Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia

Yoon Hee Lee, Gi-Ung Kang, Se Young Jeon, Setu Bazie Tagele, Huy Quang Pham, Min-Sueng Kim, Sajjad Ahmad, Da-Ryung Jung, Yeong-Jun Park, Hyung Soo Han, Jae-Ho Shin, Gun Oh Chong, Yoon Hee Lee, Gi-Ung Kang, Se Young Jeon, Setu Bazie Tagele, Huy Quang Pham, Min-Sueng Kim, Sajjad Ahmad, Da-Ryung Jung, Yeong-Jun Park, Hyung Soo Han, Jae-Ho Shin, Gun Oh Chong

Abstract

Although emerging evidence revealed that the gut microbiome served as a tool and as biomarkers for predicting and detecting specific cancer or illness, it is yet unknown if vaginal microbiome-derived bacterial markers can be used as a predictive model to predict the severity of CIN. In this study, we sequenced V3 region of 16S rRNA gene on vaginal swab samples from 66 participants (24 CIN 1-, 42 CIN 2+ patients) and investigated the taxonomic composition. The vaginal microbial diversity was not significantly different between the CIN 1- and CIN 2+ groups. However, we observed Lactobacillus amylovorus dominant type (16.7%), which does not belong to conventional community state type (CST). Moreover, a minimal set of 33 bacterial species was identified to maximally differentiate CIN 2+ from CIN 1- in a random forest model, which can distinguish CIN 2+ from CIN 1- (area under the curve (AUC) = 0.952). Among the 33 bacterial species, Lactobacillus iners was selected as the most impactful predictor in our model. This finding suggests that the random forest model is able to predict the severity of CIN and vaginal microbiome may play a role as biomarker.

Keywords: cervical intraepithelial neoplasia; machine learning; vaginal microbiome.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Phylogenetic trees of the vaginal microbiome network by metadata. From outside to inside, the stacked bar chart represents normalized relative abundances of community state type (CST)-related bacterial species; the heatmap represents each clinical stage; the bar charts were enriched in each HPV status, and the trees were grouped according to the phylum level.
Figure 2
Figure 2
Vaginal microbiome diversity in each CST. (A) Alpha diversity including Shannon index and observed operational taxonomic units (OTUs) at the species level. (B) Non-metric multidimensional scaling (NMDS) ordination plots based on Bray–Curtis. The microbial communities of each CST type were significantly (Adonis, p < 0.001) different from each other. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 3
Figure 3
Bacterial differences between CIN stages. The circular phylogenetic tree at the species level is the same as shown in Figure 1. (A) The outer circle represents the enriched bacterial species in each CIN and (B) the bar charts show mean relative abundance of the most significant species in each CIN stage.
Figure 4
Figure 4
Random forest classifier model using vaginal microbiome-derived bacterial signatures to predict the severity of CIN. (A) The 33 most important bacterial species based on their mean decrease Gini scores of the optimal random forest model. (B) The 33 bacterial species selected by optimizing the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. (C) The AUC of vaginal microbiome-based classification. Random forest classifiers were used to predict CIN 1− and CIN 2+ based on species-level vaginal microbiome composition. The green area of the curve represents the 95% confidence interval (CI) shape.

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