Evaluation of the cervical liquid-based cytology sample as a microbiome resource for dual diagnosis

被引:0
|
作者
Lim, Chiwoong [1 ]
Seo, Young-Jun [1 ]
Lee, Ji-Yeong [1 ]
Jung, Eun Sung [2 ]
Lee, Sunyoung [2 ]
Kim, Hayoung [3 ]
Kim, Kidong [4 ,5 ]
Kim, Jun-Mo [1 ]
机构
[1] Chung Ang Univ, Dept Anim Sci & Technol, Anseong, Gyeonggi Do, South Korea
[2] HEM Pharm Inc, Suwon, Gyeonggi, South Korea
[3] Ildong Biosci, Pyeongtaek, South Korea
[4] Seoul Natl Univ, Bundang Hosp, Dept Obstet & Gynecol, Seongnam, South Korea
[5] Seoul Natl Univ, Coll Med, Dept Obstet & Gynecol, Seoul, South Korea
来源
PLOS ONE | 2024年 / 19卷 / 12期
关键词
D O I
10.1371/journal.pone.0308985
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Cervical cancer, which is mainly caused by oncogenic human papillomavirus subtypes, remains a significant global health challenge. Recent studies have indicated a connection between cervical cancer and the uterine microbiome, underscoring its importance. This study explored the potential of liquid-based cytology (LBC) samples, which are typically used for cytological analysis, in investigating the cervical microbiome. Thirty women participated in the study and provided clinical information. Three samples were obtained from each participant: one for clinical purposes using LBC, another for microbiome sampling using LBC, and a third using the SWAB Microbiome kit. The LBC and traditional swab (SWAB) samples were subjected to high-throughput 16S rRNA gene sequencing for microbiome analysis. The results revealed a consistent dominance of key taxa, particularly Lactobacillus spp. The analysis of differential abundance highlighted variations in microbial abundance among individuals, which were more prominent than those resulting from the sampling methods. Functional analysis identified arachidonic acid and alpha-linolenic acid metabolism, along with a cautionary note regarding the low mean proportion values. The network analysis revealed positive correlations between indicators of structure among the networks, highlighting the robustness of microbiome similarities despite the diversity of sampling methods. Supervised machine learning has revealed challenges in distinguishing LBC and SWAB samples based on their microbiome features. Weighted co-expression network analysis revealed that the correlation between microbial clusters and the sampling method with clinical data was not significant. This study emphasizes the similarity in microbial communities observed using the LBC and SWAB methods, highlighting the potential of using dual diagnostic approaches. Additionally, the use of residual LBC samples in large-scale microbiological studies can provide comprehensive insights into cervical health and disease.
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页数:16
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