Abstract
Endometrial carcinoma (EC) is a prevalent gynecological malignancy that poses a significant threat to women's health worldwide. However, its pathogenesis and underlying mechanisms remains unclear. In this study, expression quantitative trait loci data, Mendelian randomization analysis, and differential expression analysis were performed to identify potential targets. A prognostic risk signature was subsequently constructed for EC patients based on the expression of these genes. Four bioinformatics algorithms, including generalized linear model, extreme gradient boosting, support vector machine, and random forest, were used to identify hub genes in EC. The expression of ring finger protein 144A (RNF144A) was validated using quantitative real-time polymerase chain reaction. Cellular proliferation and migration ability were evaluated using CCK-8 and Transwell assays, respectively. The genes RNF144A, ketohexokinase, and Rab interacting lysosomal protein like 2 were identified as potential targets for EC. Their differential expression was observed in EC patients, and Mendelian randomization analysis revealed a negative correlation between these genes and the development of EC. Mechanistic analyses suggested a strong association between these genes and the tumor immune microenvironment. The constructed risk signature was significantly associated with the prognosis, age, cancer stage, and grade of EC patients. Furthermore, based on interacted model algorithms, RNF144A was identified as a hub gene in EC. It was found to be significantly downregulated in EC samples, and its expression was positively correlated with the stage and grade of EC patients. In vitro experiments showed that overexpression of RNF144A significantly promoted cell growth and migration in EC cells. In conclusion, this study provides insights into the molecular mechanisms underlying EC progression and identifies preliminary candidate biomarkers for the development of EC treatment strategies.