Yuichi Hirata1, Takashi Kobayashi1, Shin Nishiumi1, Kodai Yamanaka1, Takashi Nakagawa1, Seiji Fujigaki1, Takao Iemoto1, Makoto Kobayashi2, Takuji Okusaka3, Shoji Nakamori4, Masashi Shimahara5, Takaaki Ueno5, Akihiko Tsuchida6, Naohiro Sata7, Tatsuya Ioka8, Yohichi Yasunami9, Tomoo Kosuge10, Takashi Kaneda11, Takao Kato12, Kazuhiro Yagihara13, Shigeyuki Fujita14, Tesshi Yamada2, Kazufumi Honda15, Takeshi Azuma1, Masaru Yoshida16. 1. Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan. 2. Division of Chemotherapy and Clinical Research, National Cancer Center Research Institute, Tokyo, Japan. 3. Department of Hepatobiliary and Pancreatic Oncology, National Cancer Center Hospital, Tokyo, Japan. 4. Departments of Hepato-Biliary-Pancreatic Surgery, Osaka National Hospital, National Hospital Organization, Osaka, Japan. 5. Department of Oral Surgery, Osaka Medical College, Osaka, Japan. 6. Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan. 7. Department of Surgery, Jichi Medical University, Tochigi, Japan. 8. Department of GI Cancer Screening and Surveillance, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka, Japan. 9. Islet Institute, Fukuoka University, Fukuoka, Japan. 10. Department of Gastrointestinal Surgery, JR Tokyo General Hospital, Tokyo, Japan. 11. Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan. 12. Department of Oral Implant, Nihon University School of Dentistry at Matsudo, Chiba, Japan. 13. Department of Oral Surgery, Saitama Cancer Center, Saitama, Japan. 14. Department of Oral and Maxillofacial Surgery, Wakayama Medical University, Wakayama, Japan. 15. Division of Chemotherapy and Clinical Research, National Cancer Center Research Institute, Tokyo, Japan; Japan Agency for Medical Research and Development (AMED) CREST, Tokyo, Japan. 16. Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan; Metabolomics Research, Department of Internal Related, Kobe University Graduate School of Medicine, Hyogo, Japan; AMED-CREST, AMED, Hyogo, Japan. Electronic address: myoshida@med.kobe-u.ac.jp.
Abstract
BACKGROUND: To improve prognosis of pancreatic cancer (PC) patients, the discovery of more reliable biomarkers for the early detection is desired. METHODS: Blood samples were collected by 2 independent groups. The 1st set was included 55 early PC and 58 healthy volunteers (HV), and the 2nd set was included 16 PC and 16HV. The 16 targeted metabolites were quantitatively analyzed by gas chromatography/tandem mass spectrometry together with their corresponding stable isotopes. In the 1st set, the levels of these metabolites were evaluated, and diagnostic models were constructed via multivariate logistic regression analysis, leading to validation using the 2nd set. RESULTS: In the 1st set, model X consisting of 4 candidates based on our previous report possessed higher sensitivity (74.1%) than carbohydrate antigen 19-9 (CA19-9). Model Y, consisting of 2 metabolites newly selected from 16 metabolites via stepwise method possessed higher sensitivity (70.4%) than CA19-9. Furthermore, combining model Y with CA19-9 increased its sensitivity (90.7%) and specificity (89.5%). In the 2nd set, combining model Y with CA19-9 displayed high sensitivity (81.3%) and specificity (93.8%). In particular, it displayed very high sensitivity (100%) for resectable PC. CONCLUSIONS: Quantitative analysis confirmed that metabolomics-based diagnostic methods are useful for detecting PC early.
BACKGROUND: To improve prognosis of pancreatic cancer (PC) patients, the discovery of more reliable biomarkers for the early detection is desired. METHODS: Blood samples were collected by 2 independent groups. The 1st set was included 55 early PC and 58 healthy volunteers (HV), and the 2nd set was included 16 PC and 16HV. The 16 targeted metabolites were quantitatively analyzed by gas chromatography/tandem mass spectrometry together with their corresponding stable isotopes. In the 1st set, the levels of these metabolites were evaluated, and diagnostic models were constructed via multivariate logistic regression analysis, leading to validation using the 2nd set. RESULTS: In the 1st set, model X consisting of 4 candidates based on our previous report possessed higher sensitivity (74.1%) than carbohydrate antigen 19-9 (CA19-9). Model Y, consisting of 2 metabolites newly selected from 16 metabolites via stepwise method possessed higher sensitivity (70.4%) than CA19-9. Furthermore, combining model Y with CA19-9 increased its sensitivity (90.7%) and specificity (89.5%). In the 2nd set, combining model Y with CA19-9 displayed high sensitivity (81.3%) and specificity (93.8%). In particular, it displayed very high sensitivity (100%) for resectable PC. CONCLUSIONS: Quantitative analysis confirmed that metabolomics-based diagnostic methods are useful for detecting PC early.
Authors: Vijayasarathy Ketavarapu; Vishnubhotla Ravikanth; Mitnala Sasikala; G V Rao; Ch Venkataramana Devi; Prabhakar Sripadi; Murali Satyanarayana Bethu; Ramars Amanchy; H V V Murthy; Stephen J Pandol; D Nageshwar Reddy Journal: BMC Cancer Date: 2022-07-19 Impact factor: 4.638
Authors: Alison M Farley; David R Braxton; Jonathan Li; Karl Trounson; Subhanwita Sakar-Dey; Bhavana Nayer; Tatsuhiko Ikeda; Kevin X Lau; Winita Hardikar; Kouichi Hasegawa; Martin F Pera Journal: Sci Rep Date: 2019-02-27 Impact factor: 4.379