Analysis of Differentially Expressed Genes in Esophageal Cancer Based on GEO Database

Inhalt:
OBJECTIVE: To identify hub genes abnormally expressed in esophageal cancer development through bioinformatics analysis of Gene Expression Omnibus (GEO) database datasets. METHODS: Gene expression datasets GSE45670 and GSE20347 were acquired from GEO. Differentially expressed genes (DEGs) were screened from both datasets. Subsequent analyses included Gene Ontology (GO) and KEGG pathway enrichment, construction of protein-protein interaction (PPI) networks, and identification of hub genes. The prognostic significance of identified hub genes concerning overall survival (OS) and recurrence-free survival (RFS) in ESCC patients was evaluated. Expression of key genes was validated using The Cancer Genome Atlas (TCGA) dataset. RESULTS: A total of 56 DEGs from GSE45670 and 1738 DEGs from GSE20347 were identified. Integrated bioinformatics analysis, including PPI network construction and survival analysis, pinpointed CWH43 and SLURP1 as key prognostic hub genes. Validation using TCGA data confirmed their differential expression. CONCLUSION: This study successfully identified CWH43 and SLURP1 as potential novel biomarkers for ESCC pathogenesis and prognosis through a systematic bioinformatics pipeline. These genes warrant further investigation for their clinical utility.