Arata Sakai1, Makoto Suzuki1, Takashi Kobayashi1, Shin Nishiumi1, Kodai Yamanaka1, Yuichi Hirata1, Takashi Nakagawa1, Takeshi Azuma1, Masaru Yoshida1,2,3. 1. Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan. 2. Division of Metabolomics Research, Department of Internal Related, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan. 3. AMED-CREST, AMED, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan.
Abstract
AIM: To examine a novel screening method for pancreatic cancer involving gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry-based metabolomics analysis. MATERIALS & METHODS: Sera from pancreatic cancer patients (n = 59) and healthy volunteers (n = 59) were allocated to the training set or validation set. Serum metabolome analysis was carried out using our multiplatform metabolomics system. A diagnostic model was constructed using a two-phase screening method that was newly advocated. RESULTS: When the training set was used, the constructed diagnostic model exhibited high sensitivity (100%) and specificity (80%) for pancreatic cancer. When the validation set was used, the model displayed high sensitivity (84.1%) and specificity (84.1%). CONCLUSION: We successfully developed a diagnostic model for pancreatic cancer using a multiplatform serum metabolomics system.
AIM: To examine a novel screening method for pancreatic cancer involving gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry-based metabolomics analysis. MATERIALS & METHODS: Sera from pancreatic cancerpatients (n = 59) and healthy volunteers (n = 59) were allocated to the training set or validation set. Serum metabolome analysis was carried out using our multiplatform metabolomics system. A diagnostic model was constructed using a two-phase screening method that was newly advocated. RESULTS: When the training set was used, the constructed diagnostic model exhibited high sensitivity (100%) and specificity (80%) for pancreatic cancer. When the validation set was used, the model displayed high sensitivity (84.1%) and specificity (84.1%). CONCLUSION: We successfully developed a diagnostic model for pancreatic cancer using a multiplatform serum metabolomics system.
Authors: Jaroslav Tumas; Kotryna Kvederaviciute; Marius Petrulionis; Benediktas Kurlinkus; Arnas Rimkus; Greta Sakalauskaite; Jonas Cicenas; Audrius Sileikis Journal: Med Oncol Date: 2016-11-02 Impact factor: 3.064