Molecular Signatures of Cancer Stemness Characterize the Correlations with Prognosis and Immune Landscape and Predict Risk Stratification in Pheochromocytomas and Paragangliomas

• Published in: Bioengineering (Basel) Vol. 12; no. 3; p. 219
• Main Authors: Li, Lei, Liu, Shuangyu, Guo, Zeqi, Tang, Yueming, Zhang, Yue, Qiu, Ling, Li, Yue
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21.02.2025; MDPI
• Subjects: Algorithms; Biological activity; Biomarkers; Cancer; Cancer therapies; Correlation analysis; Data mining; Development and progression; Drug resistance; Gene expression; Genes; Hypertension; Immune checkpoint; Immunity (Disease); Immunology; Immunotherapy; Infiltration; Lymphocytes; Machine learning; Macrophages; Medical prognosis; Metastases; Metastasis; Mutation; Neoantigens; Network analysis; Paraganglioma; Patients; Pheochromocytoma; pheochromocytoma and paragangliomas; Prognosis; prognostic model; Regression analysis; Risk; Risk groups; RNA; RNA sequencing; Standard scores; Stem cells; Survival analysis; Tumor antigens; Tumor microenvironment; tumor stemness; Tumors; pheochromocytoma and paragangliomas; tumor microenvironment; prognostic model; tumor stemness
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering12030219

Background: 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. Method: 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. Results: Our study found that metastatic PPGLs had higher mRNAsi scores, suggesting the degree of tumor stemness could affect metastasis and progression. HRAS, CSDE1, NF1, RET, and VHL-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. Conclusion: 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.