ArticleHereditas2025
Disulfidptosis classification of pancreatic carcinoma reveals correlation with clinical prognosis and immune profile.
Article in Hereditas, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Machine learning identifies disulfidptosis-related gene signature for pancreatic cancer prognosis and immune infiltration.Discover oncology · 2026Article
- Disulfidptosis as an immunometabolic rheostat in gastrointestinal cancers: tuning the balance between T Cell exhaustion and immunogenic cell death.Frontiers in cell and developmental biology · 2026Review
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Authors and funding
5 authors.
Funding
Abstract
backgroundDisulfidptosis, a novel form of metabolism-related regulated cell death, is a promising intervention for cancer therapeutic intervention. Although aberrant expression of long-chain noncoding RNAs (lncRNAs) expression has been associated with pancreatic carcinoma (PC) development, the biological properties and prognostic potential of disulfidptosis-related lncRNAs (DRLs) remain unclear.
methodsWe obtained RNA-seq data, clinical data, and genomic mutations of PC from the TCGA database, and then determined DRLs. We developed a risk score model and analyzed the role of risk score in the predictive ability, immune cell infiltration, immunotherapy response, and drug sensitivity.
resultsWe finally established a prognostic model including three DRLs (AP005233.2, FAM83A-AS1, and TRAF3IP2-AS1). According to Kaplan-Meier curve analysis, the survival time of patients in the low-risk group was significantly longer than that in the high-risk group. Based on enrichment analysis, significant associations between metabolic processes and differentially expressed genes were assessed in two risk groups. In addition, we observed significant differences in the tumor immune microenvironment landscape. Tumor Immune Dysfunction and Rejection (TIDE) analysis showed no statistically significant likelihood of immune evasion in both risk groups. Patients exhibiting both high risk and high tumor mutation burden (TMB) had the poorest survival times, while those falling into the low risk and low TMB categories showed the best prognosis. Moreover, the risk group identified by the 3-DRLs profile showed significant drug sensitivity.
conclusionsOur proposed 3-DRLs-based feature could serve as a promising tool for predicting the prognosis, immune landscape, and treatment response of PC patients, thus facilitating optimal clinical decision-making.
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