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논문 기본 정보

자료유형
학술저널
저자정보
Yoo Jin Choi (Seoul National University College of Medicine) Woongchang Yoon (Seoul National University) Areum Lee (Seoul National University College of Medicine) Youngmin Han (Seoul National University College of Medicine) Yoonhyeong Byun (Seoul National University College of Medicine) Jae Seung Kang (Seoul National University College of Medicine) Hongbeom Kim (Seoul National University College of Medicine) Wooil Kwon (Seoul National University College of Medicine) Young-Ah Suh (Seoul National University College of Medicine) Yongkang Kim (Seoul National University) Seungyeoun Lee (Sejong University) Junghyun Namkung (SK Telecom) Sangjo Han (SK Telecom) Yonghwan Choi (SK Telecom) Jin Seok Heo (Sungkyunkwan University School of Medicine) Joon Oh Park (Sungkyunkwan University School of Medicine) Joo Kyung Park (Sungkyunkwan University School of Medicine) Song Cheol Kim (University of Ulsan College of Medicine) Chang Moo Kang (Yonsei University College of Medicine) Woo Jin Lee (National Cancer Center) Taesung Park (Seoul National University) Jin-Young Jang (Seoul National University College of Medicine)
저널정보
대한외과학회 Annals of Surgical Treatment and Research Annals of Surgical Treatment and Research Vol.100 No.3
발행연도
2021.3
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144 - 153 (10page)

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Purpose: Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage.
Methods: Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups).
Results: The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value.
Conclusion: This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.
#Biomarkers #Enzyme-linked immunosorbent assay #Pancreatic intraductal neoplasms

목차

INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

참고문헌 (30)

참고문헌 신청
Wulfkuhle JD / 2003 / Proteomic applications for the early detection of cancer / Nat Rev Cancer 3:267~275 google schola Ilic M / 2016 / Epidemiology of pancreatic cancer / World J Gastroenterol 22:9694~9705 google schola Vincent A / 2011 / Pancreatic cancer / Lancet 378:607~620 google schola Unger K / 2018 / Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma / Oncotarget 9:23078~23090 google schola Srinivas PR / 2001 / Trends in biomarker research for cancer detection / Lancet Oncol 2:698~704 google schola

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