Abstract
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.