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Background <p>Immunogenic cell death (ICD) triggers antitumor immune responses and plays a critical role in shaping the tumor microenvironment (TME). However, its specific contribution to lung adenocarcinoma (LUAD) progression and immunotherapy response remains insufficiently explored.</p> Method <p>We integrated single-cell RNA sequencing with machine learning to characterize ICD-related transcriptional features in LUAD. ICD activity was quantified across cell types using five scoring algorithms. To develop a robust prognostic model, we evaluated over 100 machine learning algorithm combinations and selected the CoxBoost + SuperPC approach based on the highest concordance index (C-index). The resulting ICD-related gene signature (ICDRS) was validated in six external cohorts. Downstream analyses included immune infiltration, mutation profiling, drug sensitivity, and immunotherapy response. SLC2A1 was selected for functional validation using qRT-PCR, CCK-8, Transwell, colony formation, and xenograft assays.</p> Results <p>Single-cell analysis revealed that macrophages exhibited the highest ICD activity and contributed significantly to intercellular communication. Based on ICD-associated genes, the ICDRS model consisting of 11 core genes was constructed and showed superior prognostic performance over 112 published LUAD signatures across multiple cohorts. The ICDRS stratified patients into distinct risk groups with significant differences in overall survival, immune infiltration patterns, and immunotherapy benefit. Low-risk patients exhibited higher levels of CD8⁺ T cells, dendritic cells, and immune function scores, along with greater sensitivity to standard chemotherapeutics and immune checkpoint inhibitors. Functional experiments confirmed that SLC2A1 was upregulated in LUAD tissues and cell lines. Silencing SLC2A1 suppressed proliferation and invasion in vitro and inhibited tumor growth in xenograft models, supporting its oncogenic role.</p> Conclusion <p>This study highlights the crucial role of ICD in LUAD immune regulation and prognosis. The ICDRS serves as a robust biomarker for risk stratification and immunotherapy guidance, while SLC2A1 emerges as a potential therapeutic target to augment immunotherapeutic efficacy.</p>

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Integrative single-cell and machine learning approach to characterize immunogenic cell death and tumor microenvironment in LUAD

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Han Zhang,
  • Qiuqiao Mu,
  • Yuhang Jiang,
  • Xiaojiang Zhao,
  • Xiaoteng Jia,
  • Kai Wang,
  • Xin Li,
  • Daqiang Sun

摘要

Background

Immunogenic cell death (ICD) triggers antitumor immune responses and plays a critical role in shaping the tumor microenvironment (TME). However, its specific contribution to lung adenocarcinoma (LUAD) progression and immunotherapy response remains insufficiently explored.

Method

We integrated single-cell RNA sequencing with machine learning to characterize ICD-related transcriptional features in LUAD. ICD activity was quantified across cell types using five scoring algorithms. To develop a robust prognostic model, we evaluated over 100 machine learning algorithm combinations and selected the CoxBoost + SuperPC approach based on the highest concordance index (C-index). The resulting ICD-related gene signature (ICDRS) was validated in six external cohorts. Downstream analyses included immune infiltration, mutation profiling, drug sensitivity, and immunotherapy response. SLC2A1 was selected for functional validation using qRT-PCR, CCK-8, Transwell, colony formation, and xenograft assays.

Results

Single-cell analysis revealed that macrophages exhibited the highest ICD activity and contributed significantly to intercellular communication. Based on ICD-associated genes, the ICDRS model consisting of 11 core genes was constructed and showed superior prognostic performance over 112 published LUAD signatures across multiple cohorts. The ICDRS stratified patients into distinct risk groups with significant differences in overall survival, immune infiltration patterns, and immunotherapy benefit. Low-risk patients exhibited higher levels of CD8⁺ T cells, dendritic cells, and immune function scores, along with greater sensitivity to standard chemotherapeutics and immune checkpoint inhibitors. Functional experiments confirmed that SLC2A1 was upregulated in LUAD tissues and cell lines. Silencing SLC2A1 suppressed proliferation and invasion in vitro and inhibited tumor growth in xenograft models, supporting its oncogenic role.

Conclusion

This study highlights the crucial role of ICD in LUAD immune regulation and prognosis. The ICDRS serves as a robust biomarker for risk stratification and immunotherapy guidance, while SLC2A1 emerges as a potential therapeutic target to augment immunotherapeutic efficacy.