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Background <p>Breast cancer is the most prevalent malignant tumor in women globally, with its prognosis linked to immune responses, especially CD8 + and CD4 + T cell infiltration. T cell exhaustion, an immune dysfunction seen in chronic infections and cancers, is not well understood in breast cancer. This study seeks to investigate the role and prognostic significance of genes related to T cell exhaustion in breast cancer through bioinformatics, offering insights into the mechanisms of T cell exhaustion in this disease.</p> Methods <p>Breast cancer sample data from UCSC Xena and GEO databases underwent differential gene expression analysis with DESeq2. ssGSEA and WGCNA assessed T cell exhaustion-related genes. Key prognostic genes were identified through GO and KEGG analyses and PPI network construction. A prognostic model was developed using univariate Cox, Lasso regression, and multivariate Cox analyses, and its predictive performance was validated with an external dataset. Functional and immune infiltration characteristics of the prognostic genes were explored using GSEA and CIBERSORT.</p> Results <p>The study identified 2,989 differentially expressed genes and 832 key module genes. Enrichment analysis indicated that these genes were associated with immune dysfunction and T cell exhaustion‑related pathways. A risk model was developed incorporating six prognostic genes: S100B, BCL2A1, RSPH1, KCNJ10, ZMYND10, and MOB3B. Using an appropriate cutoff, patients were stratified into low-risk and high-risk groups, with the overall survival (OS) curves of these groups exhibiting significant differences. The model’s efficacy was validated using external datasets. Furthermore, the estimated IC50 values of multiple anticancer drugs showed differences between the two risk groups, warranting further investigation in the context of breast cancer therapy.</p> Conclusion <p>This study uses bioinformatic analyses to highlight the prognostic importance and mechanisms of T cell exhaustion-related genes in breast cancer, offering insights for therapeutic targets that could enhance clinical management and immunotherapy strategies.</p>

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Development and validation of a novel T cell exhaustion-related signature to predict prognosis in patients with breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Lianhe Guo,
  • Xiangjin Chen,
  • Fan Zhou

摘要

Background

Breast cancer is the most prevalent malignant tumor in women globally, with its prognosis linked to immune responses, especially CD8 + and CD4 + T cell infiltration. T cell exhaustion, an immune dysfunction seen in chronic infections and cancers, is not well understood in breast cancer. This study seeks to investigate the role and prognostic significance of genes related to T cell exhaustion in breast cancer through bioinformatics, offering insights into the mechanisms of T cell exhaustion in this disease.

Methods

Breast cancer sample data from UCSC Xena and GEO databases underwent differential gene expression analysis with DESeq2. ssGSEA and WGCNA assessed T cell exhaustion-related genes. Key prognostic genes were identified through GO and KEGG analyses and PPI network construction. A prognostic model was developed using univariate Cox, Lasso regression, and multivariate Cox analyses, and its predictive performance was validated with an external dataset. Functional and immune infiltration characteristics of the prognostic genes were explored using GSEA and CIBERSORT.

Results

The study identified 2,989 differentially expressed genes and 832 key module genes. Enrichment analysis indicated that these genes were associated with immune dysfunction and T cell exhaustion‑related pathways. A risk model was developed incorporating six prognostic genes: S100B, BCL2A1, RSPH1, KCNJ10, ZMYND10, and MOB3B. Using an appropriate cutoff, patients were stratified into low-risk and high-risk groups, with the overall survival (OS) curves of these groups exhibiting significant differences. The model’s efficacy was validated using external datasets. Furthermore, the estimated IC50 values of multiple anticancer drugs showed differences between the two risk groups, warranting further investigation in the context of breast cancer therapy.

Conclusion

This study uses bioinformatic analyses to highlight the prognostic importance and mechanisms of T cell exhaustion-related genes in breast cancer, offering insights for therapeutic targets that could enhance clinical management and immunotherapy strategies.