Abstract Background Endometriosis is a chronic inflammatory disorder affecting ~ 10% of reproductive-age women, often causing pelvic pain and infertility. Despite its prevalence, diagnosis remains delayed due to non-specific symptoms and lack of reliable non-invasive biomarkers. Emerging evidence implicates the microbiome in disease pathogenesis. Results We analyzed uterine microbiomes from 266 tissue samples collected during either the proliferative or secretory phase, using 16S rRNA gene sequencing. Genus-level analysis revealed variable Lactobacillus abundance among all individuals. Prevotella showed borderline enrichment in proliferative-phase patients. Sub-genus analyses identified a small number of differentially abundant taxa, though none remained significant after FDR correction. To capture subtle microbial shifts, we developed a feature set combining weakly differential taxa, algorithmically selected taxa via machine-learning, and a functional dysbiosis score. A supervised classifier trained on proliferative-phase data achieved moderate predictive performance (AUC = 0.70), while secretory-phase models performed poorly (AUC = 0.58). Conclusion The uterine microbiome shows phase-dependent differences in its potential to inform endometriosis status. Although no robust individual microbial biomarkers were identified, machine-learning models incorporating subtle community features from the proliferative phase yielded modest diagnostic potential. These results highlight the importance of menstrual cycle-aware sampling and support further development of microbiome-informed diagnostic tools for endometriosis.
Zhu et al. (Mon,) studied this question.