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. 2020 Jul 3;11:3288. doi: 10.1038/s41467-020-17139-y

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Characteristics of IDH wild-type GBM (N = 39), IDH mutant GBM (N = 2) and low-grade glioma (N = 9) tissue samples. Unsupervised hierarchical clustering with complete linkage was used to cluster samples based on the 1 – Jaccard coefficient as the distance metric. The type of mutations in the 8 most frequently mutated GBM genes⁵ are color-coded according to the legend. The multi-sample row displays multiple tumor samples obtained from the same patient as the same color; no color indicates unique samples. 5-ALA (within multi-sample features) indicates the intensity of the 5-aminolevulinic acid-induced fluorescence. b Overview of the multiplexed quantitative proteomic assay of glioma tissues. Trypsin-digested glioma (N = 50) and control normal tissues (N = 4) were tagged with a six-plex tandem mass tag (TMT): TMT127-131 for samples and TMT126 for the global internal standard (GIS) control. A total of 11 sets for 54 samples were prepared. High-pH fractionated peptides were subjected to liquid chromatography-tandem mass spectrometry to identify and quantify phosphopeptides and global proteins. See “Methods” for further details. c Coherence map of single-nucleotide variants (SNV) and single amino acid variants (SAVs). d Correlations between mRNA and protein levels in glioma tissue samples. (Top) Density plot of Spearman’s correlation coefficients between mRNA and protein abundance using the 8034 proteins detected in all GIS samples (N = 4071 at the gene level). Statistically significant positive correlations with a false discovery rate (FDR) <5% are indicated by the dashed-line box. (Bottom) Distribution of correlation coefficients for gene sets of interest. e Unsupervised hierarchical clustering of the 50 samples with global-proteomic data. Complete linkage and the distance metric 1 – Pearson’s correlation coefficient was used for clustering. f Genetic regulatory network activated in IDH wild-type tumors. The transcription factor–target gene regulatory network was formed by the significantly upregulated phosphoproteins and global proteins in IDH wild-type tumors using the OmniPath database⁶¹ in Cytoscape. Source data are provided as a Source Data file.