{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T14:58:03Z","timestamp":1772117883090,"version":"3.50.1"},"reference-count":47,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["febs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Molecular Oncology"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>\n                    Liver and pancreatic cancers are difficult to detect early, leading to high mortality rates. Blood\u2010based diagnostics present a viable alternative for earlier detection, potentially improving survival rates. The c\n                    <jats:italic>omprehensive serum glycopeptide spectra analysis<\/jats:italic>\n                    (CSGSA) method combines enriched glycopeptides (EGPs) with conventional tumor markers through machine learning to accurately identify early stage cancers. Here, we analyzed nine tumor markers (CA19\u20109, AFP, PSA, CEA, CA125, CYFRA, CA15\u20103, SCC antigen, and NCC\u2010ST439) in 119 patients with pancreatic cancer and 49 with hepatocellular carcinoma, alongside 590 healthy controls. We also analyzed EGPs using liquid chromatography\u2010mass spectrometry. We found that \u03b11\u2010antitrypsin with a fully sialylated biantennary glycan at asparagine 271 and \u03b12\u2010macroglobulin with a fully sialylated biantennary glycan at asparagine 70 effectively distinguished liver and pancreatic cancers. The integration of these two glycopeptides, along with the nine tumor markers and 1688 EGPs using a machine learning model enhanced diagnostic accuracy, achieving a receiver operating characteristic\u2010area under curve (ROC\u2010AUC) score of 0.996. CSGSA has the potential to minimize the need for invasive diagnostic procedures and serves as a promising tool for widespread screening.\n                  <\/jats:p>","DOI":"10.1002\/1878-0261.70084","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T02:24:52Z","timestamp":1751336692000},"page":"3499-3517","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning for identifying liver and pancreas cancers through comprehensive serum glycopeptide spectra analysis: a case\u2010control study"],"prefix":"10.1002","volume":"19","author":[{"given":"Motoyuki","family":"Kohjima","sequence":"first","affiliation":[{"name":"Department of Gastroenterology NHO Kyushu Medical Center  Fukuoka Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuko","family":"Takami","sequence":"additional","affiliation":[{"name":"Department of Hepato\u2010Biliary\u2010Pancreatic Surgery NHO Kyushu Medical Center  Fukuoka Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ken","family":"Kawabe","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology NHO Kyushu Medical Center  Fukuoka Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2671-1217","authenticated-orcid":false,"given":"Kazuhiro","family":"Tanabe","sequence":"additional","affiliation":[{"name":"Medical Solution Promotion Department Medical Solution Segment, LSI Medience Corporation  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chihiro","family":"Hayashi","sequence":"additional","affiliation":[{"name":"Medical Solution Promotion Department Medical Solution Segment, LSI Medience Corporation  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mikio","family":"Mikami","sequence":"additional","affiliation":[{"name":"Chigasaki Central Hospital Women's Center  Kanagawa Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tetsuya","family":"Kusumoto","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery and Clinical Research Institute Cancer, Research Division NHO Kyushu Medical Center  Fukuoka Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,6,30]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_2_11_3_1","unstructured":"National Cancer Center Research Institute Japan.Cancer statistics in Japan.2024.https:\/\/ganjoho.jp\/public\/qa_links\/report\/statistics\/en.html. 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