Development Of Microbiota-based Models For Early Diagnosis Of Endometriosis

Objective:Early diagnosis of endometriosis is crucial for effective disease management and improving the quality of life of patients.Although recent studies have demonstrated the potential diagnostic value of gut and genital tract microorganisms in endometriosis,there is a lack of research on the relationship between gut,reproductive tract,and pelvic microorganisms and endometriosis using paired samples.Additionally,there are currently no biomarkers based on paired samples that are suitable for constructing endometriosis diagnostic models.The aim of this study is to systematically explore the differences in microbial structure and function between endometriosis patients and controls by collecting gut,cervical mucus,and peritoneal fluid samples and utilizing bioinformatics and machine learning methods to develop a non-invasive diagnostic approach for early detection of endometriosis.Methods:To investigate the microbiome of endometriosis patients,we recruited 41 women(20 non-endometriosis,21 endometriosis)and collected 122 well-matched samples of stools,cervical mucus,and peritoneal fluid.We utilized high-throughput 16S rRNA sequencing technology,bioinformatics,and machine learning models to analyze the differences and associations between the gut,cervical mucus,and pelvic fluid microbiomes of the endometriosis and control populations.We also searched for potential biomarkers and developed predictive models for endometriosis.Results:The microbial composition of the three different sites was significantly different,with 24 overlapping genera,including Lactobacillales and Bifidobacterium spp.that were enriched in both gut and cervical mucus samples.Streptococcus,Prevotella,Enterobacteriaceae,Lactobacillaceae and Lactobacillus spp.were mainly enriched in cervical mucus samples.The average relative abundance of Streptococcaceae,Prevotella spp.,and Enterobacteriaceae was higher in the endometriosis group,while the average relative abundance of Lactobacillariophyceae and Lactobacillus spp.was higher in the non-endometriosis group.The commensal bacteria enriched in the gut included Roseburia,Faecalibacterium,Ruminococc ace ae,Barnesiella,Ruminococcus,Lachnospiraceae,Clostridiales,Firmicutes,Bacteroides,Bacteroidales,C.Clostridium and L.Clostridium.The enrichment of bacteria in the peritoneal fluid was in Oxalobacteraceae.Additionally,endometriosis patients had reduced gut microbial diversity,imbalanced flora structure,and reduced protective microorganisms such as butyric acid-producing bacteria(Lachnospiraceae,Clostridiales,Dorella spp.)and gut keystone bacteria(Prevotella spp.),while the peritoneal fluid had an increased abundance of pathogens(Pseudomonas).Dorea and Pseudomonas genera were identified as promising biomarkers in the gastrointestinal tract and peritoneal fluid,respectively,and the Dorea genus was negatively correlated with CA125 levels in serum.We also constructed insightful endometriosis classifiers using taxa selected by a robust machine learning approach.These findings indicated that the gut microbiota exceeds the cervical microbiota in identifying endometriosis.Conclusion:Although there are common microbial populations in the gut,reproductive tract,and pelvic cavity,the overall composition differs greatly.Patients with endometriosis exhibit a dysbiosis of gut microbiota,manifested by a reduction in butyrate-producing bacteria and gut keystone species,as well as a decrease in microbial diversity and an overall composition that distinguishes them from non-endometriosis individuals.Machine learning-based screening of gut-specific microbiota selected in this study is valuable for non-invasive early diagnosis of endometriosis and has a better effect than cervical mucus microbiota.This study comprehensively analyzes the microbial characteristics of endometriosis and provides new ideas and approaches for noninvasive early diagnosis of endometriosis,which has certain guiding significance for future exploration of the role of microbial populations in endometriosis.