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Παρασκευή 28 Ιουλίου 2017

Tumor Purity As an Underlying Key Factor in Glioma

Purpose: <span style="font-size: 11px;">Glioma tissues consist not only of glioma cells but also glioma-associated non-tumor cells, such as stromal cells and immune cells. These non-tumor cells dilute the purity of glioma cells and play important roles in glioma biology. Currently, the implications of variation in glioma purity are not sufficiently clarified.</span><br /><br /><br />Experimental Design: <span style="font-size: 11px;">Here, tumor purity was inferred for 2249 gliomas and 29 normal brain tissues from five cohorts. Based on the transcriptomic profiling method, we classified CGGA and TCGA-RNAseq cohorts as the RNAseq set for discovery. Cases from TCGA-microarray, REMBRANDT, and GSE16011 cohorts were grouped as a microarray set for validation. Tissues from the CGGA cohort were reviewed for histopathologic validation.</span><br /><br /><br />Results: <span style="font-size: 11px;">We found that glioma purity was highly associated with major clinical and molecular features. Low purity cases were more likely to be diagnosed as malignant entities and independently correlated with reduced survival time. Integrating glioma purity into prognostic nomogram significantly improved the predictive validity. Moreover, most recognized prognostic indicators were no longer significantly effective under different purity conditions. These results highlighted the clinical importance of glioma purity. Further analyses found distinct genomic patterns associated with glioma purity. Low purity cases were distinguished by enhanced immune phenotypes. Macrophages, microglia, and neutrophils were mutually associated and enriched in low purity gliomas, while only macrophages and neutrophils served as robust indicators for poor prognosis.</span><br /><br /><br />Conclusions: Glioma purity and relevant non-tumor cells within microenvironment confer important clinical, genomic and biological implications, which should be fully valued for precise classification and clinical prediction.



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