Frontiers in Immunology (Jan 2025)
Establishment of a prognostic signature and immune infiltration characteristics for uterine corpus endometrial carcinoma based on a disulfidptosis/ferroptosis-associated signature
Abstract
BackgroundDisulfidptosis and ferroptosis are two different programmed cell death pathways, and their potential therapeutic targets have important clinical prospects. Although there is an association between the two, the role of genes associated with these two forms of cell death in the development of endometrial cancer remains unclear.MethodsIn this study, RNA sequencing (RNA-seq) and clinical data were obtained from public databases, and comprehensive analysis methods, including difference analysis, univariate Cox regression, and Least Absolute Shrinkage and Selection Operator (LASSO) analysis were used to construct a disulfidptosis/ferroptosis-related genes (DFRGs) prognostic signature. To further explore this new feature, pathway and functional analyses were performed, and the differences in gene mutation frequency and the level of immune cell infiltration between the high- and low-risk groups were studied. Finally, we validated the prognostic gene expression profile in clinical samples.ResultsWe identified five optimal DFRGs that were differentially expressed and associated with the prognosis of uterine corpus endometrial carcinoma (UCEC). These genes include CDKN2A, FZD7, LCN2, ACTN4, and MYH10. Based on these DFRGs, we constructed a robust prognostic model with significantly lower overall survival in the high-risk group than in the low-risk group, with differences in tumor burden and immune invasion between the different risk groups. The expression of two key genes, ACTN4 and LCN2, was verified by immunohistochemistry and RT-qPCR.ConclusionThis study established a clinical prognostic model associated with disulfidptosis/ferroptosis-related genes, and the expression characteristics of key genes were validated in clinical samples. The comprehensive assessment of disulfidptosis and ferroptosis provides new insights to further guide patient clinical management and personalized treatment.
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