文献类型：期刊文献

英文题名：IFI35 and IFIT3 are potentially important biomarkers for early diagnosis and treatment of esophageal squamous cell carcinoma: based on WGCNA and machine learning analysis

作者：Wu, Hao[1];Yang, Liang[2];Weng, Xiaokun[3]

第一作者：吴颢

通信作者：Weng, XK[1]

机构：[1]Gansu Univ Chinese Med, Sch Clin Med 1, Lanzhou, Gansu, Peoples R China;[2]Shanghai Jiao Tong Univ, Dept Neurosurg, Affiliated Peoples Hosp 6, South Campus, Shanghai, Peoples R China;[3]Lishui Peoples Hosp, Dept Radiotherapy, Lishui, Zhejiang, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Lishui Peoples Hosp, Dept Radiotherapy, Lishui, Zhejiang, Peoples R China.

年份：2025

卷号：16

外文期刊名：FRONTIERS IN GENETICS

收录：;Scopus(收录号：2-s2.0-105007155321);WOS:【SCI-EXPANDED(收录号:WOS:001500536800001)】；

语种：英文

外文关键词：ESCC; IFIT3; IFI35; WGCNA; machine learning

摘要：Background Esophageal squamous cell carcinoma (ESCC) does not have distinct and highly sensitive biomarkers, making its diagnosis difficult. Consequently, identifying dependable biomarkers is critical, as these indicators can facilitate accurate ESCC diagnosis and enable effective prognostic evaluation.Methods ESCC datasets (GSE29001, GSE20347, GSE45670, and GSE161533) were sourced from the GEO, and the Limma package identified differentially expressed genes (DEGs). To characterize co-expression network, weighted gene co-expression network analysis (WGCNA) was performed, allowing for the identification of relevant co-expression modules. To assess the biological pathways of intersecting genes, we performed pathway enrichment analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The Support Vector Machine Recursive Feature Elimination (SVM), along with Least Absolute Shrinkage and Selection Operator (LASSO) regression, was applied to identify clinical biomarkers. Finally, the differences of immune cell infiltration were also detected.Results 1,019 genes were derived by integrating DEGs with co-expressed module genes. KEGG and GO revealed a strong association between these genes and processes such as chemotaxis and IL-17 signaling pathways. Two hub genes (IFIT3 and IFI35) were selected through LASSO regression and SVM. Additionally, ROC curve analysis confirmed their potential for reliable diagnostic performance. Furthermore, differences in immune cell infiltration were observed.Conclusion Collectively, IFIT3 and IFI35 emerged as promising candidate biomarkers, offering novel insights to enhance early detection and guide targeted treatment strategies for ESCC.