AUTHOR=Wu Ming , Yu Hongfei , Gao Yueqian , Li Huanrong , Wang Chen , Li Huiyang , Ma Xiaotong , Dong Mengting , Li Bijun , Bai Junyi , Dong Yalan , Fan Xiangqin , Zhang Jintian , Yan Ye , Qi Wenhui , Han Cha , Fan Aiping , Xue Fengxia TITLE=Leveraging 16S rRNA data to uncover vaginal microbial signatures in women with cervical cancer JOURNAL=Frontiers in Cellular and Infection Microbiology VOLUME=13 YEAR=2023 URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2023.1024723 DOI=10.3389/fcimb.2023.1024723 ISSN=2235-2988 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 (