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Whole Genome Analysis of Ovarian Granulosa Cell Tumors Reveals Tumor Heterogeneity and a High-Grade TP53-Specific Subgroup
Article

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

1
Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Korea
2
Department of Statistics, Seoul National University, Seoul 08826, Korea
3
Department of Core Technology, R&D Center, LG Household & Healthcare, Seoul 07795, Korea
4
MD Healthcare Inc., Seoul 03923, Korea
5
Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, Korea
6
Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
*
Authors to whom correspondence should be addressed.
These two authors contributed equally as first authors.
Cancers 2020, 12(5), 1309; https://doi.org/10.3390/cancers12051309
Received: 7 April 2020 / Revised: 15 May 2020 / Accepted: 19 May 2020 / Published: 21 May 2020
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. View Full-Text
Keywords: ovarian neoplasms; ovarian carcinoma; extracellular vesicle; microbiome; metagenomic analysis; diagnostic model ovarian neoplasms; ovarian carcinoma; extracellular vesicle; microbiome; metagenomic analysis; diagnostic model
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MDPI and ACS Style

Kim, S.I.; Kang, N.; Leem, S.; Yang, J.; Jo, H.; Lee, M.; Kim, H.S.; Dhanasekaran, D.N.; Kim, Y.-K.; Park, T.; Song, Y.S. Metagenomic Analysis of Serum Microbe-Derived Extracellular Vesicles and Diagnostic Models to Differentiate Ovarian Cancer and Benign Ovarian Tumor. Cancers 2020, 12, 1309. https://doi.org/10.3390/cancers12051309

AMA Style

Kim SI, Kang N, Leem S, Yang J, Jo H, Lee M, Kim HS, Dhanasekaran DN, Kim Y-K, Park T, Song YS. Metagenomic Analysis of Serum Microbe-Derived Extracellular Vesicles and Diagnostic Models to Differentiate Ovarian Cancer and Benign Ovarian Tumor. Cancers. 2020; 12(5):1309. https://doi.org/10.3390/cancers12051309

Chicago/Turabian Style

Kim, Se I., Nayeon Kang, Sangseob Leem, Jinho Yang, HyunA Jo, Maria Lee, Hee S. Kim, Danny N. Dhanasekaran, Yoon-Keun Kim, Taesung Park, and Yong S. Song. 2020. "Metagenomic Analysis of Serum Microbe-Derived Extracellular Vesicles and Diagnostic Models to Differentiate Ovarian Cancer and Benign Ovarian Tumor" Cancers 12, no. 5: 1309. https://doi.org/10.3390/cancers12051309

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