Translation of miRNA blood-based discovery to molecular testing for clinical diagnosis of endometriosis

24 Endometriosis is a common yet often underdiagnosed condition, partly due to the lack of reliable 25 diagnostics. This study examines the clinical feasibility of a blood-based, miRNA-driven test to 26 diagnose endometriosis and address the challenges of translating next-generation sequencing (NGS) 27 findings into clinical use. Serum from 20 patients and 20 controls underwent miRNA sequencing to 28 identify diagnostic biomarkers. A machine learning model built on all NGS-based differentially 29 expressed miRNA biomarkers achieved ≥ 90% accuracy. Validation by qPCR confirmed some but not 30 all findings, underscoring the difficulty of adapting NGS discoveries for routine diagnostics. 31 Nonetheless, serum miRNA biomarkers show strong promise for non-invasive endometriosis 32 detection, with further optimization needed for clinical translation. 33 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 3

34 Endometriosis is a chronic and often debilitating condition that affects approximately 5-10% of 35 women and adolescents within the reproductive age range of 15-49 years (1-3). However, among 36 women experiencing infertility, the prevalence of endometriosis can rise significantly, affecting 37 approximately 50% of this population. The disease can manifest as early as the first menstrual period 38 and persist until menopause, causing a wide range of symptoms that significantly impact quality of 39 life. Notably, between 50% and 80% of women who suffer from chronic pelvic pain are diagnosed 40 with endometriosis, highlighting its strong association with this symptom (1-3). 41 Despite its prevalence and impact, endometriosis remains underdiagnosed and poorly understood. 42 Early detection and timely intervention are crucial for improving patient outcomes, yet diagnosis is 43 frequently delayed by an average of 6 to 11 years (4, 5). This delay is attributed to several factors, 44 including the unknown etiology of the disease, the nonspecific and varied nature of its symptoms, 45 and the limitations of current diagnostic methods. Symptoms such as dysmenorrhea, chronic pelvic 46 pain, dyspareunia, and gastrointestinal disturbances can often mimic other gynecologic or 47 gastrointestinal conditions, leading to misdiagnosis or delayed recognition. This variability, coupled 48 with the lack of reliable diagnostic tools, poses significant challenges for both clinicians and 49 researchers (6, 7). 50 The current “Gold Standard” diagnostic endometriosis test involves an invasive laparoscopic surgical 51 procedure, which carries risks of infection, bleeding, and damage to surrounding tissues (8-10). The 52 procedure is only as reliable as the operator's surgical experience in locating and identifying 53 endometriosis lesions. As a result, its diagnostic accuracy varies greatly, with some studies reporting 54 sensitivity and specificity below 70% (10, 11). For instance, a study in Canada found that while 55 laparoscopic visualization had a sensitivity of 90%, its specificity was only 40% (12). Another study 56 reported a sensitivity of 69% and a specificity of 83% for laparoscopy in diagnosing endometriosis 57 (13). These findings highlight that while laparoscopy is a valuable diagnostic tool, it is not infallible. 58 Besides relying heavily on the operator’s experience, the heterogeneous appearance of endometriotic 59 lesions precluded a standardized visual assessment, and the presence of deep infiltrating lesions may 60 be missed during the procedure (10, 14). In addition, data suggest that some women undergoing the 61 procedure do not have the condition and are therefore unnecessarily exposed to surgical risks. 62 Further diagnostic advances may be expected from ever-evolving and indeed less invasive imaging 63 technologies, however as yet this has not achieved the gold standard status. 64 Given these limitations, there is an increasing interest in developing non-invasive diagnostic methods 65 for endometriosis (15-17). A growing body of evidence highlights the significant role of miRNA 66 expression changes in the pathogenesis and progression of various diseases, including endometriosis. 67 Numerous research groups have focused on the regulatory role of miRNAs in key genes associated 68 with endometriosis and have explored their potential as diagnostic biomarkers. For example, studies 69 have identified miR-200 and miR-17-5p as promising biomarkers in the plasma of endometriosis 70 patients (18, 19), while miR-135a has shown diagnostic potential in the saliva of these patients (20). 71 However, conflicting results have also emerged regarding the diagnostic value of miRNAs (21). For 72 example, one study found that serum miR-199a was upregulated in individuals with endometriosis 73 (22), while another study reported its downregulation in serum (23), highlighting the challenges in 74 interpreting these findings and developing reliable diagnostic tests. Several factors may contribute to 75 these discrepancies. For instance, the menstrual phase during which samples are collected may 76 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 4 influence miRNA expression, as studies have shown that miRNA levels can vary between the 77 proliferative and secretory phases of the menstrual cycle (24, 25). Additionally, variations in study 78 protocols, such as differing inclusion and exclusion criteria, may introduce further inconsistencies, 79 especially considering the heterogeneity of endometriosis as a disease. Despite the numerous 80 challenges associated with standardizing miRNA-based diagnostics, such as biological variability, 81 technical inconsistencies, and the complexity of endometriosis as a disease, considerable progress 82 has been made in developing and implementing diagnostic tools in clinical settings. One notable 83 example is the Endotest developed by Ziwig Inc., a test that utilizes a next-generation sequencing 84 (NGS) panel comprising over 100 microRNAs to evaluate disease status (26, 27). This test has been 85 primarily introduced in European healthcare systems and represents a significant advancement in 86 non-invasive diagnostics for endometriosis. Although the Ziwig Endotest holds substantial potential 87 as a laboratory-developed test (LDT), its broader implementation as a routine in vitro diagnostic 88 (IVD) tool remains limited. This is largely due to the high cost associated with NGS technologies 89 and the requirement for advanced bioinformatics infrastructure and expertise (28, 29), which may not 90 be readily available in routine clinical settings. As such, while promising, NGS-based test is 91 currently better suited for use in a centralized reference laboratory rather than widespread clinical 92 deployment in the form of IVD (30). 93 One major obstacle in translating NGS-derived miRNA biomarkers into clinically actionable 94 PCR-based assays (qPCR or ddPCR) is the challenge of selecting appropriate endogenous controls 95 for data normalization (31, 32). Unlike NGS, which employs global normalization strategies such as 96 reads per kilobase million (RPKM), and transcripts per million (TPM), PCR-based approaches 97 require specific endogenous reference genes for normalization. However, many studies rely on 98 "universal" endogenous controls, such as GAPDH, RNU48, or miR-16, without assessing their 99 stability in the specific disease contexts (31, 32). This oversight can introduce systematic bias, 100 leading to unreliable and non-reproducible biomarker quantification, thereby limiting their clinical 101 utility. 102 To address these challenges, we conducted a proof-of-concept study integrating unbiased miRNA 103 biomarker discovery via miRNA sequencing (miRNA-seq) with subsequent experimental validation 104 using qPCR. Our primary objective is to present the development and feasibility of a miRNA-based 105 panel of biomarkers that would enable a non-invasive, simple and accurate blood test in diagnosing 106 endometriosis reliably. To control for female-specific physiological processes influenced by 107 hormonal fluctuations, such as those occurring during the menstrual cycle, we limited serum 108 collection to women in the secretory phase. Another key innovation of our study is that we not only 109 identified potential biomarkers but also established endometriosis-specific reference endogenous 110 controls for normalization in clinical diagnostic settings. These newly identified endogenous controls 111 were systematically validated to ensure their stability and suitability for qPCR- and ddPCR-based 112 assays. Experimental validation of both the candidate biomarkers and the newly selected endogenous 113 controls demonstrated strong reproducibility and accuracy, supporting their potential clinical 114 application. This approach represents a critical step toward the development of a standardized, 115 reliable, and clinically translatable miRNA-based diagnostic assay for endometriosis.116 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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117 Differentially expressed miRNAs in endometriosis patients 118 In the discovery cohort of samples collected during the secretory phases, we identified a total of 85 119 differentially expressed miRNAs between the patient group and the control group (Figure 1A; 120 Supplementary Table S1). Among these, 55 miRNAs were significantly upregulated in the disease 121 group, while 30 were downregulated, indicating a distinct molecular signature associated with 122 endometriosis. Interestingly, some subjects exhibited higher expression levels of specific miRNAs 123 compared to others within the same group. This variability is likely attributable to the heterogeneous 124 nature of endometriosis, which can manifest differently across individuals in terms of severity, 125 symptom presentation, and molecular profiles. Notably, several miRNAs previously implicated in the 126 pathogenesis and progression of endometriosis were found to be highly dysregulated in the disease 127 group compared to the control group (Figure 1A-B). These included miR-9-5p and miR-21-5p, both 128 of which have been reported to play critical roles in regulating inflammatory signaling pathways, a 129 key mechanism underlying the development and progression of endometriosis. 130 To further investigate the origin and functional relevance of these differentially expressed miRNAs, 131 we performed a hypergeometric over-representation analysis using miRNet (33, 34). This analysis 132 revealed that the majority of these miRNAs predominantly originated from the bone marrow and 133 cervix (Figure 1C). The bone marrow association reflects the serum origin of the miRNA data. The 134 cervix, on the other hand, is relevant as it points to the anatomical regions where endometriosis 135 lesions can be found in proximity, providing confidence that the observed molecular signals are 136 indeed linked to the disease. This suggests that the serum circulating miRNAs may serve as 137 biomarkers for systemic changes associated with endometriosis 138 Additionally, we conducted a hypergeometric test using the gene targets of the differentially 139 expressed miRNAs against the KEGG database to identify enriched molecular pathways (34). This 140 analysis highlighted that the top non-tumor-specific molecular processes included various signaling 141 pathways and focal adhesion, which are strongly associated with endometriosis (Figure 1C). For 142 instance, focal adhesion is a critical process in the pathogenicity of endometriosis, as it involves 143 cell-matrix interactions that contribute to the attachment, survival, and invasion of endometrial cells 144 outside the uterus (35). Similarly, dysregulation in signaling pathways, such as the MAPK signaling 145 pathway has been observed in endometrial cells, leading to enhanced cell proliferation, 146 endometriosis lesion establishment, and disease persistence (36). Furthermore, MAPK signaling is 147 implicated in pain sensitization, suggesting a role in the chronic pelvic pain experienced by many 148 patients with endometriosis (37). 149 150 Assessment of differentially expressed miRNAs for prediction of endometriosis using machine 151 learning 152 To evaluate whether the identified differentially expressed miRNAs possess predictive value in 153 distinguishing individuals with endometriosis from control subjects, we implemented and assessed 154 three distinct random forest models, each utilizing a different dataset. 155 For the first model (Model #1), we constructed a random forest classifier using all 85 differentially 156 expressed miRNAs. To rigorously assess its performance, we employed a 30-fold repeated 157 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 6 subsampling cross-validation approach. Model #1 demonstrated strong predictive capability, 158 achieving an overall sensitivity of 0.91, specificity of 0.88, and an area under the curve (AUC) of 159 0.95 (Figure 2A). To further refine the model and enhance interpretability, we explored feature 160 selection based on importance scores derived from the initial model. Specifically, we developed two 161 additional models with reduced feature sets: Model #2, which utilized the top 40 most informative 162 miRNAs, and Model #3, which incorporated only the top 20 (Supplementary Table S2). These 163 refined models were constructed using the same random forest framework and evaluated with the 164 identical repeated subsampling cross-validation procedure. By focusing on the most predictive 165 miRNAs, we aimed to improve classification accuracy while eliminating noise introduced by less 166 informative features. 167 The results indicated that both reduced-feature models exhibited slightly improved performance 168 compared to Model #1. Model #2, incorporating the top 40 miRNAs, achieved a sensitivity of 0.93, a 169 specificity of 0.89, and an AUC of 0.97 (Figure 2B). Model #3, which was restricted to the top 20 170 miRNAs, further enhanced predictive accuracy, yielding a sensitivity of 0.95, a specificity of 0.90, 171 and an AUC of 0.98 (Figure 2C). These findings suggest that a substantial proportion of the 172 differentially expressed miRNAs may not meaningfully contribute to disease classification and 173 instead introduce noise into the predictive model. The improved performance of the reduced-feature 174 models underscores the value of feature selection in enhancing both the robustness and 175 interpretability of machine learning-based biomarker discovery in endometriosis. 176 177 Selection of miRNA biomarkers and endogenous controls for non-NGS clinical diagnostics 178 The translation of NGS discovery findings into actionable clinical diagnostics is essential for 179 improving patient care. While NGS provides comprehensive molecular insights, clinical diagnostic 180 applications often rely on qPCR due to their faster turnaround time, lower costs, and suitability for 181 IVD implementation without requiring the complex bioinformatics infrastructure necessary for NGS 182 analysis (28-30). Given these advantages, we explored the feasibility of translating our NGS-based 183 findings into a qPCR-based diagnostic assay. 184 One of the primary considerations in this transition is the limit of detection of qPCR. The 85 185 differentially expressed miRNAs identified in our NGS analysis exhibit a wide range of expression 186 levels across samples, spanning from as few as 10 normalized read counts (e.g., miR-4710) to over 187 1,000,000 (e.g., miR-21-5p) (Supplementary Table S1). Based on initial qPCR assessments, we 188 determined that reliable detection in qPCR is achieved for miRNAs with an average NGS normalized 189 read count exceeding 500. Applying this threshold, 38 of the differentially expressed miRNAs fell 190 below the cutoff and were deemed unreliable for qPCR-based detection (Figure 3A). This left 47 191 differentially expressed miRNAs that met the detection threshold for further investigation 192 (Supplementary Table S3). 193 To assess the predictive potential of these 47 miRNAs in a qPCR setting, we constructed two random 194 forest models. The first model incorporated all 47 differentially expressed miRNAs, while the second 195 utilized a reduced feature set consisting of the 20 most informative miRNAs selected from this pool. 196 Notably, many of the miRNAs identified as top contributors in the previous analysis using all 85 197 markers—such as miR-21-5p, miR-17-5p, and miR-15b-5p—remained among the top-ranked features, 198 suggesting that key predictive biomarkers identified via NGS may indeed be translatable to a clinical 199 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 7 qPCR assay. 200 Performance evaluation of these models revealed that the first model (using all 47 miRNAs) 201 achieved an AUC of 0.78 (Figure 3B), whereas the second model (using the top 20 miRNAs) 202 exhibited improved performance with an AUC of 0.84 (Figure 3C). As observed previously, reducing 203 the feature set led to a slight increase in performance, likely due to the removal of uninformative or 204 noisy variables. However, it is important to note that these qPCR-based models demonstrated lower 205 overall performance compared to the models trained on all 85 differentially expressed miRNAs in 206 the NGS dataset. This suggests that some of the most informative miRNAs are expressed at low 207 levels in the samples, such as miR-9-5p, which was ranked as the most predictive but had an average 208 normalized read count of only ~350. The inability of qPCR to reliably detect such lowly expressed 209 miRNAs presents a challenge for clinical translation. Addressing this limitation may require 210 optimizing primer designs, adopting ddPCR for enhanced sensitivity, or incorporating target capture 211

to improve miRNA detection. 212 A second critical factor in transitioning to a qPCR-based assay is the selection of a suitable 213 endogenous control or reference marker for data normalization. Inappropriate reference markers can 214 introduce variability, leading to unreliable expression quantification and potentially skewing 215 diagnostic outcomes. To address this, we implemented a bioinformatic pipeline in identifying 216 disease-specific endogenous controls (Ref. Materials and Methods). Briefly, we selected miRNAs 217 that display minimal inter-group variability in expression to be used as endogenous references 218 (Supplementary Table S4). One such candidate, miR-92a-3p (Figure 3D), demonstrated consistent 219 expression levels between individuals with endometriosis and control subjects. Notably, miR-92a-3p 220 has been validated in prior studies as a reliable endogenous control in blood-based miRNA analyses 221 (38). 222 To further refine our models, we performed in silico normalization of the 47 differentially expressed 223 miRNAs against miR-92a-3p, simulating the clinical qPCR diagnostic workflow. Following 224 normalization, we re-evaluated the performance of our random forest models. This approach led to 225 noticeable improvements in predictive accuracy, with the model using all 47 miRNAs achieving an 226 AUC of 0.80, and the reduced model using the top 20 miRNAs attaining an AUC of 0.88 (Figures 227 3E-F). These findings underscore the importance of proper normalization strategies in enhancing the 228 robustness of qPCR-based diagnostics and further support the potential clinical applicability of our 229 NGS-derived biomarker panel. 230 231 Experimental assessment for diagnostic qPCR assays 232 To evaluate the potential of translating our NGS discovery findings into a clinically viable qPCR 233 diagnostic assay, we selected five candidate miRNAs — miR-21-5p, miR-15b-5p, miR-17-5p, 234 miR-19b-3p, and miR-23a-3p — for experimental validation using qPCR. These miRNAs were 235 chosen based on their differential expression patterns observed in the NGS dataset, as well as their 236 biological relevance to endometriosis. In addition, miR-92a-3p was included as an endogenous 237 control for normalization, given its demonstrated stability across endometriosis and control samples 238 in both our dataset and prior studies. 239 For this validation study, we conducted qPCR assays on 69 serum samples, comprising 45 patients 240 with endometriosis and 24 control subjects whose disease status was confirmed via laparoscopic 241 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 8 surgery. The goal was to determine whether the expression patterns observed in the NGS discovery 242 dataset could be reliably recapitulated using qPCR, a crucial step in transitioning towards a clinically 243 deployable assay. Analysis of the qPCR results revealed that two of the five selected biomarkers 244 (miR-21-5p and miR-15b-5p) displayed statistically significant differences in normalized expression 245 between the endometriosis and control groups. These findings are consistent with our NGS discovery 246 dataset, reinforcing their potential utility as diagnostic biomarkers. Additionally, miR-17-5p exhibited 247 a discernible trend of differential expression between disease and control samples, as visualized in 248 the boxplot analysis. However, this difference did not reach statistical significance, suggesting that 249 while this marker may hold some biological relevance, additional optimization—such as larger 250 sample sizes or refined qPCR conditions—may be necessary to establish its diagnostic value. In 251 contrast, the remaining two miRNAs, miR-19b-3p and miR-23a-3p, did not show clear delineation 252 between patients and controls, indicating that their differential expression in the NGS dataset may 253 not translate robustly into a qPCR-based assay. This discrepancy underscores the complexities of 254 biomarker translation and highlights the need for rigorous validation and optimization at the 255 experimental level. Factors such as primer design, amplification efficiency, RNA extraction 256 variability, and technical noise could all contribute to differences between NGS and qPCR results, 257 emphasizing the importance of careful assay development. Notwithstanding, the data suggests that 258 qPCR-based assays could provide meaningful diagnostic utility with proper refinement. 259 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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260 Small RNAs such as miRNAs have been shown to play pivotal roles in numerous physiological and 261 pathological processes, influencing gene regulation in both normal and disease states. Altered 262 miRNA expression profiles have been extensively documented in the plasma and serum of patients 263 with various conditions, including diffuse large B-cell lymphoma, ovarian cancer, and type 2 264 diabetes (39, 40). These findings underscore the potential of circulating miRNAs as non-invasive 265 biomarkers for disease detection and monitoring. In this study, we demonstrate that miRNAs 266 circulating in serum can potentially serve as reliable biomarkers for the diagnosis of endometriosis. 267 The ability to analyze serum miRNA levels in a standardized manner presents a promising approach 268 in disease detection. The fluctuations in specific circulating miRNAs offer a quantifiable and 269 reproducible means of identifying endometriosis, potentially improving early diagnosis and clinical 270 management. 271 Using serum miRNAs as diagnostic biomarkers offers several key advantages over conventional 272 diagnostic methods in endometriosis. Currently, the gold standard for endometriosis diagnosis relies 273 on laparoscopy with direct visualization, an invasive surgical procedure. A serum-based miRNA 274 biomarker assay could provide a non-invasive alternative, enabling comprehensive disease 275 assessment without the need for surgery. This is particularly valuable for early detection and for 276 patients who may not have immediate access to specialized surgical evaluation. Secondly, compared 277 to invasive diagnostic procedures, a serum-based miRNA test is significantly more cost-effective. 278 The process involves routine blood collection and standard laboratory processing, making it more 279 accessible for widespread clinical implementation. Additionally, standardizing miRNA detection 280 protocols could facilitate large-scale screening efforts, improving early diagnosis and patient 281 outcomes. 282 In this study, we investigated serum miRNA expression profiles in individuals with endometriosis 283 and identified a distinct set of circulating miRNAs that may serve as potential biomarkers for disease 284 detection. To minimize variability associated with hormonal changes during the menstrual cycle, 285 particularly those impacting female-specific physiological processes, serum samples were collected 286 exclusively during the secretory phase. Our results add to the growing evidence supporting the use of 287 serum miRNA signatures as non-invasive diagnostic tools for endometriosis (15-26). Nonetheless, 288 despite their promise, several key challenges remain before miRNA-based assays can be translated 289 into clinically reliable diagnostic applications. 290 One of the primary obstacles lies in the choice of detection platform. While NGS provides a 291 comprehensive assessment of the miRNA landscape, its high cost per sample and dependence on 292 complex bioinformatics infrastructure render it impractical for routine clinical diagnostics and IVD 293 applications. Consequently, there is a need to transition toward more practical methodologies, such 294 as qPCR or ddPCR, which offer lower costs, faster turnaround times, and compatibility with IVD 295 requirements. 296 Our findings suggest that converting NGS-based miRNA discoveries into clinically applicable assays 297 is achievable, though it necessitates experimental refinement. Despite using a limited qPCR panel 298 with only five miRNA biomarkers, we demonstrated valuable diagnostic potential, albeit with a need 299 for further optimization. This highlights both the promise and the technical challenges of 300 implementing miRNA-based diagnostics in clinical practice. 301 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 1 0 To improve the reliability and diagnostic accuracy of a qPCR-based test, several key strategies 302 should be considered. (a) Primer Optimization: Refining primer designs to enhance amplification 303 efficiency and specificity, particularly for low-abundance miRNAs. (b) Adopting ddPCR for 304 Enhanced Sensitivity: ddPCR offers improved precision and sensitivity, making it a suitable 305 alternative for detecting low-expressed miRNAs that may be missed by qPCR. (c) Expanding 306 Sample Size: Increasing the number of clinical samples analyzed will improve statistical power and 307 ensure robustness across diverse patient populations. (d) Developing a Multiplex Assay: Creating a 308 multiplexed qPCR panel would allow for the simultaneous detection of multiple miRNA biomarkers, 309 streamlining workflow and improving diagnostic efficiency. 310 While initial qPCR validation of selected miRNAs shows promise, further refinement is necessary 311 and underway to enhance assay reproducibility and clinical performance. Future studies will focus on 312 validating these biomarkers in larger, independent cohorts, optimizing detection methods, and 313 standardizing protocols to ensure reproducibility across clinical testing laboratories. These efforts 314 will be critical in bridging the gap between high-throughput discovery research and real-world 315 clinical application, ultimately paving the way for a clinically deployable serum miRNA-based 316 diagnostic test for endometriosis. 317 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 1 1

318 Specimen collection 319 Peripheral blood samples were collected prospectively from women aged 18-49 years who presented 320 with mild to severe symptoms, including pelvic pain and/or menstrual bleeding. The participants were 321 enrolled under an approved institutional review board protocol (IRB-20240110-R) at the Women’s 322 Hospital of Zhejiang University School of Medicine, with informed consent obtained from all 323 individuals. The participants included in this study were collected from March 2024 till December 324 2024. All participants were clinically suspected of a gynecologic abnormal condition and were 325 scheduled to undergo laparoscopy with histopathological evaluation. To account for potential 326 variations in miRNA expression due to different menstrual cycle phases, blood samples were collected 327 exclusively from women in the secretory phase of their menstrual cycle. The menstrual phase was 328 initially determined by physicians or surgeons based on self-reported cycle days and clinical 329 assessment. To ensure accuracy, this classification was further validated through serum progesterone 330 measurements using the protein assay from Kangrun Biotech Co. Ptd (Guangdong, China), with levels 331 exceeding 1.08 ng/mL serving as a biochemical confirmation of the secretory phase according to the 332 manufacturer’s protocol. This approach minimized the influence of hormonal fluctuations on 333 biomarker expression, thereby enhancing the reliability of our findings. A total of 40 symptomatic 334 women were included in the NGS discovery cohort, with 10 mL of blood drawn into standard red-top 335 blood collection tubes prior to the laparoscopic surgery. Among them, 20 women were confirmed to 336 have endometriosis based on both laparoscopic findings and histopathology (disease group), while the 337 remaining 20 had no evidence of endometriosis and served as the control group. Serum was isolated 338 using a two-step centrifugation protocol. First, samples underwent a low-speed centrifugation at 3000 339 rpm for 10 minutes at 4°C to remove cellular components. This was then followed by a second 340 high-speed centrifugation at 16,000 g for 10 minutes at 4°C to ensure complete removal of debris and 341 platelets. The isolated serum was then aliquoted and stored at -80°C for subsequent RNA extraction 342 and downstream processing. 343 344 RNA isolation and miRNA sequencing (miRNAseq) 345 Total RNA was isolated from 300 μ L of serum using Norgen RNA extraction kit following the 346 manufacturer's instructions. Total RNAs were ligated to 3’ adapters by denaturation at 70/i3 for 2 347 minutes, and then incubated at 16/i3 for over 8 hours using NEB T4 RNA Ligase 2. Following 5’ 348 adapters were incubated with previous product using NEB T4 RNA Ligase 1 at 37/i3 for 60 minutes. 349 Ligated RNA was reverse transcribed in a thermocycler using SuperScript II Reverse Transcriptase 350 from ThermoFisher Inc. under the following conditions: an initial incubation at 50/i3 for 60 minutes, 351 followed by a heat inactivation step at 80/i3 for 10 minutes. Following cDNA synthesis, library 352 preparation was performed using the NEB Phusion High-Fidelity DNA Polymerase, adhering strictly 353 to the manufacturer's guidelines. The final libraries were then subjected to high-throughput 354 sequencing to profile the miRNA expression by LC Biosciences. 355 356 NGS miRNA differential expression profiling endogenous control selection 357 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 1 2 The raw FASTQ data obtained from miRNA sequencing underwent preprocessing and analysis using 358 the miRge3 (41) software pipeline. Initially, the sequences were quality-trimmed and adapter 359 sequences were removed using a Cutadapt (42) wrapper integrated within miRge3. The trimmed 360 reads were then aligned to the miRBase (43) reference database using Bowtie (44) optimized for 361 short reads. Following alignment, miRge3 generated a comprehensive count table summarizing the 362 abundance of each miRNA across the samples. This count table served as the input for downstream 363 differential expression analysis using the DESeq2 (45) package in R. DESeq2 was employed to 364 identify miRNAs that exhibited statistically significant differences in expression between the patient 365 and control groups. After identifying the differentially expressed miRNAs, hypergeometric 366 over-representation analyses were conducted using the miRNet platform (33). In this analysis, two 367 distinct queries were performed: first, the differentially expressed miRNAs were compared against 368 the miRNA-tissue origin database in miRNet to infer potential tissue-specific origins or associations 369 of these miRNAs; second, the predicted gene targets of these miRNAs were mapped to the KEGG 370 (Kyoto Encyclopedia of Genes and Genomes) database (34) to identify statistically enriched 371 biological pathways. This dual-level approach enabled the contextualization of the miRNA 372 expression patterns in terms of both tissue relevance and functional pathway involvement. 373 374 Endogenous control selection for in silico normalization and qPCR experimental validation 375 To bridge the findings from the next-generation sequencing (NGS) discovery cohort into clinically 376 applicable diagnostic tools, we further aimed to identify condition-specific endogenous control 377 miRNAs. These controls are essential for normalizing quantitative PCR (qPCR) or droplet digital 378 PCR (ddPCR) assays, ensuring accurate and reproducible quantification of target miRNA expression. 379 The selection of endogenous controls was guided by stringent criteria. Specifically, we evaluated the 380 expression stability of candidate miRNAs by assessing their dispersion estimates and imposing 381 constraints on log2 fold-change (|log2FC| < 0.02) between the patient and control groups. This 382 ensured that the selected controls exhibited minimal variability across conditions. Additionally, 383 candidates were filtered based on an adjusted p-value threshold (≥ 0.8), ensuring that their expression 384 was not influenced by the experimental conditions or disease state. 385 386 Disease prediction model construction using a random forest classifier 387 To assess the predictive capability of the differentially expressed miRNAs and determine the extent to 388 which they could accurately classify patients with endometriosis, we constructed a random forest 389 classifier using all the differentially expressed miRNAs identified in the discovery cohort. To ensure a 390 robust evaluation of the model's performance, we performed 30 iterations of repeated random 391 subsampling cross-validation, where in each iteration, the data was split into an 80:20 ratio for training 392 and testing, respectively. This repeated holdout validation approach allowed us to account for 393 variability in model performance due to random data partitioning and provided a reliable estimate of 394 the model's predictive accuracy. During model construction, any missing values were imputed using 395 the median of the corresponding feature. 396 397 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 1 3 Following the initial model construction, we conducted feature selection to identify the most 398 informative miRNAs for predicting endometriosis. This was achieved by evaluating the feature 399 importance scores generated by the random forest algorithm. Based on these scores, we created two 400 distinct feature sets: one comprising the top 40 most important miRNAs and another consisting of 401 the top 20 most important miRNAs. These reduced feature sets were then used to generate and assess 402 new models using the same repeated random subsampling cross-validation procedure described 403 above. Reducing the feature set is crucial for the assessment of model performance and 404 interpretability. A smaller, more informative subset of miRNAs helps prevent overfitting, ensuring 405 the model generalizes well to new data. Additionally, focusing on the most important miRNAs 406 reduces noise, leading to more reliable predictions. A reduced feature set also facilitates translation 407 into clinical diagnostics where the informative miRNAs can be assayed using qPCR and/or ddPCR 408 instead of NGS. 409 410 RNA extraction and qPCR experimental validation 411 Total RNA was extracted from 200 μ L serum using the miRNeasy Serum/Plasma Advanced Kit from 412 Qiagen, following the manufacturer's recommended protocol. Subsequently, targeted microRNAs 413 (miRNAs) were reverse transcribed into complementary DNA (cDNA) using the FastKing RT Kit II 414 from TianGen Inc. The reverse transcription reaction was carried out in a thermocycler under the 415 following conditions: an initial incubation at 42C for 15 minutes to facilitate cDNA synthesis, 416 followed by a heat inactivation step at 95C for 1 minute to terminate the reactions. The synthesized 417 cDNA was subjected to quantitative PCR analysis of the five miRNA markers. The PCR reaction 418 mixtures were prepared by miRCURY LNA miRNA SYBR Green PCR Kit from Qiagen. qPCR 419 amplification was performed in QuantStudio qPCR system following: an initial denaturation at 95/i3 420 for 2 minutes, followed of 40 cycles with denaturation at 95 /i3 for 10 seconds and annealing at 56/i3 421 for 60 seconds. 422 423 Statistical analyses 424 Differential expression analysis was performed using DESeq2, which models NGS count data with a 425 negative binomial distribution to assess statistical differences between patients and controls. For 426 machine learning-based predictions, sensitivity, specificity, and AUC were evaluated using Python’s 427 scikit-learn package. Pairwise expression comparisons between patients and controls were assessed 428 using the Wilcoxon ranked-sum test. 429 430 Study approval 431 The participants were enrolled under an approved institutional review board protocol 432 (IRB-20240110-R) at the Women’s Hospital of Zhejiang University School of Medicine, with 433 informed consent obtained from all individuals. 434 435 Data availability 436 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 1 4 Sequencing data were deposited into Genome Sequence Archive under the accession number 437 PRJCA039165438 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 1 5 Author Contributions 439 Y . Y u and W .H. Wong led the research group, analyzed the data and wrote the manuscript. Y . Yu, 440 W.H. Wong, X. Zhang and F. Bischoff conceptualized the study. X. Zhang and L. Zhu provided the 441 samples while S. Lu coordinated sample collection between the hospital and the laboratory. W.H. 442 Wong and Y . Hu performed random forest analysis. Y . Y u, Y . Shen and X. Xu performed molecular 443 experiments. 444 445 Acknowledgments 446 The authors wish to express their gratitude to Jonathan Zhao and Frank Zhang (both from Heranova 447 Lifesciences) for their valuable input on the study design and contributions to the development of the 448 manuscript. 449 450 Conflict-of-interest statement: 451 All authors, except Zhang Xinmei and Zhu Libo, are employees of Heranova Lifesciences, a 452 company engaged in the commercial development of a non-invasive test for endometriosis. Farideh 453 Bischoff holds stock options of Heranova Lifesciences. The authors declare no other conflicts of 454 interest, financial or otherwise. 455 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 1 6

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Love MI, et al. Moderated estimation of fold change and dispersion for RNA-seq with 552 DESeq2. Genome Biology. 2014;15:550 553 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 19 554 Figure 1: Differential expression analysis of miRNAs in endometriosis patients. 555 (A) Heatmap illustrating the expression profiles of miRNAs that were differentially expressed 556 between endometriosis patients and healthy controls. A total of 85 miRNAs showed significant 557 expression differences. Notably, several of these miRNAs, including miR-9-5p, miR-21-5p, 558 and let-7b-3p, have been previously associated with the pathogenesis of endometriosis and are 559 highlighted on the plot. (B) Volcano plot depicting the distribution of differentially expressed miRNAs560 based on fold change and statistical significance. (C) Pathway enrichment analysis of the differentially561 expressed miRNAs. The top panel shows inferred tissue-of-origin patterns, suggesting potential source562 tissues of the dysregulated miRNAs. The bottom panel presents enriched non-cancer-related KEGG 563 pathways derived from the predicted target genes of these miRNAs, highlighting relevant biological 564 processes potentially involved in endometriosis pathophysiology. 565 As lly rce All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 2 0 566 Figure 2: Diagnostic performance of NGS-identified differentially expressed miRNAs using 567 machine learning. 568 The diagnostic utility of differentially expressed miRNAs was assessed through machine-learning 569 analysis. Performance metrics represent the mean of 30 iterations of repeated subsampling 570 cross-validation, with error bars indicating variability across iterations. (A) Using the full set of 571 differentially expressed miRNAs, the model achieved an AUC of 0.95, with a sensitivity of 0.91 and 572 specificity of 0.88. A representative ROC curve from iteration #10 is shown. (B) Limiting the model to 573 the top 40 most informative miRNAs improved performance, yielding an AUC of 0.97, sensitivity of 574 0.93, and specificity of 0.89. The ROC curve displayed corresponds to iteration #12. (C) Further 575 refinement using the top 20 most informative miRNAs resulted in the highest diagnostic performance, 576 with an AUC of 0.98, sensitivity of 0.95, and specificity of 0.90. The representative ROC curve is also 577 from iteration #12. 578 A B C AUC Sensit ivit y Speci /i1cit y 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 AUC Sensitivit y Speci /i1c ity A U C S e ns itivity S pe c i /i1cit y 0.00 0.25 0.50 0.75 1.000.95 0.91 0.88 0.97 0.93 0.89 0.98 0.95 0.90 Cross Validation #10 (Representative) 0.2 0.4 0.6 1.0 0.8 T rue P os itive R ate F als e P os itive R ate 0 . 20 . 4 0 . 60 . 81 . 0 Model 1: All DE miRNAs Model 2: Top 40 DE miRNAs Model 2: Top 20 DE miRNAs 0.2 0.4 0.6 1.0 0.8 T rue P os itive R ate 0.2 0.4 0.6 1.0 0.8 T rue P os itive R ate F als e P os itive R ate 0.2 0.4 0.6 0.8 1.0 F als e P os itive R ate 0 . 20 . 4 0 . 60 . 81 . 0 AUC = 0.93 AUC = 0.94 AUC = 0.94 Cross Validation #12 (Represent ative) Cross Validat ion #12 (Representat ive) All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 2 1 579 Figure 3: Diagnostic performance of differentially expressed miRNAs reliably quantifiable by 580 qPCR, assessed using machine learning. Performance metrics represent mean of 30 iterations of 581 repeated subsampling cross-validation, with error bars indicating variability across iterations. (A) 582 Violin plot illustrating the expression distribution of differentially expressed miRNAs based on 583 normalized NGS read counts. miRNAs shown in blue represent those with expression levels too low 584 for reliable qPCR detection, while those in red indicate miRNAs with sufficient expression for reliable 585 quantification by qPCR. The dotted red line marks the expression threshold of 500 normalized reads, 586 used to distinguish between the two groups. (B) Predictive performance of a machine-learning model 587 using all 47 miRNAs deemed reliably detectable by qPCR, resulting in an average AUC of 0.78, with 588 a sensitivity of 0.66 and specificity of 0.69. (C) Model performance using the top 20 most informative 589 miRNAs from the qPCR-detectable set, showing a modest improvement with an average AUC of 0.84, 590 sensitivity of 0.68, and specificity of 0.74. (D) Boxplot of miR-92a-3p, a potential endogenous control 591 candidate in this disease setting, demonstrating consistent expression with minimal variability 592 between patient and control groups. The box’s lower and upper hinges correspond to the 25th and 75th 593 percentiles, respectively, with the median indicated by the line inside the box. The whiskers extend to 594 the most extreme data points within 1.5 times the interquartile range below the 25th percentile and 595 above the 75th percentile. (E) Predictive performance using all 47 reliably detectable miRNAs after in 596 silico normalization against miR-92a-3p, yielding an average AUC of 0.80, with sensitivity of 0.70 597 and specificity of 0.68. (F) Model performance using the top 20 most informative miRNAs following 598 in silico normalization with miR-92a-3p, showing further improvement with an average AUC of 0.88, 599 sensitivity of 0.77, and specificity of 0.68. 600 A B C D E F Detectable via qP CR Not det ectable via qPCR log2(average expres sion) DE miRNAs 0 5 10 15 20 0.00 0.25 0.50 0.75 1.00 AUC Sensitivit y Speci /i1cit y 0.00 0.25 0.50 0.75 1.00 AUC S ensitivit y S peci /i1cit y 0.00 0.25 0.50 0.75 1.00 AUC Sensitivit y Speci /i1c ity A U C S e ns itivity S pe c i /i1cit y 0.00 0.25 0.50 0.75 1.00 All qPCR Detectable miRNAs Top 20 qPCR Detectable miRNAs All qPCR Det ect able miRNAs normalized with miR-92a-3p Top 20 qPCR Det ect able miRNAs normalized with miR-92a-3p 0.78 0.66 0.69 0.84 0.68 0.74 0.80 0.70 0.68 0.88 0.77 0.70 C ontrols E ndometriosis 500000 750000 1000000 1250000Normalized Read Count miR -92a-3p All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint 2 2 601 Figure 4: Boxplot of qPCR performance for selected differentially expressed miRNAs. 602 All miRNAs were normalized against miR-92a-3p using qPCR. Of the five miRNAs tested, two 603 (miR-21-5p and miR-15b-5p) showed a significant difference in expression between patients and 604 controls, replicating the direction of effect observed in the NGS discovery 605 dataset. miR-17-5p exhibited a concordant trend, with a p-value of 0.065, approaching statistical 606 significance. The remaining two miRNAs, miR-19b-3p and miR-23a-3p, did not show a statistically 607 significant difference between patients and controls. The boxplot’s lower and upper hinges represent 608 the 25th and 75th percentiles, respectively, with the median indicated by the line inside the box. The 609 whiskers extend to the most extreme data points within 1.5 times the interquartile range below the 25th 610 percentile and above the 75th percentile. 611 miR-21-5p miR-15b-5p miR-17-5p miR-19b-3p miR-23a-3p 2.7 3.0 3.3 3.6 -log2(Delta CT) miR NAs normalized with miR -92a-3p p = 0.0028 p = 0.0048 p = 0.0649 p = 0.3105 p = 0.4338 Co n t r o l s n = 24 E ndometriosis n = 45 3.9 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted July 24, 2025. ; https://doi.org/10.1101/2025.07.23.25332110doi: medRxiv preprint