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Background <p>Lung adenocarcinoma (LUAD), a prevalent and aggressive malignancy, necessitates improved prognostic tools and therapeutic insights. While endoplasmic reticulum stress (ERS) and tumor-immune interactions are recognized as key cancer hallmarks, their combined prognostic potential in LUAD remains insufficiently explored.</p> Methods <p>Utilizing transcriptomic and clinical data from the TCGA-LUAD cohort, we developed an ERS-immune prognostic signature through Least Absolute Shrinkage and Selection Operator algorithm. A clinical nomogram integrating risk scores with established prognostic factors was established. Tumor microenvironment characteristics were evaluated using the CIBERSORT and ESTIMATE algorithm. The changes following the CR2 gene knockdown in NSCLC cells were evaluated through CCK-8 assay and Transwell assays.</p> Results <p>The 10-gene signature effectively stratified patients into distinct risk groups with significant survival differences. The nomogram demonstrated enhanced predictive accuracy compared to traditional staging systems. High-risk patients exhibited immunosuppressive features. CR2 knockdown significantly reduced cellular proliferation and inhibited metastatic capacity.</p> Conclusion <p>This integrated ERS-immune signature provides clinically relevant prognostic stratification and reveals potential therapeutic vulnerabilities in LUAD, offering a framework for personalized treatment strategies.</p>

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The multi-omics analysis identifies a novel endoplasmic reticulum stress and immune related genes signature in lung adenocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Danhe Huang,
  • Yuying Liu,
  • Mingyu Yuan,
  • Xiongwei Wang,
  • Lianqing Hong

摘要

Background

Lung adenocarcinoma (LUAD), a prevalent and aggressive malignancy, necessitates improved prognostic tools and therapeutic insights. While endoplasmic reticulum stress (ERS) and tumor-immune interactions are recognized as key cancer hallmarks, their combined prognostic potential in LUAD remains insufficiently explored.

Methods

Utilizing transcriptomic and clinical data from the TCGA-LUAD cohort, we developed an ERS-immune prognostic signature through Least Absolute Shrinkage and Selection Operator algorithm. A clinical nomogram integrating risk scores with established prognostic factors was established. Tumor microenvironment characteristics were evaluated using the CIBERSORT and ESTIMATE algorithm. The changes following the CR2 gene knockdown in NSCLC cells were evaluated through CCK-8 assay and Transwell assays.

Results

The 10-gene signature effectively stratified patients into distinct risk groups with significant survival differences. The nomogram demonstrated enhanced predictive accuracy compared to traditional staging systems. High-risk patients exhibited immunosuppressive features. CR2 knockdown significantly reduced cellular proliferation and inhibited metastatic capacity.

Conclusion

This integrated ERS-immune signature provides clinically relevant prognostic stratification and reveals potential therapeutic vulnerabilities in LUAD, offering a framework for personalized treatment strategies.