{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T08:06:57Z","timestamp":1789373217606,"version":"build-2803163510"},"reference-count":35,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Electrophoresis"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Thyroglobulin (Tg) is a clinically established biomarker for thyroid cancer; however, its diagnostic specificity remains limited due to confounding benign conditions. This study investigated changes in the site\u2010specific\n                    <jats:italic>N<\/jats:italic>\n                    \u2010glycosylation profiles of thyroglobulin protein derived from papillary thyroid cancer and adjacent healthy tissues. Using a bottom\u2010up mass spectrometry\u2010based glycoproteomic approach, we analyzed site\u2010specific glycan compositions in retrospective patient tissue samples. Nine distinct\n                    <jats:italic>N<\/jats:italic>\n                    \u2010glycosylation sites were identified and quantified in this study. Statistical analysis revealed significant differences in the site\u2010specific glycosylation profiles. The glycan compositions HexNAc(3)Hex(5) and HexNAc(3)Hex(6) at the Asn1365 site exhibited a discriminatory potential. Dimensionality reduction and hierarchical clustering based on the top four glycopeptide variants showed observable grouping of tumor versus normal samples. Furthermore, supervised machine learning models were used to evaluate these features, with a neural network achieving the highest diagnostic performance (area under the curve [AUC]\u00a0=\u00a00.83). Feature importance analysis indicated that Asn1365\u2010HexNAc(3)Hex(6) was the strongest contributor to the predictive power of the model. The observed alterations in specific glycan compositions, combined with machine learning evaluations, may offer improved diagnostic specificity and provide promising avenues for personalized thyroid cancer management.\n                  <\/jats:p>","DOI":"10.1002\/elps.70130","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T12:35:00Z","timestamp":1783686900000},"page":"908-918","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Glycosylation Signatures of Thyroglobulin in Papillary Thyroid Carcinoma"],"prefix":"10.1002","volume":"47","author":[{"given":"Ali","family":"Hayatu","sequence":"first","affiliation":[{"name":"Biomedical Engineering Department Faculty of Engineering and Natural Sciences Karab\u00fck University  Karab\u00fck T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esra Nur","family":"Akyildiz","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department Faculty of Engineering and Natural Sciences Karab\u00fck University  Karab\u00fck T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Betul","family":"Karabudak","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department Faculty of Engineering and Natural Sciences Karab\u00fck University  Karab\u00fck T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sena","family":"Aksoy","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department Faculty of Engineering and Natural Sciences Karab\u00fck University  Karab\u00fck T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harun","family":"Erol","sequence":"additional","affiliation":[{"name":"Pathology Department Faculty of Medicine Karab\u00fck University  Karab\u00fck T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bekir","family":"Salih","sequence":"additional","affiliation":[{"name":"Chemistry Department Faculty of Science Hacettepe University  Ankara T\u00fcrkiye"},{"name":"Turkish Academy of Sciences  Ankara T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hac\u0131 Mehmet","family":"Kayili","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department Faculty of Engineering and Natural Sciences Karab\u00fck University  Karab\u00fck T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010020\u20101995\u20104"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.18203\/2349\u20102902.isj20200040"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3892\/etm.2016.3651"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13020310"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/MED.0000000000000092"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2004\u20100986"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprot.2022.104700"},{"key":"e_1_2_8_9_1","doi-asserted-by":"crossref","unstructured":"R.Adaixo E. 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