RNA 5-Methylcytosine regulators are associated with cell adhesion and predict prognosis of endometrial cancer

BACKGROUND: RNA methylation is a significant form of post-transcriptional modification that has been implicated in various diseases, including cancers. One prominent type of RNA methylation is 5-Methylcytosine (m(5)C), which primarily regulates RNA stability, transcription, and translation. However, the role of m(5)C-related gene regulation in cell adhesion within uterine corpus endometrial carcinoma (UCEC) remains unexplored. Therefore, the objective of this study was to investigate the association between RNA m(5)C methylation and UCEC and develop a prognostic predictive model to forecast survival outcomes in UCEC patients. METHODS: The RNA datasets were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The dataset was used to explore the interaction relationships of m(5)C regulators in UCEC. Unsupervised clustering analysis identified clusters with distinct m(5)C modification patterns. Different clusters underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment level analysis to investigate the effects of pathways related to m(5)C methylation, which were further validated through in vitro cellular experiments. A prognostic predictive model was developed using the least absolute shrinkage and selection operator (LASSO) and multivariate regression analysis. RESULTS: Two clusters with distinct m(5)C modification patterns were identified using unsupervised cluster analysis. Furthermore, the prognosis of cluster 2 was found to be worse. Enrichment analysis showed alterations in cell adhesion-related pathways in both clusters, as well as differences between the clusters. Through this analysis, we identified 25 genes with significant prognostic value. Finally, a prognostic predictive model comprising NSUN2 and YBX1 was constructed. CONCLUSIONS: In conclusion, diverse m(5)C modification patterns display distinct cell adhesion properties in UCEC, which are correlated with prognosis and offer significant potential as prognostic markers for UCEC assessment. We developed a prognostic predictive model to accurately predict the prognosis of UCEC.