Blood Test for Breast Cancer Screening through the Detection of Tumor-Associated Circulating Transcripts

Sunyoung Park, Sungwoo Ahn, Jee Ye Kim, Jungho Kim, Hyun Ju Han, Dasom Hwang, Jungmin Park, Hyung Seok Park, Seho Park, Gun Min Kim, Joohyuk Sohn, Joon Jeong, Yong Uk Song, Hyeyoung Lee, Seung Il Kim

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3 Citations (Scopus)


Liquid biopsy has been emerging for early screening and treatment monitoring at each cancer stage. However, the current blood-based diagnostic tools in breast cancer have not been sufficient to understand patient-derived molecular features of aggressive tumors individually. Herein, we aimed to develop a blood test for the early detection of breast cancer with cost-effective and high-throughput considerations in order to combat the challenges associated with precision oncology using mRNA-based tests. We prospectively evaluated 719 blood samples from 404 breast cancer patients and 315 healthy controls, and identified 10 mRNA transcripts whose expression is increased in the blood of breast cancer patients relative to healthy controls. Modeling of the tumor-associated circulating transcripts (TACTs) is performed by means of four different machine learning techniques (artificial neural network (ANN), decision tree (DT), logistic regression (LR), and support vector machine (SVM)). The ANN model had superior sensitivity (90.2%), specificity (80.0%), and accuracy (85.7%) compared with the other three models. Relative to the value of 90.2% achieved using the TACT assay on our test set, the sensitivity values of other conventional assays (mammogram, CEA, and CA 15-3) were comparable or much lower, at 89%, 7%, and 5%, respectively. The sensitivity, specificity, and accuracy of TACTs were appreciably consistent across the different breast cancer stages, suggesting the potential of the TACTs assay as an early diagnosis and prediction of poor outcomes. Our study potentially paves the way for a simple and accurate diagnostic and prognostic tool for liquid biopsy.

Original languageEnglish
Article number9140
JournalInternational journal of molecular sciences
Issue number16
Publication statusPublished - 2022 Aug

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

All Science Journal Classification (ASJC) codes

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry


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