Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis

SIMPLE SUMMARY: The fecal microbiome has been suggested to be linked to invasive cervical cancer (ICC). Considering that ICC is common in women, it is important to identify bacterial signatures from fecal microbiota that contribute in classifying cervical cancer. Although previous studies have suggested possible biomarkers based on fecal microbiota, limited information exists in terms of the diagnostic ability using gut microbiota-derived signatures for detecting early ICC. The purpose of this study was to investigate the potential association between early ICC and fecal microbiota and to examine whether fecal microbiota-derived markers can be utilized as a non-invasive tool to diagnose early ICC using machine learning (ML) techniques. Further studies to incorporate quantitative and qualitative characterization of identified individual bacterial genus and validate our model in larger cohorts are imperative in terms of causality for the association between cervical cancer and microbes. ABSTRACT: The fecal microbiota is being increasingly implicated in the diagnosis of various diseases. However, evidence on changes in the fecal microbiota in invasive cervical cancer (ICC) remains scarce. Here, we aimed to investigate the fecal microbiota of our cohorts, develop a diagnostic model for predicting early ICC, and identify potential fecal microbiota-derived biomarkers using amplicon sequencing data. We obtained fecal samples from 29 healthy women (HC) and 17 women with clinically confirmed early ICC (CAN). Although Shannon’s diversity index was not reached at statistical significance, the Chao1 and Observed operational taxonomic units (OTUs) in fecal microbiota was significantly different between CAN and HC group. Furthermore, there were significant differences in the taxonomic profiles between HC and CAN; Prevotella was significantly more abundant in the CAN group and Clostridium in the HC group. Linear discriminant analysis effect size (LEfSe) analysis was applied to validate the taxonomic differences at the genus level. Furthermore, we identified a set of seven bacterial genera that were used to construct a machine learning (ML)-based classifier model to distinguish CAN from patients with HC. The model had high diagnostic utility (area under the curve [AUC] = 0.913) for predicting early ICC. Our study provides an initial step toward exploring the fecal microbiota and helps clinicians diagnose.