Expression and prognosis of NR3C1 in uterine corpus endometrial carcinoma based on multiple datasets

Abstract

Uterine corpus endometrial carcinoma (UCEC), a prevalent malignancy in the female reproductive system, has witnessed a 30% increase in recent year. Recognizing the significance of early treatment in reducing patient mortality, the identification of potential biomarkers for UCEC plays a crucial role in early diagnosis. This study was to identify key genes associated with UCEC utilizing the Gene Expression Omnibus (GEO) database, followed by validating their prognostic value across multiple databases. Analysis of four UCEC databases (GSE17025, GSE36389, GSE63678, GSE115810) yielded 72 co-expressed genes. KEGG and GO enrichment analyses revealed their involvement in physiological processes such as transcriptional misregulation in cancer. Constructing a Protein-Protein Interaction (PPI) network for these 72 genes, the top 10 genes with significant interactions were identified. Survival regression analysis highlighted NR3C1 as the gene with a substantial impact on UCEC prognostic outcomes. Differential expression analysis indicated lower expression of NR3C1 in UCEC compared to normal endometrial tissue. Cox regression analysis, performed on clinical datasets of UCEC patients, identified clinical stage III, clinical stage IV, age, and NR3C1 as independent prognostic factors influencing UCEC outcomes. The LinkedOmics online database revealed the top 50 positively and negatively correlated genes with NR3C1 in UCEC. Subsequent investigations into the relationship between NR3C1 and tumor-infiltrating immune cells were conducted using R software. Gene set enrichment analysis (GSEA) provided insights into NR3C1-related genes, showing enrichment in processes such as Ribosome, Oxidative phosphorylation in UCEC. Collectively, these comprehensive analyses suggest that NR3C1 may serve as a potential biomarker indicating the prognosis of UCEC.