Integrating ferroptosis-related genes (FRGs) and prognostic models to enhance UCEC outcome prediction and therapeutic insights.

Ferroptosis is closely associated with uterine corpus endometrial carcinoma (UCEC) development. This project aimed to identify new potential biomarkers to predict the prognosis of UCEC. In this work, UCEC transcriptome data along with clinical information was retrieved from the TCGA database including a total of 382 FRGs. We performed univariate Cox regression analysis to evaluate ferroptosis-related genes (FRGs) for prognostic significance. The genes with prognostic significance were then analyzed using LASSO-Cox to construct a prognosis model. The model genes were further characterized through various proteomic analyses and expression detection in clinical samples. A multivariate Cox regression model was constructed containing four FRGs (CDKN1A, CDKN2A, CEBPG, NOS2). Among four FRGs, higher expressions of CDKN2A, CEBPG, and NOS2 were associated with poorer overall survival probability, while higher expression of CDKN1A was associated with better overall survival probability. The area under the receiver operating characteristic curve of the risk model was 0.617, 0.688, and 0.693 for 1 year, 3 years, and 5 years, respectively. Moreover, proteomic analysis showed that the protein expression of CDKN1A, CDKN2A, and CEBPG was higher in tumor tissues than that in normal tissues. Higher protein expression of CDKN1A and CDKN2A predicted poorer survival probability. Besides, CDKN1A protein had an interaction relationship with CDKN2A protein or NOS2 protein. In clinical samples, all four FRGs were upregulated in UCEC tissues, regardless of gene expression or protein expression. Our four FRGs risk model provides new insights for predicting the prognosis of UCEC patients. © 2023. The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences.

Citation

Weiwei Jin, Xiaoping Zhuang, Yihe Lin, Xiaoying Zhao. Integrating ferroptosis-related genes (FRGs) and prognostic models to enhance UCEC outcome prediction and therapeutic insights. Journal of applied genetics. 2023 Dec;64(4):723-735

Mesh Tags

PMID: 37626211

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