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

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.

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Publication Details

Journal
Discover Oncology
Published
2025-11-20
DOI
https://doi.org/10.1007/s12672-025-03991-1
Primary Topic
Thyroid Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
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article

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

Kai-fang Xiang, Yan Wu, Dong Zhang, Ling Wang
Discover Oncology
Thyroid Cancer Diagnosis and Treatment
article

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

Kai-fang Xiang, Yan Wu, Dong Zhang, Ling Wang
article en

Abstract

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.

Discover OncologyVol. 16(1)
The First People's Hospital of Jiangxia District (CN), Wuhan Sixth Hospital (CN), Yangpu Hospital of Tongji University (CN), Wuhan University of Science and Technology (CN)
Openalex Percentile: Top 53%
Thyroid Cancer Diagnosis and Treatment
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