Systematic identification of key functional modules and genes in esophageal cancer

Abstract Background Esophageal cancer is associated with high incidence and mortality worldwide. Differential expression genes (DEGs) weighted gene co-expression network analysis (WGCNA) are important methods to screen the core as bioinformatics methods. Methods The DEGs WGCNA were combined hub genes, pathway enrichment analyses performed on module in WGCNA. CCNB1 was identified based intersection between greenyellow Expression levels prognostic values of verified UALCAN, GEPIA2, HCMDB, Kaplan–Meier plotter, TIMER databases. Results We 1,044 from dataset GSE20347, 1,904 GSE29001, 2,722 GSE111044, 32 modules revealed by gene, which upregulated tumour tissues. Moreover, esophageal patients higher showed a worse prognosis. However, ‘might not play an role immune cell infiltration. Conclusions Based key related cancer, offered novel insights into development treatment cancer.