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<p>Histone deacetylases (HDACs) correlate closely with tumor prognosis and serve as therapeutic targets for malignancies. This study aimed to explore esophageal carcinoma (ESCA) therapeutic strategies via identifying histone deacetylation-related prognostic genes. We analyzed The Cancer Genome Atlas (TCGA)-ESCA and GSE53622 datasets, along with 17 histone deacetylation-related genes (HDRGs). Differentially expressed genes (DEGs1) between tumor and normal TCGA-ESCA samples were identified; DEGs2 linked to HDRGs were screened via unsupervised clustering of TCGA-ESCA tumors. Candidate genes were defined as the intersection of DEGs1 and DEGs2, followed by functional enrichment analysis and regression analyses. TCGA-ESCA samples were stratified into high-/low-risk groups, with model validation. Independent prognostic analysis, gene set enrichment analysis (GSEA), immune/gene mutation/drug sensitivity analyses, regulatory network construction, and RT-qPCR validation were performed. The intersection of 3556 DEGs1 and 1622 DEGs2 generated 694 candidates, yielding 7 prognostic genes (UNC13C, MAP7D2, SCARA5 downregulated; SLC35D3, HSPA6, IL31RA, CXCL8 upregulated in tumors). Risk score and pathologic M were independent prognostic factors for nomogram construction, with favorable predictive value. GSEA showed enrichment in pathways like ECM receptor interaction and oxidative phosphorylation; immune correlation, drug sensitivity (e.g., WH.4.023), and RT-qPCR validation of gene expression were confirmed. This study established and verified a novel HDRG-related ESCA prognostic model. The model may serve as a potential tool for risk stratification in ESCA patients and offer new perspectives for exploring the role of histone deacetylation modifications in ESCA progression. However, its clinical translational value, including guiding immunotherapy or targeted drug selection, requires further validation in large-scale prospective studies.</p>

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Construction and mechanism of histone deacetylation related gene prognosis model in esophageal carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月18日

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  • Yao Gu,
  • Tianxing Ying,
  • Kaiyi Tao,
  • Qiqi Yang,
  • Jian Zeng

摘要

Histone deacetylases (HDACs) correlate closely with tumor prognosis and serve as therapeutic targets for malignancies. This study aimed to explore esophageal carcinoma (ESCA) therapeutic strategies via identifying histone deacetylation-related prognostic genes. We analyzed The Cancer Genome Atlas (TCGA)-ESCA and GSE53622 datasets, along with 17 histone deacetylation-related genes (HDRGs). Differentially expressed genes (DEGs1) between tumor and normal TCGA-ESCA samples were identified; DEGs2 linked to HDRGs were screened via unsupervised clustering of TCGA-ESCA tumors. Candidate genes were defined as the intersection of DEGs1 and DEGs2, followed by functional enrichment analysis and regression analyses. TCGA-ESCA samples were stratified into high-/low-risk groups, with model validation. Independent prognostic analysis, gene set enrichment analysis (GSEA), immune/gene mutation/drug sensitivity analyses, regulatory network construction, and RT-qPCR validation were performed. The intersection of 3556 DEGs1 and 1622 DEGs2 generated 694 candidates, yielding 7 prognostic genes (UNC13C, MAP7D2, SCARA5 downregulated; SLC35D3, HSPA6, IL31RA, CXCL8 upregulated in tumors). Risk score and pathologic M were independent prognostic factors for nomogram construction, with favorable predictive value. GSEA showed enrichment in pathways like ECM receptor interaction and oxidative phosphorylation; immune correlation, drug sensitivity (e.g., WH.4.023), and RT-qPCR validation of gene expression were confirmed. This study established and verified a novel HDRG-related ESCA prognostic model. The model may serve as a potential tool for risk stratification in ESCA patients and offer new perspectives for exploring the role of histone deacetylation modifications in ESCA progression. However, its clinical translational value, including guiding immunotherapy or targeted drug selection, requires further validation in large-scale prospective studies.