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<p>Thyroid cancer (TC) is a common endocrine malignancy, and the processes of anoikis resistance and epithelial–mesenchymal transition (EMT) are crucial in tumor metastasis. In this study, transcriptomic data from TC patients were obtained from The Cancer Genome Atlas (TCGA), and differential gene expression was analyzed using the Limma package. A total of 193 anoikis- and EMT-related genes were identified, and their overlap with differentially expressed genes (DEGs) was examined. Using VennDiagram, five hub genes (CARD10, DAPK1, FN1, S100A4, and SALL4) were selected for further analysis. These genes were incorporated into a prognostic risk model, developed through univariate Cox, LASSO, and multivariate regression analyses. The model showed strong predictive power, with area under the curve (AUC) values over 0.7 for 1-, 3-, and 5-year survival predictions. Immune cell infiltration analysis revealed significant differences between risk groups, with correlations to the EMT score. Furthermore, the expression of the hub genes was validated in TC tissues and cell lines, showing that four of the hub genes (excluding SALL4) were significantly associated with overall survival. This prognostic model offers valuable insights into the tumor immune microenvironment and identifies potential therapeutic targets for TC.</p>

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Prognostic value of anoikis- and epithelial–mesenchymal transition-related genes and development of a prognostic risk model in thyroid cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月20日

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  • Kai-fang Xiang,
  • Ling Wang,
  • Yan Wu,
  • Dong Zhang

摘要

Thyroid cancer (TC) is a common endocrine malignancy, and the processes of anoikis resistance and epithelial–mesenchymal transition (EMT) are crucial in tumor metastasis. In this study, transcriptomic data from TC patients were obtained from The Cancer Genome Atlas (TCGA), and differential gene expression was analyzed using the Limma package. A total of 193 anoikis- and EMT-related genes were identified, and their overlap with differentially expressed genes (DEGs) was examined. Using VennDiagram, five hub genes (CARD10, DAPK1, FN1, S100A4, and SALL4) were selected for further analysis. These genes were incorporated into a prognostic risk model, developed through univariate Cox, LASSO, and multivariate regression analyses. The model showed strong predictive power, with area under the curve (AUC) values over 0.7 for 1-, 3-, and 5-year survival predictions. Immune cell infiltration analysis revealed significant differences between risk groups, with correlations to the EMT score. Furthermore, the expression of the hub genes was validated in TC tissues and cell lines, showing that four of the hub genes (excluding SALL4) were significantly associated with overall survival. This prognostic model offers valuable insights into the tumor immune microenvironment and identifies potential therapeutic targets for TC.