The prognostic risk model of ESCA patients was constructed based on intercellular-related genes

Abstract Background Esophageal cancer is a serious malignant tumor disease. Radiotherapy is the standard treatment, but treatment tolerance often leads to failure. Cell-in-cell are observed in a variety of tumors and have been shown to correlate with prognosis. Therefore, it is particularly important to study the prognostic value and regulatory mechanism of intracellular structure-related genes in esophageal cancer. Methods TCGA Esophageal Cancer (ESCA) was included in the analysis as the training set. The differentially expressed genes in ESCA samples in the training set were analyzed, and the differentially expressed intercellular-related genes were recorded as CIC-related DEGs. Cox analysis was used to screen prognostic genes. Samples were divided into high-low-risk groups according to the median value of the ESCA sample risk score. Validation was performed in the risk model GSE53624. Morphological mapping, enrichment analysis, immune infiltration analysis, prognostic gene expression verification, molecular docking, and RT-PCR verification were established. Results A total of 38 intersection genes were obtained between the disease group and the normal group of ESCA samples. After stepwise multivariate COX analysis, three prognostic genes (AR, CXCL8, EGFR) were selected. The applicability of the risk model was verified in the GSE53624 dataset. The analysis revealed eight significantly different immune-related gene sets. The prognostic gene expression validation found that the prognostic genes reached significant differences between the disease group and the normal group in both datasets. The corresponding proteins of the three prognostic genes all interacted with Gefitinib and osimertinib. The results of PCR confirmed the differential expression of prognostic genes in esophageal cancer tissues. Conclusions Three prognostic genes, AR, CXCL8, and EGFR, were obtained in this study, and the molecular docking of prognostic genes with Gefitinib and osimertinib showed that there were interactions between them, which provided a basis for the diagnosis and treatment of ESCA.