Prognostic Biomarkers and Virtual Drug Targets for Endometrial Cancer: A Comprehensive Analysis of TCGA Database and Molecular Docking

Endometrial cancer is a prevalent malignancy of the female reproductive system. In recent years, with the changes in the pace of life, Endometrial cancer incidence and death have gradually risen. It is difficult to detect in the early stage and the mortality rate is high in the late stage. Based on this feature, it is a good choice to use biomarkers to monitor at the molecular level. In this study, the researchers downloaded 606 endometrial cancer samples and standardized them. The study used the “limma” package to conduct differential gene analysis, and strictly controlled the screening criteria. Finally, 177 differential genes ([Formula: see text]< 0.05) were obtained, and then gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), pathway analysis were performed on these 177 differential genes to explore their potential functions. Through pathway analysis, the study found that they were mainly enriched in the immune microenvironment and signal transmission pathways. This indicates that 177 genes are crucial for the emergence and development of endometrial cancer. When constructing the model, the study first used single-factor cox for analysis and obtained 26 risk genes. Afterwards, lasso regression, best subset analysis and multivariate cox regression were used in turn for statistical analysis, and finally, four genes of CYGB, CBY3, CPNE6 and CLVS1 were obtained and a predictive model was created. Meanwhile, to confirm the model’s capacity for prediction, the study performed receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (KM) survival analysis. The results showed that the model was predictive, and the area under the curve (AUC) value of the area under the ROC curve was between 0.75 and 0.85. At the same time, the results of the survival study revealed a substantial difference between the two groups, with the low-risk group having a greater rate of survival. The samples were then subjected to differential analysis, pathway analysis and immune infiltration analysis according to the risk score to explore the role of the risk score in endometrial cancer and its impact on endometrial cancer immunity. To identify potential therapeutic compounds, the researchers constructed a molecular library, which targeted the four prognostic genes using three distinct strategies. A high-throughput virtual screening approach was then applied. This involved the use of extensive molecular docking which was used to evaluate binding affinities. The scale and rigor of this screening process represent a key strength of the study. Thus, the findings in this study provide a background for further experimental validation. It is also hoped that in the near future, these findings guide the development of targeted therapies for endometrial cancer.