Scholarlink

<p>This study examines the function of chromatin-remodeling genes (CRGs) in nasopharyngeal carcinoma (NPC), with an emphasis on their potential as prognostic and diagnostic biomarkers. We examined gene expression information collected from multiple datasets (GSE12452, GSE53819, GSE61218, and GSE102349) using a multi-stage methodology; we also performed differential expression, weighted gene co-expression network analysis, and functional enrichment analyses to identify pathogenic CRGs. A prognostic signature of six key genes—<i>CDC6</i>, <i>EZH2</i>, <i>PHF14</i>, <i>PRC1</i>, <i>RAD54B</i>, and <i>UHRF1</i>—was developed through machine learning methods and further validated in independent datasets. The identified genes were used to build a diagnostic model, which performed well (AUC &gt; 0.8) in both training and validation cohorts. This model was further refined using a nomogram and demonstrated high clinical utility, as confirmed by decision curve analysis and calibration curves. Furthermore, the study of immune infiltration showed a strong correlation between immune cell types and diagnostic genes, while single-cell RNA sequencing highlighted functional differences across epithelial subpopulations in NPC. Notably, experimental validation of <i>PHF14</i> indicated its involvement in NPC malignancy, with downregulation of <i>PHF14</i>-suppressing cell migration, invasion, and proliferation. These discoveries give fresh perspectives on the molecular processes of NPC and offer potential biomarkers for clinical diagnosis and prognosis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrated machine learning and single-cell analysis identify chromatin-remodeling gene signature for diagnosis and prognosis in nasopharyngeal carcinoma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月18日

查看全文
  • Yuanyuan Chang,
  • Wanting Qin,
  • Weiling Chen,
  • Zhiyu Yan,
  • Yang Xu,
  • Shuhan Ma,
  • Xiaodong Zhu

摘要

This study examines the function of chromatin-remodeling genes (CRGs) in nasopharyngeal carcinoma (NPC), with an emphasis on their potential as prognostic and diagnostic biomarkers. We examined gene expression information collected from multiple datasets (GSE12452, GSE53819, GSE61218, and GSE102349) using a multi-stage methodology; we also performed differential expression, weighted gene co-expression network analysis, and functional enrichment analyses to identify pathogenic CRGs. A prognostic signature of six key genes—CDC6, EZH2, PHF14, PRC1, RAD54B, and UHRF1—was developed through machine learning methods and further validated in independent datasets. The identified genes were used to build a diagnostic model, which performed well (AUC > 0.8) in both training and validation cohorts. This model was further refined using a nomogram and demonstrated high clinical utility, as confirmed by decision curve analysis and calibration curves. Furthermore, the study of immune infiltration showed a strong correlation between immune cell types and diagnostic genes, while single-cell RNA sequencing highlighted functional differences across epithelial subpopulations in NPC. Notably, experimental validation of PHF14 indicated its involvement in NPC malignancy, with downregulation of PHF14-suppressing cell migration, invasion, and proliferation. These discoveries give fresh perspectives on the molecular processes of NPC and offer potential biomarkers for clinical diagnosis and prognosis.