{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T06:05:08Z","timestamp":1789538708908,"version":"build-2803163510"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Digestive malignancies remain a major cause of cancer-related mortality worldwide. Current diagnostic approaches are often limited by invasiveness, cost, and accessibility. We aimed to develop a blood-based risk stratification model for identifying individuals at increased risk of digestive malignancies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively collected clinical data from 667 patients with digestive malignancies and 795 non-cancer controls. A machine learning model was developed by integrating GDF15 with selected routine blood biomarkers. Model performance was evaluated using measures of discrimination, risk reclassification, and SHAP analysis. Blood GDF15 levels were measured by chemiluminescent immunoassay, and tissue expression was assessed by immunohistochemistry.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The GDF15-based machine learning model showed favourable performance in the independent validation cohort (AUROC = 0.886). Compared with conventional tumor markers alone, the addition of GDF15 improved model discrimination (AUROC, 0.856 vs. 0.777;\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.001) and significantly enhanced risk reclassification (NRI = 0.858; IDI = 0.151). GDF15 also contributed to the identification of patients with negative conventional tumor markers and was among the most influential variables in the model according to SHAP analysis. Higher circulating GDF15 levels were associated with larger tumor size, shorter progression-free survival (HR = 2.68), and increased expression in tumor tissues.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Integrating GDF15 with routine blood biomarkers enables effective risk stratification of individuals at increased risk of digestive malignancies. This blood-based model may provide a practical approach for risk stratification and help identify individuals who may benefit from further diagnostic evaluation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2026.1912031","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:34:39Z","timestamp":1789536879000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["GDF15-integrated blood biomarker panel for risk stratification of digestive malignancies: a multicenter retrospective study"],"prefix":"10.3389","volume":"17","author":[{"given":"Ranliang","family":"Cui","sequence":"first","affiliation":[{"name":"Department of Laboratory, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin\u2019s Clinical Research Center for Cancer","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueguo","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Laboratory, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin\u2019s Clinical Research Center for Cancer","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"94","DOI":"10.4103\/UA.UA_32_23","article-title":"Using machine learning models to predict synchronous genitourinary cancers among gastrointestinal stromal tumor patients","volume":"16","author":"Alghafees","year":"2024","journal-title":"Urol. 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