# Differential Expression of MicroRNAs between Eutopic and Ectopic Endometrium in Ovarian Endometriosis

Authors: Nicoletta Filigheddu; Ilaria Gregnanin; Paolo E. Porporato; Daniela Surico; Beatrice Perego; Licia Galli; Claudia Patrignani; Andrea Graziani; Nicola Surico
Journal: Journal of Biomedicine and Biotechnology
 (2010)

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## Abstract

Endometriosis, defined as the presence of endometrial tissue outside the uterus, is a common gynecological disease with poorly understood pathogenesis. MicroRNAs are members of a class of small noncoding RNA molecules that have a critical role in posttranscriptional regulation of gene expression by repression of target mRNAs translation. We assessed differentially expressed microRNAs in ectopic endometrium compared with eutopic endometrium in 3 patients through microarray analysis. We identified 50 microRNAs differentially expressed and the differential expression of five microRNAs was validated by real-time RT-PCR in other 13 patients. We identifiedin silico their predicted targets, several of which match the genes that have been identified to be differentially expressed in ectopic versus eutopic endometrium in studies of gene expression. A functional analysis of the predicted targets indicates that several of these are involved in molecular pathways implicated in endometriosis, thus strengthening the hypothesis of the role of microRNAs in this pathology.


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## Body

## 1. Introduction

Endometriosis, defined as the growth of endometrial tissue outside the uterine cavity, is a common gynecological disease often resulting in chronic pelvic pain and infertility. The pathogenesis of endometriosis is likely multifactorial and several hypotheses have been suggested to explain the presence of ectopic endometrial tissue and stroma, such as retrograde menstrual reflux [1], immune system defects [2–10], and ectopic presence of endometrial stem cells originating the disease [11]. In addition, there is a growing body of evidence indicating the involvement of genetic factors in the etiology of endometriosis, as it has been calculated that there is a 6–9-fold increased prevalence of this pathology among the 1st-degree relatives of women with endometriosis, compared to the general population [12–18]. Extensive investigations have been performed to characterize the differences between the eutopic and ectopic endometrium in order to better understand and define the molecular basis of the disease and, indeed, several studies have revealed a distinct pattern of gene expression in eutopic and ectopic endometrium [19–24]. The differences in gene expression reported in these works include genes encoding proteins involved in cell adhesion, extracellular matrix remodeling, migration, proliferation, immune system regulation, and inflammatory pathways, thus accounting for the multiple mechanisms hypothesized to be responsible for the establishment of ectopic endometrial implants, including the adhesion of endometrial cells to the pelvic peritoneum, invasion into the mesothelium, and survival and proliferation of the ectopic endometrial cells.MicroRNAs (miRNAs), members of a class of small non-coding RNA molecules, have a critical role in posttranscriptional regulation of gene expression by repression of target mRNAs translation. Originally identified inCaenorhabditis elegans[25], miRNAs have been shown to operate in a wide range of species, including humans. Computational predictions indicate that up to 30% of human genes are potential targets of miRNAs and that miRNAs compose 1%–5% of animal genomes [26–29]. MiRNA expression is tissue- and cell-specific [30–33]. It has been demonstrated that miRNAs are important in developmental processes as well as for other cellular activities involving cell growth, differentiation, and apoptosis. Moreover, several genes encoding miRNAs have been located at chromosomal fragile sites or regions of cytogenetic abnormalities associated with cancer and other disorders. Interestingly, miRNAs altered expression has been associated with tumorigenesis, and several studies have described differential expression of miRNAs in neoplastic versus normal tissue [34–38].Our study is aimed to investigate the differential expression of miRNAs in endometriosis by direct comparison between paired ectopic and eutopic endometrium samples. Once we identified the differentially expressed miRNAs, we validated 5 of them by an independent technique. Then, we identifiedin silico the predicted molecular targets of the differentially expressed miRNAs and we used a bioinformatics tool to investigate the molecular pathways in which these targets could be involved.


## 2. Materials and Methods

### 2.1. Tissue Collection

Subjects (n = 16) scheduled for surgery for chronic pelvic pain or infertility at the University of Piemonte Orientale-affiliated “Maggiore della Carità” Hospital were recruited to participate in this study. The study was approved by the “Maggiore della Carità” Hospital’s Institutional Review Board and informed consent was obtained from all participants. None of the authors have any conflict of interest with the study. Surgery was scheduled 6 to 12 days after the onset of menses. No patients were receiving hormone therapy at the time of the study or in the previous three months. The patients ranged in age from 24 to 48 years, with an average of 36 years. Endometriomas were removed at laparoscopy by excision of the entire cyst wall by stripping technique, preserving normal ovarian tissue. Hysteroscopy with directed biopsies, performed to obtain a sample of eutopic endometrium from the same patient, were carried out using a 4 mm Bettocchi Hysteroscope System with a 5 Fr operative channel (Karl Stortz GmbH & Co., Tuttlingen, Germany). Laparoscopy and hysteroscopy procedures were performed during the same surgical intervention. Freshly recovered tissues were rinsed in saline solution and divided in two parts. One half of the tissue was immediately snap-frozen and kept in liquid nitrogen for further processing, while the other was sent to the pathology laboratory. The endometriomas of 9 patients were classified as moderate, while 7 were classified as severe according to the ASRM guidelines [39].


### 2.2. RNA Isolation

Total RNA was extracted from tissues with the miRNeasy kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s protocol and quantified by Quant-iT RNA Assay Kit with Qubit Fluorometer (Invitrogen, Carlsbad, CA, USA).


### 2.3. MicroRNA Microarray Assay and Analysis

Microarray assay was performed using a service provider (LC Sciences).  Tenμg of  total RNA from eutopic and ectopic endometrium obtained from three patients were size fractionated using a YM-100 Microcon centrifugal filter (Millipore) and the small RNAs (<300 nt) isolated were 3′-extended with a poly(A) tail using poly(A) polymerase. An oligonucleotide tag was then ligated to the poly(A) tail for later fluorescent dye staining; two different tags were used for the two RNA samples in dual-sample experiments.  Hybridization was performed overnight on a μParaflo microfluidic chip using a microcirculation pump (Atactic Technologies) [40, 41]. On the microfluidic chip, each detection probe consisted of a chemically modified nucleotide coding segment complementary to target 475 mature human miRNA probes (Sanger miRBase sequence database 9.1) or other RNAs for control and a spacer segment of polyethylene glycol to extend the coding segment away from the substrate.  The detection probes were made by in situ synthesis using PGR (photogenerated reagent) chemistry.  The hybridization melting temperatures were balanced by chemical modifications of the detection probes. Hybridization used 100 μL 6xSSPE buffer (0.90 M NaCl, 60 mM Na2HPO4, 6 mM EDTA, pH 6.8) containing 25% formamide at 34°C.  After RNA hybridization, tag-conjugating Cy3 and Cy5 dyes were circulated through the microfluidic chip for dye staining.  Fluorescence images were collected using a laser scanner (GenePix 4000B, Molecular Device) and digitized using Array-Pro image analysis software (Media Cybernetics). Data from miRNA microarray were analyzed by the service provider first subtracting the background and then normalizing the signals using an LOWESS filter (Locally weighted Regression) [42].  The ratio of the two sets of detected signals (log2 transformed, balanced) and P-values of the t-test were calculated; differentially detected signals were those with less than  .01 P-values. Multiple sample analysis involved normalization, data adjustment, t-test, and clustering. Normalization was carried out using a cyclic LOWESS. Data adjustment included data filtering, Log2 transformation, and normalization. The t-test was performed between “control” and “test” sample groups [43]. T-values were calculated for each miRNA, and P-values were computed from the theoretical t-distribution. miRNAs with P-values <.01 were selected for cluster analysis. The clustering was done using hierarchical method and was performed with average linkage and Euclidean distance metric [44] using TIGR MultiExperiment Viewer (http://www.tm4.org/mev.html).


### 2.4. Reverse Transcription and Real-Time PCR

Real-time reverse transcription-polymerase chain reaction (real-time RT-PCR) was performed to confirm the differential expression of selected miRNAs, identified as differentially expressed by miRNA microarray, in paired samples from other 13 patients. TaqMan MicroRNA RT Kit (Applied Biosystems, Foster City, CA) was used for reverse transcription. Real-time RT-PCR reactions were carried out with a 7300 Real-Time PCR System (Applied Biosystems) according to the protocol provided by the supplier, using the TaqMan Universal PCR Master Mix No AmpErase UNG and the following TaqMan MicroRNA Assays: hsa-miR-200a, hsa-miR-200b, hsa-miR-200c, hsa-miR-182, hsa-miR-202, and U18 as endogenous control.Data from real-time RT-PCR experiments are presented as the mean± SEM. The variation among groups was compared by means of nonparametric Wilcoxon and Mann-Whitney U tests. Statistical significance was assumed for P-values <.05. Statistical analysis was performed with SPSS for Windows version 15.0 (SPSS; Chicago, IL).


## 2.1. Tissue Collection

Subjects (n = 16) scheduled for surgery for chronic pelvic pain or infertility at the University of Piemonte Orientale-affiliated “Maggiore della Carità” Hospital were recruited to participate in this study. The study was approved by the “Maggiore della Carità” Hospital’s Institutional Review Board and informed consent was obtained from all participants. None of the authors have any conflict of interest with the study. Surgery was scheduled 6 to 12 days after the onset of menses. No patients were receiving hormone therapy at the time of the study or in the previous three months. The patients ranged in age from 24 to 48 years, with an average of 36 years. Endometriomas were removed at laparoscopy by excision of the entire cyst wall by stripping technique, preserving normal ovarian tissue. Hysteroscopy with directed biopsies, performed to obtain a sample of eutopic endometrium from the same patient, were carried out using a 4 mm Bettocchi Hysteroscope System with a 5 Fr operative channel (Karl Stortz GmbH & Co., Tuttlingen, Germany). Laparoscopy and hysteroscopy procedures were performed during the same surgical intervention. Freshly recovered tissues were rinsed in saline solution and divided in two parts. One half of the tissue was immediately snap-frozen and kept in liquid nitrogen for further processing, while the other was sent to the pathology laboratory. The endometriomas of 9 patients were classified as moderate, while 7 were classified as severe according to the ASRM guidelines [39].


## 2.2. RNA Isolation

Total RNA was extracted from tissues with the miRNeasy kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s protocol and quantified by Quant-iT RNA Assay Kit with Qubit Fluorometer (Invitrogen, Carlsbad, CA, USA).


## 2.3. MicroRNA Microarray Assay and Analysis

Microarray assay was performed using a service provider (LC Sciences).  Tenμg of  total RNA from eutopic and ectopic endometrium obtained from three patients were size fractionated using a YM-100 Microcon centrifugal filter (Millipore) and the small RNAs (<300 nt) isolated were 3′-extended with a poly(A) tail using poly(A) polymerase. An oligonucleotide tag was then ligated to the poly(A) tail for later fluorescent dye staining; two different tags were used for the two RNA samples in dual-sample experiments.  Hybridization was performed overnight on a μParaflo microfluidic chip using a microcirculation pump (Atactic Technologies) [40, 41]. On the microfluidic chip, each detection probe consisted of a chemically modified nucleotide coding segment complementary to target 475 mature human miRNA probes (Sanger miRBase sequence database 9.1) or other RNAs for control and a spacer segment of polyethylene glycol to extend the coding segment away from the substrate.  The detection probes were made by in situ synthesis using PGR (photogenerated reagent) chemistry.  The hybridization melting temperatures were balanced by chemical modifications of the detection probes. Hybridization used 100 μL 6xSSPE buffer (0.90 M NaCl, 60 mM Na2HPO4, 6 mM EDTA, pH 6.8) containing 25% formamide at 34°C.  After RNA hybridization, tag-conjugating Cy3 and Cy5 dyes were circulated through the microfluidic chip for dye staining.  Fluorescence images were collected using a laser scanner (GenePix 4000B, Molecular Device) and digitized using Array-Pro image analysis software (Media Cybernetics). Data from miRNA microarray were analyzed by the service provider first subtracting the background and then normalizing the signals using an LOWESS filter (Locally weighted Regression) [42].  The ratio of the two sets of detected signals (log2 transformed, balanced) and P-values of the t-test were calculated; differentially detected signals were those with less than  .01 P-values. Multiple sample analysis involved normalization, data adjustment, t-test, and clustering. Normalization was carried out using a cyclic LOWESS. Data adjustment included data filtering, Log2 transformation, and normalization. The t-test was performed between “control” and “test” sample groups [43]. T-values were calculated for each miRNA, and P-values were computed from the theoretical t-distribution. miRNAs with P-values <.01 were selected for cluster analysis. The clustering was done using hierarchical method and was performed with average linkage and Euclidean distance metric [44] using TIGR MultiExperiment Viewer (http://www.tm4.org/mev.html).


## 2.4. Reverse Transcription and Real-Time PCR

Real-time reverse transcription-polymerase chain reaction (real-time RT-PCR) was performed to confirm the differential expression of selected miRNAs, identified as differentially expressed by miRNA microarray, in paired samples from other 13 patients. TaqMan MicroRNA RT Kit (Applied Biosystems, Foster City, CA) was used for reverse transcription. Real-time RT-PCR reactions were carried out with a 7300 Real-Time PCR System (Applied Biosystems) according to the protocol provided by the supplier, using the TaqMan Universal PCR Master Mix No AmpErase UNG and the following TaqMan MicroRNA Assays: hsa-miR-200a, hsa-miR-200b, hsa-miR-200c, hsa-miR-182, hsa-miR-202, and U18 as endogenous control.Data from real-time RT-PCR experiments are presented as the mean± SEM. The variation among groups was compared by means of nonparametric Wilcoxon and Mann-Whitney U tests. Statistical significance was assumed for P-values <.05. Statistical analysis was performed with SPSS for Windows version 15.0 (SPSS; Chicago, IL).


## 3. Results and Discussion

### 3.1. MicroRNAs Differentially Expressed in Eutopic and Ectopic Endometrial Tissue

In the present study, we used miRNA microarray technology to identify the pattern of miRNAs in paired eutopic/ectopic endometrium from the same patients, thus avoiding the variables attributable to heterogeneous genetic background between individuals and the effects of estrogenic stimulation during different phases of the menstrual cycle. Moreover, we considered the whole endometrial and endometriotic tissues in order to preserve the contribution of all the components of the tissues, including vascular and immune system components and to avoid potential changes in gene and miRNA expression due to cell isolation and manipulation.Microarray technology has allowed a global analysis of all miRNAs differentially expressed in ectopicversus eutopic endometrium. The initial analysis of miRNA expression in ectopic endometrium compared with eutopic endometrium of three patient samples generated a list of 84 miRNAs significantly differentially expressed (P-values <.01). The 50 miRNAs for which the expression value in ectopic endometrium was at least twofold higher or lower than in eutopic endometrium are listed in Table 1.Table 1
Differentially expressed miRNAs in ectopicversus eutopic endometrium. List of differentially expressed miRNAs whose expression value in ectopic endometrium was at least twofold higher or lower than in eutopic endometrium P<.01.
NameEUECP-valuehsa-miR-136.292,090.27.00E+00hsa-miR-1007,517.7318,712.43.00E+00hsa-miR-101341.512,348.69.00E+00hsa-miR-106a3,264.741,510.101.11E−16hsa-miR-106b2,996.551,414.14.00E+00hsa-miR-12610,373.8822,435.79.00E+00hsa-miR-130a1,634.845,145.94.00E+00hsa-miR-130b673.04249.86.00E+00hsa-miR-1323,699.141,261.33.00E+00hsa-miR-1438,104.2621,764.97.00E+00hsa-miR-14510,992.3627,550.33.00E+00hsa-miR-148a2,623.736,507.58.00E+00hsa-miR-1501,621.964,503.15.00E+00hsa-miR-17-5p4,517.662,059.32.00E+00hsa-miR-1821,998.92230.69.00E+00hsa-miR-183410.8341.02.00E+00hsa-miR-18656.69246.791.21E−14hsa-miR-196b380.4514.13.00E+00hsa-miR-199a4,481.2712,618.11.00E+00hsa-miR-200a582.9533.22.00E+00hsa-miR-200b17,643.11675.98.00E+00hsa-miR-200c25,249.551,391.63.00E+00hsa-miR-20249.64471.062.27E−13hsa-miR-20a5,278.722,534.219.05E−14hsa-miR-2215,368.0510,915.55.00E+00hsa-miR-2512,878.146,328.311.06E−14hsa-miR-281,465.554,589.04.00E+00hsa-miR-299-5p202.34452.175.18E−13hsa-miR-29b248.514,963.66.00E+00hsa-miR-29c295.4010,562.63.00E+00hsa-miR-30e-3p299.191,003.481.50E−14hsa-miR-30e-5p58.94428.59.00E+00hsa-miR-34a337.65861.73.00E+00hsa-miR-365264.572,071.70.00E+00hsa-miR-368297.521,882.43.00E+00hsa-miR-3751,329.8513.62.00E+00hsa-miR-376a64.20522.49.00E+00hsa-miR-379175.21601.127.19E−13hsa-miR-41162.72215.675.92E−16hsa-miR-425-5p961.30329.99.00E+00hsa-miR-4862,824.50956.89.00E+00hsa-miR-493-5p64.60355.157.22E−12hsa-miR-5032,084.95465.94.00E+00hsa-miR-63829,531.6011,202.65.00E+00hsa-miR-6634,654.421,943.144.44E−15hsa-miR-6712,052.70955.73.00E+00hsa-miR-768-3p5,841.892,901.782.81E−06hsa-miR-768-5p5,321.542,456,43.00E+00hsa-miR-932,614.63629.20.00E+00hsa-miR-99a6,766.0218,369.57.00E+00


### 3.2. Real Time RT-PCR Analysis of miRNA Expression

In order to confirm the results obtained with miRNA microarray, the expression analyses of 5 selected miRNAs was carried out by real-time RT-PCR on specimens from other 13 patients. These 5 miRNAs, namely, hsa-miR-200a, hsa-miR-200b, hsa-miR-200c, hsa-miR-182, and hsa-miR-202, were selected because their expression resulted to be highly altered in ectopic endometrium compared with the matched eutopic tissue. We verified the differential expression of the selected miRNAs in the ectopic tissue by setting as 1 the expression of eutopic miRNAs. The results obtained by real-time RT-PCR are in accordance with those obtained from the microarray. Indeed, these miRNAs showed significant differential expression (P-values <  .05) in eutopic versus ectopic tissue: hsa-miR-200a, hsa-miR-200b, hsa-miR-200c, and hsa-miR-182 levels in ectopic endometrium were reduced up to 95% (Figures 1(a)–1(d)), while hsa-miR-202 expression in ectopic endometrium was increased up to 60 folds compared to eutopic endometrium (Figure 1(e)). The analysis of data according to the severity of the endometrioma, by means of nonparametric Wilcoxon and Mann-Whitney U tests, failed to reveal any significant differences in miRNA expression levels, although this may be ascribable to the group size. Further studies increasing the cohort will be necessary to completely address this issue.Differential expression of miRNAs in ectopicversus eutopic endometrium. The miRNAs selected for independent validation were among those with wider difference in expression between eutopic and ectopic endometrium. The differential expression of the selected miRNAs in the ectopic versus eutopic tissues was evaluated by real-time RT-PCR. The expression of each miRNA in eutopic tissue was set to 1. (a) hsa-miR-200a, (b) hsa-200b, (c) hsa-miR-200c, (d) hsa-miR-182, and (e) hsa-miR-202. P*<  .05.
(a)(b)(c)(d)(e)


### 3.3. Identification of Predicted miRNA Targets andIn Silico Functional Analysis

The predicted target mRNAs of the differentially expressed miRNAs common to two different search algorithms, TARGETSCAN (http://www.targetscan.org/) and PICTAR-VERT (http://pictar.mdc-berlin.de), were 3093. The functions of these predicted targets and the molecular pathways in which they could be involved were assessed using Ingenuity Pathways Analysis software (Ingenuity IPA 7.5). The predicted targets were uploaded in IPA, and the software identified 49 significant molecular networks to which the predicted targets of the differentially expressed miRNAs belong (Table 2). Among the biological functions reported to be statistically significant by IPA there were functions known to be involved in endometriosis such as gene expression, cellular growth and proliferation, cellular development, cellular movement, cell death, cell cycle, cancer, and  reproductive system disorders. One of the subcategories of reproductive system disorders to be more represented, with P-value (calculated by Fisher’s Exact test) of 6.1·10-18, was endometriosis with 119 molecules directly involved in this pathology (Table 3).Table 2
Molecular networks constituted by the predicted miRNA targets. IPA analysis was performed in order to identify the molecular pathways and functions to which the predicted targets of the differentially expressed miRNAs belong. The networks are generated on the basis of the published literature and ranked by theP-value calculated by Fisher’s exact Test. The biological processes in which the targets are involved are determined by IPA using the GOstat application P<.01.
IDMolecules in networkP-valueFocus moleculesTop functions1ACTR1A, ADM, APP, BICD2, CABP7, CELSR1, CPSF6, DAG1, ELAVL1, EPHA2, GCH1, GNA13, HIRA, HLX, HNRNPH1, HNRNPM, IFNG, IRF2, KHDRBS1, LARGE, MAPT, MTMR3, MTMR4, MYH9, PCSK2, PLCG1, PTGS2, RASA1, SBF1, SOCS1, SOCS2, STAT6, TNPO1, TNPO2, TRIB210E−2135Cellular Development, Skeletal and Muscular Disorders, Organismal Development2AEBP2, ATAD2, C1QTNF6, CAV1, CAV2, CBX1, CCND1, CREB1, DAB2IP, DDIT4, DNMT1, DNMT3A, DNMT3B, DUSP9, EED, ESR1, EZH2, FOLR1, HSPA13, KAT2B, LEP, MED1, MED14, NCOA2, NCOA3, NCOA4, NOTCH3, PHF1, PNRC1, PRLR, RBBP7, RBM9, SIRT1, THRA (includes EG:7067), TMOD110E−2135Gene Expression, Cellular Growth and Proliferation, Developmental Disorder3AKAP13, BCL2L11, CCNE2, CDK6, CDKN1A, CDKN1B, CTGF, CTSB, CUGBP1, DUSP1, E2F1, ESRRG, ETS1, FHL2, FLI1, FOXO1, FOXO3, FOXO4, IGFBP3, IP6K3, JAG1, KRAS, MCF2, NR3C1, PRKD3, RB1CC1, SGK1, SMAD3, SP1, SPHK2, TCF7L2 (includes EG:6934), TGFBR1, TIMP3, TOPBP1, TSPYL210E−2135Cellular Growth and Proliferation, Cellular Development, Cancer4ADAM12, BCL2, CITED2, EGLN1, FGF9, FRAP1, GATA3, GNAI2, HIF1A, HSPD1, IGF1R, IKBKB, ITGA9, JUNB, KPNA1, KPNB1, MAP2K3, MAP2K5, MAP3K7, MAP3K7IP2, PIAS3, PPM1B, PRKCE, PTEN, PTPN1, RPS6KB1, SKI, SMAD7, SNAI1, SOCS3, SP2, STAT3, UBR5, WT1, ZEB110E−2135Cellular Growth and Proliferation, Cellular Movement, Cell Cycle5ANP32A, ATP2A2, CD69, CDK5R1, COL1A1, COL1A2, CREM, DDR1, DLL4, E2F3, EGR1, FBXW7, FLT1, HDAC4, IL2, IL18BP, LPL, NDRG1, NOTCH1, PHC1, PHC2, POLA1, PPARA, RANBP2, RB1, RYBP, SHC1, SP3, SP4, TRAM2 (includes EG:9697), XPO1, YBX1, YY1, ZBTB10, ZBTB7B10E−2135Organismal Injury and Abnormalities, Cardiovascular Disease, Cellular Development6ARNT, BACH1, BCL2L12, BRCA1, CLOCK, CREBBP, CYP1B1, DDX5, EP300, EPAS1, ERBB4, EREG, GABPA, GADD45A, HBEGF, HOXA13, HOXB6, LEF1, MAB21L1, MAX, NCAM1, NFYA, NPAS2, OXTR, PIN1, PPARG, PPP2CA, PTGER4, RBBP8 (includes EG:5932), RUNX1, SDC1, SLC1A2, TGFA, TRERF1, WNT5A10E−2135Gene Expression, Cancer, Genetic Disorder7ACTB, ARID1A, ARID1B, ARID4A, ARID4B, BTG2, CLIP1, DR1, ETS2, EWSR1, GTF2B, HOXA9, PFN1, RARB, RBL1, RBL2, SAP30, SAP130, SFPQ, SIN3A, SMARCA2, SMARCA4, SMARCB1, SMARCC1, SMARCC2 (includes EG:6601), SNIP1, SUMO1, TACC2, TAF4, TAF5, TAF12, TBP, TDG, TOP1, XPO610E−2135Gene Expression, Cellular Assembly and Organization, Cellular Compromise8ARHGDIA, BTRC, CASP3, CD4, CDC42, CLTC, CTTN, CXCL12, DIABLO, ELK1, ELK3, EZR, F3, FOS, FOSB, GLI3, GSK3B, HNRNPA1, HNRNPC, IL1A, ITPR1, JUND, MAP1B, MCL1, OCRL, PAK1, PGM1, PRKCI, PRKD1, PTX3, RABEP1, RAC1, SEMA3A, SRF, STK410E−2135Cell Death, Cancer, Cellular Assembly and Organization9CD47, CSF1, CSF1R, CSK, EPHA4, FASLG, FGF1, FN1, FOXP1, GRB2, IRS2, ITGA5, ITGA6, ITGA10, ITGA11, ITGAV, ITGB1, ITGB3, JAK2, KCNA3, MAP2K1, MAP2K4, MAPK1, MET, MITF, NFAT5, PDGFB, PDGFRB, PLXNB1, RAB21, SERPINE1, TNFRSF1A, TNFSF11, TRIB1, YES110E−2135Cellular Growth and Proliferation, Cell-mediated Immune Response, Cellular Movement10ACTR3, AR, ARHGEF7, ASAP1, CRKL, DYRK1A, ESR1, GDI1, KLF2, LMOD1, LRRK1, MRAS, NCK1, PFTK1, PLS3, POMT2, TEAD3, TRIP10, WAS, WEE1, WIPF1, ZMIZ110E−919Cellular Assembly and Organization, Skeletal and Muscular System Development and Function, Cancer11AKAP12, AMOTL2, ARL6IP1, ATM, BRCA1, CDC6, CHEK2, E2F1, FKBP3, HS3ST1, LATS2, LBR, MBNL2, MTDH, PPM1D, PPP1R13B, SCN3B, SH3BP4, TP53, TRIO, VCAN10E−717Cancer, Genetic Disorder, Reproductive System Disease12ANK3, CREB5, DEDD, FRK, GPRC5A, KCNK2, KRT18, MPZL2, MYCBP2, MYO1B, NRK, RAB22A, SPAG9, ZNF21710E−713Cardiovascular Disease, Cellular Development, Cell Morphology13ADAM19, CADM1, CBFA2T3, CDC42SE1, COL6A3, COL7A1, DAAM1, ERBB2, FN1, HAS3, MFAP2, MPHOSPH9, NET1, PMEPA1, RAP1B, TGFB1, THBS1, THPO, XYLT1, ZFP3610E−716Cancer, Cellular Growth and Proliferation, Dermatological Diseases and Conditions14ATP1B3, CCND1, COL3A1, COL4A1, COL5A2, CTNNB1, HOXA1, IGF2R, KLF9, LGALS3, M6PR, MAP3K10, NANOG, NPTX1, NRF1, NRIP1, PTPRC, PTTG1, RB1, SPTBN2, TCF7L2 (includes EG:6934), THRB (includes EG:7068), TP5310E−617Organismal Development, Cancer, Cell Cycle15ALDH1A3, COLQ, DUSP10, EIF4B, GPD2, HSPE1, IL6, IL13, IL1B, MMD, NR4A3, NUAK1, PTPN12, RND3, ROBO1, SEMA3C, SLC7A1, STAC, TNF, TNFSF10, TUB10E−616Small Molecule Biochemistry, Skeletal and Muscular System Development and Function, Cell-To-Cell Signaling and Interaction16ACSL3, ASXL1, EGR3, JMJD1C, PLK2, PTP4A1, RRM2, RRM1 (includes EG:6240), RRM2B, SEL1L, SFRS3, SLC2A1, SMURF2, SON, STRN3, TNF, TP53, UBE2B, ZFP36L110E−615Nucleic Acid Metabolism, Small Molecule Biochemistry, Genetic Disorder17CCND1, CCNT1, CCNT2, CDK9, CDKN1A, DNAJB9, FBXW11, GLI1, GLI2, GNAO1, GTF2F2, HSPA5, HTATSF1, ID2, JAG2, MDFIC, MXI1, MYCN, NPM1 (includes EG:4869), POLR2A, POLR2C, RB1, RPS6KA1, RXRA, SFRS1, SUPT5H, SUPT6H, TCERG1, TGFB1, TP53, ULK110E−620Gene Expression, Cellular Development, Cell Cycle18APBB2, BECN1, CAD, CDKN1A, CFL1, E2F5, ESR1, FANCA, FANCC, FGF7, GFI1, GJA1, GORASP2, HSP90AA1, LIMK1, MAX, MYC, PCBP2, PERP, PTBP1, SPTAN1, TERT, TMSB4X, TP53, XBP110E−517Cell Cycle, Connective Tissue Development and Function, Cellular Compromise19AP3M1, BCL6, CCND1, CREBL2, ENC1, FOXA1, FTH1, HNF1A, HNMT, MTA3, MUC4, NCOR1, NCOR2, NFE2L2, NFYC, NR5A2, SNX17, SSTR1, TFR2, TFRC, TMOD210E−515Cancer, Gene Expression, Drug Metabolism20ACTB, ACTL6A, CCNT1, CD9, CTCF, DMAP1, EMD, EPC1, ESR1, HABP2, HNRNPA1, HNRNPF, HNRNPK, HSP90AA1, LEMD3, MKNK2, MORF4L1, MYC, PCBP1 (includes EG:5093), SYNE2, TBP, THOC4, TNPO1, TRRAP, U2AF1, WNT1, WNT2B, YY1, ZBTB3310E−518Gene Expression, Cancer, Reproductive System Disease21BEX2, CDH1, CDH2, CDH11, CTNNA2, CTNNB1, CTNND2, DIO2, ELAVL1, EPHB3, ERBB2, ESR1, F13A1, HNRNPD, ILF3, IRS1, JUP, KHSRP, LDB1, LMO2, NHLH2, PIK3R1, PKD1, PPP3CA, PTCH1, PTPRF, TCF7L2 (includes EG:6934), TIAL1, TP53, TSC22D1, ZNF34610E−418Cell-To-Cell Signaling and Interaction, Cancer, Cellular Growth and Proliferation22ACIN1, AP2A1, BRD2, COIL, EIF4A1, EIF4G3, ICMT, LMO7, MAP7, NME1, PA2G4, PABPC1, PABPN1, PAIP1, PAIP2, PAPOLG, PNN, RNGTT, RNPS1, SAP18, SFRS11, TALDO1, TRA2B, ZNF14310E−415RNA Post-Transcriptional Modification, Protein Synthesis, Gene Expression23ADAM9, ADAM10, BMP7, CCL2, CCL5, CDH1, COL18A1, CTNNB1, DICER1, EGF, EGFR, EPS15, ERBB2, ETV1, GRB2, HGS, IL8, L1CAM, LPAR1, LPP (includes EG:4026), MAP3K14, NKRF, RALA, RELA (includes EG:5970), SHC1, SMAD5, SPG20, SRC, TBK1, TERT, TJP1, TMEM55A, TMEM55B, TNF10E−419Cell-To-Cell Signaling and Interaction, Tissue Development, Cancer24ALOX15, BZW2, CCL3, CHST2, DHCR24, FCER2, GAS7, GATA6, IGHE, IL4, IL8, IL13, MTSS1, NHLH1, NOS2, NOTCH2, PDGFC, PHLDA1, PLXNC1, RIN2, SORT1, ST8SIA410E−414Genetic Disorder, Inflammatory Disease, Respiratory Disease25CAND1, CCND2, CDC5L, CDKN1B, CUL1, CUL2, CUL3, DNTT, FBXL3, FBXW2, GPR37, PARK2, PITX2, PLRG1, PMS1, PRCC, PRPF19, PSMA2, PSMC1, PSMC5, RAD23B, RBX1 (includes EG:9978), SFRS2, SKP1, SKP2, TCEB1, VHL10E−416Post-Translational Modification, Cancer, Immunological Disease26AMOT, B4GALT5, BTG3, CCL2, CD40, CHMP2B, CLASP1, ETS1, F3, FOS, HIVEP1, IKBKB, IL2, IL6, IL15, JAK1, JUN, MAPK1, MAPK14, MVP, NEFM, NFKB1, NFKBIA, PLG, PPP2R1B, RAB32, RELA (includes EG:5970), RFWD2 (includes EG:64326), RGS2, SQSTM1, STAT1, STAT3, TNF, TYK2, ZBTB1110E−419Hematological System Development and Function, Cell Death, Cell Cycle27CCNA2, CCNB1, CCNE1, CCNE2, CD46, CD59, CDK2, CDKN1C, E2F4, EPHB2, FBXO32, HDAC9, HIVEP2, IGFBP3, KLF4, LATS1, LTC4S, MYB (includes EG:4602), MYBL2, NDC80, NUMB, PCNA, PLAU, POLD1, RALBP1, RBL1, RBL2, RFC4, RFX1, SCD, SPARC, SUZ12, TGFB1, TGFB3, TNS310E−419Cell Cycle, Cancer, Cellular Growth and Proliferation28ABL1, ADRB2, ATP1A1, ATP1A2, ATP1B1, BCAR1, BCAR3, BCR, CBL, CRK, DOCK1, FRAP1, FYN, GATA2, GRK4, ITGA2B (includes EG:3674), ITGB3, MAPK9, MGRN1, NEU2, PIAS1, PIK3R1, PLSCR1, PRKCD, PTK2, RAPGEF1, RECK, SP3, SRC, STAT1, TIMP2, TP73, TP53INP1, TSG101, VPS2810E−419Cell Death, Cellular Movement, Cellular Growth and Proliferation29AKAP11, B2M, BHLHE40, CALD1, CEBPA, CHI3L1, COL16A1, EDN1, EDNRB, EIF1AX, EMP1, HMGA1, HMGCR, HMGCS1, IDI1, INSIG1, IPO13, KIT, KITLG, LSS, MMP2, MMP3, NAMPT, NPPB, PRKCA, PTPN6, RETN, SCARB1, SERPINB1, SERPINE1, TGFBR2, TGIF2, TNC, TNF, UBE2I10E−419Cancer, Hematological Disease, Lipid Metabolism30ALOX12B, APOE, ATF7IP, BCAT2, CAMK2A, CAMK2N1, CCND3, CDKN1B, CHAF1A, CRYBB2, DPP4, EFNA5, ESR1, GSTP1, HBE1, IL4, IL13RA1, IRF4, KCNK10, MBD1, MBD2, MBD3 (includes EG:53615), MECP2, MGMT, NR1H3, NR2F2, PIP5K3, PRLR, PTPN4, PTPRM, SETDB1, SLCO3A1, TFF2, TP53BP2, TSC110E−419Behavior, Reproductive System Development and Function, Neurological Disease31ARHGEF6, BNIP2, CASP8, CHFR, CPD, CS, ELF1, IFNB1, IL8, IL16, INS, IRF1, JAK2, LMTK2, NCF2, NFKBIA, NGFR, PGAM1, PGK1, PLAGL2, PPP1CC, PPP1R12B, PRL, STAT1, TNF, TRADD10E−315Immunological Disease, Cell Death, Hematological Disease32ACHE, AGT, APP, ATP2B1, BACE1, BIK, BMP2, BTG2, CCL20, CD40LG, CDH1, CXCL2, CYCS (includes EG:54205), EFNA1, EIF4E, EIF4EBP1, GCLC, ITM2B, JUN, LAMP3, LYN, MYO6, PDK4, PPARD, PSEN1, PTGS2, PXN, SMAD1, SMPD2, SOX9, TNF, TNFAIP2, TNFSF10, TRPV6, VCL10E−318Cell Death, Cancer, Cell-To-Cell Signaling and Interaction33AHR, ANP32B, ATM, BIRC3, BTG2, CAMK2G, CEBPE, CLU, ELAVL1, ERCC1, GDF11, H2AFX, HDAC3, HNRNPD, HNRNPU, HOXA5, ILF3, NEDD8, NUP153, RAD50, RARA, RARB, RARG, TBX3, TERF2, TERF2IP, TIA1, TIAL1, TINF2, TP53, TPR, XPO1, XRCC5, XRCC6, YAP110E−318Cell Cycle, DNA Replication, Recombination, and Repair, Cell Death34ADH5 (includes EG:128), ASH2L, ATP6V0C, C16ORF53, CHRNA5, CSNK2A1, CSNK2B, DPY30, EDA, ETV4, HCFC1, HDAC1, HIST2H4A, MIER1, MLL3, MLL4, MRC2, NCOA6, OGT (includes EG:8473), PAXIP1, PKNOX1, PLAU, PLAUR, POU2F1, RBBP5, SIN3A, SP1, SP3, SSRP1, SUB1, SUPT16H, TEAD1, TRIM63, WDR5, ZBTB7A10E−318Gene Expression, Cell Morphology, Reproductive System Development and Function35APLN, BID, CASP2, CFLAR, CXCL13, CYCS (includes EG:54205), DIABLO, EIF2S1, EIF4B, EIF4E, EIF4EBP1, EIF4G1, IL21, IL1RN, INHA, INHBA, INHBB, JAK1, LEFTY1, MCL1, NFKB2, P4HA1, PPP1R15A (includes EG:23645), PRDM1, SATB1, SERPINB2, SOCS1, SOCS3, SUV39H1, TAL1, TLR4, TNF, TNFSF10, USF1, USF210E−318Protein Synthesis, Cancer, Cell Death36CEBPB, CSF1, CSF3, EGFR, FGA, GAB1, GRB2, IL6, IL1A, IL6ST, IRS1, JAK1, KIF5B, LIFR, LMO4, LPAR2, MAP2, MED28, NF2, NFKB1, OSM, OSMR, PIK3C2B, PLG, POU2F1, POU2F2, PRL, PTGS2, PTPN11, RNASE1, RNASE2, SKAP2, STAT3, TLR9, VIP10E−318Cellular Development, Cellular Growth and Proliferation, Cancer37AOF2, BAZ1A, BAZ1B, CACNA1C, CDYL, CHRAC1, CTBP1, CTBP2, EHMT1, EHMT2, GATA4, HAND1, HAND2, HDAC2, HMG20B, KCNJ3, MEF2C, MYOCD, PDS5A, PHF21A, POLE3, RAD21, RBBP4, RCOR1, RREB1, SCN5A, SFRP1, SMARCA1, SMARCA5, SMC3, SMC1A, STAG1, STAG2, WIZ, ZEB210E−318Cell Cycle, DNA Replication, Recombination, and Repair, Gene Expression38AKAP1, API5, ARHGEF12, CFTR, COL18A1, F2, F2R, FGF2, FGFR1, IL1B, IQGAP2, MPRIP, PPP1R12A, PRKAR2B, PRKG1, PTGER3, RHOA, SH3GLB1, SH3GLB2 (includes EG:56904), SLC9A3R1, SRC, STX1A, VCP10E−313Cellular Assembly and Organization, Cell Morphology, Cancer39ACVR1, ACVR1B, ACVR2A, ANTXR1, APC, ASAP2, BCAP31, BIN1, BMP2, BMP6, BMP7, BMPR2, BMPR1A, CANX, COL18A1, CTNNB1, DCTN1, EFNB2, ERBB2, F10, ICAM1, ID1, ITGB2, MAPRE1, NOG, NRP1, PLP2, SEC23A, TGFB1, TLN1, TNFRSF2110E−316Cell Signaling, Cellular Development, Connective Tissue Development and Function40ARCN1, BRCA2, BRIP1, COPB1, COPG, CYLD, EXO1, HERC2, KPNA2, KPNB1, MAD2L2, MLH1, MMS19, MSH6, PIK3C2A, PMS1, PMS2, PSD2, PSMC1, RANBP9, REV1, REV3L, RFC2, RUFY1, SACM1L, SBF2, SSB (includes EG:6741), TMED9, UBA52, UBR5, USP510E−215DNA Replication, Recombination, and Repair, Cancer, Gastrointestinal Disease41CCNB1, CD44, EGFR, EIF3A, ERBB2, ERRFI1, GAB1, IL6ST, JARID1B, KRT7, MYBL2, MYO10, NEDD9, PARP1, PIK3CA, PIK3CD, PIK3R1, PIK3R2, RAB31, SMAD2, SOLH, SOX4, TGFB1, TGIF1, TGOLN2 (includes EG:10618), TNF10E−213Cell Cycle, Cellular Growth and Proliferation, Carbohydrate Metabolism42ADCYAP1, AMPD3 (includes EG:272), CCL3, CCL4, CCL5, CD40, CD40LG, CSF3, CXCL10, DUSP1, DUSP6, FURIN, IER2, IL3, IL17A (includes EG:3605), IL1B, ITGAM, MAP2K6, MAPK3, MAPK14, MMP9, NAMPT, NFKB2, NGF, NR4A2, NSMAF, P2RX7, PLD1, PLG, PTGFR, SERPINB2, TOB1, TRAF3, TSC22D3, VEGFA10E−216Cellular Movement, Hematological System Development and Function, Immune Cell Trafficking43ATM, ATR (includes EG:545), C10ORF119, CDC6, CDC37, CDC25A, CDC25B, CHEK1, CHEK2, CSNK1A1, E2F1, FAS, GRB10, MAP3K11, MAP3K5 (includes EG:4217), MCM2, MCM3, MCM4, MCM7, MDM4, PLK1, PPP2R3A (includes EG:5523), PPP5C, RAD17, RAF1, SNAP23, SSH2, STX4, STX6, STX16, TP53, VAMP2, VAMP3, VIM, YWHAB10E−216DNA Replication, Recombination, and Repair, Cancer, Cell Cycle44BAK1, BAX, BCL2, BCL2L1, BID, BMF, BSG, CAV1, CAV3, CDC2, CDK2, CIT, CYCS (includes EG:54205), DLG4, ECT2, GIT1, GRIN2A, HINT1, HTT, IGFBP5, KIF14, KIF23, KRAS, LRP1, MEOX2, NCL, NCSTN, NT5C3, PLK1, PRC1, PSEN1, PSEN2, RACGAP1, TP53, VDAC210E−215Cell Death, Cell Cycle, Cancer45ASCL2, ASF1A, ATXN7, CCNH, CDK7, CRIP2, CSPG4, DKK1, ENO3, ERCC2, ERCC3, ESRRA, GK, GPR64, GTF2H1, GTF2H2, HMGN1, MLL2, MNAT1, NR2C2, NT5E, PPARGC1A, RBBP5, SAFB, SMAD6, TAF1, TAF2, TAF4, TAF8, TAF9, TAF11, TAF15, TFF1, TUBB, UTX10E−215Gene Expression, DNA Replication, Recombination, and Repair, Dermatological Diseases and Conditions46ADAMTS5, BAX, BCL2, BCL2L1, BRCA1, CASP3, CCL3, CCL4, CD226, CD244, CSF2, FLNB, GP9, GP1BA, IL8, IL15, IL18, IL18R1, KLRK1, LCP2, MMP1 (includes EG:4312), MNT, MOAP1, NCR1, PDIA3, RAB9A, SELL, SOD2, TERT, TP63, VDAC1, XRCC6, YWHAE, YWHAQ (includes EG:10971), YWHAZ10E−215Cell-to-Cell Signaling and Interaction, Hematological System Development and Function, Cell Death47ABCA1, AKT1, APOA1, CCDC88A, CCL2, CCL5, COL2A1, CSH1, CUL5, FKBP1A, FLOT1, IGF1, IL8, IL13, IL1B, IL1RN, ILK, INS, LOX, MMP7, PDE4D, PDPK1, PGF, RNF4, RYR1 (includes EG:6261), SLC2A4, STK38L (includes EG:23012), TNF, TRPS110E−213Cell-mediated Immune Response, Cellular Movement, Lipid Metabolism48EIF2C1, EIF2C2, TNRC6A10E−23Infection Mechanism, Cancer, Respiratory Disease49DMD, DTNA, DTNB10E−23Cellular Assembly and Organization, Nervous System Development and Function, Skeletal and Muscular System Development and FunctionTable 3
Molecules directly involved in endometriosis and networks in which they appear. IPA analysis indicated that several networks constituted by the predicted targets of the differentially expressed miRNAs include molecules known to be involved in endometriosis.
SymbolEntrez Gene NameNetworksCytokinesCD40LGCD40 ligand38CX3CL1chemokine (C-X3-C motif) ligand 112CXCL13chemokine (C-X-C motif) ligand 1336IL2interleukin 22, 29, 37IL4interleukin 429, 32, 37IL6interleukin 6 (interferon, beta 2)29IL8interleukin 828, 31IL18interleukin 18 (interferon-gamma-inducing factor)29SPP1secreted phosphoprotein 123, 46TNFtumor necrosis factor (TNF superfamily, member 2)26, 29, 30, 33, 37, 38, 40, 41, 45EnzymesCNTN1contactin 123DNMT1DNA (cytosine-5-)-methyltransferase 15, 36DNMT3ADNA (cytosine-5-)-methyltransferase 3 alpha5DNMT3BDNA (cytosine-5-)-methyltransferase 3 beta5FN1fibronectin 17, 28GNASGNAS complex locus41GSTP1glutathione S-transferase pi 149HINT1histidine triad nucleotide binding protein 148KRASv-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog3PDE4Aphosphodiesterase 4A, cAMP-specific (phosphodiesterase E2 dunce homolog, Drosophila)44PDE4Dphosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila)41PTGS2prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)7, 28RAC1ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1)5RAP1BRAP1B, member of RAS oncogene family23REV3LREV3-like, catalytic subunit of DNA polymerase zeta (yeast)39RRM1 (includes EG:6240)ribonucleotide reductase M126SAT1spermidine/spermine N1-acetyltransferase 126XRCC6X-ray repair complementing defective repair in Chinese hamster cells 642, 48Growth FactorsANGPT2angiopoietin 27CTGFconnective tissue growth factor2, 36, 40FGF2fibroblast growth factor 2 (basic)23, 31INHBAinhibin, beta A45LEPleptin6TGFB1transforming growth factor, beta 120, 26, 33, 35, 40, 45VEGFAvascular endothelial growth factor A29, 30Ion ChannelsPKD1polycystic kidney disease 1 (autosomal dominant)45PKD2 (includes EG:5311)polycystic kidney disease 2 (autosomal dominant)45KinasesCDC2cell division cycle 2, G1 to S and G2 to M32, 36CSF1Rcolony stimulating factor 1 receptor3, 34EGFRepidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)28ERBB2v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian)27, 30, 33, 35, 38, 40, 45, 47FLT1fms-related tyrosine kinase 1 (vascular endothelial growth factor/vascular permeability factor receptor)2INSRinsulin receptor26JAK1Janus kinase 1 (a protein tyrosine kinase)37KITv-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog9MAPK4mitogen-activated protein kinase 417NTRK2neurotrophic tyrosine kinase, receptor, type 245PCK1phosphoenolpyruvate carboxykinase 1 (soluble)42PDGFRAplatelet-derived growth factor receptor, alpha polypeptide11PDGFRBplatelet-derived growth factor receptor, beta polypeptide11PIK3R2phosphoinositide-3-kinase, regulatory subunit 2 (beta)17SGK1serum/glucocorticoid regulated kinase 116STC1stanniocalcin 149WEE1WEE1 homolog (S. pombe)18Ligand-Dependent Nuclear ReceptorsAHRaryl hydrocarbon receptor44ARandrogen receptor30ESR1estrogen receptor 15, 30, 44ESR2estrogen receptor 2 (ER beta)44PPARGperoxisome proliferator-activated receptor gamma12, 29PeptidasesHPR (includes EG:3250)haptoglobin-related protein24MESTmesoderm specific transcript homolog (mouse)18MMP2matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase)11PhosphatasesDUSP1dual specificity phosphatase 13PPP3R1protein phosphatase 3 (formerly 2B), regulatory subunit B, alpha isoform24PTENphosphatase and tensin homolog19PTP4A1protein tyrosine phosphatase type IVA, member 122Transcription RegulatorsBCL6B-cell CLL/lymphoma 616BRCA1breast cancer 1, early onset5, 30, 42CITED2Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 211CREB1cAMP responsive element binding protein 16EGR1early growth response 16, 26, 35EMX2empty spiracles homeobox 215FOSv-fos FBJ murine osteosarcoma viral oncogene homolog6, 35, 49FOXO1forkhead box O13GATA3GATA binding protein 32HIF1Ahypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor)10, 31, 44, 47ID1inhibitor of DNA binding 1, dominant negative helix-loop-helix protein47, 50JUNjun oncogene49JUNBjun B proto-oncogene11, 47NRIP1nuclear receptor interacting protein 136RELv-rel reticuloendotheliosis viral oncogene homolog (avian)44SMAD6SMAD family member 614SMAD7SMAD family member 711SP2Sp2 transcription factor31TP53tumor protein p5322, 27, 32, 34, 36, 37, 40, 41, 45WT1Wilms tumor 17ZFP36zinc finger protein 36, C3H type, homolog (mouse)20Transmembrane ReceptorsIL2RGinterleukin 2 receptor, gamma (severe combined immunodeficiency)37ITGB1integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 includes MDF2, MSK12)11, 28, 30ITGB3integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)7ITGB4integrin, beta 430TransportersAPOEapolipoprotein E36ATP1B1ATPase, Na+/K+ transporting, beta 1 polypeptide41ATP2B2ATPase, Ca++ transporting, plasma membrane 221SLC6A6solute carrier family 6 (neurotransmitter transporter, taurine), member 61OthersACTBactin, beta9, 49ANK3ankyrin 3, node of Ranvier (ankyrin G)24BCL2B-cell CLL/lymphoma 23, 48BIRC5baculoviral IAP repeat-containing 536BSGbasigin (Ok blood group)41CAV2caveolin 25CCNA2cyclin A243COL18A1collagen, type XVIII, alpha 147DCNdecorin28EPS15epidermal growth factor receptor pathway substrate 1528ERRFI1ERBB receptor feedback inhibitor 145EZRezrin18FBN1fibrillin 136IRS2insulin receptor substrate 26, 37ITGA6integrin, alpha 630LRP5low density lipoprotein receptor-related protein 56MARCKS (includes EG:4082)myristoylated alanine-rich protein kinase C substrate47SDC2syndecan 28TAL1T-cell acute lymphocytic leukemia 118THBS2thrombospondin 211TIMP2TIMP metallopeptidase inhibitor 211TMSB10thymosin beta 1019TRAF2TNF receptor-associated factor 226VIMvimentin19, 36An exemplificative network identified by IPA enriched for miRNA targets involved in endometriosis is  shown in Figure2. This network, converging on estrogen receptor 1 (ESR1), includes the DNA methyltransferases DNMT3A and DNMT3B that are validated targets of hsa-miR-29b and hsa-miR-29c, and of hsa-miR-29b, hsa-miR-29c, and hsa-miR-148a, respectively [45, 46]. DNA methylation is an epigenetic modification that is involved in gene silencing, chromatin remodeling, and genome stability [47]. It has been demonstrated that DNMT1, DNMT3A, and DNMT3B are disregulated in endometriosis [48], and it has been suggested that aberrant methylation of HOXA10 and of the progesterone receptor PR-B may be responsible of the disregulation of their expression in endometriosis. Thus, this network strongly suggests a possible involvement of miRNAs in these mechanisms.Figure 2
Functional analysis of all the predicted targets of the differentially expressed miRNAs. Graphical representation of network #2 obtained by IPA analysis. Genes are represented as nodes, and the biological relationship between two nodes is represented as a line. Every line is supported by at least one reference in literature. Highlighted, the genes involved in endometriosis according to IPA knowledge base.To further analyze the possible role of these differentially expressed miRNAs in endometriosis, we performed a different analysis uploading the miRNAs directly in IPA. In this way, the software identified 6 networks, 3 of which are highly significant with known biological functions including genetic disorders, connective tissue disorders, skeletal and muscular disorders, cancer, and reproductive system disorders (Table4).Table 4
Molecular networks constituted by the predicted miRNA targets. The list of differentially expressed miRNAs was directly uploaded in IPA and an analysis was performed in order to identify the molecular pathways and functions to which the predicted targets of the differentially expressed miRNAs belong. The database used by IPA to analyze miRNAs and their targets is Argonaute2  (http://www.ma.uni-heidelberg.de/apps/zmf/argonaute/). P<.01.
IDMolecules in NetworkP-valueFocus MoleculesTop Functions1AKAP3, ATP2A2, C11ORF87, CNKSR2,CREB1, CUGBP2, EIF4E3, ELK1, FLRT2, HOXB2, HOXD12, IFNG, KLHDC10, KPNB1, MIR25, MIR150, MIR186, MIR221, MIR299, MIR143 (includes EG:406935), MIR182 (includes EG:406958), MIR200A, MIR200B, MIR200C, MIR34A, MYST4, OTOF, PAQR3, PER1, RPGRIP1L, SNRPA, SRCAP, UBFD1, USP6NL, WDR4410E−2411Genetic Disorder, Skeletal and Muscular Disorders, Connective Tissue Disorders2ATP1B1, C4ORF16, CALU, DHX15, DIP2C, DNMT3A, DNMT3B, EVX2, FAM108C1, FBXL11, HOXA5, HOXA10, INO80, JPH3, KLHL18, MACF1, MAP2K6, MIR126, MIR100 (includes EG:406892), MIR130A (includes EG:406919), MIR130B (includes EG:406920), MIR132 (includes EG:406921), MIR148A (includes EG:406940), MIR20A, MIR29B, MIR29B1, MIR29B2, MIR29C, MPPED2 (includes EG:744), NUFIP2, SMARCE1, SOX6, ZFP36L2, ZNF238, ZNF318 (includes EG:24149)10E–199Genetic Disorder, Skeletal and Muscular Disorders, Infection Mechanism3ADIPOR2,AR, ARF4, CAND1, CCNT2, CDKN1A, CHSY1, FBXW7, FNDC3B, IRS1, JUN, KLF6, LASS2, MAP1D, MDM2, MIR93, MIR375, MIR1 (human), MIR106A (includes EG:406899), MIR106B (includes EG:406900), MIR145 (includes EG:406937), MIR183 (includes EG:406959), MIR196B, MIR99A, MTPN, NPAT, NPPC, PDCD4, PFTK1, PPM1D, SERP1, SERPINB5, SLC16A2, TDG, TRIM210E−189Cancer, Reproductive System Disease, Cell Cycle4MIR376A, MIR376A1, MIR376A210E−21Genetic Disorder, Skeletal and Muscular Disorders5MIR365, MIR365-1, MIR365-210E−216EZH2, MIR101, MIR101-1, MIR101-2, MYCN10E−21Cancer, Cellular Movement, Reproductive System DiseaseThe difference in the number of networks identified by IPA is ascribable to the different database used by the software, as IPA uses the Argonaute 2 databases (http://www.ma.uni-heidelberg.de/apps/zmf/argonaute/) to analyse miRNAs and their known or predicted targets, and this database identified only 118 targets for the 50 miRNAs.Next, we performed an IPA analysis on the 1203 predicted targets of the miRNAs whose differential expression between eutopic and ectopic tissue was confirmed by real-time RT-PCR. IPA software identified 49 networks and revealed that the predicted targets were enriched for biological functions such as cellular development, cell morphology, cell-mediated immune response, gene expression, cell cycle, cell death, cancer, and developmental disorders. The network with the highest score from this analysis, shown in Figure3, includes molecules that have been implicated in endometriosis such as the TNF receptor, IL10, IL6, and FOXO1 [49–55].Figure 3
Functional analysis of the predicted targets of miR-182, miR-200a, miR-200b, miR-200c, and miR-202 identified by Pictar and Targetscan: graphical representation of one of the network (P-value = 10E−37, focus molecules = 35) identified by IPA analysis of the predicted targets of the miRNAs whose differential expression in eutopic and ectopic endometrium was validated by real-time RT-PCR. Highlighted are the genes involved in endometriosis according to IPA knowledge base.Performing the analysis uploading directly the miRNAs in IPA, thus using the Argonaute2 database, the software identified only one network (Figure4), the major biological functions of which are cell cycle, cell death, and connective tissue disorders. This network contains PIK3R1, and its expression has been demonstrated to be upregulated in endometriosis, were it can play an essential role in TNF-mediated antiapoptotic signaling [56]. Another interesting molecule present in this pathway is SIP1, a validated target of the miR-200 family, which is a factor implicated in epithelial to mesenchymal transition and tumor metastasis [57].  Thus, the observed downregulation of miR-200 family in the ectopic endometrium may have a role in the endometrial lesion development.Figure 4
Functional analysis of the predicted targets of miR-182, miR-200a, miR-200b, miR-200c, and miR-202 identified by Argonaute2: graphical representation of the network  identified by IPA analysis of the miRNAs and their predicted targets using the database generated by Argonaute2 algorithm (P-value = 10E−14, focus molecules = 4).We further investigated the function of the predicted targets of the RT-PCR-validated miRNAs by using Onto-Express and Pathway-Express (http://vortex.cs.wayne.edu/) in order to categorize the targets according to Gene Ontology (GO) and KEGG pathways, respectively [58, 59]. The predicted targets of the validated miRNAs were uploaded in Onto-Express and the list of the putative targets of the 475 miRNAs assayed was used as reference. Onto-Express calculates the mRNA targets in each GO category and compares it with the expected number of targets present in the GO category. Significant differences from the expected number of genes were calculated assuming a hypergeometric distribution, and P values were adjusted with the false discovery rate correction based on the number of GO categories tested. A corrected P value <.05 was considered statistically significant. Onto-Express analysis revealed enrichment for several biological processes known to be relevant in endometriosis, such as developmental process, cell death, cell cycle, and cell adhesion (Table 5).Table 5
Gene Ontology analysis of the predicted target genes of 50 miRNAs differentially expressed. Onto-Express analysis on predicted targets of the differentially expressed miRNAs identified enrichment for biological process categories. Thegene column indicates the number of predicted targets of the differentially expressed miRNAs upon the number of the targets of all miRNAs considered for the study. Significant differences from the number of targets in each GO category with the expected number of genes were calculated with the assumption of a hypergeometric distribution and P-values were adjusted with the false discovery rate (fdr) correction. P<.05.
RankBiological process categoryGenesCorrectedP-value1Cellular process2408/6644.0Cell motion148/330.0Cell communication908/2223.0Cellular component organization546/1383.0Cellular developmental process400/949.0Cellular metabolic process1563/4269.0Regulation of cellular process1555/3840.0Cell development183/419.0Positive regulation of cellular process371/875.0Negative regulation of cellular process394/932.0Cell cycle223/5551.0E−5Cell death235/5872.0E−5Cell proliferation237/6025.0E−5Actin-filament based process90/1976.0E−5Cell fate commitment45/839.0E−5Cell aging14/217.0E−4Vescicle-mediated transport158/3977.3E−4Cell growth51/112.00286Cell fate determination15/23.00286Cellular localization224/609.00506Gene silencing16/27.00696Cell cycle process124/323.00696Translational initiation23/48.01253Cell fate specification12/20.01728Cellular response to stimulus110/292.03290Cell adhesion173/479.040942Negative regulation of biological process421/992.0Negative regulation of metabolic process193/422.0Negative regulation to cellular process394/932.0Negative regulation of developmental process129/3091.9E−4Negative regulation of response to stimulus16/29.01705Negative regulation of growth24/53.035643Multicellular organismal process820/2037.0Multicellular organismal development675/1606.0Regulation of multicellular organismal process171/4212.0E−4System process227/606.00750Respiratory gaseous exchange11/17.016394Biological regulation1656/4148.0Regulation of molecular function211/478.0Regulation of biological process1597/3961.0Regulation of biological quality281/7321.3E−45Regulation of biological process1597/3961.0Regulation of metabolic process850/2038.0Regulation of developmental process283/657.0Regulation of cellular process1555/3840.0Positive regulation of cellular process384/933.0Negative regulation of cellular process421/992.0Regulation of multicellular organismal process171/4211.4E−4Regulation of localization110/2618.7E−4Regulation of locomotion41/95.02619Regulation of growth64/164.041996Metabolic process1631/4509.0Biosynthetic process899/2354.0Negative regulation of metabolic process193/422.0Positive regulation of metabolic process201/473.0Regulation of metabolic process1563/2038.0Cellular metabolic process1563/4269.0Primary metabolic process1551/4187.0Macromolecule metabolic process1383/3644.0Oxydation reduction52/2555.0E−5Catabolic process237/665.01317Nitrogen compound metabolic process49/193.032707Developmental process821/1967.0Multicellular organismal development675/1606.0Anatomical structure morphogenesis310/710.0Embryonic development140/304.0Anatomical structure development584/1379.0Cellular developmental process400/949.0Regulation of developmental process283/657.0Positive regulation of developmental process131/2951.0E−5Anatomical structure formation involved in Morphogenesis97/2164.0E−5Pattern specification process79/1731.6E−4Negative regulation of developmental process129/3091.6E−4Pigmentation during development9/13.01264Reproductive developmental process31/68.02708Aging17/36.040828Positive regulation of biological process384/933.0Positive regulation of metabolic process201/473.0Positive regulation of cellular process371/875.0Positive regulation of developmental process131/2951.0E−5Positive regulation of homeostatic process6/8.032039Localization715/1953.0Localization of cell148/330.0Macromolecule localization247/6381.1E−4Regulation of localization110/2617.7E−4Cellular localization224/609.00422Establishment of localization577/1657.0046310Death235/5912.0E−5Cell death235/5871.0E−511Anatomical structure formation242/6291.1E−4Anatomical structure formation involved in  Morphogenesis97/2163.0E−5Cellular component assembly165/452.0127612Response to stimulus464/12762.4E−4Response to chemical stimulus185/4655.3E−4Response to endogenous stimulus59/136.00633Negative regulation to response to stimulus16/29.01844Behavior84/215.02638Cellular response to stimulus110/292.03534Response to stress253/718.0391813Multi-organism process113/286.00251Interspecies interaction between organisms71/172.00565Female pregnancy19/39.0450414Growth96/235.00334Cell growth51/112.00298Negative regulation of growth24/53.03391Regulation of growth64/164.0391615Locomotion111/277.00422Cell motility97/2233.5E−4Regulation of locomotion41/95.0243916Establishment of localization577/1657.00458Establishment of protein localization207/5363.3E−4Establishment of localization in cell209/576.0104517Reproduction117/303.00983Reproductive process116/301.0112718Reproductive process116/301.01024Reproductive developmental process31/68.03090Female pregnancy19/39.0450419Biological adhesion173/479.03486Cell adhesion173/479.0348620Rhythmic process26/59.04158Pathway-Express analysis identified 33 pathways significant at 5% level (Table6), most of which are coherent with the current knowledge on endometriosis. For instance, the most significant pathways putatively affected by the differential expression of miRNAs are MAPK and axon guidance the latter shown in Figure 5. While MAPK pathway, which is involved in several cellular functions, such as cell proliferation, migration, and differentiation, is clearly relevant for endometriosis, axon guidance, at first may appear unrelated to this pathology. However, nerves and blood vessels are highly interconnected, both physically and in their morphogenesis. Indeed, it has been demonstrated that several molecules involved in axon guidance, such as semaphorins, plexins, and neuropilins, are also strongly implicated in angiogenesis [60], a biological process essential for endometriosis. Intriguingly, this pathway contains ROBO1, and its expression, higher in ectopic endometrium compared to eutopic tissue, positively correlates with endometriosis recurrence [61], thus suggesting that miRNAs may take part in tuning ROBO1 expression and have a role in the recurrence of the pathology.Table 6
KEGG pathways containing the predicted targets of the differentially expressed miRNAs. Pathway-Express analysis identified the KEGG molecular pathways affected by the predicted targets of the differentially expressed miRNAs.P<.05.
RankPathway nameGenes in pathwayInput genes in pathwayPathway genes on chipP-value1MAPK signaling pathway2721031973.23E−082Axon guidance129671133.23E−083Melanogenesis10248748.60E−084Pathways in cancer3301192452.27E−075Regulation of actin cytoskeleton217781582.31E−056Focal adhesion203751502.31E−057Wnt signaling pathway152631271.60E−048Glioma6530502.94E−049GnRH signaling pathway10336654.86E−0410Renal cell carcinoma6934615.92E−0411Insulin signaling pathway13849987.02E−0412Adherens junction7834627.65E−0413TGF-beta signaling pathway8738728.49E−0414Prostate cancer903668.001115ECM-receptor interaction843055.001616Phosphatidylinositol signaling system763055.001617Calcium signaling pathway18254115.001618Colorectal cancer843670.001819Long-term potentiation733158.001820Adipocytokine signaling pathway672750.003221ErbB signaling pathway873469.005622Pancreatic cancer723059.005623Gap junction963367.006324Type II diabetes mellitus451831.006925Small cell lung cancer863061.009526Thyroid cancer291423.011127Ubiquitin mediated proteolysis1384294.014528Long-term depression752449.022529Non-small cell lung cancer542039.022530Acute myeloid leukemia592245.030431Melanoma712553.032332Cardiac muscle contraction872041.040233Chronic myeloid leukemia752862.0410Figure 5
The axon guidance pathway identified by Pathway-Express analysis. Pathway-Express analysis performed on the predicted targets of the 50 differentially expressed miRNAs identified, among the most significant KEGG pathways predicted to be relevant for endometriosis, theaxon guidance pathway. In yellow are the predicted targets of the differentially expressed miRNAs.


### 3.4. Genes Differentially Expressed in Endometriosis Are Predicted Targets of the Differentially Expressed miRNAs

Finally, after the identification of the predicted targets of the differentially expressed miRNAs, we investigated whether they were in accordance with the results of two studies of gene expression in endometriosis. We first analysed the genes reported to be differentially expressed in a study on paired eutopic and ectopic samples of ovarian endometriosis [23]. This study identified 701 differentially expressed transcripts (expression ≥ 0.2; fold change ±≥ 2; P≤.05), 82 of which are predicted target genes of the 50 miRNAs, 51/492 upregulated and 31/209 downregulated. A second study on peritoneal endometriosis [24] identified 622 differentially expressed transcripts (fold change ±≥ 1.5; P≤.05), 107 of which are predicted targets of the differentially expressed miRNAs, 73/232 upregulated and 34/390 downregulated. Hypothesising that the genes differentially expressed common to both studies are likely those specific to endometriosis independently from the site of the lesion, we restricted the analysis to the differentially regulated genes in eutopic and ectopic endometrium common to the two studies that are also predicted targets of the 50 miRNAs (Table 7). IPA analysis identified 5 molecular networks, the most relevant functions of which being cancer, cell cycle, and reproductive system disease (Table 8). The overlap of networks generated by IPA is shown in Figure 6. In this graphical representation the most relevant nodes are the transcription factor SP1, tumor necrosis factor (TNF), and SRC, in remarkable agreement with the nodes of the most significant networks obtained by IPA analysis performed on the distinct datasets of differentially expressed genes in ovarian and peritoneal endometriosis (data not shown).Table 7
Genes aberrantly expressed in ovarian and peritoneal endometriosis that are predicted targets of the differentially expressed miRNAs. The miRNAs predicted to regulate the expression of the genes known to be aberrantly up- (↑) or downregulated (↓) in both ovarian and peritoneal endometriosis were identified by TARGETSCAN and PICTAR algorithms. MicroRNAs whose regulation is in accordance with the resulting expression of their predicted target genes are reported in bold.
Target genesmicroRNAs upregulatedmicroRNAs downregulatedCA3 (carbonic anhydrase III)↑hsa-miR-29b; hsa-miR-29cCAV1 (caveolin 1)↑hsa-miR-199a; hsa-miR-30e-3phsa-miR-20a; hsa-miR-106bCAV2 (caveolin 2)↑hsa-miR-29b; hsa-miR-29cDMD (dystrophin)↑hsa-miR-101; hsa-miR-30e-5phsa-miR-200b; hsa-miR-200cEPHA3 (EPH receptor A3)↑hsa-miR-29b; hsa-miR-29chsa-miR-182FZD7↑ (frizzled homolog 7)hsa-miR-145; hsa-miR-1hsa-miR-20a; hsa-miR-106bGALNT3 (UDP-N-acetyl-alpha-D-galactosamine)↓hsa-miR-30e-5pKCNMA1 (potassium large conductance calcium-activated channel, subfamily M, alpha mamber 1)↑hsa-miR-186hsa-miR-93; hsa-miR-17-5p; hsa-miR-20a; hsa-miR-106bLMO3 (LIM domain only 3)↑hsa-miR-20a; hsa-miR-93; hsa-miR-17-5p; hsa-miR-183; hsa-miR-106bNFASC (neurofascin)↑hsa-miR-150hsa-miR-200b; hsa-miR-200c; hsa-miR-182PDE4DIP (phosphodiesterase 4D interacting protein)↑hsa-miR-183PLS1 (plastin 1)↓hsa-miR-30e-5phsa-miR-17-5p; hsa-miR-20a; hsa-miR-106bPTPN3 (protein tyrosine phosphatase, non-receptor type 3)↓hsa-miR-17-5p; hsa-miR-20a; hsa-miR-106bRGS2 (regulator of G-protein signalling 2)↑hsa-miR-30e-5phsa-miR-182RGS5 (regulator of G-protein signalling 5)↑hsa-miR-186RPS6KA5 (ribosomal protein S6 kinase, 90 kDa, polypeptide 5)↓hsa-miR-148ahsa-miR-93; hsa-miR-17-5p; hsa-miR-20a; hsa-miR-106bSCAP2 (src family associated phosphoprotein 2)↑hsa-miR-182SLCO3A1 (solute carrier organic anion transporter family, member 3A1)↑hsa-miR-34ahsa-miR-182SNAP25 (synaptosomal-associated protein)↑hsa-miR-130a; hsa-miR-1hsa-miR-130b; hsa-miR-200b; hsa-miR-200cTNFSF12 (tumor necrosis factor superfamily, member 12)↑hsa-miR-28Table 8
Molecular networks constituted by the common differentially expressed transcripts in ovarian and peritoneal endometriosis predicted to be targets of the 50 miRNAs. Differentially expressed genes common to both ovarian and peritoneal endometriosis that are predicted targets of the 50 differentially expressed miRNAs were uploaded in IPA in order to identify the molecular networks and functions to which they belong.P<.01.
IDMolecules in NetworkP-valueFocus MoleculesTop Functions1CAV1, CAV2, CDKN1A, ESR1, HMGA1, LPL, MMP2, NOS3, SMARCA4, SP1, SP3, SRC, TNFSF12, TP5310E−84Cancer, Cell Cycle, Reproductive System Disease2MBD1, SLCO3A110E−21Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry3RGS2, TNF10E−21Lipid Metabolism, Small Molecule Biochemistry, Cell Signaling4DMD, DTNA, DTNB10E−21Cellular Assembly and Organization, Nervous System Development and Function, Skeletal and Muscular System Development and Function5FYB, GRB2, SKAP210E−21Cell-To-Cell Signaling and Interaction, Cell-mediated Immune Response, Cellular Growth and ProliferationFigure 6
Functional analysis of the differentially regulated genes common in ovarian and peritoneal endometriosis that are predicted targets of the 50 miRNAs: Graphical representation of the overlap of the networks identified by IPA with, highlighted are the genes involved in endometriosis according to IPA knowledge base.


## 3.1. MicroRNAs Differentially Expressed in Eutopic and Ectopic Endometrial Tissue

In the present study, we used miRNA microarray technology to identify the pattern of miRNAs in paired eutopic/ectopic endometrium from the same patients, thus avoiding the variables attributable to heterogeneous genetic background between individuals and the effects of estrogenic stimulation during different phases of the menstrual cycle. Moreover, we considered the whole endometrial and endometriotic tissues in order to preserve the contribution of all the components of the tissues, including vascular and immune system components and to avoid potential changes in gene and miRNA expression due to cell isolation and manipulation.Microarray technology has allowed a global analysis of all miRNAs differentially expressed in ectopicversus eutopic endometrium. The initial analysis of miRNA expression in ectopic endometrium compared with eutopic endometrium of three patient samples generated a list of 84 miRNAs significantly differentially expressed (P-values <.01). The 50 miRNAs for which the expression value in ectopic endometrium was at least twofold higher or lower than in eutopic endometrium are listed in Table 1.Table 1
Differentially expressed miRNAs in ectopicversus eutopic endometrium. List of differentially expressed miRNAs whose expression value in ectopic endometrium was at least twofold higher or lower than in eutopic endometrium P<.01.
NameEUECP-valuehsa-miR-136.292,090.27.00E+00hsa-miR-1007,517.7318,712.43.00E+00hsa-miR-101341.512,348.69.00E+00hsa-miR-106a3,264.741,510.101.11E−16hsa-miR-106b2,996.551,414.14.00E+00hsa-miR-12610,373.8822,435.79.00E+00hsa-miR-130a1,634.845,145.94.00E+00hsa-miR-130b673.04249.86.00E+00hsa-miR-1323,699.141,261.33.00E+00hsa-miR-1438,104.2621,764.97.00E+00hsa-miR-14510,992.3627,550.33.00E+00hsa-miR-148a2,623.736,507.58.00E+00hsa-miR-1501,621.964,503.15.00E+00hsa-miR-17-5p4,517.662,059.32.00E+00hsa-miR-1821,998.92230.69.00E+00hsa-miR-183410.8341.02.00E+00hsa-miR-18656.69246.791.21E−14hsa-miR-196b380.4514.13.00E+00hsa-miR-199a4,481.2712,618.11.00E+00hsa-miR-200a582.9533.22.00E+00hsa-miR-200b17,643.11675.98.00E+00hsa-miR-200c25,249.551,391.63.00E+00hsa-miR-20249.64471.062.27E−13hsa-miR-20a5,278.722,534.219.05E−14hsa-miR-2215,368.0510,915.55.00E+00hsa-miR-2512,878.146,328.311.06E−14hsa-miR-281,465.554,589.04.00E+00hsa-miR-299-5p202.34452.175.18E−13hsa-miR-29b248.514,963.66.00E+00hsa-miR-29c295.4010,562.63.00E+00hsa-miR-30e-3p299.191,003.481.50E−14hsa-miR-30e-5p58.94428.59.00E+00hsa-miR-34a337.65861.73.00E+00hsa-miR-365264.572,071.70.00E+00hsa-miR-368297.521,882.43.00E+00hsa-miR-3751,329.8513.62.00E+00hsa-miR-376a64.20522.49.00E+00hsa-miR-379175.21601.127.19E−13hsa-miR-41162.72215.675.92E−16hsa-miR-425-5p961.30329.99.00E+00hsa-miR-4862,824.50956.89.00E+00hsa-miR-493-5p64.60355.157.22E−12hsa-miR-5032,084.95465.94.00E+00hsa-miR-63829,531.6011,202.65.00E+00hsa-miR-6634,654.421,943.144.44E−15hsa-miR-6712,052.70955.73.00E+00hsa-miR-768-3p5,841.892,901.782.81E−06hsa-miR-768-5p5,321.542,456,43.00E+00hsa-miR-932,614.63629.20.00E+00hsa-miR-99a6,766.0218,369.57.00E+00


## 3.2. Real Time RT-PCR Analysis of miRNA Expression

In order to confirm the results obtained with miRNA microarray, the expression analyses of 5 selected miRNAs was carried out by real-time RT-PCR on specimens from other 13 patients. These 5 miRNAs, namely, hsa-miR-200a, hsa-miR-200b, hsa-miR-200c, hsa-miR-182, and hsa-miR-202, were selected because their expression resulted to be highly altered in ectopic endometrium compared with the matched eutopic tissue. We verified the differential expression of the selected miRNAs in the ectopic tissue by setting as 1 the expression of eutopic miRNAs. The results obtained by real-time RT-PCR are in accordance with those obtained from the microarray. Indeed, these miRNAs showed significant differential expression (P-values <  .05) in eutopic versus ectopic tissue: hsa-miR-200a, hsa-miR-200b, hsa-miR-200c, and hsa-miR-182 levels in ectopic endometrium were reduced up to 95% (Figures 1(a)–1(d)), while hsa-miR-202 expression in ectopic endometrium was increased up to 60 folds compared to eutopic endometrium (Figure 1(e)). The analysis of data according to the severity of the endometrioma, by means of nonparametric Wilcoxon and Mann-Whitney U tests, failed to reveal any significant differences in miRNA expression levels, although this may be ascribable to the group size. Further studies increasing the cohort will be necessary to completely address this issue.Differential expression of miRNAs in ectopicversus eutopic endometrium. The miRNAs selected for independent validation were among those with wider difference in expression between eutopic and ectopic endometrium. The differential expression of the selected miRNAs in the ectopic versus eutopic tissues was evaluated by real-time RT-PCR. The expression of each miRNA in eutopic tissue was set to 1. (a) hsa-miR-200a, (b) hsa-200b, (c) hsa-miR-200c, (d) hsa-miR-182, and (e) hsa-miR-202. P*<  .05.
(a)(b)(c)(d)(e)


## 3.3. Identification of Predicted miRNA Targets andIn Silico Functional Analysis

The predicted target mRNAs of the differentially expressed miRNAs common to two different search algorithms, TARGETSCAN (http://www.targetscan.org/) and PICTAR-VERT (http://pictar.mdc-berlin.de), were 3093. The functions of these predicted targets and the molecular pathways in which they could be involved were assessed using Ingenuity Pathways Analysis software (Ingenuity IPA 7.5). The predicted targets were uploaded in IPA, and the software identified 49 significant molecular networks to which the predicted targets of the differentially expressed miRNAs belong (Table 2). Among the biological functions reported to be statistically significant by IPA there were functions known to be involved in endometriosis such as gene expression, cellular growth and proliferation, cellular development, cellular movement, cell death, cell cycle, cancer, and  reproductive system disorders. One of the subcategories of reproductive system disorders to be more represented, with P-value (calculated by Fisher’s Exact test) of 6.1·10-18, was endometriosis with 119 molecules directly involved in this pathology (Table 3).Table 2
Molecular networks constituted by the predicted miRNA targets. IPA analysis was performed in order to identify the molecular pathways and functions to which the predicted targets of the differentially expressed miRNAs belong. The networks are generated on the basis of the published literature and ranked by theP-value calculated by Fisher’s exact Test. The biological processes in which the targets are involved are determined by IPA using the GOstat application P<.01.
IDMolecules in networkP-valueFocus moleculesTop functions1ACTR1A, ADM, APP, BICD2, CABP7, CELSR1, CPSF6, DAG1, ELAVL1, EPHA2, GCH1, GNA13, HIRA, HLX, HNRNPH1, HNRNPM, IFNG, IRF2, KHDRBS1, LARGE, MAPT, MTMR3, MTMR4, MYH9, PCSK2, PLCG1, PTGS2, RASA1, SBF1, SOCS1, SOCS2, STAT6, TNPO1, TNPO2, TRIB210E−2135Cellular Development, Skeletal and Muscular Disorders, Organismal Development2AEBP2, ATAD2, C1QTNF6, CAV1, CAV2, CBX1, CCND1, CREB1, DAB2IP, DDIT4, DNMT1, DNMT3A, DNMT3B, DUSP9, EED, ESR1, EZH2, FOLR1, HSPA13, KAT2B, LEP, MED1, MED14, NCOA2, NCOA3, NCOA4, NOTCH3, PHF1, PNRC1, PRLR, RBBP7, RBM9, SIRT1, THRA (includes EG:7067), TMOD110E−2135Gene Expression, Cellular Growth and Proliferation, Developmental Disorder3AKAP13, BCL2L11, CCNE2, CDK6, CDKN1A, CDKN1B, CTGF, CTSB, CUGBP1, DUSP1, E2F1, ESRRG, ETS1, FHL2, FLI1, FOXO1, FOXO3, FOXO4, IGFBP3, IP6K3, JAG1, KRAS, MCF2, NR3C1, PRKD3, RB1CC1, SGK1, SMAD3, SP1, SPHK2, TCF7L2 (includes EG:6934), TGFBR1, TIMP3, TOPBP1, TSPYL210E−2135Cellular Growth and Proliferation, Cellular Development, Cancer4ADAM12, BCL2, CITED2, EGLN1, FGF9, FRAP1, GATA3, GNAI2, HIF1A, HSPD1, IGF1R, IKBKB, ITGA9, JUNB, KPNA1, KPNB1, MAP2K3, MAP2K5, MAP3K7, MAP3K7IP2, PIAS3, PPM1B, PRKCE, PTEN, PTPN1, RPS6KB1, SKI, SMAD7, SNAI1, SOCS3, SP2, STAT3, UBR5, WT1, ZEB110E−2135Cellular Growth and Proliferation, Cellular Movement, Cell Cycle5ANP32A, ATP2A2, CD69, CDK5R1, COL1A1, COL1A2, CREM, DDR1, DLL4, E2F3, EGR1, FBXW7, FLT1, HDAC4, IL2, IL18BP, LPL, NDRG1, NOTCH1, PHC1, PHC2, POLA1, PPARA, RANBP2, RB1, RYBP, SHC1, SP3, SP4, TRAM2 (includes EG:9697), XPO1, YBX1, YY1, ZBTB10, ZBTB7B10E−2135Organismal Injury and Abnormalities, Cardiovascular Disease, Cellular Development6ARNT, BACH1, BCL2L12, BRCA1, CLOCK, CREBBP, CYP1B1, DDX5, EP300, EPAS1, ERBB4, EREG, GABPA, GADD45A, HBEGF, HOXA13, HOXB6, LEF1, MAB21L1, MAX, NCAM1, NFYA, NPAS2, OXTR, PIN1, PPARG, PPP2CA, PTGER4, RBBP8 (includes EG:5932), RUNX1, SDC1, SLC1A2, TGFA, TRERF1, WNT5A10E−2135Gene Expression, Cancer, Genetic Disorder7ACTB, ARID1A, ARID1B, ARID4A, ARID4B, BTG2, CLIP1, DR1, ETS2, EWSR1, GTF2B, HOXA9, PFN1, RARB, RBL1, RBL2, SAP30, SAP130, SFPQ, SIN3A, SMARCA2, SMARCA4, SMARCB1, SMARCC1, SMARCC2 (includes EG:6601), SNIP1, SUMO1, TACC2, TAF4, TAF5, TAF12, TBP, TDG, TOP1, XPO610E−2135Gene Expression, Cellular Assembly and Organization, Cellular Compromise8ARHGDIA, BTRC, CASP3, CD4, CDC42, CLTC, CTTN, CXCL12, DIABLO, ELK1, ELK3, EZR, F3, FOS, FOSB, GLI3, GSK3B, HNRNPA1, HNRNPC, IL1A, ITPR1, JUND, MAP1B, MCL1, OCRL, PAK1, PGM1, PRKCI, PRKD1, PTX3, RABEP1, RAC1, SEMA3A, SRF, STK410E−2135Cell Death, Cancer, Cellular Assembly and Organization9CD47, CSF1, CSF1R, CSK, EPHA4, FASLG, FGF1, FN1, FOXP1, GRB2, IRS2, ITGA5, ITGA6, ITGA10, ITGA11, ITGAV, ITGB1, ITGB3, JAK2, KCNA3, MAP2K1, MAP2K4, MAPK1, MET, MITF, NFAT5, PDGFB, PDGFRB, PLXNB1, RAB21, SERPINE1, TNFRSF1A, TNFSF11, TRIB1, YES110E−2135Cellular Growth and Proliferation, Cell-mediated Immune Response, Cellular Movement10ACTR3, AR, ARHGEF7, ASAP1, CRKL, DYRK1A, ESR1, GDI1, KLF2, LMOD1, LRRK1, MRAS, NCK1, PFTK1, PLS3, POMT2, TEAD3, TRIP10, WAS, WEE1, WIPF1, ZMIZ110E−919Cellular Assembly and Organization, Skeletal and Muscular System Development and Function, Cancer11AKAP12, AMOTL2, ARL6IP1, ATM, BRCA1, CDC6, CHEK2, E2F1, FKBP3, HS3ST1, LATS2, LBR, MBNL2, MTDH, PPM1D, PPP1R13B, SCN3B, SH3BP4, TP53, TRIO, VCAN10E−717Cancer, Genetic Disorder, Reproductive System Disease12ANK3, CREB5, DEDD, FRK, GPRC5A, KCNK2, KRT18, MPZL2, MYCBP2, MYO1B, NRK, RAB22A, SPAG9, ZNF21710E−713Cardiovascular Disease, Cellular Development, Cell Morphology13ADAM19, CADM1, CBFA2T3, CDC42SE1, COL6A3, COL7A1, DAAM1, ERBB2, FN1, HAS3, MFAP2, MPHOSPH9, NET1, PMEPA1, RAP1B, TGFB1, THBS1, THPO, XYLT1, ZFP3610E−716Cancer, Cellular Growth and Proliferation, Dermatological Diseases and Conditions14ATP1B3, CCND1, COL3A1, COL4A1, COL5A2, CTNNB1, HOXA1, IGF2R, KLF9, LGALS3, M6PR, MAP3K10, NANOG, NPTX1, NRF1, NRIP1, PTPRC, PTTG1, RB1, SPTBN2, TCF7L2 (includes EG:6934), THRB (includes EG:7068), TP5310E−617Organismal Development, Cancer, Cell Cycle15ALDH1A3, COLQ, DUSP10, EIF4B, GPD2, HSPE1, IL6, IL13, IL1B, MMD, NR4A3, NUAK1, PTPN12, RND3, ROBO1, SEMA3C, SLC7A1, STAC, TNF, TNFSF10, TUB10E−616Small Molecule Biochemistry, Skeletal and Muscular System Development and Function, Cell-To-Cell Signaling and Interaction16ACSL3, ASXL1, EGR3, JMJD1C, PLK2, PTP4A1, RRM2, RRM1 (includes EG:6240), RRM2B, SEL1L, SFRS3, SLC2A1, SMURF2, SON, STRN3, TNF, TP53, UBE2B, ZFP36L110E−615Nucleic Acid Metabolism, Small Molecule Biochemistry, Genetic Disorder17CCND1, CCNT1, CCNT2, CDK9, CDKN1A, DNAJB9, FBXW11, GLI1, GLI2, GNAO1, GTF2F2, HSPA5, HTATSF1, ID2, JAG2, MDFIC, MXI1, MYCN, NPM1 (includes EG:4869), POLR2A, POLR2C, RB1, RPS6KA1, RXRA, SFRS1, SUPT5H, SUPT6H, TCERG1, TGFB1, TP53, ULK110E−620Gene Expression, Cellular Development, Cell Cycle18APBB2, BECN1, CAD, CDKN1A, CFL1, E2F5, ESR1, FANCA, FANCC, FGF7, GFI1, GJA1, GORASP2, HSP90AA1, LIMK1, MAX, MYC, PCBP2, PERP, PTBP1, SPTAN1, TERT, TMSB4X, TP53, XBP110E−517Cell Cycle, Connective Tissue Development and Function, Cellular Compromise19AP3M1, BCL6, CCND1, CREBL2, ENC1, FOXA1, FTH1, HNF1A, HNMT, MTA3, MUC4, NCOR1, NCOR2, NFE2L2, NFYC, NR5A2, SNX17, SSTR1, TFR2, TFRC, TMOD210E−515Cancer, Gene Expression, Drug Metabolism20ACTB, ACTL6A, CCNT1, CD9, CTCF, DMAP1, EMD, EPC1, ESR1, HABP2, HNRNPA1, HNRNPF, HNRNPK, HSP90AA1, LEMD3, MKNK2, MORF4L1, MYC, PCBP1 (includes EG:5093), SYNE2, TBP, THOC4, TNPO1, TRRAP, U2AF1, WNT1, WNT2B, YY1, ZBTB3310E−518Gene Expression, Cancer, Reproductive System Disease21BEX2, CDH1, CDH2, CDH11, CTNNA2, CTNNB1, CTNND2, DIO2, ELAVL1, EPHB3, ERBB2, ESR1, F13A1, HNRNPD, ILF3, IRS1, JUP, KHSRP, LDB1, LMO2, NHLH2, PIK3R1, PKD1, PPP3CA, PTCH1, PTPRF, TCF7L2 (includes EG:6934), TIAL1, TP53, TSC22D1, ZNF34610E−418Cell-To-Cell Signaling and Interaction, Cancer, Cellular Growth and Proliferation22ACIN1, AP2A1, BRD2, COIL, EIF4A1, EIF4G3, ICMT, LMO7, MAP7, NME1, PA2G4, PABPC1, PABPN1, PAIP1, PAIP2, PAPOLG, PNN, RNGTT, RNPS1, SAP18, SFRS11, TALDO1, TRA2B, ZNF14310E−415RNA Post-Transcriptional Modification, Protein Synthesis, Gene Expression23ADAM9, ADAM10, BMP7, CCL2, CCL5, CDH1, COL18A1, CTNNB1, DICER1, EGF, EGFR, EPS15, ERBB2, ETV1, GRB2, HGS, IL8, L1CAM, LPAR1, LPP (includes EG:4026), MAP3K14, NKRF, RALA, RELA (includes EG:5970), SHC1, SMAD5, SPG20, SRC, TBK1, TERT, TJP1, TMEM55A, TMEM55B, TNF10E−419Cell-To-Cell Signaling and Interaction, Tissue Development, Cancer24ALOX15, BZW2, CCL3, CHST2, DHCR24, FCER2, GAS7, GATA6, IGHE, IL4, IL8, IL13, MTSS1, NHLH1, NOS2, NOTCH2, PDGFC, PHLDA1, PLXNC1, RIN2, SORT1, ST8SIA410E−414Genetic Disorder, Inflammatory Disease, Respiratory Disease25CAND1, CCND2, CDC5L, CDKN1B, CUL1, CUL2, CUL3, DNTT, FBXL3, FBXW2, GPR37, PARK2, PITX2, PLRG1, PMS1, PRCC, PRPF19, PSMA2, PSMC1, PSMC5, RAD23B, RBX1 (includes EG:9978), SFRS2, SKP1, SKP2, TCEB1, VHL10E−416Post-Translational Modification, Cancer, Immunological Disease26AMOT, B4GALT5, BTG3, CCL2, CD40, CHMP2B, CLASP1, ETS1, F3, FOS, HIVEP1, IKBKB, IL2, IL6, IL15, JAK1, JUN, MAPK1, MAPK14, MVP, NEFM, NFKB1, NFKBIA, PLG, PPP2R1B, RAB32, RELA (includes EG:5970), RFWD2 (includes EG:64326), RGS2, SQSTM1, STAT1, STAT3, TNF, TYK2, ZBTB1110E−419Hematological System Development and Function, Cell Death, Cell Cycle27CCNA2, CCNB1, CCNE1, CCNE2, CD46, CD59, CDK2, CDKN1C, E2F4, EPHB2, FBXO32, HDAC9, HIVEP2, IGFBP3, KLF4, LATS1, LTC4S, MYB (includes EG:4602), MYBL2, NDC80, NUMB, PCNA, PLAU, POLD1, RALBP1, RBL1, RBL2, RFC4, RFX1, SCD, SPARC, SUZ12, TGFB1, TGFB3, TNS310E−419Cell Cycle, Cancer, Cellular Growth and Proliferation28ABL1, ADRB2, ATP1A1, ATP1A2, ATP1B1, BCAR1, BCAR3, BCR, CBL, CRK, DOCK1, FRAP1, FYN, GATA2, GRK4, ITGA2B (includes EG:3674), ITGB3, MAPK9, MGRN1, NEU2, PIAS1, PIK3R1, PLSCR1, PRKCD, PTK2, RAPGEF1, RECK, SP3, SRC, STAT1, TIMP2, TP73, TP53INP1, TSG101, VPS2810E−419Cell Death, Cellular Movement, Cellular Growth and Proliferation29AKAP11, B2M, BHLHE40, CALD1, CEBPA, CHI3L1, COL16A1, EDN1, EDNRB, EIF1AX, EMP1, HMGA1, HMGCR, HMGCS1, IDI1, INSIG1, IPO13, KIT, KITLG, LSS, MMP2, MMP3, NAMPT, NPPB, PRKCA, PTPN6, RETN, SCARB1, SERPINB1, SERPINE1, TGFBR2, TGIF2, TNC, TNF, UBE2I10E−419Cancer, Hematological Disease, Lipid Metabolism30ALOX12B, APOE, ATF7IP, BCAT2, CAMK2A, CAMK2N1, CCND3, CDKN1B, CHAF1A, CRYBB2, DPP4, EFNA5, ESR1, GSTP1, HBE1, IL4, IL13RA1, IRF4, KCNK10, MBD1, MBD2, MBD3 (includes EG:53615), MECP2, MGMT, NR1H3, NR2F2, PIP5K3, PRLR, PTPN4, PTPRM, SETDB1, SLCO3A1, TFF2, TP53BP2, TSC110E−419Behavior, Reproductive System Development and Function, Neurological Disease31ARHGEF6, BNIP2, CASP8, CHFR, CPD, CS, ELF1, IFNB1, IL8, IL16, INS, IRF1, JAK2, LMTK2, NCF2, NFKBIA, NGFR, PGAM1, PGK1, PLAGL2, PPP1CC, PPP1R12B, PRL, STAT1, TNF, TRADD10E−315Immunological Disease, Cell Death, Hematological Disease32ACHE, AGT, APP, ATP2B1, BACE1, BIK, BMP2, BTG2, CCL20, CD40LG, CDH1, CXCL2, CYCS (includes EG:54205), EFNA1, EIF4E, EIF4EBP1, GCLC, ITM2B, JUN, LAMP3, LYN, MYO6, PDK4, PPARD, PSEN1, PTGS2, PXN, SMAD1, SMPD2, SOX9, TNF, TNFAIP2, TNFSF10, TRPV6, VCL10E−318Cell Death, Cancer, Cell-To-Cell Signaling and Interaction33AHR, ANP32B, ATM, BIRC3, BTG2, CAMK2G, CEBPE, CLU, ELAVL1, ERCC1, GDF11, H2AFX, HDAC3, HNRNPD, HNRNPU, HOXA5, ILF3, NEDD8, NUP153, RAD50, RARA, RARB, RARG, TBX3, TERF2, TERF2IP, TIA1, TIAL1, TINF2, TP53, TPR, XPO1, XRCC5, XRCC6, YAP110E−318Cell Cycle, DNA Replication, Recombination, and Repair, Cell Death34ADH5 (includes EG:128), ASH2L, ATP6V0C, C16ORF53, CHRNA5, CSNK2A1, CSNK2B, DPY30, EDA, ETV4, HCFC1, HDAC1, HIST2H4A, MIER1, MLL3, MLL4, MRC2, NCOA6, OGT (includes EG:8473), PAXIP1, PKNOX1, PLAU, PLAUR, POU2F1, RBBP5, SIN3A, SP1, SP3, SSRP1, SUB1, SUPT16H, TEAD1, TRIM63, WDR5, ZBTB7A10E−318Gene Expression, Cell Morphology, Reproductive System Development and Function35APLN, BID, CASP2, CFLAR, CXCL13, CYCS (includes EG:54205), DIABLO, EIF2S1, EIF4B, EIF4E, EIF4EBP1, EIF4G1, IL21, IL1RN, INHA, INHBA, INHBB, JAK1, LEFTY1, MCL1, NFKB2, P4HA1, PPP1R15A (includes EG:23645), PRDM1, SATB1, SERPINB2, SOCS1, SOCS3, SUV39H1, TAL1, TLR4, TNF, TNFSF10, USF1, USF210E−318Protein Synthesis, Cancer, Cell Death36CEBPB, CSF1, CSF3, EGFR, FGA, GAB1, GRB2, IL6, IL1A, IL6ST, IRS1, JAK1, KIF5B, LIFR, LMO4, LPAR2, MAP2, MED28, NF2, NFKB1, OSM, OSMR, PIK3C2B, PLG, POU2F1, POU2F2, PRL, PTGS2, PTPN11, RNASE1, RNASE2, SKAP2, STAT3, TLR9, VIP10E−318Cellular Development, Cellular Growth and Proliferation, Cancer37AOF2, BAZ1A, BAZ1B, CACNA1C, CDYL, CHRAC1, CTBP1, CTBP2, EHMT1, EHMT2, GATA4, HAND1, HAND2, HDAC2, HMG20B, KCNJ3, MEF2C, MYOCD, PDS5A, PHF21A, POLE3, RAD21, RBBP4, RCOR1, RREB1, SCN5A, SFRP1, SMARCA1, SMARCA5, SMC3, SMC1A, STAG1, STAG2, WIZ, ZEB210E−318Cell Cycle, DNA Replication, Recombination, and Repair, Gene Expression38AKAP1, API5, ARHGEF12, CFTR, COL18A1, F2, F2R, FGF2, FGFR1, IL1B, IQGAP2, MPRIP, PPP1R12A, PRKAR2B, PRKG1, PTGER3, RHOA, SH3GLB1, SH3GLB2 (includes EG:56904), SLC9A3R1, SRC, STX1A, VCP10E−313Cellular Assembly and Organization, Cell Morphology, Cancer39ACVR1, ACVR1B, ACVR2A, ANTXR1, APC, ASAP2, BCAP31, BIN1, BMP2, BMP6, BMP7, BMPR2, BMPR1A, CANX, COL18A1, CTNNB1, DCTN1, EFNB2, ERBB2, F10, ICAM1, ID1, ITGB2, MAPRE1, NOG, NRP1, PLP2, SEC23A, TGFB1, TLN1, TNFRSF2110E−316Cell Signaling, Cellular Development, Connective Tissue Development and Function40ARCN1, BRCA2, BRIP1, COPB1, COPG, CYLD, EXO1, HERC2, KPNA2, KPNB1, MAD2L2, MLH1, MMS19, MSH6, PIK3C2A, PMS1, PMS2, PSD2, PSMC1, RANBP9, REV1, REV3L, RFC2, RUFY1, SACM1L, SBF2, SSB (includes EG:6741), TMED9, UBA52, UBR5, USP510E−215DNA Replication, Recombination, and Repair, Cancer, Gastrointestinal Disease41CCNB1, CD44, EGFR, EIF3A, ERBB2, ERRFI1, GAB1, IL6ST, JARID1B, KRT7, MYBL2, MYO10, NEDD9, PARP1, PIK3CA, PIK3CD, PIK3R1, PIK3R2, RAB31, SMAD2, SOLH, SOX4, TGFB1, TGIF1, TGOLN2 (includes EG:10618), TNF10E−213Cell Cycle, Cellular Growth and Proliferation, Carbohydrate Metabolism42ADCYAP1, AMPD3 (includes EG:272), CCL3, CCL4, CCL5, CD40, CD40LG, CSF3, CXCL10, DUSP1, DUSP6, FURIN, IER2, IL3, IL17A (includes EG:3605), IL1B, ITGAM, MAP2K6, MAPK3, MAPK14, MMP9, NAMPT, NFKB2, NGF, NR4A2, NSMAF, P2RX7, PLD1, PLG, PTGFR, SERPINB2, TOB1, TRAF3, TSC22D3, VEGFA10E−216Cellular Movement, Hematological System Development and Function, Immune Cell Trafficking43ATM, ATR (includes EG:545), C10ORF119, CDC6, CDC37, CDC25A, CDC25B, CHEK1, CHEK2, CSNK1A1, E2F1, FAS, GRB10, MAP3K11, MAP3K5 (includes EG:4217), MCM2, MCM3, MCM4, MCM7, MDM4, PLK1, PPP2R3A (includes EG:5523), PPP5C, RAD17, RAF1, SNAP23, SSH2, STX4, STX6, STX16, TP53, VAMP2, VAMP3, VIM, YWHAB10E−216DNA Replication, Recombination, and Repair, Cancer, Cell Cycle44BAK1, BAX, BCL2, BCL2L1, BID, BMF, BSG, CAV1, CAV3, CDC2, CDK2, CIT, CYCS (includes EG:54205), DLG4, ECT2, GIT1, GRIN2A, HINT1, HTT, IGFBP5, KIF14, KIF23, KRAS, LRP1, MEOX2, NCL, NCSTN, NT5C3, PLK1, PRC1, PSEN1, PSEN2, RACGAP1, TP53, VDAC210E−215Cell Death, Cell Cycle, Cancer45ASCL2, ASF1A, ATXN7, CCNH, CDK7, CRIP2, CSPG4, DKK1, ENO3, ERCC2, ERCC3, ESRRA, GK, GPR64, GTF2H1, GTF2H2, HMGN1, MLL2, MNAT1, NR2C2, NT5E, PPARGC1A, RBBP5, SAFB, SMAD6, TAF1, TAF2, TAF4, TAF8, TAF9, TAF11, TAF15, TFF1, TUBB, UTX10E−215Gene Expression, DNA Replication, Recombination, and Repair, Dermatological Diseases and Conditions46ADAMTS5, BAX, BCL2, BCL2L1, BRCA1, CASP3, CCL3, CCL4, CD226, CD244, CSF2, FLNB, GP9, GP1BA, IL8, IL15, IL18, IL18R1, KLRK1, LCP2, MMP1 (includes EG:4312), MNT, MOAP1, NCR1, PDIA3, RAB9A, SELL, SOD2, TERT, TP63, VDAC1, XRCC6, YWHAE, YWHAQ (includes EG:10971), YWHAZ10E−215Cell-to-Cell Signaling and Interaction, Hematological System Development and Function, Cell Death47ABCA1, AKT1, APOA1, CCDC88A, CCL2, CCL5, COL2A1, CSH1, CUL5, FKBP1A, FLOT1, IGF1, IL8, IL13, IL1B, IL1RN, ILK, INS, LOX, MMP7, PDE4D, PDPK1, PGF, RNF4, RYR1 (includes EG:6261), SLC2A4, STK38L (includes EG:23012), TNF, TRPS110E−213Cell-mediated Immune Response, Cellular Movement, Lipid Metabolism48EIF2C1, EIF2C2, TNRC6A10E−23Infection Mechanism, Cancer, Respiratory Disease49DMD, DTNA, DTNB10E−23Cellular Assembly and Organization, Nervous System Development and Function, Skeletal and Muscular System Development and FunctionTable 3
Molecules directly involved in endometriosis and networks in which they appear. IPA analysis indicated that several networks constituted by the predicted targets of the differentially expressed miRNAs include molecules known to be involved in endometriosis.
SymbolEntrez Gene NameNetworksCytokinesCD40LGCD40 ligand38CX3CL1chemokine (C-X3-C motif) ligand 112CXCL13chemokine (C-X-C motif) ligand 1336IL2interleukin 22, 29, 37IL4interleukin 429, 32, 37IL6interleukin 6 (interferon, beta 2)29IL8interleukin 828, 31IL18interleukin 18 (interferon-gamma-inducing factor)29SPP1secreted phosphoprotein 123, 46TNFtumor necrosis factor (TNF superfamily, member 2)26, 29, 30, 33, 37, 38, 40, 41, 45EnzymesCNTN1contactin 123DNMT1DNA (cytosine-5-)-methyltransferase 15, 36DNMT3ADNA (cytosine-5-)-methyltransferase 3 alpha5DNMT3BDNA (cytosine-5-)-methyltransferase 3 beta5FN1fibronectin 17, 28GNASGNAS complex locus41GSTP1glutathione S-transferase pi 149HINT1histidine triad nucleotide binding protein 148KRASv-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog3PDE4Aphosphodiesterase 4A, cAMP-specific (phosphodiesterase E2 dunce homolog, Drosophila)44PDE4Dphosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila)41PTGS2prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)7, 28RAC1ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1)5RAP1BRAP1B, member of RAS oncogene family23REV3LREV3-like, catalytic subunit of DNA polymerase zeta (yeast)39RRM1 (includes EG:6240)ribonucleotide reductase M126SAT1spermidine/spermine N1-acetyltransferase 126XRCC6X-ray repair complementing defective repair in Chinese hamster cells 642, 48Growth FactorsANGPT2angiopoietin 27CTGFconnective tissue growth factor2, 36, 40FGF2fibroblast growth factor 2 (basic)23, 31INHBAinhibin, beta A45LEPleptin6TGFB1transforming growth factor, beta 120, 26, 33, 35, 40, 45VEGFAvascular endothelial growth factor A29, 30Ion ChannelsPKD1polycystic kidney disease 1 (autosomal dominant)45PKD2 (includes EG:5311)polycystic kidney disease 2 (autosomal dominant)45KinasesCDC2cell division cycle 2, G1 to S and G2 to M32, 36CSF1Rcolony stimulating factor 1 receptor3, 34EGFRepidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)28ERBB2v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian)27, 30, 33, 35, 38, 40, 45, 47FLT1fms-related tyrosine kinase 1 (vascular endothelial growth factor/vascular permeability factor receptor)2INSRinsulin receptor26JAK1Janus kinase 1 (a protein tyrosine kinase)37KITv-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog9MAPK4mitogen-activated protein kinase 417NTRK2neurotrophic tyrosine kinase, receptor, type 245PCK1phosphoenolpyruvate carboxykinase 1 (soluble)42PDGFRAplatelet-derived growth factor receptor, alpha polypeptide11PDGFRBplatelet-derived growth factor receptor, beta polypeptide11PIK3R2phosphoinositide-3-kinase, regulatory subunit 2 (beta)17SGK1serum/glucocorticoid regulated kinase 116STC1stanniocalcin 149WEE1WEE1 homolog (S. pombe)18Ligand-Dependent Nuclear ReceptorsAHRaryl hydrocarbon receptor44ARandrogen receptor30ESR1estrogen receptor 15, 30, 44ESR2estrogen receptor 2 (ER beta)44PPARGperoxisome proliferator-activated receptor gamma12, 29PeptidasesHPR (includes EG:3250)haptoglobin-related protein24MESTmesoderm specific transcript homolog (mouse)18MMP2matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase)11PhosphatasesDUSP1dual specificity phosphatase 13PPP3R1protein phosphatase 3 (formerly 2B), regulatory subunit B, alpha isoform24PTENphosphatase and tensin homolog19PTP4A1protein tyrosine phosphatase type IVA, member 122Transcription RegulatorsBCL6B-cell CLL/lymphoma 616BRCA1breast cancer 1, early onset5, 30, 42CITED2Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 211CREB1cAMP responsive element binding protein 16EGR1early growth response 16, 26, 35EMX2empty spiracles homeobox 215FOSv-fos FBJ murine osteosarcoma viral oncogene homolog6, 35, 49FOXO1forkhead box O13GATA3GATA binding protein 32HIF1Ahypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor)10, 31, 44, 47ID1inhibitor of DNA binding 1, dominant negative helix-loop-helix protein47, 50JUNjun oncogene49JUNBjun B proto-oncogene11, 47NRIP1nuclear receptor interacting protein 136RELv-rel reticuloendotheliosis viral oncogene homolog (avian)44SMAD6SMAD family member 614SMAD7SMAD family member 711SP2Sp2 transcription factor31TP53tumor protein p5322, 27, 32, 34, 36, 37, 40, 41, 45WT1Wilms tumor 17ZFP36zinc finger protein 36, C3H type, homolog (mouse)20Transmembrane ReceptorsIL2RGinterleukin 2 receptor, gamma (severe combined immunodeficiency)37ITGB1integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 includes MDF2, MSK12)11, 28, 30ITGB3integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)7ITGB4integrin, beta 430TransportersAPOEapolipoprotein E36ATP1B1ATPase, Na+/K+ transporting, beta 1 polypeptide41ATP2B2ATPase, Ca++ transporting, plasma membrane 221SLC6A6solute carrier family 6 (neurotransmitter transporter, taurine), member 61OthersACTBactin, beta9, 49ANK3ankyrin 3, node of Ranvier (ankyrin G)24BCL2B-cell CLL/lymphoma 23, 48BIRC5baculoviral IAP repeat-containing 536BSGbasigin (Ok blood group)41CAV2caveolin 25CCNA2cyclin A243COL18A1collagen, type XVIII, alpha 147DCNdecorin28EPS15epidermal growth factor receptor pathway substrate 1528ERRFI1ERBB receptor feedback inhibitor 145EZRezrin18FBN1fibrillin 136IRS2insulin receptor substrate 26, 37ITGA6integrin, alpha 630LRP5low density lipoprotein receptor-related protein 56MARCKS (includes EG:4082)myristoylated alanine-rich protein kinase C substrate47SDC2syndecan 28TAL1T-cell acute lymphocytic leukemia 118THBS2thrombospondin 211TIMP2TIMP metallopeptidase inhibitor 211TMSB10thymosin beta 1019TRAF2TNF receptor-associated factor 226VIMvimentin19, 36An exemplificative network identified by IPA enriched for miRNA targets involved in endometriosis is  shown in Figure2. This network, converging on estrogen receptor 1 (ESR1), includes the DNA methyltransferases DNMT3A and DNMT3B that are validated targets of hsa-miR-29b and hsa-miR-29c, and of hsa-miR-29b, hsa-miR-29c, and hsa-miR-148a, respectively [45, 46]. DNA methylation is an epigenetic modification that is involved in gene silencing, chromatin remodeling, and genome stability [47]. It has been demonstrated that DNMT1, DNMT3A, and DNMT3B are disregulated in endometriosis [48], and it has been suggested that aberrant methylation of HOXA10 and of the progesterone receptor PR-B may be responsible of the disregulation of their expression in endometriosis. Thus, this network strongly suggests a possible involvement of miRNAs in these mechanisms.Figure 2
Functional analysis of all the predicted targets of the differentially expressed miRNAs. Graphical representation of network #2 obtained by IPA analysis. Genes are represented as nodes, and the biological relationship between two nodes is represented as a line. Every line is supported by at least one reference in literature. Highlighted, the genes involved in endometriosis according to IPA knowledge base.To further analyze the possible role of these differentially expressed miRNAs in endometriosis, we performed a different analysis uploading the miRNAs directly in IPA. In this way, the software identified 6 networks, 3 of which are highly significant with known biological functions including genetic disorders, connective tissue disorders, skeletal and muscular disorders, cancer, and reproductive system disorders (Table4).Table 4
Molecular networks constituted by the predicted miRNA targets. The list of differentially expressed miRNAs was directly uploaded in IPA and an analysis was performed in order to identify the molecular pathways and functions to which the predicted targets of the differentially expressed miRNAs belong. The database used by IPA to analyze miRNAs and their targets is Argonaute2  (http://www.ma.uni-heidelberg.de/apps/zmf/argonaute/). P<.01.
IDMolecules in NetworkP-valueFocus MoleculesTop Functions1AKAP3, ATP2A2, C11ORF87, CNKSR2,CREB1, CUGBP2, EIF4E3, ELK1, FLRT2, HOXB2, HOXD12, IFNG, KLHDC10, KPNB1, MIR25, MIR150, MIR186, MIR221, MIR299, MIR143 (includes EG:406935), MIR182 (includes EG:406958), MIR200A, MIR200B, MIR200C, MIR34A, MYST4, OTOF, PAQR3, PER1, RPGRIP1L, SNRPA, SRCAP, UBFD1, USP6NL, WDR4410E−2411Genetic Disorder, Skeletal and Muscular Disorders, Connective Tissue Disorders2ATP1B1, C4ORF16, CALU, DHX15, DIP2C, DNMT3A, DNMT3B, EVX2, FAM108C1, FBXL11, HOXA5, HOXA10, INO80, JPH3, KLHL18, MACF1, MAP2K6, MIR126, MIR100 (includes EG:406892), MIR130A (includes EG:406919), MIR130B (includes EG:406920), MIR132 (includes EG:406921), MIR148A (includes EG:406940), MIR20A, MIR29B, MIR29B1, MIR29B2, MIR29C, MPPED2 (includes EG:744), NUFIP2, SMARCE1, SOX6, ZFP36L2, ZNF238, ZNF318 (includes EG:24149)10E–199Genetic Disorder, Skeletal and Muscular Disorders, Infection Mechanism3ADIPOR2,AR, ARF4, CAND1, CCNT2, CDKN1A, CHSY1, FBXW7, FNDC3B, IRS1, JUN, KLF6, LASS2, MAP1D, MDM2, MIR93, MIR375, MIR1 (human), MIR106A (includes EG:406899), MIR106B (includes EG:406900), MIR145 (includes EG:406937), MIR183 (includes EG:406959), MIR196B, MIR99A, MTPN, NPAT, NPPC, PDCD4, PFTK1, PPM1D, SERP1, SERPINB5, SLC16A2, TDG, TRIM210E−189Cancer, Reproductive System Disease, Cell Cycle4MIR376A, MIR376A1, MIR376A210E−21Genetic Disorder, Skeletal and Muscular Disorders5MIR365, MIR365-1, MIR365-210E−216EZH2, MIR101, MIR101-1, MIR101-2, MYCN10E−21Cancer, Cellular Movement, Reproductive System DiseaseThe difference in the number of networks identified by IPA is ascribable to the different database used by the software, as IPA uses the Argonaute 2 databases (http://www.ma.uni-heidelberg.de/apps/zmf/argonaute/) to analyse miRNAs and their known or predicted targets, and this database identified only 118 targets for the 50 miRNAs.Next, we performed an IPA analysis on the 1203 predicted targets of the miRNAs whose differential expression between eutopic and ectopic tissue was confirmed by real-time RT-PCR. IPA software identified 49 networks and revealed that the predicted targets were enriched for biological functions such as cellular development, cell morphology, cell-mediated immune response, gene expression, cell cycle, cell death, cancer, and developmental disorders. The network with the highest score from this analysis, shown in Figure3, includes molecules that have been implicated in endometriosis such as the TNF receptor, IL10, IL6, and FOXO1 [49–55].Figure 3
Functional analysis of the predicted targets of miR-182, miR-200a, miR-200b, miR-200c, and miR-202 identified by Pictar and Targetscan: graphical representation of one of the network (P-value = 10E−37, focus molecules = 35) identified by IPA analysis of the predicted targets of the miRNAs whose differential expression in eutopic and ectopic endometrium was validated by real-time RT-PCR. Highlighted are the genes involved in endometriosis according to IPA knowledge base.Performing the analysis uploading directly the miRNAs in IPA, thus using the Argonaute2 database, the software identified only one network (Figure4), the major biological functions of which are cell cycle, cell death, and connective tissue disorders. This network contains PIK3R1, and its expression has been demonstrated to be upregulated in endometriosis, were it can play an essential role in TNF-mediated antiapoptotic signaling [56]. Another interesting molecule present in this pathway is SIP1, a validated target of the miR-200 family, which is a factor implicated in epithelial to mesenchymal transition and tumor metastasis [57].  Thus, the observed downregulation of miR-200 family in the ectopic endometrium may have a role in the endometrial lesion development.Figure 4
Functional analysis of the predicted targets of miR-182, miR-200a, miR-200b, miR-200c, and miR-202 identified by Argonaute2: graphical representation of the network  identified by IPA analysis of the miRNAs and their predicted targets using the database generated by Argonaute2 algorithm (P-value = 10E−14, focus molecules = 4).We further investigated the function of the predicted targets of the RT-PCR-validated miRNAs by using Onto-Express and Pathway-Express (http://vortex.cs.wayne.edu/) in order to categorize the targets according to Gene Ontology (GO) and KEGG pathways, respectively [58, 59]. The predicted targets of the validated miRNAs were uploaded in Onto-Express and the list of the putative targets of the 475 miRNAs assayed was used as reference. Onto-Express calculates the mRNA targets in each GO category and compares it with the expected number of targets present in the GO category. Significant differences from the expected number of genes were calculated assuming a hypergeometric distribution, and P values were adjusted with the false discovery rate correction based on the number of GO categories tested. A corrected P value <.05 was considered statistically significant. Onto-Express analysis revealed enrichment for several biological processes known to be relevant in endometriosis, such as developmental process, cell death, cell cycle, and cell adhesion (Table 5).Table 5
Gene Ontology analysis of the predicted target genes of 50 miRNAs differentially expressed. Onto-Express analysis on predicted targets of the differentially expressed miRNAs identified enrichment for biological process categories. Thegene column indicates the number of predicted targets of the differentially expressed miRNAs upon the number of the targets of all miRNAs considered for the study. Significant differences from the number of targets in each GO category with the expected number of genes were calculated with the assumption of a hypergeometric distribution and P-values were adjusted with the false discovery rate (fdr) correction. P<.05.
RankBiological process categoryGenesCorrectedP-value1Cellular process2408/6644.0Cell motion148/330.0Cell communication908/2223.0Cellular component organization546/1383.0Cellular developmental process400/949.0Cellular metabolic process1563/4269.0Regulation of cellular process1555/3840.0Cell development183/419.0Positive regulation of cellular process371/875.0Negative regulation of cellular process394/932.0Cell cycle223/5551.0E−5Cell death235/5872.0E−5Cell proliferation237/6025.0E−5Actin-filament based process90/1976.0E−5Cell fate commitment45/839.0E−5Cell aging14/217.0E−4Vescicle-mediated transport158/3977.3E−4Cell growth51/112.00286Cell fate determination15/23.00286Cellular localization224/609.00506Gene silencing16/27.00696Cell cycle process124/323.00696Translational initiation23/48.01253Cell fate specification12/20.01728Cellular response to stimulus110/292.03290Cell adhesion173/479.040942Negative regulation of biological process421/992.0Negative regulation of metabolic process193/422.0Negative regulation to cellular process394/932.0Negative regulation of developmental process129/3091.9E−4Negative regulation of response to stimulus16/29.01705Negative regulation of growth24/53.035643Multicellular organismal process820/2037.0Multicellular organismal development675/1606.0Regulation of multicellular organismal process171/4212.0E−4System process227/606.00750Respiratory gaseous exchange11/17.016394Biological regulation1656/4148.0Regulation of molecular function211/478.0Regulation of biological process1597/3961.0Regulation of biological quality281/7321.3E−45Regulation of biological process1597/3961.0Regulation of metabolic process850/2038.0Regulation of developmental process283/657.0Regulation of cellular process1555/3840.0Positive regulation of cellular process384/933.0Negative regulation of cellular process421/992.0Regulation of multicellular organismal process171/4211.4E−4Regulation of localization110/2618.7E−4Regulation of locomotion41/95.02619Regulation of growth64/164.041996Metabolic process1631/4509.0Biosynthetic process899/2354.0Negative regulation of metabolic process193/422.0Positive regulation of metabolic process201/473.0Regulation of metabolic process1563/2038.0Cellular metabolic process1563/4269.0Primary metabolic process1551/4187.0Macromolecule metabolic process1383/3644.0Oxydation reduction52/2555.0E−5Catabolic process237/665.01317Nitrogen compound metabolic process49/193.032707Developmental process821/1967.0Multicellular organismal development675/1606.0Anatomical structure morphogenesis310/710.0Embryonic development140/304.0Anatomical structure development584/1379.0Cellular developmental process400/949.0Regulation of developmental process283/657.0Positive regulation of developmental process131/2951.0E−5Anatomical structure formation involved in Morphogenesis97/2164.0E−5Pattern specification process79/1731.6E−4Negative regulation of developmental process129/3091.6E−4Pigmentation during development9/13.01264Reproductive developmental process31/68.02708Aging17/36.040828Positive regulation of biological process384/933.0Positive regulation of metabolic process201/473.0Positive regulation of cellular process371/875.0Positive regulation of developmental process131/2951.0E−5Positive regulation of homeostatic process6/8.032039Localization715/1953.0Localization of cell148/330.0Macromolecule localization247/6381.1E−4Regulation of localization110/2617.7E−4Cellular localization224/609.00422Establishment of localization577/1657.0046310Death235/5912.0E−5Cell death235/5871.0E−511Anatomical structure formation242/6291.1E−4Anatomical structure formation involved in  Morphogenesis97/2163.0E−5Cellular component assembly165/452.0127612Response to stimulus464/12762.4E−4Response to chemical stimulus185/4655.3E−4Response to endogenous stimulus59/136.00633Negative regulation to response to stimulus16/29.01844Behavior84/215.02638Cellular response to stimulus110/292.03534Response to stress253/718.0391813Multi-organism process113/286.00251Interspecies interaction between organisms71/172.00565Female pregnancy19/39.0450414Growth96/235.00334Cell growth51/112.00298Negative regulation of growth24/53.03391Regulation of growth64/164.0391615Locomotion111/277.00422Cell motility97/2233.5E−4Regulation of locomotion41/95.0243916Establishment of localization577/1657.00458Establishment of protein localization207/5363.3E−4Establishment of localization in cell209/576.0104517Reproduction117/303.00983Reproductive process116/301.0112718Reproductive process116/301.01024Reproductive developmental process31/68.03090Female pregnancy19/39.0450419Biological adhesion173/479.03486Cell adhesion173/479.0348620Rhythmic process26/59.04158Pathway-Express analysis identified 33 pathways significant at 5% level (Table6), most of which are coherent with the current knowledge on endometriosis. For instance, the most significant pathways putatively affected by the differential expression of miRNAs are MAPK and axon guidance the latter shown in Figure 5. While MAPK pathway, which is involved in several cellular functions, such as cell proliferation, migration, and differentiation, is clearly relevant for endometriosis, axon guidance, at first may appear unrelated to this pathology. However, nerves and blood vessels are highly interconnected, both physically and in their morphogenesis. Indeed, it has been demonstrated that several molecules involved in axon guidance, such as semaphorins, plexins, and neuropilins, are also strongly implicated in angiogenesis [60], a biological process essential for endometriosis. Intriguingly, this pathway contains ROBO1, and its expression, higher in ectopic endometrium compared to eutopic tissue, positively correlates with endometriosis recurrence [61], thus suggesting that miRNAs may take part in tuning ROBO1 expression and have a role in the recurrence of the pathology.Table 6
KEGG pathways containing the predicted targets of the differentially expressed miRNAs. Pathway-Express analysis identified the KEGG molecular pathways affected by the predicted targets of the differentially expressed miRNAs.P<.05.
RankPathway nameGenes in pathwayInput genes in pathwayPathway genes on chipP-value1MAPK signaling pathway2721031973.23E−082Axon guidance129671133.23E−083Melanogenesis10248748.60E−084Pathways in cancer3301192452.27E−075Regulation of actin cytoskeleton217781582.31E−056Focal adhesion203751502.31E−057Wnt signaling pathway152631271.60E−048Glioma6530502.94E−049GnRH signaling pathway10336654.86E−0410Renal cell carcinoma6934615.92E−0411Insulin signaling pathway13849987.02E−0412Adherens junction7834627.65E−0413TGF-beta signaling pathway8738728.49E−0414Prostate cancer903668.001115ECM-receptor interaction843055.001616Phosphatidylinositol signaling system763055.001617Calcium signaling pathway18254115.001618Colorectal cancer843670.001819Long-term potentiation733158.001820Adipocytokine signaling pathway672750.003221ErbB signaling pathway873469.005622Pancreatic cancer723059.005623Gap junction963367.006324Type II diabetes mellitus451831.006925Small cell lung cancer863061.009526Thyroid cancer291423.011127Ubiquitin mediated proteolysis1384294.014528Long-term depression752449.022529Non-small cell lung cancer542039.022530Acute myeloid leukemia592245.030431Melanoma712553.032332Cardiac muscle contraction872041.040233Chronic myeloid leukemia752862.0410Figure 5
The axon guidance pathway identified by Pathway-Express analysis. Pathway-Express analysis performed on the predicted targets of the 50 differentially expressed miRNAs identified, among the most significant KEGG pathways predicted to be relevant for endometriosis, theaxon guidance pathway. In yellow are the predicted targets of the differentially expressed miRNAs.


## 3.4. Genes Differentially Expressed in Endometriosis Are Predicted Targets of the Differentially Expressed miRNAs

Finally, after the identification of the predicted targets of the differentially expressed miRNAs, we investigated whether they were in accordance with the results of two studies of gene expression in endometriosis. We first analysed the genes reported to be differentially expressed in a study on paired eutopic and ectopic samples of ovarian endometriosis [23]. This study identified 701 differentially expressed transcripts (expression ≥ 0.2; fold change ±≥ 2; P≤.05), 82 of which are predicted target genes of the 50 miRNAs, 51/492 upregulated and 31/209 downregulated. A second study on peritoneal endometriosis [24] identified 622 differentially expressed transcripts (fold change ±≥ 1.5; P≤.05), 107 of which are predicted targets of the differentially expressed miRNAs, 73/232 upregulated and 34/390 downregulated. Hypothesising that the genes differentially expressed common to both studies are likely those specific to endometriosis independently from the site of the lesion, we restricted the analysis to the differentially regulated genes in eutopic and ectopic endometrium common to the two studies that are also predicted targets of the 50 miRNAs (Table 7). IPA analysis identified 5 molecular networks, the most relevant functions of which being cancer, cell cycle, and reproductive system disease (Table 8). The overlap of networks generated by IPA is shown in Figure 6. In this graphical representation the most relevant nodes are the transcription factor SP1, tumor necrosis factor (TNF), and SRC, in remarkable agreement with the nodes of the most significant networks obtained by IPA analysis performed on the distinct datasets of differentially expressed genes in ovarian and peritoneal endometriosis (data not shown).Table 7
Genes aberrantly expressed in ovarian and peritoneal endometriosis that are predicted targets of the differentially expressed miRNAs. The miRNAs predicted to regulate the expression of the genes known to be aberrantly up- (↑) or downregulated (↓) in both ovarian and peritoneal endometriosis were identified by TARGETSCAN and PICTAR algorithms. MicroRNAs whose regulation is in accordance with the resulting expression of their predicted target genes are reported in bold.
Target genesmicroRNAs upregulatedmicroRNAs downregulatedCA3 (carbonic anhydrase III)↑hsa-miR-29b; hsa-miR-29cCAV1 (caveolin 1)↑hsa-miR-199a; hsa-miR-30e-3phsa-miR-20a; hsa-miR-106bCAV2 (caveolin 2)↑hsa-miR-29b; hsa-miR-29cDMD (dystrophin)↑hsa-miR-101; hsa-miR-30e-5phsa-miR-200b; hsa-miR-200cEPHA3 (EPH receptor A3)↑hsa-miR-29b; hsa-miR-29chsa-miR-182FZD7↑ (frizzled homolog 7)hsa-miR-145; hsa-miR-1hsa-miR-20a; hsa-miR-106bGALNT3 (UDP-N-acetyl-alpha-D-galactosamine)↓hsa-miR-30e-5pKCNMA1 (potassium large conductance calcium-activated channel, subfamily M, alpha mamber 1)↑hsa-miR-186hsa-miR-93; hsa-miR-17-5p; hsa-miR-20a; hsa-miR-106bLMO3 (LIM domain only 3)↑hsa-miR-20a; hsa-miR-93; hsa-miR-17-5p; hsa-miR-183; hsa-miR-106bNFASC (neurofascin)↑hsa-miR-150hsa-miR-200b; hsa-miR-200c; hsa-miR-182PDE4DIP (phosphodiesterase 4D interacting protein)↑hsa-miR-183PLS1 (plastin 1)↓hsa-miR-30e-5phsa-miR-17-5p; hsa-miR-20a; hsa-miR-106bPTPN3 (protein tyrosine phosphatase, non-receptor type 3)↓hsa-miR-17-5p; hsa-miR-20a; hsa-miR-106bRGS2 (regulator of G-protein signalling 2)↑hsa-miR-30e-5phsa-miR-182RGS5 (regulator of G-protein signalling 5)↑hsa-miR-186RPS6KA5 (ribosomal protein S6 kinase, 90 kDa, polypeptide 5)↓hsa-miR-148ahsa-miR-93; hsa-miR-17-5p; hsa-miR-20a; hsa-miR-106bSCAP2 (src family associated phosphoprotein 2)↑hsa-miR-182SLCO3A1 (solute carrier organic anion transporter family, member 3A1)↑hsa-miR-34ahsa-miR-182SNAP25 (synaptosomal-associated protein)↑hsa-miR-130a; hsa-miR-1hsa-miR-130b; hsa-miR-200b; hsa-miR-200cTNFSF12 (tumor necrosis factor superfamily, member 12)↑hsa-miR-28Table 8
Molecular networks constituted by the common differentially expressed transcripts in ovarian and peritoneal endometriosis predicted to be targets of the 50 miRNAs. Differentially expressed genes common to both ovarian and peritoneal endometriosis that are predicted targets of the 50 differentially expressed miRNAs were uploaded in IPA in order to identify the molecular networks and functions to which they belong.P<.01.
IDMolecules in NetworkP-valueFocus MoleculesTop Functions1CAV1, CAV2, CDKN1A, ESR1, HMGA1, LPL, MMP2, NOS3, SMARCA4, SP1, SP3, SRC, TNFSF12, TP5310E−84Cancer, Cell Cycle, Reproductive System Disease2MBD1, SLCO3A110E−21Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry3RGS2, TNF10E−21Lipid Metabolism, Small Molecule Biochemistry, Cell Signaling4DMD, DTNA, DTNB10E−21Cellular Assembly and Organization, Nervous System Development and Function, Skeletal and Muscular System Development and Function5FYB, GRB2, SKAP210E−21Cell-To-Cell Signaling and Interaction, Cell-mediated Immune Response, Cellular Growth and ProliferationFigure 6
Functional analysis of the differentially regulated genes common in ovarian and peritoneal endometriosis that are predicted targets of the 50 miRNAs: Graphical representation of the overlap of the networks identified by IPA with, highlighted are the genes involved in endometriosis according to IPA knowledge base.


## 4. Conclusions

MicroRNAs are predicted to regulate a large fraction of protein-coding genes, as computational analysis reveals that an average miRNA could have as many as 100 or more target genes. On the other hand, a single gene may have target sites for several distinct miRNAs, allowing a fine tuning of gene expression by miRNAs.In the present study, we used miRNA microarray technology to identify the miRNAs differentially expressed in paired eutopic/ectopic endometrium from the same patients and bioinformatics tools to identify their predicted targets as well as the molecular networks and the biological functions they may affect.Comparing miRNA expression profiles among the different subjects, we identified 50 miRNAs differentially expressed in ectopicversus eutopic samples. Several of these miRNAs were also reported to be differentially expressed in two recent studies [62, 63], although with a modulation occasionally discordant from our results. This, joint to a notable accordance between their predicted targets and the genes reported to be differentially expressed in two studies of gene expression [23, 24], consolidates the hypothesis of a possible role of miRNAs in the pathogenesis of endometriosis.The miRNAs-predicted targets were identified by the intersection of the results from two different search algorithms, and the biological functions the differentially expressed miRNA may affect were identified by Onto-Express and IPA software. Functional analysis, performed using IPA software, was carried out uploading either the predicted targets or the differentially expressed miRNAs, thus using different databases for miRNA targets. As expected, the different algorithms used to predict miRNA targets led to the identification of different molecular networks. Still, in both cases, the identified networks contained several transcripts known to be implicated in endometriosis and with their main biological functions linked to the disease. Since the targets of miRNAs are just predictions based on mathematical algorithms, the choice  of the algorithm may radically modify on the whole the list of the predicted target genes and that of the molecular networks they belong to. For this reason, the validation of miRNA targets in vitro, in a cellular system, is essential to evaluate the contribution of each miRNA to the overall modulation of gene expression.



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*Source: 369549-2010-03-10.xml*