{"paper_id":"92db1a58-c635-4507-ab7f-a5359e2faf96","body_text":"Transcriptome profiling to identify blood biomarkers for peritoneal endometriosis \nMaja Pušić Novak1, Angelika Vižintin1, Tadeja Režen1, Helena Ban Frangež2, René Wenzl3, Tea \nLanišnik Rižner1 \n1Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, \nSlovenia \n2Department of Obstetrics and Gynecology, University Medical Centre, Ljubljana, Slovenia  \n3Department of Obstetrics and Gynecology, Medical University Vienna, Vienna, Austria  \n \n \n \nCorresponding author:  \nProf. dr. Tea Lanišnik Rižner \nInstitute of Biochemistry and Molecular Genetics \nFaculty of Medicine \nUniversity of Ljubljana  \nVrazov trg 2 \n1000 Ljubljana, Slovenia \nE-mail: tea.lanisnik-rizner@mf.uni-lj.si  \n \n \n \nFunding  \nThis study was funded by  grants J3-1755 and P3 -0449 to T.L.R. and Z3 -4522 to M.P.N. from the \nSlovenian Research and Innovation Agency).  \n \nDisclosure Statement: The authors have nothing to disclose. \n \n \nKeywords: peritoneal endometriosis, biomarkers, transcriptomics, machine learning, whole genome \nRNA sequencing \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\nABSTRACT  \nContext. Peritoneal endometriosis  (PE) remains challenging to diagnose , as  it cannot be detected \nusing standard imaging modalities and no clinically validated biomarkers are available.  \nObjective. To identify novel blood -based biomarkers for PE using whole blood transcriptomics \ncombined with machine learning approaches.  \nDesign, Setting, and Patients. This observational study enrolled 48 women undergoing laparoscopic \nsurgery for endometriosis -related symptoms at tertiary referral centre s in Slovenia and Austria.  \nPatients were classified as having PE (n=20), peritoneal and ovarian endometriosis (PE and OE, n=8), \nor no endometriosis (controls, n=20). Patients were further stratified by menstrual phase (proliferative \nor secretory). Whole blood samples were collected preoperatively.  \nMethods. Whole-blood RNA sequencing was performed, and differenti ally expressed genes (DGEs) \nand transcripts (DTEs) were identified . Sequencing data were processed using a machine learning \npipeline to select key features  and develop support vector machine  (SVM) classifiers for predicting \nendometriosis status.  \nResults. In the proliferative  group, no DGEs and only two DTEs distinguished PE from controls . In \ncontrast, in the secretory group, 1,035 DGEs and 922 DTEs were identified, with no overlap between \nmenstrual phases . Enrichment analysis of secretory phase DGEs indicated their involvement in \nangiogenesis and immune-related pathways. Feature selection identified six transcripts that achieved \nthe best SVM classification performance in distinguishing cases from controls across both menstrual \nphases (AUC = 0.92, sensitivity = 75%, specificity = 100%).  \nConclusion. This study provides  first evidence that integrating whole-blood transcriptomics  with \nmachine learning can identify potential blood-based biomarkers for PE and highlights the influence of \nmenstrual cycle phase. These findings require validation in larger, independent cohort. \n \nINTRODUCTION \nEndometriosis is a complex chronic disease affecting approximately 10  % of women in their \nreproductive age 1. It is an oestrogen-dependent, and chronic inflammatory condition associated with \ninfertility and chronic pelvic pain 2. Histologically, endometriosis is characterised  by the ectopic \npresence of endometrial stromal and epithelial cells, often accompanied by hemosiderin -containing \nmacrophages 3. Endometriotic lesions are primarily found on the surface of the peritoneum \n(superficial peritoneal endometriosis, PE), on the ovaries (ovarian endometrioma, O E) or as nodules \nthat penetrate more than 5 mm beneath the peritoneum (deep endometriosis, DE) 3,4.  \nPatients with endometriosis present with a variety of non -specific symptoms that often overlap \nwith those of other gynaecological and gastrointestinal diseases , such as infertility, uterine fibroids, \ninflammatory bowel syndrome, or pelvic inflammatory disease 5,6. Additionally, approximately 20 – 25 \n% of patients with confirmed endometriosis are asymptomatic 7,8. Despite the high prevalence of this \ndisease, endometriosis symptoms are often triviali sed, discouraging patients from seeking medical \nhelp sooner 9. Consequently, there is a significant delay in diagnosis worldwide, ranging from 4 to 11 \nyears after symptom onset 5,10,11.  \nTraditionally, laparoscopy, the surgical visualisation of endometriotic lesions, has been considered \nthe gold standard for diagnosi ng endometriosis. However, this invasive and costly procedure carries \nsurgical risks and can show variable results if not confirmed histologically 12,13. Studies have shown \nthat two-thirds of women undergoing laparoscopy are not diagnosed with endometriosis, suggesting \nthat many undergo unnecessary surgery 14. While OE and DE can be diagnosed with imaging modalities \nsuch as transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI), these techniques are \nnot useful for diagnosing superficial PE, which accounts for approximately 80 % of all endometriosis \ncases 2,15. Furthermore, common non -pigmented endometriotic lesions present in patients with \nsuperficial PE may  even be  missed at laparoscopy 16,17. Recently , the European Society of Human \nReproduction and Embryology (ESHRE) guidelines have recommended that the diagnosis of \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nendometriosis should be considered in patients with related symptoms. Confirmation should be \nachieved with imaging, reserving laparoscopy for cases where imaging results are negative or where \nempirical treatment has been unsuccessful or  is inappropriate. Clinicians are advised against using \nbiomarkers for the diagnosis of endometriosis, as there is currently no reliable or clinically validated \nbiomarker available for this condition  18. However, identification of accurate, reliable, and \nappropriately validated non-invasive biomarker s for endometriosis  is needed  to reduce current \ndiagnostic delays and accelerate patient treatment 19,20. \nTo date, non -invasive biomarkers for endometriosis have been investigated in various biological \nsamples, including peripheral blood, urine, menstrual blood, saliva, faeces and cervical mucus. Among \nthese, peripheral blood, particularly serum and plasma, has been  used most frequently  21,22. In \ncontrast to serum and plasma, whole blood does not require separation into its constituent \ncomponents, reducing potential inter-sample variability during processing and storage, and resulting \nin more reproducible results 23-25. Furthermore, whole blood collection is a standardized and routine \nprocedure, making it ideal for rapid point -of-care tests 24. As blood perfuses all organs, it provides \ninsights into human physiology and health, acting as a reporter for both systemic and locali sed \ndiseases. Consequently, transcriptome analysis of blood can identify gene expression signatures and \nbiomarkers for diagnosis, prognosis, and treatment monitoring 26-28.   \nSo far, different molecules have been considered as potential biomarkers for endometriosis, \nincluding inflammatory markers, apoptosis and endothelial markers, glycoproteins, growth factors, \noxidative stress markers, miRNAs, circRNAs and lncRNAs 25,29,30. To identify new biomarkers for \nendometriosis, studies use either hypothesis -driven approach es, or hypothesis generating - high-\nthroughput (‘omics’) techniques, or a combination of both 31. Over the past 25 years, high-throughput \nscreening technologies have been widely used to generate large-scale biological datasets to improve \nunderstanding, diagnosis and treat ment of  endometriosis 32,33. Additionally, different machine \nlearning approaches are increasingly integrated into bioinformatics studies to analyse large datasets, \nsuch as patient s clinical and lifestyle data, imaging data , and the expression of proteins, genes, \nmetabolites and their combinations, to  establish models  for the diagnosis of endometriosis 34. \nHowever, many of these studies  do not  specify the subtype of endometriosis  present among the \nenrolled patients but  instead analy se all types of the disease as  a single entity. As different \nendometriosis subtypes exhibit different pathophysiological characteristics  35, it is unlikely that to \nidentify biomarkers  that diagnose all types of endometriosis with high sensitivity and specificity . \nFurthermore, studies  often lack a proper description of the modelling approach and are rarely \nvalidated in larger, independent cohorts 34,36.  \nIn our previous studies, we searched for biomarkers of endometriosis in serum, plasma , and \nperitoneal fluid samples using different approaches , including targeted metabolomics 37,38 and \nproteomics 39-42. In the present study, our aim was to  identify a panel of blood biomarker candidates \nfor PE using whole genome transcriptomics combined with machine learning techniques.  \n \nMATERIALS AND METHODS  \n \nStudy design and patient selection \nThis study was approved by the National Medical Ethics Committee of the Republic of Slovenia \n(approval no. 120 -5412019-5), and the Republic of Austria (approval no. 545/2010). Patients were \nrecruited prospectively between January 2020 and December 2022 at the University Medical Centre \n(UMC) Ljubljana, Slovenia, and between November 2019 and January 2020 at the Medical University \nof Vienna, Austria (Figure 1). Inclusion criteria were symptoms suggestive of endometriosis (chronic \npelvic pain and/or infertility), reproductive age (18 to 39 years), and willingness to participate in the \nstudy. All recruited patients signed a written informed consent form upon inclusion. Exclusion criteria \nwere pregnancy at the time of surgery, history or presence of malignant diseases and/or autoimmune \ndiseases such as rheumatoid arthritis, inflammatory bowel disease, or autoimmune thyroid disease. \nWhole blood samples were collected from all patients. All women included underwent laparoscopic \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nsurgery and were characterized by the presence (cases) or absence (controls) of endometriosis after \nhistological examination. Patients diagnosed with endometriosis were further classified according to \nthe revised American Society of Reproductive Medicine  scoring system (rASRM). Each patient also \ncompleted a questionnaire detailing g eneral information about diet, lifestyle, smoking status, \nrecreational habits, and ethnic origin. The attending physician completed an additional questionnaire \ncovering clinical and gyn aecological data, including use of oral contraception or hormonal therapy, \nmedication use, regularity of menstrual cycle, phase of menstrual cycle at the time of surgery, and a \nsurgical report with staging and scoring of endometriosis, previous gynaecological surgeries and \npresence of other pathologies.  \nCase and control patients were carefully matched to ensure there were no significant differences \nin mean BMI or mean age. Furthermore, none of the participants had taken oral contraceptives in the \nthree months preceding laparoscopy. Patients were divided into proliferative (n=24) and secretory \n(n=24) groups based on their menstrual cycle phase at the time of surgery. In the proliferative group, \nall cases (n=12) had PE, whereas no endometriosis was detected in the control patients (n=12). In the \nsecretory group, cases (n=16) were divided into patients with PE (n=8) and those with both PE and OE \n(n=8). At the time of patient enrolment, there were insufficient numbers of patients in the secretory \nphase with PE only. Therefore, patients with combined PE and OE who matched the control criteria \nwere also included. The absence of endometriosis was confirmed laparoscopically for controls (n=8). \nThe clinical characteristics of patients in the discovery phase of the study are shown in Table 1. \n   \nFigure 1. Flowchart of the patient selection.  n- number, BMI – body mass index, PE - peritoneal endometriosis, OE – ovarian \nendometriosis, OC – oral contraception. Created with BioRender.com. \nStudy included an untargeted transcriptomic approach for biomarkers discovery , combined with  \nmachine learning techniques. Total RNA was extracted  from patients’ whole blood and subjected to \nribosomal RNA removal. Quantified cDNA libraries were then prepared, and whole-genome RNA was \nsequenced using Illumina platforms. The RNA sequencing data were used for differential gene and \ntranscript expression analysis, as well as pathway enrichment analysis. Additionally, the  sequencing \ndata were integrated into a machine learning pipeline for the selection of the most informative genes \nand transcripts using feature selection techniques, followed by the construction of support vector \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nmachine classifiers to predict the endometriosis status of patients. The flowchart of the study design \nis shown in Figure 2. \n \nFigure 2. Schematic representation of the study design. Eligible participants were recruited prospectively and underwent \nlaparoscopy, with histological confirmation of endometriotic lesions . Whole blood samples  from patients were collected, \ntotal RNA was extracted , quantified, and cDNA libraries were prepared and sequenced using Illumina platforms. The RNA \nsequencing data were used for differential gene and transcript expression analysis, functional enrichment analysis, and were \nintegrated into a machine learning pipeline for feature selection and construction of a classifier to predict the endometriosis \nstatus of patients. Created with BioRender.com. \nSample collection and processing  \nSample collection and processing w ere carried out according to a strict standard operating \nprocedure 43. Up to o ne day to one  hour before surgery, 3 ml blood samples were collected into \nTempus Blood RNA tube (Applied Biosystems , Waltham, MA, USA) at the Department of Obstetrics \nand Gynaecology at the UMC Ljubljana, Slovenia and at the Department of Gynaecology and Medical \nUniversity Centre Vienna, Austria. Immediately a fter blood collection,  the tubes were shaken \nvigorously for at least 20 seconds. The tubes were stored at 4 o C for up to 5 days and then transferred \nto -80°C until further analysis according to manufacturer’s instructions.   \n \nRNA isolation and quality analysis \nRNA was isolated according to the manufacturer’s instructions using the Tempus Spin RNA Isolation \nkit (Applied Biosystems, Waltham, M A, USA). Briefly, stabilised blood was thawed at  room \ntemperature and transferred into a 50 ml tube. Then, 3 to 4 ml of 1 x phosphate-buffered saline (PBS) \nwas added to the tube to reach  a total volume of 12 ml. The tube was vortexed vigorously for 1.5 \nminutes and centrifuged at 3000 × g for 30 minutes  at 4 °C . After centrifugation, the tube contents \nwere carefully poured off, and the RNA pellet was resuspended in 400 µl of RNA Purification \nResuspension Solution. The resuspended RNA was transferred to a purification filter and purified with \nWash Solutions  1 and 2. Finally, the RNA was eluted in Nucleic Acid Purification Elution Solution , \naliquoted and stored at -80 °C for further analysis. The concentration of isolated RNA was determined \nusing a NanoDrop O ne (Thermo  Fisher Scientific, Waltham , M A, USA), while RNA integrity was \nassessed with the Agilent Bioanalyzer Instrument  using the RNA 600 Nanokit  (Agilent Technologies \nInc., Santa Clara, CA, USA). RNA samples were subjected to strict quality control before being sent for \nsequencing. Samples with an RNA Integrity Number (RIN) > 7.5, a concentration of at least 20 ng/µl , \nand a volume of 20 µl were used for whole genome RNA sequencing (Novogene, Cambridge, UK). \n \nRNA sequencing \nFirst, ribosomal RNA was removed and rRNA free residues were cleaned using an rRNA removal kit \nand ethanol precipitation.  Subsequently, RNA fragmentation was performed, and first-strand cDNA \nwas synthesised. During second-strand cDNA synthesis, dTTPs were replaced by dUTPs in the reaction \nbuffer. The directional library was prepared after end repair, A-tailing, adapter ligation, size selection, \nUSER enzyme digestion, amplification, and purification. Quantified libraries were pooled and \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nsequenced on Illumina platforms.  The samples were sequenced in two separate batches: the first \ncomprising samples from patients in the proliferative group, and the second from those in the \nsecretory group. For alignment and calculation of read counts, we used CLC Genomics Workbench \n21.0.4 and 22.0.1 (QIAGEN Aarhus, Denmark) using the Identify and Annotate Differentially Expressed \nGenes and Pathways 1.19 and  RNA-Seq Analysis 2.7 tool with the default settings. We used the \nreference Homo sapiens GRCh38.104 (Gene, RNA). For calculation of TPM values, we used the \nDifferential Expression for RNA-seq 2.7 and 2.8 tools with the default settings.  \n \nRNA sequencing analysis  \nWe performed differential gene expression (DGE) and differential transcript expression (DTE) \nanalyses based on read counts data  using R 44 version 4.3.0 with DESeq2 45 version 1.42.1. Prior to \nanalysis, genes/transcripts with a non-zero read count in fewer than six samples were filtered out. The \nanalyses were conducted separately for patients in each of the two menstrual cycle phases. For both \nphases, w e compared patients without endometriosis (controls) to those with endometriosis, \ngrouping patients with PE only and PE and OE  together. Additionally, for the secretory phase, we \nperformed a further analysis by dividing patients into three groups: (1) patients without \nendometriosis, (2) patients with PE only, and (3) patients with both PE and OE . We then compared \neach group against the other two groups separately. Genes/transcripts were considered differentially \nexpressed if their adjusted p -value was below 0.05.  Volcano plots displaying the -log10 adjusted p-\nvalue against the log2 fold change were created using the Matplotlib library. The workflow of the \nexperimental analysis is illustrated in Figure 3.  \n \n \nFigure 3.  Scheme of experimental workflow. PE – peritoneal endometriosis, OE – ovarian endometriosis, DGE – differential \nexpressed genes, DTE - differentially expressed transcripts, TPM – transcripts per million, SVM – support vector machine.  \nGene set enrichment analysis \nOnly DEGs identified in patients from the secretory group were included in the gene set enrichment \nanalysis. The DGEs were compared across four groups: controls vs PE only, controls vs PE + OE, controls \nvs all cases (PE only and PE + OE), and PE only vs PE + OE  (Figure 3). All DGEs were divided into two \nsubsets: upregulated and downregulated genes. Within each subset, genes were ranked in ascending \norder according to their  adjusted p-values. These ranked gene lists were then individually inputted \ninto the g:GOSt tool, a component of the g:Profiler web service  46, for gene set enrichment analysis. \nThe \"ordered query\" option was selected, while other settings remained at their default values. The \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nanalysis was performed using g: Profiler version e113_eg59_p19_f6a03c19, with the database last \nupdated on Mon Jun 16 2025 46. \n \nPrincipal component analysis \nGenes/transcripts with zero  entries across all samples were filtered out . Transcript per million \n(TPM) values were centred based on genes/transcripts and log -transformed before conducting \nprincipal component analysis (PCA). PCA was separately conducted for each menstrual cycle phase as \nwell as for the combined data of all patients across both phases. This analysis was performed using \nthe scikit-learn library for dimensionality reduction and with the Matplotlib library for visualization. \n \nFeature Selection, Model Training, and Predictions Datasets \nFor this part, transcript per million (TPM) values were used. We constructed ten datasets: five at \nthe gene level and five at the transcript level.  The gene-level datasets included: 1) all participants in \nthe proliferative phase, 2) all participants in the secretory phase, 3) all participants (both proliferative \nand secretory phases), 4) participants in the secretory phase restricted to controls and cases with PE \nonly, and 5) participants in the proliferative phase restricted to a  controls and cases with PE only \n(Figure 3). The transcript-level datasets were constructed using the same participant groupings as the \ngene-level datasets. \nFirst, the genes/transcripts with zero entries across all samples were removed. Second , each \ndataset was divided into training and test sets.  In datasets containing participants from only one  \nmenstrual cycle phase, 25% of participants were allocated to the test set. In contrast, for datasets with \nparticipants from both phases, 15% of participants were assigned to the test set. The test sets were \nformed by randomly sampling  an equal number of participants from each group to maintain the \noriginal proportions  between groups in the dataset. By 'group ', we refer to patients in the same \nmenstrual phase and with the same endometriosis status (absence of endometriosis, PE, or PE + OE). \n \nFeature selection on the training set  \nConsistent with the machine learning terminology, we refer here to each gene/transcript as a \nfeature. Feature standardization was performed to normalize the data, transforming it to have a mean \nof zero and a standard deviation of one. Then, the importance of each feature was assessed by \napplying three different techniques  (separately to each of the ten training datasets ): mutual \ninformation, random forest importance  and support vector machine  (SVM) weights. All three were \nimplemented using the scikit -learn library . M utual Information assessed the mutual dependence \nbetween each feature and the endometriosis status . For random forest importance, feature \nimportance was evaluated based on how effectively each feature improved the predictive accuracy of \na trained random forest classifier. For SVM weights, a linear SVM model was trained on all features \nand then, t he weights of the features, indicat ing their influence on the decision boundary , were \nextracted. From the results of each feature selection technique, a list of the 2000 most important \ngenes/transcripts was prepared. Using this shortlist, recursive feature elimination (RFE) with an SVM \nclassifier (RFE-SVM) 47 was then performed to further refine the selection of relevant features. Initially, \nan SVM classifier was trained on data from genes/transcripts from the list, and its performance was \nassessed through leave -one-out cross-validation on the training dataset, calculating the area under \nthe curve (AUC) of the receiver-operator characteristic (ROC) curve. Subsequently, genes/transcripts \nwere sorted based on their contribution to distinguishing patients with endometriosis from those \nwithout it, and the least informative gene/transcript was removed from the list. This iterative process \ncontinued until the AUC started decreasing , i.e.  at the end of this process, the minimal set of \ngenes/transcripts that achieved an AUC of 1.0 on the training set (indicating a perfect classifier capable \nof accurately categorizing patients with or without endometriosis across all thresholds) was retrieved. \n \nPredictions on the test set \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nTo evaluate the performance of the models on unseen data, each set of genes/transcripts obtained \nfrom RFE was used for training the SVM model. The training set  was filtered to exclusively include \ngenes/transcripts from the retrieved set, and likewise, the test set was filtered accordingly. \nSubsequently, sensitivity, specificity, and the  area under th e curve  (AUC) of the ROC curve were \ncomputed. From the model’s decision function, ROC curves were calculated with the library pROC in \nR and visualized with the Matplotlib library in Python. \n \nSVM model hyperparameter tunning \nFor the linear SVM, the only hyperparameter needing selection is C. We opted for tuning C using \nthe training set comprising participants in the secretory phase, filtered to retain solely differentially \nexpressed transcripts. This decision stemmed from the dataset's apparent linear separability observed \non the PCA plot. We evaluated C across a range of values: 0.001, 0.01, 0.1, 1, 10, and 100, calculating \nthe ROC AUC via leave-one-out cross-validation on the same dataset. From this analysis C value of 0.1 \nwas selected for further utilization, being the second smallest value associated with an AUC of 1.0. \nStatistical analysis of patient’s clinical data \nPatients’ clinical data were analysed as follows. The normality of distribution was assessed using \nthe Shapiro-Wilk test, and the outliers were identified and excluded using the ROUT method (Q = 1, \nFDR < 1 %). For continuous variables, the unpaired t-test or Mann-Whitney test was used. Categorical \nclinical variables were compared using Fisher’s exact test, the Chi-squared test, or Chi-squared test for \ntrend. Statistical analysis was performed with GraphPad Prism 9.3 (GraphPad Software, San Diego, CA, \nUSA), with the significance level set at p < 0.05. \n \nRESULTS  \n \nClinical characteristics of patients  \nThe study included two groups: the proliferative group and the secretory group. In the proliferative \ngroup, 12 patients with PE and 12 control subjects were  included. The secretory group included 8 \npatients with PE, 8 patients with both PE and OE, and 8 controls (Figure 1).  \nIn both the proliferative and secretory group, there were no significant differences between cases \nand controls regarding age, body mass index (BMI), smoking status or use of oral contraceptives or \nhormonal therapy  in the last 3 months before surgery . A statistically significant difference was \nobserved in medication use prior to surgery between controls and cases only in the proliferative group \n(p=0.014). All endometriosis patients in the proliferative group were classified as rASRM stage I. In the \nsecretory group, 75 % of patients with PE were classified as stage I, 12.5% as stage II, and 12.5% as \nstage III, while none were in stage IV. Among patients with PE and OE group, 50 % were classified as \nstage II and 50 % as stage IV. Clinical characteristics of patients included in the study is shown in Table \n1.  \n \nTable 1. Clinical characteristics of patients included in the study \nPROLIFERATIVE GROUP \nCharacteristic Unit Detail Patients with PE Controls p-value \nNumber of patients n - 12 12  \nAge (mean ± SD) years - 30.33 ± 4.14 31.25 ± 4.47 0.608 \nBMI (mean ± SD) kg/m2 - 21.98 ± 2.72 22.15 ± 3.18 0.886 \nOral contraceptives in \nthe last 3 months \nn (%) Yes 0 (0) 0 (0) >0.999 \nNo 12 (100) 12 (0)  \nHormonal therapy in \nthe last 3 months  \nn (%) Yes 0 (0) 0 (0) >0.999 \nNo 12 (100) 12 (0)  \nMedication in the last \nweek \nn (%) Yes 0 (0) 6 (50) 0.014 \nNo 12 (100) 6 (50) \nSmoking status n (%) Non-smoker 8 (67) 9 (75) 0.344 \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nSmoker 3 (25) 3 (25) \nOccasional \nsmoker \n0 (0) 0 (0) \nFormer smoker 1 (8) 0 (0) \nrASRM n (%) I 12 (100) - - \nII 0 (0) - \nIII 0 (0) - \nIV 0 (0) - \nSECRETORY GROUP \nCharacteristic Unit Detail Patients \nwith PE \nPatients \nwith PE \nand OE \nControls p-value \nNumber of patients n - 8 8 8  \nAge (mean ± SD) years - 29.13 ± 4.02 31.5 ± 2.62 30.75 ± 2.12 0.297 \nBMI (mean ± SD) kg/m2 - 22.91 ± 2.07 21.45 ± 1.87 21.91 ± 2.86 0.448 \nOral contraceptives in \nthe last 3 months \nn (%) Yes 0 (0) 0 (0) 0 (0) >0.999* \n>0.999** \n \nNo 8 (100) 8 (100) 8 (100) \nHormonal therapy in \nthe last 3 months  \nn (%) Yes 1 (12.5) 0 (0) 0 (0) >0.999* \n>0.999** No 7 (87.5) 8 (100) 8 (100) \nMedication in the last \nweek \nn (%) Yes 4 (50) 3 (38) 2 (25) 0.608* \n0.999** No 4 (50) 5 (62) 6 (75) \nSmoking status n (%) Non-smoker 5 (62.5) 5 (62) 3 (37.5) 0.246* \n0.888** Smoker 2 (25) 0 (0) 1 (12.5) \nOccasional \nsmoker \n0 (0) 0 (0) 2 (25) \nFormer smoker 1 (12.5) 3 (38) 2 (25) \nrASRM n (%) I 6 (75) 0 (0) - - \nII 1 (12.5) 4 (50) - \nIII 1 (12.5) 0 (0) - \nIV 0 (0) 4 (50) - \nAbbreviations: n, number; SD, standard deviation; BMI – body mass index, PE, peritoneal endometriosis, O E, ovarian \nendometriosis; rASRM, revised American Society of Reproductive Medicine score; *PE versus controls, **PE and OE versus \ncontrols \n \nPCA analysis based on all genes and transcripts clustered patients according to their menstrual \nphase  \nPCA was carried out for each menstrual cycle phase individually and for the combined data from \nall patients in both the proliferative and secretory phases. We visualized the first six principal \ncomponents against each other, with data points colored according to various factors including \nendometriosis status, menstrual phase, and various metadata. The metadata encompassed potential \ntechnical sources of variation such as recruitment location (Slovenia or Austria), RNA isolation date, \ntime between hospitalization date, as well as clinical  and lifestyle information about patients . This \npatient clinical and lifestyle data  comprised the r ASRM score, age, age at menarche, maternal and \npaternal ethnic origins, smoking status, sport/recreation,  reports about pelvic, abdominal or back \npain, menstrual pain frequency, menstrual pain intensity, pain during sexual intercourse (in general \nand in the last 3 months), score of pain during sexual intercourses, pain during urination/defecation \n(in the last 3 months), nausea and vomiting, regularity of menstrual cycle, partus, miscarriage. \n When performing PCA on all genes/transcripts, c lustering of samples was observed when \ngenes/transcripts from both phases were clustered by menstrual phase (Supplementary Figures 1 and \n2). No clustering was observed based on endometriosis status or other metadata  (Supplementary \nFigure 1 - 3). \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nIn the secretory group, most of the differentially expressed genes and transcripts were identified \nbetween controls and patients with peritoneal endometriosis \nDGE and DTE a nalyses were performed separately for each menstrual cycle phase. For the \nproliferative phase, we compared controls to  PE only cases. For the secretory phase, we compared  \nfour groups:  controls, PE only, PE + OE, and all cases. No DGEs were identified among participants in \nthe proliferative group (Supplementary Figure 4, left). In the secretory group, 48  DGEs were identified \nbetween all controls and all cases (Figure 4, left), 1035 DGEs between controls and cases with PE only \n(Figure 4, middle), 3 DGEs between controls and cases with PE + O E (Figure 4, right), and 16 DGEs \nbetween cases with PE only and cases with both PE and PE + OE (Supplementary Figure 4, right).  \n \n \nFigure 4. Volcano plots of differentially expressed genes  across participants in the secretory group. Volcano plots display \ndifferentially expressed genes (DGEs) for the following group comparisons: all controls versus all cases (left), controls versus \ncases with PE only (middle) , and controls  versus cases with both PE and OE (right). PE – peritoneal endometriosis, OE – \novarian endometriosis. Differentially expressed genes are located above the dashed vertical line and are colored in blue. \n \nIn the proliferative group, only two differentially expressed transcripts were identified  \n(Supplementary Figure 5). In the secretory group, 110 DTEs were identified between all cases and \ncontrols (Figure 5, left), 922 DTEs between controls and cases with PE only (Figure 5, middle), 29 DTEs \nbetween controls and cases with both PE and OE (Figure 5, right), and 53 DTEs between the cases with \nPE only and cases with PE and OE (Supplementary Figure 5).  \n \n  \nFigure 5. Volcano plots of differentially expressed transcripts across participants in the secretory group. Volcano plot display \ndifferentially expressed transcripts for the following group comparisons:  all controls versus all cases (left), controls versus \ncases with PE only (middle), and controls versus cases with both PE and OE (right). PE – peritoneal endometriosis; OE – \novarian endometriosis. Differentially expressed transcripts are located above the dashed vertical line and colored in blue. \n \nGene set enrichment analysis highlights immune and inflammatory pathways in peritoneal \nendometriosis  \n \nGene set enrichment analysis was performed only for the secretory group, using the identified \nupregulated and downregulated DGEs for comparisons between controls and defined case groups (all \ncases, PE only, PE + OE). \nThe highest number of DGEs was identified between controls and cases with PE only. Functional \nenrichment analysis of the upregulated genes  in this group revealed significant enrichment across \nGene Ontology (GO) biological processes, R eactome, and KEGG pathways. The top enriched terms \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nranked by number of DGEs , were related to immune and inflammatory processes such as immune \nsystem, innate immune system, neutrophil degranulation, cytokine receptor signalling, and regulation \nof RAGE receptor binding. Additionally, pathways involved in intracellular signalling, cellular response \nto stimulus or stress, and vesicle trafficking were also prominent (Figure 6A, B). Downregulated DGEs \nfor the comparison of the same group s were enriched in pathways related to T cell activation, a key \ncomponent of the cell -based immune response (Figure 6C). Identified enriched pathways for the up \nand downregulated genes between the following groups: controls, PE only, PE+ OE, all cases (PE + PE \n+OE), are listed in Supplementary Tables 1 and 2.  \n \nFigure 6. Top enriched pathways in secretory group participants with peritoneal endometriosis. Top enriched pathways are \nranked by the number of differentially expressed genes ( DGEs) contributing to each term , identified between peritoneal \nendometriosis patients and controls in the secretory group.  Pathway enrichment was performed using g:Profiler. The Y-axis \nlists the top 10 –20 enriched terms, and the X -axis indicates the number of DGE s associated with each pathway. Pathways \nare ranked by gene count, and only statistically significant terms (adjusted p-value < 0.05) are included. Enrichment analysis \nis shown for A), B) upregulated and C) downregulated genes. \nAmong the DGEs identified between all controls and cases, three upregulated genes (CD93, CXCL8, \nNINJ1) were found to be enriched in pathways like angiogenesis, a process known to contribute to the \ndevelopment and progression of endometriosis (Supplementary Table 11). Additionally, two to three \nupregulated genes (NINJ1, CD14, PTAFR) were associated with lipopolysaccharide (LPS) binding and \nLPS immune receptor activity, which are linked to innate immune responses (Supplementary Table 2). \nOne of the three DGEs identified between controls and cases with both PE and OE endometriosis was \nCDO1, which is associated with taurine and hypotaurine metabolism (Supplementary Table 2). Among \nthe downregulated DGEs identified between cases with PE only and those with both PE and O E, \npathway enrichment analysis revealed associations with neutrophil degranulation  (CAMP, CRISP3, \nARG1, KRT1), defence response (CAMP, CRISP3, ARG1, KRT1, OASL), and innate immune response  \n(CAMP, CRISP3, ARG1, KRT1, OASL) (Supplementary Table 1).  \nVenn diagram analysis revealed one downregulated gene ( B3GAT1) that was common in three \ngroup comparisons: all controls vs. all cases, all controls vs. PE cases, and all controls vs. PE+ OE cases \n(Supplementary Figure 6, left). Among upregulated genes, 26 were shared between all controls vs. all \ncases and all controls vs. PE cases, while one downregulated gene ( CDO1) overlapped between all \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\ncontrols vs. all cases and all controls vs. PE + OE endometriosis cases (Supplementary Figure 6, right). \nThese overlaps highlight subsets of genes that may be consistently dysregulated across clinical  \npresentations of PE and patients with both PE and OE, with potential relevance to common disease \nmechanisms and biomarker development. \n \nFeature selection and model predictions analysis identified a set of six DTEs with the highest \ndiscriminatory performance in distinguishing cases from controls in both menstrual phases \n \nTable 2 and Table 3 show the sets of genes/transcripts for which the SVM models achieved the \nhighest ROC AUC values on the test set within each group. ROC curves for the overall best-performing \nmodels are shown in Figures 7 and 8, while ROC curves for the remaining top models are provided in \nSupplementary Figures 7 to 14. In Table 2, for all participants in the proliferative group, a ROC AUC of \n0.67, sensitivity of 1.0, and specificity of 0.67 were achieved with the selected 3 genes. For all \nparticipants in the secretory group, predictions based on the identified 3 genes resulted in a ROC AUC \nof 0.88, sensitivity of 0.75, and specificity of 1.0. In the secretory group, for controls and cases with PE \nonly, performance with the selected single gene was slightly worse: ROC AUC of 0.75, sensitivity of \n0.5, and specificity of 0.5. When all pa rticipants from the proliferative and secretory group were \nanalysed together, the identified set of 8 genes resulted in a ROC AUC of 0.75, sensitivity of 1.0, and \nspecificity of 0.67. For the comparison between all controls and all cases with PE only, the identified \nset of 7 genes resulted in a lower ROC AUC (0.67) and sensitivity (0.33), but a higher specificity of 1.0.  \n \nTable 2. Sets of genes identified by the feature selection pipelines for which the SVM models achieved the highest ROC AUC \nvalues on the test set within each group. \nGroup of participants Feature selection \nmethod prior RFE-\nSVM \nModels based on genes Performance on \ntest set \nall participants \n(PROLIFERATIVE GROUP)  \nmutual information Gene model 1 (3 genes)  AUC: 0.67 \nSensitivity: 1.0 \nSpecificity: 0.67 \nall participants  \n(SECRETORY GROUP)  \nmutual information Gene model 2 (3 genes)  AUC: 0.88 \nSensitivity: 0.75 \nSpecificity: 1.0 \nall controls and cases with \nperitoneal endometriosis only \n(SECRETORY GROUP)  \nrandom forest \nimportance \nGene model 3 (1 gene)  AUC: 0.75 \nSensitivity: 0.5 \nSpecificity: 0.5 \nall participants \n(PROLIFERATIVE AND \nSECRETORY GROUP) \nmutual information Gene model 4 (8 genes) AUC: 0.75 \nSensitivity: 0.75 \nSpecificity: 0.67 \nall controls and cases with \nperitoneal endometriosis only \n(PROLIFERATIVE AND \nSECRETORY GROUP)  \nSVM weights Gene model 5 (7 genes)  AUC: 0.67 \nSensitivity: 0.33 \nSpecificity: 0.67 \n*Model achieving the highest ROC AUC is highlighted bold \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\n   \nFigure 7. Classification performance and PCA visualization based on selected genes in the secretory group.  Left: Receiver \noperating characteristic ( ROC) curve showing the predictive performance of the SVM model for all participants in the \nsecretory group. The model was trained using the gene set identified through the feature-selection procedure initiated with \nmutual information. Right: Principal component analysis (PCA) plot generated using the same selected gene set for all \nparticipants in the secretory group (right).  \nIn Table 3 , it is shown that f or all participants in the proliferative group, the single selected \ntranscript yielded a ROC AUC of 0.78, sensitivity of 0.67, and specificity of 0.67. For all participants in \nthe secretory group, the two identified transcripts achieved a ROC AUC of 0.75, sensitivity of 0.75, and \nspecificity of 0.5. For the comparison between controls and cases with PE only, the performance with \nthe selected single transcript was very good, with a ROC AUC of 1.0, sensitivity of 1.0, and specifi city \nof 0.5. For all participants, from both the proliferative and secretory group , the identified set of 6 \ntranscripts, resulted in a ROC AUC of 0.92, sensitivity of 0.75, and specificity of 1.0. For the comparison \nbetween all controls and all cases  with PE only, the transcript set consisting of 3 transcripts resulted \nin a ROC AUC of 0.89, sensitivity of 0.67, and specificity of 1.0. \n \nTable 3. Sets of transcripts identified by the feature selection pipelines for which the SVM models achieved the highest \nROC AUC values on the test set within each group. \nGroup of participants Feature selection \nmethod prior RFE-\nSVM \nModels based on transcripts  Performance on \ntest set \nall participants \n(PROLIFERATIVE GROUP)  \nmutual information/ \nSVM weights \nTranscript model 1 (1 \ntranscript)  \nAUC: 0.78 \nSensitivity: 0.67 \nSpecificity: 0.67 \nall participants  \n(SECRETORY GROUP)  \nrandom forest \nimportance \nTranscript model 2 (2 \ntranscripts)  \nAUC: 0.75 \nSensitivity: 0.75 \nSpecificity: 0.5 \nall controls and cases with \nperitoneal endometriosis only \n(SECRETORY GROUP)  \nSVM weights Transcript model 3 (1 \ntranscript)  \nAUC: 1.0 \nSensitivity: 1.0 \nSpecificity: 0.5 \nall participants \n(PROLIFERATIVE AND \nSECRETORY GROUP) \nrandom forest \nimportance \nTranscript model 4 (6 \ntranscripts)  \nAUC: 0.92 \nSensitivity: 0.75 \nSpecificity: 1.0 \nall controls and cases with \nperitoneal endometriosis only \n(PROLIFERATIVE AND \nSECRETORY GROUP)  \nmutual information Transcript model 5 (3 \ntranscripts)  \nAUC: 0.89 \nSensitivity: 0.67 \nSpecificity: 1.0 \n*Model achieving the highest ROC AUC is highlighted bold \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\n \n \nFigure 8. Classification performance and PCA visualization based on selected transcripts for all the participants from both \nproliferative and secretory group. Left: Receiver operating characteristic (ROC) curve showing the predictive performance of \nthe SVM model for all participants. The model was trained using the set of transcripts identified through the feature-selection \nprocedure initiated with random forest importance. Right: Principal component analysis (PCA) plot generated using the same \nselected transcript set for all participants.  \nWhen performing PCA on the set of genes/transcript with the highest AUC of the ROC curve (Table \n2 and 3) on the test set of the SVM models build on sets and transcripts identified by the feature \nselection procedure, clustering of patients based on endometriosis status was observed (Figures 7 and \n8 and Supplementary Figures 7 to 14). \n \nDISCUSSION  \nThis is t he first study to use the whole-blood transcriptomics and machine learning methods  to \nidentify novel biomarker candidates specific to PE. The analysis identified a set of six DTEs with the \nhighest discriminatory performance between controls and cases in the test dataset (ROC AUC= 0.92, \nsensitivity = 75%, specificity = 100%). Similarly, a three gene panel achieved high performance in the \nsecretory group (AUC = 0.88, sensitivity = 75%, specificity = 100%). These findings suggest that, after \nvalidation, blood-based transcriptomic markers  may offer promising non -invasive tools for the \ndetection of PE. \nTo date, most transcriptomics studies using machine learning approaches have focused on \nanalysing gene expression data derived from ectopic, eutopic, or healthy endometrial tissue samples, \nobtained either from the Gene Expression Omnibus (GEO) database or from newly collected samples \nsubjected to microarray analysis 48-51. In contrast, fewer studies have explored non -invasive \napproaches for biomarker discovery in endometriosis. These s tudies have examined levels of miRNA \nin serum, plasma, and saliva 52-54, lncRNAs in plasma-derived extracellular vesicles 55, mRNA expression \nin menstrual fluid 56 or lncRNAs in the serum of endometriosis patients 57. Notably, most studies that \nsearched for non -invasive biomarkers for endometriosis have focused on miRNAs; however, these \ninvestigations have reported inconsistent results and limited overlap among the identified candidate \nmiRNA biomarkers 29,58. One such study developed  a salivary signature comprising 109 miRNAs ,  \ndeveloped using miRNA sequencing and a machine learning random forest model, and proposed it as \na potential diagnostic tool for endometriosis  (commonly referred to as Endotest) 54,59. Although this \nsignature was recently validated in a  multicentre study 60, it has not yet received approval from \nnational health technology assessment bodies  for routine clinical implementation, as further \nindependent, real -world validation outside controlled research settings is still required  61-63.  In \nanother study, Su et al. 64 performed biomarker discovery by analysing publicly available GEO datasets \nand using machine learning algorithms to develop an integrative model for predicting endometriosis, \nresulting in a nine -gene diagnostic panel. This model was subsequently validated on whole blood \nsamples from endometriosis patients (n=29) and controls (n= 30). However, more than 65% of patients \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nin the validation cohort had rAFS stage III –IV disease, and two of the nine genes showed suboptimal \nperformance. Further validation in a larger, multicentre cohort is therefore required.  \nIn our study, whole blood samples from patients were used to identify candidate biomarkers for \nendometriosis through a non -targeted transcriptomics approach. Previous studies have primarily \nemployed targeted strategies, such as measuring the levels of specific mRNA molecules in peripheral \nblood samples 36,65. When RNA sequencing techniques were applied, they were mostly performed on \nseparated blood components, such as serum and plasma 53,66 or on isolated peripheral blood \nmononuclear cells 67.  \nAs shown in  Tables 2 and 3, different feature selection techniques applied prior to RFE -SVM \nproduced distinct sets of genes and transcripts that yielded the best SVM classifier performance across \ngroups on the test dataset. Interestingly, overlap between feature selection techniques was observed \nonly in the transcript -based analysis of all participants in the proliferative group, where mutual \ninformation and SVM weights produced an identical final transcript set following RFE -SVM. \nFurthermore, there was minimal overlap among the identified gene/transcript sets across different \ngroups, with only two genes  being selected more than once, and no transcripts recurring between \nanalyses. Neither of these two genes has previously been linked to endometriosis.  \nIn our study, t wo SVM models based on DGEs and three models based on DTEs demonstrated \nstrong diagnostic potential, achieving an AUC of up to 1.0, and/or meeting the criteria for rule -in or \nrule-out tests (sensitivity ≥ 95% for rule-out, specificity ≥ 95% for rule-in) on the test set (Tables 2 and \n3). The model based on three DGEs showed the best performance in discriminating endometriosis \npatients from controls in the secretory group , reaching an AUC of 0.88, sensitivity of 75  % and \nspecificity of 100  %. Of these, two genes are l ong non coding RNAs (lncRNAs), previously not  \nassociated with endometriosis, while one is a small nucleolar RNA (snoRNA), found to be increased in \ncolorectal endometriosis compared to the eutopic endometrium of women with endometriosis 68. \nEncouragingly, one model based on DTEs performed well even on datasets including participants \nin both menstrual phases, effectively predicting endometriosis status regardless of the menstrual \nphase at the time of blood withdrawal. Such a menstrual -phase-independent test would be much \nmore practical for clinical use, as it simplifies and standardises sample collection, eliminating the need \nto perform the test in a specific menstrual cycle  phase. This model , also based on  a set of six \ntranscripts, showed the highest performance , reaching an AUC of 0.92, sensitivity of 75  % and \nspecificity of 100 %. Among the panel of six identified transcripts, five are protein-coding, while one is \na retained intron. \nWhen performing PCA, we did not detect sources of variation in the data originating from technical \nvariation or clinical data, except for the menstrual phase which in our case coincided with the \nsequencing batch . Therefore, it was challenging to determine conclusively whether the observed \ndifferences in PCA between patients in different phases stem from genuine biological differences were \ninstead a result of technical variations between the two sequencing batches (referred to as batch \neffects 69, and to mitigate this effect computationally. Consequently, gene/transcript differential \nexpression analysis was conducted separately for each menstrual phase. Previous studies have shown \nthat both normal endometrial tissue and endometriotic lesions exhibit phase-dependent variations in \ngene expression 70,71. Therefore, it is important to account for menstrual phase as a variable when \ntrying to identify reliable molecular biomarkers of endometriosis.  Transcriptomic profiling of whole \nblood across menstrual phases in our study revealed distinct, phase -specific expression patterns \nassociated with PE. No DGEs and only two DTEs were detected in samples from the proliferative group.  \nThe observed difference in medication use prior to surgery between controls and cases in the \nproliferative group may have influenced transcriptional profiles, and contribute d to the limited \nnumber of differentially expressed genes and transcripts detected in this group.  In contrast, in the \nsecretory group, tens to hundreds of DGEs and DTEs were identified between cases and controls. \nSpecifically, the highest numbers of DGEs (1035) and DTEs (922) w ere detected between all controls \nand PE only cases. No overlapping DGEs or DTEs were observed between the two menstrual phases, \nunderscoring the phase-specific expression profiles.  \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\nA common approach in biomarker discovery studies relies on selecting differential ly expressed \ngenes based on p -values and/or fold changes. However, this method can miss biologically relevant \nsignals, especially when expression changes are subtle or phase-specific or may yield long lists of genes \nthat are challenging and impractical to use for validation or diagnosis. In addition, analyses at the gene \nlevel do not account for transcript -specific changes that may be critical in distinguishing groups. To \naddress these limitations, machine learning methods have been increasingly applied, as they  handle \ncomplex transcriptomic data and identify patterns that conventional methods might overlook.  \nMachine learning also has limitations, one being multiplicity, whe re two distinct models achieve \nsimilar performance in the training dataset but vary greatl y on independent/test dataset s. This \ninstability, also observed in our study, indicates that model accuracy alone does not guarantee the \nidentification of biologically relevant features. Consequently, multiplicity can lead to inconsistent \nbiomarker sets, emphasising the need for cautious interpretation and experimental validation 72. \nThis study has several strengths. It analysed the whole  blood transcriptome, providing a non -\ninvasive and technically robust approach that avoids pre-analytical variability introduced by plasma or \nserum processing and captures the full range of RNA species, not limited to mRNA or miRNA. The  \nstudy focuses on the most common form of endometriosis, PE, which is still not detectable with \nimaging techniques. Strict standard operating procedures for blood collection and RNA isolation were \nfollowed, and cases and controls were carefully matched for age, BMI, hormonal therapy, and smoking \nstatus to minimi se confounding effects. Because endometriosis is an oestrogen-dependent disease \ninfluenced by hormonal fluctuations throughout the menstrual cycle, patients were stratified by \nmenstrual phase. This approach enabled the identification of biomarkers that are independent of cycle \nphase, as well as markers that are specific to phases of the cycle. The limitations of this study include \na relatively small sample size, difference in medication intake between cases and controls in the \nproliferative group, lack of technical replication across sequencing batches, and partial confounding \nof menstrual phase with sequencing batch. To minimi se technical variability, all samples were \nprocessed at the same time by the same operator using identical protocols. Additionally, the findings \nhave not yet been validated in a larger, independent cohort or in populations of different ethnicities  \nand work in progress will address this through qPCR validation of the six identified transcripts.   \n \nCONCLUSION  \nTo the best of our knowledge, this is the first study to integrate whole-genome transcriptomics \nwith machine learning techniques using whole blood samples for discovery of candidate biomarkers \nassociated with PE. Our analysis identified a six-transcript panel that performed well in distinsguishing \nendometriosis patients from controls. However, validation in larger, independent cohorts is necessary \nto confirm its diagnostic potential. The study revealed distinct gene expression profiles between \npatients in the proliferative and secretory phases of  the menstrual cycle, confirming the influence of \nhormonal status on transcriptional patterns. The differential ly expressed genes  identified as \nupregulated in  PE patients compared to controls  were associated with angiogenesis and innate \nimmune pathways, supporrting important role of these processes in the pathophysiology of PE. \n \nAUTHOR CONTRIBUTION  \nConceptualization, T.L.R.; investigation, M.P. N., T.R . and A.V. , resources, T.L.R. and H.B.F; data \ncuration, H.B.F., R.W, M.P.N., writing—original draft preparation, M.P. N. and A.V. , writing—review \nand editing, T.L.R., T.R. ; visualization, M.P.N., A.V. and T.R.; supervision, T.L.R.; project administration, \nT.L.R. funding acquisition, T.L.R. All authors  have read and agreed to the published version of the \nmanuscript. \n \nACKNOWLEDGEMENTS \nThe authors thank their study participants, who kindly donated their samples and  time and the \npersonnel of the Department of Obstetrics and Gynaecology, University Medical Centre Ljubljana, \nLjubljana, Slovenia, especially Mrs. Tatjana Lončar.  \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint \n\n \nREFERENCES  \n1. Zondervan KT, Becker CM, Missmer SA. Endometriosis. N Engl J Med. Mar 26 \n2020;382(13):1244–1256. doi:10.1056/NEJMra1810764 \n2. Saunders PTK, Horne AW. Endometriosis: Etiology, pathobiology, and therapeutic prospects. \nCell. 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No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 30, 2025. ; https://doi.org/10.64898/2025.12.23.25342915doi: medRxiv preprint","source_license":"CC0","license_restricted":false}