International Journal of Molecular Sciences

Article Open Data for Differential Network Analysis in Glioma

, Claire Jean-Quartier * † , Fleur Jeanquartier † and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. †  Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 

Abstract: The complexity of diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer expression analysis, we apply refinement as well as enrichment analysis via and conclude with graph-based visualization of involved interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as or RAS proto-. By using selected exemplary tools and open-access resources for cancer research and differential network analysis, we highlight disturbed signaling components in brain cancer subtypes of glioma.

Keywords: open data; cancer research; differential network analysis; differential ; protein-protein interaction; graph-based analysis; glioma; glioblastoma multiforme; astrocytoma; biological data integration

1. Introduction The understanding of complex diseases such as cancer requires insight into high dimensional data, underlying domain knowledge and multiple networks that provide relevant relationships among biological entities [1]. We thereby introduce the topics of open data, biological data in the context of gene expression and protein interactions, the approach of graph-based analysis, as well as the biological background for signaling in exemplary cancer diseases of glioma.

1.1. Web Resources and Open Data Cancer databases include various data types ranging from biological and pharmacological data, including information on genome, proteome as well as metabolome, and clinical information on experiments, incidence, mortality, prevalence and survival. Common issues of data retrieval are access restrictions or poor annotation due to sharing reluctance and fear about lack of control over

Int. J. Mol. Sci. 2020, 21, 547; doi:10.3390/ijms21020547 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2020, 21, 547 2 of 17 its usage and personal credit [2]. Still, the long history of producing data has been revolutionized and shaped its form of being laborious, costly and fragmentary to so-called big data generated by high throughput techniques and large multi-site collaboration projects [3]. This open sharing of data requires structures to secure common guidelines to data formats, standards, meta-data, intellectual property rights, licensing and sharing protocols. There are a wide range of datasets available open for data mining, predictive modeling or other purposes [4]. Obtainable data enable many hypotheses to be tested in silico, saving time and money, and maximizing efficiency [5]. Various data resources can be of interest including biomolecular repository hubs, additionally, offering not exclusively upload and analysis options or links to publications. In order to avoid discrepancies, one has to rely on globally normalized and quality controlled public experiments [6]. Biomolecular data types include cancer-related whole genome and large-scale genomic sequencing data, copy number alterations, DNA-methylation, different types of mutations, microarray data, microRNAs, RNA sequencing data, protein–protein interaction (PPI) probing, protein mass spectrometry, drug-target relationships, further biological and pharmacological data, as well as cancer incidence, mortality rates, prevalence and survival rates. In this paper, we focus on gene expression and PPI data.

1.2. Gene-Expression Data The unique pattern of gene expression for a given cell or tissue is referred to its molecular signature and defines a particular class of tumor. Each subtype holds distinct biological properties affecting clinical matters, such as metastasis likelihood and patient survival prognosis as well as tailored therapy strategies. Differential gene-expression data can be used for deriving marker genes indicating multiple types of or specific to individual cancers [7]. In reference to the latter group, signaling pathways have been pointed out to be consistently and highly enriched across different types of cancers, such as Wnt, and integrin signaling pathways, as well as others like phospho-APC/C-mediated degradation of A and inflammation determined by chemokine and signaling pathways. The co-occurrence of alterations in these pathways may differ between individual tumors and tumor types [8]. Differential gene expression data can be aligned to differences between cell-lines, wild-type and mutated or knock-outs, between disease and control samples or simply different disease subtypes. Gene expression is mostly assessed by transcriptomic methods [9]. Whole transcriptome analysis allows thousands of genes to be studied at once. The sheer abundance of generated data by high performance methods makes it difficult to encompass all contents. In detail, problems are raised for pre- and post-processing steps such as gene mapping, data cleaning, storage, retrieval and analysis infrastructure. The analysis requires the steps of image analysis of the microarray, normalization methods, data analysis through estimation, testing, clustering, discrimination and its biological verification and interpretation. There are several challenges, including sequence reannotation, , or simply numerical data interpretation. It has to be kept in mind that using data from microarray experiments is based on various assumptions. These include the direct relation of protein to mRNA content, equal mRNA capturing proficiency by the applied method, no impact from perturbations and foremost by no means on housekeeping genes. The different experimental settings and methods generate heterogeneous data sets that can be hardly merged and is rarely annotated. These aspects ask for a unifying framework, including standardizing methods. Official gene expression repositories are, to name a few, Expression Atlas [10], ArrayExpress [11], Gene Expression Omnibus [12], dbGAP [13], the European Genome-phenome Archive [14], IntAct [15], Japanese Genotype-phenotype Archive [16], Biological General Repository for Interaction Datasets (BioGRID) [17], NCBI PubChem BioAssay [18], cBioPortal [19], and many others, and involve the implementation of MIAME standards, the so-called minimum information about a microarray experiment. Since EMBL-EBI’s Expression Atlas [10,20] Int. J. Mol. Sci. 2020, 21, 547 3 of 17 provides gene expression from many experiments that are highly curated and normalized, we use data from this database to base our study on. Identifying patterns of gene expression supports characterizing subtypes of cancer towards a more personal treatment approach, such as in the case of glioblastoma [21]. Discrepancies when comparing cancer cells with normal cells arise from the utilization of heterogeneous cell mixtures of the organ in question instead of single cell samples of cancer cells and their nearest benign neighbor cells [22]. Recently, a framework was published for partitioning the variation in gene expression due to a variety of molecular variables, including somatic mutations, transcription factors, microRNAs, copy number alternations, methylation and germ-line genetic variation [23]. Meta-analysis allows for expression data from various public repositories to be merged in order to make group comparisons that have not been considered before [24]. Recently, a web-based application was published which can be used to download, collect and manage gene expression data from public databases [25]. Integrating gene expression data and PPI networks has been presented for the prediction of essential proteins [26].

1.3. PPI Networks and Graph Analysis Protein interactions determine cellular communication based on signal transduction cascades. These resemble molecular circuits consisting of receptor proteins, kinases, primary and secondary messengers which modulate the gene transcription or the activity of other proteins. Databases provide disease-specific as well as general PPI information [27–31]. PPI data can be based on wet experimentation as well as prediction [32,33]. Integrative tools for visualization of PPI networks thereby facilitate exploration and analysis tasks. PPI networks are commonly modeled via graphs whose nodes represent proteins and whose undirected edges connect pairs of interacting proteins [34]. These graphs easily comprise large quantities of elements in a four to five-digit range or even more. Analysis thereof often requires automated methods for subnetwork identification with topological or functional characteristics [35]. Visual representation of interaction networks has the objective to give a review on data and to reveal otherwise hidden patterns by augmenting cognition in order to make sense of often large quantities of abstract information [36]. Graphs can be visualized by modern approaches mapping abstract data by visual transformation and subsequent rendering, such as the prefuse force-directed layout [37]. There are several graph-level features that suit the comparison task according to the existing literature on graph comparison [38–40]. Network analysis comprises several computations based on the number of network elements such as nodes and edges, the network diameter, path length, clustering coefficients, degrees and shared neighbors as well as computational construction of differential subnetworks. Thereby, common graph-based algorithms have been developed, such as Molecular Complex Detection (MCODE) [41] or Clustering with Overlapping Neighborhood Expansion (ClusterOne) [42]. Graph-based approaches to cellular network analysis have been comprehensively reviewed before [39,43,44]. Finding clustering entities as separated part of a network also involves outliers that are different from the remaining dataset or overlapping subnetworks with hub nodes belonging to multiple entities. Protein complexes could be identified as being part of dense regions containing many connections in PPI networks, but multifunctional proteins could be part of several clusters [42]. Biological network analysis requires both the topological information and the biological background which is commonly represented by Gene Ontology (GO) terms comprising cellular components, biological or molecular functions.

1.4. Signaling Background Cancer is based on oncogenic mutations resulting in aberrant signaling in tumor cells and effecting increased mitogenesis, prevention of , as well as increased cell motility and invasion [45]. Cancerous growth occurs upon an unbalance within genes that control cell proliferation [46]. Cell proliferation is defined by cell cycling behavior and activity, which is known as the growth Int. J. Mol. Sci. 2020, 21, 547 4 of 17 fraction whereas the term describes the increase in mass and cell proliferation relates an increase in cell number [47]. We concentrate our study further on genes associated to proliferation. As exemplary class of tumors, we chose to focus on glioma. Glioma comprise different subtypes of glial tumors including astrocytoma and glioblastoma multiforme (GBM). Thereby, the latter group is the most aggressive type and to date, non-curable [48,49]. Astrocytoma derive from star-shaped glia cells that are called astrocytes [50]. Glioma cells firstly experience disrupted pathways of cell cycle control, including mutations in , CDK-4, and RB1 [51]. Pro-apoptotic signals are hindered mostly through tumor protein p53 (TP53) mutations. These tumor cells further exhibit overexpressed growth factors and receptors, such as the transforming growth factor alpha (TGFA) and the receptor (EGFR) based on mutations [52]. The vascular endothelial growth factor (VEGF) is further postulated as angiogenic switch and tumor progression [51]. Invasion and migration are modulated by signaling events as the overexpression of extracellular matrix molecules and cell surface receptors. In more detail, glioma can be roughly subdivided into four grades, including low (LGG) and high grade (HGG) types. HGG, grade IV, also called glioblastoma multiforme (GBM) are the most frequent and malignant brain tumor. Thereby, glioblastoma can be distinguished, as primary when developing rapidly without a less malignant precursor, or as secondary, progressing from LGG as astrocytoma [53]. Secondary glioblastoma exhibit mutations in isocitrate dehydrogenase 1 (IDH1) and TP53, uncommon in pediatric malignant glioma [54,55]. IDH1 can be used as biomarker for secondary glioblastomas in addition to several newly identified key biomarker genes, such as PRDX1, based on Support Vector Machine Learning using differentially expressed genes [56]. Our study focuses on GBM in comparison to low-grade astrocytoma and general glioma, that surpasses several diseases within the general group of glioma tumors, including both IDH-wildtype and IDH-mutant glioma.

2. Results We identified NetworkAnalyst [57], Navigator [58], OmicsNet [59], WebGestalt [60] and Cytoscape [61] to freely create and visualize a PPI network for comparison for further analysis of selected genes. Among these, Cytoscape, NetworkAnalyst and OmcisNet are suitable tools for creating protein-coding gene networks based on gene lists. Both NetworkAnalyst and OmicsNet are web-based tools that offer a StringDB interface for calculating PPI, while Cytoscape does not offer a StringDB interface, but, among others, a BioGRID based one. NetworkAnalyst and OmicsNet use the same confidence level of 90% and include only those relations that are already marked with evidence by default. These two tools compute similar results regarding subnetworks, nodes, edges and so called “seed” proteins. Since the calculation is mostly the same, with differences between 2 nodes and edges only, we can assume that the PPI computation is unambiguous. We therefore proceed with only comparing the different results between the graph result of BioGRID and the one of StringDB. Though, StringDB has been shown to be the more comprehensive source [31], the sum of interactions calculated by BioGRID is far more compared to StringDB. This is due to the fact that StringDB-based networks were filtered on a high confidence level of evidence-based information, and BioGRID-based network construction includes all available data without any evidence or confidence filter. In NetworkAnalyst, one can specify the organism human at the beginning. Instead, each constructed BioGrid network included data from all available taxonomies. By filtering on human taxonomy, the networks were thus reduced around 15% in number of nodes. Constructed PPI graphs from the different network analysis tools using filtered gene expression data from EMBL-EBI Expression Atlas as input are illustrated in Figure S1 for BioGrid-based networks and in Figure S2 for String-DB constructed networks. Networks were then filtered to unique nodes by subtracting the merged intersection of all three disease type-specific networks, illustrated in Figure1 in case of BioGrid-constructed networks and in Figure2 based on StringDB. The resulting numbers of nodes and edges as well as calculated parameters from network analysis, including network diameter, clustering coefficient and connected components for each disease- and database-specific network, are summarized in Table1. Int. J. Mol. Sci. 2020, 21, 547 5 of 17

The comparison of graphs in Figures1 and2 illustrates that the StringDB-based graphs’ size is far smaller and does not show any potential overlapping protein complexes. This is also true for the comparison of originally constructed networks without subtraction of the intersection equal to all disease subtypes, presented in Figures S1 and S2. All BioGrid-based networks consist of around five times as many elements and interactions compared to those constructed via StringDB, as shown by Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 5 of 16 the number in Table1. Several subnetworks could be distinguished by cluster analysis via Cytoscape withinwithin the the larger larger networks networks while while only only a small a small number number of of clusters clusters could could be be observed observed in in graphs graphs based based on theon the latter latter database. database. (A) (B) (C)

Figure 1. Graph clusters from BioGRID showing unique difference networks between the intersection of allFigure three 1. disease-type Graph clusters constructed from BioGRID networks showing and each unique type-specific difference networknetworks with betw (Aeen) general the intersection glioma, (B)of glioblastoma all three disease-type multiforme andconstructed (C) low-grade networ astrocytoma.ks and each Graphs type-specific were rendered network with with Prefuse (A) general Force Directedglioma, Layout, (B) glioblastoma clustered by multiforme ClusterOne and (grey: (C) outlier,low-grade yellow: astrocytoma. overlap, red:Graphs cluster). were rendered with Prefuse Force Directed Layout, clustered by ClusterOne (grey: outlier, yellow: overlap, red: cluster). Merged intersections and unions of the different disease- and database-specific networks as well as their uniqueMerged diff erencesintersections among and each unions other of are the summarized different disease- in Table and1 anddatabase-specific visualized in Figuresnetworks1 and as well2. Networkas their data unique exported differences as cys-files among can each be found other onare https: summarized//github.com in Table/schokine 1 and/ Cytoscape-glioma-CFvisualized in Figures .1 and 2. Network data exported as cys-files can be found on https://github.com/schokine/Cytoscape- glioma-CF.Table 1. Di fferences between constructed networks from StringDB and BioGRID: counts of nodes and edges as well as cluster analysis. Table 1. Differences between constructed networks from StringDB and BioGRID: counts of nodes and Clustering Connected Network Isolated edgesSamples as well as cluster Nodes analysis. Edges Coefficient Components Diameter Nodes BioGRID-based networks computedClustering by CytoscapeConnected Network Isolated Samples Nodes Edges glioblastoma multiforme 2507 6108Coefficient 0.076 Components 4 9Diameter Nodes 3 general glioma 1907BioGRID-based 4100 networks 0.063 computed by Cytoscape 4 8 3 low-gradeglioblastoma astrocytoma multiforme 1842 2507 3813 6108 0.035 0.076 44 89 3 3 mergedgeneral intersection glioma 1718 1907 3439 4100 0.008 0.063 144 98 13 3 merged union 2567 6340 0.076 4 9 3 low-grade astrocytoma 1842 3813 0.035 4 8 3 merged intersection StringDB-based 1718 networks 3439 computed 0.008 by NetworkAnalyst 14 9 13 glioblastomamerged multiforme union 538 2567 911 6340 0.229 0.076 54 99 0 3 general gliomaStringDB-based 392 559 networks computed 0.130 by NetworkAnalyst 6 10 0 low-grade astrocytoma 368 489 0.103 5 12 0 mergedglioblastoma intersection multiforme 333 538 379 911 0.048 0.229 185 109 10 0 mergedgeneral union glioma 565 392 1005 559 0.217 0.130 36 1210 0 0 low-grade astrocytoma 368 489 0.103 5 12 0 merged intersection 333 379 0.048 18 10 10 2.1. Network Analysis of GO Terms and Genes of Interest merged union 565 1005 0.217 3 12 0 The GO-term proliferation has been originally used to filter overexpressed genes within the extensive2.1. Network datasets Analysis of each of GO general Terms and glioma, Genes glioblastoma of Interest multiforme and low-grade astrocytoma. ProliferationThe GO-term is related proliferation to malignancy has andbeen was originally thereby us chosened to filter as exemplary overexpressed key termgenes for within further the analysis.extensive This datasets filtering of step each was general computationally glioma, glioblastoma necessary duemultiforme to the high and number low-grade of genesastrocytoma. which couldProliferation not properly is related function to asmalignancy input for theand diversewas thereby tools withoutchosen as error-free exemplary processing. key term Thefor further list of genesanalysis. was thereby This filtering reduced step from was the computationally magnitude of multiplenecessary thousands due to the to high less number than hundred of genes entries. which Filteredcould genenot properly lists that function were used as in asput input for forthe networkdiverse tools creation without can e berror-free found forprocessing. each of theThe threelist of genes was thereby reduced from the magnitude of multiple thousands to less than hundred entries. Filtered gene lists that were used as input for network creation can be found for each of the three glioma subtypes on https://github.com/schokine/Cytoscape-glioma-CF/. Filtered datasets were used to create PPI networks using BioGRID and StringDB, illustrated in Figures S1 and S2. The difference networks from unique nodes to each sample dataset, isolated by subtraction from the intersection of all three sample networks, were used for further GO analysis to highlight significant biological functions. Exemplary GO-terms with low p-values within all three sample

Int. J. Mol. Sci. 2020, 21, 547 6 of 17 glioma subtypes on https://github.com/schokine/Cytoscape-glioma-CF/. Filtered datasets were used to create PPI networks using BioGRID and StringDB, illustrated in Figures S1 and S2. The difference networks from unique nodes to each sample dataset, isolated by subtraction from the intersection of all three sample networks, were used for further GO analysis to highlight significant biological functions. Exemplary GO-terms with low p-values within all three sample networks are presented in Figure3 and Table S1. The general glioma network comprises 219 nodes related to transmembrane receptor protein tyrosine kinase signaling pathway with higher significance compared to other sample networks. The GBM network comprises 446 nodes related to cell cycle regulation at highest significance in comparison to other sample networks. The low-grade astrocytoma network does not further show any biological function unique to its disease subtype in comparison to general glioma or GBM. Within Figure S1, there are three unconnected nodes common to all three BioGRID based networks, which can be easily recognized within the merged union-network of all three disease subtypes in Figure4. These nodes represent the protein-coding genes HDAC4 with its corresponding ensembl-ID ENSG00000068024, PPP2R5A as ENSG00000066027 and MAP2K7 as ENSG00000076984. These genes are overexpressed in all three datasets of general glioma, glioblastoma multiforme as well as low-grade astrocytoma. 4 (HDAC4) is involved in deacetylation of histones, leading to a repression of transcription [62]. regulatory subunit B56 alpha (PPP2R5A) has been implicated in a variety of regulatory processes, including cell growth and division, muscle contraction, and gene transcription based on the regulatory mechanism, protein phosphorylation, which is commonly employed in multiple cellular processes such as cell cycle progression, growth factor signaling, and cell transformation [62]. Mitogen-activated protein kinase kinase 7 (MAP2K7) acts as an essential component of the MAP kinase signal transduction pathway [63]. The general function of mitogen-activated protein kinase (MAPK) cascades is to relay environmental signals to the transcriptional machinery in the nucleus and thus modulate gene expression [62]. There are several matching genes within the top hub nodes of networks constructed by BioGRID compared to the far smaller ones by StringDB, presented in Figure5 and further details in Tables S2–S4. Top hub nodes from glioblastoma multiforme networks contain six matches, namely cyclin dependent kinase 2 (CDK2), (CCNA2), (CCNB1), erb-b2 receptor tyrosine kinase 2 (HER2), (CCNE1), and cyclin D3 (CCND3). Five out of the ten top hub nodes from general glioma networks from StringDB and BioGRID are matching genes containing erb-b2 receptor tyrosine kinase 2 (HER2), erb-b2 receptor tyrosine kinase 4 (HER4), cyclin D3 (CCND3), TEK tyrosine kinase (TEK), and transforming growth factor alpha (TGFA). Seven out of the ten top hub nodes from both BioGRID and StringDB constructed networks of low-grade astrocytoma samples accord with each other, namely erb-b2 receptor tyrosine kinase 4 (HER4), annexin A1 (ANXA1), vascular endothelial growth factor A (VEGFA), (CCNE2), interleukin 6 receptor (IL6R), transforming growth factor beta 2 (TGFB2), and CD86 molecule (CD86). Some of the identified genes are hub nodes within clusters, as specified in Tables S2–S4. BioGrid constructed networks include several clusters. Top ten hub nodes of glioblastoma multiforme involve CDK2, HER2, CCNE1, HIF1A, CDKN1A and CDK1 as part of cluster 1. Further interrelation was given by CCNA2, CCNB1 and CCND3 belonging to clusters 3 and 4. In case of general glioma, top hub nodes included HER2 and HER4 as part of one cluster; as for astrocytoma, the top hub nodes listed CCNE2 and CDK2 as part of one cluster and VEGF and NRP1 as components of another cluster. In case of the StringDB constructed networks, there were hardly any clusters isolated due to the smaller number of nodes and edges. Still, in case of glioblastoma multiforme, HER2 was identified to be a part of cluster 1 also involving other top hub nodes of proto-oncogenes KRAS, HRAS and NRAS. The epidermal growth factor receptor (EGFR, HER1) is a major regulator of proliferation in tumor cells and a well-known target in glioblastoma treatment [64]. The human EGFR related family of receptor tyrosine kinases (HER) includes next to HER1/EGFR, additional members HER2 to HER4. The latter genes are well studied for their involvement in aberrant signaling of but also Int. J. Mol. Sci. 2020, 21, 547 7 of 17

within other tumors including glioma [52]. ANXA1 is also known to be involved in proliferation, differentiation and apoptosis, serves as a substrate for EGFR, and is implicated in tumor progression of Int. J. Mol.astrocytoma Sci. 2020, 21, [x65 FOR]. VEGFAPEER REVIEW is involved in promoting tumor-induced angiogenesis and implicated7 of 16 with brain tumor progression [66]. Transforming growth factor receptor beta (TGFB) is likewise involved in expressionInt. J. has Mol. been Sci. 2020 shown, 21, x asFOR a predictorPEER REVIEW of poor survival in glioma effecting tumor progression [68]. 7 of 16 the regulation of cell proliferation and postulated as a target for glioma therapy [67]. IL6R expression CD86 as ligand for the costimulatory receptor CD28 is involved in intratumoral T-cell stimulation has been shown as a predictor of poor survival in glioma effecting tumor progression [68]. CD86 as [69,70]. expressionTEK is known has beenfor glioma shown tumor as a predictor progression of poor [71]. su Cyclinsrvival in as glioma key cell effecting cycle factors tumor are progression part of [68]. ligand for the costimulatory receptor CD28 is involved in intratumoral T-cell stimulation [69,70]. TEK is the regulationCD86 as machinery ligand for behind the costimulatory tumor cell proliferation receptor CD28 [72]. is involved in intratumoral T-cell stimulation known for glioma tumor progression [71]. as key cell cycle factors are part of the regulation [69,70]. TEK is known for glioma tumor progression [71]. Cyclins as key cell cycle factors are part of (Amachinery) behind tumor cell( proliferationB) [72]. (C) the regulation machinery behind tumor cell proliferation [72]. (A) (B) (C)

Figure 2. Graph clusters from StringDB showing unique difference networks between the intersection of all threeFigure disease-type 2. Graph clustersconstructed from networ StringDB ks and showing each type-specific unique difference network networks with (A between) general the glioma, intersection (B) glioblastomaofFigure all three 2. Graphmultiforme disease-type clusters and constructed from (C) StringDBlow-grade networks showing astrocytoma. and unique each type-specific Graphs difference were networks network rendered with between with (A) general Prefusethe intersection glioma, Force Directed(Bof) glioblastomaall three Layout, disease-type multiformeclustered constructed by and ClusterOne (C) low-grade networ (grey:ks astrocytoma.and outlier, each yellow:type-specific Graphs overlap, were network red: rendered cluster). with with(A ) general Prefuse glioma, Force Directed(B) glioblastoma Layout, clustered multiforme by ClusterOne and (C) low-grade (grey: outlier, astrocytoma. yellow: overlap,Graphs red:were cluster). rendered with Prefuse Force Directed Layout, clustered by ClusterOne (grey: outlier, yellow: overlap, red: cluster). transmembrane receptor protein tyrosine general glioma kinase signaling pathway

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glioblastoma post-translational protein modification multiforme regulation of phosphorylation astrocytoma regulationregulation of cell cycle of phosphorylation astrocytoma 0 10203040 regulation of cell cycle -log10 (p-value) 0 10203040 Figure 3. Significant GO-terms within PPI networks of GBM, general glioma and low-grade astrocytoma -log10 (p-value) Figure dissections:3. Significant based GO-terms on elevated within expression PPI networks levels ofof genes GBM, associated general withglioma GO-term and low-grade “proliferation”, astrocytomaenriched dissections: via BioGRID, based diff erenceon elevated from mergedexpression network levels intersection; of genes associated significance with expressed GO-term as p-value, “proliferation”,calculatedFigure 3.enriched by Significant BinGO. via BioGRID,GO-terms difference within PPI from networks merged ofnetwork GBM, integeneralrsection; glioma significance and low-grade expressedastrocytoma as p-value, dissections:calculated by based BinGO. on elevated expression levels of genes associated with GO-term “proliferation”, enriched via BioGRID, difference from merged network intersection; significance expressed as p-value, calculated by BinGO.

Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 8 of 16 Int. J. Mol. Sci. 2020, 21, 547 8 of 17

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API4 HNRPC DIWS PCLP RPL32 EBP DAD1 TIH1 AIP ENX-1 ALY/R EFIP O A1 PSMA5 P0 PSATD NONO eIF3-p36ARC4 1 sid2895 PGD INC A1 MYOD FAM113A PI-9DUTT1 DCD NMMHCBHNRNPMCMT2N GDIL MGC :152 43 MPP -6 Rpn6 EIF3 HNRNPF SPR210 EF2 GIG9 RP3-395M20.6SFRS7 RP11-297I6.1 POR GUA HUPF1 PORIN SERRS WC RF13U21.1 5 RP5-908M14.8KPR P FER 1L3 HSD48 TKT1 FH RPS14 FAM133B NSE RPL8RP11-290D2.1 SERCA3 EFP1 RP11-151D14.2MOR F 4L1 RRAS2 RPL41 RP3-357D13.2RA-A47 TUBA1 C H1s-2 AHCY ZIP LIMCH1A PSMD9RP11-223J15.3 RP1p21-Rac1 GIG38 CDCD2 SUPT16H EIF3S 3 H2B.1 PSMB6 LRRF IP2 ATP 5 B S3 CTPS1eIF3-p46 CTG-B45d EIF-5A2 MS L-1 SGII YWHA1 KOC 1 RAD6AHCRNN4 RADIL MYR 1 2 SKAP NPYY5-R CCT3PCTK1 SLC25A3 AB LIM1 RPL30 HEL-S -303 PARP10 BMP2K HEL-STMO -128m D1 MAT1GIG8 P44BDP LT15 SYND4 KMT2E CGI10HDR UNQ601 ST38 CLIP3 BTBD2 S12 GID3 NPEPPS FBX20 FASN LOC100419906 ZAG RPL31 HLH4 SMC-4 H2B/f RP4-697E16.3 FAM39B MT1 RPS28 ALS 2CR7 L18 P LTN MC VD MAC F2 PNN ABC G8 PCBP2 ERP5 RP1-120G22.10 CMD1W DBPA PRO2446AAC1 HIP4S23 ALTAP DC HADHA mitAAT ES L-1 DDX17UNQ374/PRO7 10 S11 RP11-175D17.1RPS17 LDHAserrate TRA2A hBUB1 CT103 RP11-228B15.2 H2AH S10 COPB SDCCAG3 Hs.89862 GSNRIR1 hCG_21329ST13 NMP-22 RPL35 TC4 BC2 GAL7 LDHB CAP-Rf G3BP2 CPN10 FLI EIF2 PCMT1 TES RPS7RPL12 FBP ANAC BXLBV68 USP6NLP8 Cctq RPS16 Cep110 SMAD2H9 NCBP2-AS2PCBP1TMO D3 MUK3 4 BMP2 CGI-90 HEIR-1 ALS 1 8 MIG 1 0 UNQ891/PRO1906 p116Rip NAP1ATPRBM17 5D H-YPT3 CYB5R3 PSMD8 EB1 KMT3 B CTSA RP11-13G14 LCF S 2 CPAMD6 CEBPG H2AFM p12DOC-1 S24 RBM7 FLN-A RPS9 DHRS1 MBC2MDA20QARS SEC23A RAB15 emb-5 CRHBP RPL34 PHB1 P5CSLUZP 1 MDCR DSP PRDX2L26 SVIL AC TL6 A bHLHe32 CDHF1 ATV CAD LYZ UNQ314/PRO357/PRO1282 SYNPO NAP1 Nomo DADB-110M10.1 P AXIP 1 DFNA26 UGPP1 CPP32B YE AT S 2 MS N CBF-C MLP LLREPIC ER 3hVam6pTHO1 E L-II SIR2 EPB41 RPL36 PNADHKE3RP4-740B20.2 SCKL6 WDF1 RP13-444K19.2BAP PA2G4 LEURSma go h LATS 1 TMSL37 A HSPC139 K6 IR SMA3 3 87 IL1eIF3-p110 5R A VAT1WDR1 YWH AE IBP -9 NCBP 1 PTP4A3 TGFB1I4IMP D2 AMCX1 SRB SMAD1DBP1CBF-B HELB p100 CDC16 IL-1RAc P ABP L ADF DBA1 RBPSUHP3 COXPD7BIRC6SF3B3DFNB37 GIP TAF II7 0 RP5-975D15.1 CTHBP AKR 7 RNASEH2C NEDD-8 RPS4X CCNB3AFAP 1 RP11-269F19.3FLG2 SNF2L2 PTB3 RPL10_5_1358 SPTB2 NP UBC12 CD20L L27 TG T GADD45B RFG7 ERH SSAC15orf42 ZC3H18 DCTN5 KR T1 8 ARVD MYBL2 RBM4 MS TP 01 3 PKCLS3A PLEKHG2 GADD45gamma beta-globin D DX3 Y TAL-H p23 SPTAN1 TO P1 MT RBPMS PC3 CABP BBC1 APOOS13 MARCKS UBA80 ZNFN1 A5 HARP 11 My021 GMNN UKVH5dSNL KSRP DFNB2 4 CAF-1 D0S117E GOLGA2 M130AP OD TAF 2F ELF3 UNQ437/PRO868 RP3-447F3.5 L19 HEL-S -72 KIAA14 67 PIG50 GANAB SET ALKBH8 SDHAF2 CDW4 PIG5 LCRMC MP B1 IDP POLA1 DBC2 DRP2 KAP 1 LP C2 PEF1B CCNA1RP5-1009E24.3C14orf43 MTHF D1 DCTN4 JBS HPR1 MAP K10 TSCsa-19 C CKBB RP1-3J17.2 WRAP 73A1U CKS1BP7 FATP2 MDH HHR23A HsCDC1 8 HEL-220 RBP5 CARKDRP11-529I10.1 PPP1R163 D6S231E TP M4 hGCN5 PP1221 VDUP 1 MR D1 5 RPTOR UIMC1 GMPPB p23 AS TE 1 HUBSNF2 A-2 70 G 1. 2 TP P 2 RP11-80I15.2 TG F B2 NSUN1 NHP2L1 PUF60 HsCDC7CCT-epsilon CBF-B ERp-72 BS69 RP1-76K10.1 GID4 NEURL4 CEBPA MPP2 RBM3 MNB AAC2 EIF2B1NORPEG PRIC295 DA2B PHA2E BAT1NIN USP22 LGL2 RP11-296L22.3 PRAT4A RP6-239D12.2 CYPB COII SUG-1HEL-S -47e PDE4DN2HuCHA60 OFD1 COXPD6 FKBP r38 p33 COPS5 CAC1 TID3 FBX30 MRLC2 RP11-159J2.3HMFN0395 MIR K GLBA AAT3RP11-350E12.2 CCT2p34 HERC5 H10BH FXR1 DOC1 SEH1L CCDC14PRO2900 C2CD5 H1F0 OSCP PDIA1 RP11-307C12.6 CDC20 IP O 9 PRDM14 ERV29 G17P1 GR ASP65 THC4 RNF200 AKAP8 CDC20C RPC8FAD CLIC4 PPP2CA EFEMP2 PRKCI ACTR1A RHOGAP2 PRP10 O-GLCNACAR H1 2 ORC2L NRIP2 IR AK Gp95 SLBP S MTN L2 CAPZBH12.3 CNOT6L HERC1 TUBB2CRP11-552M11.5 SRA4 H1.5 RBL1 H1F1 STK1 ZC 3HC1 SCDGF-B RPIB9 CDS1 CYS1 MITPOLD OTIN PRDX3RP11-437A18.1 SEL1L EPPK RP11-472F14.2 Zfp291AUNA1 ESPL1 RP11-47G11.1 MAR C H-V C10orf18 MAWD ATP 6 AP 1 CFL1 Dp-1 RBAP1 CCNA2 PR MSTP 01 9FLAP1RAR RALBP1 ZADH2 DCTN6 BCAR3 bca REEP4 IL-13RAP S ANX6 RP11-125A15.2 PDIA3 NPAT RBBP1 PSC5 ZNF661 NOELIN1TARBP -B TUBB2A CCNB2 USP37 PPP1CB S5 MAD2 DBS NTRK1 ALS 21 PHI ALS 6CYCE2 KAT2RBL2UBCH5 B B PRPSAP2 ALP S2B POTE-2 RPL4 LP GP 1 CRIg RP5-952N6.2ER K7 CCNE1 GSK3B Mud1 RP1-177P22.2 SRAC14orf18 EEA1 RP3-422H11.1 HDPTP HAAP ADE 2 H1 KLF1 0 PSMA6 IR 1 B4 PNAS-27PARVA Alb FBX1 KLCt PUF XAB2 RCC2 TRAP 3 GCP-1 VAP -B FEN1 NPDC1 ANAP C4CCNB1CDC27Hs DSPA2c STAR IS Y1 HEL-S -113 CDKN1CRB1 TAR S L2 MMI-b e ta ADOHR CKSHS2 CNA43RIS2 CDK3 TCEB1L UBF-1 FAN RP11-57G10.3 B56B HCA88 HYPT8RP1-14O9.1 NCAM ZNF280C HEP-COP CDC2L5RP11-354A16.1 HEL-S -87p CDKN1A p45 HIST1H4A NGB GMPR2 MYL6TAR S RHOBTB3 HEL-S-71 SRRF PDB3 IKAP POLD2 CD79B DTWD2 HEL-S -82p RLFB Lag SIRT6CUL1 SGK HOX2 HYD EFCAB3 HSPA9 HDDC1 CCBP 2 IP O A5 C XXC 9 CEF1 DOPEY2 CDHF5 RLKDIP13alpha TP ID RINGO3 C DK6 CETN ER81 GPATC1 hSlu7 CSBP CDABP0061 -4 AP C11 HSPC238 HRH1 GSTT1 TNIK RP11-30C23.1AR MC 6 CRPM SMC3L1 RPL14 PIG54 CYITMS A Im p 7 UHRF1BP1L NRIF3 p300 ZNF144 PFTAIRE1 B-raf S100A7cRP11-181E22.3 NXF2 RER1 TIMD3 PTDSS1 FKBP52 Sm-F AH NA K SSB p202 HECTH2 TMEM2RP11-108L7.3 PAGE3 LIMA1 CSTA ATIC PCNAC DKN4 CAMIISP1 PSMD7 C 11o rf5 KIAA114 3 RP11-4O1.1BTC p38 RNF12 S20EF-Tu MTBP P34CDC2RNF53 CCND2 MRE11 GAS2L1 BTG1 COXPD21 AC AD7 TO P I UBL5 E2-CDC34 ORC1SRRM2 STAU MRX7 4 HSGT1 SLITRK3 G7A CBPCLH-17FBXO4 RBP6 PC3 RP11-66D17.10-003 NBP 2 TG FA PRR14 HUSS Y-17 PTC4 PSMD10 HOX3 SLC7A6OS VAC 1 4 ISPK-1HLC14-06-P PS1TP5BP1UCHL1 HKD CCND3 REV3 HIP2 DCTN1 RNF213 DPM1 CGI-17 FBL KU80 MYO1 B RAD6B L28 P1.1-MCM3 IRF1 COIL PPP1R59 XAP 2 DFNB35 DNCH1 VPS35 PPP1R126 NDR1 CCNG2 Cwc1 CML33 TEC PHF23 EAP TS P YL2 C AND1 PPP1CC Sm-B/B FNEPPK TUBB5 RP1-295C6.1 C OG7 PRKCE TRAP 2 CYPH BR INP1 TS S C4 Mp n1 LMBRSC1 1 NSEP1 PCDHGA3 EVA ATPSRA 5JG LKP TDP X2 EOMFC Sm-D2 CDK5 RANBP1 DCLREC 1C NNE PARK11XR RA1 DCE R2 ZNF364 FBHOk HEL35GPIP137 Nbla10236 TRAP D PRKN My0 3 1 KU70 RPRGL3C DK1 1 -p 58 Caf1b MAF1ALPFBXL3A S1 A C4.4A SCKL1 CD51 PC4 AS E 1 KPC1 hfzo2 C14orf41 PP14434 H2AFY SH2D5 DIMT1 DUSP 20 RP11-112J3.1D6S230E LMNL1 AP P CDK4 Rsc8PP-1AZNF363 CDHF4hCAF-1 LIG4S HSF1 CPR2 PDP1 AR GBP2 BRCC45 LARP 4 S H UJ UN -2 PPP1R160 RNF75 HDAC3 AS CC 3L1DDX35 HCERN3 RBM41 WW C2 AL DH 3 A2 MFAP 1 NSHUR HU-1 EIF4A1 PLAC-24 SERTAD1 RP11-88M19.2 C14orf11 RP6-213H19.1 HYCC2 CGI-25 PLK1 TO K-1 PLS3ARF6 PRKBA AS NS D H2B/a PRPF6 PTD015 DNAH7 HMG3 FAM68ACDC25A2RPS27 PRAD1 HRCA1IPZK418.4 O A3 CG-NAP CLR11.1 RPRD1A XPO4H2-ALPHA CAPH2 DBR1 AP105A XP O1 PPP1R170 SSK1 CGI-23 MAP1 LC3ADJ10 PIS1 BS-84SEC10 LATS 2 CTRCT30 Fbx31 P16INK4A PTPAUSP7 p97MAPKCARMA1 JFY-1 SEC6 MC G1 8 P2 bTrCP IG HA1 CGI-21 IL4 R DOK2 TIM MRXHF2 DECp44 CUL4A BA2R BAP MYST3 RP11-108L7.8 CHD3IP NBEAL2 SLAP-2hCG_2004980Mt-SS B MAS1GRP-170HMG2 ESR MADH3 DDX60 se20-10 RSRC2SLFN11 EEF1GRPL23 HOGACDC25 RP-A flj32422DYNAMITIN MORG 1 SLX1A KIAA1008FT1 ERBB2IP GLIF PSCTK3 RAP1 GDS1 DNAPRP11-109A6.1 K UNQ570/PRO1132MP FD PR61G C14orf145 OTB1RABEP1 BCAS3 CCDC47MTDPS 5 RAB3ALRP -8 SMIM5 CBX2 PP2A-Abeta YAR S COPS6 FBL2 CNOT1 FBL4 AQR SNX1 ATAD1 CLC-6 PGII HSPA5 TR X1 UBC DXS423ESAPK1c PLA2G4A PRPF28 RP11-14H3.2 SHC3IRS 1 PKC-ZETAOSBP1 YWHAQ CIRBP SNRNP220 Sm-D3 KRTAP15-1PANK4 PLD2 LIAG LYN PROMMPHKD RPS15A hCDC14 SPEN SOCS7 VR1 P R KAA1 PSEC0121THD6S182 CYT3 ILRS Fip3p CDC21 NXF1 BARD1 BTRCP2 NF-AT3 SPACIA1 HEXDCZCCHC18 EIF3S5 PTPRT MSTD BCLAF1 MFD1 KID EHD1 HsSwt1 TRAP P C3 LRRC 40SLASTAI4 UTP15 XP O 2 SSA c- C-ERBA-BETA fS AP b AR HG AP 2 9 SMG1 GID5 MTS 1 NPM1 NCOA1 HECTD3 EFTUD2 Clf1 FAH MP 7 8 TBL3SFRS2A RNF126 TAFIDHM 2C RP11-413M3.10 YWHAAMR D19 PIG32 AAC 2 ANCRKLKL3 STAP-1 PPP3CB RIGB RP5-1029K14.1PAK1 HADHB IBMP F D3 PPP2R5D HMBOX1 TO R4A PDK1ANKR D1 3B PAX6NEB NTHL1 MXRA1 VIP 2 1 GRNFLG3 UBE2I SPICE1 SMYD4 CDH5 FAK2 CD282 RP4-655J12.1 HMG17G3PD CCNJ LIP D CTA-268H5.7 CAP-H C21orf106PSMC3IPIG HG 3 NAALADL2 ANKRD13 SEC15B CDH2 D2DRRASSF4RP11-284B18.5 RGS4 TRP 53 PRMT5 KRTAP10-7 NURIT RP11-205M9.4 PIG43 ZC3HDC2 TMEM111 DC22 PMI1 ATP 5C VAP -3 3 RPA135 CFF EIF2AK1NMHC-II-A HDAC2 SMD1 T160 Ta t-C T1NDUFA9RP11-212K13.2 SGPL1PPARG1 VAV-2 YWHAS RIN1 TIF1 B FBXW6 BTD NEDD1 ICER E-1 MKK2 VRSTAT1 AP CTNNG CD340PDCD6IP A1 FOXO1ANEUROD1CREB2 HsT18361 TIP 49A IRS -2 C6 JTK3 TFALCAM R MYL YWHAD IDD M2 0 VTN GON-10IA1 CAMKI CCNG NCOA2 VAN MYS T4 UBOX4 SESN2 RCCP1 IP O 13 FRAP1 RP11-243J16.11-006ATP 2 B1 DDB1 P AK7LTBP -3 AKAP 12 EIF2B2 USP8 PLSCR1MMP4CT129 GABRA3 PPP1R140GBF1 IMB1 USP9 RP11-513I15.2 DBF4 IP O A7 UBADC1GCIP HEATR4 KBTBD7MSTPImp4 070 PIXB hLON M6 B GNB2 CPSF4L DARS C5orf28 SH2BC3orf6 SHIP2 SH3BGRLBARR2 EGF IT K RP11-107M16.3 TP C PNMA1 Sb1 ICA1 AIO PIGEA14 GA17 v- NFKB3 P105 OPRT SDR12C1 FAKBLKNbla00127 EB-1 HSPA8 CLTA EFTU PRPPRB1 DNAJA1 HSPABP2LCAMB ODF3L2 P58 AR RVB2MC G40 308 NCOA-62 PRKM1 RAINB1 NADSYN1 IG HV12 CCD1RP11-452K12.5AP 50 NBAT IG FIR GOT1MGC:9072L7 PPM1B RP11-490F3.2 ZZZ4 BTG3 HBA-T3A1 RCN2 RASA4BSAST170 FLAF3 SOCS1 NCK2 LDLCQ4 CFC PTPN1 ZNF114 KIAA1217 MR T5 RGS16 NCKAP2 ATP 5AL2 CCDC88A RP11-298J23.2 NCDN PRPF16 CK-40 JADE-3 PLAAPTPRU PON2 HSP90N ING 5 NEK8 NUCKS IQ G AP 3 JIP-1 tcag7.1075ADMIO LCAM HUMORFA01 CCTETA ORC4 c- NTR1 STAF54 PDE UBC7 PPP6C RP11-227D2.1RP11-753C18.1VIL2 CSK SHC1 CLONE-23970 AAG6IF T5 4 MO V1 0 DBP PLEKHA4 CNOT11 dJ342K12.1 TD AG 51 PY160 PRKM8IPL PARK1 RAM2 GCSH TRS31 FAC1 MLS TD2 EIF5BSPTLC1RP11-498N2.1SHEP1 RFC5HPTPA CAV2MAS A P85BPLC-IV RP11-177A2.3 JADE-2 C14orf123AAA-TOB3Atg21 LGALSNS5 1 RP11-118B23.1 ERCC1 JM21 RP5-823N20.1TTC9 B NWASP TUBB4 ZFLGMD2L 21 mGlu4ITGB1TENS 1R-PTP-kappa CLPX FGR PCPTP1C1orf155P P 2C -DELTA PSD95 B-ALP HA-1 HEL-S -101 NEDD2TS R3 CUL7 BRD1 ATX1 HT019 RP11-108L7.13-001 EPN1 ErbB-3 DHTR MIS 1 4 MEAF 6 MS T0 9 6FAM118B RP11-297A16.3 TOMM70A VAV3 SOS2OSMRB PTPN18 CMS CSNK2A1 POLRA ERG28 JADE1 MYO9A bA311H10.1 MG C : 13UNQ440/PRO873 7 5 IT S N2 PIGS MIG 1 SEC61B YBE Y TRP M-2 RP11-278J17.2 TCF-3 GAK CMSS1 GSTK1 ABC A1 STAP2DOPEY1 EWS YAP 1 ARD1 CIG PPP2R5E LP C1 MYS T2 DAZL PIM KIAA0368 FAM98A TU LA-2 GPATCH9 STYX CTNNA1 TAT-SF 1 RP5-1049G16.3 BUB3L SERPINB12 SOCS3 HSP70-1 SYKEEN-B1GAB1 CIS-1 omentin AS H2 hHLP1 APC A CTBP1UBE2S EPS8 ORC5 SLI ARX ANKS1 RBAF600 SCCE SMARCE1 P89 RAB1A FerT DLK LIG1 GLCECLN2 MS T3 BRPF1 TRN2 NSFSDF2 TS G 10 RCCP2 VAV PIG61 GS1-358P8.4UNQ175/PRO201DFN7 P38BETA2 C1orf123 SH2BP1APLP 1 MR G P R G-AS 1ILV2 H SS SSH1L SMURF2H23AG hsp70 S3 MAP 1S RIBC1 CNP1ANKRD13D SFRS14 NUP93MG ST -I SFDGAL3 CDC37 HSP70-2 KI-RAS OBSL1 hSod1 CDK9 WDR26EXOC8 AF-1OST1 PRP11-142I17.1 C21orf56 DOK4 WWOX AUF1 REL BPM90 HU-K4 UNQ1941/PRO4424 PSMA3 CD98HC HsT287 EIF2B5TMEM25PTK6 TF AP 2 C MEAI KMS D DX1 9 B HNUP1 53 PRKACA CLAPG1 SH3KBP1 PRO34088p38 PPP1R13BRANBP16 MKK1 IN C E NP H2AA RP11-481A12.7cam-PDE 1C ZNF573 APE1 GrbX CDG2L RIP CHK1 AP C TEX13A ZNF294RP11-336K24.3 CREAP-1 FAM66E MS TP 01 0 PRLTS3 C1orf42 PTK5 NSLL STAT5AEKV3 HECW2 CYTSA ZP R9 RNPC7GRL IG FBP 2 CRKL RSS PFK-1 H1.10 ZF P -161 LARP 2 EAR7 C23 CM-AVMTNK2 C2orf61LAP 1B ZBTB1 RP11-164L23.1HEL-S -43 HYX CCDC67 NANOS2 ARFG EF1 BCIE AR R B 1 SNX2 TUBB6 XLPD NEDD4-1SBEM PEG1 PCNX CBL-SLPGC1HEATR2 RP4-547C9.3 HTT UNC80 ZTNF R 9HRMT1L3AP 2 -ALP HA p55-GAMMAGRB7 P120CASERRFI1 BM-015 RP11-98D18.5 FKSG13EHD4 CTBP2 C TTN HIP-9 ARF4 HIST1H3A RP3-524E15.1 FLN29 HOOK2 RME8 HXB TRN ERBB4MY08PC 76G05 NRPS998 MLL2 SMT3H2SIRT7 OSR1 MLXIP L IP O 11 SNX29 DPP4 BSCL3NET2 SH3GL3RP5-963E22.1 UCK2 INrf2 GPIG4 KR S2 POC7A EGFL10 HCAP1 METR S CLNK LC-PTP MAP 4K1 PGD2 C13orf11BND7 RP5-1039K5.11-004GluK4 SEC15L1HGS MU-1A ATXN7L3BHIST2H2AA3VHR JUND MLL1RP11-48O20.1 BIG-3 TF G CK8 ATG 4B MON2 SULT1A1 NRG1-IT2DD AP 2-BETA STIP1 XLMRRAB5C IN G 4 BAG-5 XP OT TAG-1PERB11.2 ACTR2 B55A hdm2 P53BP2 KIAA1712 RP11-93H12.1 PAR17 EJ30 KCTD9PCCDC146 2 MINO S1DNAJC11 GPR69A nck-1FE65LAAG1SAP1 RAD WAIT1 IN C AM-1 00 MR X9 9 SEC8 RAB31 SHC4 RP11-263K15.1p85-ALPHA GLCLRAT P 1 B L27A SIN3A TAF 1D ORCASDR32C1 NELL1 RET RP11-148B2.1 NKAT-5 CD49DINO80R NUMBLMIHA HUTCH-I TULAP90 FBLN7 SHP-1LP130Cas FAM186A HSPC250 PRO1635ARF1 C1orf103 LSH RING5 IRE M-3UNQ517/PRO812HUSSSFRS10 Y18 NT5DC2 MTS S K SDP1 SLSN4 CD130 GFAP CCDC123 MEGF6 ZNF297 GTP WAVE 3 AAA LTK POPX2 EIF2B3 LMWDS P 3 HEATR3 H3.4 SOX2 MO B4 C3G GINGF NCKAP3 MKP 6 PTK7 SLX1B CT61 KNTC 1AP GPR69B TAFII170 RP11-356B19.6AP 1 G2 hTID-1 RFC38 dJ468O1.1 HCF-2 SFT ANXA4 SNRPA1 RP4-756G23.7 AAMP CAML UBXD8ER C21orf13 HYLTK ATP4A PCT EML6 HEL-S -77 p PAK4 RBQ3 NF-kappa-BEWS-WT1 ATF 7IP 2 GPATC2DYNC1LI1EPPKDOK5LQT1 TIM50HLAA P62DOK TO M1HADH2 L1 dJ579F20.2 DWFC RP11-357H24.1 COG4 EDPF1 PFKFB3 COL17A1 CPR3 TEL2 ProSAP1TNFR60 EXO70 C1-TEN ANX3 1 HFAF1s CSD MR T39 AP3M1 COA3 TES S P 2 hCG_2040524 XP O5 STAT5B hCGBP SCKL5 SPG30 AP MC F1 FANCD2REPRIMO RP11-98G7.2 RPUSD3 USPL1 KIAA15 2STA 4 SLCO3A1 PPM1A BAI1 COG1 UGT1A10 RASSF10 EEF1B2 DSCD75 SEC13MIHB AF6q21 RP3-407F17.2 SLP-76 PFK-P Km CUL5 ANLN MAP 4 RABGEF1 ACOT9 ZAP -7 0 FN3KLZF YVE28 C15orf29 hCG_2043352NIK ECS M4 ASCT2 STAT2 HDAC7A ADAM5 HEL-S-102MVC D1 U2AF2 SLC45A4 DOCK10 LYP ClC-5 A7MYCBP 32 2 HIST2H2BA RPA3HN1A MTDP S6 RP11-739N20.5AFGPRO1304 3 L2 PST LCC S5 KIAA05 53R2 HCK SERTAD4PHP1BVEPTPHC90 UCPH RP11-72J7.1 RP11-353C18.2TMEM159 PID1 PFKL NDFIP1 RP11-435O5.3EIF1AYSLC45A2 L38 Fbx6b KNS L1 PP2AAALPHAOR2F2 LUZP 5 IL 1 R A P L2 RP5-944N15.1 FES IT GB4 DNAH8 WUGS C:H_GS034D21.1LIS CH7 DEF1 FANCH CD4 STILBTBD18 CTPP1 RIOK2 RP4-580O19.1 SHAP GTC90 TGFBR5 THADA Ulp1 TEKT2SPCS2 SPAG7 NGLY1 R NAS E N BAG2CEBPB SETX BANF AR FL1 POLR2B TTC27 PSP1ERR-PTP-ETA LEC2 DOK5LXP O6 SELP JNK2B DALRD1 p49 RIPK6 DITHP USP25 MYO1 D BS CI-30ARHGAP 32 GEMIN6 PCH1A DHFR SH2D1B ME D1 2 HTF9C TRIML2 IGFBPCESP-1 8 HSPUM EM9 PRKCB2 UREB1 TUBB4 phosphacan FOXB1NB GOT1B MU-1B NOL1 MIR2 2HG H2AX EIF 2B4 SMAD1 DFS70 OBFC2B LOC4 004 99 H-IDHB VE GF R 2 Rpn2 TRN-S R LAT1 HEXIM1 COQ10D4 ADAMTS 1 CEP104ADRM1 UNQ243/PRO276 LRRTM4IP O 8ARMCMAP X5 2K4 PCTA1 HSP70-2SLC25A49 C11 orf84 PCH1B SAPS1 ZF R1 HSD11B1PSMA4 HIST4H4 P110BETA TO P AZ1 FADS2IMD2STT3A RRP20 AF6 ATXN2 KAP 18.9HNRPH1 UFS2 PRO1843 POTE2alpha PRKAG1 HYA22 CALU GPSN2 OR2W3 EDM3 GRB14GABARAPL2asw SEC61A1 ARFIP 2 C6orf120RPML8DYRK5 IDH3A NS7 HSPC017 FR OUNT L17 PDXDC1HIHGHH p55 BM88 TTC37 GC P CNTF p100 bA366E13.1 FSGSERCC4RP3-393D12.1 SMC2L1 Sigma3A PPP5 SJS2 PRO2743 P136 CRP CIS6 SPDA1 CBR1 CPSD MAP 2 K3NCLN HOX3I KR T2 DTS G3BP1 CLCP1MAP3K3CALM CTLN2 SYT17 IG LL5 SHGC-10760 CVAK104 MS TP 00 8 HKII SNX4 CCDC60PTPRD NDUFA4L2 mint3 UNQ2507/PRO5995RP4-559A3.4 HsT441 FP793 smg-10 SUP45L1HbpAKRRP10 4 EDEM1 RAP55 SRRM4 P3.58 NRP1 hBLVR NTG MBD3 AC C HTR3AIG HG 1 USP34 HSPC028 GCP-2 RP1-37M3.7 PGR10EPB41L2AC TA2 P45 MUC16 IBP-7 CAIR-1 RHOGAP1SERAC1DYMPLE RNF125 KRT3 T10 TAF A4 N4WBP5 A LRS AM1 PSEC0109 AMP ACT CNTFR SLC7A5 SERPINB4 FKSG23 CHCHD3 PP1201 HVLP LTR P C3 FDPS PCDH-BETA11 PLVAP SRPR NACT HBA-T2 ZZZ3C HAS M PRKAR1B FBX25 MC 3DN5 HACC2 75 OSM C12orf58 AKAP-17AHCR-NTPaseTBK1 RUSC2 P23 CSBP PGRMC1 PRRG4 HNRNPL hCG_39321 ESA GEMIN3HK33 ABC D3 C1orf3 FLT1 PR130 ARD1 DDD1 TRMT10 C C17orf26 PRPF4 HILDA SGSM2 YNL0 2 6 W CT114 hHb6 OC-116kDaTJP1 LFA-3 CKMT1 P11 GPC1 UBIAD1 Prp2MAC 30 WBP -2 HIP1 AZI OK/SW-cl.10 ZBTB1 6 RP11-34E5.1 SLC12A2 SPRKPWCR RP11-528G1.4 RAP46 WD R 6 WS S HASPP28 RWDD1 AAC11 PDGFR2 USP31 LASS2 CLTD RPS15 SCGB1D1 BSF3 BCL6 hNRD2 TO M22 EPM1B TCR E E4 -DBP EIF3-P116 BAR NY-MEL-3 VEGF L POP101 CTA-215D11.1 CSTF3 MRT4 THES 2 HSP-CBF ZC3H4 MMP7 HAR COL1A2 CD3Q PDGFRB SPARC DIABLO MIHC COL1A1 STX16 UNQ288/PRO327 MLAS A1 SMGP21 UNQ597/PRO1183 RNF137 DUSP 22 MC5DN2 PDGF-A RAB5 RP11-5K23.1 SDOS EIF4EL3 NME2P1 CBP HAL SCAN-1 PARK15 RPTPG RAB14 CEB TIMM1 7A DBP UNQ174 /PRO200 ADAM 21 RP4-675E8.4 PNAS-136 MTC H 2 DKIR PTRH2 eLOX3 CCNYL1 C-H-RAS SVIP PRLTS4 AR F5 H_LUCA16.1 HqkRP11-472G23.5 Mcl-1 IKBIP GNA-11 FAT1 LRF N3 PP3610 INP P 5 A NKG2DL2 HLP2 Gi Ipi3 NRN1 RP11-263F14.1 TLDC1 DUSP 28 OCC1 JM4 S TL1 CYB5B LAMTO R4 KC TD21 CSNK1G3 IBG C5 RAB9 SRP54 RP11-132G19.2BSVD Ragulator1 SB144 s LZIP GNA13 GNG5 UBTD2 DUSP 23 PALM TXNDC CUEDC1 COL3A1 OPLL ERGIC-32 C2orf33 HOX1 ADIP Q TL1 AR CND1 NM23H3 VTI1L ATF C 14o rf24 PICT-1 GNA14 C17orf35GNAO1 HAI SYNGR1 HIBDL DJ473B4 RAB10 NIR RPL26P1 HO-2 EBNA1BP2 PTCD3 HsT17016 PEREC1 RBM34 BRIX DXS9 82 2 DCAKD KR I1 CRLZ1 TPA AP OL-II RP11-494N1.1 MYBBP1A MR P -S 5 L15 ERICH5 RP4-696P19.1 NSA2 FATJ NOC4 Cwc26 DG6 TMX3 p22 RBM28 bMRP47 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Figure 4. Merged union network of general glioma, glioblastoma multiforme and low-grade astrocytoma samples, constructed from BioGrid in Cytoscape, rendered with Prefuse Force Directed Layout, clustered by ClusterOne (grey: outlier, yellow: overlap, red: cluster, blue: unassigned/default). Figure 4. Merged union network of general glioma, glioblastoma multiforme and low-grade astrocytoma samples, constructed from BioGrid in Cytoscape, rendered with Prefuse Force Directed Layout, clustered by ClusterOne (grey: outlier, yellow: overlap, red: cluster, blue: unassigned/default).

Int. J. J. Mol. Mol. Sci. Sci. 20202020,, 2121,, x 547 FOR PEER REVIEW 9 of 1716

general glioma glioblastoma multiforme low-grade astrocytoma

BioGrid BioGrid BioGrid

StringDB StringDB StringDB

HER2 HER4 CCND3 TEK TGFA CDK2 CCNB1 CCNA2 HER4 ANXA1 VEGF CCNE2 IL6R TGFB2 CD86 HER2 CCNE1 CCND3 Figure 5. High-degree genes of general glioma, glioblastoma multiforme and low-grade astrocytoma samples,Figure 5. presentHigh-degree in BioGrid genes andof general StringDB glioma, constructed glioblas networkstoma multiforme upon top and ten low-grade hubs. astrocytoma samples, present in BioGrid and StringDB constructed networks upon top ten hubs. 2.2. Comparison and Mapping of Different Database-Constructed Graphs 2.2. ComparisonWe compared and StringDBMapping of to D BioGRIDifferent Database-Constructed networks by mapping Graphs BioGRID table columns via Biomart differentWe compared node identifiers. StringDB While, to BioGRID by using networks Ensembl by gene mapping identifiers, BioGRID we could table findcolumns 41 similar via Biomart nodes withindifferent both node glioblastoma identifiers. multiformeWhile, by using networks, Ensemb byl usinggene identifiers, the Ensembl we protein could find identifiers, 41 similar we nodes could findwithin 42 both matching glioblastoma nodes, and multiforme by using BioGRID’snetworks, humanby using readable the Ensembl label within protein Cytoscape identifiers, to mapwe could with StringDB’sfind 42 matching labelprovided nodes, and by by NetworkAnalyst, using BioGRID’ similars human to readable HUGO Gene label Nomenclaturewithin Cytoscape Committee to map (HGNC)with StringDB’s symbols, label we found provided 137 intersecting by NetworkAnalyst, nodes between similar the two to GBM HUGO networks. CommitteeWe then (HGNC) tried to symbols, use Cytoscape’s we found build-in 137 intersecting column mapping nodes between tool by mappingthe two GBM HGNC networks. symbols to EnsemblWe then Gene tried IDs. to Since use Cytoscape’s HGNC labels build-in are written column di ffmappingerently depending tool by mapping on database, HGNC formatsymbols and to tool,Ensembl this Gene mapping IDs. isSince far from HGNC comprehensive. labels are writte Wen further differently tried depending to map on database, gene ids toformat Ensembl and andtool, couldthis mapping map over is 80%far from of the co nodes.mprehensive. Missing We 30 Ensemblfurther tried gene to identifier map Entrez were gene added ids manuallyto Ensembl to theand Ensemblcould map gene over id column80% of the of thenodes. imported Missing network 30 Ensembl from Biogrid.gene identifier The column were added was then manually complete to butthe Ensembl one gene. gene Moreover, id column ensembl of the hasimported limitations, network such from as Biogrid. there being The nocolumn ensembl was gene then idcomplete for the recombiningbut one gene. binding Moreover, protein ensembl suppressor has limitations, of hairless pseudogenesuch as there 3 (RBPJP3),being no thoughensembl of gene no relevance id for the in ourrecombining use case. Webinding experienced protein comparablesuppressor columnof hairless mapping pseudogene results 3 insofar (RBPJP3), as mapping though HGNCof no relevance symbols resultedin our use only case. in We less experienced than half of comparable the Ensembl colu genemn identifier, mapping whileresults adding insofar them as mapping up with HGNC Entrez mappingssymbols resulted to complete only nearlyin less allthan (in half glioma, of the only Ensembl eight had gene to beidentifier, added manually while adding and in them astrocytoma, up with alsoEntrez only mappings eight). to complete nearly all (in glioma, only eight had to be added manually and in astrocytoma,We further also merged only eight). disease-specific networks from StringDB and BioGrid, respectively, by using the Ensembl Gene Id as intersecting nodes. Table2 shows the consensus results. In the case of GBM, the resultingTable intersection 2. Merge network Results from counts StringDB 411 unconnected and BioGRID-based nodes, general disease-specific glioma intersectionsnetworks. resulted in 301 unconnected nodes, and astrocytoma intersections resulted in 285 unconnected nodes. The Samples Intersecting Nodes Union of Nodes complete node lists as well as Cytoscape source files can be found at https://github.com/schokine/ glioblastoma multiforme 411 2634 Cytoscape-glioma-CF. general glioma 301 1997 low-grade astrocytoma 285 1949 Table 2. Merge Results from StringDB and BioGRID-based disease-specific networks.

We further merged disease-specificSamples networks Intersecting from NodesStringDB and Union BioGrid, of Nodes respectively, by using the Ensembl Gene glioblastomaId as intersecting multiforme nodes. Table 2 411shows the consensus 2634 results. In the case of GBM, the resulting intersectiongeneral network glioma counts 411 un 301connected nodes, 1997general glioma intersections resulted in 301 unconnectedlow-grade nodes, astrocytoma and astrocytoma 285 intersections resulted 1949 in 285 unconnected nodes. The complete node lists as well as Cytoscape source files can be found at https://github.com/schokine/Cytoscape-glioma-CF.Top hub nodes, for each glioma sub-type presented in Tables S2–S4, were compared manually in orderTop to circumvent hub nodes, mapping for each digliomafficulties sub-type between presen theted various in Tables gene identifiers.S2–S4, were compared manually in order to circumvent mapping difficulties between the various gene identifiers.

Int. J. Mol. Sci. 2020, 21, 547 10 of 17

3. Discussion The construction of PPI networks on the basis of overexpressed genes within disease samples can vary according to the applied tools and web resources. The extensive datasets on gene expression within chosen cancer samples posed the first challenge. These data had to be scaled down in order to be applicable as input for the diverse tools. Filtering data to a required minimum expression value will reduce entries. But it has to be kept in mind that low changes in expression levels can still be significant and could present a relevant or even crucial biological impact. Data entries in the five to six-digit range exceeded computing power and resources for online information retrieval from data repositories by the selected tools. In order to downsize sample data entries, we concentrated genes associated to the general biological function of proliferation as basic principle corresponding to cancer. This filtering step is one of many possibilities to analyse selected datasets in more detail. Other biological functions as apoptosis or migration could function likewise as approach to answer various questions for cancer research. We constructed our PPI networks on expression profiles from upregulated genes within disease samples. Likewise, PPI graphs could be compared on the basis of downregulated genes which could equally possess significant biological impact such as regulation of apoptosis and prevent an imbalance of proliferation. Differential graph analysis allowed us to compare the differences as well as similarities among datasets of selected disease types. GO analysis of dissected sample networks could highlight unique characteristics to disease subtypes and even point to possible biomarkers or novel molecular targets for treatment. Calculating PPI networks from gene lists is based on estimations and therefore connected to an uncertainty [39,73]. We confined the PPI networks in NetworkAnalyst to only those interactions between proteins that are based on evidence at a high confidence level. In case of BioGRID, PPI networks data were refined to human taxonomy only while Cytoscape did not offer an option to choose between confidence scores or types of interaction evidence from BioGRID imported networks. However, all the respective StringDB and Biogrid-based networks share common subgraphs that show some similarities between the different database approaches, while the list of intersecting nodes could also be used for further analysis. Hubs presenting high degree nodes can be used to evaluate signaling key points within samples. These can be of interest for understanding molecular mechanisms up to possible targets for treatment. Still, well-known and highly investigated proteins will be more likely assigned to a multiplicity of interactions than unstudied proteins. Thus, nodes with low number of interactions or even isolated outliers could hold relevant biological function within the disease. Results from corresponding BioGRID and StringDB high degree nodes list several genes that have been investigated to a certain extent or have been postulated to be involved in glioma signaling. The difference in top hubs between low-grade and high-grade glioma in comparison to general glioma can further point to tumor progression and malignancy. Further studies on unconnected nodes seem to be equally of interest. Differential graph analysis pinpoints three example genes which could be essential to glioma signaling. They have been indicated as possible prognostic biomarkers in different cancer diseases and require further evaluation respectively. Comparative Toxicogenomics Database lists HDAC4 an PP2R5A listing inferred gene-disease associations to glioma, mainly curated via chemicals, and no association between glioma and MAP2K7. Hub nodes within clusters could point to protein complexes since numerous proteins function within interacting aggregates. These interactions relate to biological functions and can be pointed out as signaling steps in disease. The analysis of the first ten top high-degree nodes already depicted several possible interacting complexes such as HER-signaling interrelated with cell cycle proteins which have been given much attention as targets in cancer treatment [74]. The examples of HER2 and CDK1/2 as well as CDKN1a and CCNE1 has yet to be evaluated. Expression of HER2 and HIF1A has been shown to correlate within other cancer subtypes [75]. Findings in general glioma samples of HER2 and HER4 to be part of the same protein cluster go along with studies on childhood Int. J. Mol. Sci. 2020, 21, 547 11 of 17 medulloblastoma and other cancer subtypes showing coexpression of the two proteins and further receptor heterodimerization between them [76,77]. Cluster analysis within astrocytoma samples listed only VEGF and NRP1 within one complex. The corresponding proteins have been identified to interact in tumor biology and suggested as antitumor targets [78]. The only result from StringDB based graphs for possible protein complexes within glioblastoma multiforme listed HER2 and several RAS proto-oncogenes. HER2 gene amplification has been shown in glioblastoma multiforme in correlation to KRAS and NRAS mutations [79,80]. We further included data from a comparison of gene expression profiles of anaplastic glioma with or without the mutated IDH1/2 gene [81]. Using the above described protocol, these data included only three upregulated genes which are related to proliferation. These IDH wildtype and mutant specific data were not suitable for the comparison and analysis process described above, since the graph analysis resulted in small networks created by BioGrid and StringDB, which are shown in Figure S3. Unfortunately, we could not find additional gene expression data related to IDH mutation specifically. In this case, future experiments will be necessary to retrieve relevant results on network changes related to IDH-gene mutation using the above-described method. Still, most of the example signaling components have been evaluated within various non-brain tumors and yet have to be studied in more detail for the various glioma diseases.

4. Methods The main idea is to compare gene expression in general glioma including GBM and low-grade astrocytoma via graph-based analysis of different PPI databases using network analysis tools. At first, we use EMBL-EBI Expression Atlas [20] to download glioblastoma data from the PanCancer Analysis of Whole Genomes (PCAWG) [82] dataset. By filtering data from homo sapiens in the region of brain and by the selected disease types, we then took out the cutoff expression level < 3, and downloaded data in fragments per kilobase of transcript per million mapped reads (FPKM), a well-established normalized expression data format that can be found in many datasets provided as open data. This cutoff range is similar to a cutoff level < 10 for transcripts per million (TPM). Data were further filtered by GO biological function [83] in order to reduce entries within datasets. PantherDB [84] was used to get GO information on selected genes to filter on the term “proliferation”. We used NetworkAnalyst [57] and Cytoscape [61] to create and visualize a PPI network for comparison for further analysis of selected genes. Cytoscape PPI construction and enrichment was processed via BioGRID using human taxonomy results only. NetworkAnalyst PPI construction and enrichment was based on StringDB at a confidence level of 90%. We manually filtered the BioGRID-based networks by human nodes within Cytoscape by filtering on the column taxonomy id 9606 for human. We further processed the network data by using gene name mapping to PPI data via BIomart Martview service (available online: www.biomart.org/ biomart/martview) and g:Profiler g:convert (available online: https://biit.cs.ut.ee/gprofiler/convert) including the removal of duplicates, and also used the column mapping functions in Cytoscape. In order to compare data for glioblastoma multiforme to other projects, we repeated the same steps, downloading the data for the disease types of general glioma data from the PCAWG study [82] and low-grade astrocytoma data from Expression Atlas [10,85], detailed in the beneath Section 4.2. Network clustering was computed via ClusterOne app within Cytoscape. BinGO app was used for gene ontology analysis of significant biological functions within created networks. Relevant gene results from network analysis were checked within the Comparative Toxicogenomics Database [86,87] for possible gene-disease associations.

4.1. Data Sources Genomic data on glioblastoma multiforme, general glioma and low-grade astrocytoma and anaplastic glioma with or without the mutated IDH1/2 gene were obtained from EMBL-EBI Expression Atlas [20] from the PCAWG dataset [82], E-MTAB-3708 [85] and E-GEOD-52942 [81]. Glioma data were Int. J. Mol. Sci. 2020, 21, 547 12 of 17

filtered on expression level cutoff 3 and FPKM data format in experiment E-MTAB-5200, on 30 September 2019 at 20:01:31. Glioblastoma data were filtered on expression level cutoff 3 FPKM in experiment E-MTAB-5200, on 22 September 2019 at 14:13:19. Astrocytoma data were filtered on expression level cutoff 3 FPKM in experiment E-MTAB-3708, on 30 September 2019 at 19:44:01. Gene expression profiles of anaplastic glioma with or without the mutated IDH1/2 gene were downloaded as a table of differentially expressed genes with p-value < 0.05 (DESeq) and log2fold cutoff 1 in experiment E-GEOD-52942. The list of genes was further filtered by upregulated genes only, on 20 Dec 2019 at 20:02:12.

4.2. Software and Web Resources AmiGO version 2.5.12 [88] • BinGO version 3.0.3 [89] • Biomart, Ensembl release 98, 2019-09 [90] • ClusterOne version 1.0 [42] • ClusterViz version 1.0.3 [91] • Comparative Toxicogenomics Database, revision 15923 [86] • Cytoscape version 3.7.2 [92] • EMBL-EBI Expression Atlas release 31, 2019-05 [10] • Gene Ontology release 2019-07-01 [84,93] • g:Profiler release 2019-10-02 [94] • NetworkAnalyst release 2019-10-07 [57] • Omicsnet release 2019-08-07 [59] • PantherDB version 14.1 [84] • Generated data can be found on https://github.com/schokine/Cytoscape-glioma-CF.

5. Conclusions There are several open-access resources available for cancer research. We present and compare exemplary web-based and stand-alone tools for differential network analysis using open gene expression data on various glioma diseases as input. This approach highlights disturbed signaling components in general glioma, glioblastoma multiforme, as well as low-grade astrocytoma.

Supplementary Materials: Supplementary materials can be found at http://www.mdpi.com/1422-0067/21/2/547/s1. Author Contributions: Conceptualization, C.J.-Q. and F.J.; methodology, C.J.-Q.; software, F.J.; implementation, C.J.-Q. and F.J; validation C.J.-Q.; writing—original draft preparation, C.J.-Q. and F.J.; writing—review and editing, A.H. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: We thank the PCAWG project as well as tool providers such as EMBL-EBI with its Expression Atlas, the STRING consortium and cBioPortal’s maintainers and its collaborators for providing data on cancer and also all the other data providers to make open science possible at all. We dedicate our work in memoriam to our family members and friends we have lost. If we may contribute even tiny steps to help to save lives in the future our mission was worth our passion, enthusiasm and effort. Please visit our project homepage at: https://hci-kdd.org/project/tugrovis. Conflicts of Interest: The authors declare no conflict of interest. Int. J. Mol. Sci. 2020, 21, 547 13 of 17

Abbreviations

ANXA1 annexin 1 CDK cyclin-dependent kinase CCNA/B/D/E EGFR epidermal growth factor receptor GBM glioblastoma multiforme GDC Genomic Data Commons GO Gene Ontology HDAC4 histone deacetylase HER2/HER4 erb-b2 receptor tyrosine kinase 2/4 HGG ICGC International Cancer Genome Consortium IDH1 isocitrate dehydrogenase 1 IL6R interleukin 6 receptor LGG low grade glioma MAP2K7 mitogen-activated protein kinase kinase 7 MAPK NCI National Cancer Institute TCGA The Cancer Genome Project PPI Protein Protein Interaction PCAWG Pancancer Analysis of Whole Genomes PPP2R5A protein phosphatase 2 regulatory subunit B56 alpha TEK TGFA/TGFB transforming growth factor alpha/beta TP53 TPM transcripts per millions FPKM fragments per kilobase of transcript per million VEGFA

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