ArticleDiscover oncology2026
Machine learning identifies disulfidptosis-related gene signature for pancreatic cancer prognosis and immune infiltration.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPancreatic cancer (PC) is a leading cause of cancer-related mortality due to late diagnosis and limited treatments. Disulfidptosis, a novel form of regulated cell death, is implicated in disease pathogenesis. This study explores disulfidptosis-related gene (DRG) signatures to identify prognostic biomarkers and immune infiltration patterns in PC using bioinformatics and single-cell analyses.
methodsDifferential analysis of PC gene expression identified overlapping DRGs. Hub genes were selected via machine learning, and a prognostic model was built, validated with GSE28735. Single-cell RNA sequencing (scRNA-seq) from GSE212966 examined DRG expression in the tumor microenvironment.
resultsAmong 42 DRGs, 19 prognostic genes were first identified by univariate Cox analysis. Using the R package Mime integrating 10 machine learning algorithms, and further evaluated by StepCox[both] + RSF, 8 hub genes (NDUFA11, MYH14, TRIP6, ACTN1, ANP32E, PML, CD2AP, RPN1) were extracted. An optimized disulfidptosis-based risk score model was constructed via StepCox[both] combined with the random survival forest (RSF) algorithm, achieving a concordance index (C-index) of 0.9 in the training set though external validation yielded moderate performance (C-index = 0.6), indicating potential for refinement. RPN1 and PML were significantly associated with survival and enriched in cell cycle and immune regulation pathways. scRNA-seq revealed RPN1 upregulation in macrophages, suggesting an immunosuppressive role. Immune infiltration and drug sensitivity analyses highlighted distinct profiles between high- and low-risk groups.
conclusionThis study establishes a disulfidptosis-based prognostic model for PC, identifying RPN1 and PML as key biomarkers. These findings provide novel insights into PC prognosis and immune dynamics, supporting the development of targeted diagnostic and therapeutic strategies.
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