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Esophageal carcinoma (ESCA) is one of the most frequent types of malignant tumor that has a dismal prognosis. This research applied datasets aimed from the GEO and TCGA to create a prognostic signature for forecasting the clinical outcome of ESCA patients on the basis of a circRNA-associated regulatory network. Methods. A regulatory network associated with ESCA was established based on transcriptome data of circRNAs, miRNAs, and mRNAs. Functional annotations were implemented to further explore the mechanism of ESCA. Cox relative regression method was applied to create a risk signature. Besides, the immune microenvironment of the signature was investigated by utilizing the CIBERSORT algorithm. Results. Based on 27 DEcircRNAs, 65 DEmiRNAs, and 780 DEmRNAs, the circRNA-miRNA-mRNA network was finally set up. Functional enrichment unearthed that the regulatory network might participate in phosphorylation negative regulation, MAPK pathway, and PI3K/AKT pathway. This study established a risk scoring signature based on the seven immune-related genes (IRGs) (MARP14, RASGR1, PTK2, HMGB1, DKK1, RARB, and IGF1R), which was validated for its reliability. A stable and accurate nomogram combining immune-related risk scores with clinical features was constructed. Furthermore, we observed that the risk model was also related to the immunocyte infiltration. Conclusion. Our study successfully created a circRNA-associated regulatory network and further developed an immune-related model to forecast the clinical outcome of ESCA patients as well as to assess their immune status.

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