Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study’s integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.

嗜铬细胞瘤/副神经节瘤（PGPG）是一种罕见的神经内分泌肿瘤。氨基酸代谢对于能量产生、氧化还原平衡和肿瘤细胞增殖的代谢途径至关重要。本研究旨在利用氨基酸代谢相关基因建立风险模型，增强 PGPG 的诊断和治疗决策。我们分析了 GEO 数据集中 PCPG 队列的 RNA 测序数据作为我们的训练集，并使用 TCGA 数据集和其他临床队列验证了我们的研究结果。 WGCNA 和 LASSO 用于识别中心基因并开发风险预测模型。单样本基因集富集分析、MCPCOUNTER和ESTIMATE算法计算了PCPG中氨基酸代谢与免疫细胞浸润之间的关系。 TIDE算法预测了PCPG患者的免疫治疗效果。分析确定了 292 个具有差异表达的基因，这些基因涉及氨基酸代谢和免疫途径。六个基因（DDC、SYT11、GCLM、PSMB7、TYRO3、AGMAT）被确定对风险预测模型至关重要。高风险患者表现出免疫浸润减少，但免疫治疗可能带来更高的益处。值得注意的是，DDC 和 SYT11 显示出强大的诊断和预后潜力。通过定量实时聚合酶链反应和免疫组织化学的验证证实了它们的差异表达，强调了它们在 PCPG 诊断和预测免疫治疗反应中的重要性。这项研究将氨基酸代谢相关基因整合到风险预测模型中，为 PCPG 风险分层、潜在的免疫治疗反应、药物开发和治疗计划提供了关键的临床见解，标志着这种复杂疾病的管理向前迈出了重要一步。