{"paper_id":"2af80fb5-7044-4a3d-9aff-0bf994eca67c","body_text":"REVIEW Open Access\n© The Author(s) 2024. Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, \nsharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and \nthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this \narticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included \nin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will \nneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The \nCreative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available \nin this article, unless otherwise stated in a credit line to the data.\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nhttps://doi.org/10.1186/s12967-024-05474-3\nJournal of Translational \nMedicine\n*Correspondence:\nTao Zhang\ntaozhang@cuhk.edu.hk\nChi Chiu Wang\nccwang@cuhk.edu.hk\nFull list of author information is available at the end of the article\nAbstract\nBackground Endometriosis is one of the most common gynaecological diseases, yet it lacks efficient biomarkers \nfor early detection and unravels disease mechanisms. Proteomic profiling has revealed diverse patterns of protein \nchanges in various clinical samples. Integrating and systematically analysing proteomics data can facilitate the \ndevelopment of biomarkers, expediting diagnosis and providing insights for potential clinical and therapeutic \napplications. Hence, this systematic review and meta-analysis aimed to explore potential non-invasive diagnostic \nbiomarkers in various biological samples and therapeutic targets for endometriosis.\nMethods Online databases, including Scopus, PubMed, Web of Science, MEDLINE, Embase via Ovid, and Google \nScholar, were searched using MeSH terms. Two independent authors screened the articles, extracted the data, and \nassessed the methodological quality of the included studies. GO and KEGG analyses were performed to identify the \npathways that were significantly enriched. Protein-protein interaction and hub gene selection analyses were also \nconducted to identify biomarker networks for endometriosis.\nResults Twenty-six observational studies with a total of 2,486 participants were included. A total of 644 differentially \nexpressed proteins (180 upregulated and 464 downregulated) were identified from 9 studies. Proteins in peripheral \nblood exhibited a sensitivity and specificity of 38-100% and 59-99%, respectively, for detecting endometriosis, while \nproteins in urine had a sensitivity of 58-91% and specificity of 76-93%. Alpha-1-antitrypsin, albumin, and vitamin D \nbinding proteins were significantly DEPs in both serum and urine. Complement C3 is commonly expressed in serum, \nmenstrual blood, and cervical mucus. Additionally, S100-A8 is commonly expressed in both menstrual blood and \ncervical mucus. Haptoglobin is commonly detected in both serum and plasma, whereas cathepsin G is found in \nProteomics approach to discovering \nnon-invasive diagnostic biomarkers \nand understanding the pathogenesis \nof endometriosis: a systematic review \nand meta-analysis\nGetnet Gedefaw Azeze1,2 , Ling Wu1 , Bekalu Kassie Alemu1,3 , Wing Fong Lee1, Linda Wen Ying Fung1,  \nEva Chun Wai Cheung1, Tao Zhang1*  and Chi Chiu Wang1,4*\n\nPage 2 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nBackground\nEndometriosis is characterized by the development of \nendometrium-like tissue and/or stroma outside the endo-\nmetrium and myometrium [ 1, 2]. It is a chronic inflam -\nmatory disease that affects more than 170 million women \nworldwide, predominantly women of reproductive age, \nwith a wide range of clinical symptoms, including dys -\nmenorrhea, dyspareunia, dyschezia, dysuria, chronic pel -\nvic pain, and infertility, affecting women’s health from the \ntime of menarche to menopause, regardless of their eth -\nnicity or social status [1, 3].\nIn clinical settings, the gold standard diagnostic \nmethod for confirming endometriosis is laparoscopy, \na minimally invasive surgical procedure that involves \ninserting an imaging tube through a small incision in the \nabdomen [ 4]. Although laparoscopy is effective and the \ngold standard, it has potential complications, requires \ngeneral anaesthesia, and demands advanced surgical \nskills [5– 7]. Moreover, it is not always available or acces -\nsible, particularly in low- and middle-income countries \nwhere healthcare facilities and resources are lacking \n[5]. Ultrasound is the first-line non-invasive diagnos -\ntic method for detecting endometriosis [ 8]. It has been \nwidely used to enhance the diagnosis and identification \nof endometriomas and nodules in adjacent structures of \nthe pelvis but lacks both sensitivity and specificity for \nurine, serum, and plasma. GO and KEGG enrichment analyses revealed that proteoglycans in cancer pathways, which \nregulate cell-to-cell interactions, modulate the extracellular matrix, and promote the proliferation and invasion of \nendometrial cells, are commonly enriched in serum and urine.\nConclusion This comprehensive study revealed potential proteomes that were significantly differentially expressed in \nwomen with endometriosis utilizing various non-invasive clinical samples. Exploring common differentially expressed \nproteins in various biological samples provides insights into the diagnosis and pathophysiology of endometriosis, as \nwell as potential clinical and therapeutic applications.\nGraphical abstract \nKeywords Proteomics, Endometriosis, Meta-analysis, Blood, Urine, Cervical mucus, Biomarker\n\nPage 3 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nruling out peritoneal endometriosis, endometriosis-asso -\nciated adhesions, and deep infiltrating endometriosis [ 9, \n10]. Imaging techniques such as transvaginal ultrasound \n(TVS), transrectal ultrasound (TRS), and magnetic reso -\nnance imaging (MRI) can bridge the gap between clinical \nand surgical diagnosis by providing a non-invasive visual \ndiagnosis that can be achieved more quickly, safely, and \naccessibly than surgery. However, different studies have \nreported wide variation in diagnostic accuracy between \nMRI and TVS, mainly due to the variability of tech -\nniques, examiners’ experience, and anatomic locations of \nthe lesions/subtypes of the disease [11]. Given these chal-\nlenges, non-invasive diagnostic approaches for endome -\ntriosis are urgently needed.\nWhile various non-invasive diagnostic modalities \ninvolving blood, cervicovaginal fluid, and urine have been \nproposed, a definitive diagnostic biomarker for endome -\ntriosis remains elusive. Despite extensive research into \nblood and urine tests and the investigation of altered lev -\nels of cytokines, angiogenic factors, and growth factors, \nnone of these biomarkers have been used to conclusively \ndiagnose endometriosis [ 12– 15]. In addition, numerous \nstudies have demonstrated that nanoparticles, which are \nmaterials with dimensions smaller than 100 nanometers, \nhold promise for improving diagnostic and imaging tech-\nniques for non-invasive detection, understanding target \nsignalling pathways, and identifying therapeutic options \nfor diverse diseases [ 16– 19]. Notably, nanoparticles can \nserve as carriers for transporting anti-inflammatory, anti-\noxidant, anti-angiogenic, or immunomodulatory mol -\necules to specific locations [ 20– 23], owing to their low \ntoxicity, high stability, and capacity for conjugating with \nvarious biomolecules [ 21, 24, 25]. Moreover, nanotech -\nnology may offer a promising non-invasive diagnostic \nmethod for detecting endometriosis by identifying spe -\ncific biomarkers, such as proteins or genetic materials \n[26]. Although studies have shown that CA 19 − 9 and \nCA-125 have been detected in blood using immuno -\nchemical sensing [ 27, 28], the recognition of iron oxide \nnanoparticles as contrast agents for magnetic resonance \nimaging [26, 29], and the investigation of gold nanorods \nand carbon nanotubes as photoacoustic imaging agents \nfor visualizing endometriosis lesions in vivo [ 26, 30]. \nHowever, it is important to note that none of the bio -\nmarkers/methods have been clinically proven biomarkers \nfor endometriosis detection. Among all techniques, pro -\nteomic approaches are essential for identifying biomark -\ners by characterizing the protein content of biological \nsamples [31]. These approaches enable proteome profil -\ning, comparative expression analysis of proteins in vari -\nous biological samples, identification of posttranslational \nmodifications, and identification of protein–protein \ninteractions. Notably, proteomic analysis is invaluable \nbecause proteins, unlike DNA or RNA, directly mediate \ncellular functions and disease mechanisms [ 32, 33]. Mass \nspectrometry (MS) proteomic methods have appeared to \nbe powerful platforms for discovering novel and poten -\ntial diagnostic and prognostic biomarkers for various \ndiseases. MS-based approaches are substantially helpful \nfor consistently identifying proteins with high diagnos -\ntic accuracy for endometriosis [ 34]. Furthermore, pro -\nteomics studies offer functional insights into expressed \nproteins and significantly enriched pathways, providing \nvaluable information for understanding the pathogenesis \nof this disease.\nOur hypothesis is that biomarkers of endometrio -\nsis commonly found in various biological samples may \nhave substantial significance and have a direct impact \non the development and progression of endometriosis. \nTherefore, our aim is to gain a thorough understanding \nof the diagnosis, pathogenesis, and possible therapeu -\ntic approaches for endometriosis utilizing diverse clini -\ncal samples, which could ultimately result in improved \npatient outcomes and quality of care. Hence, this system-\natic review aims to assess the utility of proteomic (MS-\ntargeted) analysis for biomarker discovery and navigate \nthe pathogenesis of endometriosis development. Addi -\ntionally, this study explored the sensitivity and specific -\nity of expressed proteins as promising biomarkers for \ndetecting endometriosis. Moreover, this study involved \nmass spectrometry-based diagnostic testing for endo -\nmetriosis and a comprehensive understanding of the \npathogenesis of endometriosis in various non-invasive \nbiological samples, including peripheral blood, cervi -\ncal mucus, menstrual blood, and urine. Remarkably, this \nstudy examined commonly enriched pathways associated \nwith disease conditions to better understand the mecha -\nnism of disease development.\nMethods\nProtocol registration\nFollowing the PRISMA 2020 checklist, we conducted \na systematic review and registered the protocol with \nPROSPERO (registration ID: CRD42023397217).\nStudy search strategy\nSearches were performed in the following databases: \nPubMed, EMBASE through OVID, Google Scholar, \nScopus, and Web of Science. The following terms were \nused in the search strategy, with alternatives as shown \nusing Boolean operators: “mass spectrometry” AND \n(“diagnostic” OR “test”) AND (“endometriosis” OR \n“endometrioma”) & (‘’proteomics’’ OR’’ proteome’’ AND \n(‘’endometriosis’’ OR’’ endometrioma”).\nIn addition, manual searches were performed for the \nreference lists of all studies identified by the search strat -\negy described above. Web sources and databases were \nsearched for published articles and preprint research \n\nPage 4 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \npapers written in the English language up to January 31, \n2024.\nStudy participants\nThe participants in the study were reproductive-aged \nwomen who underwent laparoscopy or abdominal sur -\ngery for one of the following reasons: pelvic pain, infer -\ntility, dysmenorrhea, abnormal pelvic examination, or a \ncombination of the aforementioned conditions, an ovar -\nian mass regardless of symptoms, or other pelvic pathol -\nogies. Only confirmed cases with laparoscopy and/or \nhistology data were included in the review after surgery, \nwhile women with confirmed benign pelvic patholo -\ngies, such as uterine fibroids, ovarian cysts, unexplained \ninfertility, and fertile healthy women were considered as \ncontrols.\nStudy selection\nFrom the initial 2,273 retrieved articles, we included 22 \ncase-control, 2 cross-sectional, and 2 prospective cohort \nstudies that met our eligibility criteria. Laparoscopy or \nlaparotomy with or without histological confirmation \nand mass spectrometry techniques were used as refer -\nence standards and index tests, respectively.\nInclusion criteria\nIn this study, women with a confirmed diagnosis of \nendometriosis, either combined with one phenotype (I) \novarian endometriosis, (II) deep pelvic infiltrating endo -\nmetriosis (DIE), and (III) peritoneal endometriosis, were \nenrolled as cases, whereas women with benign uterine \nconditions such as uterine fibroids and ovarian cysts and \nhealthy women (self-declaration) were enrolled as con -\ntrols. All observational studies, such as cohort, cross-\nsectional, and case-control studies, that were published \nexclusively in the English language were considered for \ninclusion.\nExclusion criteria\nEndometriosis with other coincidental pelvic patholo -\ngies, such as pelvic malignancy, adenomyosis, polycys -\ntic ovarian syndrome (PCOS), and pelvic inflammatory \ndisease (PID), studies conducted with approaches other \nthan mass spectrometry-based series, proteomics stud -\nies with invasive sources of samples, such as peritoneal \nfluid, endometrial biopsy, follicular fluid, and endome -\ntrial fluid, and studies reporting proteins with other \nindex tests, such as enzyme-linked immunosorbent assay \n(ELISA), polymerase chain reaction (PCR/qPCR), and \nwestern blot, were excluded from the study. Addition -\nally, case reports or series, articles without full text and \nabstracts, duplicated studies, anonymous reports, edito -\nrial reports, reviews, perspectives, and book sections or \nchapters were also excluded.\nData extraction\nThe authors’ names, year of study, country, diagnostic \ncriteria for endometriosis, type of sample, protein altera -\ntions, menstrual phase, proteomics platform, sensitivity, \nand specificity of biomarkers with a molecular weight \nof m/z were extracted from each article (Table  1). In \naddition, for the bioinformatics analysis, the protein ID \n(UniProt), protein accession, and fold change (up- and \ndownregulated) were extracted. Moreover, the protein \nlists from the 8 articles were extracted, including the \nidentification codes and the level of regulation (up/down-\nregulated). The UniProt website ( https://www.uniprot.\norg/) was used to standardize the protein identification \ncodes. Subsequently, a comparison was conducted on the \nsignificantly differentially expressed proteins extracted \nfrom the 8 papers to identify consistent proteins. Stud -\nies reporting the p value (p < 0.05) and fold change (FC) \nof differentially expressed proteins were included in the \nmeta-analysis.\nRisk of bias and applicability\nTwo authors (GGA & BKA) conducted independent \nassessments of risks associated with bias and applicability \nusing the Diagnostic Precision Study Quality Assessment \nTool (QUADAS-2) for the studies included in the diag -\nnostic accuracy review [ 35]. Conflicts between the two \nauthors were evaluated and reviewed by a third author \n(LW). Patient selection, index test, reference standard, \nand flow and timing were the four domains used to eval -\nuate the risk of bias, whereas patient selection, index test, \nand reference standard were the domains employed to \nassess the applicability of each article. The distribution of \nrisk-of-bias and applicability judgments within each bias \ndomain was assessed (Figure S1).\nIdentification and enrichment of DEPs\nGene Ontology (GO) and Kyoto Encyclopedia of Genes \nand Genomes (KEGG) pathway enrichment analyses \nwere performed to elucidate the biological characteris -\ntics of the overlapping DEGs via the online tool database \nfor annotation, visualization, and integrated discov -\nery (DAVID) ( https://david.ncifcrf.gov/). GO annota -\ntion and KEGG pathway analyses were performed with \nMetascape ( https://metascape.org/). Furthermore, a sci -\nence and research online plot (SRplot) ( https://www.bio-\ninformatics.com.cn/en) was used to present the findings. \nGO and KEGG analyses were performed for each clini -\ncal sample separately, such as peripheral blood (serum, \nplasma), urine, and menstrual blood. DEPs from the \nsupernatant and mesenchymal stem cells derived from \nmenstrual blood were combined and analysed as men -\nstrual blood-expressed proteins. For each given gene list, \npathway and process enrichment analyses were carried \nout with KEGG and GO pathway analyses. Metascape \n\nPage 5 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nAuthor \n(year) & \ncountry\nStudy \ndesign\nAge of the patient/con-\ntrol (mean ± SD/SEM, \nmedian with IQR)\nDiagnosis criteria Type of \nSample\nMenstru-\nal cycle\nNumber of subjects \n(case/control)\nProteomics platform/\nsurface\nMain findings: Pro-\nteins (m/z), in Da.\nSensitiv-\nity/speci-\nficity (%)\nZhang et al. \n2023 [93],\nChina\nCase \ncontrol\n35.375 ± 4.069/34 ± 4.721# Histology menstrual \nblood\nmenstrual \nphase\n8/8 LC‒MS/MS 95 DEPs (64↑ and 31↓) NR\nVišnić et al., \n2023 [94], \nCroatia\nCase \ncontrol\nNA Histology urine Prolifera-\ntive\nphase\n16/16 LC‒MS/MS 17 DEPs (15↑ and 2↓) NR\nSasamoto et \nal. 2022 [62], \nUSA\nCross \nsectional\n18/22# Laparoscopy plasma NR 142/78 SOMAscan 63 proteins associated \nwith endometriosis \n(36↑, 27↓)\nNR\nPenariol et \nal. 2022 [95], \nBrazil\nCase \ncontrol\n18–40* Laparoscopy with histology menstrual \nblood\nmenstrual \nphase\n10/9 UHPLC-MS/MS 1373 proteins \nexpressed\nNR\nGiuseppe et \nal. 2020 [96], \nItaly\nCase \ncontrol\n30–40** Laparoscopy with histology urine NR 8/5 LC‒MS/MS 11 proteins ↑ and one \n↓ in endometriosis\nNR\nChen et al. \n2019 [41], \nChina\nCase \ncontrol\n35.8 ± 8.3/38.4# Laparoscopy with histology urine NR 25/72 LC‒MS/MS Histone 4 70/80\nManouso-\npoulou et \nal. 2018 [97], \nUK\nCase \ncontrol\n32.5 ± 4.5/31.8 ± 4.7 Laparoscopy serum prolifera-\ntive phase\n4/4 LC‒MS/MS 404 (DEPs) identified in \nendometriosis\nNR\nGrande et \nal. 2017 [98], \nItaly\nCase \ncontrol\n30–40** Laparoscopy with histology cervical \nmucus\nperiovula-\ntory phase\n10/10 LC‒MS/MS 6 and 9 proteins were \nquantitatively ↑ and ↓ \nin endometriosis\nNR\nZhao et al. \n2015 [99], \nChina\nCase \ncontrol\n37.8/38.9 # Laparoscopy with histology serum NR 50/40 2DE with MALDI-TOF/MS 5 proteins, with m/z of \n4210, 5904, and 2660\n96.67/100\nDutta et al. \n2014 [100],\nIndia\nCase \ncontrol\n28.18 /28.49# Laparoscopy serum NR 547/79 2D-DIGE combined with \nMALDI-TOF/TOF-MS\n25 DEPs identified & \nalpha-1B-glycoprotein \n(A1BG) identified as a \npromising diagnostic \nbiomarker\nNR\nHwang et al. \n2014 [47],\nSouth Korea\nCase \ncontrol\nNA Histology plasma prolifera-\ntive phase\n15/15 2DE, ESI-Q-TOF/MS C4A and α-2 M \nprotein expression ↑in \nendometriosis\nHp, ApoL-1, and LRG \n↓in endometriosis\nNR\nWang et al. \n2014 [101],\nChina\nCase \ncontrol\n30.5 + 3.4/31.5 + 4.2# Laparoscopy urine prolif-\nerative & \nsecretory \nphases\n60/62 MALDI-TOF/LC‒MS/MS 5 proteins, with m/z of \n433.9, 1599.4, 2085.6, \n6798.0 and 3217.2.\n90.9/92.9\nTable 1 Proteomics studies of endometriosis\n\nPage 6 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nAuthor \n(year) & \ncountry\nStudy \ndesign\nAge of the patient/con-\ntrol (mean ± SD/SEM, \nmedian with IQR)\nDiagnosis criteria Type of \nSample\nMenstru-\nal cycle\nNumber of subjects \n(case/control)\nProteomics platform/\nsurface\nMain findings: Pro-\nteins (m/z), in Da.\nSensitiv-\nity/speci-\nficity (%)\nWilliams \net al. 2014 \n[102], USA\nCase \ncontrol\nNA Laparoscopy/Laparotomy urine NR 17/44 iTRAQ & 2D LC‒MS/MS 1025 DEPs are identi-\nfied. Uromodulin, \nserum albumin, \nkeratins, various im-\nmunoglobulins, actin, \nand collagen were \namong the proteins \ndetected in the major-\nity of samples\nNR\nZhang et al. \n2013 [103], \nChina\nCase \ncontrol\n24–38** Laparotomy serum NR 32/34 MALDI-TOF-MS 1 protein, with m/z \nof 4180\n100/90\nFassbender \net al. 2012 \n[104],\nBelgium\nCase \ncontrol\n31.44 + 4.24/\n32.32 + 0.19#\nLaparoscopy with histology plasma menstrual, \nsecretory \n& prolif-\nerative \nphases\n165/89 SELDI-TOF-MS (CM10, SPA) 5 proteins, with m/z \nof 9,926.31, 10,072.2, \n6,753.04, 4,302.67, \n9,328.49\n38/85\n5 proteins, with m/z \nof 1,366.3, 5,712.69, \n10,070.7, 3,017.68, \n3,824.44\n53/82\n5 proteins, with m/z \nof 2,831.02,7,554.66, \n4,241.29, 2,953.25, and \n9,927.73\n66/99\nFaserl et al. \n2011 [38], \nAustria\nCross \nsectional\nNA Laparoscopy serum NR 56/20 2DE with MALDI-TOF/MS vitamin D-binding \nprotein was higher in \nall endometrioses by \n3 folds\nNR\nCho et al. \n2011 [37],\nKorea\nCase \ncontrol\n34.2 ± 6.88 /32.7 ± 10.26# Laparoscopy/histology urine prolif-\nerative & \nsecretory \nphases\n57/38 2DE with LC‒MS/MS VDBP protein, with \nm/z of 52,930.\n58/76\nTable 1 (continued)\n \n\nPage 7 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nAuthor \n(year) & \ncountry\nStudy \ndesign\nAge of the patient/con-\ntrol (mean ± SD/SEM, \nmedian with IQR)\nDiagnosis criteria Type of \nSample\nMenstru-\nal cycle\nNumber of subjects \n(case/control)\nProteomics platform/\nsurface\nMain findings: Pro-\nteins (m/z), in Da.\nSensitiv-\nity/speci-\nficity (%)\nEl-Kasti et al. \n2011 [105], \nUK\nProspec-\ntive \nrandom-\nized pilot \nstudy\n35# Laparoscopy urine periovula-\ntory & \nsecretory \nphase\n23/16 MALDI-TOF/MS 1 during the periovula-\ntory phase and 1 \nduring the luteal \nphase with m/z of \n1,767.1 and 1,824.3, \nrespectively (control \nvs. moderate/severe \nEndometriosis)\n75/75 and \n84.6/71.4\n2 peptide markers (1 \nduring the periovula-\ntory phase) 1 during \nthe luteal phase with \nm/z of 3,280.9 and \n1,933.8, respectively \n(minimal/mild vs. \nmoderate/severe \nEndometriosis)\n81.8/75 \nand \n87.5/75\nSeeber et al. \n2010 [106], \nUSA\nCase \ncontrol\nNA Laparoscopy serum NR 61/78 SELD-TOF-MS/CM10 6 proteins, with m/z of \n1629 3047, 3526, 3774, \n5046 & 5068\n66/99\nTokushige \net al. 2010 \n[107],\nAustralia\nCase \ncontrol\n32.8/38.6# Laparoscopy with histology urine prolif-\nerative & \nsecretory \nphase\n11/6 2DE with MALDI-TOF/MS 133 DEPs were \nsignificantly different \nbetween women \nwith and without \nendometriosis.\nCytokeratin-1 is highly \n↑ in endometriosis\nNR\nJing et al. \n2009 [108], \nJapan\nCase \ncontrol\n22–48 ** Laparoscopy with histology serum NA 30/31 SELDI-TOF-MS/IMAC30 2 proteins, with m/z of \n5,830 Da and 8,865\n86.67/96.77\nZhang et al. \n2009 [109], \nChina\nCase \ncontrol\nNA Histology serum NA 36/24 SELDI-TOF-MS/CM10 3 proteins, with m/z of \n4974, 5813 and 4290\n91.7/95.8\nLiu et 2009 \n[110], China\nCase \ncontrol\nNA Laparoscopy plasma NR 36/35 SELDI-TOF-MS 3 proteins, with m/z \nof 3956, 11 710, and \n6 986\n92/83\nWolfler et al. \n2009 [111],\nGermany\nProspec-\ntive cohort\n32.5/31.9# Laparoscopy with histology serum prolif-\nerative & \nsecretory \nphases\n51/39 SELDI-TOF-MS/Q10 5 proteins, with m/z of \n4159, 5264, 5603, 9861 \nand 10,533\n78.4/59\nTable 1 (continued)\n \n\nPage 8 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \n(https://metascape.org/) default parameters: terms with a \np value < 0.05, a minimum count of 3, and an enrichment \nfactor > 1.5 were deemed significant. Moreover, p values \nare calculated based on the cumulative hypergeometric \ndistribution, and q-values are calculated using the Ben -\njamini‒Hochberg procedure to account for multiple tests \n[36].\nProtein‒protein interaction (PPI) network construction and \nanalysis\nThe PPI network was constructed with the STRING \n(https://string-db.org/) database with a threshold of a \ncombined score > 0.4, and the interaction networks were \nvisualized with Cytoscape (version 3.10.1). In addition, \nthe molecular complex detection (MCODE) plug-in was \nused to screen strongly interconnected modules in the \nPPI network with default parameters (degree cut-off = 2, \nnode score cut-off = 0.2, and K-score = 2).\nHub gene selection and analyses\nThe Cyto-Hubba plug-in in Cytoscape (version 3.10.1) \nwas used to select hub genes in the PPI network. Based \non the evidence in the literature, we selected five of the \n12 algorithms in the cyto-Hubba plug-in and took the \nintersection of the five parameters (degree, edge perco -\nlated component, maximum neighborhood component, \nmaximal clique centrality, and eccentricity) to determine \nthe hub genes in each biological sample.\nResults\nStudy characteristics\nA total of 2,273 articles were identified from the online \ndatabases with the search strategy. After removing 351 \nduplicate results, 1922 articles remained. Moreover, \n1851 articles were excluded after reviewing the title and \nabstract, and 71 articles met the eligibility criteria for \nfull-text review and further consideration. Finally, 26 of \nthe 71 identified articles met the eligibility criteria. All \nselected studies were performed in Asian, American, and \nEuropean countries (9 in China, 1 in India, 1 in Japan, \n3 in the USA, 2 in South Korea, 1 in Belgium, 1 in Ger -\nmany, 1 in Austria, 2 in Italy, 1 in Australia, 2 in the UK, \n1 in Brazil, and 1 in Croatia). Platforms for proteomics \nincluded surface-enhanced laser desorption/ionization-\ntime of flight mass spectrometry (SELDI-TOF-MS) (8 \nstudies), SOMA scanning (1 study), electrospray ioniza -\ntion quadrupole time-of-flight mass spectrometry (ESI-\nQ-TOF-MS) (1 study), liquid chromatography‒mass \nspectrometry (LC‒MS/MS) (9 studies), and matrix-\nassisted laser desorption ionization-time of flight mass \nspectrometry (MALDI-TOF/MS) (7 studies). The biolog -\nical samples included in this study were peripheral blood \n(15 studies), urine (8 studies), cervical mucus (1 study), \nand menstrual blood (2 studies). A PRISMA flow chart \nAuthor \n(year) & \ncountry\nStudy \ndesign\nAge of the patient/con-\ntrol (mean ± SD/SEM, \nmedian with IQR)\nDiagnosis criteria Type of \nSample\nMenstru-\nal cycle\nNumber of subjects \n(case/control)\nProteomics platform/\nsurface\nMain findings: Pro-\nteins (m/z), in Da.\nSensitiv-\nity/speci-\nficity (%)\nWang et al. \n2008 [112], \nChina\nCase \ncontrol\n36/38* Laparoscopy with histology Serum NR 36/30 SELDI-TOF-MS/H4 5 proteins, with m/z of \n8142, 5640, 5847, 8940, \nand 3269\n91.7/90.0\nLiu et al. \n2007 [113], \nChina\nCase \ncontrol\n24–46** Histology plasma NR 52/46 SELDI-TOF-MS/WAX2 3 proteins, with m/z of \n3,956.83, 11,710.70 & \n6,986.45\n87.5/85.7\n#, Mean with/without standard deviation (SD), **, range; *, median, NR, not reported; DEP, differentially expressed protein; ↑--increased; ↓, decreased; SELDI-TOF-MS, surface-enhanced laser desorption/ionization-time \nof flight mass spectrometry; 2DE, two-dimensional gel electrophoresis with MALDI-TOF/MS, matrix-assisted laser desorption ionization–time-of-flight mass spectrometry; iTRAQ, isobaric tag for relative and absolute \nquantitation, LC ‒MS, liquid chromatography ‒mass spectrometry; 2D DIGE, two-dimensional difference gel electrophoresis, ESI-TOF-MS, electrospray ionization time-of-flight-mass spectrometry; UHPLC, ultrahigh-\nperformance liquid chromatography; m/z, mass-to-charge ratio; DEP , differentially expressed protein\nTable 1 (continued)\n \n\nPage 9 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nthat depicts each step is shown in Fig. 1. The studies anal-\nysed in this review were all conducted from 2007 to 2023, \nand a total of 2,486 women were enrolled.\nDiagnostic accuracy of proteins\nVarious proteomic techniques have been used to inves -\ntigate potential biomarkers for detecting endometriosis. \nPeripheral blood (serum and plasma) protein biomarker \nanalysis has a sensitivity of 38–100% and a specificity of \n59–99% for detecting endometriosis (Table  2). Addition-\nally, urine proteomic profiling revealed that single and/\nor combined proteins could detect endometriosis with a \nsensitivity ranging from 58 to 91% and a specificity rang -\ning from 76 to 93% (Table 2).\nFig. 1 PRISMA flowchart\n \n\nPage 10 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nCommon DEPs in endometriosis\nIn endometriosis, different proteins are expressed in \nvarious biological samples. Peroxiredoxin-6, angio -\npoietin-related protein, heterogeneous nuclear \nribonucleoproteins, peroxiredoxin-1, leucine-rich alpha-\n2-glycoprotein, alpha-2-macroglobulin, apolipoprotein \nL1 and haptoglobin are commonly expressed proteins in \nplasma and serum samples. Alpha-1-antitrypsin, alpha-\nenolase, albumin, and vitamin D-binding protein are \ncommonly expressed in both urine and serum, whereas \nS100-A8 and complement proteins are expressed in cer -\nvical mucus and menstrual blood as well as serum. Addi -\ntionally, dynamin-1-like protein, rho GTPase-activating \nprotein 6, rho GTPase-activating protein 18, zinc finger \nprotein 185, FYN-binding protein 1, rho GTPase-acti -\nvating protein 45, neurosecretory protein VGF, cartilage \noligomeric matrix protein, stromal interaction molecule \n1, polymeric immunoglobulin receptor, adipogenesis reg-\nulatory factor, complement C3, serum amyloid A-1 pro -\ntein, fibrinogen gamma chain and ATP-dependent RNA \nhelicase A are differentially expressed proteins in both \nserum and menstrual blood (Fig. 2 and Table S1).\nGO, KEGG, and PPI analyses of the DEPs in women with \nendometriosis\nA total of 644 DEPs (180 upregulated and 464 downregu -\nlated) were identified from 9 studies in different clinical \nsamples, such as peripheral blood (serum, plasma), men -\nstrual blood, cervical mucus, and urine. Among these \nstudies, 8 met the eligibility requirements for meta-anal -\nysis, and the remaining cervical mucus clinical samples \nwere comprehensively reviewed and described (Fig. 3).\nPlasma\nThe DEPs from plasma samples were analysed using \nGO terms that were categorized into molecular func -\ntions, cellular components, and biological processes. The \nmolecular functions of the DEPs were primarily enriched \nin signalling receptor activator activity, signalling recep -\ntor regulator activity, and kinase activity. The GO terms \nin the cellular component category were mainly related \nto the collagen-containing extracellular matrix, the exter-\nnal secretory granule lumen, and the extracellular matrix. \nThe biological process GO terms were primarily involved \nin the regulation of cell activation, regulation of leuko -\ncyte activation, and regulation of lymphocyte activation \n(Fig.  4 & Fig. S6). The enriched GO networks are also \nillustrated in Fig. S2.\nWe conducted KEGG pathway enrichment analysis of \nDEPs from plasma samples to explore DEP-related gene \npathways in endometriosis. Nitrogen metabolism path -\nways, the phosphatidylinositol 3 kinase-protein kinase B \n(PI3K-Akt) pathway, and microRNAs in cancer pathways \nwere the most significant (Fig.  4 & Fig. S7). In general, \nGO and KEGG analyses revealed that cell proliferation, \nadhesion, migration, and inflammation are involved in \nthe pathophysiology of endometriosis.\nPPI network analysis was performed for the 69 DEPs \nusing the STRING database. After removing proteins \nwithout standard symbols, a total of 68 nodes and 121 \nedges were obtained that represented the interaction net-\nwork with a p value of 1.98e-10. The top five hub genes \nidentified using the cyto-Hubba plugin included casein \nkinase II subunit alpha (CSNK2A1, CSNK2A2), mamma -\nlian topoisomerase 1 (TOP1), cAMP-dependent protein \nkinase catalytic subunit alpha (PRKACA) and RNA-\nbinding protein 39 (RBM39) (Fig. 2). The MCODE plugin \nTable 2 Sensitivity and specificity of proteins/peptides detected from (A) peripheral blood (serum/plasma) and (B) urine in \nendometriosis\nA)\nAuthor (Year) Mw (kDa*) of proteins/peptides TP FP FN TN Sensitivity (95%CI) Specificity (95%CI)\nFassbender et al. 2012 a 2,831.02, 7,554.66, 4,241.29, 2,953.25, & 9,927.73 25 5 40 28 0.38 (0.27, 0.51) 0.85(0.68,0.95)\nFassbender et al. 2012 b 9,926.31, 10,072.2, 6,753.04, 4,302.67, & 9,328.49 18 4 27 18 0.4 (0.26, 0.56) 0.82(0.6,0.95)\nFassbender et al. 2012 c 11,365.3, 5,712.69, 10,070.7, 3,017.68 & 3,824.44 29 6 26 27 0.53 (0.39, 0.66) 0.82(0.65,0.93)\nSeeber et al. 2010 1629.00 3047.00, 3526.00, 3774.00, 5046.00 & 5068.00 40 1 21 77 0.66 (0.52, 0.77) 0.99(0.93,1)\nWolfler et al. 2009 4159.00, 5264.00, 5603.00, 9861.00 & 10,533.00 40 16 11 23 0.78 (0.65, 0.89) 0.59(0.42,0.74)\nLiu et al. 2009 3,956.00, 11,710.00 & 6,986.00 14 3 2 12 0.88 (0.62, 0.98) 0.8(0.52,0.80)\nLiu et al. 2007 3,956.83, 11,710.70, & 6,986.45 32 4 4 28 0.89 (0.74, 0.97) 0.88(0.71,0.80)\nZhang et al. 2009 4974, 5813 & 4290 33 1 3 23 0.92 (0.78, 0.98) 0.96(0.79,1)\nZhang et al. 2013 4,180 29 3 0 32 1.00 (0.88, 1.00) 0.91(0.77,0.98)\nB)\nAuthor (Year) Mw (kDaa*)/name of proteins/peptides TP FP FN TN Sensitivity (95%CI) Specificity (95%CI)\nWang et al. 2014 1433.9, 1599.4, 2085.6, 6798.0 & 3217.2 10 1 1 14 0.91(0.59,1.00) 0.93(0.93,1.00)\nChen et al. 2019 Histone H4 18 14 7 58 0.72(0.51,0.88) 0.81(0.7,0.89)\nCho et al. 2012 VDBP-Cr 33 9 24 29 0.58(0.44,0.71) 0.76(0.60, 0.89)\n*kDa; kilodalton, Mw; molecular weight\n\nPage 11 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \ndistinguished two cluster networks, and all the top five \nhub genes, CSNK2A2, CSNK2A1, TOP1, PRKACA and \nRBM39, were included in the cluster with the highest \nscore.\nSerum\nCategory-based GO analysis of the DEPs from serum \nsamples was performed. The cellular component of the \nDEPs was predominantly enriched in collagen-containing \nextracellular matrix binding, extracellular matrix, and \nsecretory vesicle lumen. The molecular function category \nwas mainly involved in cell adhesion molecule binding, \nkinase binding, and actin binding (Fig.  4 & Fig S6). Actin \nfilament organization, supramolecular fiber organization, \nand regulation of body fluid levels are predominantly \ninvolved in biological processes. The enriched GO term \nnetworks are also illustrated in Fig S3.\nKEGG enrichment pathway analysis was carried out on \nserum samples to elucidate the pathogenesis of endome -\ntriosis. The top ten enriched pathways are illustrated in \nFig. 4 and Fig. S7. The complement and coagulation cas -\ncades, platelet activation, neutrophil extracellular trap \nformation, and tight junction pathways were the most \nenriched KEGG pathways.\nPPI network analysis of the 428 DEPs in serum was \nperformed using the STRING database. A total of 396 \nnodes and 3186 edges associated with the PPI network \nwere identified after removing proteins with no sym -\nbol name (PPI enrichment p value: < 1.0e-16). The five \ntop hub genes were identified using the cytoHubba plu -\ngin and included albumin (ALB), actin, cytoplasmic 1 \n(ACTB), glyceraldehyde-3-phosphate dehydrogenase \n(GAPDH), fibronectin (FN1), and apolipoprotein A-I \n(APOA1) (Fig. 2). The MCODE plugin distinguished two \ncluster networks, and all the top five hub genes, ALB, \nACTB, GAPDH, FN1, and APOA1, were included in the \ncluster with the highest score.\nMenstrual blood\nGO analysis demonstrated that DEPs derived from \nmenstrual blood are involved in the pathophysiology of \nendometriosis. In addition, the GO analysis results were \ncategorized into three components, i.e., molecular func -\ntions, cellular components, and biological processes. The \nmolecular functions of the DEPs were mainly enriched in \nprotease binding, receptor‒ligand activity, and fatty acid \nbinding. The GO terms in the cellular component cat -\negory were mainly involved in the vesicle lumen, secre -\ntory granule lumen and cytoplasmic vesicle lumen (Fig.  4 \n& Fig. S6). Granulocyte migration, granulocyte chemo -\ntaxis, and leukocyte chemotaxis are the main biological \nprocesses involved. The enriched GO networks are also \nshown in Fig. S4.\nThe enriched KEGG pathways of DEPs from men -\nstrual blood samples were used to further investigate \nDEP-related gene pathways. The top ten enriched path -\nways are illustrated in Fig.  4 and Fig. S7. IL-17 signal -\nling pathway, complement and coagulation cascades, \nFig. 2 The distribution of DEPs (overexpressed) in endometriosis patients in different clinical samples supplemented with Table S1: List of differentially \nexpressed proteins\n \n\nPage 12 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \ncytokine‒cytokine receptor interaction, and TNF signal -\nling pathway. In conclusion, the GO and KEGG enrich -\nment pathway analyses revealed that angiogenesis, cell \nproliferation, differentiation, and the induction of inflam-\nmation are highly important for the pathogenesis of \nendometriosis.\nThe STRING database was used for the PPI network \nanalysis of 110 DEPs. After identifying proteins with no \nsymbol name, there were 89 nodes and 134 edges associ -\nated with the PPI network (p value < 1.0e-16).\nThe top five hub genes identified using the cyto-Hubba \nplugin included protein S100 calcium-binding protein A9 \n(S100-A9), C-X-C motif chemokine ligand 1 (CXCL1), \ninterleukin-1 receptor antagonist protein (IL1RN), \ncystatin-A (CSTA), and protein S100-A8 (Fig.  2). The \nMCODE plugin illustrated three cluster networks (clus -\nter one: 7 nodes (desmoglein 1 & 3 (DSG1&DSG3), small \nproline-rich protein 3 (SPRR3), CSTA, small proline-rich \nprotein 1B (SPRR1B), ajuba LIM protein (JUB) and ser -\npin Family B Member 13 (SERPINB13), 19 edges; clus -\nter two: 7 nodes (S100-A8, S100-A9, myeloperoxidase \n(MPO), IL1RN, C-X-C motif chemokine ligand 1 & 5 \n(CXCL1, CXCL5); and growth differentiation factor 15 \n(GDF15), 14 edges; and cluster three: 3 nodes (haptoglo -\nbin (HP), cyclic adenosine 3′,5′-monophosphate (CAMP) \nand resistin (RETN) and 3 edges). The top five hub genes, \nS100A9, IL1RN, CSTA, S100A8, and CXCL1, were \nincluded in the cluster with the highest score.\nUrine\nThe three categories of GO term analysis, i.e., molecular \nfunctions, cellular components, and biological processes, \nof the DEPs from urine samples were notably involved \nin the pathophysiology of endometriosis. The molecular \nfunctions of the DEPs were mainly enriched in collagen \nbinding, cytokine binding, and transforming growth fac -\ntor binding. The GO terms in the cellular components \ncategory were mainly involved in the collagen-containing \nextracellular matrix, secretory vesicle lumen, and extra -\ncellular matrix (Fig.  4 & Fig. S6). Cell‒cell adhesion, \nplasminogen activity regulation, and body fluid level \nFig. 3 Top five DEPs in (a) plasma, (b) serum, (c) menstrual blood, and (d) urine of women with endometriosis\n \n\nPage 13 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nregulation are the main biological processes involved. \nThe enriched GO networks are also illustrated in Fig S5.\nThe KEGG pathway enrichment of DEPs from urine \nsamples revealed the DEP-related gene pathways that are \ninvolved in the mechanism of endometriosis pathogene -\nsis. ECM receptor interactions and microRNAs in cancer \npathways were the pathways most significantly associated \nwith endometriosis development (Fig.  4 & Fig. S7). Gen-\nerally, GO and KEGG analyses revealed that cell growth \nand invasion, adhesion, and angiogenesis were implicated \nin the pathophysiology of endometriosis.\nPPI network analysis of the 22 DEPs was performed \nusing the STRING database, which revealed 22 nodes \nand 39 edges associated with the PPI network ( p value: \n9.7e-14). The top five hub genes identified using the cyto-\nHubba plugin included thrombospondin-1 (THBS1), \nalbumin (ALB), CD44 antigen (CD44), annexin A2 \n(ANXA2), and (LUM) (Fig.  2). The MCODE plugin dis -\ntinguished two cluster networks. In cluster one, CD44, \nalkaline phosphatase (ALP), zinc-alpha-2-glycoprotein \n(AZGP1), alpha-1-antitrypsin (SERPINA1), ANAX2, \nand enolase 1 (ENO1) were the most sub connected pro -\nteins, whereas transforming growth factor beta receptor \n2 (TGFBR2), endoglin (ENG), THBS1 and LUM were the \nmost highly connected subnetworks in cluster two.\nDiscussion\nThis is a comprehensive systematic review and meta-\nanalysis of proteomics data to explore common pathways \nand non-invasive diagnostic biomarkers for detecting \nendometriosis. Proteomic platforms offer an extraordi -\nnary opportunity to overcome the challenges associated \nwith endometriosis by providing valuable insights into \nthe mechanisms underlying the disease and identifying \npotential markers for diagnosis and therapeutic target -\ning. Hence, this study focused on recent improvements \nin proteomics technology aimed at identifying poten -\ntial non-invasive diagnostic biomarkers and establishing \nmechanistic pathways to understand the pathogenesis of \nendometriosis.\nAlteration of proteins in endometriosis\nThis study investigated DEPs in peripheral blood, cer -\nvical mucus, menstrual blood, and urine from women \nwith endometriosis. Although many proteins are altered \nin women with endometriosis, this review illustrates the \ncommon DEPs in diverse biological samples from women \nwith endometriosis. DEPs commonly found in multiple \nbiological samples, including vitamin D binding protein \n(VDBP), haptoglobin, S100-A8, cathepsin G, and com -\nplement component 3, are discussed.\nVDBP is one of the most common proteins whose \nexpression is altered in women with endometriosis. A \nline of evidence has shown that the expression of VDBP \nFig. 4 GO and KEGG analyses of the DEPs in women with endometriosis\n \n\nPage 14 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nis substantially increased in the urine [ 37] and serum \n[38] of women with endometriosis compared to women \nwithout endometriosis. Similarly, the expression level of \nVDBP is markedly higher in endometrial tissue [ 39] but \nlower in peritoneal fluid [ 40] in women with endome -\ntriosis. Although studies have shown that VDBP may be \nimplicated in the pathogenesis of endometriosis because \nof its chemotactic characteristics and ability to attract \nimmune cells [ 39, 40], inconsistent patterns of VDBP \nexpression have been observed across studies. The poten-\ntial reasons for discrepancies may be observed in vari -\nous studies, attributing them to differences in biological \nspecimens, protein extraction procedures, centrifugal \nforces, and analysis platforms. Regarding the abundance \nof VDBP , studies have described diverse techniques for \nsample handling and analysis, such as 2DE-gel electro -\nphoresis with LC‒MS/MS [ 37, 38] and ELISA [ 41, 42]. \nThese disparities highlight the potential influence of \nmethodological applications, as evidenced by (1) the \nsuperior sensitivity of LC‒MS/MS compared to ELISA, \n(2) the possibility of cell loss in the supernatant, affect -\ning the abundance and concentration of proteins when \nemploying low centrifugation force or short processing \ntime, and (3) the superior sensitivity and ability of ELISA \nto detect very small amounts of target proteins compared \nto 2DE-gel electrophoresis [ 43, 44]. These perspectives \nhighlight the clinical utility of LC‒MS/MS, which is a \nstandard and high-throughput proteomics technology \nwith a lesser tendency for bias or interference, as well as \ngreater quantitative agreement among laboratories and \nbiological samples [45, 46]. Given the wide range of varia-\ntion within biological samples that does not adequately \nexplore protein alterations across the severity and phe -\nnotype of endometriosis, conducting further large-scale \nmulti-omics studies would be helpful to elucidate the \nassociation between VDBP and the underlying mecha -\nnism of endometriosis.\nThe expression level of haptoglobin decreased in the \nplasma and serum of women with endometriosis [ 47]. \nHowever, this finding contradicts the findings of Wöl -\nfler et al., who demonstrated that the alteration of hap -\ntoglobin is significantly increased in the peritoneal fluid \nof patients with ovarian and peritoneal endometriosis \n[48]. The potential variation may be due to the diverse \nphenotypes of endometriosis, including ovarian, peri -\ntoneal, and deep endometriotic lesions, as well as the \ntiming of sample collection. The upregulation of estro -\ngen and the estrogen receptor on macrophages in the \nperitoneal cavity generates an abnormal immune micro -\nenvironment, potentially resulting in increased hapto -\nglobin production [ 49]. In addition to the phenotype of \nendometriosis, the depletion of proteins should also be \nconsidered for the variations that ensue. Some studies \ndepleted the most abundant proteins, such as albumin \nand globulin, to detect low-abundance proteins, which \nmay be putative disease biomarkers in biological samples \n[47], whereas other studies did not mention the deple -\ntion process during protein extraction and identification \n[50– 52]. Therefore, protein depletion can affect the hap -\ntoglobin concentration during protein extraction via dif -\nferent mechanisms, including reduced solubility, altered \nprotein‒ligand interactions, and competitive binding \n[53– 55]. The proteomics analysis platform is also another \nconfounding factor. The two common analysis platforms \nare mass spectrometry and enzyme-linked immunoassay. \nBoth techniques are used to detect the concentration and \nexpression of proteins. However, compared with ELISA, \nmass spectrometry (MS)-based proteomics analysis [ 47, \n50] provides high accuracy, resolution, reproducibility, \nand sensitivity in identifying and quantifying proteins \nin a complex mixture, often not allowing differentiation \nbetween the peptide and its derivatives or degradation \nfragments [49, 56].\nThis study similarly demonstrated that the protein \nS100-A8 is markedly reduced in the cervical mucus of \nwomen with endometriosis. This finding supports the \nfindings of a study conducted in France, which identified \nS100-A8 as a promising endometrial diagnostic marker \nfor both the proliferative and secretory phases [57]. Addi-\ntionally, another study showed that S100A8 is predomi -\nnant in the peritoneal fluid of women with early-stage \ndeep endometriosis [ 51]. In addition, the presence of \nhigher levels of S100A8 in the peritoneal fluid of women \nwith endometriosis suggests its potential contribution \nto the development and formation of lesions within the \nperitoneal cavity through inflammatory pathways by acti-\nvating neutrophils [58, 59].\nThis study also revealed that cathepsin G is a common \nDEP in the urine, serum, and plasma of women with \nendometriosis. This finding supports the findings of a \nstudy conducted in Poland, which revealed that cathep -\nsin G is significantly elevated in the endometrial tissue \nof women with endometriosis and may play a role in dis -\nease development and progression [ 60]. Several lines of \nevidence have demonstrated that cathepsin G plays an \nessential role in the pathogenesis of endometriosis by \npromoting extracellular matrix degradation and invasion \n[61], activating collagen production [ 61], and stimulat -\ning the inflammatory process [ 62], which facilitates the \nimplantation and growth of endometrial tissue outside \nthe uterus.\nThis comprehensive study also showed that comple -\nment C3 levels are significantly higher in women with \nendometriosis than in those without endometriosis. \nSimilarly, it has been reported that the abundance of C3 \nis significantly higher in peritoneal fluid [ 63] and endo -\nmetrial tissue [64, 65] in women with endometriosis. The \ninvolvement of complement C3, as expressed by ectopic \n\nPage 15 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nendometrial tissue, in the formation of endometriotic \nlesions is mediated by mast cell activation. Additionally, \nit may be generated locally by ectopic endometrial tis -\nsue and can promote the engraftment of endometriotic \ncysts [65, 66]. Moreover, cyto-hub gene analysis revealed \nthat CSNK2A1, CSNK2A2, TOP1, PRKACA, RBM39 \n(plasma), ALB, ACTB, GAPDH, FN1, APOA1 (serum), \nS100-A9, CXCL1, IL1RN, CSTA, S100-A8 (menstrual \nblood) and THBS1, ALB, CD44, ANXA2, and LUM \n(urine) were the top 5 proteins expressed in women with \nendometriosis. Among all the proteins, ALB is commonly \nexpressed in both serum and urine. These disparities \nwere also revealed by a study conducted by Donal S et al., \nwho reported that the percentages of proteins in venous \nblood, menstrual blood, and vaginal fluid were 61%, 36%, \nand 35%, respectively. These body fluid-derived proteins \ncould contribute to augmenting the diagnosis of endome-\ntriosis combined with imaging techniques and physical \nexaminations. Nevertheless, to enhance the diagnostic \naccuracy of non-invasive biological sample-derived pro -\nteins, further comprehensive functional and validation \nmulti-omics studies with large sample sizes are needed.\nGO analysis revealed that the modulation of molecu -\nlar, functional, and cellular processes contributes to the \npathophysiology of endometriosis through the activa -\ntion of the collagen-containing extracellular matrix, \nextracellular matrix, secretory granule lumen, and others \n[67]. These GO terms play a role in cell migration, adhe -\nsion, angiogenesis, immune response, lymphocyte acti -\nvation, tissue survival, and facilitating the implantation \nand potential growth of ectopic endometrial lesions [ 13, \n68– 71].\nKEGG enrichment analysis revealed that nitrogen \nmetabolism [ 72], PI3K-Akt [ 73], platelet activation [ 74], \nthe NOD-like receptor signalling pathway [ 75], ECM-\nreceptor interactions [ 76], cytokine‒cytokine receptor \ninteractions [76], IL-17 signalling [ 77], complement and \ncoagulation cascades [ 78], TNF signalling and proteogly -\ncans in cancer [79] have been implicated in the pathogen-\nesis of endometriosis. These pathways play a significant \nrole in the cellular growth and survival of endometriotic \nlesions [80– 82]. The ECM pathway plays a key role in cell \nmigration, adhesion, and tissue remodelling through the \nmodulation of matrix metalloproteinases that interact \nwith various growth factors and proinflammatory cyto -\nkines, such as transforming growth factor-beta (TGF-\nβ), interleukin-1 (IL-1), and tumor necrosis factor-alpha \n(TNF-α) [83– 85].\nThe NOD-like receptor pathway is an important sig -\nnalling pathway that is involved in the pathogenesis of \nendometriosis [ 86]. This pathway encompasses the fam -\nily pyrin domain containing 3 (NLRP3), an intracellular \nFig. 5 Newly proposed approach for the integrative study of endometriosis\n \n\nPage 16 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nreceptor that initiates the release of proinflammatory \ncytokines such as interleukin-1β (IL-1β) upon the acti -\nvation of NLRP3. Abnormal activation of the NLRP3 \ninflammasome has been observed within ectopic endo -\nmetrial lesions, peritoneal fluid, and the eutopic endome-\ntrium of women with endometriosis. This dysregulated \nactivation significantly contributes to persistent inflam -\nmation and accompanying pain related to the condition \n[86– 89]. Cytokine‒cytokine receptor interactions and \nthe IL-17 signalling pathway have been implicated in the \npathogenesis of endometriosis. IL-17 has been shown to \npromote the production of other proinflammatory cyto -\nkines, such as IL-1α and IL-1β, involved in the pathogen -\nesis of endometriosis [ 77]. Additionally, an interaction \nbetween the complement system and coagulation system \nmight contribute to the pathophysiology of endometrio -\nsis following the monthly shedding of endometrial tis -\nsues, triggering complement activation resulting from \nthe activation of the microenvironment in women diag -\nnosed with endometriosis [90].\nProteoglycans involved in cancer pathways are com -\nmonly enriched in both the serum and urine of women \nwith endometriosis. Proteoglycans are complex mol -\necules that are secreted by cancer cells and stromal cells \nand are composed of glycosaminoglycan (GAG) chains \n[91]. The literature has shown that proteoglycans play \na significant role in regulating cell-to-cell and cell-to-\nmatrix interactions, releasing growth factors and cyto -\nkines that can promote cell proliferation and invasion \n[92]. Hence, the trapping and release of angiogenic fac -\ntors and cytokines that trigger proliferation and invasion \nare implicated in the pathophysiology of endometriosis.\nOverall, this proteomics study provides insights into \nthe expression of common and distinct proteins that are \nexpressed in women with endometriosis. Given the dif -\nferent conditions of the study participants, the pheno -\ntype and severity of endometriosis, sample handling, \nand processing methods, proteomic platforms, and dif -\nferent menstrual cycles, we recommend the use of an \nintegrated multi-OMICS study in which all non-invasive \nbiological samples from the same patients are adjusted \nfor confounders to enhance the mechanism of disease \ndevelopment and provide an opportunity to identify \nnovel diagnostic and therapeutic targets for endometrio -\nsis (Fig. 5).\nStrengths and limitations\nThis is a comprehensive systematic review and meta-\nanalysis to explore the applicability of the proteomics \napproach to discover novel diagnostic biomarkers and \nunravel therapeutic targets from non-invasive bio -\nlogical samples. Additionally, this study serves as an \ninput for further multi-OMICS studies to uncover and \nestablish novel diagnostic and therapeutic targets in \nendometriosis. There are some limitations in our study. \nFirst, there is a lack of sufficient studies on the overall \ndiagnostic accuracy of individual or combined proteins \nbased on the expression molecular weight of proteins/\npeptides in different phases of the menstrual cycle. \nAlthough the literature has shown protein expression \nin endometriosis during different phases of the human \nmenstrual cycle, the difference in protein expression \nbetween the proliferative and secretory phases remains \ncontroversial. Therefore, further evidence is required to \nexplore the diagnostic accuracy of protein biomarkers \nconcerning the m/z ratio in different phases of the men -\nstrual cycle. Second, the lack of available raw data and/\nor full protein lists allowed us to focus only on the dif -\nferentially expressed protein lists, which could affect the \nconclusions of the findings. Additionally, the lack of stud-\nies did not allow us to look at the differentially expressed \nproteins across the stages (early vs. advanced, subtypes \nof endometriosis (ovarian, peritoneal & deep infiltrating) \nand menstrual cycles (secretary, proliferative and men -\nstrual phases).\nConclusion\nIn summary, this comprehensive meta-analysis of dif -\nferentially expressed proteins from non-invasive clinical \nsamples highlights the pathophysiology of endometriosis \nwith GO and enriched KEGG pathways. Moreover, pro -\nteomics holds promise for the discovery of peripheral \nblood, menstrual blood, cervical mucus, and urine-based \nbiomarkers for endometriosis. Various upregulated and \ndownregulated proteins have been identified, suggesting \ntheir potential utility as promising non-invasive biomark-\ners for endometriosis detection and disease development \nmechanisms.\nFurthermore, this review explored how the expres -\nsion of different proteins and pathways in multiple clini -\ncal samples from non-invasive sources can be used to \nelucidate the pathophysiology of endometriosis. Finally, \nour findings provide new knowledge that will be helpful \nin understanding the pathophysiology of endometriosis, \nand future integrated studies involving peripheral blood, \nmenstrual blood, and urine samples are needed. The \nidentified proteins and pathways not only expand our \nunderstanding of the disease but also offer promising tar-\ngets for future research. Furthermore, validation of these \nfindings, exploration of hub genes for diagnostic accu -\nracy, and further research across a wider range of sam -\nples and endometriosis types are key to revealing new \noptions for non-invasive diagnosis and helping to explore \nmore effective potential treatment options. Moreover, \nfurther research is needed to validate these findings and \npotentially help to improve the diagnosis, enhance patho-\nphysiology, and offer hints for potential treatments for \nendometriosis.\n\nPage 17 of 19\nAzeze et al. Journal of Translational Medicine          (2024) 22:685 \nSupplementary Information\nThe online version contains supplementary material available at https://doi.\norg/10.1186/s12967-024-05474-3.\nSupplementary Material 1: Figure S1. QUADAS-2 tool: The distribution \nof risk-of-bias (A) and applicability (B) judgments within each bias domain. \nFigure S2. Network of enriched GO terms in peripheral blood (plasma): \n(a) biological process, (b) cellular component and (c) molecular function. \nFigure S3. Network of enriched GO terms in peripheral blood (serum): (a) \nbiological process, (b) cellular component and (c) molecular function. \nFigure S4. Network of enriched GO terms in menstrual blood. (a) biological \nprocess (b) cellular component and (c) molecular function. Figure S5. \nNetwork of enriched GO terms in urine: (a) biological process, (b) cellular \ncomponent and (c) molecular function. Figure S6. GO term analysis of \nDEPs in plasma, serum, menstrual blood, and urine from patients with \nendometriosis\nSupplementary Material 2: Table S1. List of differentially expressed \nproteins\nAcknowledgements\nNot applicable.\nAuthor contributions\nG.G.A. initially began the review and wrote the protocol with help from \nW.C.C. and Z.T. G.G.A. and B.A.K. performed the data extraction and quality \nassessment for the selected articles. The analysis was carried out by G.G.A. and \nW.L. G.G.A. wrote the first draft of the manuscript with the help of W.C.C., Z.T., \nC.E.C.W., L.W.F., F.L.W.Y. and W.L., who provided feedback on the review and \nmodifications. All authors contributed to and approved the final version of this \narticle.\nFunding\nNA.\nData availability\nThe data underlying this article are available upon the request of the \ncorresponding authors.\nDeclarations\nEthics approval and consent to participate\nNot applicable.\nConsent for publication\nNot applicable.\nConflict of interest\nThe authors declare that there are no conflicts of interest.\nAuthor details\n1Department of Obstetrics and Gynaecology, Faculty of Medicine, Prince \nof Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong \nKong SAR\n2Department of Midwifery, College of Medicine and Health Sciences, \nInjibara University, Injibara, Ethiopia\n3Department of Midwifery, College of Medicine and Health Sciences, \nDebre Markos University, Debre Markos, Ethiopia\n4School of Biomedical Sciences; Li Ka Shing Institute of Health Sciences; \nChinese University of Hong Kong – Sichuan University Joint Laboratory \nin Reproductive Medicine, The Chinese University of Hong Kong, Shatin, \nHong Kong SAR\nReceived: 12 April 2024 / Accepted: 3 July 2024\nReferences\n1. 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Fertil Steril. 2007;87(4):988–90.\nPublisher’s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in \npublished maps and institutional affiliations.","source_license":"CC0","license_restricted":false}