Zhang Yu, Zhou Gongwei
School of Public Health, Ningxia Medical University, Yinchuan, China.
Department of Health Medical Big Data Office, Statistical Information Center of the National Health Commission, Beijing, China.
Medicine (Baltimore). 2025 Aug 29;104(35):e44193. doi: 10.1097/MD.0000000000044193.
This study developed a prognostic risk prediction model for endometrial carcinoma (EC) by integrating data from The Cancer Genome Atlas and Gene Expression Omnibus for bioinformatics analysis. The relevant data of EC were downloaded from The Cancer Genome Atlas database and the GSE17025 dataset of the Gene Expression Omnibus database. Based on the R language, the differentially expressed genes (DEGs) and weighted gene co-expression network analysis were used to identify the gene modules with the strongest correlation with clinical features, and intersected with the DEGs of GSE17025 dataset. Subsequently, univariate and multivariate Cox regression analyses were conducted to construct and validate a prognostic risk prediction model for EC. Weighted gene co-expression network analysis identified 6 gene modules, with the turquoise module exhibiting the strongest correlation with EC prognosis and survival. By intersecting with DEGs from GSE17025 dataset, 65 candidate genes were identified. Univariate Cox regression revealed 19 genes significantly associated with overall survival, and multivariate Cox regression identified 5 prognostic genes. A 5-gene risk prediction model, including PDZ domain containing ring finger 3, KN motif and ankyrin repeat domains 4, prion protein, phosphoserine aminotransferase 1, and Annexin A1, was constructed. Kaplan-Meier survival curve analysis demonstrated that patients in the high-risk group had significantly lower overall survival compared to the low-risk group (P < .001). The ROC curve confirmed the model's robust prognostic predictive performance. This study presents a 5-gene prognostic risk prediction model for EC, including PDZ domain containing ring finger 3, KN motif and ankyrin repeat domains 4, prion protein, phosphoserine aminotransferase 1, and Annexin A1, which can effectively predict patients' prognosis and provide a reference for the clinical diagnosis and targeted therapy of EC.
本研究通过整合来自癌症基因组图谱（The Cancer Genome Atlas, TCGA）和基因表达综合数据库（Gene Expression Omnibus, GEO）的数据进行生物信息学分析，开发了一种子宫内膜癌（EC）的预后风险预测模型。从TCGA数据库和GEO数据库的GSE17025数据集中下载了EC的相关数据。基于R语言，利用差异表达基因（DEGs）和加权基因共表达网络分析来识别与临床特征相关性最强的基因模块，并与GSE17025数据集的DEGs进行交集分析。随后，进行单因素和多因素Cox回归分析，以构建和验证EC的预后风险预测模型。加权基因共表达网络分析确定了6个基因模块，其中蓝绿色模块与EC预后和生存的相关性最强。通过与GSE17025数据集的DEGs进行交集分析，确定了65个候选基因。单因素Cox回归显示19个基因与总生存显著相关，多因素Cox回归确定了5个预后基因。构建了一个包含含PDZ结构域的环指蛋白3、KN基序和锚蛋白重复结构域4、朊病毒蛋白、磷酸丝氨酸转氨酶1和膜联蛋白A1的5基因风险预测模型。Kaplan-Meier生存曲线分析表明，高风险组患者的总生存率显著低于低风险组（P < 0.001）。ROC曲线证实了该模型具有强大的预后预测性能。本研究提出了一种用于EC的5基因预后风险预测模型，包括含PDZ结构域的环指蛋白3、KN基序和锚蛋白重复结构域4、朊病毒蛋白、磷酸丝氨酸转氨酶1和膜联蛋白A1，该模型可以有效预测患者的预后，并为EC的临床诊断和靶向治疗提供参考。
