Potential biomarkers for early detection of endometriosis: current state of art (what we know so far)

Endometriosis is a chronic gynecological condition characterized by the presence of endometrial-like tissue outside the uterine cavity. Its diagnosis remains a significant clinical challenge, often delayed by 7 to 12 years, leading to considerable socio-economic burden and a substantial decline in patients’ quality of life, including potential infertility. Consequently, there is an urgent need to identify reliable biomarkers that would allow for earlier and more accurate detection. This review provides a comprehensive and up-to-date analysis of potential biomarkers for the diagnosis of endometriosis, including hormonal, inflammatory, genetic, epigenetic, immunological, metabolic, and imaging-based markers. Their diagnostic value and limitations are critically assessed, with particular emphasis on the advantages of multimarker and integrated diagnostic approaches to enhance early detection. The findings of this review offer valuable insights for clinicians, researchers, and healthcare professionals working to develop better diagnostic methods and improve patient outcomes. Moreover, the integra- tion of emerging technologies, such as artificial intelligence, offers promising opportunities to revolutionize endometriosis diagnostics through personalized and precise medical care.

Endometriosis · Biomarkers · Artificial intelligence · Machine learning · Personalized medicine Abbreviations AGD Anogenital distance AI Artificial intelligence AUC Area under the curve HE4 Human epididymis protein 4 MRI Magnetic resonance imaging ML Machine learning TGFBI Transforming growth factor Beta-induced protein TNF alpha Tumor Necrosis Factor-alpha TVUS Transvaginal ultrasound

Endometriosis is an estrogen-dependent inflammatory disease defined by the presence of endometrial-like tissue outside the uterine cavity, affecting approximately 10% of women of reproductive age worldwide (Bulun et al. 2019). Although endometriosis affects a significant portion of the female population, the path to diagnosis is often prolonged, with many patients enduring years of misdiagnoses and clinical uncertainty before receiving a correct identifica - tion of the disease—typically delayed by 7 to 12 years from symptom onset (Swift et al. 2024). The diagnostic challenges are compounded by the hetero- geneity of its clinical manifestations—ranging from severe pelvic pain, dysmenorrhea, and infertility to entirely asymp- tomatic presentations—often mimicking other gynecologi- cal or gastrointestinal disorders (Murphy 2002; Lukac et al. 2022). This enigmatic disorder not only has profound implica- tions for women’s physical and emotional well-being but also imposes a substantial socio-economic burden. Women with endometriosis often experience reduced quality of life, due to chronic pain, infertility, and the psychological toll of living with a long-term condition. These challenges extend Communicated by: Kamila Kusz-Zamelczyk * Klaudia Kulczyńska-Figurny [email protected] 1 Department of Biochemistry and Molecular Biology, Poznan University of Medical Sciences, Święcickiego 6 St., 61-701 Poznan, Poland 2 Institute of Human Genetics, Polish Academy of Sciences, Strzeszyńska 32 St., 60-479 Poznan, Poland Journal of Applied Genetics beyond the individual to the broader society, contributing to substantial healthcare costs and work absenteeism. A study by Simoens et al. (2012) estimated that the annual total cost of treating a woman with endometriosis in referral centres was €9579, with €6298 attributed to lost productivity and €3113 to healthcare costs. The burden was found to increase with the severity of the disease, the presence of pelvic pain, infertility, and the duration of the diagnostic delay, further emphasizing the significant socio-economic implications of this condition (Simoens et al. 2012). Currently, the gold standard for diagnosis remains lapa- roscopic surgery with histological confirmation, an inva- sive approach that underscores the pressing need for non- invasive, reliable diagnostic alternatives (Simko and Wright 2022). Endometriosis presents a unique conundrum in the realm of biomarkers—it is a condition that manifests in diverse ways, affecting women of varying ages, and may be accom- panied by a spectrum of symptoms, from the debilitating to the entirely asymptomatic. In recent years, advances in molecular biology and clini- cal diagnostics have spurred growing interest in the identifi- cation of disease-specific biomarkers. Biomarkers—defined as measurable indicators of normal or pathological biologi- cal processes—hold the potential to transform the diagnos- tic landscape of endometriosis (Encalada Soto et al. 2022). They could facilitate earlier detection, predict disease pro- gression, assess therapeutic responses, and ultimately pave the way for personalized treatment strategies. Among the most promising areas of biomarker discovery in endometriosis are genetic and epigenetic factors. Family studies and twin concordance rates indicate a strong heredi- tary component, with an estimated heritability of up to 50%. Genome-wide association studies (GWAS) have identified multiple risk loci—including SNPs in genes such as WNT4, VEZT, and GREB1—that are consistently associated with endometriosis across populations (Nyholt et al. 2012). Addi- tionally, epigenetic modifications such as DNA methylation patterns and dysregulated miRNA expression have emerged as crucial contributors to disease onset, lesion development, and hormonal resistance. These molecular alterations not only enhance our understanding of disease pathophysiol - ogy but also offer a promising foundation for non-invasive diagnostic strategies and polygenic risk modeling. Recent advances in biomarker research have shown prom- ising potential in revolutionizing the diagnosis of this com- plex disorder. Biomarkers, defined as objective and quan- tifiable indicators of biological processes, offer hope for the early detection of endometriosis, assessment of disease severity, and monitoring of its progression. This review provides an in-depth evaluation of cur - rent and emerging biomarkers in endometriosis research, including hormonal, inflammatory, genetic, epigenetic, immunological, metabolic, and imaging-related mark - ers. Each category reflects a distinct biological dimension of the disease and contributes to a more nuanced under - standing of its complex pathophysiology. In addition, the potential of multimarker panels and integrated diagnostic platforms—including artificial intelligence and omics-based approaches—will be explored, offering a vision of future precision medicine applications in endometriosis care. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) into the diagnostic process holds enormous potential (Hanna et al. 2025). AI algorithms, trained on vast datasets of patient information, could assist in identifying patterns and correlations that may not be apparent to the human eye. ML models, in particular, are well-suited for analyzing complex, multidimensional data such as biomarker profiles, imaging results, and clinical his- tories. These models could help clinicians predict disease progression, assess treatment responses, and even identify personalized therapeutic strategies tailored to individual patients (Bekbolatova et al. 2024). The combination of AI and biomarkers has the poten- tial to revolutionize not only the detection of endometrio- sis but also its management. By integrating omics-based approaches—such as genomics, proteomics, and metabo- lomics—into AI-driven diagnostic platforms, a more com- prehensive understanding of the disease could emerge. These platforms could enable clinicians to offer person- alized, precision medicine approaches, moving beyond a one-size-fits-all model to a more tailored treatment strategy that optimally addresses the unique characteristics of each patient's condition. As we move forward, the continuous evolution of AI, ML, and omics technologies promises to reshape the landscape of endometriosis diagnosis and care, offering women earlier diagnoses, more effective treatments, and a better overall quality of life (Dungate et al. 2024). Types of biomarkers To date, researchers have investigated various types of bio- markers for the diagnosis of endometriosis, including hor - monal, inflammatory, genetic, epigenetic, immunological, metabolic, and imaging biomarkers (Fig. 1 ). However, cur- rently no single biomarker can diagnose endometriosis with high accuracy and specificity. The combination of these bio- markers, especially when supported by advanced technolo- gies such as machine learning, offers new opportunities for improved early detection. Hormonal biomarkers Hormonal biomarkers have long been central to under - standing endometriosis, as this condition exhibits hormonal Journal of Applied Genetics dependencies. In addition to classical hormones such as estrogen, progesterone, LH, and FSH, increasing attention has been paid to enzymes and metabolites involved in estro- gen metabolism, which may serve as diagnostic and prog- nostic biomarkers. Elevated estrogen levels and disrupted hormone ratios have been identified as potential indicators of endometriosis. Aromatase (CYP19A1), the enzyme responsible for convert- ing androgens into estrogens, shows increased expression in endometrial tissues of patients with endometriosis. A meta- analysis including 17 studies and 1279 participants demon- strated that aromatase had the highest diagnostic accuracy among evaluated hormonal biomarkers, with a pooled sensi- tivity of 79% and specificity of 89%, outperforming estrogen receptors (ERα/β), serum prolactin, and 17β-hydroxysteroid dehydrogenase type 2 (17βHSD2) (Gao et al. 2019). Recent findings indicate that the expression levels of aromatase, steroidogenic factor-1 (SF-1), and HSD17B2 in menstrual blood could effectively differentiate patients with endometriosis from healthy controls, with aromatase achieving an area under the curve (AUC) of 0.977 (Amanda et al. 2024). Elevated levels of 2-hydroxyestradiol (2OHE2) and 4-hydroxyestradiol (4OHE2) have been reported in the eutopic endometrium of women with endometriosis com- pared to controls (Othman et al. 2021). Moreover, urinary concentrations of 2-hydroxyestrone (2OHE1) are signifi- cantly higher in affected individuals, suggesting potential for non-invasive diagnostic applications (Othman et al. 2021). Additionally, recent studies have identified overexpres- sion of nicotinamide N-methyltransferase (NNMT) in endo- metrial stromal cells, induced by estrogen and macrophage interaction, which modulates cell proliferation via the NNMT-ERBB4-PI3K/AKT signaling pathway, contribut- ing to the development of endometriosis (Hou et al. 2024). Progesterone resistance is another key hormonal aspect of endometriosis. Reduced expression of progesterone recep- tors and disrupted signaling pathways have been impli- cated in lesion persistence. Decreased FKBP4 levels and alterations in microRNA-29c regulation have been linked to impaired progesterone responsiveness (Joshi et al. 2017) . Moreover, studies have shown that loss of progesterone receptor-B (PR-B) in stromal cells of endometriotic lesions is a hallmark of progesterone resistance, contributing to infertility in endometriosis patients. Dual inhibition of AKT and ERK1/2 pathways has been proposed to restore pro- gesterone responsiveness in these cases (Dutta et al. 2018). Recent studies suggest that circulating testosterone, par - ticularly in its free and bioavailable forms, may serve as a potential biomarker for endometriosis diagnosis and risk stratification (Zhao et al. 2023; McGrath et al. 2023; Gjor- goska and Rizner 2024). Lower levels of total testosterone, Fig. 1 Proposed categories of potential biomarkers for early detection of endometriosis, including hormonal, inflamma- tory, immunological, metabolic, genetic/epigenetic, and imaging markers, which together illus- trate the multifactorial nature of the disease. Part of the illustra- tion was adapted from Servier Medical Art, licensed under a Creative Commons Attribu- tion 4.0 International License (https:// smart. servi er. com) Journal of Applied Genetics bioavailable testosterone, and DHEAS have been geneti- cally linked to a higher risk of endometriosis, highlighting a potential hormonal profile characteristic of the disease (McGrath et al. 2023). Furthermore, reduced testosterone concentrations in fol- licular fluid observed in endometriosis patients undergoing assisted reproductive technologies suggest its utility as a non-invasive biomarker for impaired folliculogenesis and infertility in these individuals (Huang et al. 2021). Although current findings are inconsistent, integrating testosterone levels with other hormonal, genetic, and inflammatory markers could enhance the sensitivity and specificity of biomarker panels for early detection of endometriosis. Inflammatory biomarkers Endometriosis is characterized by chronic inflammation, with recent studies highlighting its role in the pathophysiol- ogy of the disease. This persistent inflammatory response contributes to pain, infertility, and lesion progression, and serves as the foundation for exploring inflammatory bio- markers in early diagnosis. Biomarkers in endometriosis play a significant role in understanding its pathophysiology and in the development of non-invasive diagnostic tests. Cytokines, both pro-inflammatory and anti-inflammatory, have been identified in biopsy specimens from individuals with endometriosis, suggesting their involvement in the dis- ease's etiopathogenesis, similarly to what has been observed in various cancers (AlAshqar et al. 2021). For instance, mac- rophage migration inhibitory factor (MIF), alongside inter - leukin-1 (IL-1), has been found to play a regulatory role in immune responses, angiogenesis, and estrogen production, all of which are critical in the progression of endometriosis (Cao et al. 2005; Veillat et al. 2010). Furthermore, cytokines such as IL-6, IL-8, and Tumor Necrosis Factor-alpha (TNF-alpha) are implicated in the pain, embryonic implantation, and angiogenesis associ- ated with endometriosis. However, these cytokines still require validation regarding their specificity, sensitivity, and diagnostic significance (Weisheng et al. 2019). Proin- flammatory cytokines, such as IL-1β (Taketani et al. 1992; Ho et al. 1996), TNF-α (Overton et al. 1996; Harada et al. 1997), IL-6 (Punnonen et al. 1996; Harada et al. 1997), and IL-8 (Arici et al. 1996) are elevated in the peritoneal fluid of women with endometriosis. They are secreted by perito- neal macrophages and ectopic endometrial lesions. These cytokines promote the development of endometriosis by inducing COX-2 expression and triggering PGE2 produc- tion, creating a positive feedback loop (Wu et al. 2002; Carli et al. 2009). In particular, IL-1β upregulates COX-2 mRNA stability and promoter activity in ectopic endometrial cells, enhancing their migration and invasiveness (Taketani et al. 1992; Ho et al. 1996). Similarly, TNF-α stimulates IL-6 and IL-8 secretion, further driving inflammation and the forma- tion of endometriotic lesions (Overton et al. 1996; Harada et al. 1997). Non-invasive diagnostic approaches utilizing antibody arrays have shown that IL-31 could be a potential biomarker, though traditional markers like CA-125 lack the necessary sensitivity and specificity for accurate diagnosis (Waelkens et al. 2020). Additionally, pro-inflammatory cytokines like IL-17 and IL-33, commonly associated with both endometriosis and cardiovascular diseases, further complicate the development of reliable biomarkers (Rafi et al. 2021). Oxidative stress is another important factor in the progression of endometriosis, with damage-associated molecular patterns (DAMPs) like HMGB1 and TLR4 being identified as key components in the inflammatory response associated with the disease (Yun et al. 2016). Moreover, cytokines such as IL-6 and IL-10, along with factors like TNF-alpha, are involved in the growth of endo- metriotic cells and the potential carcinogenic effects of endometriosis (Wang et al. 2018) . However, as studies con- tinue, more evidence is needed to confirm the full potential of these cytokines as reliable biomarkers for diagnosis and therapeutic targeting in endometriosis. As inflammation is closely intertwined with immune dys- regulation, further exploration of immunological markers may offer additional insights into the underlying mecha- nisms and diagnostic potential in endometriosis. Immunological biomarkers The immune system plays a crucial role in the pathogen- esis of endometriosis. It has been proposed that defects in immune surveillance and clearance mechanisms permit endometrial cells to survive, implant, and proliferate outside the uterine cavity. The frequent coexistence of endometriosis with autoimmune diseases, such as systemic lupus erythe- matosus or Hashimoto’s thyroiditis, supports the hypoth- esis of underlying immune dysregulation in this condition (Blanco et al. 2025). Alterations in both innate and adaptive immune responses have been documented in women with endometriosis. Among innate immune cells, macrophages exhibit a skewed polarization toward the M2 phenotype, contributing to tissue remodeling, angiogenesis, and immune tolerance. Addition- ally, natural killer (NK) cells display reduced cytotoxicity and altered cytokine secretion profiles, impairing their abil- ity to eliminate ectopic endometrial cells (Jeung et al. 2016). Dysregulated populations of dendritic cells and neutrophils have also been observed, further implicating innate immu- nity in the persistence of endometriotic lesions (Jeung et al. 2016). Journal of Applied Genetics From the adaptive immune perspective, changes in T lym- phocyte subsets, including an imbalance between Th1/Th2 and Th17/Treg cells, have been reported in both peritoneal fluid and peripheral blood (Tarokh et al. 2019; Pashizeh et al. 2020; Olkowska-Truchanowicz et al. 2021). These alterations may favor a pro-inflammatory environment con- ducive to lesion survival and progression. B cells, though less extensively studied, are also involved and may contrib- ute by producing autoantibodies associated with endome- triosis (Riccio et al. 2017; Harden et al. 2023). Several studies have identified circulating autoantibod- ies targeting specific autoantigens, such as stomatin-like protein 2 (SLP2), tropomodulin 3 (TMOD3), tropomyosin 3 (TPM3), and PDIK1L. These autoantibodies have been particularly associated with early-stage endometriosis and may serve as promising non-invasive biomarkers for early detection (Gajbhiye et al. 2017). Another component of the immune system implicated in endometriosis is the complement system. Aberrant activa- tion and increased expression of complement factors such as C3 and C5 have been observed in the peritoneal fluid and tis- sues of affected individuals. These molecules may contribute to chronic inflammation, angiogenesis, and immune evasion by ectopic endometrial cells (Rahal et al. 2021). Dysregulation of cell adhesion molecules, such as integ- rin β3 (CD61), has also been observed in the endometrium of women with endometriosis. Abnormal expression of these molecules may facilitate ectopic implantation and lesion per- sistence (May et al. 2011). From a clinical perspective, immunological biomarkers offer potential utility not only for diagnosis but also for dis- ease staging and therapeutic targeting. Recent transcriptomic and proteomic analyses have highlighted several immune- related genes and pathways—including CXCL12, PECAM1, NGF, CTGF, and WNT5A—that are differentially expressed in endometriotic lesions and may serve as future therapeu- tic targets (Yang et al. 2023; Zhang et al. 2025). Although immunological markers are still under investigation and require validation in large, diverse cohorts, their integra- tion into multimodal diagnostic strategies could improve the early detection, stratification, and personalized management of endometriosis. Genetic and epigenetic biomarkers Recent advances in genetic and epigenetic research have greatly advanced the understanding of endometriosis, revealing key molecular markers and mechanisms that con- tribute to the disease's pathogenesis. Studies have identi - fied several critical genes associated with endometriosis, including CUX2, CLMP, CEP131, EHD4, CDH24, ILRUN, LINC01709, HOTAIR, SLC30A2, and NKG7, which have been proposed as potential biomarkers for diagnosis and treatment. These findings were supported by machine learning-based approaches using transcriptomic datasets from patients with endometriosis and healthy controls. The Bagged CART model, for example, achieved high classifi- cation metrics, including 85.7% accuracy, 100% sensitivity, and 75% specificity (Kucukakcali et al., 2025). In addition to genetic factors, epigenetic modifications such as DNA methylation have been shown to play a crucial role in the disease. A recent study identified 51 methylation quantitative trait loci (mQTLs) associated with endometrio- sis susceptibility, distributed across 21 genomic loci. These mQTLs offer valuable insights into tissue-specific epigenetic regulation and its potential impact on the development of the disease (Mortlock et al. 2023). Another recent systematic review further emphasized the role of DNA methylation pat- terns in the pathogenesis of endometriosis, suggesting that the identification of specific methylation markers could sup- port novel diagnostic and therapeutic approaches (Ducreux et al. 2025). MicroRNAs (miRNAs) have also been found dysregu- lated in endometriosis, potentially contributing to proges- terone resistance. MiR-199a-3p, miR-1-3p, miR-146a-5p, and miR-125b-5p were upregulated in ectopic lesions com- pared to eutopic tissue (Hon et al. 2024). At the same time, ERα and ERβ expression was altered, while PR-A and PR-B levels remained unchanged. Predicted target genes of these miRNAs (e.g., SCD, CDK6, DDIT4) are involved in pro- liferation and survival pathways, suggesting that miRNA- driven hormonal dysregulation plays a key role in endome- triosis pathogenesis and resistance to progestins (Hon et al. 2024). Moreover, several miRNAs have been consistently identi- fied as promising biomarkers for non-invasive diagnostics. These include miR-17-5p, miR-451a, and let-7b-5p, which have consistently been found to be dysregulated across mul- tiple studies (Vanhie et al. 2024). New studies also propose that differences in miRNA expression can be detected in body fluids, offering opportunities for non-invasive diag- nostic tests (Ravaggi et al. 2024), and that salivary miRNA signatures might help identify peritoneal superficial endo- metriosis (Bendifallah et al. 2024). Endometriosis is a complex, estrogen-dependent condi- tion characterized by the ectopic growth of endometrial- like tissue. It exhibits a strong heritable component, with heritability estimated at approximately 50%. Large-scale genome-wide association studies (GWAS) have identified multiple single nucleotide polymorphisms (SNPs) associated with increased disease risk. These findings open the door to early risk stratification and precision diagnostics. A landmark meta-analysis by Sapkota et al. (2017) uncov- ered several key SNPs. Among them, rs12700667 on chro- mosome 7p15.2 showed strong and consistent association with all disease stages. Though intergenic, this variant likely Journal of Applied Genetics regulates nearby gene expression and has been replicated across diverse populations. Another key SNP, rs2235529, resides within the WNT4 gene (1p36.12), a major regulator of female reproductive tract development and decidualiza- tion. Dysregulation of WNT4 may impair tissue differentia- tion, contributing to lesion formation (Sapkota et al. 2017). Additional risk variants include rs10859871 near the VEZT gene (12q22), which influences cell adhesion and epithelial barrier function—both relevant to the invasive nature of endometriotic lesions. Furthermore, rs13394619 (Nyholt et al. 2012) near GREB1 (2p25.1), a gene critical for estrogen receptor signaling, has been implicated in dis- ease susceptibility. Finally, variants near FN1 (fibronectin 1) at 2q35, a gene involved in extracellular matrix remod- eling, may drive tissue invasion and fibrosis characteristic of advanced endometriosis (Lalami et al. 2021). In summary, the most promising SNPs identified across recent studies include: • rs12700667 (7p15.2) – intergenic, consistently associated with endometriosis risk. • rs2235529 (WNT4, 1p36.12) – involved in reproductive tract development. • rs10859871 (VEZT, 12q22) – affects cell adhesion and epithelial structure. • rs13394619 (GREB1, 2p25.1) – modulates estrogen response. • Variants near FN1 (2q35) – associated with tissue remod- eling and lesion invasiveness. These discoveries not only provide insights into the molecular basis of endometriosis but also offer promising candidates for integration into polygenic risk scores. With further functional validation, these SNPs could serve as the foundation for non-invasive genetic screening tools, ena- bling earlier diagnosis and personalized management of endometriosis (Table  1). In addition to GWAS-identified SNPs, recent transcrip- tomic and machine learning approaches have uncovered additional candidate genes that may serve as promising non-invasive biomarkers. These include CUX2, a tran- scription factor involved in cell cycle regulation and tissue remodeling, which has been shown to be upregu- lated in endometriotic lesions (Fan et al. 2021). CLMP, a cell adhesion molecule, has been implicated in disrup- tion of cell–cell junctions and immune-related pathways, potentially facilitating ectopic implantation of endometrial tissue. CEP131, a centrosomal protein essential for cili- ogenesis and cell cycle control, was also identified as dif- ferentially expressed, pointing toward dysregulated signal- ing and division in disease progression. EHD4, associated with endocytic recycling and vesicular transport, may con- tribute to altered membrane dynamics and lesion survival. Among genes implicated in adhesion and invasion, CDH4 (cadherin-4) regulates epithelial–mesenchymal transition (EMT), a process critical for lesion estab- lishment, and its dysregulation has been proposed as a biomarker of aggressiveness (Sapkota et al. 2017). Immune-related genes such as ILRUN, a regulator of type I interferon responses, and NKG7, involved in NK cell degranulation, highlight immune dysregulation as a central mechanism; downregulation of NKG7 in particular corre- lates with impaired NK cell cytotoxicity (Fan et al. 2021). Non-coding RNAs also show strong biomarker poten- tial. LINC01709, a long non-coding RNA, has been sug- gested as a diagnostic marker due to its dysregulation in endometriotic samples, while HOTAIR, a well-character - ized oncogenic lncRNA, promotes invasion, proliferation, and epigenetic reprogramming in ectopic endometrium. Given their stability in circulation, both may serve as attractive candidates for minimally invasive assays (Sap- kota et al. 2017 ; Fan et al. 2021). Finally, SLC30A2N, a zinc transporter-related gene, reflects perturbations in metal ion homeostasis, which may contribute to oxidative stress and inflammatory signaling in lesions. Collectively, these candidate genes—CUX2, CLMP, CEP131, EHD4, CDH4, ILRUN, LINC01709, HOTAIR, SLC30A2N, and NKG7—represent a promising panel of molecular biomarkers identified through integrative genomic and transcriptomic analyses. Their validation in large, multicenter cohorts could provide the basis for accu- rate, non-invasive diagnostic tools for endometriosis (Fan et al. 2021; Zondervan et al. 2020; Sapkota et al. 2017. Table 1 The table summarizes selected SNPs linked to endometriosis risk, including their chromosomal loci, genomic regions, and odds ratios with 95% confidence intervals SNP Locus (ChR) Genomic region/gene Odds (95% CI) Source (Citation) rs12700667 7p15.2 Intergenic (probable regulatory) 1.20 (1.13–1.27) (Nyholt et al. 2012) rs2235529 1p36.12 WNT4 (LINC00339–WNT4 locus) 1.29 (1.18–1.40) (Albertsen et al. 2013) rs10859871 12q22 Near VEZT (Not reported in source) (Nyholt et al. 2012) rs13394619 2p25.1 GREB1 (intronic) 0.92 (0.88–0.96) (Sapkota et al. 2017) rs1250248 (proxy FN1) 2q35 FN1 1.87 (1.34–2.61) (Matalliotaki et al. 2019) Journal of Applied Genetics Both intergenic and gene-associated variants are shown, reflecting their potential roles in gene regulation, reproduc- tive tract development, extracellular matrix remodeling, and estrogen signaling Metabolic and mitochondrial biomarkers Alterations in metabolic pathways have been observed in women with endometriosis. Metabolomic studies—utilizing blood, urine, and tissue samples—have identified changes in the levels of specific metabolites, offering insight into disease-related metabolic disturbances and highlighting their potential diagnostic and therapeutic relevance. Metabolomic analyses have revealed specific alterations in biochemical pathways among affected individuals. For instance, elevated serum levels of amino acids such as leu- cine, lysine, alanine, valine, tyrosine, and phenylalanine have been associated with the presence of endometriosis, as reported by Wang et al. (2018) (Dutta et al. 2018). Addi- tionally, disruptions in purine metabolism have also been notedincreased levels of hypoxanthine inosine guanosine and xanthosine decreased concentrations of uric acid. These changes suggest altered nucleotide Turnover and enhanced cellular stress, as described by Li et al 2018 (Li et al. 2018). The diagnostic utility of these metabolic markers has shown promise. For example, a diagnostic model combin- ing hypoxanthine, uric acid, and lysophosphatidylethanola- mine achieved a sensitivity of 66.7% and specificity of 90% in detecting early-stage endometriosis. Another biomarker panel, including 3-hydroxybutyrate, threonic acid, and ala- nine, yielded an area under the curve (AUC) of 0.91, indicat- ing high diagnostic accuracy (Li et al. 2018). Lipidomic profiling has further demonstrated that patients with endometriosis show increased levels of lysophosphati- dylethanolamine and omega-3 arachidonic acid in endome- trial tissues, indicating dysregulation of membrane Lipid composition and inflammatory Lipid mediators. This was explored in detail by Ortiz et al 2021 (Ortiz et al. 2021). Although these findings are encouraging, further validation through large-scale, multicenter studies is essential. Con - firming the clinical applicability of metabolic biomarkers could pave the way for their integration into routine diag- nostics, particularly when combined with other omics-based approaches to enhance diagnostic sensitivity and specificity. Imaging and anthropometric biomarkers Imaging biomarkers, including anthropometric indicators and advanced radiological techniques, have gained (Zapar - diel et al. 2016)increasing attention in the context of endo- metriosis diagnostics, providing non-invasive alternatives or complementary tools to laparoscopy. Anthropometric markers such as the second-to-fourth digit ratio known as (2D:4D) have been studied as indicators of prenatal hor - monal exposure. Numerous studies have demonstrated that women with endometriosis tend to have a higher 2D:4D ratio, particularly on the right hand, which may reflect lower prenatal androgen exposure and higher estrogen lev - els—both of which are associated with increased disease susceptibility, as shown by Ribeiro et al. (2023) (Buggio et al. 2023). Another antropometric biomarker—anogenital distance (AGD)—is a significant marker that reflects prenatal andro- gen exposure and has been studied as a potential diagnostic biomarker for endometriosis. AGD can be measured using different urogenital landmarks. For instance, AGDAC refers to the distance from the anterior surface of the clitoris to the upper edge or center of the anus, while AGDAF is measured from the posterior fourchette to the upper edge or center of the anus, and AGDCt is taken from the tip of the clitoris to the center of the anus (Mendiola et al. 2012). Recent studies, including a meta-analysis and systematic review, have suggested that a shorter AGDAF may be a prom- ising biomarker for endometriosis diagnosis. However, the

remain inconsistent, and further research is required. For example, Crestani et al. (2023) found that both AGDAC and AGDAF were significantly shorter in women with endo- metriosis in a French population (Crestani et al. 2020). In contrast, Peters et al. (2021) in the Netherlands observed that only AGDAC was shorter in women with endometrio- sis compared to controls (Peters et al. 2020). Conversely, a study in Spain showed that only AGDAF was associated with endometriosis (Sánchez-Ferrer et al. 2017). These discrepan- cies highlight the need for more extensive studies to better understand the role of AGD in diagnosing endometriosis. Additionally, birth weight has been explored as a poten- tial biomarker, with population studies suggesting that women with low birth weight are at increased risk of devel- oping endometriosis later in life. For example, Borghese et al. (2015), in a study involving 743 women, found that those with histologically confirmed endometriosis had sig- nificantly lower average birth weights. Moreover, a birth weight below 2500 g was associated with a higher risk of deep infiltrating endometriosis, suggesting that intrauterine growth restriction may contribute to the disease’s etiology (Borghese et al. 2015). Beyond these developmental markers, imaging techniques such as transvaginal ultrasound (TVUS) remain the first- line diagnostic tool in suspected endometriosis cases, par - ticularly for identifying ovarian endometriomas and deeply infiltrating nodules in the rectovaginal septum, uterosacral ligaments, or bladder (Baușic et al. 2023). However, the sen- sitivity and specificity of ultrasound are highly dependent on the operator's skill and may be limited in detecting peri- toneal lesions or early-stage disease. According to the con- sensus statement by the International Deep Endometriosis Journal of Applied Genetics Analysis (IDEA) group, systematic transvaginal ultrasound conducted by experienced examiners can reach high diag- nostic accuracy. Guerriero et al. (2016) report that for deep endometriosis, the sensitivity of TVUS ranges from 79 to 98%, and specificity from 94 to 100%, depending on lesion location and scanning protocol (Bazot et al. 2004). Magnetic resonance imaging (MRI) has become a power- ful complementary method. It’s excellent soft tissue contrast resolution and multiplanar imaging capabilities, which allow for precise mapping of endometrial lesions, particularly in complex or deeply infiltrating cases. Studies have shown that MRI sensitivity ranges from 77 to 93%, and specificity from 90 to 98%, depending on the imaging protocol and the radiologist's expertise (Fiaschetti et al. 2012). Functional MRI (fMRI), although primarily used in neuroscience, has recently been applied to study altered brain connectivity in women with chronic pelvic pain and endometriosis (Szabo et al. 2022). Preliminary studies have demonstrated abnormal activa- tion of pain-processing networks and altered connectivity in regions such as the anterior cingulate cortex and the insula. These findings suggest that central sensitization may be a hallmark of chronic endometriosis and could serve as a potential neuroimaging biomarker of disease severity and pain chronicity (As-Sanie et al. 2016). Together, these imaging and anthropometric biomark - ers reflect both peripheral and central manifestations of endometriosis, offering a multifaceted approach that may improve early diagnosis, patient risk stratification, and the understanding of disease mechanisms. Advanced imaging techniques such as ultrasound, MRI, and more recently, functional MRI (fMRI), have been employed to visualize endometriotic lesions and assess their characteristics. Recent studies have honed in on specific imaging features, patterns, and potential quantitative measures that can enhance the accuracy of diagnosis and characterization. Multimodal approaches and precision medicine The diagnosis of endometriosis is not a one-size-fits-all endeavor. The interplay of various biomarkers, clinical parameters, and advanced technologies underscores the importance of multimodal diagnostic approaches. Addition- ally, the concept of precision medicine is emerging, where individualized care is tailored based on a patient's unique biomarker profile. While remarkable progress has been made in identifying potential biomarkers for endometriosis, the road to clinical implementation is ongoing. Emerging technologies, including artificial intelligence and machine learning, promise to revolutionize diagnostic accuracy and efficiency. In conclusion, these biomarkers offer a glimpse into the complexity of the disease, emphasizing the potential for enhanced diagnostic accuracy, individualized treatment, and improved patient outcomes. Ongoing research and col- laborative efforts are vital in driving the field forward and harnessing the full potential of these biomarkers. The most promising biomarkers for endometriosis detection CA‑125: a widely studied marker CA-125, widely recognized as a tumor marker, has been extensively studied in the context of endometriosis (Feduniw et al. 2024). Recent research continues to explore its role as a diagnostic biomarker. While not specific to endometrio- sis, elevated CA-125 levels have shown promise in distin- guishing this condition from other gynecological disorders (Magalhães et al. 2021). This chapter focuses on the most recent findings regarding CA-125 as a diagnostic tool. CA-125 is one of the most studied metabolic biomarkers in the context of non-invasive diagnosis of endometriosis. Although it lacks sufficient sensitivity to be used as a stan- dalone diagnostic test, it can serve as a useful complemen- tary tool, particularly in cases of moderate to severe disease. CA-125 is a membrane-bound glycoprotein often elevated in various pathological conditions, including ovarian cancer, pelvic inflammatory disease, menstruation, and endometrio- sis. Its serum levels have been shown to correlate with dis- ease stage and lesion burden in endometriosis. A comprehensive meta-analysis of 22 studies involving 3626 women a threshold of 30 U/ml resulted in a sensitiv - ity of 52 percent and specificity of 93 percent, indicating that CA-125 may be more effective as a rule-in rather than a rule-out diagnostic marker particularly in more advanced stages of the disease (Nisenblat et al. 2016). Sensitivity was significantly higher in moderate to severe endometriosis at 63 percent compared to only 24 percent in minimal disease, suggesting that lesion volume and location significantly influence circulating levels of this marker (Nisenblat et al. 2016). A prospective cohort study confirmed similar findings showing that a CA-125 threshold of 30 U/ml yielded a sen- sitivity of 57 percent and specificity of 96 percent with a positive likelihood ratio of 15.8 which provides substantial support for its diagnostic value in symptomatic patients (Szubert et al. 2012). Additionally, other studies have dem- onstrated that women with stage III and IV endometriosis had mean CA-125 levels around 50 U/ml whereas women without endometriosis had mean levels closer to 7.8 U/ml (Somigliana et al. 2004). Although CA-125 levels can be influenced by menstrua- tion and other gynecological conditions its significant ele- vation in advanced endometriosis supports its inclusion in Journal of Applied Genetics diagnostic panels for selected patients CA-125 may also be useful in tracking treatment response or recurrence although its clinical utility in monitoring remains to be validated in larger longitudinal studies (Mol et al. 1998) despite its

CA-125 continues to be the most accessible and widely used metabolic biomarker in endometriosis and ongoing research aims to improve its specificity and sensi- tivity through combination with other markers or imaging techniques (May et al. 2010). Chen et al. (2021) examined differences in blood cells and tumor biomarkers between endometriosis patients and controls. They found notable discrepancies in blood cell counts and tumor markers, with endometriosis patients hav- ing altered levels of eosinophil, neutrophil count, and others. A diagnostic model using HGB, CA199, CA-125, and HE4 showed a sensitivity of 85.4%, specificity of 78.83%, and an AUC of 0.900, suggesting enhanced diagnostic accuracy for early endometriosis detection (Chen et al. 2021). HE4: a complementary marker Human Epididymis Protein 4 (HE4) is a glycoprotein traditionally associated with ovarian cancer diagnosis. Recent studies have explored its potential as a biomarker for endometriosis, particularly in differentiating it from other gynecological conditions. For instance, a multicenter prospective study involving 981 patients assessed the util - ity of HE4 in distinguishing endometriosis from adnexal malignancies. The findings indicated that HE4 was positive in only 1.5% of endometriosis cases, compared to 64.6% positivity for CA125. This suggests that HE4 has high specificity in excluding malignant disease in patients with endometriosis, especially when CA125 levels are elevated (Zapardiel et al. 2016).Another study evaluated the diag- nostic performance of HE4 compared to CA125 in differ - entiating between ovarian cancer and endometriosis. In this sub-analysis, HE4 demonstrated superior performance with an area under the curve (AUC) of 0.91, compared to 0.81 for CA125. The Risk of Ovarian Malignancy Algo- rithm (ROMA), which incorporates both HE4 and CA125, achieved an AUC of 0.95, indicating enhanced diagnostic accuracy when combining these biomarkers (Braicu et al. 2022). Furthermore, research has shown that HE4 levels are not elevated in patients with endometriosis or benign ovar - ian masses, whereas they are significantly higher in ovarian cancer patients. This reinforces the potential of HE4 as a specific marker to distinguish between benign and malignant ovarian conditions (Anastasi et al. 2013). In conclusion, HE4 holds promise as a specific marker for distinguishing endometriosis from ovarian malignancies, especially when used alongside CA125. The specificity of HE4 can improve diagnostic accuracy, especially in cases where CA125 is elevated (Abdalla et al. 2016). TGFBI and CA‑125 Transforming Growth Factor Beta-Induced protein (TGFBI) is an extracellular matrix protein encoded by the TGFBI gene and regulated by TGF-β signaling. It plays a key role in cell adhesion, migration, and tissue remodeling. and is involved in several physiological and pathological processes including inflammation, fibrosis, and tumor progression. In the context of endometriosis, elevated TGFBI levels may reflect abnormal tissue remodeling and immune activity associated with the disease. A study by Janša et al. (2023) assessed the diagnostic value of TGFBI in combination with CA-125 for non- invasive detection of endometriosis. (Janša et al. 2023 ). The analysis was performed in two phases: discovery and validation. In the discovery cohort, (32 patients, 24 con- trols), TGFBI levels were significantly higher in patients, with a receiver operating characteristic (ROC) AUC of 0.77, sensitivity of 58%, and specificity of 84%. When combined with CA-125 using a support vector machine (SVM) model, diagnostic performance improved notably (AUC 0.91, sen- sitivity 88%, specificity 75%) (Janša et al. 2023). Validation on a larger cohort (166 patients, 71 controls) confirmed the utility of the combined marker set, achieving an AUC of 0.83, sensitivity of 83%, and specificity of 67%. CA-125 alone produced similar AUC (0.83) but with lower sensitiv- ity (73%) and higher specificity (80%). Importantly, in early- stage disease (rASRM I–II), TGFBI outperformed CA-125 (AUC 0.74 vs. 0.63). For advanced stages (rASRM III–IV), the combined model showed high diagnostic accuracy (AUC 0.94, sensitivity 95%).(Janša et al. 2023). These findings highlight TGFBI as a promising biomarker that enhances diagnostic accuracy, particularly in early-stage endometrio- sis when used alongside CA-125. Further large-scale studies are warranted to validate these results and explore clinical integration. Inflammatory cytokines: insights into disease activity Recent research has identified several interleukins and related cytokines as promising non-invasive biomarkers for the diagnosis and monitoring of endometriosis. These immune mediators, involved in regulating inflammation and tissue remodeling, often display altered expression pat- terns in patients with endometriosis compared to healthy individuals. Interleukin-6 (IL-6): is a pro-inflammatory cytokine frequently elevated in the serum and peritoneal fluid of women with endometriosis. A systematic review and meta-analysis confirmed significantly higher IL-6 lev - els in affected individuals, highlighting its potential as a Journal of Applied Genetics diagnostic biomarker. Furthermore, IL-6 has been pro - posed as a predictor of endometriosis-associated infertility (Krygere et al. 2024). Interleukin-1β (IL-1β): IL-1β, another pro-inflamma- tory cytokine, has been found at elevated levels in the serum of women with endometriosis. Research indicates that IL-1β, along with IL-6 and TNF-α, could be used as predictors for endometriosis (Malutan et al. 2015). Tumor Necrosis Factor-alpha (TNF-α): a key regulator of systemic inflammation, is also elevated in the serum of endometriosis patients. Its involvement in promoting angiogenesis and inflammatory signaling supports its util- ity as a diagnostic indicator (Krygere et al. 2024). Interleukin-1 Receptor Antagonist (IL-1RA): is an anti- inflammatory cytokine that inhibits the activities of IL-1β. Elevated levels of IL-1RA have been reported in the serum and peritoneal fluid of patients with endometriosis, indi- cating its involvement in the disease's pathophysiology (Werdel et al. 2024). Interleukin-10 (IL-10), another anti-inflammatory cytokine, suppresses immune responses and may contrib- ute to immune tolerance mechanisms in endometriosis. Elevated IL-10 levels have been reported in both serum and peritoneal fluid of affected individuals (Suen et al. 2014). Interleukin-8 (IL-8): is a chemokine associated with chronic inflammation. Its expression is particularly increased during the early stages of the disease and in patients with endometriomas, indicating its potential utility as a biomarker for early detection (Arici et al. 1996; Cardoso et al. 2023). Interleukin-16 (IL-16): has been found at significantly higher concentrations in the peritoneal fluid of patients with advanced-stage endometriosis. This cytokine may play a role in initiating and sustaining peritoneal inflammation (Koga et al. 2005; Krygere et al. 2024). Interleukin-18 (IL-18): involved in immune modulation, has shown elevated levels in the peritoneal fluid of women with minimal-to-mild endometriosis, although serum lev - els did not differ significantly. Further studies are needed to assess its diagnostic potential (Oku et al. 2004; Luo et al. 2006). Combining multiple cytokines into a panel may enhance diagnostic performance. For instance, a study combining IL-6, IL-8, TNF-α, high-sensitivity C-reactive protein, CA-125, and CA-19.9 achieved a sensitivity of 100% and specificity of 84% for moderate-to-severe endometriosis (Krygere et al. 2024). Large-scale, multicenter studies are essential to validate these findings and to develop standardized, cytokine-based non-invasive diagnostic tools. Measuring inflammatory cytokines such as IL-6, IL-8, and TNF-α not only offers diagnostic value but also provides insights into disease activ- ity and highlights potential therapeutic targets. MicroRNAs (miRNAs): Epigenetic biomarkers in endometriosis Epigenetic biomarkers, particularly microRNAs (miRNAs), have gained substantial attention in the last few years. These small RNA molecules play a role in the regulation of gene expression and have been found to exhibit distinct profiles in endometriosis. Research articles have identified specific miRNA signatures associated with endometriosis, raising the exciting prospect of non-invasive diagnostic tests based on miRNA patterns. Furthermore, these biomarkers hold the potential for predicting disease progression and thera- peutic response, paving the way for personalized treatment strategies. In a 2023 study, researchers explored the potential of a microRNA (miRNA) panel in combination with CA-125 as a diagnostic biomarker for endometriosis. The aim was to assess how effectively this miRNA panel could detect early- stage endometriosis and distinguish it from more advanced stages of the disease. The study analyzed the serum levels of miR-199a, miR-122, miR-145, and miR-141 alongside CA-125, a well-established biomarker for endometriosis, in women diagnosed with endometriosis (early and advanced stages) compared to a control group of healthy women. The team utilized quantitative polymerase chain reaction (qPCR) to profile miRNAs and enzyme-linked immunosorb- ent assay (ELISA) to measure CA-125 levels. Diagnostic accuracy was evaluated based on sensitivity, specificity, and the AUC from the receiver operating characteristic (ROC) curve. The results showed that the combination of the four miRNAs with CA-125 achieved excellent diagnostic perfor- mance, with 81.8% sensitivity, 92.6% specificity, and a 0.939 AUC—indicating strong diagnostic accuracy, especially for distinguishing early-stage from advanced-stage endometrio- sis. The panel's ability to identify early stages of the disease is particularly important for timely diagnosis and interven- tion (Chen et al. 2023). The study concludes that combining miRNA panels with CA-125 could significantly improve the non-invasive diagnostic approach to endometriosis. While the results are promising, further validation in larger, mul - ticenter trials is needed to confirm these findings and assess the clinical applicability of this biomarker panel in routine clinical practice. Future prospects: the role of artificial intelligence and machine learning This section concludes by exploring the potential of emerg- ing technologies—particularly artificial intelligence (AI) and machine learning (ML)—in analyzing complex biomarker datasets to improve the diagnosis of endometriosis. Recent advancements in these technologies have demonstrated significant promise in enhancing non-invasive diagnostic Journal of Applied Genetics approaches through sophisticated data integration and pat- tern recognition. A 2022 study introduced a non-invasive, blood-based diagnostic model using AI to analyze the human miRNome, achieving 96.8% sensitivity, 100% specificity, and an AUC of 98.4%, These results suggest the potential of AI to replace invasive diagnostic procedures such as laparoscopy (Ben- difallah et al. 2024 ). Similarly, a 2024 study applied ML algorithms to serum biomarkers, demonstrating that the random forest model combining CA125 and neutrophil-to- lymphocyte ratio (NLR) achieved an accuracy of 78.16%, sensitivity of 86.21%, and AUC of 0.85 (Zhao et al. 2024). In another application, ML was used to identify 11 immune-related genes regulated by 8 miRNAs as poten- tial diagnostic biomarkers, underlining the relevance of the immune microenvironment in disease development (Tu et al. 2024). Moreover, a 2022 study utilizing clinical and symp- tom-based features applied various ML algorithms to screen for endometriosis, achieving AUCs between 0.91 and 0.95, which supports their potential as diagnostic aids in primary care settings (Bendifallah et al. 2022). A comprehensive scoping review from the same year examined 36 studies employing AI in endometriosis research and found that models such as logistic regression, deci- sion trees, random forests, and support vector machines had pooled sensitivities ranging from 81.7% to 96.7% and specificities between 70.7% and 91.6%, utilizing diverse data inputs including clinical, imaging, and biomarker data (Sivajohan et al. 2022). Collectively, these findings collec- tively highlight the transformative potential of AI and ML in advancing non-invasive, accurate, and early diagnosis of endometriosis through the integration of biomarker data. Next-generation sequencing (NGS) has also contributed significantly to biomarker discovery. Multiple studies have identified circulationg microRNAs in plasma with differ - ential expression between women with laparoscopically confirmed endometriosis and healthy controls, identifying up to 41 miRNAs as potential diagnostic markers (Nguyen et al., 2020). Similarly, NGS combined with qRT-PCR has further validated these candidate miRNAs (Suryawanshi et al., 2020). Broader genomic analyses using NGS have revealed multiple genes implicated in endometriosis patho- genesis, emphasizing its value in biomarker and therapeutic discovery (Ali et al., 2024). However, no clinically validated diagnostic test based solely on NGS has yet been imple- mented, reflecting the translational gap between discovery and clinical application. In contrast, AI and ML approaches have advanced closer to practical use. Systematic reviews indicate that AI mod- els trained on clinical, biomarker, imaging, and omics data achieve high diagnostic accuracy, with sensitivity ranging from 81.7% to 96.7% and specificity from 70.7% to 91.6%, using methods such as logistic regression, random forest, and support vector machines (Tao et al., 2022). Projects like IMAGENDO demonstrated that AI-assisted integration of MRI and transvaginal ultrasound improved diagnostic accu- racy for detecting posterior cul-de-sac obliteration, raising the AUC from approximately 65% to 90.6% (Avery et al. 2024, IMAGENDO study). Deep learning architectures including Xception, Inception-V4, ResNet50, DenseNet, and EfficientNetB2 applied to ultrasound data achieved strong discriminatory power with AUC values of 0.85–0.90 (Chen et al. 2023). Furthermore, explainable AI (XAI) approaches, such as U-Net with attention mechanisms and Grad-CAM, are being developed to enhance interpretability and clini - cal trustworthiness of AI predictions (Zhang et al. 2025). Multimodal frameworks like HAICOMM, integrating MRI data with multi-rater expert assessments, have outperformed individual radiologists in diagnostic classification (Wang et al. 2024). A notable example of combining NGS and ML in a non- invasive diagnostic is the saliva microRNA assay, commonly referred to as Endotest. This assay sequences hundreds of salivary miRNAs using NGS and applies an ML classifier, reporting very high diagnostic accuracy (AUC ≈0.98–0.99) in prospective case–control cohorts, including early and non-ovarian disease. However, independent, real-world validation and reimbursement are still pending, and national health technology assessors have so far judged the evidence promising but insufficient for routine use (Bendifallah et al. 2024). In summary, while NGS has advanced biomarker discovery, AI-based methods—particularly in imaging and multimodal data integration—have reached a stage where diagnostic performance rivals or exceeds expert evaluation. Both approaches, however, require further validation, stand- ardization, and regulatory approval before widespread clini- cal implementation. To capture the current landscape of biomarker research in endometriosis, Table  2 provides a summary of the main biomarker categories, representative examples, and their potential clinical applications.

The development of biomarkers for early detection of endo- metriosis has made significant strides, particularly in the integration of hormonal, genetic, epigenetic, immunologi- cal, metabolic, imaging, and multimodal approaches (Luo et al. 2006; Anastasiu et al. 2020; Azeze et al. 2024) . These markers not only offer new insights into the pathophysiol- ogy of the disease but also promise improved diagnostic precision, allowing for earlier detection and personalized treatment strategies. Hormonal biomarkers, such as HE4 and CA-125, remain central to the diagnostic process, with HE4 showing promise Journal of Applied Genetics Table 2 Summary of major biomarker categories studied in endometriosis and their potential clinical applications Biomarker Type Example(s) Sample Source Diagnostic Role/Comments Ref Classical serum markers CA-125, HE4, TGFBI Serum (blood) Moderate sensitivity and specificity; best used in combination with other biomarkers CA-125 already used clinically (support- ive, not definitive), HE4, TGFBI- Under investigation, not yet routine (Zhao et al. 2024) (Feduniw et al. 2024) (Zapardiel et al. 2016) (Braicu et al. 2022) (Janša et al. 2023) Inflammatory cytokines IL-6, IL-8, TNF-α, IL-10. IL-16, IL-18 Serum (blood) or peritoneal fluid Reflects local and systemic inflammation; currently under active investigation for diagnostic use. Under investigation (Krygere et al. 2024) (Malutan et al. 2015) (Suen et al. 2014) (Cardoso et al. 2023) (Q. Luo et al. 2006; Oku et al. 2004) Hormonal markers Estrogen, Progesteron, LH, FSH Serum (blood) Altered hormonal profiles observed; potential supportive markers in specific clinical contexts Used clinically (hormonal profiling, sup- portive, not diagnostic-specific) (Gao et al. 2019) (Gjorgoska & Rizner 2024; McGrath et al., 2023; B. Zhao et al. 2022) MicroRNAs miR-17-5p, miR-451a, let-7b-5p Serum, plasma, tissue, saliva Emerging as promising non-invasive biomarkers; with potential for early and accurate diagnosis Under investigation (Hon et al. 2024) (Vanhie et al. 2024) Epigenetic markers DNA methylation profiles, mQTLs Blood or tissue DNA/RNA Help identify disease susceptibility and progression; still largely in the explora- tory research phase. Under investigation (Bendifallah et al. 2024) (Mortlock et al. 2023) (Ducreux et al. 2025) (Ortiz et al. 2021) Genetic markers Variants in CUX2, CLMP, CEP131, etc Blood or tissue DNA Associated with disease risk; may inform personalized therapeutic strategies in the future Under investigation (Kucukakcali et al., 2025) Immune markers Regulatory T cell levels, cytokine profiles Serum (blood) or tissue samples Reflect immune dysregulation; potential role in disease staging and monitoring. Under investigation (Tarokh et al. 2019; Pashizeh et al. 2020; Olkowska-Truchanowicz et al. 2021) Metabolites Altered metabolic signatures Serum (blood) or urine Under active study for diagnostic panels; strong potential for non-invasive test- ing. Under investigation (Dutta et al. 2018) (Li et al. 2018) Multimodal approaches Combined biomarker panels, AI- enhanced analysis (e.g., Endotest saliva miRNA test, IMAGENDO MRI/TVUS integration, Random forest with CA125 + NLR) Blood, imaging, multi-omics Improve diagnostic sensitivity and speci- ficity; represent the future of precision diagnosis. Under investigation (Krygere et al. 2024) (Bendifallah et al. 2024) (Sivajohan et al. 2022) Journal of Applied Genetics in distinguishing endometriosis from other gynecological conditions (Anastasi et al. 2013; Braicu et al. 2022). The combination of these markers significantly enhances diag- nostic sensitivity and specificity, particularly in the context of early-stage disease. TGFBI, a marker of tissue fibrosis and extracellular matrix remodeling, shows potential in the monitoring of dis- ease progression (Janša et al. 2023). It can serve as a useful biomarker to assess disease activity, aiding in the prediction of endometriosis recurrence and the evaluation of therapeu- tic interventions. Inflammatory cytokines, particularly IL-6, IL-1β, TNF- α, and other interleukins, play a crucial role in the inflam- matory response associated with endometriosis (Oală et al. 2024; Krygere et al. 2024). The levels of these cytokines correlate with disease activity and can be used to monitor the inflammatory status, providing valuable information for treatment strategies. MiRNA profiles represent an evolving approach in bio- marker diagnostics. Changes in miRNA expression pat- terns, especially in circulating samples, offer insights into the molecular mechanisms underlying endometriosis and its progression (Wang et al. 2013; Leonova et al. 2021). The ability to detect these miRNAs non-invasively could signifi- cantly improve early diagnosis, particularly when used in conjunction with other biomarkers such as CA-125. The 2D:4D ratio, a measure of prenatal hormone expo- sure, is a promising hormonal biomarker that may help iden- tify individuals at higher risk of developing endometriosis (Buggio et al. 2023). While the 2D:4D ratio warrants further exploration, it offers a potential early indicator for personal- ized risk assessment. The AGD has gained attention for its role in the develop- ment and progression of endometriosis. Changes in AGD may offer insights into the prenatal hormonal environment and the molecular mechanisms underlying the disease, potentially assisting in the identification of patients at higher risk of severe disease progression (Zamani et al. 2023). Bioimaging techniques are emerging as vital tools in the non-invasive diagnosis and monitoring of endometriosis. Advances in MRI, ultrasound, and other imaging modali- ties allow for the visualization of endometriotic lesions and provide real-time monitoring of disease progression (Avery et al. 2024). These techniques are integral in improving the accuracy of endometriosis diagnosis, especially when combined with biomarker analysis, offering a more holistic approach to patient care. Epigenetic markers, including DNA methylation patterns, histone modifications, and non-coding RNAs, offer valuable insights into the molecular changes that underpin the patho- genesis of endometriosis (Luo et al. 2024). These markers not only enhance the understanding of the disease but also provide the potential for identifying new therapeutic targets. This table summarizes the main classes of biomarkers explored for endometriosis detection, including examples, sample sources, and diagnostic relevance. The integration of classical mark - ers, inflammatory profiles, hormonal alterations, microRNAs, genetic and epigenetic factors, immune signatures, and metabolic changes reflects the complexity of the disease. Multimodal approaches combining multiple biomarkers with artificial intelligence represent the future direction of improving diagnostic precision. Table 2 (continued) Biomarker Type Example(s) Sample Source Diagnostic Role/Comments Ref Imaging (Neuroimaging) MRI, fMRI, Transvaginal ultrasound Detect ovarian and deep lesions, assess CNS changes Combine imaging with metabolic bio- markers to improve diagnostic accuracy fMRI- evaluates central sensitization; assesses activation of brain pain net- works Already used clinically (imaging stand- ards in diagnosis) (Borghese et al. 2015) (Fiaschetti et al. 2012) (Baușic et al. 2023) Phenotypic 2D:4D ratio, birth weight, AGD Early identification of at-risk individuals Merge phenotypic traits with imaging to improve patient stratification and risk profiling Under investigation (phenotypic/epide- miological risk markers (Buggio et al. 2023) (Zamani et al. 2023) (Borghese et al. 2015) Journal of Applied Genetics Epigenetic modifications are also crucial for understanding the transgenerational impact of the disease, particularly in the context of maternal transmission of endometriosis risk. Furthermore, multimodal approaches that integrate bioimaging with molecular biomarkers, such as miRNAs, cytokines, and genetic markers, represent the future of endo- metriosis diagnostics. The application of artificial intelli- gence (AI) to analyze complex datasets can significantly enhance diagnostic accuracy, enabling personalized medi- cine strategies and improving patient outcomes. Future directions in clinical implementation The early and accurate detection of endometriosis is para - mount, particularly in avoiding the long-term complications associated with the disease, such as infertility. One of the most significant challenges in current clinical practice is the reliance on invasive diagnostic techniques, which often

in delayed diagnoses and the potential for irreversible damage. Therefore, there is an urgent need to identify and implement non-invasive biomarkers that can provide quick, reliable, and minimally disruptive diagnostics. By incorporating hormonal, genetic, epigenetic, inflam- matory, and imaging biomarkers, we move towards the possibility of diagnosing endometriosis in its early stages, before it has the opportunity to cause extensive damage to the reproductive organs. The use of bioimaging technologies combined with circulating biomarkers, such as miRNAs, inflammatory cytokines, and protein markers, presents a promising non-invasive approach for detecting endometrio- sis. This not only improves the speed of diagnosis but also reduces the need for invasive surgeries that carry risk and can lead to delays in treatment. Implementing non-invasive diagnostic methods would revolutionize clinical manage- ment, enabling earlier intervention that could prevent the disease from causing significant tissue damage, reducing the risk of infertility, and improving overall patient outcomes. With such methods, the focus could shift from reactive, inva- sive procedures to proactive, preventive care, empowering clinicians to manage endometriosis more effectively and pro- viding patients with the opportunity for timely, personalized treatment. Thus, advancing the search for non-invasive diagnostic solutions is essential in transforming the approach to endo- metriosis care, offering a way to diagnose this debilitating disease early and reduce its impact on reproductive health. Author contribution Michalina Kliber: conceptualization, writ- ing—original draft, review and editing, literature searching; Klaudia Kulczynska-Figurny: writing-review and editing, figure prepara- tion; Andrzej Pławski editing, supervision; Paweł Piotr Jagodziński supervision. Data Availability No new data were generated or analyzed in this study. All information presented is based on previously published studies, which are appropriately cited in the manuscript. Declarations Ethics approval This article does not contain any studies with human participants or animals performed by any of the authors. Consent to participate Not applicable/not required. Consent for publication Not applicable/not required. Competing interest The authors declare that there is no conflicts of interest. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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