Lim Chiwoong, Seo Young-Jun, Lee Ji-Yeong, Jung Eun Sung, Lee Sunyoung, Kim Hayoung, Kim Kidong, Kim Jun-Mo
Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do, Republic of Korea.
HEM Pharma Inc., Suwon, Gyeonggi, Republic of Korea.
PLoS One. 2024 Dec 11;19(12):e0308985. doi: 10.1371/journal.pone.0308985. eCollection 2024.
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.
宫颈癌主要由致癌性人乳头瘤病毒亚型引起，仍然是一项重大的全球健康挑战。最近的研究表明宫颈癌与子宫微生物群之间存在联系，凸显了其重要性。本研究探讨了通常用于细胞学分析的液基细胞学（LBC）样本在研究宫颈微生物群方面的潜力。30名女性参与了该研究并提供了临床信息。从每位参与者处获取了三份样本：一份用于LBC临床目的，另一份用于LBC微生物群采样，第三份使用SWAB微生物群试剂盒。对LBC和传统拭子（SWAB）样本进行高通量16S rRNA基因测序以进行微生物群分析。结果显示关键分类群具有一致的优势，尤其是乳杆菌属。差异丰度分析突出了个体间微生物丰度的差异，这些差异比采样方法导致的差异更为显著。功能分析确定了花生四烯酸和α-亚麻酸代谢，同时也提到了低平均比例值的警示。网络分析揭示了网络间结构指标之间的正相关，突出了尽管采样方法多样，但微生物群相似性的稳健性。监督机器学习揭示了基于微生物群特征区分LBC和SWAB样本存在的挑战。加权共表达网络分析表明，微生物簇与采样方法和临床数据之间的相关性不显著。本研究强调了使用LBC和SWAB方法观察到的微生物群落的相似性，突出了使用双重诊断方法的潜力。此外，在大规模微生物学研究中使用剩余的LBC样本可以为宫颈健康和疾病提供全面的见解。
