Risk model based on adenylate uridylate (AU)-rich elements genes for assessing esophageal cancer prognosis and immune...

Despite their established role in post-transcriptional regulation, the functions of adenylate uridylate-rich elements genes (AREGs) in esophageal cancer (EC) remain incompletely understood. Therefore, this study aimed to identify critical AREGs and evaluate their potential as prognostic biomarkers for EC.

To delineate AREGs expression in EC, we conducted differential expression analysis using public databases. A prognostic model was subsequently constructed by integrating least absolute shrinkage and selection operator and multivariate Cox regression analysis. For immune microenvironment assessment, algorithms including CIBERSORT, ESTIMATE, and single sample gene set enrichment analysis (GSEA) were utilized. For functional characterization, GSEA, gene ontology enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed. Finally, molecular subtyping was achieved through non-negative matrix factorization (NMF) clustering.

A reliable prognostic signature based on eight AREGs was constructed to stratify patients. Those in the high-risk group experienced worse survival, a phenotype associated with cell cycle activation. In contrast, the low-risk group was associated with cell adhesion processes, and patients in this group might have derived greater clinical benefit from anti-programmed cell death ligand 1 immune checkpoint inhibitor therapy. NMF categorized all samples into three molecular subtypes with unique features.

This research investigates the intricate tumor microenvironment and molecular functions of AREGs in EC, offering novel perspectives on the pathological mechanisms and clinical treatment strategies for this disease.