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

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.