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Background <p>Cuproptosis and oxidative stress (COS) are emerging regulators in cancer biology. However, their link to long non-coding RNAs (lncRNAs) and clinical outcomes in ovarian cancer remains unclear.</p> Objective <p>This study aimed to construct and validate a prognostic signature based on COS-related lncRNAs to improve risk stratification and provide insights into therapeutic strategies for ovarian cancer.</p> Methods <p>RNA-seq and clinical data from TCGA and GEO were analyzed to identify COS-related lncRNAs via WGCNA and Pearson correlation. Prognostic lncRNAs were screened using Cox regression and modeled using multiple machine learning algorithms. Immune profiles, mutation patterns, drug sensitivity, and experimental validation were performed.</p> Results <p>A 12-lncRNA signature was established that stratified patients into high- and low-risk groups with significant survival differences (HR = 22.6145, <i>p</i> &lt; 0.001). The model showed strong predictive performance (AUCs: 0.91/0.967/0.974) and was validated externally. High-risk patients exhibited greater mutation burden, altered immune pathways (interferon, TGF-β), and differential predicted sensitivity to agents like Axitinib and Pazopanib. RT-qPCR confirmed the expression patterns of 11 out of 12 lncRNAs.</p> Conclusion <p>This study proposes a robust 12-lncRNA signature linking cuproptosis and oxidative stress with prognosis and therapy response in ovarian cancer, offering preliminary insights for personalized treatment guidance.</p>

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Multiple machine learning algorithms construct cuproptosis genes and oxidative stress genes-related LncRNAs signature with prognostic and therapeutic relevance in ovarian cancer

  • 期刊论文

  • 发表日期：

    2025年11月27日

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  • Ruyue Pan,
  • Yan Yang,
  • Qinghuo Kong,
  • Xin Hu,
  • Jie Yu,
  • Jiaxu Chen

摘要

Background

Cuproptosis and oxidative stress (COS) are emerging regulators in cancer biology. However, their link to long non-coding RNAs (lncRNAs) and clinical outcomes in ovarian cancer remains unclear.

Objective

This study aimed to construct and validate a prognostic signature based on COS-related lncRNAs to improve risk stratification and provide insights into therapeutic strategies for ovarian cancer.

Methods

RNA-seq and clinical data from TCGA and GEO were analyzed to identify COS-related lncRNAs via WGCNA and Pearson correlation. Prognostic lncRNAs were screened using Cox regression and modeled using multiple machine learning algorithms. Immune profiles, mutation patterns, drug sensitivity, and experimental validation were performed.

Results

A 12-lncRNA signature was established that stratified patients into high- and low-risk groups with significant survival differences (HR = 22.6145, p < 0.001). The model showed strong predictive performance (AUCs: 0.91/0.967/0.974) and was validated externally. High-risk patients exhibited greater mutation burden, altered immune pathways (interferon, TGF-β), and differential predicted sensitivity to agents like Axitinib and Pazopanib. RT-qPCR confirmed the expression patterns of 11 out of 12 lncRNAs.

Conclusion

This study proposes a robust 12-lncRNA signature linking cuproptosis and oxidative stress with prognosis and therapy response in ovarian cancer, offering preliminary insights for personalized treatment guidance.