Comprehensive Gene Expression Analysis in Papillary Thyroid Carcinoma Reveals a Transcriptional Profile Associated with Reduced Radioiodine Avidity.

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Tác giả: Vincenzo Condello, Catharina Ihre-Lundgren, C Christofer Juhlin, Carlotta Marchettini, Joachim N Nilsson

Ngôn ngữ: eng

Ký hiệu phân loại: 398.352 Persons without paranormal powers

Thông tin xuất bản: United States : Endocrine pathology , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 247059

Papillary thyroid carcinoma (PTC) is the most common form of well-differentiated thyroid cancer (WDTC) and generally has a favorable prognosis. However, subsets of these tumors can metastasize, leading to aggressive disease progression and poorer clinical outcomes. Radioactive iodine (RAI) therapy is routinely given in the adjuvant setting following thyroidectomy and lymph node dissection for WDTC. Nevertheless, its therapeutic efficacy is limited to tumors with high iodine avidity. Early post-surgical classification of thyroid cancers as either iodine-avid or refractory is crucial for enabling more personalized and effective treatment strategies. In this study, we aimed to identify transcriptomic determinants associated with RAI refractoriness (RAI-R) to improve prognostication. We collected clinicopathologic data and conducted RNA-seq on 36 tissue samples (18 high-avidity and 18 low-avidity), each uniquely characterized by ex vivo iodine concentration measurements taken directly from surgical specimens. Whole-transcriptomic analysis identified 63 differentially expressed genes, with six (S100A4, CRTC2, ANO1, WWTR1, DEPTOR, MT1G) showing consistent deregulation. The expression of ANO1, an established iodine transporter at the apical membrane of the thyroid follicular cells, correlated significantly with iodine avidity (r = 0.54). Validation via RT-qPCR confirmed differential expression trends. Gene ontology and pathway enrichment analyses highlighted thyroid hormone synthesis, PI3K-AKT, and MAPK signaling pathways as key regulators of RAI avidity. A refined multivariate predictive model incorporating ANO1 mRNA expression, histological subtypes, and sample type demonstrated strong predictive performance (adjusted R
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