Key points are not available for this paper at this time.
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. 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. 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). 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.
Yu et al. (2025) studied this question.