한빛사 논문
Hyunku Shin1, Seunghyun Oh2, Soonwoo Hong1, Minsung Kang3, Daehyeon Kang2, Yong-gu Ji4, Byeong Hyeon Choi6,7, Ka-Won Kang5, Hyesun Jeong8, Yong Park5, Sunghoi Hong8, Hyun Koo Kim6,7,*, and Yeonho Choi1,2,3,4,*
1Department of Bio-convergence Engineering, Korea University, Seoul 02841, Republic of Korea.
2School of Biomedical Engineering, Korea University, Seoul 02841, Republic of Korea.
3Department of Bioengineering, Korea University, Seoul 02841, Republic of Korea.
4Exopert corporation, Seoul, 02841, Republic of Korea.
5Division of Hematology-Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul 02841, Republic of Korea.
6Department of Biomedical Sciences, College of Medicine, Korea University, Seoul 02841, Republic of Korea.
7Department of Thoracic and Cardiovascular Surgery, College of Medicine, Korea University Guro Hospital, Seoul 08308, Republic of Korea.
8School of Biosystems and Biomedical Sciences, Korea University, Seoul 02841, Republic of Korea.
*Corresponding authors:
Abstract
Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.
KEYWORDS:exosome, liquid biopsy, lung cancer diagnosis, deep learning, surface-enhanced Raman spectroscopy (SERS)
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