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

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, w...

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Autores principales: Lee, Yoon Hee, Kang, Gi-Ung, Jeon, Se Young, Tagele, Setu Bazie, Pham, Huy Quang, Kim, Min-Sueng, Ahmad, Sajjad, Jung, Da-Ryung, Park, Yeong-Jun, Han, Hyung Soo, Shin, Jae-Ho, Chong, Gun Oh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7761147/
https://www.ncbi.nlm.nih.gov/pubmed/33256024
http://dx.doi.org/10.3390/diagnostics10121013
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author Lee, Yoon Hee
Kang, Gi-Ung
Jeon, Se Young
Tagele, Setu Bazie
Pham, Huy Quang
Kim, Min-Sueng
Ahmad, Sajjad
Jung, Da-Ryung
Park, Yeong-Jun
Han, Hyung Soo
Shin, Jae-Ho
Chong, Gun Oh
author_facet Lee, Yoon Hee
Kang, Gi-Ung
Jeon, Se Young
Tagele, Setu Bazie
Pham, Huy Quang
Kim, Min-Sueng
Ahmad, Sajjad
Jung, Da-Ryung
Park, Yeong-Jun
Han, Hyung Soo
Shin, Jae-Ho
Chong, Gun Oh
author_sort Lee, Yoon Hee
collection PubMed
description 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.
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spelling pubmed-77611472020-12-26 Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia Lee, Yoon Hee Kang, Gi-Ung Jeon, Se Young Tagele, Setu Bazie Pham, Huy Quang Kim, Min-Sueng Ahmad, Sajjad Jung, Da-Ryung Park, Yeong-Jun Han, Hyung Soo Shin, Jae-Ho Chong, Gun Oh Diagnostics (Basel) Article 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. MDPI 2020-11-26 /pmc/articles/PMC7761147/ /pubmed/33256024 http://dx.doi.org/10.3390/diagnostics10121013 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Yoon Hee
Kang, Gi-Ung
Jeon, Se Young
Tagele, Setu Bazie
Pham, Huy Quang
Kim, Min-Sueng
Ahmad, Sajjad
Jung, Da-Ryung
Park, Yeong-Jun
Han, Hyung Soo
Shin, Jae-Ho
Chong, Gun Oh
Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia
title Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia
title_full Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia
title_fullStr Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia
title_full_unstemmed Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia
title_short Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia
title_sort vaginal microbiome-based bacterial signatures for predicting the severity of cervical intraepithelial neoplasia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7761147/
https://www.ncbi.nlm.nih.gov/pubmed/33256024
http://dx.doi.org/10.3390/diagnostics10121013
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