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Pancreatic adenocarcinoma (PAAD) is a prevalent and aggressive malignancy in the digestive tract, requiring accurate prediction and effective treatment strategies. Recently, the discovery of disulfidptosis, a novel form of programmed cell death characterized by abnormal disulfide accumulation, has sparked interest in its role in PAAD. In this study, we aimed to investigate the involvement of disulfidptosis-related genes (DRGs) in PAAD. Using publicly available databases, we conducted a comprehensive analysis exploring the complex relationships between DRGs and important aspects of PAAD, including gene expression, immune response, mutation, drug sensitivity, and functional enrichment. Notably, we observed significant heterogeneity among different disulfidptosis subclusters and identified specific differentially expressed genes in PAAD. Through machine learning techniques, we identified SLC7A11, S100A4, DIAPH3, PRDX1, and SLC7A7 as the most relevant hub genes. We further validated their significance in PAAD by considering their expression patterns, prognostic value, diagnostic potential, diagnostic model, and immune infiltration. This study presents exciting opportunities and challenges in unraveling the underlying mechanisms of PAAD prognosis. It also establishes a foundation for personalized cancer care and the development of innovative immunotherapeutic strategies. By shedding light on the role of DRGs, particularly hub genes, we enhance our understanding and management of PAAD.

Lower-grade glioma (LGG), a prevalent malignant tumor in the central nervous system, requires accurate prediction and treatment to prevent aggressive progression. We aimed to explore the role of disulfidptosis-related genes (DRGs) in LGG, a recently discovered form of programmed cell death characterized by abnormal disulfide accumulation. Leveraging public databases, we analyzed 532 LGG tumor tissues (The Cancer Genome Atlas), 1,157 normal samples (Genotype-Tissue Expression), and 21 LGG tumor samples with 8 paired normal samples (GSE16011). Our research uncovered intricate relationships between DRGs and crucial aspects of LGG, including gene expression, immune response, mutation, drug sensitivity, and functional enrichment. Notably, we identified significant heterogeneity among disulfidptosis sub-clusters and elucidated specific differential gene expression in LGG, with myeloid cell leukemia-1 (MCL1) as a key candidate. Machine learning techniques validated the relevance of MCL1, considering its expression patterns, prognostic value, diagnostic potential, and impact on immune infiltration. Our study offers opportunities and challenges to unravel potential mechanisms underlying LGG prognosis, paving the way for personalized cancer care and innovative immunotherapeutic strategies. By shedding light on DRGs, particularly MCL1, we enhance understanding and management of LGG.