Diagnostic model for pancreatic cancer using a multi-biomarker panel

Yoo Jin Choi, Woongchang Yoon, Areum Lee, Youngmin Han, Yoonhyeong Byun, Jae Seung Kang, Hongbeom Kim, Wooil Kwon, Young Ah Suh, Yongkang Kim, Seungyeoun Lee, Junghyun Namkung, Sangjo Han, Yonghwan Choi, Jin Seok Heo, Joon Oh Park, Joo Kyung Park, Song Cheol Kim, Chang Moo Kang, Woo Jin LeeTaesung Park, Jin Young Jang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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-2glycoprotein [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.

Original languageEnglish
Pages (from-to)144-153
Number of pages10
JournalAnnals of Surgical Treatment and Research
Issue number3
Publication statusPublished - 2021 Mar

Bibliographical note

Funding Information:
This research was supported by a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea. (HI14C2640) and by a grant of the Korean Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI16C2037).

Publisher Copyright:
Copyright 2021, the Korean Surgical Society

All Science Journal Classification (ASJC) codes

  • Surgery


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