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Background <p>Ovarian cancer ranks as the fifth most common cause of cancer-related deaths in women worldwide. Macrophages M2 is believed to support tumor growth by suppressing immune responses and promoting angiogenesis.</p> Methods <p>A macrophage M2-related signature (MRS) was developed by applying machine learning methods that included 10 algorithms and utilized data from the TCGA, GSE14764 and GSE140082 datasets. The predictive capacity of the MRS for immunotherapy response was evaluated through various methods, including immunophenoscore, TIDE score, TMB score, immune escape score, as well as two immunotherapy cohorts (IMvigor210 and GSE91061).</p> Results <p>The optimal MRS, developed using the lasso algorithm, served as an independent prognostic factor and demonstrated stable performance in predicting overall survival rates in ovarian cancer. In the TCGA dataset, the AUC values for the 1-, 3-, and 5-year ROC curves were 0.874, 0.808, and 0.813, respectively. The C-index of our MRS was superior to that of clinical stage, tumor grade, and several other established prognostic signatures. Ovarian cancer patients with low risk score exhibited higher ESTIMATE score, increased levels of immune cells, elevated PDI&amp;CTLA4 immunophenoscore, higher TMB score, reduced TIDE score, and lower immune escape score. Additionally, the survival prediction nomogram displayed significant potential for clinical application in estimating the 1-, 3-, and 5-year overall survival rates of ovarian cancer patients.</p> Conclusion <p>Our study developed a novel MRS for ovarian cancer, which could act as an indicator for predicting the prognosis and immunotherapy response in ovarian cancer.</p>

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Development of macrophage M2 relate signature for predicting prognosis and immunotherapy response in ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月13日

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  • Yifei Xiong,
  • Yan Tong

摘要

Background

Ovarian cancer ranks as the fifth most common cause of cancer-related deaths in women worldwide. Macrophages M2 is believed to support tumor growth by suppressing immune responses and promoting angiogenesis.

Methods

A macrophage M2-related signature (MRS) was developed by applying machine learning methods that included 10 algorithms and utilized data from the TCGA, GSE14764 and GSE140082 datasets. The predictive capacity of the MRS for immunotherapy response was evaluated through various methods, including immunophenoscore, TIDE score, TMB score, immune escape score, as well as two immunotherapy cohorts (IMvigor210 and GSE91061).

Results

The optimal MRS, developed using the lasso algorithm, served as an independent prognostic factor and demonstrated stable performance in predicting overall survival rates in ovarian cancer. In the TCGA dataset, the AUC values for the 1-, 3-, and 5-year ROC curves were 0.874, 0.808, and 0.813, respectively. The C-index of our MRS was superior to that of clinical stage, tumor grade, and several other established prognostic signatures. Ovarian cancer patients with low risk score exhibited higher ESTIMATE score, increased levels of immune cells, elevated PDI&CTLA4 immunophenoscore, higher TMB score, reduced TIDE score, and lower immune escape score. Additionally, the survival prediction nomogram displayed significant potential for clinical application in estimating the 1-, 3-, and 5-year overall survival rates of ovarian cancer patients.

Conclusion

Our study developed a novel MRS for ovarian cancer, which could act as an indicator for predicting the prognosis and immunotherapy response in ovarian cancer.