Leveraging a Disulfidptosis-related LncRNA Prognostic Signature to Indicate Immune Features and Drug Sensitivity of
Xinyu Pan1, Mei Yang2, Lilan Fan1
1Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Introduction:
Disulfidptosis is a newly identified, programmed cell death that could influence the progression, immune microenvironment, and therapeutic response of stomach adenocarcinoma (STAD). This mechanism is potentially regulated by long non-coding RNAs (lncRNAs).
Materials And Methods:
We analyzed TCGA datasets to identify 187 disulfidptosis-related lncRNAs (DRlncRNAs). Prognostic candidates were screened using univariate Cox, LASSO, and multivariate Cox analyses. Molecular subtypes were stratified via consensus clustering. Additionally, we construct a prognostic model based on key DRlncRNAs and evaluate its clinical relevance in terms of immune infiltration, functional pathways, drug responsiveness, and tumor mutation burden (TMB). Finally, expression validation of these DRlncRNAs was performed in STAD cell lines using qRT-PCR.
Results:
We established a three-DRlncRNA prognostic model demonstrating robust predictive performance with high AUC values. Patients in the low-risk subgroup exhibited favorable survival outcomes and greater sensitivity to immunotherapy. Conversely, the high-risk subgroup showed reduced survival, increased tumor malignancy, and poorer response to immunotherapy. qRT-PCR validation revealed significant expression differences of these three DRlncRNAs between gastric cancer cell lines and normal gastric epithelial cell lines.
Discussion:
DRlncRNAs were identified as potential biomarkers for prognostic and immune profiling in STAD. The identified signature may reflect biological activities associated with disulfidptosis and suggest potential therapeutic targets. Further mechanistic and translational studies are needed to determine whether these DRlncRNAs directly regulate disulfidptosis and to support clinical applications.
Conclusion:
This DRlncRNA-based prognostic model demonstrates strong predictive power for survival outcomes in STAD, offering guidance for personalized treatment strategies.
