Metagenomic Analysis of Serum Microbe-Derived Extracellular Vesicles and Diagnostic Models to Differentiate Ovarian Cancer and Benign Ovarian Tumor

Abstract

We aimed to develop a diagnostic model identifying ovarian cancer (OC) from benign ovarian tumors using metagenomic data from serum microbe-derived extracellular vesicles (EVs). We obtained serum samples from 166 patients with pathologically confirmed OC and 76 patients with benign ovarian tumors. For model construction and validation, samples were randomly divided into training and test sets in the ratio 2:1. Isolation of microbial EVs from serum samples of the patients and 16S rDNA amplicon sequencing were carried out. Metagenomic and clinicopathologic data-based OC diagnostic models were constructed in the training set and then validated in the test set. There were significant differences in the metagenomic profiles between the OC and benign ovarian tumor groups; specifically, genus Acinetobacter was significantly more abundant in the OC group. More importantly, Acinetobacter was the only common genus identified by seven different statistical analysis methods. Among the various metagenomic and clinicopathologic data-based OC diagnostic models, the model consisting of age, serum CA-125 levels, and relative abundance of Acinetobacter showed the best diagnostic performance with the area under the receiver operating characteristic curve of 0.898 and 0.846 in the training and test sets, respectively. Thus, our findings establish a metagenomic analysis of serum microbe-derived EVs as a potential tool for the diagnosis of OC.

The role of the microbiome in ovarian cancer: mechanistic insights into oncobiosis and to bacterial metabolite signaling

“…Another field, where bacterial dysbiosis and metabolite signaling can be exploited, is therapy and diagnostics. There are numerous studies in the literature indicating that microbiome profiles in the intratumoral area (Wang et al 2020 ; Poore et al 2020 ), genital tract (Wang et al 2020 ; Zhou et al 2019a ), the serum (Kim et al 2020 ), or the peritoneum (Miao 2020 ) can be exploited as biomarkers to diagnose ovarian cancer. Metabolomics studies also revealed exploitable biomarkers (Turkoglu et al 2016 ).…”

“…Oncobiosis was identified in several compartments, including vaginal, cervicovaginal (Ness et al 2003 ; Nené et al 2019 ), upper genital tract (Zhou et al 2019a ; Brewster et al 2016 ), ovarian, intratumoral (Wang et al 2020 ; Banerjee et al 2017 ; Shanmughapriya et al 2012 ; Poore et al 2020 ), peritoneal (Miao et al 2020 ), serum (Kim et al 2020 ), and fecal (Mori et al 2019 ) compartments (Table 1 , Fig. 1 ).…”

“…In other compartments, such as the serum, oncobiosis does not interfere with either α and β diversity. (Kim et al 2020 ). [For the explanation of the diversity indices we refer the reader to (Alpha and beta diversity [ http://www.metagenomics.wiki/pdf/definition/alpha-beta-diversity ]; Vida et al 2020 )].…”

Circulating 16S RNA in Biofluids: Extracellular Vesicles as Mirrors of Human Microbiome?

“…The most recent investigation regarding the use of extracellular vesicles-derived 16S for sequencing-based taxonomy identification was focused on the development of diagnostic models to differentiate ovarian cancer (OC) and benign ovarian tumor (BOT) [ 65 ]. In 2020, Kim and co-workers described the isolation of EVs from the serum of 166 patients with OC and 76 patients with BOT.…”

Microbiome Markers of Pancreatic Cancer Based on Bacteria-Derived Extracellular Vesicles Acquired from Blood Samples: A Retrospective Propensity Score Matching Analysis

“…They can help evaluate microbiota composition [ 13 , 14 , 15 ]. A recent study, aimed to develop a diagnostic model for ovarian cancer, used microbiome profiles from serum bacteria-derived EVs alongside clinicopathologic data [ 16 ]. However, to the best of our knowledge, the association between the blood microbiome, assessed with bacteria-derived EVs, and PC has not been previously studied.…”