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Background <p>Endometrial carcinoma is a leading gynecological malignancy worldwide and is frequently associated with poor prognosis in advanced-stage patients. Immunotherapy has emerged as a promising treatment modality in endometrial cancer. T cells are recognized as vital mediators of immunosurveillance and cancer eradication.</p> Methods <p>In this study, we leveraged single-cell RNA sequencing data from the GSE173682 dataset to identify T cell marker genes. Additionally, we collected bulk RNA sequencing data and clinical data from 544 endometrial cancer patients to construct a predictive model. We further explored the correlations between this model and immunotherapy response, drug sensitivity, and mutation status.</p> Results <p>We developed an eight-gene prediction signature based on T cell marker genes, which demonstrated high accuracy in predicting the prognosis of endometrial cancer patients. The area under the receiver operating characteristic curve was 0.838 at 5 years. This T cell-related signature was strongly associated with immune-related function scores and showed potential for predicting responses to both immunotherapy and chemotherapy in endometrial cancer patients.</p> Conclusion <p>We have established a robust prognostic model based on T cell genes. This model may contribute to the development of more precise therapeutic strategies in the management of endometrial cancer, though further validation is needed.</p>

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Characterization of T cell markers in endometrial carcinoma through single-cell RNA sequencing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • Menghuang Zhao,
  • Lulu Zhou

摘要

Background

Endometrial carcinoma is a leading gynecological malignancy worldwide and is frequently associated with poor prognosis in advanced-stage patients. Immunotherapy has emerged as a promising treatment modality in endometrial cancer. T cells are recognized as vital mediators of immunosurveillance and cancer eradication.

Methods

In this study, we leveraged single-cell RNA sequencing data from the GSE173682 dataset to identify T cell marker genes. Additionally, we collected bulk RNA sequencing data and clinical data from 544 endometrial cancer patients to construct a predictive model. We further explored the correlations between this model and immunotherapy response, drug sensitivity, and mutation status.

Results

We developed an eight-gene prediction signature based on T cell marker genes, which demonstrated high accuracy in predicting the prognosis of endometrial cancer patients. The area under the receiver operating characteristic curve was 0.838 at 5 years. This T cell-related signature was strongly associated with immune-related function scores and showed potential for predicting responses to both immunotherapy and chemotherapy in endometrial cancer patients.

Conclusion

We have established a robust prognostic model based on T cell genes. This model may contribute to the development of more precise therapeutic strategies in the management of endometrial cancer, though further validation is needed.