A plasma-derived extracellular vesicle mRNA classifier for the detection of breast cancer

Geng-Xi Cai; Li Lin; Xiang-Ming Zhai; Zhi-Wei Guo; Ying-Song Wu; Guo-Lin Ye; Qing Liu; Lu-Shi Chen; Guan-Yu Xing; Qiao-Hong Zhao; Li-Ling Tang; Shun-He Mai; Bo-Jian Ye

doi:10.21037/gs-21-275

A plasma-derived extracellular vesicle mRNA classifier for the detection of breast cancer

Gland Surg. 2021 Jun;10(6):2002-2009. doi: 10.21037/gs-21-275.

Authors

Geng-Xi Cai^{1

2}, Li Lin³, Xiang-Ming Zhai³, Zhi-Wei Guo³, Ying-Song Wu³, Guo-Lin Ye², Qing Liu², Lu-Shi Chen², Guan-Yu Xing², Qiao-Hong Zhao², Li-Ling Tang², Shun-He Mai², Bo-Jian Ye²

Affiliations

¹ Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
² Department of Breast Surgery, The First People's Hospital of Foshan, Foshan, China.
³ Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medical and Biotechnology, Southern Medical University, Guangzhou, China.

Abstract

Background: According to the global cancer burden data released in 2020, breast cancer (BC) has become the most common cancer in the world. Similar to those of other cancers, the present methods used in clinic for diagnosing early BC are invasive, inaccurate, and insensitive. Hence, new non-invasive methods capable of early diagnosis are needed.

Methods: We applied next-generation sequencing and analyzed the messenger RNA (mRNA) profiles of plasma extracellular vesicles (EVs) derived from 14 BC patients and 6 patients with benign breast lesions. We used 3 regression models, namely support vector machine (SVM), linear discriminate analysis (LDA), and logistic regression (LR), to develop classifiers for use in making predictive BC diagnoses; and used 259 plasma samples, including those obtained from 144 patients with BC, 72 patients with benign breast lesions, and 43 healthy women, which were divided into training groups and validation groups to verify their performances as classifiers by quantitative reverse transcription polymerase chain reaction (RT-qPCR). The area under the curve (AUC) and accuracy, sensitivity, and specificity of the classifiers were cross-validated with the leave-1-out cross-validation (LOOCV) method.

Results: Among all combinations assessed with the 3 different regression models, an 8-mRNA combination, named EXOB_mRNA, exhibited high performance [accuracy =71.9% and AUC =0.718, 95% confidence interval (CI): 0.652 to 0.784] in the training cohort after LOOCV was performed, showing the largest AUC in the SVM model. The mRNAs in EXOB_mRNA were HLA-DRB1, HAVCR1, ENPEP, TIMP1, CD36, MARCKS, DAB2, and CXCL14. In the validation cohort, the AUC of EXOB_mRNA was 0.737 (95% CI: 0.636 to 0.837). In addition, gene function and pathway analyses revealed that different levels of gene expression were associated with cancer.

Conclusions: We developed a high-performing predictive classifiers including 8 mRNAs from plasma extracellular vesicles for diagnosing breast cancer.

Keywords: Breast cancer (BC); diagnosis; extracellular vesicles (EVs); messenger RNAs (mRNAs); predictive classifier.