Mining in Endometrial Cancer Based on the TCGA Database and Constructing Prognostic Models

Area of Science:

• Oncology
• Genomics
• Metabolism

Background:

• Endometrial cancer (EC) incidence is rising globally, driven by aging and obesity.
• Lymph node metastasis is a common challenge in Uterine Corpus Endometrial Carcinoma (UCEC), highlighting the need for better prognostic markers.
• Understanding molecular pathways like nicotinamide metabolism is crucial for advancing EC treatment.

Purpose of the Study:

• To identify genetic factors in nicotinamide metabolism associated with endometrial cancer.
• To develop a prognostic model for UCEC based on key metabolic genes.
• To explore the role of nicotinamide metabolism in EC progression and metastasis.

Main Methods:

• Analysis of UCEC data from The Cancer Genome Atlas (TCGA) and validation with Gene Expression Omnibus (GEO) datasets.
• Identification of 18 differentially expressed genes in nicotinamide metabolism (NMRDEGs).
• Gene Set Enrichment Analysis (GSEA), univariate Cox regression, and Protein-Protein Interaction (PPI) network analysis were employed.

Main Results:

• Eighteen NMRDEGs were identified, implicated in processes like oxidative stress, hypoxia, glycolysis, and apoptosis.
• A six-gene prognostic model (AURKA, CDKN3, FOXM1, CDKN2A, TK1, CDK1) demonstrated significant prognostic value.
• Hub genes including CDK2, CCNA2, TP53, and FOXM1 were identified through PPI analysis.
• Nicotinamide metabolism was significantly correlated with EC progression.

Conclusions:

• The identified six key genes and the developed risk model hold potential for guiding clinical decisions in UCEC.
• Nicotinamide metabolism plays a significant role in endometrial cancer progression.
• This research provides novel insights into nicotinamide metabolism's involvement in EC, suggesting therapeutic targets.