Construction of a novel risk model for esophageal squamous cell carcinoma associated with purinergic signaling pathways and chemoradiotherapy sensitivity genes

Background Esophageal squamous cell carcinoma (ESCC) significantly impacts public health. Variability exists in patients’ responses to chemoradiotherapy (CRT), and the role of purinergic signaling (PS) in ESCC, related to tumor migration, remains unclear. Methods Survival-related genes among PS-related genes were identified using univariate Cox analysis. Patients from The Cancer Genome Atlas (TCGA)-ESCC dataset were categorized based on optimal scoring thresholds. Differential expression analysis was employed to identify differentially expressed genes (DEGs) between tumors and controls and across scoring groups. Intersecting genes from DEGs were identified and further analyzed in the GSE45670 dataset to identify genes associated with CRT sensitivity, which were designated as candidate genes. A prognostic signature was developed, and gene set enrichment analysis (GSEA), mutation analysis, immune infiltration, and drug sensitivity were implemented. In addition, we used Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) for experimental validation to further screen potential anti-tumor drug targets. Finally, the expression of prognostic signature genes in pan-cancer were explored. Results A total of four prognostic signature genes (ANXA10, ERICH5, HRG, and AMN) were identified. GSEA linked the risk group to pathways like cell adhesion molecules. Mutational analysis suggested a potential missense mutation in ANXA10. High-risk patients were associated with increased infiltration of plasmacytoid dendritic cell and T follicular helper cells. Drugs like VX.702 and BMS.754807 were more effective in the high-risk group. In esophageal cancer, ANXA10 was remarkably down-regulated, and HRG was up-regulated. ESCC tumor tissues exhibited markedly decreased AMN and ERICH5 expression but increased HRG compared to normal controls. Conclusion This study identified prognostic signature genes associated with CRT and PS in ESCC and constructed a risk model that may predict patient survival, which could provide valuable insights for future research on ESCC diagnosis and treatment.