Abstract: Objective The aim of this study was to screen diagnostic and prognostic markers for esophageal cancer,explore their potential mechanisms of action,and provide new insights for early diagnosis and precision treatment of esophageal cancer. Methods The protein-coding genes in transcriptome sequencing data(RNA-seq)of esophageal cancer from The Cancer Genome Atlas(TCGA)were extracted and analyzed.The KEGG pathways and protein-protein interaction(PPI)network relationships enriched in differentially expressed genes(DEGs)were used as prior information.The prior incorporation mixed graphical model(piMGM)was employed to construct an integrative regulatory network.Genes related to disease status,survival time,and survival outcomes were identified as diagnostic and prognostic markers.The prediction models and calculate the risk score were constructed using multivariate Cox regression analysis. Results A total of 180 DEGs between tumor and normal tissues were obtained,which were mainly enriched in KEGG pathways such as cell cycle,cellular senescence,gastric acid secretion,p53 signaling pathway,and IL-17 signaling pathway.MT1M,SLC9A4,GPER1,MT1A,CCL20,and MDFI were identified as key genes through gene regulatory network analysis.Together with clinical variables,a prognostic prediction model was constructed and the risk score was calculated.According to the optimal cutoff value,the patients were divided into the high-and low-risk groups with significantly different prognoses:the area under the curve(AUC)of the esophageal cancer diagnosis model was 0.978(95% CI:0.935-0.996),the AUCs of the 1-year and 3-years overall survival prediction models were 0.783(95% CI:0.646-0.896)and 0.779(95% CI:0.598-0.999),respectively,and the AUCs of the 1-year and 3-years disease-free survival prediction models were 0.787(95% CI:0.664-0.848)and 0.762(95% CI:0.575-0.900),respectively. Conclusion The six markers identified in this study can effectively predict the incidence and prognosis of patients with esophageal cancer,laying a solid foundation for the development of efficient diagnostic tools and precise treatment regimens for esophageal cancer.

Key words: Biological priori information, Gene regulatory network, Esophageal cancer, Biomarker