Prognostic Biomarkers Identification in Esophageal Cancer Based on WGCNA and Single-Cell Analysis

Background. Esophageal cancer is one of the most common cancers worldwide. Dysregulation of genes plays an important role in cancer. In this study, we aimed to investigate the prognostic biomarkers in esophageal cancer based on comprehensive bioinformatics analysis including WGCNA and single cell analysis. Methods. RNA sequencing data of esophageal cancer was downloaded from GSE75241 dataset in the GEO database. We also selected esophageal cancer patients from public databases (Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA)). WGCNA was used to construct a scale-free coexpression network of genes. Multifactor Cox analysis model was constructed as the prognostic model in esophageal cancer. Furthermore, single-cell gene analysis was used to discover the mechanism of hub genes in esophageal cancer. Results. WGCNA discovered 182 genes for further analysis. Among 182 genes, four genes including ANGPT2, VCAN, MS4A4A, and FOS had significant prognostic value in esophageal cancer. In single cell analysis, seven types of cells subsets were distinguished including T cells, B cells, NK cells, monocytes, macrophages, DCs, neutrophils. The expression of four hub genes (ANGPT2, VCAN, MS4A4A, and FOS) in inflammatory cell subsets was evaluated, respectively. Hub genes were correlated with inflammatory cells in esophageal cancer. In addition, the subgroups of specific inflammatory cells such as macrophages, monocytes, and DCs were analyzed to identify the function of hub genes, either. Hub genes were correlated with differentiation of inflammatory cells including monocytes, macrophages, and DCs in tumor environment. Conclusions. We identified specific hub genes correlated with prognosis of esophageal cancer. These hub genes play critical roles by regulating inflammatory cells status in esophageal cancer.