Identification and Validation of eRNA as a Prognostic Indicator for Cervical Cancer

Area of Science:

• Oncology
• Molecular Biology
• Bioinformatics

Background:

• Cervical squamous cell carcinoma (CESC) patient survival is linked to enhancer RNA (eRNA) expression.
• Understanding eRNA's role is vital for developing novel prognostic tools.

Purpose of the Study:

• To identify a robust eRNA-based prognostic model for CESC patients.
• To evaluate the model's predictive performance and explore therapeutic strategies.

Main Methods:

• Downloaded eRNA expression, clinical, and gene expression data from TCeA and TCGA.
• Utilized limma analysis to identify differentially expressed eRNAs.
• Applied univariate Cox, LASSO, and multivariate Cox regression analyses to build an 8-eRNA prognostic model.
• Validated the model using survival curves, ROC analysis, and nomograms.
• Performed Gene Set Enrichment Analysis (GSEA) to compare pathway diversity.

Main Results:

• Identified 7936 differentially expressed eRNAs.
• Developed and validated an 8-eRNA signature with high prognostic accuracy, independent of clinicopathological features.
• The model effectively stratified patients into high-risk and low-risk groups.
• GSEA revealed distinct pathway differences between risk groups.

Conclusions:

• The 8-eRNA model serves as a novel and reliable prognostic indicator for CESC.
• This model can guide personalized treatment strategies by identifying high-risk patients.
• Further research into the identified pathways may reveal new therapeutic targets.