Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment
Fig 1
Schematic of a multi-omics approach to study the complex interplay between HPV, host and microbiota in women across cervical neoplasia.
In this multicenter study n = 72 women were enrolled with invasive cervical carcinoma (ICC), high- and low-grade squamous intraepithelial lesions (HSIL, LSIL), as well as, HPV-positive and healthy HPV-negative controls (Ctrl). Two vaginal swabs and cervicovaginal lavage (CVL) were collected from each participant. Vaginal swabs were used for microbiome analysis and to evaluate vaginal pH. CVL samples were used for metabolome and immunoproteome analyses. The vaginal microbiota compositions were determined by 16S rRNA gene sequencing revealing 763 amplicon sequencing variants (ASVs). Cervicovaginal metabolic fingerprints in CVL samples were profiled by liquid chromatography-mass spectrometry and identified 467 unique metabolites. Levels of immune mediators and other cancer-related proteins in CVL samples (68 targets) were evaluated using multiplex cytometric bead arrays. Principal component, hierarchical clustering, neural network (mmvec) and Random Forest analyses were utilized to explore associations among multi-omics data sets to predict Lactobacillus dominance (dominant vs. non-dominant), vaginal pH (low ≤5 vs. high >5), evidence of genital inflammation (high, low, none) and disease status (Ctrl HPV–, Ctrl HPV+, LSIL, HSIL, ICC).