Transcriptome profiling to identify blood biomarkers for peritoneal endometriosis

ContextPeritoneal endometriosis (PE) remains challenging to diagnose, as it cannot be detected using standard imaging modalities and no clinically validated biomarkers are available. ObjectiveTo identify novel blood-based biomarkers for PE using whole blood transcriptomics combined with machine learning approaches. Design, Setting, and PatientsThis observational study enrolled 48 women undergoing laparoscopic surgery for endometriosis-related symptoms at tertiary referral centres in Slovenia and Austria. Patients were classified as having PE (n=20), peritoneal and ovarian endometriosis (PE and OE, n=8), or no endometriosis (controls, n=20). Patients were further stratified by menstrual phase (proliferative or secretory). Whole blood samples were collected preoperatively. MethodsWhole-blood RNA sequencing was performed, and differentially expressed genes (DGEs) and transcripts (DTEs) were identified. Sequencing data were processed using a machine learning pipeline to select key features and develop support vector machine (SVM) classifiers for predicting endometriosis status. ResultsIn the proliferative group, no DGEs and only two DTEs distinguished PE from controls. In contrast, in the secretory group, 1,035 DGEs and 922 DTEs were identified, with no overlap between menstrual phases. Enrichment analysis of secretory phase DGEs indicated their involvement in angiogenesis and immune-related pathways. Feature selection identified six transcripts that achieved the best SVM classification performance in distinguishing cases from controls across both menstrual phases (AUC = 0.92, sensitivity = 75%, specificity = 100%). ConclusionThis study provides first evidence that integrating whole-blood transcriptomics with machine learning can identify potential blood-based biomarkers for PE and highlights the influence of menstrual cycle phase. These findings require validation in larger, independent cohort.