#AI reads Urine# A potential association between the characteristics of the multi-organ microbiota and lymph node metastasis in cervical cancer
Published 20 March, 2026
This pioneering study explores the potential link between the multi-organ microbiota and lymph node metastasis (LNM) in cervical cancer (CC). By performing 16S rDNA sequencing on oral swab, fecal, urine, and vaginal secretion samples from CC patients, the research found that compared with the non-LNM group, the LNM group showed significantly reduced α-diversity of urinary microbiota, along with notable differences in the community structures of gut and urinary microbiota. The study screened microbiota biomarkers associated with LNM at multiple sites and constructed a predictive model based on three specific oral flora: "Erysipelotrichaceae" UCG-003 sp., "Eubacterium hallii" group, and "Staphylococcus". This model demonstrated good predictive performance, with an AUC of 0.798, a Youden index of 0.520, a sensitivity of 57.9%, and a specificity of 94.1%. Additionally, functional pathway analysis revealed significant differences in seven KEGG pathways of urinary microbiota between the two groups. The study concludes that CC patients with LNM exhibit distinct multi-site microbial dysbiosis, and the oral flora-based predictive model provides a non-invasive and convenient method for assessing LNM in CC, offering new insights for clinical diagnosis and personalized treatment of the disease. However, the study has limitations such as the lack of HPV infection history records, absence of dietary information, and a single-center, small-sample design, which need to be addressed in future research.
Front Cell Infect Microbiol. 2026 Jan 20:15:1639811. doi: 10.3389/fcimb.2025.1639811.
Youhe Gao
Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.
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