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.
Copyright: © 2024 Lim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.