Disulfidptosis is a novel form of glucose starvation-induced cell death, yet its prognostic implications in gastric cancer (GC) remain largely undefined. We integrated single-cell and bulk multi-omics data to identify core disulfidptosis-related genes. A robust Cox regression-based prognostic model was constructed. Machine learning combined with single-cell interaction analyses was used to delineate the mechanistic roles of key genes across different patient strata. Multi‑omics profiling was performed to characterize mutation landscapes and immune microenvironment features. Cell‑cell interaction analysis was conducted to explore ligand‑receptor pathways. SPARC , CYR61 , and TIMP1 were identified as core disulfidptosis-related genes. High disulfidptosis scores correlated with IGHA1/IGHA2 mutations and a depleted immune microenvironment, whereas low scores were driven by LYZ amplification and active immune infiltration. Machine learning identified SPARC and CYR61 as core genes, predominantly expressed in endothelial cells and fibroblasts. Cell‑cell interaction analysis revealed that SPARC + /CYR61 + endothelial cells inhibited B‑cell function via the SELPLG‑SELL signaling pathway, while SPARC + /CYR61 + fibroblasts suppressed multiple immune populations (T cells, macrophages, mast cells, and B cells) through the SPP1‑CD44 signaling. Disulfidptosis features serve as critical prognostic biomarkers in gastric cancer and actively orchestrate immunosuppression within the tumor microenvironment. These insights present promising translational avenues for patient specific immunotherapeutic strategies.
Xu et al. (Fri,) studied this question.
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