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Background <p>The complex metabolic and immune characteristics of ovarian cancer and their interconnections can promote tumor immune evasion, ultimately leading to immunotherapy failure. A comprehensive understanding of these relationships can inform the development of diagnostic markers and more effective therapeutics.</p> Methods <p>Mendelian randomization analysis identified the metabolic and immune characteristics of non-mucinous ovarian cancer. Glycosphingolipid metabolic gene signature was selected using Lasso and univariate Cox regression. Single-cell transcriptomics, proteomics, and spatial transcriptomics were applied to elucidate the expression of these genes and their association with immunological features, while cell-cell communication analysis explored potential molecular mechanisms. Prognostic models were constructed using multiple machine learning algorithms.</p> Results <p>13 metabolic and immune characteristics showed causally associations with non-mucinous ovarian cancer, including CD20 + B cells and the glycosphingolipid metabolism. PPIA-BSG mediated the cell-cell communication between CD20 + B cells and SUMF1 + malignant cells, promoting immune evasion driven by extracellular matrix remodeling. Prognostic models based on extracellular matrix genes co-experessed with SUMF1 demonstrated good predictive accuracy and generalizability across multiple independent datasets (maximum AUC = 0.732).</p> Conclusions <p>In non-mucinous ovarian cancer, we identified two distinct cell subpopulations: CD20 + B cells and SUMF1 + malignant cells, which interact through the PPIA-BSG signaling axis. These findings may aid in identifying diagnostic markers and clinical therapeutic targets for this disease.</p>

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Multi-omics reveals the immune and metabolic characteristics and associations in non-mucinous ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月24日

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  • Hongkai Yu,
  • Chang You,
  • Ting Xu,
  • Ziyan Zhao,
  • Danyang Wang,
  • Lu Hu,
  • Liangzhe Dai,
  • Wanlin Zheng,
  • Luyao Wang,
  • Minghui Ji,
  • Tianchi Zhuang,
  • Yingqi Yang

摘要

Background

The complex metabolic and immune characteristics of ovarian cancer and their interconnections can promote tumor immune evasion, ultimately leading to immunotherapy failure. A comprehensive understanding of these relationships can inform the development of diagnostic markers and more effective therapeutics.

Methods

Mendelian randomization analysis identified the metabolic and immune characteristics of non-mucinous ovarian cancer. Glycosphingolipid metabolic gene signature was selected using Lasso and univariate Cox regression. Single-cell transcriptomics, proteomics, and spatial transcriptomics were applied to elucidate the expression of these genes and their association with immunological features, while cell-cell communication analysis explored potential molecular mechanisms. Prognostic models were constructed using multiple machine learning algorithms.

Results

13 metabolic and immune characteristics showed causally associations with non-mucinous ovarian cancer, including CD20 + B cells and the glycosphingolipid metabolism. PPIA-BSG mediated the cell-cell communication between CD20 + B cells and SUMF1 + malignant cells, promoting immune evasion driven by extracellular matrix remodeling. Prognostic models based on extracellular matrix genes co-experessed with SUMF1 demonstrated good predictive accuracy and generalizability across multiple independent datasets (maximum AUC = 0.732).

Conclusions

In non-mucinous ovarian cancer, we identified two distinct cell subpopulations: CD20 + B cells and SUMF1 + malignant cells, which interact through the PPIA-BSG signaling axis. These findings may aid in identifying diagnostic markers and clinical therapeutic targets for this disease.