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First page of Identification of GALNT12 as a Novel Potential Diagnostic and Prognostic Marker for Esophageal Squamous Cell Carcinoma by Integrated Bioinformatics Analysis

Background: Esophageal squamous cell carcinoma (ESCC) is a highly fatal cancer with unclear molecular underpinnings. This study utilized bioinformatics to uncover key genes and pathways associated with ESCC and to identify prognostic markers. Methods: We identified the differentially expressed genes (DEGs) using three datasets (GSE53625, GSE67269, and GSE23400-GPL96). Meanwhile, Weighted gene co-expression network analysis (WGCNA) constructed gene co-expression networks based on the GSE23400-GLP97 dataset. Machine-learning algorithms further identified the most critical genes. Additionally, we validated the expression and diagnostic potential of the hub genes using the GSE161533 and GSE38129 datasets. Survival analysis and Gene Set Enrichment Analysis (GSEA) revealed the prognostic value and potential functions of the hub genes, respectively. Results: The study identified 240 DGEs (103 upregulated and 137 downregulated). Concurrently, WGCNA pinpointed 209 genes associated with ESCC. Subsequently, machine-learning algorithms identify four hub genes, including KIF14, GALNT12, MGLL, and EMP1. Moreover, their expression differences and potential as diagnostic biomarkers for ESCC were validated. Survival analysis indicated that elevated GALNT12 expression was associated with a poor prognosis of ESCC patients. GSEA delineated the involvement of GALNT12 in critical biological pathways. Conclusions: Our results identified GALNT12 as a novel potential diagnostic and prognostic marker for ESCC.

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Research Article
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S. Karger AG, Basel
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2025
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