{"paper_id":"9c155701-d8ce-4b08-8a77-432b7042baf2","body_text":"1 \nIntegrated eutopic endometrium and non-depleted serum quantitative proteomic 1 \nanalysis identifies candidate serological markers of endometriosis  2 \n 3 \nAntigoni Manousopoulou1#, Mukhri Hamdan2#, Miltiadis Fotopoulos1, Diana J. Garay-4 \nBaquero1, Jie Teng1,3, Spiros D. Garbis1,4* and Ying Cheong5,6* 5 \n 6 \n1Institute for Life Sciences, University of Southampton, Southampton, UK; 2Department of Obstetrics and 7 \nGynaecology, Faculty of medicine, University Malaysia, 50603 Kuala Lumpur, Malaysia; 3School of Pharmacy, 8 \nTianjin Medical University, Tianjin, China; 4Cancer Sciences Unit, Faculty of Medicine, University of Southampton, 9 \nSouthampton, UK; 5Human Development and Health, University of Southampton, Southampton, UK; 6Complete 10 \nFertility Centre, Southampton, Princess Anne Hospital, Coxford Road, SO16 5YA Southampton. 11 \n 12 \n#These authors contributed equally to the study 13 \n* SDG and YC jointly led the study and are co-corresponding authors 14 \n 15 \nThe authors report no conflict of interest 16 \n 17 \nCorresponding authors: 18 \nYing Cheong 19 \nFaculty of Medicine | Human Development and Health | University of Southampton 20 \nY.Cheong@soton.ac.uk 21 \n 22 \nSpiros D. Garbis 23 \nFaculty of Medicine | Institute for Life Sciences | University of Southampton 24 \nS.D.Garbis@soton.ac.uk 25 \n 26 \nWord count: 5,002 27 \nRunning title: Proteomics of serum and tissue in endometriosis 28 \nKeywords: proteomics, iTRAQ, LC-MS 29 \n\n 2 \nAbstract 30 \nBackground: Endometriosis affects about 4 % of women in the reproductive age and is 31 \nassociated with subfertil ity. The aim of the present study was to examine the quantitative 32 \nproteomic profile of eutopic endometrium and serum from women with endometriosis 33 \ncompared to controls in order to identify candidate disease-specific serological markers. 34 \nMethods: Eutopic endometrium and serum from patients with endometriosis (n=8 for tissue 35 \nand n=4 for serum ) was respectively compared t o endometrium and serum from females 36 \nwithout endometriosis (n=8 for tissue  and n=4 for serum ) using a shotgun quantitative 37 \nproteomics method. All study participants were at the proliferative phase of their menstrual 38 \ncycle. 39 \nResults: At the tissue and serum level, 1,214 and 404 proteins were differentially expressed 40 \n(DEPs) in eutopic endometrium and serum  respectively of women with endometriosis vs. 41 \ncontrol. Gene ontology analysis showed that terms related to immune response  | 42 \ninflammation, cell adhesion | migration and blood coagulation were significantly enriched in 43 \nthe DEPs of eutopic endometrium as well as serum. Twenty-one DEPs had the same trend of 44 \ndifferential expression in both matrices and can be further examined as potential disease- and 45 \ntissue-specific serological markers of endometriosis. 46 \nConclusions: The present in-depth proteomic profiling of eutopic endometrium and serum from 47 \nwomen with endometriosis identified promising serological markers that can be further 48 \nvalidated in larger cohorts for the minimally invasive diagnosis of endometriosis. 49 \n 50 \n 51 \n 52 \n 53 \n 54 \n 55 \n 56 \n 57 \n\n 3 \nIntroduction 58 \nEndometriosis is a gynaecological condition in which endometrial glands and stroma is 59 \nimplanted outside the uterine cavity, usually on the ovaries, Fallopian tubes and surrounding 60 \ntissue within the peritoneal cavity (1). Endometriosis affects approximately 3 to 4% of women 61 \nin the reproductive age  (2) and the most common symptoms include pelvic pain, especially 62 \nduring menstruation, and subfertility (3).  63 \nThe exact pathophysiology of endometriosis, related to infertility i s still unknown.  64 \nEndometriosis can be detrimental to fertility directly by distorting tubo-ovarian anatomy (4), or 65 \nindirectly by invoking inflammatory (5) and oxidative damage (6,7) on the oocytes resulting in 66 \npoorer quality oocytes. A non- invasive method of diagnosis is not available and currently, 67 \nendometriosis can be only definitively diagnosed through laparoscopic surgery. Transvaginal 68 \nsonography (TVS), as described in the consensus statement of the International Deep 69 \nEndometriosis Analysis (IDEA) group, can also be used as a first -line imaging technique in 70 \norder to examine women with suspected endometriosis (8). 71 \nEndometriosis persists in both the proli ferative and secretory phases of the menstrual 72 \ncycle. Rai et al. (9) reported an altered endometrial proteomic profile between proliferative and 73 \nsecretory/luteal phases of the menstrual cycle. Previous biomarker discovery studies have 74 \nfocused primarily on the secretory phase of the menstrual cycle, at the time where there is a 75 \nsignificant level of protein turnover, modification and regeneration. The secret ory or luteal 76 \nphase can vary in individuals and in the context of fertility and implantation, the ‘luteal phase 77 \ndefect’ (10), coupled with the recent evidence around the non- specific timeframe of the 78 \n‘implantation window’ within the secretory phase of the menstrual cycle (11), means that the 79 \nproteins related to the secretory phase are much more heterogeneous and may inadvertently 80 \nconceal the discovery of non- menstrual cycle related endometriosis specific m arkers. For 81 \nthese reasons, in the present study, we focused on the proliferative phase for both controls 82 \nand patients with endometriosis.  83 \nNon-targeted global proteomics, supported by recent technological advances in mass 84 \nspectrometry, is gradually becoming an indispensable analytical tool in clinical research since 85 \n\n 4 \nthe unbiased protein expression profiling of tissue or serum/plasma can provide novel 86 \nendophenotypic insight for a given pathophysiological state with unsurpassed analytical 87 \nconfidence. Such a strategy also provides great promise in the detection of novel diagnostic, 88 \nprognostic and therapeutic targets that can eventually influence clinical practice (12-15).  89 \nThere is a limited number of studies that have examined the global proteomic portrait of 90 \neutopic endometrium in women with endometriosis (16-18), and the serum/plasma proteomic 91 \nprofile of endometriosis patients ( 19-21) in order to identify tissue or blood level biomarkers 92 \nfor the diagnosis  of endometriosis . However, the integrated quantitative global proteomic 93 \nanalysis of eutopic endometrium and non -depleted serum samples from women with 94 \nendometriosis for the identification of candidate tissue- and disease-specific biomarkers using 95 \nisobaric tags and state -of-the-art ultra-high precision LC -MS based methods has not been 96 \nreported to date. 97 \nThe aim of the present study was to apply an in- depth quantitative proteomics 98 \nmethodology in combination with comprehensive bioinformatics analysis t o eutopic 99 \nendometrium and serum from women with endometriosis during the proliferative phase of the 100 \nmenstrual cycle compared to healthy controls in order to identify potential serological markers 101 \nfor the minimally invasive diagnosis of endometriosis . An ov erview of the study workflow is 102 \npresented in Figure 1. 103 \n 104 \nMaterials and Methods 105 \nData recording, sample collection and tissue storage in this study were performed 106 \naccording to the World Endometriosis Research Foundation (WERF) Endometriosis Phenome 107 \nand Bioban king Harmonisation Project (EPHect)  (22-24). This study has institutional and 108 \nregional review board approval by the University Hospital Southampton (RHMO&G160) and 109 \nHampshire B ethical committees (MREC08/ HO502/162).  110 \n 111 \n 112 \n 113 \n\n 5 \nInclusion and exclusion criteria  114 \nWomen in the endometriosis group had a laparoscopic diagnosis of endometriosis 115 \n(laparoscopy or laparotomy) with the disease stage documented according to the ASRM 116 \nclassification [Stage I: minimal; Stage II: mild; Stage III: moderate; Stage IV: severe] (25, 26). 117 \nWomen undertaking endometrial biopsy had transvaginal ultrasonography or hysteroscopic 118 \ninspection of their uterine cavity and this did not reveal any endometrial pathology. Patients 119 \nwith pelvic inflammatory disease were excluded from the study. The control group consisted 120 \nof women with no endometriosis as diagnosed by a negative laparoscopy. Since the 121 \nendometrial proteomic profile may vary in the different phases of the menstrual cycle, all 122 \nparticipants (patients with endometriosis and healthy control s) were consistently at the non-123 \nmenstruating proliferative phase of the menstrual cycle. Subfertility was defined as trying to 124 \nconceive for more than 1 year without a successful outcome, while having regular sexual 125 \nintercourse and not using any contraceptive methods.  126 \nWomen were excluded from the study if they were age 45 years old and above,  at the 127 \nsecretory phase of the menstrual cycle (15-28 day of menstrual cycle), on hormonal treatment 128 \nwithin three months prior to the procedure, had a BMI of more than 30, or a current smoker. 129 \nA systematic review and meta-analysis showed no association between smoking status and 130 \nthe development of endometriosis (2 7). However, smokers  were excluded from our study 131 \nbecause smoking has been shown to alter the blood plasma/serum proteomic profile (28). 132 \nDue to the small number of subjects included in the present study we would be unable to 133 \ncorrect for this potential confounder. 134 \n We selected women with regular cycles in order to more accurately define the 135 \nproliferative phase. We included women with a history of regular menstrual cycles, and 136 \nconfirmed their stage of menstrual phase by their retrospective last menstrual date and the 137 \nprospective date of menstruation.  This may mean we excluded women with endometriosis 138 \nand irregular cycles, but as menstrual cycle regularity has not been found to be significantly 139 \nassociated with endometriosis (29 ), we do not expect this inclusion criterion to significantly 140 \nconfound our results.  141 \n\n 6 \nPatient recruitment  142 \nThis study was performed at the Princess Anne Hospital, Southampton where suitable 143 \ncandidates were given an information sheet outlining the study and signed a consent form. 144 \nPatients were grouped into those with endometriosis and those without (control) in accordance 145 \nwith the findings during laparoscopy. The findings of the laparoscopy were documented in the 146 \nproforma. Whenever possible photographic evidence was obtained.  147 \n 148 \nEndometrial tissue collection  149 \nEndometrial tissue was collected using endometrium sampler (Endocell®, Wallach, 150 \nUSA). Sample collection was performed before any uterine manipulation or procedure. 151 \nEndometrial tissues that were suctioned in the tube were collected into individual falcon tubes 152 \ncontaining normal saline. The procedure was repeated at least twice or until an adequate 153 \ntissue sample was obtained.  154 \n 155 \nProcessing of endometrium sample and storage  156 \nThe collected tissue samples were processed up to 4 hours from the collection. Tissues 157 \nwere transferred into a petri dish and were gently teased apart with a tissue forceps and then 158 \nwashed repeatedly with Phosphate Buffered Saline (PBS) to remove any blood. Healthy 159 \ntissues that were free from blood were cut into smaller pieces (approximately 15mm in length) 160 \nusing a pair of tissue scissors. The processed tissues were then transferred into at least 3 161 \nseparate Cryovials (Greiner, UK). These vials were snap frozen in -80 °C freezer. 162 \nEndometrium was transported on solid carbon dioxide (dry ice) inside a polystyrene box. 163 \n 164 \nQuantitative proteomics sample processing 165 \nTwo independent multiplex experiments were performed to include specimens from 16 166 \nsubjects (n=8 controls; n=8 females with endometriosis). Specimens were dissolved in 0.5 M 167 \ntriethylammonium bicarbonate, 0.05% sodium dodecyl sulphate and subjected to pulsed 168 \nprobe sonication (Misonix, Farmingdale, NY, USA). Lysates were centrifuged (16,000 g, 10 169 \n\n 7 \nmin, 4oC) and supernatants were measured for protein content using infrared spectroscopy 170 \n(Merck Millipore, Darmstadt, Germany). Lysates were then reduced, alkylated and subjected 171 \nto trypsin proteolysis. Peptides were labelled using the eight -plex isobaric Tag for Relative 172 \nand Absolute Quantitation (iTRAQ) reagent kit (Label assignment, Experiment A: 113=control 173 \n1, 114=control 2, 115= control 3, 116= control 4, 117= endometriosis patient 1, 118= 174 \nendometriosis patient 2, 119= endometriosis patient 3, 121= endometriosis patient 4 ; 175 \nExperiment B: 113=control 5, 114=control 6, 115= control 7, 116= control 8, 117= 176 \nendometriosis patient 5, 118= endometriosis patient 6 , 119= endometriosis patient 7, 121= 177 \nendometriosis patient 8 ) and analysed using multi -dimensional liquid chromatography and 178 \ntandem mass spectrometry as reported previously by the authors (30-34).  179 \n 180 \nSerum procurement and proteomic analysis  181 \nThe procurement and handling of sera was in accordance with the recommendations of 182 \nthe Standard Operating Procedure Integration Working Group (SOPIWG) as adopted by the 183 \nauthor’s method ( 35). One eight-plex s erum proteomics experiment was  performed (n=4 184 \ncontrols; n=4 patients with endometriosis). Serum specimens were freshly thawed and 185 \nvortexed for 2 minutes. For each participant, 100uL of unprocessed serum were mixed with 186 \n400uL 6M Guanidine Hydrochloride and subjected to global quantitative serum proteomic 187 \nanalysis using our reported depletion-free methodology (12-14). In summary, high -188 \nperformance Size Exclusion Chromatography using three serially connected Waters KW-804 189 \ncolumns at 0.75 ml/min flow rate and 30°C was used to separate the proteins based on their 190 \nmolecular weight differences. The separ ated low -molecular weight protein segments 191 \n(molecular weight cutoff 3 kDa) were dialysis purified and lyophilized to dryness. One-hundred 192 \nμg of protein from each sample was subjected to trypsin proteolysis and the peptides were 193 \nchemically labelled using the eight-plex iTRAQ reagent kit (Label assignment, 113=control 9, 194 \n114=control 10, 115= control 11, 116= control 12, 117= endometriosis patient 9, 118= 195 \nendometriosis patient 10 , 119= endometriosis patient 11 , 121= endometriosis patient 12 ), 196 \npooled, and offline fractionated with high pH C4 reverse phase chromatography. Each fraction 197 \n\n 8 \nwas analysed using ultra- high performance low pH C 18 nano-liquid chromatography 198 \nhyphenated with high- resolution tandem mass spectrometry using the FT -Orbitrap Elite 199 \nplatform. 200 \n 201 \nDatabase searching 202 \nUnprocessed raw files were submitted to Proteome Discoverer 1.4 for target decoy 203 \nsearch against the UniProtKB homo sapiens database comprised of 20,159 entries (release 204 \ndate January 2015), allowing for up to two missed cleavages, a prec ursor mass tolerance of 205 \n10ppm, a minimum peptide length of six and a maximum of two variable (one equal) 206 \nmodifications of; iTRAQ 8-plex (Y), oxidation (M), deamidation (N, Q), or phosphorylation (S, 207 \nT, Y). Methylthio (C) and iTRAQ (K, Y and N -terminus) were set as fixed modifications. FDR 208 \nat the peptide level was set at <0.05. Percent co-isolation excluding peptides from quantitation 209 \nwas set at 50. Reporter ion ratios from unique peptides only were taken into consideration for 210 \nthe quantitation of the respective protein. The iTRAQ ratios of proteins were median -211 \nnormalized and log2transformed.  212 \nA one- sample Student’s T- Test was performed to identify differentially expressed 213 \nproteins in tissue and serum samples from endometriosis patients vs. controls. Significance 214 \nwas set at p ≤ 0.05. Only proteins with a one- sample Student’t T-Test p-value<0.05, a mean 215 \niTRAQ log2ratio higher than ±0.3 and identified with at least two unique peptides in adherence 216 \nto the Paris Publication Guidelines for the analysis and documentation of peptide and protein 217 \nidentifications (http://www.mcponline.org/site/misc/ParisReport_Final.xhtml), were 218 \nconsidered differentially expressed and subjected to bioinformatics analysis. All mass 219 \nspectrometry proteomics data have been deposited to the ProteomeXchange Consortium via 220 \nthe PRIDE partner repository with the dataset identifier PXD009090 (eutopic endometrium 221 \nproteomic analysis) and PXD011091 (serum proteomic analysis). 222 \n 223 \n 224 \n 225 \n\n 9 \nBioinformatics analysis 226 \nDAVID (https://david.ncifcrf.gov/), STRING (https://string-db.org/), BiNGO in Cytoscape 227 \nand MetaCore (Clarivate Analytics, Philadelphia, PA, USA) were applied to differentially 228 \nexpressed proteins in order to identify over-represented gene ontology terms, pathway maps 229 \nand direct protein interaction networks in endometriosis vs. control. P -values ≤ 0.05 were 230 \nconsidered significant.  231 \n 232 \nResults 233 \nTwenty-four patients were recruited between September 2013 and September 2015. Of 234 \nthese, eutopic endometrium from 16 subjects was used for the tissue proteomic analysis (n=8 235 \npatients with endometriosis; n=8 controls) and serum from eight subjects for the serum 236 \nproteomics analysis (n=4 patients with endometriosis; n=4 controls). The clinical 237 \ncharacteristics of the participants are presented in Table 1. All patients were in the proliferative 238 \nphase and had a regular menstrual cycle.  There was no significant difference in age, body 239 \nmass index, and baseline FSH between the two groups.  240 \n 241 \nTissue and serum proteomic analysis 242 \nTissue proteomic analysis resulted in the profiling of 10,929 proteins whereas serum 243 \nproteomic analysis quantitatively identified 2,010 proteins (peptide FDR p<0.05). Of these, 244 \n1,214 ( Supplementary Table 1) and 404 ( Supplementary Table 2) were identified as 245 \ndifferentially expressed at the tissue and serum level respectively and were further subjected 246 \nto bioinformatics analysis.  Forty-four DEPs were common between the two matrices, 21 of 247 \nwhich with the same trend of differential expression (i.e. up-regulated or down- regulated in 248 \nendometriosis vs. control at both tissue and serum level). 249 \nDAVID gene ontology analysis of the tissue and serum DEPs showed a significant 250 \nenrichment for gene ontology terms related to Immune response | Inflammation, Cell adhesion 251 \n| Migration , Blood coagulation and other terms (e.g. receptor -mediated endocytosis, high -252 \ndensity lipoprotein particle remodelling and G2/M transition of mitotic cycle) in both matrices 253 \n\n 10 \n(Figure 2A). Forty-four DEPs were observed at both tissue and serum level and these are 254 \npresented in heatmap format in Figure 2B. The 21 proteins with the same trend of modulation 255 \nat both tissue and serum level are highlighted in grey.  Ingenuity Pathway Analysis showed 256 \nthat carbohydrate | lipid metabolism and organ development protein networks were enriched 257 \nin the 21 DEPs analysed in tissue and serum of patients with endometriosis vs. control (Figure 258 \n3). 259 \n 260 \nDiscussion 261 \nThe present study reports the integrated  quantitative proteomic profiling of eutopic  262 \nendometrial tissue and non-depleted serum from women diagnosed with endometriosis 263 \ncompared to healthy controls. Bioinformatics analysis  of differentially expressed proteins 264 \n(DEPs) showed a significant enrichment for processes related to immune  265 \nresponse/inflammation, cell adhesion/migration, blood coagulation in both matrices, in 266 \nkeeping with the known inflammatory and adhesive nature of endometriosis. 267 \nAbnormalities in immune responses have been suggested to play an important role in 268 \nthe perpetuation of endometriosis (36, 3 7). Endometrial cells in the peritoneal cavity can 269 \nescape clearance from immune cells through a mechanism coined as “immunoescaping” (38). 270 \nDysregulation of immune response can thus allow the proliferation, implantation and 271 \nangiogenesis of ectopic endometrial tissue (39). Furthermore, previous studies of peritoneal 272 \nfluid from patients with endometriosis have shown disease- related abnormalities in the 273 \nimmune response (40, 41). 274 \nStudies have shown that eutopic  endometrial stromal cells from females with 275 \nendometriosis exhibit an altered cell -adhesion molecular profile compared to stromal cells 276 \nfrom healthy controls  (42). Extracellular matrix has been shown to control cell proliferation, 277 \ndifferentiation and apoptosis (43).  278 \nTwo proteins were found to be up-regulated in both the eutopic endometrium and serum 279 \nproteomic analysis of patients with endometriosis vs. control, Na(+)/H(+) exchange regulatory 280 \ncofactor NHERF-1 (NHERF-1) (gene name SLC9A3R1) and thymosin beta-4 (Tb 4) (gene 281 \n\n 11 \nname TMSB4X). Increased expression of a particular protein is more easily and reliably 282 \ndetected compared to lower expression levels, thus these two proteins may represent the 283 \nmost promising serological markers of endometriosis  for further larger scale investigative 284 \nstudies. 285 \nNHERF-1 is a scaffold protein expressed primarily in the plasma membrane of polarized 286 \nepithelial cells and mediates signals connecting the membrane to the cytoskeleton. The role 287 \nof NHERF-1 in uterine physiology remains unknown, with few studies reporting its involvement 288 \nwith pathological conditions such as endometrial cancer and polycystic ovaries syndrome 289 \n(PCOS). NHERF-1 contributes to the organization of microvilli in polarized epitheliums, but 290 \nalso regulates the acti vity of growth factor receptors, ion channels and the endocytic 291 \nmachinery (44-46). A study showed that NHERF-1 expression is transcriptionally regulated by 292 \noestrogens in human endometrium, and that it is expressed at higher levels during the 293 \nproliferative phase of the menstrual cycle (47). The role of NHERF-1 in endometriosis warrants 294 \nfurther investigation. 295 \nTb4, a member of the beta- thymosins family, is an N -terminally acetylated peptide 296 \ncomposed of 43 amino acid residues  (48). Tb4 interacts with monomeric actin (48) and 297 \nmodulates actin polymerization (49). As a secreted factor, Tb4 has been found to modulate 298 \nthe immune response and participate in hormonal activities  (48, 50). Tb4 is also involved in 299 \ninflammatory response, angiogenesis, blood coagulation, wound healing and apoptosis  (51-300 \n54). Using a mouse model, Kawahara et al.  (55) showed that Tb4 over-expression could 301 \nparticipate in musculature disintegration and the development of adenomyosis . The role of 302 \nTb4 in endometriosis should be assessed in future studies. 303 \nThe main limitation of the study is its small size. Power calculation of sample size (n=16 304 \nfor tissue analysis and n=8 for serum analysis) was based on ensuring a statistical power of 305 \nover 0.7, taking into consideration a 30% measurement error and a log2ratio fold change > 0.3 306 \nbetween biological replicates, as reported in a similar simulation study ( 56). Validating the 307 \nproteins at the tissue and serum level in a larger cohort using mass spectrometry or an 308 \nalternative analytical method to mass spectrometry (e.g. ELISA or western blot) to confirm 309 \n\n 12 \ntheir clinical utility was beyond the scope of the present study and constitutes a future 310 \nperspective.  311 \nIn conclusion, the integrated eutopic endometrium and serum global proteomic profiling 312 \nidentified candidate serological targets that can be further validated for their clinical utility in 313 \nthe non-invasive diagnosis of endometriosis. 314 \n 315 \n 316 \n 317 \n 318 \n 319 \n 320 \n 321 \n 322 \n 323 \n 324 \n 325 \n 326 \n 327 \n 328 \n 329 \n 330 \n 331 \n 332 \n 333 \n 334 \n 335 \n 336 \n 337 \n\n 13 \nAcknowledgements 338 \nWe are indebted to Mr. Roger Allsopp, Mr. Derek Coates and Hope for Guernsey for 339 \nestablishing the clinical mass spectrometry infrastructure at the University of Southampton. 340 \nThe authors are grateful to the support of King Saud University, Deanship of Scien tific 341 \nResearch Chair, Prince Mutaib Bin Abdullah Chair for Biomarkers of Osteoporosis, College of 342 \nScience, as well as the Visiting Professor Program of King Saud University, Riyadh, Saudi 343 \nArabia.  344 \n 345 \nDisclosure of interests 346 \nThe authors declare no conflict of interest  347 \n 348 \nContribution to Authorship 349 \nAM performed experiments, analysed/ interpreted data and wrote manuscript; MH collected 350 \nsamples, performed experiments, analysed/ interpreted data; MF, DJGB and JT performed 351 \nexperiments and analysed data;  SDG and YC designed study, supervised the execution of 352 \nexperiments, interpreted the experimental results and wrote manuscript.   353 \n 354 \nDetails of ethics approval 355 \nThis study has institutional and regional review board approval by the University Hospital 356 \nSouthampton (RHMO&G160) and Hampshire B ethical committees (MREC08/ HO502/162) 357 \n(Approval date: 24 October 2008). 358 \n 359 \nFunding 360 \nJT was supported by the China Scholarship Council and the China Postdoctoral Science 361 \nFoundation (2013T60260). 362 \n 363 \n 364 \n 365 \n\n 14 \nReferences 366 \n 367 \n1. 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Safer D, Golla R, Nachmias VT (1990)  Isolation of a  5-kilodalton actin-508 \nsequestering peptide from human blood platelets. Proc Natl Acad Sci U S A 87, 2536-40. 509 \n55. Kawahara R, Matsuda M, Imaoka T et al.  (2003) Up-regulation of thymosin beta 4 gene 510 \nexpression in experimentally-induced uterine adenomyosis in mice. In Vivo 17, 561-5. 511 \n56. Levin Y (2011) The role of statistical power analysis in quantitative proteomics. Proteomics 512 \n11, 2565–7. 513 \n 514 \n 515 \n 516 \n 517 \n 518 \n  519 \n 520 \n 521 \n 522 \n 523 \n 524 \n 525 \n 526 \n 527 \n 528 \n 529 \n 530 \n\n 20 \nTable and Figure Legends 531 \n 532 \nTable 1. Clinical characteristics of study participants 533 \n 534 \nFigure 1. Study design  535 \nFigure 2. A. DAVID gene ontology analysis of the DEPs showed a significant enrichment for 536 \ngene ontology terms related to Immune response | Inflammation, Cell adhesion | Migration, 537 \nBlood coagulation in both tissue and serum from patients with endometriosis vs. control.  B. 538 \nHeatmap of fourty-four DEPs observed at both tissue and serum level. Proteins with the same 539 \ntrend of modulation at both matrices are highlighted in grey. 540 \nFigure 3. Ingenuity Pathway Analysis showed that carbohydrate | lipid metabolism and organ 541 \ndevelopment protein networks were enriched in the 21 DEPs analysed in tissue and serum of 542 \npatients with endometriosis vs. control 543 \n 544","source_license":"CC0","license_restricted":false}