An integrated multi-tissue approach for endometriosis candidate biomarkers: a systematic review

We systematically searched the PubMed and Embase databases for research articles on endometriosis biomarkers published between January 2005 and September 2022. Of the 879 publications retrieved after exclusion of duplicates, 278 focused on comparisons i) among endometriosis patients or between tissues in endometriosis patients or ii) between endometriosis and cancer patients (Fig.  1 ). We excluded these articles since their usability for endometriosis diagnosis is limited. 270 articles did not include information on the patient endometriosis phenotypes according to either the rARSM classification or lesions localization (Fig.  1 ). These articles were also excluded, as we chose to study how each candidate biomarker was potentially relevant to specific subtypes of patients. In total, we retained 447 publications for further analysis (Additional Table 1 ), of which 387 identified at least one biomarker with significantly modified levels in endometriosis patients compared to controls (Fig.  1 ). Information on endometriosis phenotypes was a mandatory inclusion criterion in this study, and 73% of selected publications adjusted the results accordingly, either intentionally or indirectly by including only a particular phenotype (Fig.  2 ). Biomarker levels can vary with menstrual cycle phases [ 11 ], but only 47% of selected publications provided information about cycle phase. Just over half took this parameter into account when analysing the results (Fig.  2 ), and mainly because surgical teams operated on patients either in the follicular phase or in the luteal phase. Regarding treatments, 42% of articles provided some information (Fig.  2 ). Non-use of hormonal treatments in the 3 to 6 months prior to inclusion is often specified (and sometimes non-use of anti-inflammatory drugs in the days before inclusion), explaining why only 3% of the analysed publications adjust results for hormonal or symptomatic treatments (anti-inflammatory, painkillers, etc.; Fig.  2 ). Although endometriosis symptoms are very diverse, this aspect is the least documented, with only 32% of analysed articles taking symptoms into account (Fig.  2 ). Of these, 17% reported subgroup analyses depending on symptoms (Fig.  2 ). Infertility, a consequence of endometriosis often regarded as a symptom, was the most commonly considered. Fig. 2 Intermediate analyses carried out on 387 articles. Analyses performed i) on the adjustments of the results according to the subtype of endometriosis, the menstrual cycle phases, the treatments and the symptoms and ii) on the different biological compartments studied Intermediate analyses carried out on 387 articles. Analyses performed i) on the adjustments of the results according to the subtype of endometriosis, the menstrual cycle phases, the treatments and the symptoms and ii) on the different biological compartments studied Among 447 retained publications, 387 identified a significant biomarker in at least one biological compartment (Fig.  1 and 2 ). The majority studied peripheral blood (183 articles, 47,3%) (Fig.  2 , Additional Table  2 ). Other studied compartments were eutopic endometrium (93 articles, 24%), peritoneum and peritoneal fluid (74 articles, 19,1%), ovary (mainly follicular fluid or cumulus cells; 21 articles, 5,4%), urine (12 articles, 3,1%), menstrual blood (2 articles, 0,5%), saliva (2 articles, 0,5%), feces (1 article, 0,3%) and cervical mucus (1 article, 0,3%) (Fig.  2 ). 444 biomarkers were identified in peripheral blood, 408 in eutopic endometrium, 188 in peritoneum and peritoneal fluid, 96 in compartments related to ovary, 77 in urine, 23 in menstrual blood, 13 in saliva, 6 in feces and 1 in cervical mucus (Additional Table  2 ). In total, we listed 1107 candidate biomarkers of endometriosis, several of which were identified in different compartments. Interestingly, only a minority were reproducibly found in independent articles within the same compartment (Additional Table  2 ), questioning the standardisation and reproducibility of endometriosis candidate biomarker studies. Moreover, only 74 were found in several biological compartments by at least two independent research teams (Table  1 ). These 74 biomarkers significantly modified in endometriosis were classified into molecular subtypes (Table  1 ) and used for the following selections. The names of the 74 candidate biomarkers identified, their direction of variation in the different biological compartments studied and the cohort sizes of the selected articles are detailed in Table  1 . Table 1 Biomarkers found in different biological compartments by independent teams Molecular subtypes Biomarkers found in several tissues Biological compartments References by compartment: B for Blood, P for Peritoneum/Peritoneal fluid, E for Endometrium, O for ovary, U for Urine, M for menstrual blood, and Sa for Saliva B P E O U M Sa n  = X&Y with X the number of samples from women without endometriosis, Y the number of samples from women with endometriosis Up/Down (in endometriosis patients vs controls) is indicated when studies are inconsistent Immunity related markers / Cytokines CXCL8 (IL8) ↑ ↑ ↑ ↓* ↑* B: [ 12 ] n  = 93&201 ; [ 13 ] n  = 25&19; [ 14 ] n  = 12&75 P: [ 15 ] n  = 30&48 ; [ 16 ] n  = 16&67; [ 17 ] n  = 20&57; [ 18 ] n  = 27&36; [ 19 ] n  = 40&58 ; [ 20 ] n  = 34&124 ; [ 21 ] n  = 38&56 ; [ 22 ] n  = 45&126 ; [ 23 ] n  = 35&45 E: [ 24 ] n  = 11&24; [ 25 ] n  = 8&15; [ 26 ] n  = 5&5 O: [ 27 ] n  = 9&9 (cumulus cells) – M: [ 28 ] n  = 3&3 TNFa ↑↓ ↑ ↑ ↑ B: Up [ 13 ] n  = 25&19; Up [ 29 ] n  = 103&190 , Down [ 12 ] n  = 93&201 ; Down [ 30 ] n  = 121&232 P: [ 31 ] n  = 22&30; [ 32 ] n  = 17&33; [ 17 ] n  = 20&57; [ 23 ] n  = 35&45 ; [ 33 ] n  = 59&73 E: [ 24 ] n  = 11&24 – O: [ 34 ] n  = 279&47 (follicular fluid) (s)ICAM-1 ↑↓ ↑↓ ↑ ↓* B: Up [ 35 ] n  = 20&48; Up [ 36 ] n  = 48&49; Down [ 30 ] n  = 121&232; Down [ 37 ] n  = 86&170 P: Up [ 38 ] n  = 6&12; Up in peritoneal tissue in menstrual phase, Down in luteal phase [ 24 ] n  = 11&24 E: [ 26 ] n  = 5&5 – O: [ 27 ] n  = 9&9 (cumulus cells) IFNɣ ↑↓ ↑ ↓ ↑ B: Up [ 39 ] n  = 68&70 ; Up [ 13 ] n  = 25&19; Down [ 30 ] n  = 121&232 P: [ 40 ] n  = 30&50 ; [ 17 ] n  = 20&57 – O: [ 41 ] n  = 29&20 (follicular fluid) M: [ 42 ] (in the supernatant of menstrual blood derived stem cells with an allogeneic stimulation) n  = 6&6 IL6 ↑ ↑ ↑ ↑* B: [ 43 ] n  = 72&38 ; [ 39 ] n  = 68&70 ; [ 44 ] n  = 31&38 ; [ 12 ] n  = 93&201 ; [ 32 ] n  = 17&33; [ 13 ] n  = 25&19; [ 35 ] n  = 20&48; [ 45 ] n  = 22&47; [ 46 ] n  = 35&45 ; [ 47 ] n  = 60&80 ; [ 48 ] n  = 32&40 ; [ 49 ] n  = 35&43 ; [ 29 ] n  = 103&190 ; [ 14 ] n  = 12&75; P: [ 24 ] n  = 11&24; [ 31 ] n  = 22&30; [ 16 ] n  = 16&67; [ 32 ] n  = 17&33; [ 50 ] n  = 28&70; [ 51 ] n  = 42&36 ; [ 19 ] =  40&58 ; [ 35 ] n  = 20&48; [ 45 ] n  = 22&47 [ 23 ]; n  = 35&45 ; [ 46 ] n  = 35&45 ; [ 22 ] n  = 45&126 ; [ 48 ] n  = 32&40 ; E: [ 26 ] n  = 5&5 – M: [ 28 ] n  = 3&3 CCL5 (RANTES) ↑ ↑ ↑ B: [ 13 ] n  = 25&19 – P: [ 17 ] n  = 20&57; [ 52 ] n  = 20&74 – E: [ 53 ] n  = 5&15 CXCL10 (IP-10) ↓ ↑↓ ↑* B: [ 54 ] n  = 70&77 – E: [ 55 ] n  = 8&8 P: [ 56 ] Up n  = 32&101 ; [ 33 ] Up n  = 59&73 ; Up [ 38 ] n  = 6&12; [ 54 ] Down n  = 70&77 ; [ 52 ] n  = 20&74 Immunity related markers / Cytokines IL6R ↓* ↑ ↓* B and P: [ 45 ] n  = 22&47 – E: [ 55 ] n  = 8&8 IL4 ↑ ↑ ↑ B: [ 13 ] n  = 25&19; [ 47 ] n  = 60&80 P: [ 57 ] n  = 31&38 ; [ 17 ] n  = 20&57 – O: [ 41 ] n  = 29&20 (follicular fluid) IL17A ↓ ↑↓ ↓ B: [ 58 ] n  = 16&27 – P: [ 52 ] Up n  = 20&74 [ 22 ], Down n  = 45&126 O: [ 41 ] n  = 29&20 (follicular fluid) IL2 ↑↓ ↑ ↓ B: Up [ 13 ] n  = 25&19, Down [ 44 ] n  = 31&38 , Down [ 58 ] n  = 16&27; P: [ 58 ] n  = 15&27 – O: [ 59 ] n  = 5&5 (follicular fluid) IL13 ↑↓ ↓ ↑ B: Up [ 13 ] n  = 25&19, Down [ 60 ] n  = 46&57 P: [ 21 ] n  = 38&56 – O: [ 41 ] n  = 29&20 IL10 ↑ ↑ ↑ B: [ 13 ] n  = 25&19; [ 58 ] n  = 16&27; P: [ 51 ] n  = 42&36 ; [ 23 ] 2018 n  = 35&45 – M: [ 42 ] n  = 6&6 CCL2 (MCP-1) ↑ ↑ ↑ B: [ 61 ] n  = 31&18; [ 62 ] n  = 60&102 ; [ 39 ] n  = 68&70 ; [ 29 ] n  = 103&190 ; P: [ 56 ] n  = 32&101 ; [ 18 ] n  = 27&36; [ 21 ] n  = 38&56 M: [ 42 ] n  = 6&6 IL9, IL37 ↑ ↑ B: IL37 [ 58 ] n  = 36&27, IL9: [ 13 ] n  = 25&19, B and P: [ 48 ] n  = 32&40 P: IL9 [ 17 ] n  = 20&57 GM-CSF ↑ ↑ ↓* B: [ 13 ] n  = 25&19 – P: [ 17 ] n  = 20&57 – O: [ 27 ] n  = 9&9 IL1β ↑↓ ↑ ↑* B: Up [ 13 ] n  = 25&19; [ 49 ] Up n  = 35&43 , Down [ 30 ] n  = 121&232 P: [ 31 ] n  = 22&30; [ 57 ] n  = 31&38 ; [ 17 ] n  = 20&57; [ 33 ] n  = 59&73 M: [ 28 ] n  = 3&3 IL12 ↑↓ ↑ B: Up [ 13 ] n  = 25&19, Down [ 60 ] n  = 46&57 – P: [ 63 ] n  = 33&72 , [ 33 ] n  = 59&73 FAS/CD95 +  ↑↓ ↑* B: [ 64 ] Up (soluble) n  = 30&30 , [ 40 ] Down (T cell surface) n  = 30&50 , P: [ 65 ] (in NK cells) n  = 24&46; [ 66 ] n  = 18&26 IL1α ↑ ↑↓* P: [ 52 ] n  = 20&74 O: [ 41 ] Up n  = 29&20 (folicular fluid), [ 27 ] Down n  = 9&9 (cumulus cells) IL3 ↓ ↑ P [ 33 ]: n  = 59&73 – O: [ 41 ] n  = 29&20 (folicular fluid) CCL22 (MDC) ↑ ↓ P: [ 52 ] n  = 20&74 – O: [ 41 ] n  = 29&20 (folicular fluid) IL32 ↑ ↑* B: [ 67 ] n  = 35&50 – E: [ 55 ] n  = 8&8 CD8 + (cytotoxic T cells) ↓ ↑ B: [ 40 ] n  = 30&50 , [ 68 ] n  = 20&54 – E: [ 69 ] n  = 15&15 CD3 + or CD4 + (T cells) ↑ ↑ CD3 ↑ CD4 B: [ 40 ] n  = 30&50 , [ 68 ] n  = 20&54 P: [ 16 ] = 16&67 E: [ 69 ] n  = 15&15 CD25HIGH/FOXP3 + /CD4 +  (Treg cells) ↓ ↑ B: [ 70 ] n  = 15&17 P: [ 70 ] n  = 15&17, [ 50 ] n  = 28&70; [ 71 ] n  = 25&25 Immunity related markers / Cytokines CD68 (macrophages) ↑ ↑ P [ 15 ]: n  = 30&48 ; [ 31 ] n  = 22&30; [ 72 ] n  = 18&38; [ 45 ] n  = 22&47 E: [ 73 ] n  = 36&37 GAL-3 ↑ ↓ P: [ 74 ] n  = 8&15 – E: [ 75 ] n  = 34&34 PTGS2 ↑ ↓ E: [ 76 ] n  = 21&26, O: [ 77 ] n  = 40&38 (Cumulus cells) CXCL12 (SDF-1) ↑ ↑* B: [ 78 ] n  = 10&11, O: [ 27 ] n  = 9&9 ECM/Cell Markers/ Cell fate MMP-9 (or MMP-9/NGAL) ↑ ↑ ↑ ↑ ↑* B: [ 79 ] n  = 31&60 ; [ 80 ] n  = 26&50; [ 81 ] n  = 140&200 – P [ 80 ]: n  = 26&50 O: [ 81 ] n  = 140&200 ; U: [ 82 ] n  = 58&73 – M: [ 28 ] n  = 3&3 MMP-2 ↑ ↓ ↑* O: [ 81 ] n  = 140&200 – U: [ 83 ] n  = 25&25; M: [ 28 ] n  = 3&3 MMP-3 ↑ ↑ ↑ B: [ 84 ] n  = 20&40; [ 85 ] n  = 20&40 P: [ 24 ] n  = 11&24; E: [ 24 ] n  = 11&24, [ 86 ] n  = 20&23 MMP-7 ↑ ↑ P: [ 20 ] n  = 34&124 ; E: [ 87 ] n  = 110&109 OSTEOPONTIN, PERIOSTIN ↑ ↑ ↑ Osteopontin: B and E: [ 88 ] n  = 41&40 – P: [ 38 ] n  = 6&12 Periostin: B and P: [ 89 ] n  = 80&104 – E: [ 90 ] n  = 11&14 CA-125 aka MUC-16 ↑ ↑ B: [ 25 ] n  = 32&71 ; [ 91 ] n  = 37&47 ; [ 92 ] n  = 17&35; [ 93 ] n  = 52&52 P: [ 94 ] n  = 43&65 ; [ 92 ] n  = 17&35; [ 93 ] n  = 52&52 ; [ 25 ] n  = 32&71 [ 91 ] ; n  = 37&47 CA-19–9 ↑ ↑ B: [ 95 ] n  = 36&50 ; [ 96 ] n  = 40&60 ; [ 91 ] n  = 37&47 – P: [ 20 ] n  = 34&124 CYTOKERATIN-19 ↑ ↑ B: [ 97 ] n  = 35&44 – U: [ 97 ] n  = 35&44 ; [ 98 ] n  = 6&11 TIMP-1 ↓ ↑ ↓ B: [ 81 ] n  = 140&200 – P: [ 22 ] n  = 45&126 – O: [ 81 ] n  = 140&200 FIBRONECTIN ↓ ↑ E: [ 99 ] n  = 18&40 – O: [ 100 ] n  = 10&20 TGF-β ↑ ↑ ↑ ↑ B: [ 81 ] n  = 140&200 – P: [ 50 ] n  = 28&70 E: [ 101 ] n  = 10&10 – O: [ 81 ] n  = 140&200 BCL2 ↑ ↑* E: [ 102 ] n  = 20&20 – M: [ 28 ] n  = 3&3 SOX2 ↑ ↑* E: [ 103 ] n  = 16&26 – M: [ 28 ] n  = 3&3 Metabolites GLUTAMINE, ALANINE ↑ ↓* ↓ B: Glutamine: [ 104 ] n  = 15&22; Alanine: [ 105 ] n  = 23&22 E: [ 106 ] n  = 24&95 – O: [ 107 ] n  = 9&7 (samples n  = 50&29) LYSINE ↑ ↑↓ ↓ B: [ 105 ] n  = 23&22 – U: [ 108 ] n  = 36&45 E: Up [ 109 ] n  = 29&37; Down [ 106 ] n  = 24&95 LEUCINE ↑ ↑↓ B: [ 105 ] n  = 23&22 E Down: [ 106 ] n  = 24&95; Up: [ 109 ] n  = 29&37 ISOLEUCINE ↓ ↑ B: [ 105 ] n  = 23&22 – O: [ 107 ] n  = 9&7 (samples n  = 50&29) THREONINE ↑ ↓ B: [ 105 ] n  = 23&22 – O: [ 107 ] n  = 9&7 (samples n  = 50&29) VALINE ↑ ↑ B: [ 105 ] n  = 23&22; [ 110 ] n  = 23&50 U: [ 108 ] n  = 36&45 Metabolites TYROSINE ↑ ↓ E: [ 109 ] n  = 29&37; [ 106 ] n  = 24&95 O: [ 107 ] n  = 9&7 (samples n  = 50&29) TAURINE ↑ ↑ E: [ 106 ] n  = 24&95 – U: [ 108 ] n  = 36&45 LACTATE, 3-HYDROXYBUTYRATE ↑ ↑ B: [ 105 ] n  = 23&22 O: [ 107 ] n  = 9&7 (samples n  = 50&29) GLUCOSE ↓ ↑* ↓ B: [ 29 ] n  = 103&190 ; [ 105 ] n  = 23&22 – E [ 106 ]: n  = 24&95 O: [ 107 ] n  = 9&7 (samples n  = 50&29) Hormones/ Growth Factors ERα ↑ ↑↓ ↑* P: [ 31 ] n  = 22&30 – M: [ 28 ] n  = 3&3 E: [ 111 ] Up n  = 18&38, [ 112 ] Down n  = 26&28 ERβ ↑ ↓ P: [ 31 ] = 22&30, E [ 112 ]: n  = 26&28 LEPTIN ↑ ↓* B: [ 113 ] n  = 30&30 P: [ 19 ] n  = 40&58 AMH ↓ ↑* B: [ 114 ] n  = 93&57 , [ 115 ] n  = 17&17 – E: [ 116 ] n  = 20&23 FGF-2 ↑* ↑ P: [ 52 ] n  = 20&74, [ 117 ] n  = 18&38 VEGF/VEGF-A ↑↓ ↑ ↑* ↑* B: Up [ 30 ] n  = 121&232; Up [ 29 ] n  = 103&190; Up [ 118 ] n  = 822&1109; Up [ 119 ] n  = 30&30; Down [ 25 ] n  = 32&71; Down [ 60 ] n  = 46&57; P [ 22 ]: n  = 45&126 – E: [ 26 ] n  = 5&5 – M: [ 28 ] n  = 3&3 PDGF-A ↑* ↓ P: [ 52 ] n  = 20&74 – E: [ 25 ] n  = 8&15 ACTIVI N -A ↑* ↑* B: [ 120 ] n  = 75&139 – P: [ 121 ] n  = 3&6 miRNAs MIR-451/MIR-451A ↑↓ ↑ ↑ ↓ B: Up [ 122 ] n = 24&24; Up [ 123 ] n = 99&89 ; Down [ 124 ] n = 66&80 P: [ 22 ] n = 45&126 – E: [ 125 ] n = 20&40 – O: [ 126 ] n = 30&30 (follicular fluid) MIR-122, MIR-199A ↑ ↑ B: [ 127 ] n  = 25&60, [ 46 ] n  = 35&45 – P: [ 46 ] n  = 35&45 MIR-15A-5P ↓ ↓ B: [ 128 ] n  = 30&60 – E: n  = 3&3 MIR-17/MIR-17-5P ↓ ↓ B: [ 47 ] n  = 60&80 ; [ 128 ] n  = 30&60 – E: [ 129 ] n  = 51&51 MIR-135A ↑↓ ↑ B: Down [ 130 ] n  = 24&24; Up [ 131 ] n  = 17&17 – Sa: [ 131 ] n  = 17&17 Other GLYCODELIN-A ↑ ↑ B: [ 37 ] n  = 86&170 ; [ 30 ] n  = 121&232 [ 19 ] ; n  = 42&57 ; [ 35 ] n  = 20&48 P: [ 37 ] n  = 86&170 ; [ 19 ] n  = 42&57 ; [ 32 ] n  = 17&33, [ 35 ] n  = 20&48 HAPTOGLOBIN ↓ ↑ B: [ 132 ] n  = 15&15 – P: [ 133 ] n  = 8&16 PGP9.5 ↑ ↑* P: [ 72 ] n  = 18&38 – E: [ 134 ] n  = 20&20 Arrows indicate the direction of change in endometriosis patients. *: change detected only in a subgroup of patients (either because of patient inclusion criteria restricting to a subgroup of patients, or detected only in a specific subgroup of all included patients). ILX Interleukin X, CA-X Cancer Antigen X, MMP-X Matrix Metalloproteinase-X, CDX Cluster of Differentiation X, CXCLX Chemokine (C-X-C motif) ligand X, TNFα Tumor Necrosis Factor alpha, (s)ICAM-1 (soluble) Intercellular adhesion molecule-1, IFNy Interferon gamma, CCL5 Chemokine (C-C motif) ligand 5, IP-10 Interferon gamma induced protein 10, IL6R Interleukin 6 receptor, MCP-1 Monocyte Chemoattractant protein-1, GM-CSF Granulocyte/macrophage-colony stimulating factor, MDC Macrophage-derived chemokine, GAL-3 Galectin-3, VEGF Vascular Endothelial Growth Factor, NGAL Neutrophil Gelatinase-Associated Lipocalin, TIMP-1 TIMP metallopeptidase inhibitor-1, MUC-16 Mucin-16, TGF-β Transforming growth factor beta, BCL2 B-cell lymphoma 2, ERα Estrogen receptor alpha, ERβ Estrogen receptor beta, AMH Anti-Müllerian hormone, FGF-2 Fibroblast growth factor 2, PDGF Patelet-derived growth factor, PGP9.5 Protein gene product 9.5, PTGS2 Prostaglandin-endoperoxide synthase 2, SDF-1 Stromal cell-derived facotr-1. Biomarkers found in different biological compartments by independent teams Immunity related markers / Cytokines B: [ 12 ] n  = 93&201 ; [ 13 ] n  = 25&19; [ 14 ] n  = 12&75 P: [ 15 ] n  = 30&48 ; [ 16 ] n  = 16&67; [ 17 ] n  = 20&57; [ 18 ] n  = 27&36; [ 19 ] n  = 40&58 ; [ 20 ] n  = 34&124 ; [ 21 ] n  = 38&56 ; [ 22 ] n  = 45&126 ; [ 23 ] n  = 35&45 E: [ 24 ] n  = 11&24; [ 25 ] n  = 8&15; [ 26 ] n  = 5&5 O: [ 27 ] n  = 9&9 (cumulus cells) – M: [ 28 ] n  = 3&3 B: Up [ 13 ] n  = 25&19; Up [ 29 ] n  = 103&190 , Down [ 12 ] n  = 93&201 ; Down [ 30 ] n  = 121&232 P: [ 31 ] n  = 22&30; [ 32 ] n  = 17&33; [ 17 ] n  = 20&57; [ 23 ] n  = 35&45 ; [ 33 ] n  = 59&73 E: [ 24 ] n  = 11&24 – O: [ 34 ] n  = 279&47 (follicular fluid) B: Up [ 39 ] n  = 68&70 ; Up [ 13 ] n  = 25&19; Down [ 30 ] n  = 121&232 P: [ 40 ] n  = 30&50 ; [ 17 ] n  = 20&57 – O: [ 41 ] n  = 29&20 (follicular fluid) M: [ 42 ] (in the supernatant of menstrual blood derived stem cells with an allogeneic stimulation) n  = 6&6 B: [ 43 ] n  = 72&38 ; [ 39 ] n  = 68&70 ; [ 44 ] n  = 31&38 ; [ 12 ] n  = 93&201 ; [ 32 ] n  = 17&33; [ 13 ] n  = 25&19; [ 35 ] n  = 20&48; [ 45 ] n  = 22&47; [ 46 ] n  = 35&45 ; [ 47 ] n  = 60&80 ; [ 48 ] n  = 32&40 ; [ 49 ] n  = 35&43 ; [ 29 ] n  = 103&190 ; [ 14 ] n  = 12&75; P: [ 24 ] n  = 11&24; [ 31 ] n  = 22&30; [ 16 ] n  = 16&67; [ 32 ] n  = 17&33; [ 50 ] n  = 28&70; [ 51 ] n  = 42&36 ; [ 19 ] =  40&58 ; [ 35 ] n  = 20&48; [ 45 ] n  = 22&47 [ 23 ]; n  = 35&45 ; [ 46 ] n  = 35&45 ; [ 22 ] n  = 45&126 ; [ 48 ] n  = 32&40 ; E: [ 26 ] n  = 5&5 – M: [ 28 ] n  = 3&3 B: [ 54 ] n  = 70&77 – E: [ 55 ] n  = 8&8 P: [ 56 ] Up n  = 32&101 ; [ 33 ] Up n  = 59&73 ; Up [ 38 ] n  = 6&12; [ 54 ] Down n  = 70&77 ; [ 52 ] n  = 20&74 B: [ 13 ] n  = 25&19; [ 47 ] n  = 60&80 P: [ 57 ] n  = 31&38 ; [ 17 ] n  = 20&57 – O: [ 41 ] n  = 29&20 (follicular fluid) B: [ 58 ] n  = 16&27 – P: [ 52 ] Up n  = 20&74 [ 22 ], Down n  = 45&126 O: [ 41 ] n  = 29&20 (follicular fluid) B: Up [ 13 ] n  = 25&19, Down [ 44 ] n  = 31&38 , Down [ 58 ] n  = 16&27; P: [ 58 ] n  = 15&27 – O: [ 59 ] n  = 5&5 (follicular fluid) B: Up [ 13 ] n  = 25&19, Down [ 60 ] n  = 46&57 P: [ 21 ] n  = 38&56 – O: [ 41 ] n  = 29&20 B: [ 13 ] n  = 25&19; [ 58 ] n  = 16&27; P: [ 51 ] n  = 42&36 ; [ 23 ] 2018 n  = 35&45 – M: [ 42 ] n  = 6&6 B: [ 61 ] n  = 31&18; [ 62 ] n  = 60&102 ; [ 39 ] n  = 68&70 ; [ 29 ] n  = 103&190 ; P: [ 56 ] n  = 32&101 ; [ 18 ] n  = 27&36; [ 21 ] n  = 38&56 M: [ 42 ] n  = 6&6 B: IL37 [ 58 ] n  = 36&27, IL9: [ 13 ] n  = 25&19, B and P: [ 48 ] n  = 32&40 P: IL9 [ 17 ] n  = 20&57 B: Up [ 13 ] n  = 25&19; [ 49 ] Up n  = 35&43 , Down [ 30 ] n  = 121&232 P: [ 31 ] n  = 22&30; [ 57 ] n  = 31&38 ; [ 17 ] n  = 20&57; [ 33 ] n  = 59&73 M: [ 28 ] n  = 3&3 B: [ 64 ] Up (soluble) n  = 30&30 , [ 40 ] Down (T cell surface) n  = 30&50 , P: [ 65 ] (in NK cells) n  = 24&46; [ 66 ] n  = 18&26 P: [ 52 ] n  = 20&74 O: [ 41 ] Up n  = 29&20 (folicular fluid), [ 27 ] Down n  = 9&9 (cumulus cells) B: [ 40 ] n  = 30&50 , [ 68 ] n  = 20&54 P: [ 16 ] = 16&67 E: [ 69 ] n  = 15&15 CD25HIGH/FOXP3 + /CD4 + (Treg cells) B: [ 70 ] n  = 15&17 P: [ 70 ] n  = 15&17, [ 50 ] n  = 28&70; [ 71 ] n  = 25&25 Immunity related markers / Cytokines P [ 15 ]: n  = 30&48 ; [ 31 ] n  = 22&30; [ 72 ] n  = 18&38; [ 45 ] n  = 22&47 E: [ 73 ] n  = 36&37 ECM/Cell Markers/ Cell fate B: [ 79 ] n  = 31&60 ; [ 80 ] n  = 26&50; [ 81 ] n  = 140&200 – P [ 80 ]: n  = 26&50 O: [ 81 ] n  = 140&200 ; U: [ 82 ] n  = 58&73 – M: [ 28 ] n  = 3&3 B: [ 84 ] n  = 20&40; [ 85 ] n  = 20&40 P: [ 24 ] n  = 11&24; E: [ 24 ] n  = 11&24, [ 86 ] n  = 20&23 Osteopontin: B and E: [ 88 ] n  = 41&40 – P: [ 38 ] n  = 6&12 Periostin: B and P: [ 89 ] n  = 80&104 – E: [ 90 ] n  = 11&14 B: [ 25 ] n  = 32&71 ; [ 91 ] n  = 37&47 ; [ 92 ] n  = 17&35; [ 93 ] n  = 52&52 P: [ 94 ] n  = 43&65 ; [ 92 ] n  = 17&35; [ 93 ] n  = 52&52 ; [ 25 ] n  = 32&71 [ 91 ] ; n  = 37&47 B: [ 81 ] n  = 140&200 – P: [ 22 ] n  = 45&126 – O: [ 81 ] n  = 140&200 B: [ 81 ] n  = 140&200 – P: [ 50 ] n  = 28&70 E: [ 101 ] n  = 10&10 – O: [ 81 ] n  = 140&200 B: Glutamine: [ 104 ] n  = 15&22; Alanine: [ 105 ] n  = 23&22 E: [ 106 ] n  = 24&95 – O: [ 107 ] n  = 9&7 (samples n  = 50&29) B: [ 105 ] n  = 23&22 – U: [ 108 ] n  = 36&45 E: Up [ 109 ] n  = 29&37; Down [ 106 ] n  = 24&95 B: [ 105 ] n  = 23&22 E Down: [ 106 ] n  = 24&95; Up: [ 109 ] n  = 29&37 B: [ 105 ] n  = 23&22; [ 110 ] n  = 23&50 U: [ 108 ] n  = 36&45 E: [ 109 ] n  = 29&37; [ 106 ] n  = 24&95 O: [ 107 ] n  = 9&7 (samples n  = 50&29) B: [ 105 ] n  = 23&22 O: [ 107 ] n  = 9&7 (samples n  = 50&29) B: [ 29 ] n  = 103&190 ; [ 105 ] n  = 23&22 – E [ 106 ]: n  = 24&95 O: [ 107 ] n  = 9&7 (samples n  = 50&29) Hormones/ Growth Factors P: [ 31 ] n  = 22&30 – M: [ 28 ] n  = 3&3 E: [ 111 ] Up n  = 18&38, [ 112 ] Down n  = 26&28 B: [ 113 ] n  = 30&30 P: [ 19 ] n  = 40&58 B: Up [ 30 ] n  = 121&232; Up [ 29 ] n  = 103&190; Up [ 118 ] n  = 822&1109; Up [ 119 ] n  = 30&30; Down [ 25 ] n  = 32&71; Down [ 60 ] n  = 46&57; P [ 22 ]: n  = 45&126 – E: [ 26 ] n  = 5&5 – M: [ 28 ] n  = 3&3 B: Up [ 122 ] n = 24&24; Up [ 123 ] n = 99&89 ; Down [ 124 ] n = 66&80 P: [ 22 ] n = 45&126 – E: [ 125 ] n = 20&40 – O: [ 126 ] n = 30&30 (follicular fluid) B: [ 37 ] n  = 86&170 ; [ 30 ] n  = 121&232 [ 19 ] ; n  = 42&57 ; [ 35 ] n  = 20&48 P: [ 37 ] n  = 86&170 ; [ 19 ] n  = 42&57 ; [ 32 ] n  = 17&33, [ 35 ] n  = 20&48 Arrows indicate the direction of change in endometriosis patients. *: change detected only in a subgroup of patients (either because of patient inclusion criteria restricting to a subgroup of patients, or detected only in a specific subgroup of all included patients). ILX Interleukin X, CA-X Cancer Antigen X, MMP-X Matrix Metalloproteinase-X, CDX Cluster of Differentiation X, CXCLX Chemokine (C-X-C motif) ligand X, TNFα Tumor Necrosis Factor alpha, (s)ICAM-1 (soluble) Intercellular adhesion molecule-1, IFNy Interferon gamma, CCL5 Chemokine (C-C motif) ligand 5, IP-10 Interferon gamma induced protein 10, IL6R Interleukin 6 receptor, MCP-1 Monocyte Chemoattractant protein-1, GM-CSF Granulocyte/macrophage-colony stimulating factor, MDC Macrophage-derived chemokine, GAL-3 Galectin-3, VEGF Vascular Endothelial Growth Factor, NGAL Neutrophil Gelatinase-Associated Lipocalin, TIMP-1 TIMP metallopeptidase inhibitor-1, MUC-16 Mucin-16, TGF-β Transforming growth factor beta, BCL2 B-cell lymphoma 2, ERα Estrogen receptor alpha, ERβ Estrogen receptor beta, AMH Anti-Müllerian hormone, FGF-2 Fibroblast growth factor 2, PDGF Patelet-derived growth factor, PGP9.5 Protein gene product 9.5, PTGS2 Prostaglandin-endoperoxide synthase 2, SDF-1 Stromal cell-derived facotr-1. The majority of selected studies included between 10 and 50 women in each group with and without endometriosis (Fig.  3 ). Unsurprisingly, all studies including more than 500 women per group focused on peripheral blood. Biological compartments are also very unequally studied in cohorts with 100 to 500 women per group: 84.3% studied peripheral blood, 8.6% eutopic endometrium, 5.7% peritoneal fluid and 1.4% follicular fluid. Biological compartments that can be assessed non-invasively (urine, menstrual blood, saliva, feces, cervical mucus), have not been studied on a large scale. Fig. 3 Cohort size distribution. Analyses performed among the 387 articles kept and illustrating the distribution of studies according to cohort size Cohort size distribution. Analyses performed among the 387 articles kept and illustrating the distribution of studies according to cohort size We highlighted 4 candidate biomarkers identified by at least two different research teams in 3 biological compartments or more, with at least one well-powered study per compartment including 30 or more controls and 30 or more patients with endometriosis (Table  2 ). Here, we summarized the main lines of evidence supporting each of these 4 biomarkers as potential diagnostic elements for endometriosis. Table 2 Candidate biomarkers found by independent teams in at least 3 biological compartments in large cohort Biomarker Patients Potential confunding factors Type of measured molecule related to the indicated biomarker Main results Missing information Adjustements Statistics as regards diagnostic accuracy References Control group (n) Endometriosis group (n) Age BMI (endometriosis patients vs controls) Biological sample type Global variation (endometriosis patients vs control) Concerning endometriosis phenotype, cycle phase, hormonal treatments or symptoms Endometriosis subtype Cycle phase Symptoms Hormonal treatments AUC Sensitivity Specificity TNF-alpha Healthy volunteers without detected endometriosis by ultrasound examination ( n  = 103) Surgically and histologically proven ovarian endometriosis ( n  = 190) Stage I/II ( n  = 77) and III/IV ( n  = 113) NS ND Protein Serum Increased 1.4 times Cycle phase, treatment for controls, symptoms Higher in stage III/IV vs I/II NA ND or NA ND or NA 0.776 (alone) 0.913 (with VEGF, sFlt-1, IL6 and MCP1) ND (alone) 85.79% (with VEGF, sFlt-1, IL6 and MCP1) ND (alone) 87.38% (with VEGF, sFlt-1, IL6 and MCP1) [ 29 ] Without endometriosis (laparoscopic exam) ( n  = 93) Endometriosis patients ( n  = 201) Stage I/II ( n  = 132) and III/IV ( n  = 69) ND ND or NA Protein Plasma Reduced 14.6 times Pain symptoms, lesions localisations Significant reduction for I/II and III/IV vs controls NS I/II vs III/IV Change detected in all and secretory phase ND or NA NP 0.758 (all phase) 0.787 (secretory) 79.5% 80.6% 73.7% 73.7% [ 12 ] Without endometriosis (laparoscopic exam) ( n  = 121) Endometriosis patients ( n  = 232) Training subset ( n  = 155), test subset ( n  = 67) Stage I/II ( n  = 148) and III/IV ( n  = 84) US negative ( n  = 175) NS ND or NA Protein Plasma Reduced 1.2 times (only in training set) Lesions localisations ND (for stages) Change detected in all and proliferative phase ND NP 0.65 (for training set US negative patients in follicular phase) 78% 57% [ 30 ] Without endometriosis (laparoscopic exam) ( n  = 35) Surgically and histologically proven endometriosis ( n  = 45) Stage I/II/III/IV ( n  = 10/8/18/9) PE ( n  = 39), OE ( n  = 18), DIE ( n  = 18) NS Lower Protein Plasma NS Pain symptoms other than dysmenorrhea Higher in patients with vs without DIE NS (between phase) NS ND ND ND ND [ 23 ] Peritoneal fluid Increased ~ 2 times Without endometriosis (laparoscopic exam) ( n  = 59) Endometriosis ( n  = 73) Stage I/II ( n  = 31) a n d III/IV ( n  = 42) PE ( n  = 17), OE ( n  = 30), DIE ( n  = 14) NS NS or NA Protein Peritoneal fluid NS (All endometriosis) Increased 3.39 times (OE, proliferative phase only) None Higher in OE vs DIE (all phases) Higher OE vs PE (proliferative phase) Main result only found in proliferative phase ND NP ND ND ND [ 33 ] Infertile patients undergoing ICSI without endometriosis as assessed by laparoscopy ( n  = 279) Surgically and histologically proven endometriosis, infertile patients undergoing ICSI ( n  = 47) Stage I/II ( n  = 33) and III/IV ( n  = 14) NS NS Protein Follicular fluid Increased 2 times Pain symptoms, lesions localisations ND NP NA NP ND ND ND [ 34 ] Biomarker Patients Potential confunding factors Type of measured molecule related to the indicated biomarker Main results Missing information Adjustements Statistics as regards diagnostic accuracy References Control group (n) Endometriosis group (n) Age BMI (endometriosis patients vs controls) Biological sample type Global variation (endometriosis patients vs control) Concerning endometriosis phenotype, cycle phase, hormonal treatments or symptoms Endometriosis subtype Cycle phase Symptoms Hormonal treatments AUC Sensitivity Specificity MMP-9 or MMP-9/NGAL Healthy volunteers without endometriosis (laparoscopic exam) ( n  = 31) Surgically proven endometriosis ( n  = 60) OE ( n  = 31) NS NS Protein Serum Increased 1.25 times Cycle phase and symptoms Higher in stage III/IV vs I/II NA NA NP Predictive accuracy of MMP-9 for severe endometriosis (threshold: 14.13 pg/ml) 0.878 80% 73.3% [ 79 ] Infertile women without endometriosis but with tubal-factor infertility undergoing IVF ( n  = 200) Infertile women with endometriosis confirmed by laparoscopy and histological analysis, undergoing IVF Stage III/IV ( n  = 140) NS NS Protein Serum Increased 1.5 times Pain symptoms, lesions localisations NP NP NA/NP for infertility Progesterone supplementation decrease (1.4 times) MMP-9 serum level in pregnant women ND ND ND [ 81 ] Follicular fluid Increased 1.5 times Surgically and histologically or clinically verified no evidence of endometriosis ( n  = 58) Surgically and histologically verified endometriosis or clinical diagnosis of endometriosis ( n  = 73) NA NA Protein Urine NA Cycle phase, hormonal treatments, symptoms and lesions localisations NP NA NA NA The odds ratio of having MMP-9 or MMP-9/NGAL in the urine and having endometriosis was 7.8 (95% CI: 2.5–25.1; p < 0.001) and 6.3 (95% CI: 1.7–22.8; p < 0.001), respectively ND ND [ 82 ] TIMP-1 Infertile women without endometriosis but with tubal-factor infertility undergoing IVF ( n  = 200) Infertile women with endometriosis confirmed by laparoscopy and histological analysis, undergoing IVF Stage III/IV ( n  = 140) NS NS Protein Serum Reduced 1.2 times Pain symptoms, lesions localisations NP NP NA/NP for infertility NP ND ND ND [ 81 ] Follicular fluid Reduced 1.2 times Healthy volunteers without endometriosis (laparoscopic exam) ( n  = 45) Surgically proven endometriosis ( n  = 126) NA NA Protein Peritoneal fluid Increased 1.9 times Pain symptoms, lesions localisations No statistical difference between stage I/II and III/IV Increase only in secretory phase NA/Same range of increase regardless of fertility status NP ND ND ND [ 22 ] Biomarker Patients Potential confunding factors Type of measured molecule related to the indicated biomarker Main results Missing information Adjustements Statistics as regards diagnostic accuracy References Control group (n) Endometriosis group (n) Age BMI (endometriosis patients vs controls) Biological sample type Global variation (endometriosis patients vs control) Concerning endometriosis phenotype, cycle phase, hormonal treatments or symptoms Endometriosis subtype Cycle phase Symptoms Hormonal treatments AUC Sensitivity Specificity miR-451/miR-451a Healthy volunteers without endometriosis (laparoscopic exam) ( n  = 99) Surgically proven endometriosis ( n  = 89) Stage I/II/III/IV ( n  = 27/17/36/19) NS NS miRNA Serum Increased 4.5 times (qRT-PCR) Symptoms, lesions localisations Significant increase for I/II and III/IV vs controls NS I/II vs III/IV NS NA NS 0.84 (alone) 90% 72.9% [ 123 ] 0.939 (in combination with miR-125b, miR-150, miR-342, miR-3613 and let-7b) 83% 96% Healthy volunteers without endometriosis ( n  = 66) Infertile women with endometriosis confirmed by laparoscopy and histological analysis, undergoing IVF/ICSI ( n  = 80) Stage I/II (60) and III/IV (20) NS NS miRNA Serum Decreased 1.7 times (qRT-PCR) Other symptoms than infertility for patients, symptoms and treatment for control, cycle phase, lesions localisations NP NA NA NA 0,978 ND ND [ 124 ] Without endometriosis (laparoscopic exam) ( n  = 45) Surgically proven endometriosis ( n  = 126) NA NA miRNA Peritoneal fluid Increased 2.5 times (qRT-PCR) Pain symptoms, lesions localisations No statistical difference between stage I/II and III/IV Significantly up-regulated during menstrual phase compared to proliferative and secretory phase in endometriosis women but not in control NA/No impact of the fertility status NP ND ND ND [ 22 ] Infertile women without endometriosis (laparoscopic exam), undergoing IVF ( n  = 30) Infertile women with endometriosis confirmed by laparoscopy, undergoing IVF Stage III/IV All with OE ( n  = 30) NS NS miRNA Follicular fluid Decreased 2 times (qRT-PCR) Pain symptoms NP NP NA/NP for infertility NP ND ND ND [ 126 ] ND : Not disclosed/done (despite available information), NA Not available, NS No significant difference/correlation, NP : Not possible (same for all samples), PE Peritoneal Endometriosis, OE Ovarian Endometriosis, DIE Deep Infiltrating Endometriosis, ICSI IntraCytoplasmic Sperm Injection, IVF In Vitro Fertilization, US Ultrasound, VEGF Vascular Endothelial Growth Factor, sFLt-1 Soluble Fms-like tyrosine kinase 1, MCP1 Monocyte Chemoattractant protein 1, Il6 Interleukin 6, TNF-alpha Tumor Necrosis Factor-alpha, MMP-9 Matrix Metalloproteinase-9, NGAL Neutrophil Gelatinase-Associated Lipocalin, TIMP-1 TIMP metallopeptidase inhibitor 1 Candidate biomarkers found by independent teams in at least 3 biological compartments in large cohort Surgically and histologically proven ovarian endometriosis ( n  = 190) Stage I/II ( n  = 77) and III/IV ( n  = 113) Higher in stage III/IV vs I/II 0.776 (alone) 0.913 (with VEGF, sFlt-1, IL6 and MCP1) ND (alone) 85.79% (with VEGF, sFlt-1, IL6 and MCP1) ND (alone) 87.38% (with VEGF, sFlt-1, IL6 and MCP1) Without endometriosis (laparoscopic exam) ( n  = 93) Endometriosis patients ( n  = 201) Stage I/II ( n  = 132) and III/IV ( n  = 69) Significant reduction for I/II and III/IV vs controls NS I/II vs III/IV 0.758 (all phase) 0.787 (secretory) 79.5% 80.6% 73.7% 73.7% Endometriosis patients ( n  = 232) Training subset ( n  = 155), test subset ( n  = 67) Stage I/II ( n  = 148) and III/IV ( n  = 84) US negative ( n  = 175) Reduced 1.2 times (only in training set) Without endometriosis (laparoscopic exam) ( n  = 35) Surgically and histologically proven endometriosis ( n  = 45) Stage I/II/III/IV ( n  = 10/8/18/9) PE ( n  = 39), OE ( n  = 18), DIE ( n  = 18) Without endometriosis (laparoscopic exam) ( n  = 59) Endometriosis ( n  = 73) Stage I/II ( n  = 31) a n d III/IV ( n  = 42) PE ( n  = 17), OE ( n  = 30), DIE ( n  = 14) NS (All endometriosis) Increased 3.39 times (OE, proliferative phase only) Higher in OE vs DIE (all phases) Higher OE vs PE (proliferative phase) Surgically and histologically proven endometriosis, infertile patients undergoing ICSI ( n  = 47) Stage I/II ( n  = 33) and III/IV ( n  = 14) Healthy volunteers without endometriosis (laparoscopic exam) ( n  = 31) Surgically proven endometriosis ( n  = 60) OE ( n  = 31) Predictive accuracy of MMP-9 for severe endometriosis (threshold: 14.13 pg/ml) 0.878 Infertile women with endometriosis confirmed by laparoscopy and histological analysis, undergoing IVF Stage III/IV ( n  = 140) Surgically and histologically or clinically verified no evidence of endometriosis ( n  = 58) Surgically and histologically verified endometriosis or clinical diagnosis of endometriosis ( n  = 73) Infertile women with endometriosis confirmed by laparoscopy and histological analysis, undergoing IVF Stage III/IV ( n  = 140) Healthy volunteers without endometriosis (laparoscopic exam) ( n  = 45) Surgically proven endometriosis ( n  = 126) Healthy volunteers without endometriosis (laparoscopic exam) ( n  = 99) Surgically proven endometriosis ( n  = 89) Stage I/II/III/IV ( n  = 27/17/36/19) Significant increase for I/II and III/IV vs controls NS I/II vs III/IV Healthy volunteers without endometriosis ( n  = 66) Infertile women with endometriosis confirmed by laparoscopy and histological analysis, undergoing IVF/ICSI ( n  = 80) Stage I/II (60) and III/IV (20) Without endometriosis (laparoscopic exam) ( n  = 45) Surgically proven endometriosis ( n  = 126) Infertile women without endometriosis (laparoscopic exam), undergoing IVF ( n  = 30) Infertile women with endometriosis confirmed by laparoscopy, undergoing IVF Stage III/IV All with OE ( n  = 30) ND : Not disclosed/done (despite available information), NA Not available, NS No significant difference/correlation, NP : Not possible (same for all samples), PE Peritoneal Endometriosis, OE Ovarian Endometriosis, DIE Deep Infiltrating Endometriosis, ICSI IntraCytoplasmic Sperm Injection, IVF In Vitro Fertilization, US Ultrasound, VEGF Vascular Endothelial Growth Factor, sFLt-1 Soluble Fms-like tyrosine kinase 1, MCP1 Monocyte Chemoattractant protein 1, Il6 Interleukin 6, TNF-alpha Tumor Necrosis Factor-alpha, MMP-9 Matrix Metalloproteinase-9, NGAL Neutrophil Gelatinase-Associated Lipocalin, TIMP-1 TIMP metallopeptidase inhibitor 1 Tumor necrosis factor alpha (TNF-α), a pro-inflammatory cytokine, was consistently reported as increased in larger cohorts of women with endometriosis in two biological compartments: peritoneal fluid [ 23 , 135 ], and follicular fluid [ 34 ]. In the peritoneal fluid, this increase was further consolidated by consistent results from smaller cohorts [ 17 , 31 , 32 ], although two other studies found no significant differences between women with and without endometriosis [ 21 , 51 ]. How TNF-α changes in peritoneal fluid tie in with endometriosis phenotypes and menstrual phases was unclear, with reported increases in both stages I/II and III/IV [ 32 ], only in stage III/IV with no difference between proliferative and secretory phases, or only in patients with endometrioma and in proliferative phase [ 135 ] – some of which may reflect inappropriate statistical power to control for Type II errors when stratifying cohorts. Another increase was also detected in the endometrium at the mRNA level during menstrual phase [ 24 ]. While TNF-α was also reported as modified in blood, results were inconsistent, reporting increases [ 13 , 29 ], decreases [ 12 , 30 ] or no change [ 32 , 39 , 44 , 60 , 67 ], both within small or large cohorts. When focusing on large cohorts, increases were observed in serum while the decreases were in plasma, suggesting an importance on the blood collection method (Table  2 ). Unfortunately, diagnostic accuracy of TNFα was only assessed in blood and yielded low specificity and sensitivity, which is unsurprising in light of the discrepancies across studies. Enzyme matrix metalloproteinase (MMPs), including MMP-9, are involved in extracellular matrix remodeling via proteolytic activity. They play a key role in physiological (like embryogenesis and wound healing) and pathophysiological (invasion and tissue destruction mechanisms) uterine processes [ 80 ]. In this review, we observed that MMP-9 levels appeared to be increased in endometriosis in all studies and regardless of the biological compartment studied. Interestingly, fertility status and menstrual cycle phases do not seem to affect the variations of this biomarker [ 79 – 82 , 136 ]. Although still to be confirmed, the diagnostic value of this biomarker seems to be appropriate [ 79 , 82 ]. We note that a therapeutic approach to reduce MMP-9 level through progesterone supplementation to improve IVF success rates in endometriosis patients showed promising success, suggesting that MMP-9 may have treatment as well as diagnosis value in endometriosis [ 81 ]. TIMP-1, a metalloproteinase inhibitor, is involved in extracellular matrix remodeling which is particularly intense in ovary during follicular development and cyst formation and in endometrium during dynamic cyclic changes across the menstrual cycle [ 81 ]. TIMP-1 showed inconsistent regulation between different biological compartments in women with endometriosis, with reported decreases in blood and ovarian tissue and an increase in peritoneal fluid across well-powered cohorts [ 22 , 81 ]. Although this remains to be confirmed, this candidate biomarker does not appear discriminative for disease stage and fertility status, but seems impacted by menstrual cycle phases [ 22 ]. MiRNAs are small endogenous noncoding functional RNAs [ 122 ]. As they are released into the circulation, their interest as biomarkers has been the subject of numerous studies, specific miRNA expression patterns are hallmarks for numerous diseases [ 122 , 123 ]. These associations between miRNA expression profiles and diseases are often obtained by non-targeted screening (microarray or miRNome sequencing), and their mechanistic roles in physiology or pathophysiology are poorly studied. miR-451 seemed to perform well as a diagnostic marker of endometriosis across different biological fluids and study settings, particularly in combination with other miRNAs [ 123 , 124 ]. This biomarker does not seem to be correlated with endometriosis severity [ 22 , 123 ]. Understanding the observed discrepancies in the direction of variation will require studies with systematic adjustments for disease severity, menstrual cycle phases, treatments, symptoms, clinical characteristics of the cohorts and associated comorbidities.

Pubmed and Embase (excluding Medline articles) were searched for English-language articles as follows: Endometriosis in the title AND ‘biomarkers’ as Medical Subject Heading (MeSH) or All Fields terms. The strategy was designed in association with the referral Inter-University Library of Medicine of Université Paris Cité, France. All articles involving human subjects and published between 2005/01/01 and 2022/09/01 were selected for screening. The databases were last consulted on 30 September 2022. The review was conducted in accordance with The PRISMA 2020 statement for systematic review and not registered [ 10 ]. PRISMA 2020 Checklist is included in Additional Material . The study selection strategy is summarized as a flowchart in Fig.  1 . Both clinical and basic research studies were considered. A total of 879 original publications were manually screened. At each step of the selection process, titles/abstracts/methods or full-text were screened by 2 independent reviewers (A.B. and one of the other co-authors) regarding eligibility criteria. Discrepancies concerning studies inclusion were resolved by A.B. and L.D. All review articles, editorial, animal subject studies and publications not written in English were excluded. Articles with the two following mandatory inclusion criteria were selected for further evaluation: i) presence of a control group without endometriosis and without malignant diseases, and ii) available information on endometriosis phenotype(s). For the remaining 447 articles, the full text was screened to extract the biomarkers studied, the significance and the direction of the variation observed between control and endometriosis groups and the biological compartment in which these changes were found. Only biomarkers significantly deregulated in endometriosis were considered. Biomarkers deregulated in ectopic endometrium only, and articles focusing exclusively on ectopic endometrium were excluded from this review because ectopic endometrium has no equivalent tissue in the control group. At this step, we obtained a list of 387 articles with 1107 significantly deregulated biomarkers in endometriosis and affected biological compartment(s). This list from the remaining 387 publications, validated independently by 2 reviewers, was reviewed a third time to extract information and/or adjustments for disease phenotypes, menstrual cycle phases, treatments and symptoms, which represent parameters of major importance in endometriosis. A list of candidate biomarkers per compartment was also created. Identified biomarkers and their instances in each compartment were tallied using a custom Perl script identifying unique character strings (case insensitive). To focus on sustained multi-tissue evidence, we selected articles related to the biomarkers identified by at least 2 different research teams (regardless of biological compartment) and across at least 3 biological compartments. For each candidate biomarker, at least one study per compartment including 30 or more controls and 30 or more patients with endometriosis was mandatory. All extracted data were analysed descriptively. Fig. 1 PRISMA flowchart for the systematic review. Flowchart highlighting the different steps in the selection of articles included in the review, giving details of the inclusion and exclusion criteria and the analyses carried out PRISMA flowchart for the systematic review. Flowchart highlighting the different steps in the selection of articles included in the review, giving details of the inclusion and exclusion criteria and the analyses carried out The data related to our final set of candidate biomarkers of interest in this review were extracted from 18 articles (see flowchart in Fig.  1 ). For each article, the following data were extracted by 2 independent reviewers: the quality of the control groups and the homogeneity of the confounding factors between the groups, the level of expression (mRNA, protein) and the direction and amplitude of variation of the candidate biomarker, the adjustments of the results according to the subtypes of endometriosis, the phase of the menstrual cycle, the treatments and the symptoms and the ROC curve analysis to highlight diagnostic accuracy if available. Due to the great disparity in study designs (clinical and basic research) available for each candidate biomarkers of interest, we did not use ROC curve analysis as a selection criterion to address robustness, but instead included the presence of at least one study per biological compartment including at least 30 individuals per group as a mandatory criterion. For the creation of lists of candidate biomarkers by biological compartments, to avoid computing different aliases of the same gene/protein as different biomarkers, all identified candidate biomarkers were manually curated to unify writing styles and conventions before processing. Aliases were identified through the HUGO Gene Nomenclature Committee (HGNC) Multi symbol checker tool ( https://www.genenames.org/tools/multi-symbol-checker/ ), replacing 76 aliases by their official gene symbols.

It appears necessary to rethink endometriosis candidate biomarkers research by designing studies that can be integrated at different levels: i) local and systemic biological compartments; ii) different disease phenotypes with improved characterisation; iii) treatments and their impacts; iv) symptoms; and v) menstrual cycle phases. Access to these parameters will require harmonisation of data collection methods following recommendations of the EPHect project [ 139 ]. Such harmonisation would enable meta-analyses, yield a considerable increase in cohort sizes, and facilitate investigations into the effects of these stratifying variables. As endometriosis biomarker discovery remains challenging, sensitivity may be improved by combining biomarkers from different molecular pathways. However, combining biomarkers across biological compartments seems unsustainable in clinical practice, and identifying the most relevant biological compartment remains an important challenge. To this regard, our study pinpoints numerous discrepancies in the results obtained in peripheral blood. Local approaches may lead to more consistent results, as is the case in peritoneal fluid, which can unfortunately not be assessed non-invasively. We therefore highlight the need to further investigate non-invasively accessible biological fluids, especially locally accessible such as menstrual fluid or cervical mucus.

Endometriosis biomarker research investigated a large variety of biological compartments so far, some of which are relevant to the local mechanisms of endometriosis pathophysiology (eutopic endometrium, peritoneal fluid, ovary, menstrual blood, cervical mucus), while others approached endometriosis as a systemic disorder resulting in body-wide dysregulations (peripheral blood, urine, feces, saliva). A first valuable outcome of our review was that non-invasively accessible biological compartments (urine, menstrual blood, feces, saliva, cervical mucus) remained drastically understudied despite their potential to transform endometriosis diagnosis. These compartments can address disease modifications both at the systematic and local levels, and deserve more focused attention in the future. We identified a total of 1107 candidate biomarkers across all nine studied biological compartments, suggesting that endometriosis is potentially associated with widespread molecular modifications. However, agreement between studies, protocols and laboratories was strikingly low, with few candidate markers consistently modified within the same compartment and exhibiting similar directions of change. This suggests that many reported candidate biomarkers were either highly dependent on technical considerations, or represent false positives due to unaccounted confounders. Only 4 of these candidates were reproducibly detected across several compartments by different research teams and with appropriately powered cohorts, and we argued that these markers with widespread modifications should be first-line candidates for investigation in more accessible biological compartments. As highlighted in this review, the relationships between marker variations and biological compartments were often obscured by uneven consideration of fundamental variables such as disease stage, symptoms, treatments, and menstrual cycle phase. Symptoms and treatments were the major missing elements in many study designs. These variables were rarely analyzed and often absent altogether. Regarding symptomatic treatments (painkillers, anti-inflammatory drugs), neither their effectiveness nor their frequency of use was reported. Menstrual cycle phases were also key variables as many metabolic and regulatory pathways vary throughout the cycle, including one-carbon metabolism [ 137 ] and miRNAs [ 128 , 138 ], but were frequently overlooked. Most articles used the revised ASRM classification to rank phenotypes from endometriosis stage I (minimal) to stage IV (severe) [ 2 ]. Deep infiltrating endometriosis is then classified as stage III or IV regardless of the presence of endometrioma. However, presence of endometriomas seems decisive for some biomarkers regulation, especially metabolites. A more accurate classification like ERZIAN scoring may allow for better discrimination between different disease phenotypes [ 2 ]. All these parameters may contribute to explain the lack of reproducibility between studies, and standardizing data records may help alleviate this issue in future studies. The top 4 candidate biomarkers of interest identified here belong to different molecular categories (miRNAs, extra-cellular matrix, and cytokines), and are involved in pathophysiological processes common to many diseases, especially extra-cellular matrix remodeling and inflammation. Previous studies have generally combined elements of the same molecular category together [ 122 , 123 ], but combinations involving different molecular families are more rarely studied [ 30 ]. The 4 candidate biomarkers identified in this work were present in blood, an accessible and relevant biological compartment for diagnostic test development. They have never been combined together to test their diagnostic performance in endometriosis, but their association should be evaluated. Comparing sensitivity and specificity across studies to identify potential combinations of markers of interest remains difficult, as designs and cut-offs varied between studies and between biological compartments. We noted that formal meta-analyses of endometriosis biomarkers were largely absent, and will likely remain challenging due to the heterogeneity in study designs and data collection records that we highlighted above, limiting the reusability of available information. Finally, this systematic study also came with some limitations. First, and despite our best efforts, we may have missed biomarkers that meet our selection criteria but are listed under different aliases during manual literature curation. We however expect that these instances were rare and did not affect the overarching conclusions of this study. Another important limiting factor was the design of the selected studies, which typically excluded rather than accounted for stratifying parameters of interest. Most of the highlighted adjustments were by exclusion of other categories of patients, for example by including only a single endometriosis phenotype in the cohort, or enrolling women in the same phase of the cycle. In this context, rigorously assessing the impact of adjustment and the differential effects of endometriosis subtypes, cycle phases, symptoms and treatments on biomarker levels remained challenging. Another potential source of bias was the heterogeneity of the control groups, a problem widely recognized for endometriosis research. Indeed, supposedly healthy donors may contain asymptomatic endometriosis patients, while most laparoscopically examined controls with confirmed absence of endometriosis had other gynecological or fertility issues. In most cases, these women presented benign comorbidities (e.g. leiomyomas, ovarian cysts) which were not matched with the case group and may impact the levels of certain markers. While these markers were also of interest to eliminate other diseases during endometriosis diagnosis, they addressed a separate question compared to diagnosing endometriosis at large in the population. A final issue that may interfere with reproducibility concerned the methodology of the studies. At this time, few untargeted studies with large discovery and validation cohorts used omics technologies for high-throughput biomarker discovery. The majority of studies focused on a limited panel of predefined targets and many potential biomarkers were therefore not evaluated. We chose to focus on biomarkers reported by independent research teams and in multiple tissues to improve the relevance and the strength of evidence, but numerous biomarkers were probably unconfirmed because they have not been evaluated so far by independent team and in several compartments.

Endometriosis is a chronic gynecological condition affecting 6%-10% of women of reproductive age [ 1 ]. Histologically, endometriosis corresponds to the dissemination of endometrial-like tissue, or lesions, outside the uterus. The reference method to diagnose endometriosis is surgery, through lesion visualisation and anatomical pathology evaluation. Endometriosis staging is currently based on surgeons' observations. The most widely used scoring is from the American Society for Reproductive Medicine (ASRM) that ranges endometriosis from stage I “minimal” to stage IV “severe”, based on lesion localisation, size, appearance and presence of adhesions [ 2 ]. Currently, patient management practices promote non-invasive diagnostic methods such as transvaginal ultrasonography and magnetic resonance imaging (MRI) [ 3 ]. Not all staging features are accessible with non-invasive diagnostic approaches, which makes the ASRM classification difficult to use in this context. An alternative classification defines three phenotypes: superficial peritoneal lesions (located less than 5 mm below the peritoneum), ovarian endometriomas, and deep infiltrating endometriosis (located more than 5 mm below the peritoneum). The last two can usually be detected by imaging. Clinically, endometriosis is associated with a wide range of symptoms and consequences: pelvic pain, severe pain during periods (dysmenorrhea), painful sex, painful urination (dysuria) and/or defecation (dyschesia), alternance of diarrhea/constipation, heavy menstrual bleeding, mood disorders, chronic fatigue and infertility [ 4 ]. Endometriosis is increasingly considered as a systemic disease rather than a pelvic pathology [ 5 ]. Because of its complex pathophysiology, symptoms heterogeneity, and diagnostic requirements, diagnosis delay for endometriosis ranges from 4 to 11 years [ 5 ]. Biomarker candidate research is therefore a key avenue to improve diagnosis. Despite extensive efforts, no single or combination of biomarkers has reached clinical validation for endometriosis, as extensively reviewed in Cochrane’s reviews in 2016 [ 6 – 9 ]. While dozens of potential biomarkers have been investigated across varied biological compartments, few have been validated in independent studies, making their relevance unclear. To address this, we propose an original integrative review of the endometriosis biomarker literature across biological compartments. Our hypothesis is that endometriosis biomarkers recurrently identified across multiple tissues may be particularly relevant and play a more direct role in disease physiopathology, and an integrative multi-tissue approach could highlight and prioritize these candidates, which may eventually lead to enhanced patient care. For all considered studies, we highlight if endometriosis subtypes, menstrual cycle phases, treatments and symptoms were accounted for. We particularly focus on biomarkers reproducibly detected by at least two independent research teams, and found in different biological compartments.

Additional file 1: Additional material.  PRISMA checklist Additional file 2:  Additional Table 1.  References of the 447 selected articles Additional file 3: Additional Table 2.  Quantitative summary of significantly modified biomarkers in endometriosis identified by biological compartment Additional file 1: Additional material.  PRISMA checklist Additional file 2:  Additional Table 1.  References of the 447 selected articles Additional file 3: Additional Table 2.  Quantitative summary of significantly modified biomarkers in endometriosis identified by biological compartment