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. 2017 Sep 6;8(52):89811-89823.
doi: 10.18632/oncotarget.20688. eCollection 2017 Oct 27.

A combination of circulating miRNAs for the early detection of ovarian cancer

Akira Yokoi  1   2 , Yusuke Yoshioka  1 , Akihiro Hirakawa  3 , Yusuke Yamamoto  1 , Mitsuya Ishikawa  3 , Shun-Ichi Ikeda  3 , Tomoyasu Kato  4 , Kaoru Niimi  2 , Hiroaki Kajiyama  2 , Fumitaka Kikkawa  2 , Takahiro Ochiya  1
Affiliations

A combination of circulating miRNAs for the early detection of ovarian cancer

Akira Yokoi et al. Oncotarget. .

Abstract

Ovarian cancer is the leading cause of gynecologic cancer mortality, due to the difficulty of early detection. Current screening methods lack sufficient accuracy, and it is still challenging to propose a new early detection method that improves patient outcomes with less-invasiveness. Although many studies have suggested the utility of circulating microRNAs in cancer detection, their potential for early detection remains elusive. Here, we develop novel predictive models using a combination of 8 circulating serum miRNAs. This method was able to successfully distinguish ovarian cancer patients from healthy controls (area under the curve, 0.97; sensitivity, 0.92; and specificity, 0.91) and early-stage ovarian cancer from patients with benign tumors (0.91, 0.86 and 0.83, respectively). This method also enables subtype classification in 4 types of epithelial ovarian cancer. Furthermore, it is found that most of the 8 miRNAs were packaged in extracellular vesicles, including exosomes, derived from ovarian cancer cells, and they were circulating in murine blood stream. The circulating miRNAs described in this study may serve as biomarkers for ovarian cancer patients. Early detection and subtype determination prior to surgery are crucial for clinicians to design an effective treatment strategy for each patient, as is the goal of precision medicine.

Keywords: biomarkers; circulating microRNAs; exosomes; liquid biopsy; ovarian cancer.

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Conflict of interest statement

CONFLICTS OF INTEREST The authors have declared that no conflicts of interest exists.

Figures

Figure 1
Figure 1. An overview of the process for selecting candidate miRNAs
(A) Flowchart of the process for the development of biomarkers for early-stage ovarian cancer. A total of 301 serum samples were prepared from patients with ovarian tumors, and 250 samples were analyzed. Eighty-three samples from healthy controls were also prepared. Candidate miRNAs were selected based on the results of miRNA-seq, and further narrowed down using multiple validation steps. LPM: low potential of malignancy. (B) Results of selection from miRNA-seq using method 1. The heat maps show the distribution of read counts for miRNAs using the data with 0 or 1 mismatch allowed. The yellow line encloses results from healthy controls. The miRNAs shown in bold were selected. The bar charts on the right show the read counts of 2 miRNAs (miR-98 and miR-26a-5p) as examples. Read counts are on the vertical axis, and samples are on the horizontal axis. (C) Results of selection from miRNA-seq using method 2. The heat maps show the distribution of read counts for miRNAs using the data with 0 or 1 mismatch allowed. The yellow line encloses results from healthy controls. The miRNAs shown in bold were selected. The bar charts on the right show the read counts of 2 miRNAs (miR-487b and miR-330-5p) as examples. Read counts are on the vertical axis, and samples are on the horizontal axis.
Figure 2
Figure 2. Distributions of 8 selected miRNAs in trial cohort
(A) Serum levels of miRNAs. The dot plots are overlaid with box plots. The vertical axis shows 2^⊿Ct values, which were normalized to the values for cel-miR-39. Descriptions of the data points are shown below the graphs. (B) Statistical analyses. The Mann–Whitney U-test was used. *p < 0.01, ** p < 0.05.
Figure 3
Figure 3. Diagnostic outcomes in each model for the prediction of ovarian cancer
(A) ROC curve for identification of patients with ovarian cancer (N = 155) versus healthy controls (N = 63) using 8 miRNAs. (B) ROC curve for identification of patients with ovarian cancer (N = 155) versus healthy controls (N = 63) using 6 miRNAs and CA-125. (C) ROC curve for identification of early-stage patients with ovarian cancer (N = 65) versus patients with benign ovarian tumors (N = 43) using 7 miRNAs. The AUC values are shown on the graphs.
Figure 4
Figure 4. Diagnostic outcomes in each model for the prediction of histopathological subtypes ROC curves for the discrimination of each histopathological subtype versus other subtypes
AUC values are shown on the graphs. N = 155 (serous: 112; mucinous: 11; Endometrioid: 13; clear-cell: 19).
Figure 5
Figure 5. Validation of selected miRNAs in ovarian cancer cell lines
(A) Heat map showing the levels of miRNAs in exosomes derived from ovarian cancer cell lines. (B) Table showing the signal intensity of 8 selected miRNAs in EVs derived from ovarian cancer cell lines in microarray analysis. Each spot is colored according to the Z-score. (C) Schematic of the protocol for identifying circulating miRNAs in EVs derived from ovarian cancer cells. Orthotopic mouse models were established with A2780 cells and ES-2 cells, and blood was collected from the mice on day 10 (ES-2 cells) and day 21 (A2780 cells). (D) Levels of miR-766-3p in mouse serum EVs as assessed by qRT-PCR. The vertical axis indicates the ⊿Ct value normalized to the levels of miR-766-3p. A2780_1 and _2: orthotopic mouse model with A2780 cells. ES-2_1 and _2: orthotopic mouse model with ES-2 cells. Ctrl_1 and _2: mouse without human ovarian cancer cells.
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