Frontiers in cell and developmental biology · 2026
Multi-omics integration of the vaginal microbiome and inflammatory proteome for non-invasive prediction of infertility: a machine learning approach.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Feng Yangkun, Zhang Yu, Chen Dandan, Wang Aifen, Liu Zhenxing, Zhang Yun, Yu Ruixi
Аннотация
BACKGROUND: Infertility remains a global health challenge that often necessitates invasive and costly diagnostic procedures. While the vaginal microenvironment is known to influence reproductive health, its potential as a source of non-invasive biomarkers for infertility remains insufficiently explored. This study integrated vaginal microbiome and inflammatory proteomic profiling using machine learning to develop and validate an objective, non-invasive diagnostic model for identifying women at high risk of infertility.
METHODS: A total of 205 women (76 infertility patients and 129 healthy controls) were enrolled between October 2025 and February 2026. Vaginal swabs were collected for 16S rRNA sequencing and Olink Target 96 Inflammation panel analysis. LASSO, Boruta, and RFE were applied for feature selection, and eight machine learning algorithms were established to the training cohort (n = 143). Model performance was validated in the test cohort (n = 62) using AUC and DeLong tests. SHAP analysis was utilized for biological interpretability.
RESULTS: 4 Olink features and 14 microbial features were retained for analysis. The microbiome-based SVM model demonstrated the highest predictive performance with an AUC of 0.815 (95% CI: 0.710-0.920), significantly outperforming the proteomics-based XGBoost model (AUC = 0.629, 95% CI: 0.489-0.768). Multi-omics based SVM model yielded an AUC of 0.799 (95% CI: 0.691-0.993). SHAP analysis identified the genera Thomasclavelia, unclassified Ruminococcaceae, and Megamonas as the top 3 contributing features in the predictive model. Furthermore, all Olink biomarkers retained in the prediction model, including MMP-1, MMP-10, CCL20, and CXCL5, were identified as contributing risk factors associated with infertility.
CONCLUSION: The vaginal microbiome is a potent non-invasive biomarker for infertility, offering superior diagnostic accuracy compared to host inflammatory proteins. This machine learning-based multi-omics approach provides a promising tool for early risk stratification and personalized reproductive management.
METHODS: A total of 205 women (76 infertility patients and 129 healthy controls) were enrolled between October 2025 and February 2026. Vaginal swabs were collected for 16S rRNA sequencing and Olink Target 96 Inflammation panel analysis. LASSO, Boruta, and RFE were applied for feature selection, and eight machine learning algorithms were established to the training cohort (n = 143). Model performance was validated in the test cohort (n = 62) using AUC and DeLong tests. SHAP analysis was utilized for biological interpretability.
RESULTS: 4 Olink features and 14 microbial features were retained for analysis. The microbiome-based SVM model demonstrated the highest predictive performance with an AUC of 0.815 (95% CI: 0.710-0.920), significantly outperforming the proteomics-based XGBoost model (AUC = 0.629, 95% CI: 0.489-0.768). Multi-omics based SVM model yielded an AUC of 0.799 (95% CI: 0.691-0.993). SHAP analysis identified the genera Thomasclavelia, unclassified Ruminococcaceae, and Megamonas as the top 3 contributing features in the predictive model. Furthermore, all Olink biomarkers retained in the prediction model, including MMP-1, MMP-10, CCL20, and CXCL5, were identified as contributing risk factors associated with infertility.
CONCLUSION: The vaginal microbiome is a potent non-invasive biomarker for infertility, offering superior diagnostic accuracy compared to host inflammatory proteins. This machine learning-based multi-omics approach provides a promising tool for early risk stratification and personalized reproductive management.