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Background <p>Although immune checkpoint inhibitors show promise in papillary thyroid carcinoma (PTC), their efficacy is limited by immune resistance. This study constructed a prognostic model based on genes associated with the sensitivity of tumor cells to T cell-mediated tumor killing (GSTTKs) to evaluate their role in prognostic prediction for PTC.</p> Methods <p>Differentially expressed genes between PTC and normal tissues were identified from the TCGA dataset and intersected with GSTTKs to obtain TTK-related differentially expressed genes. A prognostic model was constructed through regression analysis, and predictive performance was evaluated using ROC curves. The impact of model genes on the malignant progression of PTC was validated through cell experiments, and the regulatory relationship between key transcription factors and target genes was verified using ChIP-qPCR.</p> Results <p>A prognostic model constructed based on five TTK-related genes (including B3GLCT, EPS15L1, and FGFR4) exhibited robust predictive performance in PTC. EPS15L1, P2RY11, and B3GLCT may serve as candidate therapeutic targets for PTC, whereas knockdown of PSORS1C1 repressed malignant progression of PTC cells under the transcriptional regulation of PRDM10.</p> Conclusion <p>A prognostic model built on genes associated with intrinsic T-cell killing sensitivity of tumor cells enables risk stratification of PTC patients independent of immune cell infiltration abundance, and the identified drug-target interactions as well as the PRDM10/PSORS1C1 regulatory axis offer novel insights for precision therapy.</p>

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Characterizing risk groups in papillary thyroid carcinoma through T-Cell mediated tumor cell killing-related genes: a pathway to therapeutic predictions

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Yan Zhong,
  • Guanghui Ren,
  • Tao Huang,
  • Yiming Sang,
  • Jizong Zhang

摘要

Background

Although immune checkpoint inhibitors show promise in papillary thyroid carcinoma (PTC), their efficacy is limited by immune resistance. This study constructed a prognostic model based on genes associated with the sensitivity of tumor cells to T cell-mediated tumor killing (GSTTKs) to evaluate their role in prognostic prediction for PTC.

Methods

Differentially expressed genes between PTC and normal tissues were identified from the TCGA dataset and intersected with GSTTKs to obtain TTK-related differentially expressed genes. A prognostic model was constructed through regression analysis, and predictive performance was evaluated using ROC curves. The impact of model genes on the malignant progression of PTC was validated through cell experiments, and the regulatory relationship between key transcription factors and target genes was verified using ChIP-qPCR.

Results

A prognostic model constructed based on five TTK-related genes (including B3GLCT, EPS15L1, and FGFR4) exhibited robust predictive performance in PTC. EPS15L1, P2RY11, and B3GLCT may serve as candidate therapeutic targets for PTC, whereas knockdown of PSORS1C1 repressed malignant progression of PTC cells under the transcriptional regulation of PRDM10.

Conclusion

A prognostic model built on genes associated with intrinsic T-cell killing sensitivity of tumor cells enables risk stratification of PTC patients independent of immune cell infiltration abundance, and the identified drug-target interactions as well as the PRDM10/PSORS1C1 regulatory axis offer novel insights for precision therapy.