. J. Neuropathol. Exp. Neurol. 2021, 80, 254–264. [Google Scholar] [CrossRef] [PubMed]
  61. Xu, F.; Zhang, Q.; Cheng, W.; Zhang, Z.; Wang, J.; Ge, J. Effect of miR-29b-1* and miR-29c knockdown on cell growth of the bladder cancer cell line T24. J. Int. Med. Res. 2013, 41, 1803–1810. [Google Scholar] [CrossRef] [PubMed][Green Version]
  62. Datta, C.; Subuddhi, A.; Kumar, M.; Lepcha, T.T.; Chakraborty, S.; Jana, K.; Ghosh, Z.; Mukhopadhyay, A.K.; Basu, J.; Kundu, M. Genome-wide mRNA-miRNA profiling uncovers a role of the microRNA miR-29b-1-5p/PHLPP1 signalling pathway in Helicobacter pylori-driven matrix metalloproteinase production in gastric epithelial cells. Cell. Microbiol. 2018, 20, e12859. [Google Scholar] [CrossRef] [PubMed]
  63. Long, B.; Li, N.; Xu, X.-X.; Li, X.-X.; Xu, X.-J.; Guo, D.; Zhang, D.; Wu, Z.-H.; Zhang, S.-Y. Long noncoding RNA FTX regulates cardiomyocyte apoptosis by targeting miR-29b-1-5p and Bcl2l2. Biochem. Biophys. Res. Commun. 2018, 495, 312–318. [Google Scholar] [CrossRef]
  64. Drago-Ferrante, R.; Pentimalli, F.; Carlisi, D.; De Blasio, A.; Saliba, C.; Baldacchino, S.; Degaetano, J.; Debono, J.; Caruana-Dingli, G.; Grech, G.; et al. Suppressive role exerted by microRNA-29b-1-5p in triple negative breast cancer through SPIN1 regulation. Oncotarget 2017, 8, 28939–28958. [Google Scholar] [CrossRef] [PubMed][Green Version]
  65. Kurihara-Shimomura, M.; Sasahira, T.; Shimomura, H.; Nakashima, C.; Kirita, T. The Oncogenic Activity of miR-29b-1-5p Induces the Epithelial-Mesenchymal Transition in Oral Squamous Cell Carcinoma. J. Clin. Med. 2019, 8, 273. [Google Scholar] [CrossRef] [PubMed][Green Version]
  66. Liang, Y.; Song, X.; Li, Y.; Su, P.; Han, D.; Ma, T.; Guo, R.; Chen, B.; Zhao, W.; Sang, Y.; et al. circKDM4C suppresses tumor progression and attenuates doxorubicin resistance by regulating miR-548p/PBLD axis in breast cancer. Oncogene 2019, 38, 6850–6866. [Google Scholar] [CrossRef]
  67. Zhang, J.; Chang, Y.; Xu, L.; Qin, L. Elevated expression of circular RNA circ_0008450 predicts dismal prognosis in hepatocellular carcinoma and regulates cell proliferation, apoptosis, and invasion via sponging miR-548p. J. Cell. Biochem. 2019, 120, 9487–9494. [Google Scholar] [CrossRef]
  68. Zhou, L.; Hussain, M.M. Human MicroRNA-548p Decreases Hepatic Apolipoprotein B Secretion and Lipid Synthesis. Arterioscler. Thromb. Vasc. Biol. 2017, 37, 786–793. [Google Scholar] [CrossRef][Green Version]
  69. Hu, X.-M.; Yan, X.-H.; Hu, Y.-W.; Huang, J.-L.; Cao, S.-W.; Ren, T.-Y.; Tang, Y.-T.; Lin, L.; Zheng, L.; Wang, Q. miRNA-548p suppresses hepatitis B virus X protein associated hepatocellular carcinoma by downregulating oncoprotein hepatitis B x-interacting protein. Hepatol. Res. Off. J. Jpn. Soc. Hepatol. 2016, 46, 804–815. [Google Scholar] [CrossRef]
  70. Liu, C.; Yang, H.; Xu, Z.; Li, D.; Zhou, M.; Xiao, K.; Shi, Z.; Zhu, L.; Yang, L.; Zhou, R. microRNA-548l is involved in the migration and invasion of non-small cell lung cancer by targeting the AKT1 signaling pathway. J. Cancer Res. Clin. Oncol. 2015, 141, 431–441. [Google Scholar] [CrossRef] [PubMed]
  71. Cai, H.; Zhou, H.; Miao, Y.; Li, N.; Zhao, L.; Jia, L. MiRNA expression profiles reveal the involvement of miR-26a, miR-548l and miR-34a in hepatocellular carcinoma progression through regulation of ST3GAL5. Lab. Investig. J. Tech. Methods Pathol. 2017, 97, 530–542. [Google Scholar] [CrossRef][Green Version]
  72. Medina-Trillo, C.; Aroca-Aguilar, J.-D.; Ferre-Fernández, J.-J.; Méndez-Hernández, C.-D.; Morales, L.; García-Feijoo, J.; Escribano, J. The Role of hsa-miR-548l Dysregulation as a Putative Modifier Factor for Glaucoma-Associated FOXC1 Mutations. MicroRNA Shariqah United Arab Emir. 2015, 4, 50–56. [Google Scholar] [CrossRef]
  73. Li, X.; Chen, C.; Wang, Z.; Liu, J.; Sun, W.; Shen, K.; Lv, Y.; Zhu, S.; Zhan, P.; Lv, T.; et al. Elevated exosome-derived miRNAs predict osimertinib resistance in non-small cell lung cancer. Cancer Cell Int. 2021, 21, 428. [Google Scholar] [CrossRef]
  74. Yao, Y.; Jiao, D.; Liu, Z.; Chen, J.; Zhou, X.; Li, Z.; Li, J.; Han, X. Novel miRNA Predicts Survival and Prognosis of Cholangiocarcinoma Based on RNA-seq Data and In Vitro Experiments. BioMed Res. Int. 2020, 2020, 5976127. [Google Scholar] [CrossRef]
  75. Schepeler, T.; Holm, A.; Halvey, P.; Nordentoft, I.; Lamy, P.; Riising, E.M.; Christensen, L.L.; Thorsen, K.; Liebler, D.C.; Helin, K.; et al. Attenuation of the beta-catenin/TCF4 complex in colorectal cancer cells induces several growth-suppressive microRNAs that target cancer promoting genes. Oncogene 2012, 31, 2750–2760. [Google Scholar] [CrossRef][Green Version]
  76. Song, A.; Yang, Y.; He, H.; Sun, J.; Chang, Q.; Xue, Q. Inhibition of Long Non-Coding RNA KCNQ1OT1 Attenuates Neuroinflammation and Neuronal Apoptosis Through Regulating NLRP3 Expression via Sponging miR-30e-3p. J. Inflamm. Res. 2021, 14, 1731–1742. [Google Scholar] [CrossRef] [PubMed]
  77. Gao, X.; Wang, X.; He, H.; Cao, Y. LINC02308 promotes the progression of glioma through activating mTOR/AKT-signaling pathway by targeting miR-30e-3p/TM4SF1 axis. Cell Biol. Toxicol. 2021, 1–14. [Google Scholar] [CrossRef] [PubMed]
  78. Liu, Y.; Xu, Y.; Ding, L.; Yu, L.; Zhang, B.; Wei, D. LncRNA MEG3 suppressed the progression of ovarian cancer via sponging miR-30e-3p and regulating LAMA4 expression. Cancer Cell Int. 2020, 20, 181. [Google Scholar] [CrossRef] [PubMed]
  79. Wang, D.; Zhu, C.; Zhang, Y.; Zheng, Y.; Ma, F.; Su, L.; Shao, G. MicroRNA-30e-3p inhibits cell invasion and migration in clear cell renal cell carcinoma by targeting Snail1. Oncol. Lett. 2017, 13, 2053–2058. [Google Scholar] [CrossRef][Green Version]
  80. Gramantieri, L.; Pollutri, D.; Gagliardi, M.; Giovannini, C.; Quarta, S.; Ferracin, M.; Casadei-Gardini, A.; Callegari, E.; De Carolis, S.; Marinelli, S.; et al. MiR-30e-3p Influences Tumor Phenotype through MDM2/TP53 Axis and Predicts Sorafenib Resistance in Hepatocellular Carcinoma. Cancer Res. 2020, 80, 1720–1734. [Google Scholar] [CrossRef]
  81. Su, B.; Wang, X.; Sun, Y.; Long, M.; Zheng, J.; Wu, W.; Li, L. miR-30e-3p Promotes Cardiomyocyte Autophagy and Inhibits Apoptosis via Regulating Egr-1 during Ischemia/Hypoxia. BioMed Res. Int. 2020, 2020, 7231243. [Google Scholar] [CrossRef]
  82. Yeap, S.K.; Mohd Ali, N.; Akhtar, M.N.; Razak, N.A.; Chong, Z.X.; Ho, W.Y.; Boo, L.; Zareen, S.; Kurniawan, T.A.; Avtar, R.; et al. Induction of Apoptosis and Regulation of MicroRNA Expression by (2E,6E)-2,6-bis-(4-hydroxy-3-methoxybenzylidene)-cyclohexanone (BHMC) Treatment on MCF-7 Breast Cancer Cells. Molecules 2021, 26, 1277. [Google Scholar] [CrossRef] [PubMed]
  83. Quintanilha, J.C.F.; Cursino, M.A.; Borges, J.B.; Torso, N.G.; Bastos, L.B.; Oliveira, J.M.; Cobaxo, T.S.; Pincinato, E.C.; Hirata, M.H.; Geraldo, M.V.; et al. MiR-3168, miR-6125, and miR-4718 as potential predictors of cisplatin-induced nephrotoxicity in patients with head and neck cancer. BMC Cancer 2021, 21, 575. [Google Scholar] [CrossRef] [PubMed]
  84. Ormseth, M.J.; Solus, J.F.; Sheng, Q.; Chen, S.-C.; Ye, F.; Wu, Q.; Oeser, A.M.; Allen, R.; Raggi, P.; Vickers, K.C.; et al. Plasma miRNAs improve the prediction of coronary atherosclerosis in patients with rheumatoid arthritis. Clin. Rheumatol. 2021, 40, 2211–2219. [Google Scholar] [CrossRef] [PubMed]
  85. Fedorchak, G.; Rangnekar, A.; Onks, C.; Loeffert, A.C.; Loeffert, J.; Olympia, R.P.; DeVita, S.; Leddy, J.; Haider, M.N.; Roberts, A.; et al. Saliva RNA biomarkers predict concussion duration and detect symptom recovery: A comparison with balance and cognitive testing. J. Neurol. 2021, 268, 4349–4361. [Google Scholar] [CrossRef]
  86. Blondal, T.; Brunetto, M.R.; Cavallone, D.; Mikkelsen, M.; Thorsen, M.; Mang, Y.; Pinheiro, H.; Bonino, F.; Mouritzen, P. Genome-Wide Comparison of Next-Generation Sequencing and qPCR Platforms for microRNA Profiling in Serum. Methods Mol. Biol. 2017, 1580, 21–44. [Google Scholar] [CrossRef] [PubMed]
  87. Hicks, S.D.; Onks, C.; Kim, R.Y.; Zhen, K.J.; Loeffert, J.; Loeffert, A.C.; Olympia, R.P.; Fedorchak, G.; DeVita, S.; Rangnekar, A.; et al. Diagnosing mild traumatic brain injury using saliva RNA compared to cognitive and balance testing. Clin. Transl. Med. 2020, 10, e197. [Google Scholar] [CrossRef] [PubMed]
  88. Lee, I.H.; Hong, X.; Mathur, S.C.; Sharma, M.; Rastogi, A.; Sharma, P.; Christenson, L.K.; Bansal, A. A detailed analysis of next generation sequencing reads of microRNA expression in Barrett’s esophagus: Absolute versus relative quantification. BMC Res. Notes 2014, 7, 212. [Google Scholar] [CrossRef][Green Version]
  89. ’t Hoen, P.A.C.; Ariyurek, Y.; Thygesen, H.H.; Vreugdenhil, E.; Vossen, R.H.A.M.; de Menezes, R.X.; Boer, J.M.; van Ommen, G.-J.B.; den Dunnen, J.T. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Res. 2008, 36, e141. [Google Scholar] [CrossRef][Green Version]
  90. Agrawal, S.; Tapmeier, T.; Rahmioglu, N.; Kirtley, S.; Zondervan, K.; Becker, C. The miRNA Mirage: How Close Are We to Finding a Non-Invasive Diagnostic Biomarker in Endometriosis? A Systematic Review. Int. J. Mol. Sci. 2018, 19, 599. [Google Scholar] [CrossRef][Green Version]
  91. Setti, G.; Pezzi, M.E.; Viani, M.V.; Pertinhez, T.A.; Cassi, D.; Magnoni, C.; Bellini, P.; Musolino, A.; Vescovi, P.; Meleti, M. Salivary MicroRNA for Diagnosis of Cancer and Systemic Diseases: A Systematic Review. Int. J. Mol. Sci. 2020, 21, 907. [Google Scholar] [CrossRef] [PubMed][Green Version]
  92. Moustafa, S.; Burn, M.; Mamillapalli, R.; Nematian, S.; Flores, V.; Taylor, H.S. Accurate diagnosis of endometriosis using serum microRNAs. Am. J. Obstet. Gynecol. 2020, 223, 557.e1–557.e11. [Google Scholar] [CrossRef]
  93. Ghafouri-Fard, S.; Shoorei, H.; Taheri, M. Role of Non-coding RNAs in the Pathogenesis of Endometriosis. Front. Oncol. 2020, 10, 1370. [Google Scholar] [CrossRef]
  94. Marí-Alexandre, J.; Sánchez-Izquierdo, D.; Gilabert-Estellés, J.; Barceló-Molina, M.; Braza-Boïls, A.; Sandoval, J. miRNAs Regulation and Its Role as Biomarkers in Endometriosis. Int. J. Mol. Sci. 2016, 17, 93. [Google Scholar] [CrossRef][Green Version]
  95. Wang, Z.; Wu, X.; Hou, X.; Zhao, W.; Yang, C.; Wan, W.; Chen, L. miR-548b-3p functions as a tumor suppressor in lung cancer. Lasers Med. Sci. 2020, 35, 833–839. [Google Scholar] [CrossRef] [PubMed]
  96. Sha, M.-X.; Huang, X.-W.; Yin, Q. MiR-548b-3p inhibits proliferation and migration of breast cancer cells by targeting MDM2. Eur. Rev. Med. Pharmacol. Sci. 2020, 24, 3105–3112. [Google Scholar] [CrossRef]
  97. Lee, W.-C.; Li, L.-C.; Ng, H.-Y.; Lin, P.-T.; Chiou, T.T.-Y.; Kuo, W.-H.; Lee, C.-T. Urinary Exosomal MicroRNA Signatures in Nephrotic, Biopsy-Proven Diabetic Nephropathy. J. Clin. Med. 2020, 9, 1220. [Google Scholar] [CrossRef][Green Version]
  98. Garcia-Lacarte, M.; Mansego, M.L.; Zulet, M.A.; Martinez, J.A.; Milagro, F.I. miR-1185-1 and miR-548q Are Biomarkers of Response to Weight Loss and Regulate the Expression of GSK3B. Cells 2019, 8, 1548. [Google Scholar] [CrossRef][Green Version]
  99. Sun, Y.; Yang, Z.; Zheng, B.; Zhang, X.-H.; Zhang, M.-L.; Zhao, X.-S.; Zhao, H.-Y.; Suzuki, T.; Wen, J.-K. A Novel Regulatory Mechanism of Smooth Muscle α-Actin Expression by NRG-1/circACTA2/miR-548f-5p Axis. Circ. Res. 2017, 121, 628–635. [Google Scholar] [CrossRef] [PubMed]
  100. Chacolla-Huaringa, R.; Moreno-Cuevas, J.; Trevino, V.; Scott, S.-P. Entrainment of Breast Cell Lines Results in Rhythmic Fluctuations of MicroRNAs. Int. J. Mol. Sci. 2017, 18, 1499. [Google Scholar] [CrossRef] [PubMed][Green Version]
  101. Zhang, F.; Liu, X.-L.; Wang, W.; Dong, H.-L.; Xia, Y.-F.; Ruan, L.-P.; Liu, L.-P. Expression of MMIF, HIF-1α and VEGF in Serum and Endometrial Tissues of Patients with Endometriosis. Curr. Med. Sci. 2018, 38, 499–504. [Google Scholar] [CrossRef]
  102. Herington, J.L.; Bruner-Tran, K.L.; Lucas, J.A.; Osteen, K.G. Immune interactions in endometriosis. Expert Rev. Clin. Immunol. 2011, 7, 611–626. [Google Scholar] [CrossRef] [PubMed][Green Version]
  103. Králíčková, M.; Vetvicka, V. Immunological aspects of endometriosis: A review. Ann. Transl. Med. 2015, 3, 153. [Google Scholar] [CrossRef] [PubMed]
  104. Milewski, Ł.; Barcz, E.; Dziunycz, P.; Radomski, D.; Kamiński, P.; Roszkowski, P.I.; Korczak-Kowalska, G.; Malejczyk, J. Association of leptin with inflammatory cytokines and lymphocyte subpopulations in peritoneal fluid of patients with endometriosis. J. Reprod. Immunol. 2008, 79, 111–117. [Google Scholar] [CrossRef] [PubMed]
  105. Keenan, J.A.; Chen, T.T.; Chadwell, N.L.; Torry, D.S.; Caudle, M.R. IL-1 beta, TNF-alpha, and IL-2 in peritoneal fluid and macrophage-conditioned media of women with endometriosis. Am. J. Reprod. Immunol. 1995, 34, 381–385. [Google Scholar] [CrossRef] [PubMed]
  106. Gmyrek, G.B.; Sieradzka, U.; Goluda, M.; Gabrys, M.; Sozanski, R.; Jerzak, M.; Zbyryt, I.; Chrobak, A.; Chełmonska-Soyta, A. Flow cytometric evaluation of intracellular cytokine synthesis in peripheral mononuclear cells of women with endometriosis. Immunol. Investig. 2008, 37, 43–61. [Google Scholar] [CrossRef] [PubMed]
  107. Wu, M.-H.; Sun, H.S.; Lin, C.-C.; Hsiao, K.-Y.; Chuang, P.-C.; Pan, H.-A.; Tsai, S.-J. Distinct mechanisms regulate cyclooxygenase-1 and -2 in peritoneal macrophages of women with and without endometriosis. Mol. Hum. Reprod. 2002, 8, 1103–1110. [Google Scholar] [CrossRef] [PubMed][Green Version]
  108. Laganà, A.S.; Salmeri, F.M.; Ban Frangež, H.; Ghezzi, F.; Vrtačnik-Bokal, E.; Granese, R. Evaluation of M1 and M2 macrophages in ovarian endometriomas from women affected by endometriosis at different stages of the disease. Gynecol. Endocrinol. Off. J. Int. Soc. Gynecol. Endocrinol. 2020, 36, 441–444. [Google Scholar] [CrossRef] [PubMed]
  109. Bulun, S.E.; Monsavais, D.; Pavone, M.E.; Dyson, M.; Xue, Q.; Attar, E.; Tokunaga, H.; Su, E.J. Role of estrogen receptor-β in endometriosis. Semin. Reprod. Med. 2012, 30, 39–45. [Google Scholar] [CrossRef][Green Version]
  110. Kästingschäfer, C.S.; Schäfer, S.D.; Kiesel, L.; Götte, M. miR-142-3p is a novel regulator of cell viability and proinflammatory signalling in endometrial stroma cells. Reprod. Biomed. Online 2015, 30, 553–556. [Google Scholar] [CrossRef] [PubMed][Green Version]
  111. Okamoto, M.; Nasu, K.; Abe, W.; Aoyagi, Y.; Kawano, Y.; Kai, K.; Moriyama, M.; Narahara, H. Enhanced miR-210 expression promotes the pathogenesis of endometriosis through activation of signal transducer and activator of transcription 3. Hum. Reprod. Oxf. Engl. 2015, 30, 632–641. [Google Scholar] [CrossRef] [PubMed]
  112. Abe, W.; Nasu, K.; Nakada, C.; Kawano, Y.; Moriyama, M.; Narahara, H. miR-196b targets c-myc and Bcl-2 expression, inhibits proliferation and induces apoptosis in endometriotic stromal cells. Hum. Reprod. Oxf. Engl. 2013, 28, 750–761. [Google Scholar] [CrossRef] [PubMed]
  113. Tsai, E.-M.; Wang, Y.-S.; Lin, C.-S.; Lin, W.-Y.; Hsi, E.; Wu, M.-T.; Juo, S.-H.H. A microRNA-520 mirSNP at the MMP2 gene influences susceptibility to endometriosis in Chinese women. J. Hum. Genet. 2013, 58, 202–209. [Google Scholar] [CrossRef] [PubMed]
  114. Laganà, A.S.; Salmeri, F.M.; Vitale, S.G.; Triolo, O.; Götte, M. Stem Cell Trafficking During Endometriosis: May Epigenetics Play a Pivotal Role? Reprod. Sci. 2018, 25, 978–979. [Google Scholar] [CrossRef] [PubMed]
  115. Gomes, M.K.O.; Ferriani, R.A.; Rosa e Silva, J.C.; Japur de Sá Rosa e Silva, A.C.; Vieira, C.S.; Cândido dos Reis, F.J. The levonorgestrel-releasing intrauterine system and endometriosis staging. Fertil. Steril. 2007, 87, 1231–1234. [Google Scholar] [CrossRef] [PubMed]
  116. Laschke, M.W.; Menger, M.D. Anti-angiogenic treatment strategies for the therapy of endometriosis. Hum. Reprod. Update 2012, 18, 682–702. [Google Scholar] [CrossRef][Green Version]
  117. Laschke, M.W.; Menger, M.D. In vitro and in vivo approaches to study angiogenesis in the pathophysiology and therapy of endometriosis. Hum. Reprod. Update 2007, 13, 331–342. [Google Scholar] [CrossRef][Green Version]
  118. Mousa, N.A.; Bedaiwy, M.A.; Casper, R.F. Aromatase inhibitors in the treatment of severe endometriosis. Obstet. Gynecol. 2007, 109, 1421–1423. [Google Scholar] [CrossRef]
  119. Bedaiwy, M.A.; Allaire, C.; Alfaraj, S. Long-term medical management of endometriosis with dienogest and with a gonadotropin-releasing hormone agonist and add-back hormone therapy. Fertil. Steril. 2017, 107, 537–548. [Google Scholar] [CrossRef] [PubMed][Green Version]
  120. de Andres, M.P.; Lopes, L.A.; Baracat, E.C.; Podgaec, S. Dienogest in the treatment of endometriosis: Systematic review. Arch. Gynecol. Obstet. 2015, 292, 523–529. [Google Scholar] [CrossRef]
  121. Papkoff, J.; Rubinfeld, B.; Schryver, B.; Polakis, P. Wnt-1 regulates free pools of catenins and stabilizes APC-catenin complexes. Mol. Cell. Biol. 1996, 16, 2128–2134. [Google Scholar] [CrossRef][Green Version]
  122. Clevers, H.; Nusse, R. Wnt/β-catenin signaling and disease. Cell 2012, 149, 1192–1205. [Google Scholar] [CrossRef] [PubMed][Green Version]
  123. Janda, C.Y.; Dang, L.T.; You, C.; Chang, J.; de Lau, W.; Zhong, Z.A.; Yan, K.S.; Marecic, O.; Siepe, D.; Li, X.; et al. Surrogate Wnt agonists that phenocopy canonical Wnt and β-catenin signalling. Nature 2017, 545, 234–237. [Google Scholar] [CrossRef]
  124. Licht-Murava, A.; Paz, R.; Vaks, L.; Avrahami, L.; Plotkin, B.; Eisenstein, M.; Eldar-Finkelman, H. A unique type of GSK-3 inhibitor brings new opportunities to the clinic. Sci. Signal. 2016, 9, ra110. [Google Scholar] [CrossRef] [PubMed]
  125. Eldar-Finkelman, H.; VanHook, A.M. Science Signaling Podcast for 15 November 2016: A new type of kinase inhibitor. Sci. Signal. 2016, 9, c22. [Google Scholar] [CrossRef]
  126. Dey, A.; Varelas, X.; Guan, K.-L. Targeting the Hippo pathway in cancer, fibrosis, wound healing and regenerative medicine. Nat. Rev. Drug Discov. 2020, 19, 480–494. [Google Scholar] [CrossRef]
  127. Zanconato, F.; Cordenonsi, M.; Piccolo, S. YAP/TAZ at the Roots of Cancer. Cancer Cell 2016, 29, 783–803. [Google Scholar] [CrossRef][Green Version]
  128. Noguchi, S.; Saito, A.; Nagase, T. YAP/TAZ Signaling as a Molecular Link between Fibrosis and Cancer. Int. J. Mol. Sci. 2018, 19, 3674. [Google Scholar] [CrossRef][Green Version]
  129. Young, V.J.; Ahmad, S.F.; Duncan, W.C.; Horne, A.W. The role of TGF-β in the pathophysiology of peritoneal endometriosis. Hum. Reprod. Update 2017, 23, 548–559. [Google Scholar] [CrossRef] [PubMed][Green Version]
  130. Jiao, S.; Li, C.; Hao, Q.; Miao, H.; Zhang, L.; Li, L.; Zhou, Z. VGLL4 targets a TCF4-TEAD4 complex to coregulate Wnt and Hippo signalling in colorectal cancer. Nat. Commun. 2017, 8, 14058. [Google Scholar] [CrossRef][Green Version]
  131. Dong, L.; Lin, F.; Wu, W.; Liu, Y.; Huang, W. Verteporfin inhibits YAP-induced bladder cancer cell growth and invasion via Hippo signaling pathway. Int. J. Med. Sci. 2018, 15, 645–652. [Google Scholar] [CrossRef] [PubMed][Green Version]
  132. Guo, J.-C.; Yang, Y.-J.; Zheng, J.-F.; Zhang, J.-Q.; Guo, M.; Yang, X.; Jiang, X.-L.; Xiang, L.; Li, Y.; Ping, H.; et al. Silencing of long noncoding RNA HOXA11-AS inhibits the Wnt signaling pathway via the upregulation of HOXA11 and thereby inhibits the proliferation, invasion, and self-renewal of hepatocellular carcinoma stem cells. Exp. Mol. Med. 2019, 51, 1–20. [Google Scholar] [CrossRef] [PubMed]
  133. Poncelet, C.; Leblanc, M.; Walker-Combrouze, F.; Soriano, D.; Feldmann, G.; Madelenat, P.; Scoazec, J.-Y.; Daraï, E. Expression of cadherins and CD44 isoforms in human endometrium and peritoneal endometriosis. Acta Obstet. Gynecol. Scand. 2002, 81, 195–203. [Google Scholar] [CrossRef]
  134. Graesslin, O.; Cortez, A.; Uzan, C.; Birembaut, P.; Quereux, C.; Daraï, E. Endometrial tumor invasiveness is related to metalloproteinase 2 and tissue inhibitor of metalloproteinase 2 expressions. Int. J. Gynecol. Cancer Off. J. Int. Gynecol. Cancer Soc. 2006, 16, 1911–1917. [Google Scholar] [CrossRef] [PubMed]
  135. Lee, I.I.; Kim, J.J. Influence of AKT on progesterone action in endometrial diseases. Biol. Reprod. 2014, 91, 63. [Google Scholar] [CrossRef] [PubMed]
  136. Kacan, T.; Yildiz, C.; Baloglu Kacan, S.; Seker, M.; Ozer, H.; Cetin, A. Everolimus as an mTOR Inhibitor Suppresses Endometriotic Implants: An Experimental Rat Study. Geburtshilfe Frauenheilkd. 2017, 77, 66–72. [Google Scholar] [CrossRef] [PubMed][Green Version]
  137. Zhang, L.; Li, H.; Yuan, M.; Li, D.; Sun, C.; Wang, G. Serum Exosomal MicroRNAs as Potential Circulating Biomarkers for Endometriosis. Dis. Markers 2020, 2020, 2456340. [Google Scholar] [CrossRef][Green Version]
  138. Cho, S.; Mutlu, L.; Grechukhina, O.; Taylor, H.S. Circulating microRNAs as potential biomarkers for endometriosis. Fertil. Steril. 2015, 103, 1252–1260.e1. [Google Scholar] [CrossRef] [PubMed][Green Version]
  139. Wang, W.-T.; Zhao, Y.-N.; Han, B.-W.; Hong, S.-J.; Chen, Y.-Q. Circulating microRNAs identified in a genome-wide serum microRNA expression analysis as noninvasive biomarkers for endometriosis. J. Clin. Endocrinol. Metab. 2013, 98, 281–289. [Google Scholar] [CrossRef][Green Version]
  140. Nisenblat, V.; Sharkey, D.J.; Wang, Z.; Evans, S.F.; Healey, M.; Ohlsson Teague, E.M.C.; Print, C.G.; Robertson, S.A.; Hull, M.L. Plasma miRNAs Display Limited Potential as Diagnostic Tools for Endometriosis. J. Clin. Endocrinol. Metab. 2019, 104, 1999–2022. [Google Scholar] [CrossRef] [PubMed]
  141. Nothnick, W.B.; Falcone, T.; Joshi, N.; Fazleabas, A.T.; Graham, A. Serum miR-451a Levels Are Significantly Elevated in Women With Endometriosis and Recapitulated in Baboons (Papio anubis) With Experimentally-Induced Disease. Reprod. Sci. 2017, 24, 1195–1202. [Google Scholar] [CrossRef] [PubMed]
  142. Talbi, S.; Hamilton, A.E.; Vo, K.C.; Tulac, S.; Overgaard, M.T.; Dosiou, C.; Le Shay, N.; Nezhat, C.N.; Kempson, R.; Lessey, B.A.; et al. Molecular phenotyping of human endometrium distinguishes menstrual cycle phases and underlying biological processes in normo-ovulatory women. Endocrinology 2006, 147, 1097–1121. [Google Scholar] [CrossRef] [PubMed][Green Version]
  143. Kao, L.C.; Germeyer, A.; Tulac, S.; Lobo, S.; Yang, J.P.; Taylor, R.N.; Osteen, K.; Lessey, B.A.; Giudice, L.C. Expression profiling of endometrium from women with endometriosis reveals candidate genes for disease-based implantation failure and infertility. Endocrinology 2003, 144, 2870–2881. [Google Scholar] [CrossRef] [PubMed][Green Version]
Figure 1. miRNA serum expression according to endometriosis stage.
Figure 1. miRNA serum expression according to endometriosis stage.
Diagnostics 12 00175 g001
Figure 2. miRNA expression level according to BMI.
Figure 2. miRNA expression level according to BMI.
Diagnostics 12 00175 g002
Figure 3. miRNA expression level according to Age.
Figure 3. miRNA expression level according to Age.
Diagnostics 12 00175 g003
Figure 4. miRNA expression level according to fertility status.
Figure 4. miRNA expression level according to fertility status.
Diagnostics 12 00175 g004
Figure 5. miRNA expression level according to tobacco.
Figure 5. miRNA expression level according to tobacco.
Diagnostics 12 00175 g005
Figure 6. miRNA expression level according to hormonal treatment use.
Figure 6. miRNA expression level according to hormonal treatment use.
Diagnostics 12 00175 g006
Table 1. Demographic characteristics of the patients included in the ENDOmiRNA cohort.
Table 1. Demographic characteristics of the patients included in the ENDOmiRNA cohort.
N (%)
N = 47
N (%)
N = 153
Age:mean (SD)30.92 (13.79)31.17 (10.78)0.19
BMI: mean (SD)24.84 (11.10)24.36 (8.38)0.53
Tobacco use22 (14.4)0 (0)<0.01
rASRM classification
-  I–II
80 (52)
73 (48)
Control diagnoses
-  No abnormality
-  Leiomyoma
-  Cystadenoma
-  Teratoma
-  Other gynecological disorders
24 (51)
1 (2)
5 (11)
11 (23)
6 (13)
Dysmenorrhea47 (100)153 (100)
Abdominal pain outside menstruation21 (44)89 (58.2)0.70
Pain suggesting sciatica10 (21)70 (45.6)0.02
Dyspareunia: mean (SD)4.95 (3.52)5.28 (3.95)<0.01
Lower back pain outside menstruation20 (42)101 (66.0)0.049
Painful defecation: mean (SD)2.84 (2.76)4.35 (3.47)<0.01
Right shoulder pain near or during menstruation3 (9)26 (17.0)0.22
Urinary pain during menstruation: mean (SD)2.84 (2.76)4.35(3.36)<0.01
Blood in the stools during menstruation4 (12)30 (19.6)0.24
Blood in urine during menstruation8 (17)21 (13.7)0.42
Mode of diagnostic -
Surgery47 (100)83 (54.2)-
Magnetic Resonance Imaging-70 (45.8)-
BMI: Body Mass Index; rASRM: revised American Society for Reproductive Medicine.
Table 2. miRNAs-associated benign and malignant disorders.
Table 2. miRNAs-associated benign and malignant disorders.
miRNAsUp/Down RegulatedBenign DisordersMalignant
miR-515-5p [36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54]UpAtherosclerosisHepato-cellular carcinoma, retinoblastoma, prostate cancer, Breast cancer, Lung cancer
miR-29b-1-5p [55,56,57,58,59,60,61,62,63,64,65]UpHelicobacter Pilori (Gastric cells), Spinal cord injury,Breast cancer, Colon cancer, Oral squamous cell carcinoma, Bladder cancer
miR-548p [66,67,68,69]--Hepatitis B-mediated hepatocarcinoma
miR-548l [70,71,72]-GlaucomaHepatocellular carcinoma, Lung cancer
miR-3913-5p [73,74]--Lung cancer, Cholangiocarcinoma
miR-30e-3p [75,76,77,78,79,80,81]--Glioma, Hepatocellular carcinoma, ovarian cancer, colorectal cancer, clear cell renal cell carcinoma
miR-6813-5p [82]--Breast cancer
miR-3168 [83,84]DownCoronary atherosclerosis in patients with rheumatoid arthritis-
miR-548j-5p-Never reportedNever reported
miR-6502-5pUpNever reportedNever reported
miR-4748UpNever reportedNever reported
miR-3137DownNever reportedNever reported
Link to Pollutants; Ster/Horm: Steroidogenesis or Hormonal influence; Therap Sens: Therapeutic sensitivity; EMT: Epithelium to Mesenchymal transition.
Table 3. miRNA-associated pathophysiologic pathways.
Table 3. miRNA-associated pathophysiologic pathways.
ProlifApoptAngioInfEMRMet/MigImmune Resp/escTNeuro fLTPSter/HormTherap sensOther
XXX---X----XDecreases Hepatic Apolipoprotein B Secretion and Lipid Synthesis
-XX-X---X---Cardiomyocyte autophagy
Ad/Inv: Adhesion/Invasion, Prolif: Proliferation; Apopt: Apoptosis; Angio: Angiogenesis; Inf: Inflammation; EMR: Extracellular Matrix Remodeling; Met/Mig: Metastasis and Migration; Immune Resp/esc: Immune Response or escape; Neuro f: Neurogenic function; LTP. “-” is for “unreported or absent” and “X” is for “present”.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Dabi, Y.; Suisse, S.; Jornea, L.; Bouteiller, D.; Touboul, C.; Puchar, A.; Daraï, E.; Bendifallah, S. Clues for Improving the Pathophysiology Knowledge for Endometriosis Using Serum Micro-RNA Expression. Diagnostics 2022, 12, 175.

AMA Style

Dabi Y, Suisse S, Jornea L, Bouteiller D, Touboul C, Puchar A, Daraï E, Bendifallah S. Clues for Improving the Pathophysiology Knowledge for Endometriosis Using Serum Micro-RNA Expression. Diagnostics. 2022; 12(1):175.

Chicago/Turabian Style

Dabi, Yohann, Stéphane Suisse, Ludmila Jornea, Delphine Bouteiller, Cyril Touboul, Anne Puchar, Emile Daraï, and Sofiane Bendifallah. 2022. "Clues for Improving the Pathophysiology Knowledge for Endometriosis Using Serum Micro-RNA Expression" Diagnostics 12, no. 1: 175.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop