{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T19:50:33Z","timestamp":1772999433592,"version":"3.50.1"},"reference-count":52,"publisher":"Wiley","issue":"15","license":[{"start":{"date-parts":[[2025,8,10]],"date-time":"2025-08-10T00:00:00Z","timestamp":1754784000000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Thyroid cancer is one of the most common endocrine malignancies globally, with a markedly higher incidence in women. Although pregnancy\u2010induced hypertension is recognized as a risk factor, the underlying mechanisms linking hypertension and thyroid cancer remain poorly understood. This study explores the gender\u2010specific associations between family history of hypertension and thyroid cancer, integrating clinical characteristics with genetic insights.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this large\u2010scale cross\u2010sectional study conducted in China, clinical and lifestyle data were collected from 52,963 participants, including 296 thyroid cancer cases. An interpretable ensemble machine learning model was constructed to evaluate risk factors, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Additionally, the Cox proportional hazards model was applied to identify significant hypertension\u2010related genes, and Kaplan\u2013Meier analyses were used to compare overall survival, clinical stages, and immune cell infiltration between high\u2010 and low\u2010risk groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Our analysis revealed that individuals with a family history of hypertension exhibited a significantly altered rate of thyroid cancer (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). In particular, among women, a positive family history increased thyroid cancer risk (OR\u2009=\u20091.53, 95% CI: 1.09\u20132.14,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.04) and emerged as a key predictor in the machine learning model. Genetic analyses identified overlapping genes\u2014most notably FOXD3, F10, and SLC12A5\u2014whose aberrant expression was associated with poorer five\u2010year survival and distinct immune cell profiles.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our study provides novel clinical and genetic evidence that family history of hypertension is significantly associated with thyroid cancer in women. Integrating hypertension\u2010related screening with genetic profiling may enhance risk stratification and aid in the development of personalized management strategies to reduce overtreatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71031","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T05:40:21Z","timestamp":1754890821000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Family History of Hypertension Predicts Thyroid Cancer Risk in Women: A Population\u2010Based Cross\u2010Sectional Study With Integrative Machine Learning and Genomic Analyses"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9158-4280","authenticated-orcid":false,"given":"Zijin","family":"Wang","sequence":"first","affiliation":[{"name":"The Second Clinical Medicine School Southern Medical University  Guangzhou Guangdong 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