Screening of diagnostic and prognostic markers for esophageal cancer based on biological prior information and gene regula-tory network
10.11904/j.issn.1002-3070.2025.03.006
- VernacularTitle:基于生物学先验信息和基因调控网络筛选食管癌诊断及预后标志物
- Author:
Xinyu WANG
1
;
Qi ZHANG
Author Information
1. 国家儿童医学中心 首都医科大学附属北京儿童医院大数据中心(北京 100045)
- Publication Type:Journal Article
- Keywords:
Biological priori information;
Gene regulatory network;
Esophageal cancer;
Biomarker
- From:
Practical Oncology Journal
2025;(3):208-215
- CountryChina
- Language:Chinese
-
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.Meth-ods 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 em-ployed to construct an integrative regulatory network.Genes related to disease status,survival time,and survival outcomes were identi-fied 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 pa-tients with esophageal cancer,laying a solid foundation for the development of efficient diagnostic tools and precise treatment regimens for esophageal cancer.
