Integrated Bioinformatics Analysis Revealed Stemness Features and a Novel Stemness-Related Gene Signature for Risk Stratification in Pheochromocytomas and Paragangliomas

Abstract

Abstract Numerous studies have shown that tumor stemness is closely related to the heterogeneous growth of tumor cells and their proliferation, distant metastasis, and resistance to chemotherapy. However, comprehensive studies on the stemness of pheochromocytomas and paragangliomas (PPGLs) are still lacking. The mRNA expression-based stemness indices (mRNAsi) reflecting tumor cell stemness were calculated using the OCLR machine-learning algorithm and PPGLs patients' RNAseq data from The Cancer Genome Atlas (TCGA). The relationship between clinical, molecular and immune microenvironment characteristics of PPGLs patients and mRNAsi values was investigated based on 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. The higher mRNAsi may be associated with tumor metastasis in SDHB wild-type PPGLs patients, meanwhile also demonstrated lower immune, stromal, and ESTIMATE scores and suppressive tumor immune microenvironment than the low mRNAsi group. The stemness scoring system could be used for the prognostic prediction of PPGLs patients with the high predictive ability (AUC = 0.908), and the patients with lower stemness-related risk scores demonstrated improved immunotherapy responsiveness in the TCGA-PPGLs patient cohort and the real-world cohort of patients receiving immunotherapy. In contrast, PPGLs patients with high stemness-related risk scores showed higher sensitivity to multiple chemotherapeutic agents.We developed and verified a novel stemness scoring system that can be applied to predict prognosis and guide the choice of treatment strategies.