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However, current clinical practices rely on TNM classification, including histological confirmation of metastatic disease in lymph nodes, often involving elective neck dissection, a procedure that can cause post\u2010operative morbidity. Here it is shown that zinc imidazole framework\u20108 (ZIF\u20108) electrochemical biosensors can effectively distinguish non\u2010metastatic (N0) from lymph node metastatic (N+) OSCC saliva samples. By monitoring the OSCC biomarkers cystatin B (CSTB), leukotriene A 4 hydrolase (LTA4H), and collagen type VI alpha 1 chain (COL6A1) in human saliva through electrochemical impedance spectroscopy and antigen\u2013antibody immunoreactions, elevated biomarker levels in N0 samples are observed. The biosensor displays high accuracy, specificity, and reproducibility, with limits of detection lower than 0.4\u00a0ng mL\n                    <jats:sup>\u22121<\/jats:sup>\n                    . Supervised bioinformatic analysis, using 34 machine learning classifiers, indicates LTA4H as the most accurate biomarker for distinguishing prognostic groups, confirming previous mass spectrometry findings. Notably, the AdaBoost model, integrating the combined detection of biomarkers, achieves a 76% accuracy rate in identifying metastatic saliva samples. This non\u2010invasive biosensor technology, combined with bioinformatics, presents a sensitive and reliable approach to improve clinical assessments and guiding therapeutic decisions for OSCC patients.\n                  <\/jats:p>","DOI":"10.1002\/smll.202504278","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T12:50:09Z","timestamp":1753793409000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Noninvasive and Sensitive Biosensor for the Detection of Oral Cancer Prognostic Biomarkers"],"prefix":"10.1002","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5838-0443","authenticated-orcid":false,"given":"Luciana D.","family":"Trino Albano","sequence":"first","affiliation":[{"name":"Center for Information Technology Renato Archer (CTI Renato Archer)  Campinas S\u00e3o Paulo 13069\u2010901 Brazil"},{"name":"Brazilian Biosciences National Laboratory (LNBio) Brazilian Center for Research in Energy and Materials (CNPEM)  Campinas S\u00e3o Paulo 13083\u2010970 Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9901-8686","authenticated-orcid":false,"given":"Daniela C.","family":"Granato","sequence":"additional","affiliation":[{"name":"Brazilian Biosciences National Laboratory (LNBio) Brazilian Center for Research in Energy and Materials (CNPEM)  Campinas S\u00e3o Paulo 13083\u2010970 Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5606-4287","authenticated-orcid":false,"given":"Luiz G. 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