# Characterization of cervical microbiota in cervical intraepithelial neoplasia and cervical cancer using low-coverage whole genome sequencing

## TL;DR

This study uses low-coverage whole genome sequencing to track changes in cervical microbiota from benign conditions to cervical cancer, identifying microbial biomarkers linked to disease progression.

## Contribution

The study pioneers the use of LC-WGS/UCAD to profile cervical microbiota across cervical cancer stages and identifies severity-associated microbial biomarkers.

## Key findings

- Lactobacillus depletion and enrichment of pathobionts like Gardnerella and Bacteroides correlate with increasing cervical lesion severity.
- CC is marked by high microbial diversity, HPV16 dominance, and significant Bacteroidetes enrichment.
- Random Forest modeling identifies Mobiluncus curtisii and HPV16 as key discriminators of cervical cancer.

## Abstract

This study characterized compositional shifts in cervical microbiota across disease stages from benign conditions through cervical intraepithelial neoplasia (CIN) to cervical cancer (CC) and investigated interactions with high-risk HPV (hr-HPV) infection using species-resolution profiling to identify severity-associated biomarkers. Cervical exfoliated epithelial cells from 50 patients (eight normal/CIN1, 15 CIN2, 19 CIN3, 5 CC) were analyzed using Low-Coverage Whole Genome Sequencing combined with the Ultrasensitive Chromosomal Aneuploidy Detector (UCAD), a technology featuring a two-step normalization framework that systematically converts raw microbial reads into statistically validated abundance deviations. This enables quantitative identification of pathologically relevant microbiota through cohort-wide Z-score benchmarking. Microbial diversity, differential biomarkers, and HPV-microbiota interactions were assessed using Kruskal-Wallis tests, LEfSe, and Random Forest modeling. Results revealed progressive Lactobacillus depletion (e.g., Lactobacillus crispatus: 32.9% in ≤CIN2 vs. 8.8% in CC) and enrichment of pathobionts like Gardnerella and Bacteroides with lesion severity. CC exhibited the highest microbial diversity (Shannon index: CC vs. CIN2, P=0.045), dominated by HPV16 (11.8%), Bacteroides (55.4%), and Porphyromonas (25.2%). LEfSe identified HPV16, HPV35, Parvimonas micra, and Anaerococcus lactolyticus as CC-specific markers, while Random Forest highlighted Mobiluncus curtisii (importance score=2.0) and HPV16 as key discriminators. CC microbiota showed significant Bacteroidetes enrichment (82% at class level) and reduced Firmicutes abundance. These findings suggest carcinogenesis-associated microbial restructuring, marked by Lactobacillus loss, anaerobic proliferation, and HPV16/35 dominance, potentially modulating disease progression. The identified signatures may inform diagnostic development and microbiome-targeted therapies.

Our study pioneers an LC-WGS/UCAD approach to characterize microbial across the spectrum from benign lesions through precancerous cervical intraepithelial neoplasia to invasive cervical carcinoma. By identifying lesion-specific microbial biomarkers and HPV-associated cofactors, this work advances mechanistic understanding of microbiota-driven oncogenesis and informs future strategies for microbiota-targeted cervical cancer prevention.