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Diagnostic model for pancreatic cancer using a multi-biomarker panel

Authors :
Sangjo Han
Jae Seung Kang
Joon Oh Park
Wooil Kwon
Seungyeoun Lee
Yoo Jin Choi
Woojin Lee
Jin-Young Jang
Junghyun Namkung
Yongkang Kim
Yong-Hwan Choi
Young Ah Suh
Areum Lee
Joo Kyung Park
Taesung Park
Hongbeom Kim
Youngmin Han
Chang Moo Kang
Woongchang Yoon
Jin Seok Heo
Yoonhyeong Byun
Song Cheol Kim
Source :
Annals of Surgical Treatment and Research
Publication Year :
2021
Publisher :
The Korean Surgical Society, 2021.

Abstract

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.

Details

Language :
English
ISSN :
22886796 and 22886575
Volume :
100
Issue :
3
Database :
OpenAIRE
Journal :
Annals of Surgical Treatment and Research
Accession number :
edsair.doi.dedup.....4478a90b35279240ad47e86fd020beec