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• W2765407575 abstract "// Jiyoung Park 1, 2, * , Yonghwan Choi 3, 14, * , Junghyun Namkung 3 , Sung Gon Yi 3 , Hyunsoo Kim 1, 2 , Jiyoung Yu 1, 2 , Yongkang Kim 4 , Min-Seok Kwon 4 , Wooil Kwon 5 , Do-Youn Oh 6 , Sun-Whe Kim 5 , Seung-Yong Jeong 5 , Wonshik Han 5 , Kyu Eun Lee 5 , Jin Seok Heo 7 , Joon Oh Park 8 , Joo Kyung Park 9 , Song Cheol Kim 10 , Chang Moo Kang 11 , Woo Jin Lee 12 , Seungyeoun Lee 13 , Sangjo Han 3 , Taesung Park 4 , Jin-Young Jang 5 and Youngsoo Kim 1, 2 1 Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea 2 Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea 3 Immunodiagnostics R&D Team, IVD Business Unit 5, SK Telecom, Seoul, Korea 4 Department of Statistics, Seoul National University, Seoul, Korea 5 Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea 6 Department of Internal Medicine and Cancer Research Institute, Seoul National University Hospital, Seoul, Korea 7 Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 8 Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 9 Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea 10 Department of Surgery, University of Ulsan College of Medicine and Asan Medical Center, Seoul, Korea 11 Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea 12 Center for Liver Cancer, National Cancer Center, Seoul, Korea 13 Department of Mathematics and Statistics, Sejong University, Seoul, Korea 14 School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea * The authors have contributed equally to this work Correspondence to: Taesung Park, email: taesungp@gmail.com Jin-Young Jang, email: jangjy4@snu.ac.kr Youngsoo Kim, email: biolab@snu.ac.kr Keywords: pancreatic cancer, proteomics, diagnostic biomarker, multimarker panel, multiple reaction monitoring Received: July 10, 2017      Accepted: August 29, 2017      Published: October 16, 2017 ABSTRACT Due to its high mortality rate and asymptomatic nature, early detection rates of pancreatic ductal adenocarcinoma (PDAC) remain poor. We measured 1000 biomarker candidates in 134 clinical plasma samples by multiple reaction monitoring-mass spectrometry (MRM-MS). Differentially abundant proteins were assembled into a multimarker panel from a training set (n=684) and validated in independent set (n=318) from five centers. The level of panel proteins was also confirmed by immunoassays. The panel including leucine-rich alpha-2 glycoprotein (LRG1), transthyretin (TTR), and CA19-9 had a sensitivity of 82.5% and a specificity of 92.1%. The triple-marker panel exceeded the diagnostic performance of CA19-9 by more than 10% (AUC CA19-9 = 0.826, AUC panel = 0.931, P < 0.01) in all PDAC samples and by more than 30% (AUC CA19-9 = 0.520, AUC panel = 0.830, P < 0.001) in patients with normal range of CA19-9 (<37U/mL). Further, it differentiated PDAC from benign pancreatic disease (AUC CA19-9 = 0.812, AUC panel = 0.892, P < 0.01) and other cancers (AUC CA19-9 = 0.796, AUC panel = 0.899, P < 0.001). Overall, the multimarker panel that we have developed and validated in large-scale samples by MRM-MS and immunoassay has clinical applicability in the early detection of PDAC." @default.
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• W2765407575 date "2017-10-16" @default.
• W2765407575 modified "2023-10-16" @default.
• W2765407575 title "Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel" @default.
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• W2765407575 doi "https://doi.org/10.18632/oncotarget.21861" @default.
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