Abstract
Background: Tumor purity is defined as the proportion of cancer cells in the tumor tissue, and its effects on molecular genetics, the immune microenvironment, and the prognosis of children’s central nervous system (CNS) tumors are under-researched. Methods: We applied random forest machine learning, the InfiniumPurify algorithm, and the ESTIMATE algorithm to estimate the tumor purity of every child’s CNS tumor sample in several published pediatric CNS tumor sample datasets from Gene Expression Omnibus (GEO), aiming to perform an integrated analysis on the tumor purity of children’s CNS tumors. Results: Only the purity of CNS tumors in children based on the random forest (RF) machine learning method was normally distributed. In addition, the children’s CNS tumor purity was associated with 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 the immune microenvironment, three immune-related genes, namely, CD8A, CXCR2, and TNFRSF14, were negatively related to MB purity. In contrast, no significant correlation was detected between immunotherapy-associated markers, such as PD-1, PD-L1, and CTLA4; most infiltrating immune cells; and MB purity. In the tumor purity–related survival analysis of MB, ependymoma (EPN), and children’s high-grade glioma, we discovered a minor effect of tumor purity on the survival of the aforementioned pediatric patients with CNS tumors. Conclusion: Our purity pediatric pan-CNS tumor analysis provides a deeper understanding and helps with the clinical management of pediatric CNS tumors.
Highlights
As the most frequent solid tumors in children, pediatric tumors of the central nervous system (CNS) represent an array of molecularly and clinically diverse entities
Regarding the tumor purity distribution of the GSE85218 dataset (MB) (Figure 1B), the tumor purity based on the InfiniumPurify algorithm was bimodal, while that based on the ESTIMATE algorithm was skewed and focused on 80% or more of the total area, but the tumor purity resulting from the random forest (RF) algorithm was normal, with an average tumor purity of 73.7 ± 4.5%
For the GSE64415 and GSE65362 datasets (EPN) (Figures 1D,E), the tumor purity based on the ESTIMATE algorithm was skewed, with an average value of 85.95 ± 8.01%, and that based on InfiniumPurify was skewed, with an average value of 67.1 ± 22.4%, but that based on the RF algorithm was normal, with an average value of 68.4 ± 4.6%
Summary
As the most frequent solid tumors in children, pediatric tumors of the central nervous system (CNS) represent an array of molecularly and clinically diverse entities. For the past few years, high-throughput techniques have been increasingly applied in the field of pediatric CNS tumors (Kumar et al, 2018). These techniques offer some new means for the clinical diagnosis, prognostic prediction, and precise classification of pediatric CNS tumors. The DNA and RNA extracted from such a mixture are from all of the cells involved, so the measurement result is a kind of mixed signal (Zheng et al, 2017). Such a sample mixture may bias the downstream analyses and could mask true biologically meaningful signals. Tumor purity is defined as the proportion of cancer cells in the tumor tissue, and its effects on molecular genetics, the immune microenvironment, and the prognosis of children’s central nervous system (CNS) tumors are under-researched
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