An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods - Yang, Jian; Wang, Jiajia; Tian, Shuaiwei; Wang, Qinhua; Zhao, Yang; Wang, Baocheng; Cao, Liangliang; Liang, Zhuangzhuang; Zhao, Heng; Lian, Hao; Ma, Jie (Frontiers in Genetics, 2021)
Background: Tumor purity is defined as the proportion of cancer cells in the tumor tissue, and its effects on molecular genetics, immune microenvironment, and prognosis of children’s central nervous system (CNS) tumors remain under-researched. Methods: We applied random forest machine learning, InfiniumPurify algorithm, and ESTIMATE algorithms to estimate the tumor purity of every children’s CNS tumor sample in several published common children’s CNS tumor sample datasets from Gene Expression Ominus (GEO), aiming to perform an integrated analysis on the tumor purity of children’s CNS tumors. Results: Only the purity of CNS tumor in children based on random forest (RF) machine learning method was normally distributed. In addition, children’s CNS tumor purity was associated with the primary clinical pathological and molecular indicators. Enrichment analysis of biological pathways related to the purity of medulloblastoma (MB) revealed some classical signaling pathways associated with MB biology and development-related pathways. According to the correlation analysis between MB purity and immune microenvironment, three immune-related genes, namely CD8A, CXCR2 and TNFRSF14, were negatively related to MB purity. By contrast, no significant correlation was detected between immunotherapy-associated markers, such as PD-1, PD-L1 and CTLA4, and most of infiltrating immune cells, and MB purity. Lastly, in the tumor purity-related survival analysis of MB, ependymoma (EPN) and children’s high-grade glioma, we discovered minor effect of tumor purity on the survival of aforesaid pediatric patients of CNS tumor. Conclusions: The purity of children’s CNS tumors and relevant nonmalignant cells within tumor microenvironment confer vital genomic, biological, and clinical implications, which should be fully valued for clinical management.