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Purpose <p>Ferroptosis is a novel form of iron-dependent programmed cell death that is associated with the progression of various tumors and cancer treatment responses. However, its role in the clinical treatment of lung cancer, particularly in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), remains poorly understood. This study aims to explore the prognostic value of ferroptosis-related genes in lung cancer and establish a reliable predictive model.</p> Methods <p>We collected the GSE4573, TCGA-LUAD, and TCGA-LUSC datasets, comprising a total of 1,271 samples and a ferroptosis gene set of 717 genes. Weighted gene coexpression network analysis (WGCNA) was used to identify ferroptosis-related markers, followed by the application of 101 machine learning algorithms combining 10 different approaches to develop a ferroptosis-related signature (FRS) for lung cancer prognosis.</p> Results <p>The FRS demonstrated superior performance in predicting the survival of lung cancer patients, significantly outperforming traditional TNM and American Joint Committee on Cancer (AJCC) staging systems. External validation using the GSE13213 dataset also confirmed its robustness. Furthermore, the low-risk group exhibited higher immune microenvironment scores, suggesting a more active anti-tumor immune response, while the high-risk group showed elevated cell proliferation, migration, T-cell exclusion, and TIDE scores, indicating a more aggressive tumor phenotype. Additionally, the low-risk group demonstrated higher sensitivity to multiple drugs, including cisplatin, cyclophosphamide, paclitaxel, erlotinib, Niraparib, Rapamycin, Fulvestrant, and Venetoclax, highlighting its potential for guiding personalized treatment strategies.</p> Conclusion <p>The FRS represents a powerful and clinically relevant tool for predicting the survival of lung cancer patients, offering new insights into personalized treatment and therapeutic decision-making.</p>

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Machine learning constructs a ferroptosis related signature for predicting prognosis and drug sensitivity in lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • Zihao Li,
  • Yibing Chen,
  • Benxin Hou,
  • Yanjun Mi,
  • Chunlin Fu,
  • Zhaoyang Han,
  • Qing Tang,
  • Weihong Sun,
  • Qing Xia,
  • Yuan Liao,
  • Zhengzhi Zou

摘要

Purpose

Ferroptosis is a novel form of iron-dependent programmed cell death that is associated with the progression of various tumors and cancer treatment responses. However, its role in the clinical treatment of lung cancer, particularly in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), remains poorly understood. This study aims to explore the prognostic value of ferroptosis-related genes in lung cancer and establish a reliable predictive model.

Methods

We collected the GSE4573, TCGA-LUAD, and TCGA-LUSC datasets, comprising a total of 1,271 samples and a ferroptosis gene set of 717 genes. Weighted gene coexpression network analysis (WGCNA) was used to identify ferroptosis-related markers, followed by the application of 101 machine learning algorithms combining 10 different approaches to develop a ferroptosis-related signature (FRS) for lung cancer prognosis.

Results

The FRS demonstrated superior performance in predicting the survival of lung cancer patients, significantly outperforming traditional TNM and American Joint Committee on Cancer (AJCC) staging systems. External validation using the GSE13213 dataset also confirmed its robustness. Furthermore, the low-risk group exhibited higher immune microenvironment scores, suggesting a more active anti-tumor immune response, while the high-risk group showed elevated cell proliferation, migration, T-cell exclusion, and TIDE scores, indicating a more aggressive tumor phenotype. Additionally, the low-risk group demonstrated higher sensitivity to multiple drugs, including cisplatin, cyclophosphamide, paclitaxel, erlotinib, Niraparib, Rapamycin, Fulvestrant, and Venetoclax, highlighting its potential for guiding personalized treatment strategies.

Conclusion

The FRS represents a powerful and clinically relevant tool for predicting the survival of lung cancer patients, offering new insights into personalized treatment and therapeutic decision-making.