Identification of E3 ubiquitin ligase-associated prognostic genes and construction of a prediction model for uterine cervical cancer based on bioinformatics analysis.

E3 ligases are engaged in a variety of physiological processes within cells and use ubiquitin-labeled substrates to control their activity and stability. Although some research has indicated that E3 ligases or particular substrates have an impact on the treatment that cervical cancer patients get after their diagnosis, The exact purpose of these enzymes in the occurrence and evolution of cancer of the cervical region (CC) is not clear. In order to extract and analyze relevant mRNA gene expression data as well as clinical patient data, we used open databases. A reliable risk prediction model was developed by applying the least absolute shrinkage and selection operator (LASSO) technique in conjunction with Cox regression analysis. Column-line plots were combined to analyze the predictive model, and the GSE44001 dataset served as an external validation.Four gene models:proteasome (prosome, macropain) 26S subunit, non-ATPase, 14(PSMD14),proteasome (prosome, macropain) subunit, alpha type, 4(PSMA4,),zinc finger and BTB domain containing 16(ZBTB16),and ankyrin repeat domain 9(ANKRD9). Gene expression levels in both healthy and cancerous tissues have been confirmed by the HPA database. Next, the investigation focused on immunological state and tumor mutation load. The high-risk group and Cluster B had distinct levels of immune cell infiltration and a worse prognosis. Additionally, KEGG and GO analyses of differentially expressed genes (DEGs) between the high- and low-risk groups were performed, as well as tumor microenvironment (TME) investigations. Targeting E3 ligases may be an efficient strategy to treat cervical cancer (CC), according to a novel and comprehensive E3 ubiquitination ligase-associated gene model that has been presented.