A novel cuproptosis-related gene signature for overall survival prediction in uterine corpus endometrial carcinoma (UCEC)

一种用于预测子宫体子宫内膜癌 (UCEC) 总体生存率的新型杯状凋亡相关基因特征

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作者:Shitong Lin, Yashi Xu, Binghan Liu, Lingling Zheng, Canhui Cao, Peng Wu, Wencheng Ding, Fang Ren

Abstract

Cuproptosis is a copper-dependent model of cell death involved in tumor genesis and progression. Its roles in uterine corpus endometrial carcinoma (UCEC) remains elusive. Here, we aimed to explore the expression and prognostic values of cuprotosis-related genes (CRGs) in UCEC. Expression profiles and clinical data of UCEC were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into testing or training cohort (1:1 ratio). The CRG signature was identified by LASSO regression analysis. The differentially expressed genes and their functional enrichment analysis were performed by the "limma" R package and Metascape, respectively. The immunocytes infiltration was measured by TIMER, and "GSVA" R package. In total, seven differentially expressed prognostic genes of CRGs in UCEC were identified, and four genes (GLS, CDKN2A, PC, and SUCLG1) were selected to construct a predictive model in training cohort. UCEC patients from training and testing cohorts were further divided into high- or low-risk groups according to the median risk score. High-risk group favored poor prognosis compared to low-risk group. Functional enrichment analysis revealed this CRG signature were got involved in the process of cell-cell adhesion and immune activities (e.g., IL-1 signaling pathway, cellular response to cytokine stimulus). Further analyses revealed there were significant differences between high- and low-risk patients regarding immunocytes infiltration, chemokines, and chemokine receptors. Finally, the expression and biological functions of identified CRGs were confirmed by UCEC samples and experimental methods in vitro. In summary, the CRG signature was significantly correlated with patients' overall survival, which could provide insights into the diagnosis and prognosis prediction for UCEC.

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