Serum metabolomic profiles for human pancreatic cancer discrimination

Takao Itoi, Masahiro Sugimoto, Junko Umeda, Atsushi Sofuni, Takayoshi Tsuchiya, Shujiro Tsuji, Reina Tanaka, Ryosuke Tonozuka, Mitsuyoshi Honjo, Fuminori Moriyasu, Kazuhiko Kasuya, Yuichi Nagakawa, Yuta Abe, Kimihiro Takano, Shigeyuki Kawachi, Motohide Shimazu, Tomoyoshi Soga, Masaru Tomita, Makoto Sunamura

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresis–mass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel–Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946–0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650–1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.

Original languageEnglish
Article number767
JournalInternational Journal of Molecular Sciences
Volume18
Issue number4
DOIs
Publication statusPublished - 2017 Apr 1

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Metabolomics
Biliary Tract Neoplasms
Metabolites
Pancreatic Neoplasms
serums
discrimination
Antigens
Logistic Models
cancer
Mucinous Adenocarcinoma
Carcinoma, Intraductal, Noninfiltrating
metabolites
Papillary Carcinoma
profiles
Serum
Area Under Curve
antigens
Logistics
Carcinoembryonic Antigen
Tumor Biomarkers

Keywords

  • Biliary tract cancers
  • Capillary electrophoresis mass spectrometry
  • Metabolomics
  • Pancreatic cancer

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Computer Science Applications
  • Spectroscopy
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

Cite this

Itoi, T., Sugimoto, M., Umeda, J., Sofuni, A., Tsuchiya, T., Tsuji, S., ... Sunamura, M. (2017). Serum metabolomic profiles for human pancreatic cancer discrimination. International Journal of Molecular Sciences, 18(4), [767]. https://doi.org/10.3390/ijms18040767

Serum metabolomic profiles for human pancreatic cancer discrimination. / Itoi, Takao; Sugimoto, Masahiro; Umeda, Junko; Sofuni, Atsushi; Tsuchiya, Takayoshi; Tsuji, Shujiro; Tanaka, Reina; Tonozuka, Ryosuke; Honjo, Mitsuyoshi; Moriyasu, Fuminori; Kasuya, Kazuhiko; Nagakawa, Yuichi; Abe, Yuta; Takano, Kimihiro; Kawachi, Shigeyuki; Shimazu, Motohide; Soga, Tomoyoshi; Tomita, Masaru; Sunamura, Makoto.

In: International Journal of Molecular Sciences, Vol. 18, No. 4, 767, 01.04.2017.

Research output: Contribution to journalArticle

Itoi, T, Sugimoto, M, Umeda, J, Sofuni, A, Tsuchiya, T, Tsuji, S, Tanaka, R, Tonozuka, R, Honjo, M, Moriyasu, F, Kasuya, K, Nagakawa, Y, Abe, Y, Takano, K, Kawachi, S, Shimazu, M, Soga, T, Tomita, M & Sunamura, M 2017, 'Serum metabolomic profiles for human pancreatic cancer discrimination', International Journal of Molecular Sciences, vol. 18, no. 4, 767. https://doi.org/10.3390/ijms18040767
Itoi, Takao ; Sugimoto, Masahiro ; Umeda, Junko ; Sofuni, Atsushi ; Tsuchiya, Takayoshi ; Tsuji, Shujiro ; Tanaka, Reina ; Tonozuka, Ryosuke ; Honjo, Mitsuyoshi ; Moriyasu, Fuminori ; Kasuya, Kazuhiko ; Nagakawa, Yuichi ; Abe, Yuta ; Takano, Kimihiro ; Kawachi, Shigeyuki ; Shimazu, Motohide ; Soga, Tomoyoshi ; Tomita, Masaru ; Sunamura, Makoto. / Serum metabolomic profiles for human pancreatic cancer discrimination. In: International Journal of Molecular Sciences. 2017 ; Vol. 18, No. 4.
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AU - Itoi, Takao

AU - Sugimoto, Masahiro

AU - Umeda, Junko

AU - Sofuni, Atsushi

AU - Tsuchiya, Takayoshi

AU - Tsuji, Shujiro

AU - Tanaka, Reina

AU - Tonozuka, Ryosuke

AU - Honjo, Mitsuyoshi

AU - Moriyasu, Fuminori

AU - Kasuya, Kazuhiko

AU - Nagakawa, Yuichi

AU - Abe, Yuta

AU - Takano, Kimihiro

AU - Kawachi, Shigeyuki

AU - Shimazu, Motohide

AU - Soga, Tomoyoshi

AU - Tomita, Masaru

AU - Sunamura, Makoto

PY - 2017/4/1

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N2 - This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresis–mass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel–Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946–0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650–1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.

AB - This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresis–mass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel–Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946–0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650–1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.

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KW - Pancreatic cancer

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