Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment.

ABSTRACT: Emerging evidence suggests that host-microbe interaction in the cervicovaginal microenvironment contributes to cervical carcinogenesis, yet dissecting these complex interactions is challenging. Herein, we performed an integrated analysis of multiple "omics" datasets to develop predictive models of the cervicovaginal microenvironment and identify characteristic features of vaginal microbiome, genital inflammation and disease status. Microbiomes, vaginal pH, immunoproteomes and metabolomes were measured in cervicovaginal specimens collected from a cohort (n = 72) of Arizonan women with or without cervical neoplasm. Multi-omics integration methods, including neural networks (mmvec) and Random Forest supervised learning, were utilized to explore potential interactions and develop predictive mo

SUBMITTER: Bokulich NA 

PROVIDER: S-EPMC8901057 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature