Li Lei, Liu Shuangyu, Guo Zeqi, Tang Yueming, Zhang Yue, Qiu Ling, Li Yue
Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China.
Department of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China.
Bioengineering (Basel). 2025 Feb 21;12(3):219. doi: 10.3390/bioengineering12030219.
Pheochromocytoma and paragangliomas (PPGLs) caused refractory hypertension in clinics. The sustained risk of local or metastatic recurrences or new tumor development prompted more research on diagnosis, prognosis prediction, and immunotherapy.
The tumor stemness is closely related to the heterogeneous growth of tumor, metastasis, and drug-resistance, and mRNA expression-based stemness indices (mRNAsi) could reflect tumor stemness. This was calculated based on OCLR machine learning algorithm and PPGLs patients' TCGA RNAseq data. The relationship between clinical, molecular, and tumor microenvironment (TME) features and tumor stemness was analyzed through the hub genes that best captured the stem cell characteristics of PPGLs using weighted gene co-expression network analysis (WGCNA), Cox, and LASSO regression analysis.
Our study found that metastatic PPGLs had higher mRNAsi scores, suggesting the degree of tumor stemness could affect metastasis and progression. , , , , and -mutant subtypes displayed significant difference in stemness expression. Patients were divided into stemness high-score and low-score subtypes. High-score PPGLs displayed the more unfavorable prognosis compared with low-score, associated with their immune-suppressive features, manifested as low macrophages M1 infiltration and downregulated expression of immune checkpoints. Furthermore, from the viewpoint of stemness features, we established a reliable prognostic for PPGLs, which has the highest AUC value (0.908) in the field so far. And this could stratify PPGLs patients into high-risk and low-risk subtypes, showing the significant differences in prognosis, underlying mechanisms correlated with specific molecular alterations, biological processes activation, and TME. Notably, high immune infiltration and tumor neoantigen in low-risk patients and further resulted in more responsive to immunotherapy.
We indicated that tumor stemness could act as the potential biomarker for metastasis or prognosis of PPGLs, and integrated multi-data sources, analyzed valuable stemness-related genes, developed and verified a novel stemness scoring system to predict prognosis and guide the choice of treatment strategies.
嗜铬细胞瘤和副神经节瘤（PPGLs）在临床上可导致难治性高血压。局部或转移性复发或新肿瘤发生的持续风险促使人们对诊断、预后预测和免疫治疗进行更多研究。
肿瘤干性与肿瘤的异质性生长、转移和耐药性密切相关，基于mRNA表达的干性指数（mRNAsi）可以反映肿瘤干性。这是基于OCLR机器学习算法和PPGLs患者的TCGA RNAseq数据计算得出的。通过使用加权基因共表达网络分析（WGCNA）、Cox和LASSO回归分析，通过最能捕捉PPGLs干细胞特征的枢纽基因，分析临床、分子和肿瘤微环境（TME）特征与肿瘤干性之间的关系。
我们的研究发现，转移性PPGLs具有更高的mRNAsi评分，表明肿瘤干性程度可能影响转移和进展。 、 、 、 和 -突变亚型在干性表达上显示出显著差异。患者被分为干性高分和低分亚型。与低分PPGLs相比，高分PPGLs的预后更差，这与其免疫抑制特征有关，表现为巨噬细胞M1浸润低和免疫检查点表达下调。此外，从干性特征的角度，我们建立了一个可靠的PPGLs预后模型，该模型在目前该领域具有最高的AUC值（0.908）。这可以将PPGLs患者分为高风险和低风险亚型，显示出预后的显著差异，其潜在机制与特定的分子改变、生物过程激活和TME相关。值得注意的是，低风险患者的免疫浸润高和肿瘤新抗原多，进而对免疫治疗更敏感。
我们表明肿瘤干性可作为PPGLs转移或预后的潜在生物标志物，并整合多数据源，分析有价值的干性相关基因，开发并验证了一种新的干性评分系统以预测预后并指导治疗策略的选择。
