{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:53:36Z","timestamp":1776214416597,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"3","content-domain":{"domain":["eje.bioscientifica.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,9,1]]},"abstract":"<jats:sec>\n<jats:title>Objective<\/jats:title>\n<jats:p>The minimally invasive fine-needle aspiration cytology (FNAC) is the current gold standard for the diagnosis of thyroid nodule malignancy. However, the correct discrimination of follicular neoplasia often requires more invasive diagnostic techniques. The lack of suitable immunohistochemical markers to distinguish between follicular thyroid carcinoma and other types of follicular-derived lesions complicates diagnosis, and despite most of these tumours being surgically resected, only a small number will test positive for malignancy. As such, the development of new orthogonal diagnostic approaches may improve the accuracy of diagnosing thyroid nodules.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Design<\/jats:title>\n<jats:p>This study includes a retrospective, multi-centre training cohort including 54 fresh-frozen follicular-patterned thyroid samples and two independent, multi-centre validation cohorts of 103 snap-frozen biopsies and 33 FNAC samples, respectively.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>We performed a genome-wide genetic and epigenetic profiling of 54 fresh-frozen follicular-patterned thyroid samples using exome sequencing and the Illumina Human DNA Methylation EPIC platform. An extensive validation was performed using the bisulfite pyrosequencing technique.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>Using a random forest approach, we developed a three-CpG marker-based diagnostic model that was subsequently validated using bisulfite pyrosequencing experiments. According to the validation cohort, this cost-effective method discriminates between benign and malignant nodules with a sensitivity and specificity of 97 and 88%, respectively (positive predictive value (PPV): 0.85, negative predictive value (NPV): 0.98).<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Our classification system based on a minimal set of epigenetic biomarkers can complement the potential of the diagnostic techniques currently available and would prioritize a considerable number of surgical interventions that are often performed due to uncertain cytology.<\/jats:p>\n<\/jats:sec>\n<jats:sec>    \n<jats:title>Significance statement<\/jats:title>\n<jats:p>In recent years, there has been a significant increase in the number of people diagnosed with thyroid nodules. The current challenge is their etiological diagnosis to discount malignancy without resorting to thyroidectomy. The method proposed here, based on DNA pyrosequencing assays, has high sensitivity (0.97) and specificity (0.88) for the identification of malignant thyroid nodules. This simple and cost-effective approach can complement expert pathologist evaluation to prioritize the classification of difficult-to-diagnose follicular-patterned thyroid lesions and track tumor evolution, including real-time monitoring of treatment efficacy, thereby stimulating adherence to health promotion programs.<\/jats:p>\n<\/jats:sec>","DOI":"10.1530\/eje-22-0012","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T11:35:58Z","timestamp":1656934558000},"page":"335-347","update-policy":"https:\/\/doi.org\/10.1530\/crossmarkpolicy-7","source":"Crossref","is-referenced-by-count":9,"title":["Classification of follicular-patterned thyroid lesions using a minimal set of epigenetic biomarkers"],"prefix":"10.1093","volume":"187","author":[{"given":"Sandra","family":"Rodr\u00edguez-Rodero","sequence":"first","affiliation":[{"name":"Nanomaterials and Nanotechnology Research Center (CINN), Spanish National Research Council (CSIC), El Entrego, Asturias, 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