BioMed Research International

BioMed Research International / 2010 / Article

Research Article | Open Access

Volume 2010 |Article ID 369549 | 29 pages |

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

Academic Editor: Sorin Draghici
Received12 Mar 2009
Revised10 Aug 2009
Accepted19 Dec 2009
Published10 Mar 2010


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 identified in 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.

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 [210], 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 [1218]. 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 [1924]. 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 in Caenorhabditis 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 [2629]. MiRNA expression is tissue- and cell-specific [3033]. 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 [3438].

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 identified in 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 ( = 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). Ten g 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 -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 Paraflo 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  L 6 SSPE buffer (0.90 M NaCl, 60 mM Na2HPO4, 6 mM EDTA, pH 6.8) containing 25% formamide at . 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 -values of the -test were calculated; differentially detected signals were those with less than  .01 -values. Multiple sample analysis involved normalization, data adjustment, -test, and clustering. Normalization was carried out using a cyclic LOWESS. Data adjustment included data filtering, Log2 transformation, and normalization. The -test was performed between “control” and “test” sample groups [43]. -values were calculated for each miRNA, and -values were computed from the theoretical -distribution. miRNAs with -values 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 (

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 tests. Statistical significance was assumed for -values . 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 ectopic versus 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 ( -values ). 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.

NameEUEC 𝑃 -value


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 ( -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 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.

3.3. Identification of Predicted miRNA Targets and In Silico Functional Analysis

The predicted target mRNAs of the differentially expressed miRNAs common to two different search algorithms, TARGETSCAN ( and PICTAR-VERT (, 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 -value (calculated by Fisher’s Exact test) of , was endometriosis with 119 molecules directly involved in this pathology (Table 3).

IDMolecules in network 𝑃 -valueFocus moleculesTop functions

1ACTR1A, 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 Development

2AEBP2, 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 Disorder

3AKAP13, 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, Cancer

4ADAM12, 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 Cycle

5ANP32A, 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 Development
6ARNT, 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 Disorder

7ACTB, 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 Compromise

8ARHGDIA, 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 Organization

9CD47, 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 Movement

10ACTR3, 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, Cancer

11AKAP12, 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 Disease

12ANK3, CREB5, DEDD, FRK, GPRC5A, KCNK2, KRT18, MPZL2, MYCBP2, MYO1B, NRK, RAB22A, SPAG9, ZNF21710E−713Cardiovascular Disease, Cellular Development, Cell Morphology
13ADAM19, 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 Conditions

14ATP1B3, 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 Cycle

15ALDH1A3, 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 Interaction

16ACSL3, 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 Disorder

17CCND1, 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 Cycle

18APBB2, 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 Compromise

19AP3M1, BCL6, CCND1, CREBL2, ENC1, FOXA1, FTH1, HNF1A, HNMT, MTA3, MUC4, NCOR1, NCOR2, NFE2L2, NFYC, NR5A2, SNX17, SSTR1, TFR2, TFRC, TMOD210E−515Cancer, Gene Expression, Drug Metabolism

20ACTB, 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 Disease
21BEX2, 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 Proliferation

22ACIN1, 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 Expression

23ADAM9, 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, Cancer

24ALOX15, 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 Disease

25CAND1, 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 Disease

26AMOT, 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 Cycle

27CCNA2, 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 Proliferation
28ABL1, 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 Proliferation


30ALOX12B, 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 Disease

31ARHGEF6, 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 Disease

32ACHE, 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 Interaction

33AHR, 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 Death
34ADH5 (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 Function

35APLN, 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 Death

36CEBPB, 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, Cancer

37AOF2, 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 Expression

38AKAP1, 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, Cancer

39ACVR1, ACVR1B, ACVR2A, ANTXR1, APC, ASAP2, BCAP31, BIN1, BMP2, BMP6, BMP7, BMPR2, BMPR1A, CANX, COL18A1, CTNNB1, DCTN1, EFNB2, ERBB2, F10, ICAM1, ID1, ITGB2, MAPRE 1 , NOG, NRP1, PLP2, SEC23A, TGFB1, TLN1, TNFRSF2110E−316Cell Signaling, Cellular Development, Connective Tissue Development and Function
40ARCN1, 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 Disease

41CCNB1, 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 Metabolism

42ADCYAP1, 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 Trafficking

43ATM, 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 Cycle

44BAK1, 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, Cancer

45ASCL2, 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 Conditions
46ADAMTS5, 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 Death

47ABCA1, 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 Metabolism

48EIF2C1, EIF2C2, TNRC6A10E−23Infection Mechanism, Cancer, Respiratory Disease

49DMD, DTNA, DTNB10E−23Cellular Assembly and Organization, Nervous System Development and Function, Skeletal and Muscular System Development and Function

SymbolEntrez Gene NameNetworks

CytokinesCD40LG CD40 ligand38
CX3CL1 chemokine (C-X3-C motif) ligand 112
CXCL13 chemokine (C-X-C motif) ligand 1336
IL2 interleukin 22, 29, 37
IL4 interleukin 429, 32, 37
IL6 interleukin 6 (interferon, beta 2)29
IL8 interleukin 828, 31
IL18 interleukin 18 (interferon-gamma-inducing factor)29
SPP1 secreted phosphoprotein 123, 46
TNF tumor necrosis factor (TNF superfamily, member 2)26, 29, 30, 33, 37, 38, 40, 41, 45

EnzymesCNTN1 contactin 123
DNMT1 DNA (cytosine-5-)-methyltransferase 15, 36
DNMT3A DNA (cytosine-5-)-methyltransferase 3 alpha5
DNMT3B DNA (cytosine-5-)-methyltransferase 3 beta5
FN1 fibronectin 17, 28
GNAS GNAS complex locus41
GSTP1 glutathione S-transferase pi 149
HINT1 histidine triad nucleotide binding protein 148
KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog3
PDE4A phosphodiesterase 4A, cAMP-specific (phosphodiesterase E2 dunce homolog, Drosophila)44
PDE4D phosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila)41
PTGS2 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)7, 28
RAC1 ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1)5
RAP1B RAP1B, member of RAS oncogene family23
REV3L REV3-like, catalytic subunit of DNA polymerase zeta (yeast)39
RRM1 (includes EG:6240) ribonucleotide reductase M126
SAT1 spermidine/spermine N1-acetyltransferase 126
XRCC6 X-ray repair complementing defective repair in Chinese hamster cells 642, 48
Growth FactorsANGPT2 angiopoietin 27
CTGF connective tissue growth factor2, 36, 40
FGF2 fibroblast growth factor 2 (basic)23, 31
INHBA inhibin, beta A45
LEP leptin6
TGFB1 transforming growth factor, beta 120, 26, 33, 35, 40, 45
VEGFA vascular endothelial growth factor A29, 30

Ion ChannelsPKD1 polycystic kidney disease 1 (autosomal dominant)45
PKD2 (includes EG:5311) polycystic kidney disease 2 (autosomal dominant)45

KinasesCDC2 cell division cycle 2, G1 to S and G2 to M32, 36
CSF1R colony stimulating factor 1 receptor3, 34
EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)28
ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian)27, 30, 33, 35, 38, 40, 45, 47
FLT1 fms-related tyrosine kinase 1 (vascular endothelial growth factor/vascular permeability factor receptor)2
INSR insulin receptor26
JAK1 Janus kinase 1 (a protein tyrosine kinase)37
KIT v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog9
MAPK4 mitogen-activated protein kinase 417
NTRK2 neurotrophic tyrosine kinase, receptor, type 245
PCK1 phosphoenolpyruvate carboxykinase 1 (soluble)42
PDGFRA platelet-derived growth factor receptor, alpha polypeptide11
PDGFRB platelet-derived growth factor receptor, beta polypeptide11
PIK3R2 phosphoinositide-3-kinase, regulatory subunit 2 (beta)17
SGK1 serum/glucocorticoid regulated kinase 116
STC1 stanniocalcin 149
WEE1 WEE1 homolog (S. pombe)18

Ligand-Dependent Nuclear ReceptorsAHR aryl hydrocarbon receptor44
AR androgen receptor30
ESR1 estrogen receptor 15, 30, 44
ESR2 estrogen receptor 2 (ER beta)44
PPARG peroxisome proliferator-activated receptor gamma12, 29

PeptidasesHPR (includes EG:3250) haptoglobin-related protein24
MEST mesoderm specific transcript homolog (mouse)18
MMP2 matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase)11

PhosphatasesDUSP1 dual specificity phosphatase 13
PPP3R1 protein phosphatase 3 (formerly 2B), regulatory subunit B, alpha isoform24
PTEN phosphatase and tensin homolog19
PTP4A1 protein tyrosine phosphatase type IVA, member 122

Transcription RegulatorsBCL6 B-cell CLL/lymphoma 616
BRCA1 breast cancer 1, early onset5, 30, 42
CITED2 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 211
CREB1 cAMP responsive element binding protein 16
EGR1 early growth response 16, 26, 35
EMX2 empty spiracles homeobox 215
FOS v-fos FBJ murine osteosarcoma viral oncogene homolog6, 35, 49
FOXO1 forkhead box O13
GATA3 GATA binding protein 32
HIF1A hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor)10, 31, 44, 47
ID1 inhibitor of DNA binding 1, dominant negative helix-loop-helix protein47, 50
JUN jun oncogene49
JUNB jun B proto-oncogene11, 47
NRIP1 nuclear receptor interacting protein 136
REL v-rel reticuloendotheliosis viral oncogene homolog (avian)44
SMAD6 SMAD family member 614
SMAD7 SMAD family member 711
SP2 Sp2 transcription factor31
TP53 tumor protein p5322, 27, 32, 34, 36, 37, 40, 41, 45
WT1 Wilms tumor 17
ZFP36 zinc finger protein 36, C3H type, homolog (mouse)20

Transmembrane ReceptorsIL2RG interleukin 2 receptor, gamma (severe combined immunodeficiency)37
ITGB1 integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 includes MDF2, MSK12)11, 28, 30
ITGB3 integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)7
ITGB4 integrin, beta 430

TransportersAPOE apolipoprotein E36
ATP1B1 ATPase, Na+/K+ transporting, beta 1 polypeptide41
ATP2B2 ATPase, Ca++ transporting, plasma membrane 221
SLC6A6 solute carrier family 6 (neurotransmitter transporter, taurine), member 61

OthersACTB actin, beta9, 49
ANK3 ankyrin 3, node of Ranvier (ankyrin G)24
BCL2 B-cell CLL/lymphoma 23, 48
BIRC5 baculoviral IAP repeat-containing 536
BSG basigin (Ok blood group)41
CAV2 caveolin 25
CCNA2 cyclin A243
COL18A1 collagen, type XVIII, alpha 147
DCN decorin28
EPS15 epidermal growth factor receptor pathway substrate 1528
ERRFI1 ERBB receptor feedback inhibitor 145
EZR ezrin18
FBN1 fibrillin 136
IRS2 insulin receptor substrate 26, 37
ITGA6 integrin, alpha 630
LRP5 low density lipoprotein receptor-related protein 56
MARCKS (includes EG:4082) myristoylated alanine-rich protein kinase C substrate47
SDC2 syndecan 28
TAL1 T-cell acute lymphocytic leukemia 118
THBS2 thrombospondin 211
TIMP2 TIMP metallopeptidase inhibitor 211
TMSB10 thymosin beta 1019
TRAF2 TNF receptor-associated factor 226
VIM vimentin19, 36

An exemplificative network identified by IPA enriched for miRNA targets involved in endometriosis is shown in Figure 2. 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.

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 (Table 4).

IDMolecules in Network 𝑃 -valueFocus MoleculesTop Functions

1AKAP3, 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, WDR44 10E−2411Genetic Disorder, Skeletal and Muscular Disorders, Connective Tissue Disorders

2ATP1B1, 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 Mechanism

3ADIPOR2, 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, TRIM2 10E−189Cancer, Reproductive System Disease, Cell Cycle

4MIR376A, MIR376A1, MIR376A2 10E−21Genetic Disorder, Skeletal and Muscular Disorders

5MIR365, MIR365-1, MIR365-2 10E−21

6EZH2, MIR101, MIR101-1, MIR101-2, MYCN 10E−21Cancer, Cellular Movement, Reproductive System Disease

The 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 ( 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 Figure 3, includes molecules that have been implicated in endometriosis such as the TNF receptor, IL10, IL6, and FOXO1 [4955].

Performing the analysis uploading directly the miRNAs in IPA, thus using the Argonaute2 database, the software identified only one network (Figure 4), 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.

We further investigated the function of the predicted targets of the RT-PCR-validated miRNAs by using Onto-Express and Pathway-Express ( 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 values were adjusted with the false discovery rate correction based on the number of GO categories tested. A corrected value 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).

RankBiological process categoryGenesCorrected 𝑃 -value

1Cellular process2408/6644.0
 Cell motion148/330.0
 Cell communication908/2223.0
 Cellular component organization546/1383.0
 Cellular developmental process400/949.0
 Cellular metabolic process1563/4269.0
 Regulation of cellular process1555/3840.0
 Cell development183/419.0
 Positive regulation of cellular process371/875.0
 Negative regulation of cellular process394/932.0
 Cell cycle223/5551.0E−5
 Cell death235/5872.0E−5
 Cell proliferation237/6025.0E−5
 Actin-filament based process90/1976.0E−5
 Cell fate commitment45/839.0E−5
 Cell aging14/217.0E−4
 Vescicle-mediated transport158/3977.3E−4
 Cell growth51/112.00286
 Cell fate determination15/23.00286
 Cellular localization224/609.00506
 Gene silencing16/27.00696
 Cell cycle process124/323.00696
 Translational initiation23/48.01253
 Cell fate specification12/20.01728
 Cellular response to stimulus110/292.03290
 Cell adhesion173/479.04094

2Negative regulation of biological process421/992.0
 Negative regulation of metabolic process193/422.0
 Negative regulation to cellular process394/932.0
 Negative regulation of developmental process129/3091.9E−4
 Negative regulation of response to stimulus16/29.01705
 Negative regulation of growth24/53.03564

3Multicellular organismal process820/2037.0
 Multicellular organismal development675/1606.0
 Regulation of multicellular organismal process171/4212.0E−4
 System process227/606.00750
 Respiratory gaseous exchange11/17.01639

4Biological regulation1656/4148.0
 Regulation of molecular function211/478.0
 Regulation of biological process1597/3961.0
 Regulation of biological quality281/7321.3E−4
5Regulation of biological process1597/3961.0
 Regulation of metabolic process850/2038.0
 Regulation of developmental process283/657.0
 Regulation of cellular process1555/3840.0
 Positive regulation of cellular process384/933.0
 Negative regulation of cellular process421/992.0
 Regulation of multicellular organismal process171/4211.4E−4
 Regulation of localization110/2618.7E−4
 Regulation of locomotion41/95.02619
 Regulation of growth64/164.04199

6Metabolic process1631/4509.0
 Biosynthetic process899/2354.0
 Negative regulation of metabolic process193/422.0
 Positive regulation of metabolic process201/473.0
 Regulation of metabolic process1563/2038.0
 Cellular metabolic process1563/4269.0
 Primary metabolic process1551/4187.0
 Macromolecule metabolic process1383/3644.0
 Oxydation reduction52/2555.0E−5
 Catabolic process237/665.01317
 Nitrogen compound metabolic process49/193.03270

7Developmental process821/1967.0
 Multicellular organismal development675/1606.0
 Anatomical structure morphogenesis310/710.0
 Embryonic development140/304.0
 Anatomical structure development584/1379.0
 Cellular developmental process400/949.0
 Regulation of developmental process283/657.0
 Positive regulation of developmental process131/2951.0E−5
 Anatomical structure formation involved in Morphogenesis97/2164.0E−5
 Pattern specification process79/1731.6E−4
 Negative regulation of developmental process129/3091.6E−4
 Pigmentation during development9/13.01264
 Reproductive developmental process31/68.02708

8Positive regulation of biological process384/933.0
 Positive regulation of metabolic process201/473.0
 Positive regulation of cellular process371/875.0
 Positive regulation of developmental process131/2951.0E−5
 Positive regulation of homeostatic process6/8.03203

 Localization of cell148/330.0
 Macromolecule localization247/6381.1E−4
 Regulation of localization110/2617.7E−4
 Cellular localization224/609.00422
 Establishment of localization577/1657.00463
 Cell death235/5871.0E−5

11Anatomical structure formation242/6291.1E−4
 Anatomical structure formation involved in  Morphogenesis97/2163.0E−5
 Cellular component assembly165/452.01276

12Response to stimulus464/12762.4E−4
 Response to chemical stimulus185/4655.3E−4
 Response to endogenous stimulus59/136.00633
 Negative regulation to response to stimulus16/29.01844
 Cellular response to stimulus110/292.03534
 Response to stress253/718.03918

13Multi-organism process113/286.00251
 Interspecies interaction between organisms71/172.00565
 Female pregnancy19/39.04504

 Cell growth51/112.00298
 Negative regulation of growth24/53.03391
 Regulation of growth64/164.03916

 Cell motility97/2233.5E−4
 Regulation of locomotion41/95.02439

16Establishment of localization577/1657.00458
 Establishment of protein localization207/5363.3E−4
 Establishment of localization in cell209/576.01045

 Reproductive process116/301.01127

18Reproductive process116/301.01024
 Reproductive developmental process31/68.03090
 Female pregnancy19/39.04504

19Biological adhesion173/479.03486
 Cell adhesion173/479.03486

20Rhythmic process26/59.04158

Pathway-Express analysis identified 33 pathways significant at 5% level (Table 6), 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.

RankPathway nameGenes in pathwayInput genes in pathwayPathway genes on chip 𝑃 -value

1MAPK signaling pathway2721031973.23E−08
2Axon guidance129671133.23E−08
4Pathways in cancer3301192452.27E−07
5Regulation of actin cytoskeleton217781582.31E−05
6Focal adhesion203751502.31E−05
7Wnt signaling pathway152631271.60E−04
9GnRH signaling pathway10336654.86E−04
10Renal cell carcinoma6934615.92E−04
11Insulin signaling pathway13849987.02E−04
12Adherens junction7834627.65E−04
13TGF-beta signaling pathway8738728.49E−04
14Prostate cancer903668.0011
15ECM-receptor interaction843055.0016
16Phosphatidylinositol signaling system763055.0016
17Calcium signaling pathway18254115.0016
18Colorectal cancer843670.0018
19Long-term potentiation733158.0018
20Adipocytokine signaling pathway672750.0032
21ErbB signaling pathway873469.0056
22Pancreatic cancer723059.0056
23Gap junction963367.0063
24Type II diabetes mellitus451831.0069
25Small cell lung cancer863061.0095
26Thyroid cancer291423.0111
27Ubiquitin mediated proteolysis1384294.0145
28Long-term depression752449.0225
29Non-small cell lung cancer542039.0225
30Acute myeloid leukemia592245.0304
32Cardiac muscle contraction872041.0402
33Chronic myeloid leukemia752862.0410

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; ), 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; ), 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).

Target genesmicroRNAs upregulatedmicroRNAs downregulated

CA3 (carbonic anhydrase III) hsa-miR-29b; hsa-miR-29c
CAV1 (caveolin 1) hsa-miR-199a; hsa-miR-30e-3phsa-miR-20a; hsa-miR-106b
CAV2 (caveolin 2) hsa-miR-29b; hsa-miR-29c
DMD (dystrophin) hsa-miR-101; hsa-miR-30e-5phsa-miR-200b; hsa-miR-200c
EPHA3 (EPH receptor A3) hsa-miR-29b; hsa-miR-29chsa-miR-182
FZD7 (frizzled homolog 7)hsa-miR-145; hsa-miR-1hsa-miR-20a; hsa-miR-106b
GALNT3 (UDP-N-acetyl-alpha-D-galactosamine) hsa-miR-30e-5p
KCNMA1 (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-106b
LMO3 (LIM domain only 3) hsa-miR-20a; hsa-miR-93; hsa-miR-17-5p; hsa-miR-183; hsa-miR-106b
NFASC (neurofascin) hsa-miR-150hsa-miR-200b; hsa-miR-200c; hsa-miR-182
PDE4DIP (phosphodiesterase 4D interacting protein) hsa-miR-183
PLS1 (plastin 1) hsa-miR-30e-5phsa-miR-17-5p; hsa-miR-20a; hsa-miR-106b
PTPN3 (protein tyrosine phosphatase, non-receptor type 3) hsa-miR-17-5p; hsa-miR-20a; hsa-miR-106b
RGS2 (regulator of G-protein signalling 2) hsa-miR-30e-5phsa-miR-182
RGS5 (regulator of G-protein signalling 5) hsa-miR-186
RPS6KA5 (ribosomal protein S6 kinase, 90 kDa, polypeptide 5) hsa-miR-148ahsa-miR-93; hsa-miR-17-5p; hsa-miR-20a; hsa-miR-106b
SCAP2 (src family associated phosphoprotein 2) hsa-miR-182
SLCO3A1 (solute carrier organic anion transporter family, member 3A1) hsa-miR-34ahsa-miR-182
SNAP25 (synaptosomal-associated protein) hsa-miR-130a; hsa-miR-1hsa-miR-130b; hsa-miR-200b; hsa-miR-200c
TNFSF12 (tumor necrosis factor superfamily, member 12) hsa-miR-28

IDMolecules in Network 𝑃 -valueFocus MoleculesTop Functions

1CAV1, CAV2, CDKN1A, ESR1, HMGA1, LPL, MMP2, NOS3, SMARCA4, SP1, SP3, SRC, TNFSF12, TP5310E−8 4Cancer, Cell Cycle, Reproductive System Disease

2MBD1, SLCO3A110E−2 1Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry

3RGS2, TNF10E−2 1Lipid Metabolism, Small Molecule Biochemistry, Cell Signaling

4DMD, DTNA, DTNB10E−2 1Cellular Assembly and Organization, Nervous System Development and Function, Skeletal and Muscular System Development and Function

5FYB, GRB2, SKAP210E−2 1Cell-To-Cell Signaling and Interaction, Cell-mediated Immune Response, Cellular Growth and Proliferation

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 ectopic versus 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.


The authors gratefully acknowledge Flavia Prodam for assistance with statistical analysis, Francesca Riboni for skilled help in collecting samples, Paolo Borasio and Chiara Airoldi for the assistance in databases analysis, and Michele Ferrara for his valuable help in preparing this manuscript. N. Filigheddu and I. Gregnanin contributed equally to this work


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Copyright © 2010 Nicoletta Filigheddu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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