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<p>Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer; however, indeterminate cytology in fine-needle aspiration biopsy (FNAB) and the limited sensitivity of single–mutation–based biomarkers remain important clinical challenges. This study aimed to identify a robust multi-gene expression signature for diagnostic evaluation and exploratory prognostic assessment of PTC. An integrative bioinformatics framework was applied to TCGA-THCA RNA-sequencing data (505 tumors and 59 normal samples). Differentially expressed genes were subjected to LASSO regression to construct a diagnostic model, which was externally validated in two independent GEO cohorts (GSE33630 and GSE60542). A seven-gene signature consisting of four upregulated genes (<i>GABRB2</i>, <i>SERINC2</i>, <i>PTCHD4</i>, <i>HAPLN1</i>) and three downregulated genes (<i>SLC6A15</i>, <i>UGT2B11</i>, <i>GRIA1</i>) demonstrated excellent diagnostic performance in the discovery cohort (AUC = 0.995) and remained highly accurate in external validation datasets (AUC = 0.940 and 0.938). Kaplan–Meier analysis showed significant stratification for disease-free interval (p = 0.034), although this association did not remain independent in multivariable analysis. Exploratory analyses suggested potential epigenetic regulation of several genes and distinct patterns in immune microenvironments between risk groups. Overall, these findings reveal a robust seven-gene expression signature with strong diagnostic capacity and potential prognostic relevance in PTC, highlighting its value for molecular characterization. However, further biological validation and prospective evaluation in clinically relevant cohorts are required before translational applicability can be established.</p>

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A seven-gene diagnostic and prognostic signature for papillary thyroid carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月21日

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  • Berna Hukkamlı,
  • Burak Dagdelen,
  • Feyza Sönmez Aydın

摘要

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer; however, indeterminate cytology in fine-needle aspiration biopsy (FNAB) and the limited sensitivity of single–mutation–based biomarkers remain important clinical challenges. This study aimed to identify a robust multi-gene expression signature for diagnostic evaluation and exploratory prognostic assessment of PTC. An integrative bioinformatics framework was applied to TCGA-THCA RNA-sequencing data (505 tumors and 59 normal samples). Differentially expressed genes were subjected to LASSO regression to construct a diagnostic model, which was externally validated in two independent GEO cohorts (GSE33630 and GSE60542). A seven-gene signature consisting of four upregulated genes (GABRB2, SERINC2, PTCHD4, HAPLN1) and three downregulated genes (SLC6A15, UGT2B11, GRIA1) demonstrated excellent diagnostic performance in the discovery cohort (AUC = 0.995) and remained highly accurate in external validation datasets (AUC = 0.940 and 0.938). Kaplan–Meier analysis showed significant stratification for disease-free interval (p = 0.034), although this association did not remain independent in multivariable analysis. Exploratory analyses suggested potential epigenetic regulation of several genes and distinct patterns in immune microenvironments between risk groups. Overall, these findings reveal a robust seven-gene expression signature with strong diagnostic capacity and potential prognostic relevance in PTC, highlighting its value for molecular characterization. However, further biological validation and prospective evaluation in clinically relevant cohorts are required before translational applicability can be established.