Dataset Information

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.

SUBMITTER: Kim SI

PROVIDER: S-EPMC7281409 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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

Kim Se Ik SI Kang Nayeon N Leem Sangseob S Yang Jinho J Jo HyunA H Lee Maria M Kim Hee Seung HS Dhanasekaran Danny N DN Kim Yoon-Keun YK Park Taesung T Song Yong Sang YS

Cancers 20200521 5

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 se ...[more]

PMID: 32455705

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Project description:Epithelial ovarian carcinoma (EOC) is one of the most common gynecologic malignancies with a high mortality rate. Serum biomarkers and imaging approaches are insufficient in identifying EOC patients at an early stage. This study is to set up a combination of proteins from serum small extracellular vesicles (sEVs) for the diagnosis of early-stage EOC and to determine its performance. A biomarker for early-stage ovarian cancer (BESOC) cohort was used as a Chinese multi-center population-based biomarker study and registered as a Chinese Clinical Trial ChiCTR2000040136. The sEV protein levels of CA125, HE4, and C5a were measured in 299 subjects. Logistic regression was exploited to calculate the odds ratio and to create the sEV protein model for the predicted probability and subsequently receiver-operating characteristic (ROC) analysis. The combined sEV marker panel of CA125, HE4, and C5a as a sEV model obtained an area under curve (AUC) of 0.912, which was greater than the serum model (0.809), by ROC analysis to identify EOC patients from the whole cohort. With the cutoff of 0.370, the sensitivity and specificity of the sEV model were 0.80 and 0.89, which were much better performance than the serum markers (sensitivity: 0.55~0.66; specificity: 0.59~0.68) and the risk of ovarian malignancy algorithm (ROMA) index approved by the U.S. Food and Drug Administration (sensitivity: 0.65; specificity: 0.61), to identify EOC patients from patients with benign ovarian diseases or other controls. The sEV levels of CA125 significantly differed among early-stage and late-stage EOC (p < 0.001). Moreover, the AUC of ROC to identify early-stage EOC patients was 0.888. Further investigation revealed that the sEV levels of these 3 proteins significantly decreased after cytoreductive surgery (CA125, p = 0.008; HE4, p = 0.025; C5a, p = 0.044). In summary, our study showed that CA125, HE4, and C5a levels in serum sEVs can identify EOC patients at the early stage, elucidating the possibility of using a sEV model for the diagnosis of early-stage EOC.

Dataset Information

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

Publications

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

Similar Datasets

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