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.
Highlights
Ovarian cancer (OC) is the deadliest gynecologic cancer worldwide [1]
The risk of malignancy index (RMI) scoring system, consisting of serum cancer antigen 125 (CA-125), menopausal status, and ultrasound features, as well as the risk of ovarian malignancy algorithm (ROMA), a biomarker-based algorithm consisting of serum CA-125 and human epididymis protein 4 (HE4), have been developed [6,7]
After 2:1 random distribution of the patients into training and test sets, OC patients were still older than those with benign ovarian tumors in the training set, whereas patients’ ages were similar in the test set. Both in the training and test sets, no differences in body mass index (BMI), menopausal status, and comorbidities were observed between the OC
Summary
Ovarian cancer (OC) is the deadliest gynecologic cancer worldwide [1]. In the United States, the number of new cases of OC and cancer deaths from OC in 2019 were estimated to be 22,530 (2.5% of all female cancers) and 13,980 (4.9% of female cancer deaths), respectively [2]. The risk of malignancy index (RMI) scoring system, consisting of serum CA-125, menopausal status, and ultrasound features, as well as the risk of ovarian malignancy algorithm (ROMA), a biomarker-based algorithm consisting of serum CA-125 and human epididymis protein 4 (HE4), have been developed [6,7]. Both RMI and ROMA are reliable tools and perform well in differentiating OC from adnexal masses [8,9]
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