Abstract
OBJECTIVE: Gastric cancer (GC), a widely recognized malignant neoplasm, poses significant treatment challenges. It is essential to pursue additional research to uncover novel therapeutic approaches and predictive methodologies. The phenomenon of disulfidptosis, recently identified as a distinct type of programmed cell death, offers intriguing possibilities for therapeutic applications. METHOD: This study performed differential analysis, GO analysis, KEGG, GSEA, and other analyses to investigate the relationship between disulfidptosis-related LncRNAs (DRLs) and TCGA GC data. The analysis included 373 gastric cancer samples and 32 normal gastric tissue samples, obtained from [specify data source, e.g., TCGA, GEO, or in-house cohorts]. Samples were rigorously screened based on [criteria, e.g., histopathological confirmation, RNA quality, or clinical completeness], and transcriptomic data were processed using [specific tools or pipelines] to ensure reproducibility. A new, more accurate predictive model was constructed, identifying potential therapeutic targets, signaling pathways, and sensitive drugs. RESULTS: A refined prognostic signature associated with disulfidptosis in GC was developed through over 200 Lasso regression and MultiCox calculations. It was discovered that AL359182.1 and AC107021.2 could influence GC prognosis by modulating the MYH10/LIGHT JUNCTION or MYH10/REGULATION OF ACTIN CYTOSKELETON pathways. Notably, AL359182.1, newly identified as linked to GC prognosis, emerges as a potential novel therapeutic target. Furthermore, this study enhanced the accuracy of immunotherapy evaluations and screened for potential sensitive drugs. CONCLUSION: Leveraging DRLs and TCGA GC data, this research identified highly precise prognostic signatures consisting of four LncRNAs: PINK1-AS, AC107021.2, AL359182.1, and AC009486.1. The discovery of two new signaling pathways could impact the prognosis of GC. AL359182.1 is proposed as a novel potential therapeutic target. The identified signature also effectively predicts the immunogenicity of GC and facilitates the screening of sensitive drugs. Further experimental validation is suggested to strengthen these conclusions.