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Development of a metabolite calculator for diagnosis of pancreatic cancer

Authors
 Munseok Choi  ;  Minsu Park  ;  Sung Hwan Lee  ;  Min Jung Lee  ;  Young-Ki Paik  ;  Sung Il Jang  ;  Dong Ki Lee  ;  Sang-Guk Lee  ;  Chang Moo Kang 
Citation
 CANCER MEDICINE, : epub, 2023-06 
Journal Title
CANCER MEDICINE
Issue Date
2023-06
Keywords
biomarker ; calculator ; diagnosis ; metabolomics ; pancreatic cancer
Abstract
Background: Carbohydrate antigen (CA) 19-9 is a known pancreatic cancer (PC) biomarker, but is not commonly used for general screening due to its low sensitivity and specificity. This study aimed to develop a serum metabolites-based diagnostic calculator for detecting PC with high accuracy.

Methods: A targeted quantitative approach of direct flow injection-tandem mass spectrometry combined with liquid chromatography-tandem mass spectrometry was employed for metabolomic analysis of serum samples using an Absolute IDQ™ p180 kit. Integrated metabolomic analysis was performed on 241 pooled or individual serum samples collected from healthy donors and patients from nine disease groups, including chronic pancreatitis, PC, other cancers, and benign diseases. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on characteristics of 116 serum metabolites distinguished patients with PC from those with other diseases. Sparse partial least squares discriminant analysis (SPLS-DA) was also performed, incorporating simultaneous dimension reduction and variable selection. Predictive performance between discrimination models was compared using a 2-by-2 contingency table of predicted probabilities obtained from the models and actual diagnoses.

Results: Predictive values obtained through OPLS-DA for accuracy, sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were 0.9825, 0.9916, 0.9870, 0.9866, and 0.9870, respectively. The number of metabolite candidates was narrowed to 76 for SPLS-DA. The SPLS-DA-obtained predictive values for accuracy, sensitivity, specificity, balanced accuracy, and AUC were 0.9773, 0.9649, 0.9832, 0.9741, and 0.9741, respectively.

Conclusions: We successfully developed a 76 metabolome-based diagnostic panel for detecting PC that demonstrated high diagnostic performance in differentiating PC from other diseases.
Files in This Item:
T202303633.pdf Download
DOI
10.1002/cam4.6233
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Laboratory Medicine (진단검사의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
Yonsei Authors
Kang, Chang Moo(강창무) ORCID logo https://orcid.org/0000-0002-5382-4658
Lee, Dong Ki(이동기) ORCID logo https://orcid.org/0000-0002-0048-9112
Lee, Sang-Guk(이상국) ORCID logo https://orcid.org/0000-0003-3862-3660
Jang, Sung Ill(장성일) ORCID logo https://orcid.org/0000-0003-4937-6167
Choi, Munseok(최문석) ORCID logo https://orcid.org/0000-0002-9844-4747
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/195555
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