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Background <p>Thyroid carcinoma (TC) presents a rising global incidence, with a subset of cases progressing aggressively despite standard therapies. The tumor microenvironment (TME), particularly the functional state of CD8⁺ T cells, is crucial in disease progression, yet a systematic, high-resolution understanding of the cellular interactions associated with immune exhaustion and tumor evolution in TC remains limited.</p> Methods <p>To address this, we performed an integrated single-cell and spatial transcriptomic analysis of human TC samples. Our approach combined scRNA-seq data processing, cell communication inference, spatial co-localization validation, and machine learning-based prognostic modeling.</p> Result <p>We identified a distinct tumor cell subcluster (Trem/T01) characterized by <i>APOE</i> expression and a CD8⁺ exhausted T cell (Tex) subset. Computational and spatial analyses revealed a potential <i>APOE</i> (from Trem)-<i>NCF1</i> (from CD8⁺ Tex) interaction axis, with these cell types demonstrating significant spatial proximity in tumor tissues. Furthermore, we derived a prognostic gene signature from this network, constructing a robust risk stratification model validated across independent cohorts.</p> Conclusion <p>This study uniquely delineates a specific tumor-immune interactive niche in TC, providing insights into potential mechanisms into immune evasion via the <i>APOE-NCF1</i> axis and delivering a translatable framework for patient prognostication.</p>

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Spatially resolved single cell analysis suggests an APOE NCF1 associated immunosuppressive niche and its prognostic signature in thyroid cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月30日

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  • Han Su,
  • Zheng Zhang,
  • Nuo Li,
  • Zimo Zhang,
  • Renju Zhang,
  • Ziyi Li,
  • Fen Chen,
  • Xinyue Wang,
  • Shuyuan Zhang,
  • Chao Zhang

摘要

Background

Thyroid carcinoma (TC) presents a rising global incidence, with a subset of cases progressing aggressively despite standard therapies. The tumor microenvironment (TME), particularly the functional state of CD8⁺ T cells, is crucial in disease progression, yet a systematic, high-resolution understanding of the cellular interactions associated with immune exhaustion and tumor evolution in TC remains limited.

Methods

To address this, we performed an integrated single-cell and spatial transcriptomic analysis of human TC samples. Our approach combined scRNA-seq data processing, cell communication inference, spatial co-localization validation, and machine learning-based prognostic modeling.

Result

We identified a distinct tumor cell subcluster (Trem/T01) characterized by APOE expression and a CD8⁺ exhausted T cell (Tex) subset. Computational and spatial analyses revealed a potential APOE (from Trem)-NCF1 (from CD8⁺ Tex) interaction axis, with these cell types demonstrating significant spatial proximity in tumor tissues. Furthermore, we derived a prognostic gene signature from this network, constructing a robust risk stratification model validated across independent cohorts.

Conclusion

This study uniquely delineates a specific tumor-immune interactive niche in TC, providing insights into potential mechanisms into immune evasion via the APOE-NCF1 axis and delivering a translatable framework for patient prognostication.