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<p>Pheochromocytomas (PCCs) and paragangliomas (PGLs), collectively PPGLs, are rare tumors with significant molecular heterogeneity, which complicates prognosis and treatment. This study analyzed publicly available datasets (TCGA-PPGLs, GSE19422, GSE60459) to identify differentially expressed genes (DEGs) related to mitochondrial autophagy and ferroptosis in PPGLs. We identified 6,286 DEGs, including 31 mitochondrial ferroptosis-related DEGs (MFRDEGs). A prognostic model based on four genes (<i>AMBRA1</i>,<i> EIF2S1</i>,<i> SRC</i>,<i> PHGDH</i>) demonstrated high predictive accuracy (AUC &gt; 0.9). Functional enrichment analysis highlighted key pathways, including mitophagy and Fc epsilon receptor I (FcεRI) signaling. Protein-protein interaction (PPI) and ceRNA network analyses revealed potential regulatory mechanisms. Calibration and decision curve analyses confirmed the model’s clinical utility. These findings offe<i>r</i> insights into PPGL molecular mechanisms, suggest prognostic biomarkers, and propose candidate therapeutic targets to improve risk stratification and personalized treatment. However, experimental validation is required to confirm their biological relevance before clinical application.</p>

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Identification of prognostic signatures in pheochromocytomas and paragangliomas based on mitochondrial autophagy and ferroptosis in TCGA and GEO datasets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Qingke Chen,
  • Zhiyong Xian,
  • Qian Zou,
  • Junming Bi

摘要

Pheochromocytomas (PCCs) and paragangliomas (PGLs), collectively PPGLs, are rare tumors with significant molecular heterogeneity, which complicates prognosis and treatment. This study analyzed publicly available datasets (TCGA-PPGLs, GSE19422, GSE60459) to identify differentially expressed genes (DEGs) related to mitochondrial autophagy and ferroptosis in PPGLs. We identified 6,286 DEGs, including 31 mitochondrial ferroptosis-related DEGs (MFRDEGs). A prognostic model based on four genes (AMBRA1, EIF2S1, SRC, PHGDH) demonstrated high predictive accuracy (AUC > 0.9). Functional enrichment analysis highlighted key pathways, including mitophagy and Fc epsilon receptor I (FcεRI) signaling. Protein-protein interaction (PPI) and ceRNA network analyses revealed potential regulatory mechanisms. Calibration and decision curve analyses confirmed the model’s clinical utility. These findings offer insights into PPGL molecular mechanisms, suggest prognostic biomarkers, and propose candidate therapeutic targets to improve risk stratification and personalized treatment. However, experimental validation is required to confirm their biological relevance before clinical application.