Anovel serum metabolomics-based diagnostic approach to pancreatic cancer

Takashi Kobayashi, Shin Nishiumi, Atsuki Ikeda, Tomoo Yoshie, Aya Sakai, Atsuki Matsubara, Yoshihiro Izumi, Hidetaka Tsumura, Masahiro Tsuda, Hogara Nishisaki, Nobuhide Hayashi, Seiji Kawano, Yutaka Fujiwara, Hironobu Minami, Tadaomi Takenawa, Takeshi Azuma, Masaru Yoshida

Research output: Contribution to journalArticle

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Abstract

Background: To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer. Methods: Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study. Results: In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers. Conclusions: Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis. Impact: It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis.

Original languageEnglish
Pages (from-to)571-579
Number of pages9
JournalCancer Epidemiology Biomarkers and Prevention
Volume22
Issue number4
DOIs
Publication statusPublished - Apr 1 2013
Externally publishedYes

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Metabolomics
Pancreatic Neoplasms
Chronic Pancreatitis
Serum
Healthy Volunteers
Validation Studies
Sensitivity and Specificity
Tumor Biomarkers
Information Systems
Gas Chromatography-Mass Spectrometry
Libraries
Logistic Models
Regression Analysis

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Oncology

Cite this

Kobayashi, T., Nishiumi, S., Ikeda, A., Yoshie, T., Sakai, A., Matsubara, A., ... Yoshida, M. (2013). Anovel serum metabolomics-based diagnostic approach to pancreatic cancer. Cancer Epidemiology Biomarkers and Prevention, 22(4), 571-579. https://doi.org/10.1158/1055-9965.EPI-12-1033

Anovel serum metabolomics-based diagnostic approach to pancreatic cancer. / Kobayashi, Takashi; Nishiumi, Shin; Ikeda, Atsuki; Yoshie, Tomoo; Sakai, Aya; Matsubara, Atsuki; Izumi, Yoshihiro; Tsumura, Hidetaka; Tsuda, Masahiro; Nishisaki, Hogara; Hayashi, Nobuhide; Kawano, Seiji; Fujiwara, Yutaka; Minami, Hironobu; Takenawa, Tadaomi; Azuma, Takeshi; Yoshida, Masaru.

In: Cancer Epidemiology Biomarkers and Prevention, Vol. 22, No. 4, 01.04.2013, p. 571-579.

Research output: Contribution to journalArticle

Kobayashi, T, Nishiumi, S, Ikeda, A, Yoshie, T, Sakai, A, Matsubara, A, Izumi, Y, Tsumura, H, Tsuda, M, Nishisaki, H, Hayashi, N, Kawano, S, Fujiwara, Y, Minami, H, Takenawa, T, Azuma, T & Yoshida, M 2013, 'Anovel serum metabolomics-based diagnostic approach to pancreatic cancer', Cancer Epidemiology Biomarkers and Prevention, vol. 22, no. 4, pp. 571-579. https://doi.org/10.1158/1055-9965.EPI-12-1033
Kobayashi, Takashi ; Nishiumi, Shin ; Ikeda, Atsuki ; Yoshie, Tomoo ; Sakai, Aya ; Matsubara, Atsuki ; Izumi, Yoshihiro ; Tsumura, Hidetaka ; Tsuda, Masahiro ; Nishisaki, Hogara ; Hayashi, Nobuhide ; Kawano, Seiji ; Fujiwara, Yutaka ; Minami, Hironobu ; Takenawa, Tadaomi ; Azuma, Takeshi ; Yoshida, Masaru. / Anovel serum metabolomics-based diagnostic approach to pancreatic cancer. In: Cancer Epidemiology Biomarkers and Prevention. 2013 ; Vol. 22, No. 4. pp. 571-579.
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AU - Matsubara, Atsuki

AU - Izumi, Yoshihiro

AU - Tsumura, Hidetaka

AU - Tsuda, Masahiro

AU - Nishisaki, Hogara

AU - Hayashi, Nobuhide

AU - Kawano, Seiji

AU - Fujiwara, Yutaka

AU - Minami, Hironobu

AU - Takenawa, Tadaomi

AU - Azuma, Takeshi

AU - Yoshida, Masaru

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N2 - Background: To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer. Methods: Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study. Results: In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers. Conclusions: Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis. Impact: It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis.

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