An 11-GRG Predicting Model for UCEC Prognosis

Abstract

Abstract Background Endometrial cancer (EC) is indeed one of the most prevalent gynecological malignancies. Further research is required to gain insights into the molecular pathways involved in EC tumorigenesis and to develop more accurate prognostic prediction methods.Method Data from the Cancer Genome Atlas( TCGA) database was used and validated using two GEO datasets, specifically GSE6008 and GSE17025. Various bioinformatics analyses were performed, including the Least Absolute Shrinkage and Selection Operator regression (lasso) regression, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis Gene Set Variation Analysis (GSVA), gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network analysis, COX regression, calibration curves, and decision curve analysis (DCA). These analyses aimed to explore the associations and functions of GRGs in EC development, as well as develop a predictive model for prognosis assessment.Results There were 11 GRGs identified as significantly associated with EC by the Lasso regression, GSEA, and PPI. The further analysis identifies 61 miRNA molecules, 24 RBPs, 139 TFs, and 21 potential drugs or molecular compounds that might have links to these 11 key genes. These 11 GRGs were made into a Cox regression prediction model, among which the PGK2 shows significantly higher utility than other variables. Calibration analysis and DCA indicate that the clinical predictive performance of this 11-GRGs multivariate Cox regression model is highest at 5 years, followed by 3 years and 1 year. There were 6 genes (GPI, HK1, NUP188, PDHA1, PDHA2, PGK2) that exhibited higher predictive accuracy in time-dependent ROC curves.Conclusion The highly enriched GRGs that have been identified might provide a new understanding of the development of EC and its treatment. Moreover, the 11-GRGs model that has been constructed holds significant clinical implications for evaluating prognosis and providing specific therapy guidance to individuals with EC. Among the variables in the 11-GRG model, PGK2 demonstrates notably higher usefulness, highlighting its potential clinical value in EC.