Plasma-Derived Extracellular Vesicles Circular RNAs Serve as Biomarkers for Breast Cancer Diagnosis

Abstract

Breast cancer is the second cause of cancer-associated death among women and seriously endangers women's health. Therefore, early identification of breast cancer would be beneficial to women's health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BC_ExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.

Keywords: breast cancer; cancer diagnosis; circular RNA; extracellular vesicles; predictive classifier.

Figure 1

Study design. To develop classifiers for the early diagnosis of breast cancer, the workflow of our study consists of three stages, including the discovery stage, training, and validation stage. In the discovery stage, we used whole-genome sequencing to identify circRNAs with significantly different levels. In the training stage, we developed classifiers with three regression models by using the circRNAs levels detected by qPCR. In the validation stage, the predictive efficacy of the classifiers was validated. qPCR, quantitative real-time polymerase chain reaction. circRNA, circular RNA.

Figure 2

RNA composition in EVs. (A) The types of RNAs in EVs. (B) Annotation of circRNAs. (C) Source of circRNAs. (D) Length distribution of circRNAs. nt, Nucleotide. All, all of individuals. Overlap, sense overlapping circRNAs.

Figure 3

circRNAs with significantly different levels. (A) Volcano plots of circRNAs with significant different levels (|log2 fold change| ≥ 2 and P-value < 0.05 produced by edgR package of R software) between cancer and control groups. (B) PCA analysis of genome-wide RNA sequencing data derived from 14 breast cancer patients and 6 benign patients. (C) Heat map of the z-scores of circRNAs with significantly different levels. (D) Gene function enrichment analysis of the host genes of the circRNAs with significantly different levels. Decrease, circRNAs with decreased levels. Non, circRNAs with non-significant changes. Increase, circRNAs increased levels.

Figure 4

Performance of classifiers for breast cancer prediction. The performance of training cohort (A, B), testing cohort (C, D) and all sample (E, F) with three models was showed. The AUC in the training cohort was cross-validated using leave one out cross-validation (LOOCV). Receiver operating characteristic, ROC; Acc, accuracy; Sen, sensitivity; SVM, support vector machine; LDA, linear discriminate analysis; LR, logistic regression; Spe, specificity; P, the P-value of DeLong's test.