ArticleFrontiers in immunology2026
Construction and validation of a novel diagnostic model for esophageal squamous cell carcinoma: an integrated analysis of multi-omics data.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Esophageal squamous cell carcinoma (ESCC), highly prevalent in China, has a limited number of ideal genes for early diagnosis, highlighting the need for the development of novel biomarkers to improve detection capabilities. The purpose of this study is to develop and validate a new genetic diagnostic model for ESCC. Materials and methods: Publicly available bulk RNA-seq datasets (GSE23400, GSE17351, GSE20347) were merged to identify differentially expressed genes (DEGs) between ESCC and adjacent normal tissues. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) were performed to identify hub genes associated with ESCC. We identified the intersecting genes between the DEGs and those within the ESCC-related module identified by WGCNA. We subsequently refined these intersecting genes via LASSO regression and then constructed a diagnostic model for ESCC using multivariate logistic regression. ESCC samples from the TCGA database were used as the external validation set. Validation of the identified protective factor was conducted through Western blotting (WB) in mouse ESCC models and immunofluorescence (IF) in human tissues. Additionally, single-cell RNA analysis was conducted to explore the cell types expressing the marker genes. Results: 113 upregulated and 173 downregulated genes were found in the ESCC groups. WGCNA identified the blue module (13 genes) as most correlated with ESCC. We obtained a total of 13 intersecting genes. Among them, five genes formed the diagnostic model: Logit(P) = -24.4547 + 2.0567× Conclusion: We developed an ESCC diagnostic model and identified
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