ArticleFrontiers in cellular and infection microbiology2023
Leveraging 16S rRNA data to uncover vaginal microbial signatures in women with cervical cancer.
Article in Frontiers in cellular and infection microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- The microbiota-host metabolic axis in cervical cancer: from homeostatic disruption to mechanisms of therapy resistance.Frontiers in cellular and infection microbiology · 2026Review
- Mapping the Vaginal Metabolic Profile in Dysbiosis, Persistent Human Papillomavirus Infection, and Cervical Intraepithelial Neoplasia: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Insights into the tripartite relationship between cervical cancer, human papillomavirus, and the vaginal microbiome: a mega-analysis.Human genomics · 2025Article
- Strain-level variation among vaginal Lactobacillus crispatus and Lactobacillus iners as identified by comparative metagenomics.NPJ biofilms and microbiomes · 2025Article
- Integrated analysis of microbiome and metabolome reveals insights into cervical neoplasia aggravation in a Chinese cohort.Frontiers in cellular and infection microbiology · 2025Article
- Diagnostic and prognostic potential of the intra-tumoral microbiota profile in HPV-independent endocervical adenocarcinoma.Frontiers in cellular and infection microbiology · 2024Article
Corrections and comments
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Authors and funding
18 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbiota-relevant signatures have been investigated for human papillomavirus-related cervical cancer (CC), but lack consistency because of study- and methodology-derived heterogeneities. Here, four publicly available 16S rRNA datasets including 171 vaginal samples (51 CC versus 120 healthy controls) were analyzed to characterize reproducible CC-associated microbial signatures. We employed a recently published clustering approach called VAginaL community state typE Nearest CentroId clAssifier to assign the metadata to 13 community state types (CSTs) in our study. Nine subCSTs were identified. A random forest model (RFM) classifier was constructed to identify 33 optimal genus-based and 94 species-based signatures. Confounder analysis revealed confounding effects on both study- and hypervariable region-associated aspects. After adjusting for confounders, multivariate analysis identified 14 significantly changed taxa in CC versus the controls (
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Registered trials
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