Identification of Anoikis-Related Genes in Endometrial Cancer and Their Applications in Treatment Sensitivity and Prognostic Evaluation

Background Endometrial Cancer (EC) is a common type of gynecological malignancy, with a rising incidence rate each year. However, the prognostic value of Anoikis-related genes in EC remains unclear. This study aims to investigate the roles of Anoikis-related genes in EC diagnosis, prognosis, and drug treatment prediction. Methods Differentially expressed Anoikis-related genes were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients were categorized based on consensus clustering analysis of these genes. Functional analysis of differentially expressed genes between subgroups was conducted using Gene Set Variation Analysis (GSVA) to explore the functional state. A prognostic risk model for EC was constructed and its independent prognostic value was evaluated using univariate Cox proportional hazards regression analysis and LASSO regression analysis. Additionally, the roles of these genes in the tumor microenvironment, their association with tumor immune cell infiltration, and their relationship with drug sensitivity were investigated. Results Key Anoikis-related genes, including BUB1, PLK1, UBE2C, and BIRC5, were identified, and an Anoikis-related prognostic risk model was successfully constructed, in which the indicators G3, High Grade, and risk score were especially significant (p < 0.001) and could independently serve as prognostic markers for UCEC patients. A nomogram score was developed to predict patient survival rates in the future. Based on the median risk score, patients were divided into two groups. In the test dataset, patients with highrisk scores generally had lower survival probabilities and died earlier than those with lowrisk scores (p < 0.001). The risk model also demonstrated the ability to predict the immune microenvironment in EC patients and was closely associated with treatment resistance in EC. Conclusion The prognostic risk model based on Anoikis-related genes can predict the overall survival rate of EC patients and provides insight into the tumor immune microenvironment. The expression level of Anoikis-related genes may influence the sensitivity of various drugs to EC treatment. This study offers a theoretical basis for the discovery of new molecular markers and therapeutic targets in EC.