Article Reverse Engineering of Ewing Regulatory Network Uncovers PAX7 and RUNX3 as Master Regulators Associated with Good Prognosis

Marcel da Câmara Ribeiro-Dantas 1,2 , Danilo Oliveira Imparato 1 , Matheus Gibeke Siqueira Dalmolin 3 , Caroline Brunetto de Farias 3,4 , André Tesainer Brunetto 3 , Mariane da Cunha Jaeger 3,4 , Rafael Roesler 4,5 , Marialva Sinigaglia 3 and Rodrigo Juliani Siqueira Dalmolin 1,6,*

1 Multidisciplinary Environment—IMD, Federal University of Rio Grande do Norte, Natal 59078-400, Brazil; [email protected] (....-D.); [email protected] (D.O.I.) 2 Laboratoire Physico Chimie Curie, UMR168, Institut Curie, Université PSL, Sorbonne Université, 75005 Paris, France 3 Children’s Institute, Porto Alegre 90620-110, Brazil; [email protected] (M.G.S.D.); [email protected] (C..d..); [email protected] (A..B.); [email protected] (M.d.C..); [email protected] (M.S.) 4 Cancer and Neurobiology Laboratory, Experimental Research Center, Clinical Hospital (CPE HCPA),   Porto Alegre 90035-903, Brazil; [email protected] 5 Department of Pharmacology, Institute for Basic Health Sciences, Federal University of Rio Grande do Sul, Citation: Ribeiro-Dantas, M.d.C.; Porto Alegre 90050-170, Brazil 6 Imparato, D..; Dalmolin, M..S.; Department of —CB, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil * Correspondence: [email protected] de Farias, C.B.; Brunetto, A.T.; da Cunha Jaeger, M.; Roesler, R.; Sinigaglia, M.; Siqueira Dalmolin, R.J. Simple Summary: Ewing Sarcoma is a rare cancer that, when localized, has an overall five-year Reverse Engineering of Ewing survival rate of 70%. Patients with metastasis have a five-year survival rate of 15 to 30%. Early analysis Sarcoma Regulatory Network of patient prognosis can be crucial to provide adequate treatment and increase chances of survival. Uncovers PAX7 and RUNX3 as Besides, it is a disease with several gaps in our understanding, including regulation of and Master Regulators Associated with which factors are master regulators. This work addresses these two topics by inferring Good Prognosis. Cancers 2021, 13, regulatory networks that allow us to identify putative master regulators to predict patient 1860. https://doi.org/10.3390/ prognosis. We were able to identify two sets of master regulators that can predict good and bad cancers13081860 patient outcomes.

Academic Editors: Alan Hutson, Abstract: Ewing Sarcoma () is a rare malignant tumor occurring most frequently in adolescents Song Liu and Aykut Uren and young adults. ES hallmark is a chromosomal translocation between the 11 and

Received: 24 February 2021 22 that results in an aberrant (TF) through the fusion of genes from the FET and Accepted: 1 April 2021 ETS families, commonly EWSR1 and FLI1. The regulatory mechanisms behind the ES transcriptional Published: 13 April 2021 alterations remain poorly understood. Here, we reconstruct the ES regulatory network using public available transcriptional data. Seven TFs were identified as potential MRs and clustered into two Publisher’s Note: MDPI stays neutral groups: one composed by PAX7 and RUNX3, and another composed by ARNT2, CREB3L1, GLI3, with regard to jurisdictional claims in MEF2C, and PBX3. The MRs within each cluster act as reciprocal agonists regarding the regulation of published maps and institutional affil- shared genes, regulon activity, and implications in clinical outcome, while the clusters counteract iations. each other. The regulons of all the seven MRs were differentially methylated. PAX7 and RUNX3 regulon activity were associated with good prognosis while ARNT2, CREB3L1, GLI3, and PBX3 were associated with bad prognosis. PAX7 and RUNX3 appear as highly expressed in ES biopsies and ES lines. This work contributes to the understanding of the ES regulome, identifying candidate MRs, Copyright: © 2021 by the authors. analyzing their methilome and pointing to potential prognostic factors. Licensee MDPI, Basel, . This article is an open access article Keywords: pediatric cancer; transcription factor; systems biology; cancer of unknown primary; distributed under the terms and regulome conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cancers 2021, 13, 1860. https://doi.org/10.3390/cancers13081860 https://www.mdpi.com/journal/cancers Cancers 2021, 13, 1860 2 of 24

1. Introduction Ewing Sarcoma (ES) is an aggressive bone and soft tissue sarcoma, occurring most frequently in adolescents and young adults, of which the hallmark is a chromosomal translocation between chromosomes 11 and 22, involving genes from the FET and the ETS transcription factor families. Bone is the most frequent site of origin, but about 15% to 20% of Ewing sarcoma emerges in bone-associated soft tissue [1–3]. EWSR1-FLI1 has been reported as the most frequent fusion in ES, coding for a chimeric that functions as an aberrant transcription factor [4]. The EWSR1-FLI1 chimeric protein is known to alter the leading to mistargeting, dysregulation of chromatin state, and eventually transcriptional impairing [5,6]. The impact of EWSR-FLI1 fusion on ES transcriptome is still poorly understood, and the regulatory mechanisms behind those transcriptional alterations have not yet been deeply investigated. ES was first described as a presumed diffuse endothelioma of bone [7]. The key ES prognostic factor is the presence of detectable metastasis at diagnosis, and patients with bony metastases (with or without involvement) have a very poor prognosis, mostly when compared to patients with exclusively lung metastasis [8]. It is known that the chimeric protein alters the transcriptional pattern, and some of ES transcriptional abnormalities have been associated with epigenetic modifications induced by EWSR1- FLI1 [4]. There is no ES cell of origin identified so far, and several ES tissues of origin were proposed, including endothelial, vascular pericytes or smooth muscle, primitive vascular , pluripotential uncommitted mesenchyme, osteoblastic (based on collagen matrix synthesis patterns) and small cell osteosarcoma, and/or mesenchymal chondrosarcoma [2,9]. In recent decades, studies converged on two putative ES cells of origin: mesenchymal stem cells (hMSC) and human cells (hNCC) [4]. There are also studies that point towards a neuro-mesenchymal [10]. is regulated by the activity of regulatory molecules such as transcrip- tion factors. It is well described that a small number of transcription factors, which acts as master regulators (MRs), might handle the cell fate in different cellular models [11,12]. The reconstruction of regulatory networks based on transcriptional information has been successfully applied to different types of cancer, including , pancreatic ductal adenocarcinoma, and [13–16]. The regulatory network reconstruction aims to identify those transcription factors at the top of the transcriptional regulatory hierarchy, the MRs [17,18]. Identifying such MRs could help predict patient prognostic and point- ing out putative biomarkers that could lead to better diagnosis and treatment protocols. Compared with other solid cancers, and cancers that have recurrent , the number of recurrent somatic mutations in ES is limited [19–22]. Therefore, ES development is known as a transcription-related phenomenon, which points to regulatory analysis as a promising strategy in ES investigation. Here, we have inferred the ES regulatory network based on transcriptional data available in public databases. We also identified a set of seven putative transcription factors which acts as master regulators in ES and analyzed their profile. Those master regulators clustered in two groups with antagonistic behavior between the groups regarding the regulation of shared genes, regulon activity, and implications in clinical outcome.

2. Materials and Methods 2.1. Data Acquisition and Processing Data used to infer the ES regulatory networks were obtained from Gene Expression Omnibus (GEO) [23] (accession numbers GSE34620 and GSE63157). The dataset GSE34620 comprises expression data (Affymetrix U133 Plus 2.0 Array) from 117 ES biopsies [24] and the dataset GSE63157 comprises expression data (Affymetrix Human 1.0 ST Array) from 85 ES biopsies [25]. Data used to infer ES gene signature was also obtained from GEO (accession numbers GSE73610 and GSE67073) [26,27]. The dataset GSE73610 comprises expression data (RNA-Seq profiling Illumina Genome Analyzer IIx, Homo sapiens) from 3 ES cell lines and 2 hMSC cell lines and the dataset GSE67073 Cancers 2021, 13, 1860 3 of 24

comprises expression data (RNA-Seq profiling Illumina HiSeq 2000, Homo sapiens) from four induced pluripotent stem cells (iPSC)-derived neural crest populations from familial dysautonomia patients and 2 iPSC-derived neural crest populations from healthy volun- teers. Data used for survival analysis was obtained from GSE63157 (also used for Ewing Sarcoma regulatory network inference) and from GSE17618, both retrieved from GEO. The dataset GSE17618 comprises expression data (Affymetrix Human Genome U133 Plus 2.0 Array) from 44 ES biopsies and 11 ES cell line samples [27]. To assess the gene expression of the master regulators in different tissues and solid pediatric cancers, we used once again the dataset GSE34620 for ES, in addition to the fol- lowing: GSE16476, comprising expression data (Affymetrix Human Genome U133 Plus 2.0 Array) from 88 Neuroblastoma biopsies; GSE53224 comprising expression data (Affymetrix Human Genome U133 Plus 2.0 Array) from 53 biopsies of Wilm’s tumor; GSE75271 com- prising expression data (Affymetrix Human Genome U133 Plus 2.0 Array) from 55 biopsies of hepatoblastoma, GSE87437 comprising expression data (Affymetrix Human Genome U133 Plus 2.0 Array) from 21 biopsies of high-grade osteosarcoma, GSE29684 compris- ing expression data (Affymetrix Human Genome U133 Plus 2.0 Array) from 20 biopsies of retinoblastoma, GSE66533 comprising expression data (Affymetrix Human Genome U133 Plus 2.0 Array) from 58 biopsies of and GSE3526 comprising expression data (Affymetrix Human Genome U133 Plus 2.0 Array) from 353 samples of 65 different healthy tissues. The criterion chosen for this analysis was the most common solid pediatric tumors, microarray platform type, and the number of samples. Microarray raw data (GSE34620 and GSE17618, Affymetrix Human Genome U133 Plus 2.0 Array) normalization (Robust Multichip Average, RMA method) and quality control was performed with the Affy Bioconductor/R package [28]. For GSE63157 (Affymetrix Human Exon 1.0 ST Array) normalization (Robust Multichip Average, RMA method) and quality control was performed with oligo Bioconductor/R package [29]. Annotation data for Affymetrix Human Genome U133 Plus 2.0 Array was obtained from hgu133plus2.db and for Affymetrix Human Exon 1.0 ST Array from huex10sttranscriptcluster.db R packages. RNA-seq data were processed according to the Tuxedo protocol. Briefly, we used fastqdump to convert SRA compressed files into FASTQ format files and fastqc to assess the quality of them. Then we run TopHat to align reads to the hg19 reference transcriptome. For the comparison of gene expression of the master regulators among solid pediatric tumors and healthy tissues, the datasets GSE34620, GSE16476, GSE53224, GSE75271, GSE87437, GSE29684, GSE66533, and GSE3526 were normalized together. Looking for samples on the same platform type was important here since different microarray platforms have a different set of probes. In this work, we analyzed three datasets with ES biopsies. For GSE63157, data was from biopsies collected from patients on Children’s Oncology Group (COG) and EuroEw- ing. Molecular analysis of COG and EuroEwing tumors was performed using RT-PCR for EWS-FLI1 and EWS-ERG fusions. Forty-six samples obtained from the COG Biorepository in Columbus, Ohio (Cooperative Human Tissue Network - CHTN) were prospectively acquired from patients on clinical trials INT-0154 (CCG-7942, POG-9354) and AEWS0031, the two most recent COG clinical protocols for patients with localized Ewing sarcoma (ES). An independent set of 39 tumor samples was obtained from the EuroEwing tumor biorepos- itory in Muenster, Germany and were prospectively acquired from patients registered on EICESS 92 (European Intergroup Cooperative Ewing’s Sarcoma Study) and EuroEwing 99.46 out of the 85 samples have information about the primary tumor site (Under three categories: other, extremity and pelvis) [25]. For GSE17618, the patient material for study was taken prior to any treatment in 29 cases, and in 15 cases, chemotherapeutics, , and/or surgical treatment was applied before material was collected [30]. In the case of GSE34620, samples were from the CIT program (Cartes d’Identité des Tumeurs research program) from the french Ligue Nationale Contre le Cancer. The published dataset article details that all cases included in the study showed a specific EWSR1-ETS fusion and the molecular diagnosis was performed in Institut Curie [24]. Cancers 2021, 13, 1860 4 of 24

2.2. Regulatory Network Inference and Master Regulator Analysis The regulatory network inferences and the master regulator analyses were performed using RTN (Reconstruction of Transcriptional Networks), a Bioconductor/R package that provides several tools for the reconstruction and analyses of transcriptional networks, such as the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe), and the Master Regulator Analysis (MRA) algorithms [15]. Briefly, the RTN algorithm measures statistical dependence of gene expression data along with a previously defined set of transcription factors (TFs) to infer a TF-centric regulatory network. The Transcriptional Network Algorithm (TNI) computes the association between a transcription factor and each potential regulated gene, removing spurious associations by permutation analysis (BH adjusted p-value 0.05). We have inferred two ES regulatory networks by using two datasets containing ES expression data from 117 biopsies (GSE34620), used to infer Network 1, and 85 biopsies (GSE63157), used to infer Network 2, and a list of 1388 human transcription factors available in Fletcher2013b Bioconductor/R package [15]. The MRA looks for regulatory units (regulons) that are enriched for a gene signature (BH adjusted p-value 0.05), pointing out putative transcription factors that are relevant to the disease, known as Master Regulators (MRs). Here, we obtained two ES signatures: one by performing a differential expression analysis comparing hMSC (GSE73610 = 2) against ES cell lines (GSE73610 N = 3) and another by performing a differential expression analysis comparing hNCC (GSE67073 N = 2) against the same ES cell line samples (GSE73610 N = 3). RNAseq samples were processed with the Tuxedo protocol, and Cuffdiff was used to quantify gene expression in each condition and test for differential expression. Genes with adjusted p-value lesser or equal than 0.05 were considered to be differentially expressed. We used both signatures to perform the MRA pipeline to network 1 and network 2 (Figure A1).

2.3. Differential Methylation Analysis The differential methylation analysis was performed comparing 15 samples from different ES cell lines with 9 samples from hMSC cell lines. These 9 samples from hMSC are divided between 6 samples from patients with ES and 3 from healthy donors. The complete study consists of 24 samples (GSE118872) [31]. We used the methylationArrayAnalysis package from Bioconductor [32], and followed their pipeline instructions. Data normaliza- tion was performed using the Genome Studio, the standard software provided by Illumina, through the function preprocessIllumina. Quality control was performed looking for failed positions (this is defined as both the methylated and unmethylated channel reporting background signal levels), as rec- ommended in the pipeline manual, and with a p-value threshold of 0.01. At the end, no sample was required to be filtered out. After that, probe-wise differential methylation analysis was performed (Bonferroni adjusted p-value < 0.01). We then used Fisher’s Exact Test (Bonferroni adjusted p-value < 0.01) to check if the regulons of the putative master regulators inferred in the Master Regulator Analysis were significantly enriched with genes that were found to be differentially methylated in the probe-wise differential methylation analysis. This enrichment analysis was performed 2 times: (a) with regulons inferred in Network 1, and (b) with regulons inferred in Network 2.

2.4. Functional Enrichment To investigate the functional enrichment of genes regulated by each of the master regulators, we merged the regulons of each of the master regulators from Network 1 and 2. The Gene Ontology functional enrichment was performed using the clusterProfiler R Package aimed at the Biological Processes ontology [33]. The adjusted p-value < 0.05 (Benjamini-Hochberg correction) was considered to be significant.

2.5. Network Visualization We used the RedeR Bioconductor/R package [34] to visualize two types of networks generated by the RTN package: association maps and tree-and-leaf representations. In as- Cancers 2021, 13, 1860 5 of 24

sociation maps, nodes in the network are transcription factors, and the edge width between any two nodes is related to the number of genes mutually regulated by each pair of tran- scription factors, i.., the edge width refers to the regulatory overlap between regulons. Tree-and-leaf representation is similar to the association map, with the exception that edge width is fixed and nodes are hierarchically organized throughout the network according to the overlap of regulated genes. The regulon clusters are formed according to the overlap of genes regulated by these transcription factors. Regulons with less than 15 genes are not represented in the networks, a default cut-off in the RTN package.

2.6. Master Regulators Activity We used the RTN package to measure MR regulatory activity, i.e., the enrichment score of the regulatory activity of each regulon compared to the other regulons in the set for every patient. The RTN package measures for every patient how the expression of every gene deviates towards the average expression of that gene for all patients in the cohort and then applies the Two-Tailed Gene Set Enrichment Score analysis (GSEA2). Briefly, gene expression was first converted into -score and, in each sample, all genes were sorted by the z-score and then used as the reference list in GSEA2. The TFs activity level was approximated by the Normalized Enrichment Score (NES) computed for its regulon. The results of the two analyses were plotted as a heatmap along with dendrograms.

2.7. Survival Analysis From the ES datasets used in this study, only GSE63157 (N = 85) and GSE17618 (N = 44) had survival data. The survival R package was used to analyze patient overall survival in terms of the activity of the regulon of each putative master regulator. Samples were divided into two groups based on the median of the regulatory activity of each regulon. The regulons with activity values above the median were classified as “high regulon activity” and values below the median as “low regulon activity”. The median was calculated for every regulon of each patient. The p-values for the comparison of the survival curves in the Kaplan-Meier estimator were calculated with the log- test. For the GSE17618 cohort, with too few samples to perform network inference, we used the regulon structure of the cohort one (the cohort with the largest sample size used for network inference, N = 117), i.e., which genes were associated with each putative master regulator, but with the expression values of the GSE17618 cohort.

2.8. RT-qPCR Analysis For the in vitro expression gene analysis of master regulators identified in silico, we used different representative cell lines of Ewing Sarcoma (RD-ES and SK-ES), Neurob- lastoma (-SY5Y and SK-N-Be(2)), Hepatoblastoma (HepG2) and Medulloblastoma (Daoy and D283). The total RNA extraction was performed with SV Total RNA Isolation System (Promega, Madison, USA) according to manufacturer’s instructions and quantified in Nanodrop (Thermo Fisher Scientific, Waltham, USA). The cDNA was obtained using the GoScript Reverse System (Promega) according to manufacturer’s instructions. The qPCR of the master regulators cDNA (PAX7, RUNX3, ARNT2, CREB3L1, GLI3, MEF2C, PBX3) were amplified using PowerUp SYBR Green Master Mix (Thermo Fisher Scientific.) and the relative gene expression was analysed using 2(−DDCt) method [35]. The RD-ES was used as control and ACTB was used as internal control. Statistical analysis was performed by one-way analysis of variance (ANOVA) followed by Bonferroni post-hoc test; p values under 0.05 were considered to indicate statistical significance.

3. Results 3.1. Regulatory Network Inference and Master Regulator Analysis We have inferred ES regulatory networks based on two cohorts (GSE34620 and GSE63157) containing transcriptional data from 117 patients (regulatory network 1) and 85 patients (regulatory network 2) (Figures1 and A1). A gene signature is required to infer Cancers 2021, 13, 1860 6 of 24

potential master regulators. A common strategy to obtain a gene signature for a given tu- mor is through differential expression analysis by comparing the cancer cells against the cell of origin. Since the cell of origin of ES is still a matter of debate, we have used two different cell types to infer ES signature: human mesenchymal stem cells (hMSC) and human neural crest cells (hNCC), the most accepted cells of origin in the literature [4]. The two signatures were used to perform the MRA for network 1 and network 2 (Figure A1). Figure1A,B show tree-and-leaf representations of both regulatory networks (for the regulatory net- works with fully regulon names, see Figures A2 and A3). Each network is depicted based on regulon overlap, and the nodes represent the inferred regulons containing at least 15 genes. Networks 1 and 2 contain 645 and 568 regulons, respectively. The MRA performed in the network 1 identified 44 master regulators (Table A1) for the hMSC signature and 13 master regulators for the hNCC signature (Table A2). In the network 2, MRA identified 66 master regulators for the hMSC (Table A3) signature and 42 master regulators for the hNCC signature (Table A4).

Figure 1. Tree-and-leaf representation of Ewing Sarcoma regulatory networks and methylation profile. Regulatory networks 1 (A) and 2 (B) inferred from ES datasets GSE34620 and GSE63157, respectively. Nodes represent regulatory units (regulons) labeled by their transcription factors, and edges represent their relationship as the overlap of mutually regulated genes. Network nodes are colored according to the master regulator analysis (MRA) results, carried out with the disease signatures obtained with hMSC and hNCC as ES cells of origin. Methylation profile (C) of the seven regulons composing the intersection of both networks (networks 1 and 2), and both signatures (hMSC and hNCC). The color code in C represents methylation p-value as indicated.

Among those four sets of inferred master regulators (MRs), seven are always present: ARNT2, CREB3L1, GLI3, MEF2C, PBX3, PAX7, and RUNX3 (Figure1A,B) and Figure A1). From that point, we chose to work with those seven master regulators compos- ing the intersection of both networks, and both signatures (Figure A1). In network 1, five master regulators (CREB3L1, GLI3, MEF2C, PBX3, and PAX7) out of the seven common to all MRA analyses are located close to each other at a small region of the network (Figure1A). In network 2, five of those master regulators (CREB3L1, GLI3, PBX3, RUNX3, and PAX7) are also located close to each other at a small region of the network (Figure1B).

3.2. Differential Methylation Analysis As a further regulon validation, we investigated the regulon methylation profile in an independent dataset involving ES cell lines and hMSC cell lines. Figure1C shows that the regulons of the seven identified master regulators (ARNT2, CREB3L1, GLI3, MEF2C, PBX3, PAX7, and RUNX3) have more genes differentially methylated as expected by change in both network 1 and network 2.

3.3. Biological Function of Master Regulators Associated Genes To perform the functional enrichment of the genes regulated by the seven master regulators (MRs), we considered collectively the genes associated with each MR in both Cancers 2021, 13, 1860 7 of 24

networks 1 and 2. According to Figure2, PAX7 regulated genes are associated with glyco- protein metabolic process, proteoglycan , and protein deacetylation. There are no biological functions significantly enriched for RUNX3 regulated genes, while ARNT2 associated genes are involved with synapse and postsynapse organization, and axogenesis. The regulons CREB3L1, GLI3, PBX3, and MEF2C share biological functions, mainly func- tions involved with dynamics. For example, the GO term extracellular matrix organization is enriched in those four regulons. GO terms extracellular structure or- ganization, connectivity tissue development, and chondrocyte differentiation are enriched in regulons CREB3L1, GLI3, and MEF2C, while MRs PBX3 and MEF2C regulates genes involved with endothelial and epithelial cell migration.

Figure 2. Gene Ontology functional enrichment using clusterProfileR to show the main biological functions performed by each regulon of the seven putative master regulators. The p-value cutoff used is 0.05 and the p-value was adjusted by Benjamini-Hochberg correction. Each regulon in this analysis consists of the genes regulated by each master regulator in Network 1 and 2. 3.4. Regulon Activity The regulon activity is calculated based on the expression of genes into the regulon. In other words, a regulon is considered to be activated when the genes identified as positively regulated by the transcription factor (in this case, the master regulator) are up- regulated in the GSEA, and the genes negatively regulated by the TF are down-regulated in the GSEA. On the contrary, the regulon is considered inhibited when negatively regulated genes are on the GSEA top, and positively regulated genes are on the GSEA tail. We assessed regulon activity in the different samples used for networks 1 and 2 inference. Figure3A shows the heatmaps involving the activity of the seven MR for the 117 sam- ples used to infer network 1 (GSE34620) and the 85 samples used to infer network 2 (GSE63157). In both heatmaps, it is possible to observe two clusters: one formed by regu- lons PAX7 and RUNX3 and another formed by regulons ARNT2, CREB3L1, GLI3, MEF2C, and PBX3. The regulatory activity of these two clusters is antagonistic to each other in both cohorts: in general, patients with high RUNX3 and PAX7 regulon activity have low ARNT2, CREB3L1, GLI3, MEF2C, and PBX3 regulon activity and vice-versa (Figure3A). Figure3B shows two subnetworks extracted from the regulatory networks 1 and 2. Both subnetworks Cancers 2021, 13, 1860 8 of 24

include only the seven regulons used for heatmap analyses (Figure3A). Similar to Figure1, the network is depicted according to regulon overlapping (i.e., according to genes mutually regulated), but here the edges indicate whether each pair of transcription factors regulates the shared genes in the same direction (agonistic regulation) or in the opposite direction (antagonistic regulation). As seen in Figure3B, it is possible to observe two groups of regulons. PAX7 and RUNX3 regulate shared genes in the same direction. Similarly, the group formed by ARNT2, CREB3L1, GLI3, MEF2C, and PBX3 regulates the shared genes in the same direction (Figure3A and Figures A4 and A5). However, transcription factors of different groups always regulate simultaneously regulated genes in the opposite direction (Figure3B ). According to Figure3, regulons PAX7 and RUNX3 are simultaneously acti- vated or inhibited and act coordinately by regulating shared genes in the same direction. The same occurs among regulons ARNT2, CREB3L1, GLI3, MEF2C, and PBX3. Moreover, both groups seem to work as antagonists between each other.

Figure 3. Regulon activity analyses. (A) Heatmaps of regulatory activity obtained for both datasets (GSE34620 N = 117 and GSE63157 N = 85). (B) Association map of subnetwork 1 (GSE34620) and subnetwork 2 (GSE63157) involving only the 7 MRs identified in both networks when using the two gene signatures (hMSC and hNCC). Node size reflects the number of regulated genes into regulon, and edge width reflects the number of mutually regulated genes between each regulon. Nodes are colored according to the cluster they belong to. Dotted edges represent regulatory antagonism, while solid edges represent regulatory agonism.

3.5. Survival Analysis To evaluate the impact of the seven regulon activity in ES outcome, we accessed survival data available for the 85 patients from the cohort used to infer ES regulatory network 2 (GSE63157). The samples into the cohort were classified according to the activity of each regulon. Figure4 shows the seven Kaplan-Meier plots corresponding to each regulon where six of them were significantly related to patient outcome. Again, PAX7 and RUNX3 present similar behavior regarding patient outcome with both regulons associated with a good prognosis when activated. In contrast, regulons ARNT2, CREB3L1, GLI3, and PBX3 are associated with bad prognosis when activated. Cancers 2021, 13, 1860 9 of 24

Figure 4. Kaplan-Meier plot of ES patients. High regulatory activity of PAX7 and RUNX3 is associated with better patient overall survival, while high regulatory activity of ARNT2, GLI3, PBX3, and CREB3L1 is associated with worse overall survival. Box colors reflect the cluster each regulon belongs to. p-values are presented inside each box. Survival and expression data were obtained from ES dataset GSE63157 (N = 85).

Unfortunately, there is no survival data available regarding patients from cohort 1 (GSE34620). To verify the implication of regulon activity in patient outcome, we access another ES cohort composed by 44 patients (GSE17618). We evaluated the activity of the seven regulons inferred for network 1 in each of 44 patients with data available in GSE17618. Figure5A shows the heatmap clustering based on regulon activity. The heatmap presents the same pattern observed in Figure3A. PAX7 and RUNX3 regulons cluster together, in contrast to regulons ARNT2, CREB3L1, GLI3, and PBX3. Additionally, both PAX7 and RUNX3 are significantly related to good prognosis when activated. Among the other regulons, only ARNT2 was significantly related to patient outcome, being associated with bad prognosis when activated.

3.6. Master Regulators Expression in Ewing Sarcoma We evaluated the expression of ES master regulators significantly associated with patient outcome (i.e., PAX7, RUNX3, ARNT2, GLI3, PBX3, and CREB3L1) in ES samples as well as in samples of the most common solid pediatric tumors: neuroblastoma, Wilm’s tu- mor, hepatoblastoma, osteosarcoma, retinoblastoma, and rhabdomyosarcoma [36]. We also evaluated the expression of the above master regulators in a set of samples (N = 353) from 65 different healthy tissues (Figure6). PAX7 and RUNX3 genes were highly expressed in ES samples when compared with the other evaluated pediatric tumors and normal tissues. RUNX3 was also highly expressed in osteosarcoma when compared with normal tissue, neuroblastoma, Wilm’s tumor, hepatoblastoma, retinoblastoma, and rhabdomyosarcoma. However, RUNX3 expression in ES was significantly higher when compared with the expression in osteosarcoma. The expression of the other four master regulators (ARNT2, GLI3, PBX3, and CREB3L1) had no significant difference among the samples, except by CREB3L1 gene which was highly expressed in osteosarcoma samples (Pairwise Wilcox test with Bonferroni correction, p-value < 0.01). Cancers 2021, 13, 1860 10 of 24

Figure 5. Regulatory activity and survival analyses (ES dataset GSE17618, N = 44). (A) Heatmaps of regulatory activity (B) Kaplan Meier plots obtained with survival and gene expression data obtained from GSE17618. High regulatory activity of PAX7 and RUNX3 regulons is associated with better overall survival, while high regulatory activity of ARTN2 is associated with worse overall survival. Box colors reflect the cluster each regulon belongs to. p-values are presented inside each box.

In sense to validate the transcriptome data, the transcript levels of ES master regulators genes were evaluated in representative cell lines of ES, neuroblastoma, hepatoblastoma and medulloblastoma (Figure6B). The expression of PAX7 and RUNX3 were similar between ES cell lines and were significantly higher in RD-ES cell line in comparison to other tumor cell lines (p < 0.01). ARNT2 and PBX3 expressions were lower in ES cell line RD-ES than neuroblastoma cell line SH-5Y5Y (p < 0.001). GLI3 was highly expressed in medulloblastoma cell line D283 compared to RD-ES (p < 0.01). CREB3L1 expression was higher in the ES cell line RD-ES compared to neuroblastoma, hepatoblastoma (p < 0.001) and D283 cell line (p < 0.01), whereas no significant difference was observed in Daoy cells. Cancers 2021, 13, 1860 11 of 24

A PAX7 RUNX3 B PAX7 RUNX3 10.0 1.5 1.0

7.5 1.0

0.5 5.0 0.5 ** ** ***

ARNT2 GLI3 ARNT2 GLI3 *** 4 40 **

xpression 10.0

e 3 30

7.5 2 20 ed probe 5.0 10 1 Relative mRNA level mali z

PBX3 CREB3L1 PBX3 CREB3L1 No r *** 1.25 10.0 10.0 1.00

7.5 0.75 7.5 * 5.0 0.50

5.0 2.5 *** ** 0.25

Ewing Ewing lastomalastoma lastoma lastomalastoma lastoma yosarcoma mal tissue yosarcoma mal tissue Wilms'No tumorr Wilms'No tumorr NeurobOsteosarcoma NeurobOsteosarcoma RD−ESSK−ES − Ewing − Ewing RD−ESSK−ES − Ewing − Ewing Hepatob Retinob Hepatob Retinob Rhabdom Rhabdom D283 −Daoy Meduloblast. − Meduloblast. D283 −Daoy Meduloblast. − Meduloblast. HepG2SH−SY5Y − Hepatoblast. − Neuroblast. HepG2SH−SY5Y − Hepatoblast. − Neuroblast. SK−N−Be (2) − Neuroblast. SK−N−Be (2) − Neuroblast. Figure 6. Ewing Sarcoma Master Regulators Expression. The boxplots (A) show the expression of the six ES master regulators significantly associated with patient outcome (PAX7, RUNX3, ARNT2, GLI3, PBX3, and CREB3L1). The expression of each MRs was assessed in biopsies from ES (N = 117), Hepatoblastoma (N = 55), Neuroblastoma (N = 88), Osteosarcoma (N = 21), Retinoblastoma (N = 20), Rhabdomyosarcoma (N = 58), Wilm’s tumor (N = 53) and a dataset from 65 different healthy tissues as control (N = 353). Colored boxes indicate the group was significantly different compared to all the other groups (Pairwise Wilcox test with Bonferroni correction, p < 0.05). Bar plots (B) represent the relative expression measured by RT-qPCR of the indicated MRs in different cell lines originated from ES and other pediatric tumors. The differences are always related to RD-ES cell line (* p < 0.05, ** p < 0.01, *** p < 0.0001).

4. Discussion The primary goal of this study was to reconstruct the ES regulatory network to understand the ES regulatory properties and, particularly, to identify transcription factors potentially relevant to that cancer. However, the uncertainty regarding ES cell of origin hinders the identification of master regulators. To surpass that limitation, we inferred two signatures using the two most accepted ES cells of origin: hMSC and hNCC [4]. The number of inferred master regulators vary according to the used signatures, but the master regulators ARNT2, CREB3L1, GLI3, MEF2C, PBX3, PAX7, and RUNX3 were found using either signature in both networks. It would be naive to consider only those seven TFs as Ewing Sarcoma regulators. It is reasonable to assume that other TFs identified as master regulators only when using hMSC signature could be relevant to ES transcriptional regulation, especially if hMSC were the ES cell of origin. The same is true for the hNCC signature. As example, the transcription factors NR0B1 and NKX2-2 are well-known to be associated with Ewing Sarcoma [37]. NR0B1 have been associated with the tumor phenotype mediated by EWS/FLI chimera in cell lines [38]. NKX2–2, a homeodomain transcription factor involved in neuroendocrine/glial differentiation and a downstream target of EWSR1-FLI1, has been reported as an immunohistochemical marker for Ewing sarcoma [39]. We identified both NR0B1 and NKX2-2 as MRs in our analysis. NR0B1 was Cancers 2021, 13, 1860 12 of 24

identified as MR in network 2 using both hMSC and hNCC signatures (Tables A3 and A4, and Figures A2 and A3), while NKX2-2 was identified in network 1 using hNCC signature (Tables A2 and Figure A2), and in network 2 using the two signatures (Tables A3 and A4, and Figure A3). Because they were not always present in the four master regulator analysis (i.e., networks 1 and 2, using both hMSC and hNCC signatures), they were left out of the further analysis. Another sensitive point is the Ewing Sarcoma cell of origin. Despite hMSC and hNCC being the most supported cells of origin by the literature [2,10,40–44], we can not neglect the possibility of other cells of origin. However, our stringent strategy assures that the seven master regulators inferred here are involved in the ES transcriptional regulation. According to our results, the regulons ARNT2, CREB3L1, GLI3, MEF2C, and PBX3 act coordinately: (i) they are collectively activated or collectively inhibited in the majority of the patients from the three cohorts evaluated here; (ii) those five regulons always regulate shared genes in the same direction; (iii) except for MEF2C, all of those regulons are significantly related with poor prognosis in cohort GSE63157 (N = 85) when activated. The same statement is true for PAX7 and RUNX3, except that they are both associated with good prognosis when activated. We also evaluated overall survival of a small cohort (GSE17618) with 44 patients with similar results: PAX7 and RUNX3 are significantly associated with good prognosis when activated, and ARNT2 is significantly associated with poor prognosis when activated. Interestingly, both clusters—the first formed by ARNT2, CREB3L1, GLI3, MEF2C, and PBX3, and the second formed by PAX7 and RUNX3— act collectively as reciprocal antagonists to each other regarding activation, regulation of shared genes, and implication in patient outcome. A similar behavior can be observed in the biological functions performed by the regulons. For instance, CREB3L1, GLI3, PBX3, and MEF2C share biological functions involved with extracellular matrix dynamics, and all those regulons are agonist among each other and cluster together. The results of our methylation analysis show that all the seven regulons discussed in the are differentially methylated in an independent set of ES samples, reinforcing our findings and highlighting the importance of epigenomic reprogramming in the tumour regulation, as demonstrated by Sheffield et al that pointed out epigenetic heterogeneity in genetically homogeneous developmental cancers [45]. However, it was not possible to associate the methylation experiment with the regulon activity since we do not have the same set of samples with both methylation and expression experiments. It is not clear why some patients have the cluster composed of PAX7 and RUNX3 regulons activated, while the same cluster is inhibited in other patients, and further in- vestigations are needed to elucidate it. However, the differential regulon activity among patients seems to be important to tumor prognostic since we showed it is associated with overall survival. Similar result was observed in breast cancer, where it was possible to stratify a patient cohort based on regulon activity [13].In the same work, the authors have shown that the pharmacological inhibition of estrogen drastically suppresses the ESR1 regulon. The ESR1 regulon has either estrogen-induced and estrogen-repressed genes. Therefore, their function as regulator occurs oppositely when the regulon is repressed and when the regulon is activated [13]. It suggests that the MR influence can be through the activation of its targets or by target inhibition, and both activation and repression of an MR might modulate the cell fate. In other studies, ARNT2, CREB3L1, GLI3, and PBX3 have been associated with tumor progression. The role of CREB3L1 in tumor phenotype is controversial: while some studies associated this TF with metastasis promotion, other studies suggest its role in metastasis inhibition [46,47]. ARNT2 was pointed out as a key TF in the control of glioblastoma cell aggressiveness by regulating the expression of TFs related to a tumorigenic/stem glioblastoma signature [48]. PBX3 is well described as associated with poor prognostic in several cancer types [49], and the same is correct for GLI3 [50,51]. However, the role of those TFs has not been previously reported in ES. Previous studies have suggested that high expression of CREB3L1, GLI3, MEF2C, and PBX3 induces invasion and metastasis Cancers 2021, 13, 1860 13 of 24

by promoting epithelial–mesenchymal transition (EMT) [47,51,52], a process reported as critical to induce metastasis in ES [53]. ARTN2 is associated with hypoxia response and acts as a dimerization partner of hypoxia-inducible factor 1α (HIF1α), which acts is adaptive stress response as well as required for tumor growth and metastasis [54,55]. Functional enrichment analysis has shown ARNT2, CREB3L1, GLI3, and PBX3 regu- lons related to several processes associated with cell migration such as extracellular matrix organization, collagen metabolic process, glycosaminoglycan (GAG) process, epithelial cell migration, and regulation of angiogenesis. Cell migration is an essential process for regu- lating cancer invasion. An initial step in cancer metastasis is the migration of tumor cells through the extracellular matrix (ECM) and into the lymphatic or vascular systems [56]. Progression to metastasis is associated with some biomechanical particularities, such as the restructuring of the extracellular matrix, collagen organization, ECM environments rich in the GAG, angiogenesis process, and epithelial cell migration [57–60] all process regulated by the ARNT2, CREB3L1, GLI3, and PBX3 regulons according to our results. Therefore, the high activity of ARNT2, CREB3L1, GLI3, and PBX3 regulons could be related to ES aggressiveness through stress adaptation, angiogenesis, and mesenchymal-like phenotype induction [61]. Recent studies suggested that EWSR1-FLI1 chimera regulates the expression of PAX7 and RUNX3 [62,63]. According to the available evidence, PAX7 is required for neural crest formation and adult skeletal muscle progenitor development [64]. PAX7 is described to be expressed in ES, subsets of rhabdomyosarcoma, and rare synovial [65,66]. Baldauf et al 2018, interrogated Chaville’s findings, since the increased expression of the PAX7 gene was observed in a dataset that compares samples of CIC-DUX4-positive sarcomas with EWSR1-NFATc2-positive sarcomas. According Baldauf et al. 2018, EWSR1- NFATC2-positive sarcomas are transcriptionally distinct from tumors with EWSR1-FLI1 translocation and should be treated as an entity distinct from EWSR1-ETS tumors [67]. On the other hand, Toki et al. 2018 using monoclonal against PAX7 identified the PAX7 expression in 27 of 30 molecularly confirmed Ewing Sarcomas (90%) [66]. However, its role in tumor biology is uncertain. Charville and collaborators suggested that EWSR1- FLI1 binds to the PAX7 , being the chimeric protein required for PAX7 expression in ES [62]. The transcription factor RUNX3 has been described as a tumor suppressor [68]. Bledsoe and collaborators verified that RUNX3 is expressed in ES cell lines as well as in tumor biopsies. Additionally, RUNX3 inhibition alters the expression of a set of genes regulated by EWSR1-FLI1 in A673 cell line. The authors also observed that suppression of RUNX3 expression in A673 reduced cell growth [63]. On the other hand, several studies demonstrated RUNX3 inhibition in different cancers, such as colorectal cancer, glioma, melanoma, and breast cancer [69–72]. The opposite behavior of PAX7 and RUNX3 when compared with ARNT2, CREB3L1, GLI3, and PBX3 suggests that the first two TFs could act by antagonizing the last four TFs action. As mentioned before, ARNT2, CREB3L1, GLI3, and PBX3 are well described in several tumors, while PAX7 and RUNX3 seem to be more specific to ES. When taken together, our results allow us to hypothesize that PAX7 and RUNX3 activation in ES could help mitigate the damaging effect caused by ARNT2, CREB3L1, GLI3, and PBX3 activation. PAX7 is known to be expressed in ES, subsets of rhabdomyosarcoma, and rare cases of synovial sarcomas, though only in ES samples it was found positive in all evaluated cases [65]. Charville and collaborators suggest that significant expression of PAX7 is a unique feature of rhabdomyosarcoma and ES, and put forward PAX7 as a diagnostic marker for ES diagnosis [62]. Additionally, we also identified RUNX3 as highly expressed in ES, corroborating with the similar regulatory behavior shared by those two transcription factors in ES regulatory network. High expression of PAX7 and RUNX3 genes could help mitigate EMT promoted by the cluster formed by ARNT2, CREB3L1, GLI3, and PBX3, contributing to avoid metastasis and therefore an aggressive behavior that often leads to death. Even though there is no evidence that PAX7 and RUNX3 promote mesenchymal- Cancers 2021, 13, 1860 14 of 24

epithelial-transition (MET), which is the opposite of EMT, they may be able to avoid metastasis only by avoiding EMT.

5. Conclusions The regulatory network analysis sheds on the Ewing Sarcoma regulatory be- havior by identifying PAX7 and RUNX3 as promising master regulators for this cancer. Both regulons are agonists, are simultaneously activated or inhibited in patient samples, and are both associated with a good prognosis. The analysis evinces another cluster of regulon consisting of ARNT2, CREB3L1, GLI3, MEF2C, and PBX3, which counteracts PAX7 and RUNX3 in all the parameters mentioned above, suggesting that the last two regulons counteract the former five regulons.

Author Contributions: Conceptualization, R.J.S.D.; methodology, R.J.S.D. and M.S.; software, M.d.C.R.-D., D.O.I., M.G.S.D., M.d.C.J.; validation, M.d.C.R.-D., D.O.I., M.G.S.D., M.d.C.J.; formal analysis, M.d.C.R.-D., D.O.I., M.G.S.D., M.d.C.J.; investigation, R.J.S.D., M.S., M.d.C.R.-D.; resources, C.B.d.F., A.T.B., M.d.C.J., R.R., M.S.; data curation, M.d.C.R.-D., D.O.I., M.G.S.D., M.d.C.J.; writing—original draft preparation, R.J.S.D., M.S., M.d.C.R.-D., C.B.d.F., A.T.B., R.R.; writing—review and editing, R.J.S.D., M.S., M.d.C.R.-D., C.B.d.F., A.T.B., R.R.; visualization, M.d.C.R.-D., D.O.I.; supervision, R.J.S.D. and M.S.; project administration, R.J.S.D. and M.S.; funding acquisition, R.J.S.D., M.S. and A.T.B. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by CAPES, CNPq, PRONON/Ministry of Health, Brazil (number 25000.202751/2016-65 to C.B.F), the Rafael Koff Acordi Project Research Fund (Children’s Cancer Institute), and PROPESQ-UFRN. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: MicroArray and RNA sequenced samples mentioned in this research can be accessed at the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/ (accessed on: 22 February 2021)) with the accession numbers: GSE34620, GSE63157, GSE73610, GSE67073, GSE17618, GSE16476, GSE53224, GSE75271, GSE87437, GSE29684, GSE66533, and GSE3526. Source code of the analyses mentioned in this study can be found at https://github.com/dalmolingroup/ ewing-mra-netinfer. Acknowledgments: We would like to thank the institutions and projects that funded this research, the High Performance Processing Unit (NPAD) for the high-performance computing infrastructure required for some of the analyses performed in this work, as well as the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI) for hosting the public datasets. We would also like to thank the authors of the studies that generated the public data used in this work. Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations The following abbreviations are used in this manuscript:

2GSEA Two-Tailed Gene Set Enrichment Analysis ANOVA Analysis of Variance ARACNE Algorithm for the Reconstruction of Accurate Cellular Networks CHTN Cooperative Human Tissue Network CIT Cartes d’Identité des Tumeurs research program COG Children’s Oncology Group EICESS European Intergroup Cooperative Ewing’s Sarcoma Study ES Ewing Sarcoma GSEA Gene Set Enrichment Analysis GSE GEO Series GSM GEO Sample hMSC Human Mesenchymal Stem Cells Cancers 2021, 13, 1860 15 of 24

hNCC Human Neural Crest Cells iPSC Induced Pluripotent Stem Cells MR Master Regulator MRA Master Regulator Analysis RT-PCR Real-time polymerase chain reaction TF Transcription Factor

Appendix A

Figure A1. Master regulator analysis flowchart. Human mesenchymal stem cells (hMSC) and human neural crest cells (hNCC) were used along with ES cell lines to obtain the ES signatures. One analysis using hMSC as control and another using hNCC as control. Gene expression data from GSE34620 (N = 117) and GSE63157 (N = 85) were used to infer the regulatory networks 1 and 2, respectively. The networks were interrogated with the two gene signatures (disease signature with hMSC as control and disease signature with hNCC as control), and a set of shared master regulators were identified: ARNT2, CREB3L1, GLI3, MEF2C, PAX7, PBX3, and RUNX3. The meta-PCNA signature was used to filter for transcription factors commonly associated with cancer development. However, none from the ES MRs were filtered. Cancers 2021, 13, 1860 16 of 24

CBFBHMGA2 TCF21TFCP2L1 FOXF1 DLX2FOXF2SMAD6 PRDM16ELF5 ZNF669 SCML1 THRB ZNF394 RNPEP SIX2RUNX2 GATA6 CEBPA HSF4 RREB1NR4A2 AFF3 YWHAZZNF160 ZNF557 HOXA11 GATAD1 IRX5 PHTF2 VDR BLZF1NFIC LZTS1KLF7 FOXA1 KLF13 SATB2 RORC PBX2ZNF696ZFY ATF4 RARB FOXA2 CTNNB1 NFX1FOXL1 HOXD11 PHTF1 SMAD9POU3F1 ZKSCAN1 ETV1 ZNF665ZNF136 ARNTL2 KLF5 EP300 ZNF185 XBP1 ELF3 NFIX ZNF45 HOXD4 ELF4 HOXA1 CREBBP ZNF444 MYOD1 STAT6ZBTB25 ZNF426 THRAFBXL8 MGA SIX1 ZNF7C7orf61 ZNF264 MYOG CREBL2 ESRRB MYF6 HIF1A MADD IRF6 CREG1 ERF NFE2L1 MEIS2 DLX4 CREB3L2 BCL6 NR1H3 NR5A1 ELK3 BACH2 ZNF324ZNF580 VEZF1 TCF7L1 ZNF235 TEF VAV1 MEF2C ZNF222 TCF7L2 HR ETS1 RUNX3 ZNF217 PRDM1HEY1 ZNF34 NRL SMAD4TFDP2 MEF2D KLF15 HIC1 ZNF226 STAT5B TBX2 ERG ARNTL ZBTB16 ZNF331 DR1ZNF430 MYT1 HOXA4ZNF174 PAX9 TCEAL1 SOX18 NPAS2 FOXJ2 HAND2MSC HEYL FOXH1 PAX7 HOXD10 NKX2−2 FOXJ3 HIF3A ZNF274 ZNF10MNT HHEX DLX5 SIX5ZNF236 TAF5L PPARG ARNT2 TAL1HOXD1 POU4F2 MAFGREST ETV5 RERE ZNF195 NFRKB ZNF395 TSC22D3 HLF HIVEP3CITED2 HOXC11 STAT3 CREB3L1 ZMYM3 ZNF334MITF NFATC4 HEY2 GATA2 ZNF33B HOXD13 MAF SOX15 ZNF175ZNF507 PBX3 ZNF446E4F1 POU6F1TCF4 FOXK2NR6A1 ZNF337 ZNF287LMO4MSRB2 AHRE2F5 TFDP1NKX2−5 CEBPB ESR1 PLAGL1 HOXB13 SOX5 HIVEP2HOXA3SHOX2 GAS7 MYCZNF16ZNF576 SOX10 HMG20A RFXANK TCF7CRXZNF205 SP1 LZTFL1 NR2F6 ZNF335 ZNF593DAXX ZNF277 ZNF688HMGB2 MEOX2 HSF2SUPT4H1 PITX3 EDF1 CNOT8 HMG20B ZNF443CDX2 NEUROG1 ZFHX4 ZNF365 YWHAE NR1I3BAZ1B FOXP3 ELK1 ZNF574 TCF15 GLI3 RUNX1T1 NFYB PROP1 AEBP1 COMMD5 ZNF253ZBTB17ILF2 RNF4 ZNF227 TRPS1PRRX1 ESRRA SMARCA4 NOTCH2SNAPC2ZNF550ZNF423 ZNF3 ATF5 MTA2TP53 RXRB HAND1 NHLH1 TFCP2 TFAM NR0B2 MLXIP ZNF282 AATF ZNF85 TBX4 EMX1TFAP4 POU3F4 REXO4 YPEL3 YY1 ZNF221 NR2E1 BRPF1 ZNF330 PHOX2A HSF1 SCAND1 CEBPZPLAGL2 T FOXN1 CEBPEEVX1ZNF384 ZNF668 MLXZNF672MYBL2 SP4 ZNF646 BUD31 CDX4 ref ZNF212 ZNF592NR3C1PREB ETV4 ZNF358 LMO1 NPAT FEZF2 ZNF230NFKB2NFYA PHOX2B ZNF207SMAD1 ALX4 both KLF6 SLC26A3GATA1 VSX1 PGRRARA ZNF83 MAFF ATF3 FOSL2 ZNF682 MXD1ZNF408 MAFKTSC22D1CLOCK KLF9 JUNB DMTF1 ONECUT1 TBX6 hmsc ZNF614 SP3 FOSB CNOT7 KLF12 OVOL1 HNF4A FOSL1 NFIL3 HOXC4 FOXI1 SP140 ZNF460 KLF10 HES2 IKZF1 BATFPOU2F2HCLS1ZNF667 TCF25 KLF11 ZNF350 FOXP1 ZNF552 RAX hncc MAXPPARD SHOX TBX21 VENTX TFEC AFF4 ATF2 CREM ETV3 ETS2 CTBP1 OLIG2 IRF8 IRF5PKNOX1 HBP1 LHX5 SPDEF STAT4 RLF NFATC3 CDHR5 none FUBP3 NEUROG3USF2 CIITA ZFXDDIT3 ATOH1 SOX21PAX4 PHF2 LBX1 NFE2 ELK4 MEF2A TTC36 ZNF451 LHX3NKX2−8 CDX1 TLX2LMX1B ZNF471 SPI1ASCL2 GBX2 ZNF211ZNF215 ZNF528ZHX2 RELA LHX6 CNBP ESR2 ZNF611 NCOR1NFAT5 MZF1 ZNF24 BTAF1SREBF1 MTF1 ZMYM2 ZNF343TGIF2ZNF652SNAPC5 ZNF248 ZNF345 ZNF654ARID4A PRDM2 ZNF225 ENO1 CEBPG ZNF432 ZNF606 CREB3 PURA ATF6 TEAD4 FUBP1ZNF529TARDBP MAZ SOX12 NR1H2JUND ARNT ZNF232 IRF3 ZBTB6 NPAS3 EGLN1 ZNF148 ZNF551ZNF573 KLF3 UBP1 ZNF586 MTA1ZFP36L1 ZNF22ZNF12TAF1B RORA RFXAP CBFA2T2 CITED1 ZBTB7ADRAP1 TSC22D4 MBD1 NFE2L2 NFYC TRIM28ELF1SNAPC4 ZNF302 RFX2 ZNF692 ZNF562BRD8 ZNF500JUN ZNF202 MLLT10 STAT2 NRF1 HOXD9 HCFC1 PFDN1 AFF1 SMAD5 NR2C2 ZNF250 ZNF516SIM2 TULP4 REL TEAD1 MAFB ZNF544ZNF234 POU6F2 BRF1 EWSR1 ATF1 ZNF91 ZNF146 RB1 LZTR1YBX1 RELBZNF318 ZBTB38 ZNF329ZNF223 ZNF675ZNF711FMNL2 UBN1CBL IRF1 POU2F1RBL2 CREB1 NR0B1 ZNF135 MYCN TFAP2B ZNF468 SNAI1 SLC2A4RGSNAI2TRERF1POU3F2 ZNF142 ZNF710 NKRF PCGF2 ZNF189 ZNF184 CTBP2NFATC1BACH1 SOX11 ZNF292ZNF14 SALL2 ZFP37 CTCF ZNF74 ZNF79 ZNF200ZNF143 LOC100506639STAT5A ZNF510 ZNF133 MECP2 ZNF589 YEATS4 ADNP ZNF706 ZNF239 ZNF354A NR2C1 GTF2IRD1 NFKB1 ZNF623 ZNF473 ZNF536 E2F6 IKZF4 RXRATFEB VAX2ZNF140KNTC1 ZNF32GATAD2A ZNF124 VPS72 ZFP36L2 ZNF629 ZNF532ZNF93 ZNF304 ZNF467 GMEB1 ZNF281 ZNF132 CEBPD TRIM22 TSPY26PZNF197 IRF4 ZNF415 EPAS1 STAT1 ZNF268 PBX1SMAD7 IRF7ETV7 HMGB1 SOX1 SLC30A9 ZNF35 ZMYM4 DEK PLAG1 E2F8 SREBF2PTTG1 HMGA1 PML KLF2 NKX6−1 FOXM1 MEIS1TCFL5IRF2 RFX5

Figure A2. Tree-and-leaf representation of network 1 (GSE34620). Nodes are representation of regu- lons, labeled by the transcription factor that regulates them and colored according to its classification as master regulator for the hMSC signature (light green), hNCC signature (light blue), both signatures (red) and not a master regulator (black). The edges are the result of a hierarchical analysis on regulon overlap, i.e., the more regulated genes two regulons have in common, the closer they are in the representation.

MYCNZNF331 NFRKB FOXJ2 CTBP1 ZNF592 SP2 ZNF132 RELA MITF NFE2L1RXRB TCF25 RNPEP LZTR1 TEAD3ZNF335 HMG20B ZNF692 NFIC ERF RXRGMYBL2 CREBBP MAZ MBD1USF2 HOXB5 ZNF263THRA SMARCA4 NFAT5ATF1 E2F2 ZNF467 ZNF35CLOCK ZNF212 GATAD1 TP53ZMYM3 CITED2 HIVEP2 ZNF552 GTF2IRD1 HOXB13 PHTF1 SMAD1 HOXC11 ZNF395 ZNF350ZNF227 MSC ZNF550 PLAGL2 ZNF175ZNF223 ZNF7 ZNF304 SUPT4H1 ZNF274 MTA2 ZNF34 HIRA POU2F2 ZNF174 ZNF200 RARA VPS72 ZNF528ZNF226 LZTS1 ZNF606PKNOX1 SMAD6ZNF493 RUNX3 CEBPB GATAD2A YY1 ZNF688FOXD3 RXRA TFDP1TULP4 ZNF586TRERF1FOXE3SLC2A4RG ZNF160 TEAD1VAV1 CBL TRERF1 ZNF468 ZNF239 ARID3A ZNF85 SOX4 DLX4 AATF TBX19 CREBL2 TCF7L2 NR1H3 ZBTB16 KNTC1E2F3 ZNF267 GMEB1 BUD31 ZNF143RLF ESRRBSTAT6FOXP1RREB1 ZNF3ZNF329 SNAPC5 NKRF CTCF CNBP ILF2 PTTG1 SMAD2 FOXN1 ZNF529VDR NFATC3 HMGB2 ZNF426SMAD4 OVOL1 DRAP1 RBL2CEBPD RORC MSX1 MYOD1DLX6 RUNX2FUBP3VEZF1 YPEL3 ZNF593 EWSR1 ZNF711 ZKSCAN1 MLXZNF701HSF1EN2PAX9 ZNF273 E2F8 ZNF277 POU2F3 POU6F2 KLF5 HSF2 CNOT8 MLXIPL NKX2−2 IRF6 NFYB HIVEP3 VENTX IRF4IKZF1 TMOD4 IRF1 ZNF135BRD8HHEXRARB DR1 TRIM29 MYOG FOSB TCF4CREB3L2 MAX CIITA MYF6 KLF2 ZNF133NR0B1 DEK TBR1 BNC1 NFX1 SNAI2 ZNF337 ZHX3 IRF5 ZNF665 YEATS4 TP73 TBX4 ZNF195 ref MEF2C ZNF667 MADD MAFK MEIS1 PPARA NHLH2 FOXI1 SUPT6HZNF652 CNOT7ZNF473 LMO1 NEUROD2CEBPZ BTAF1 TAF5L ZNF623 HNF4A KLF9 both TRIM22 ESRRGLHX2TFCP2L1 MAFG CITED1 POU4F1 TCEAL1RFXAP ZNF669 ADNPHMG20A FEV JUND ZNF202 SATB2TRPS1 NFIX ZNF407 SIX2 SP4UBN1 HEY2 TEAD4 ZNF124 ZNF14 TCFL5 REST FOSL2JUNB NR2C2 ZBTB25 FOXJ3 RFX3 SNAI1 hmsc STAT1 ZFP36L1 WT1ZNF215 SOX17 ZNF142 AHREPAS1 ZNF345 POU2F1 ZNF91ZNF197 MYT1L ZNF224 CEBPG ZNF318 AFF3 IKZF4 NR5A2 XBP1 ELF4 STAT2 LZTFL1 PITX1 SALL1 ETS1 ERGRUNX1T1HCLS1 ZNF281 RFX2ZNF668 ARNT2MYT1ARNT hncc TFECCREG1 UBP1 POU3F3 RELB ATF3 PRDM1 CREB3L1AEBP1 CTBP2NR2E1 ZMYM2 PCGF2 SRF RUNX1 BAZ1BZNF131 HRTCF15 TBX2 ZNF507ARID4A NFATC4HLFTFAP2B KLF4 PPARG REL YBX1 PAX2 IRF7 ZNF415 TEAD1 STAT3 none ETV4 ZNF185ZNF12 HIF1ANR2F6 ZNF471 THRB DDIT3 HOXB9 ZNF671 PKNOX2 ZNF510 NRF1 SALL2 CREM ETV1 TLX2ZNF500 C7orf61 NR2F1 NR4A1 BACH2 VAX2 ZNF45 MEIS2 NFIL3 MAF PAX7 FOXL1 ZNF234ZNF81 ZNF8 ETV5PLAG1SCML1 JUN GLI3MEOX2 ZBTB17 MLLT10 ZNF235 SMAD9 ZNF696 NR3C1 MAFKTFEB RFX1 NCOR1 ELF1IRF2 PBX3ARNTL2 ZFP36L2 E2F1 ZMYM4 FUBP1 NKX6−1 ZNF146 CEBPA ZNF516NFKB1 ETV7ATOH1 BLZF1MYNN ZNF140ZNF410 SOX13 TFCP2 SOX18TAL1 SOX1SOX11 TFDP3 TAF1B SPI1ZNF10 SIX6 SHOX2ZNF22 ZNF287 NFE2L3 ELF5 SPDEFRFX5 POU3F4 SP1 HEYL GATA3 E2F6 GBX2NKX2−5 FOXJ1 TFAP4 HEY1 FOXF1INSM1FOXH1FOXE1 ZNF205 GATA1 GATA6 OR56B1 ESR2 FOXK2ZNF74 NR5A1 HMGA1 PPARDPML TSPY26P NKX2−8POU3F1 CDX4 SLC30A9 ZNF219 NFKB2 ZNF460 SNAPC4 NPAT ELK1 TEF ZNF654 ZNF614PURA HOXD10 HBP1 PROP1HAND2 ZNF646 ZBTB6 SIX5 MLXIP FOXA1 HES2 ZNF408 ETV3 ZNF710 HMBOX1 ZNF189 ZNF384 NHLH1NEUROG3 CBFB NEUROG1 PFDN1BACH1 ZNF148 SOX21 HAND1 CREB1 BRF1 TBX10 NFE2 CTNNB1 HIC1 PAX4 FOXP3 AR SMAD5 ZNF574 CEBPESOX15 LMX1B TBX21 ESR1 HIVEP1KLF15 BRPF1 CDX2FOSL1 DAXX VSX1 PHF2ZNF672 CDX1 PRRX2 NR0B2 NR1I2 PREB EMX1 DLX2 ELF3PHOX2B HSF4 ZNF79 SNAPC2 GLI2MAFF SIX3 ASCL1PAX1 TBX6 SCAND1 CREB3 NR2E3 ZFP36L1 RFXANK HOXA1 ATF2 TSC22D4 PAX8 ATF6IRF3 ESRRA LBX1EVX1 KLF1 NR1I3 ZNF365 ZBTB7A PRDM16 LHX6CDHR5 MNT ZNF358 MZF1 TCF7 ALX4 SOX10 ASCL2 SOX12 HOXD12 PITX3 PGRST18 DBP TFE3 T PHOX2A ZNF444ZNF446 ZNF282E2F4 NRLZNF576 HCFC1

Figure A3. Tree-and-leaf representation of network 2 (GSE63157). Nodes are representation of regu- lons, labeled by the transcription factor that regulates them and colored according to its classification as master regulator for the hMSC signature (light green), hNCC signature (light blue), both signatures (red) and not a master regulator (black). The edges are the result of a hierarchical analysis on regulon overlap, i.e., the more regulated genes two regulons have in common, the closer they are in the representation. Cancers 2021, 13, 1860 17 of 24

CMBL NKIRAS1 POU3F1 SORD IRS2 PRPSAP2 NAF1 FOPNL TOM1L1 FAM162A KIAA1958 KIAA1191 SPIN1 GSTM5 TSPY26P IFT74 RIBC1 SHOX2ARSB RUNX2KCNK6 GLCE MMP14 TSEN34 COL6A1DYNC1I1 ITGA10 DCP1A PRMT6 LRRC10BCOASY RRBP1 PLPPR2 PIH1D2 SLC39A11 MELTF TMEM2FBLIM1PLOD2SLC16A7BCAR3 HYAL2 FAM169A GATA2 KAZALD1 LINC02544 TEX9 TEX264 FKBP10CBLN4 LRRC6 JUP PIGO MIPEP OMD NQO2 UBXN8NPTN RTL8C PTPRD S100A4 ABCC9 SNHG18 SLC41A2 ERMN SP7 ZXDB BRF2 TRIM36 GPI KCTD12 MMP13 SDC2 NT5C3A ADCY7 GLMP FRMD6-AS1 ADO OSGIN2 ACADS TNFRSF19 ATP10A LOC100506885 PAMR1 HRH1 BMP1 PAPSS2 VASN CALHM5 TMEM71 PEX11G SNN GPR161 ZFHX4-AS1 IBSP NT5E WBP4 ZBTB25 RPS6KA6FSD1 FNDC3BCPZ SEC24D CARHSP1 DMRTC1 HSBP1 TMEM106A ZNF469 HIP1R ALDH16A1 MDK ARHGAP42 TFEC SLC39A13 VDR TRIM35 NUDT10 PIK3R3 RBP4 WFDC1 SIX2 CAMK2G CHKA JADE3 DNAJC6 CACNB3 HHIPL1 OARD1 LRRC49 CSK JDP2 ARHGAP23 HEPH WNT5B ALPK2DTX4 RAB27B PIPOX ADRB3 PAPLN LRP1 ADAMTS18 ARHGAP22 ARF4 IFITM10 C16orf45 NEDD4L SCD5 VANGL1 C1QTNF6 SCIN SYNJ2 PHF21BGOLGA2P10 ARL9 PTX3 METRN CSF1R FAM84B PTTG1IP TVP23A TUBB2B IKZF4 PALM3 RHBDD2 LINC01122 GBF1 GPR68SLC8A3 LRCH2 MARCKS PLBD2 PLA2G4A TMEM214 ITGB8 FAM226B S100A6 P3H1 USP27X-AS1 KRT17 DBH-AS1 ASPHD2 AMPD2 DLG3 POC1B BICD1 HS3ST3B1 VPS52 LYRM1 GPR173 SEPT3 CERK TMEM151B MRAS FBXO28CDR2L HDAC7 IL17RD PTGFR IGF2 CTSK SYT11 BTBD11 TMEM116 MAST3HLF TRIM17 LMO4 JPH4 RALGPS1 TRIL PIANP TMEM216 UNC5C YIPF2 TCEAL2 HS3ST3A1PLD1 FCHSD1 B3GALT2 ESR1 F7 TNFRSF4 KLHL15 GOLGA2P7 SKIDA1 KCNK2 HOXA11 FSTL3 GFOD2 MYH10ZSWIM5 PPIB ACTN1 ACAN CRIP2 SATB2 FAM114A1 GOLGA7 IGSF9 HSPA12B TCEAL7 IL17D C1GALT1C1L FKBP11 FBXL22HTRA3 EPOR MMP23B CAPNS1 TENM4 EXOG CDC42EP1 IGLON5 AMOTL1WNT2B SPNS2 EZH2 NDRG4 CFHR2 SMIM15 RAB33A SEC31A S100A11 MIAT NFIB REC8 TP53I11 SPRYD4 TNFSF4 SGMS2 SH2B3 CTIF CADM3 TMEM59L SLC17A9 PPOX LY6E EOGT MLKL LPGAT1 SPINT2 CREB3L1 CHPF COL5A1 KIF5C CHPF2 CD63 GALNT5 PCYOX1L FAM241B LDHA AKAP5 ZNRF1 CHST6 ALPL COL24A1 SRGAP1FRRS1L C2orf88 RFPL2 LSP1 TMEM99TM9SF4ZNF33A ARNT2 CRABP2 RGS3 TRIM46 LOC100506497 CNKSR2 SMIM30 ADAM19 KDELR3 PPP6R1 CEP70 SPATS2L PDGFRL CXXC4 CCDC136SVBP GTF2H4 SESTD1 LRRC15OLFML2B LENG1 ZNF670 ABHD17C DLL3 LOC100506125 SLC35F2 ZNF821 PRR36 DCXR FERMT3 RBFOX1 ZNF365 LZTS3 BACH2 SCN3A FAXC KIF5A RNF144A KIF26B SERP2 NFASC PPFIBP2 VASH2 TMEM205 COPZ2 FXYD6 SMCO4 CD99L2 MPZL1 ING5 ASPHD1 NRP2 COPS7BBEX1 HEBP2TMEM200A TMEM44 CYHR1 MICAL1 KLK1 LIMK2 PLA2R1MRC2 TMEM41B NR2F2 SIPA1L2 MS4A7 AKR1A1 GATM NTNG1 MYLK PTGFRN NOVA2 NR2F1 NT5DC2PDZRN3 PTGIS PCOLCE FAM110B GNG2 LINC00667 KITLG DOCK11DPYD NR2F1-AS1 MAPRE2 MGLL TXNDC5 ST6GALNAC3 GNPTAB TCF7L1 SELENON PCNX2A1BG-AS1 FAM69A PCSK5 DAP ZNF423 C1R PEAK1 GGTA1P RERG RASSF8-AS1 EHD3 TGFBR2C1QA CTSC ALDH18A1 AIG1 PLPP7 TMEM14A BTAF1 MFAP2 ZC2HC1A NIPAL2 TSPAN12GOLM1 TSPAN18 PTPN22 NHSL1 RASSF8 DKK3 C9orf3 GAL3ST4 GBE1 MRPL24 TFB1M GLI3 S100A13 CDK6 NCK2 FSCN1IGDCC4COL6A2 VCAN CAMK2D LOC729970 SMTN METTL8 GPR34 SH3RF3 LOX ADAMTS9WIPI1 MEIS2 MAMDC2 COL12A1 CMKLR1 RHOBTB1 FRMD6 EXOSC5 FBN1 C1QTNF5 FAP MSR1 PIEZO2 PPIC CPXM1 EPS8 HADHB GULP1 GAS6 FST TM6SF1 FAM57A KIF7 KLHL35 ETV5 ST13 DUSP28 GHR VEGFC CELA1 LINC00244 DNAL4 DCBLD1 PDZD4 CD7 NR2C2APPPT1 C11orf74 PBX3 SELENOM TMEM133 NOTCH2 GARNL3 YTHDF3-AS1 MGC16275 QSOX2 ATP5L ZNF608 DNAH9 SLC40A1 TAX1BP3 MSRB3 ST3GAL6 LRRC56 MMP2 SMIM17 IDH3G NUAK1 PRR16TAGLN SECTM1 RAB40B PDGFRB RARRES2 TFAP2B PDGFRA TXNDC15 RUNX3 LYSMD2CPED1 FOLR2 CLEC2L LINC02361 FNDC11 DISC1 DUSP19 ERI2 LINC01431 MAPT USP35 PYGL RPL35 HPS3 ABCD1 CERKLTBK1 SMIM8 XG RADIL TRANK1 CCDC155 CLDN1 LAMA4 OPLAH PDE5A RFTN1 MPP7 GTF2IRD1 C1QC TMEM255A MAN1A1 ASPSCR1CCDC191 SNHG5 MOB4 SLC17A7 SLC8A1 A2M SRPX FABP5 TNS3 SMIM10L2ACADPS2 RHOJ SUSD1 COL1A1 RNASEH1-AS1ATG13 KATNAL2RPL10A LINC00471 UBE2E2 ECSCR HSPG2 SLC43A3 LPP VAMP8 CD200 CTHRC1 CACNA1H SCHIP1 MXRA5 SWT1 ERF SNX7 LHFPL2 PRICKLE2 SLC16A8 EBNA1BP2GPAT4 ZFHX4 TIE1 NRP1 SPARC COL3A1 UMPS GPX8 DPYSL3 LAMC1 CHPT1 PPM1J C1QB ADGRL2 GRIK3 C2orf76 NHS PTN DOCK10 RTN4RL1 TRAM1L1 SCPEP1 SLC46A3 NID2 ZNF571-AS1 CCDC30 OTUD5PRRT3-AS1 CARD10 DCLK1 SPTBN1 PLK2 TM4SF1 FAM198B NRK TECPR1 PALLD WDR31 ASIC2 CCDC92 VWF WHRN ZNF503 CHN1 PDZRN4 RASSF4 NR2F2-AS1 DEAF1 IGSF21 PMPCAISYNA1 HLA-DMA ITGA9 HTRA1 SUPV3L1 ESAM MMRN2 CLDN16REEP6 FEZ1 H19 ADCY10P1 ZNF416 CORO2B LGMN MID1 ROCK2 B3GNTL1 WWC1ARTN SLCO2B1 CYB5D1 HACD1 TNNC2 ZNF703 NPW BVES CARMN PALMD TJP1 KLF16 TOX2 ZNF502 SIAH3 RCC1L LOC101927610 ZNF395 CEBPZ RBFA NIFK-AS1 MEF2C TNNI1 GIMAP6 DYSF BNIP3 FHOD3 UBA2 CLBA1 TCP10L TSPAN7 SEZ6L2 PLA2G6 GMPR DOCK9 PLN CD34 INPP4B TP73-AS1 PLVAP MTCL1 ADAMTS5 F13A1HEY1 PAX7 ARHGAP29 MOAP1 TMCC3 ARAP3FILIP1L PSD NID1 ZNF521 TMTC1 HIST1H2BH UBXN2A EMCN ROBO4 GSN PPP1R3A POLR3C FCGRT CAVIN4 CNN2 MECOM MAPT-AS1 TACC2 AQP1 H2BFS GYG1 NEXN NDUFA6-AS1 ZSWIM6CHD3 SPINT1-AS1JAKMIP2 SHISA4 TMEM204SPARCL1 GPX3 SYTL2GUCY1A3GTF2F1TOPORS IL13RA1 NPY1R CYP26B1 LIMCH1 ITIH3 L3MBTL3 NES SHISA2 LAMA5 TM4SF18DMD AOC3 ZNF570CITED2 PHKA1ARNTL ARHGEF9UBXN7-AS1PHF13 CYP2E1 ADGRF5 ZNF827 SYNM DOCK4 ARHGAP6CNN3 CMA1 COX6A2 IL32 TMEM70TIMM17B EGFR IL7 PRKAG2-AS1 FAT4 H2AFY2 HOOK1 FRY PRKG1 ITGA6 C1RL SPRY1 GRB10 ALPK3 PRUNE2 SELENOW PDK4 PCDH8 GYG2 HOXC4 STON2 RPRD2 PLEKHG3 MYORG DPF3 TMEM178BRPL39 PECAM1 FAM83C ANO9 NR0B1 HS3ST4 IL25 FABP4 NFE2L1 CD93 UNC45B CNMD PTPRB FHL1 PTPRM PLA2G16KDR CHI3L1 BAHCC1 MLC1 CAPRIN1ST8SIA5 MYL9 RPS6KA2 CRACR2ATUBGCP2 LRRC75ATHEM6 CDKL2 ZNF302 ZEB1 CDK2AP1 NPAP1 CHP2 TUBB4ACMTM1 TRPC1 APLNR RCAN2 ADGRL4 FRMD4A PDLIM1 IGFN1 ADCYAP1 ENPP3 HOXD10 ZSCAN25 IQCA1 RIPOR3 RUNX1T1 LPL AKR1C2 PAPPA NPY5R KIAA1671 CA3 ADD3-AS1 SMIM10 HOXB13 CDH5 TEK SORBS2 PDS5B KIAA1522 STAB1 TTC32 S100A2 AMER2 LINC01607 MEOX2 SORBS1HOTS CCNJL GINM1 SGCE LGALS1 LOC102546294 SLC45A3 WWTR1 EPB41L4A-AS2 NECTIN4FAM105A AK1 RCSD1 PXDN MED12L TESC REPS2 RBMS3 CCDC124PRNT FMO3 SOX5 ACTA2 CMTM8 COL15A1 AFAP1L1 COPRS PHF10 KANK1 GIMAP8 TMEM246 ZNF385D PODXL CCDC174 PWP1 APOL4 LCN2 CIB2 C9orf66AKR1C3TPD52L1 SPATA2L HNRNPK TRHR MS4A2 FAM187A HIST1H2AM CCL11 PPARG EXOC6B DUS4L PIK3CG CST4 SEC61B ZNF134 TXNRD3CPA3 PLS1 WDR83

Figure A4. Regulatory map (TF-centric regulatory network) of the seven common master regulators in network 1 (GSE34620). Gray squares are transcription factors (TFs) that are here inferred as mater regulators (MRs) and circles are genes regulated by at least one of the seven MRs. The edges indicate regulation and the color of the edge indicates the type of regulation (red for positive regulation, activation, and blue for negative regulation, inhibition). The circles follow the same color scheme, except that whenever a gene is positively regulated by one TF and negatively regulated by another TF, it is colored gray.

3081525 3919124 3829751 3299970 3645779 2343231 2817464 3597338 2348569 2940202 3745351 3626826 3752888 2854445 3428596 4004044 2476671 2365958 2923270 3397589 3214800 3671202 3962781 3471427 2429556 3300115 2692909 2513758 2748923 3468888 3577078 3214825 3389954 2436526 2827057 3457160 2867873 3461795 2727226 34215112435383 2724094 2331213 3382296 2373336 2363689 3901055 2584134 3174121 26367263556288 2903219 3017030 2893130 2362991 2794704 3068097 3458451 3532393 3992408 3125571 2351171 3936256 2473735 2967249 3617719 2652410 2626097 2712754 2738664 33901434013549 2325526 3157385 3944637 255873625018353382410 3307580 2958325 3474958 3184896 3682135 2615360 2684187 3634256 2713709 3601931 31023722318656 3349293 2784027 2830465 2866576 3301512 3030448 3699616 3570599 2673181 3136888 3451375 3376529 3838845 4003017 2835021 3165825 2704441 3346147 2939232 2500803 2931970 28453513568310 2842194 3473436 30730132363562 3494137 2655387 3252690 3284302 3426502 3329343 3798778 4022447 37555803944826 3903598 2510056 3351498 3699080 2872848 2378369 3865275 3511698 2677853 3091077 2955556 2464005 3349437 27903682676167 3979762 3186966 3985008 3557350 3978155 2647154 2491661 4001850 3442249 3777991 3325307 3385769 3788560 3803418 3802602 3465274 2950329 3234760 2343823 2830818 3430462 2981912 2779543 3958475 3635737 3291151 3061805 2524301 3061319 2511603 2862696 2728938 2649824 3672886 3418249 2958172 3233049 2705748 3352948 3777470 3509473 3384704 3389450 2423175 3319137 2995667 3147173 2874371 3211938 2979679 2350952 3625271 3200648 3922664 37970322587961 3699426 2591837 311335240023942589291 2475407 3081862 3829687 23722112955638 4045676 3301263 3268059 3904691 27777142361154 3420442 2863885 2777113 2648677 2438458 3556128 3420151 2810805 3134511 2476510 3927480 3768412 3784344 3663287 3960782 2424102 MEF2C 3354535 2514413 3839920 2636125 3244488 23710653925639 3260383 2379314 2348992 3549575 3023103 3988874 3031533 2680046 2772876 2318455 2783316 39453143241316 3298557 2704267 3246888 3368707 3376556 3372420 2840425 2806091 3566495 3825244 2461037 3317117 34057482363042 3443348 2720584 2696113 3220977 2676454 3768474 3462816 PBX3 2660029 3212008 3970338 3214845 28731052818517 ARNT2 2708720 3529725 3021158 2796951 2970086 2508520 3767480 2725061 3198346 3013565 3319119 3220673 2903189 2865390 2615060 3618333 3595315 3150579 3400236 3311417 2922972 2688813 3832256 2727762 2351572 2961177 2791419 3323556 3175119 2427619 2877918 296134739738392373842 2423829 3474461 3376560 3510066 3376914 245330734652483766796 3399678 3046197 2519229 3352847 2522728 2597389 2332528 3329069 2699145 3988538 3305801 2569908 3137530 29672762548699 2473376 3913821 2515627 3374934 2705706 38864103862650 2591643 2548617 2867443 2681114 2829947 2971801 389555239634763939125 2751066 3564919 3365757 3522398 2581000 3930360 2441043 2519577 36230312882098 2945440 4018454 2700404 3323052 3181728 3624513 2522094 2977510 2615938 30065722427208 3102096 3176209 3459956 3191877 35238814047493 3448152 3784602 2928690 26459513469180 2580802 3481410 3361811 3801943 28026963338192 3039177 3786471 2745547 3652867 2416522 3212848 39005452353477

2738697

2705266 3791482 3601955 3928668 2863964 3058209 2500275 3078348 3278401 3679533 27054452797393 2438411 3853345 3730322 3564620 GLI3 3721452 2782545 2603897 3512719 3133233 3599709 3888835 3948640 32651402645387 3121198 3878619 3553228 35297993908358 2991150 3396593 2899152 3230811 3960875 3333603 3408733 3079005 3392332 3410056 3380080 3966891 3653123 3082874 3556966 36856103094157 4014029 3075136 3698919 2821417 2871241 3506153 3062193 3410322 3599758 PAX7 3441011 3949722 2321238 39993953132016 3129065 2898499 2831350 3958658 3722417 3159483 3813198 3888613 3224650 3211579 3946510 3694215 4049835 2423422 2922631 3407926 3775211 3160735 3167731 2902736 3362795 2766456 2468622 3497790 4003857 3292413 2323172 3709213 2957227 3753500 3873629 2783596 3526831 3405297 2945518 2809628 28533252357961 3359121 3282601 3739867 38190163213120 3838624 2863535 3326950 3250602 2792127 3886294 3169331 3375545 3424442 3737874 3579114 3430959 34449582929870 3506093 2731636 2601995 36390073458337 3723755 3709244 3723687 3523499 3091403 2474240 3868587 2769063 292587129026333653786 4007689 3551566 3838665 2559189 3761054 3087501 3217123 2773719 3248999 CREB3L1 3744013 2573016 3363266 3936951 2351004 3948259 3808854 3790259 3019158 3942681 3379452 2971724 2417272 2735221 314364324496932347732 3261009 2431112 30495222409004

39243723361971 3924424 3643580 4054612 2939469 3774906 23335743589141 2701109 3013054 3264059 39092473571059 3082990 3518418 29037823513549 3899173 2909948 3439378 3570373 3386737 3995666 3509910 3905145 24745273887452 2460422 RUNX3 3592214 2994835 3551485 35652063502829 3164086 2801694 3163982 3628498 3774883 23597803016380 3848243 2899176 3701297 3282463 3419849 2504328 3944758 38943223258221 3702547 3894513 30485172528159 2776088 2604998 3646118 2837970 2614369 3829471 3162529 2969677 3853108 25055292525989 3453405 3293187 2719361 3999568 2604390 3377569 2802963 2949885 3883819 2529546 3572982 2730746 3550485 264057938674432400212 2320581 250276238312013521484 2338625 2847967 2600218 39626783379644 3188697 3326183 3482219 23181703564210 3293215 2587520 3070309 3178611 3400034 2396201 3945396 2500722 23963622385146 3864808 32495872330002 2409507 3622239 235799625950423470446 3693314 39102602687840 29869993867065 2332812 2333481 2674526 3131789 3545022 2816298 4026487 2434746 2768981 2574984 2674501 3744463 3009959 3427876 3615579 387664533210553630450

Figure A5. Regulatory map (TF-centric regulatory network) of the seven common master regulators in network 2 (GSE63157). Gray squares are transcription factors (TFs) that are here inferred as mater regulators (MRs) and circles are genes regulated by at least one of the seven MRs. The edges indicate regulation and the color of the edge indicates the type of regulation (red for positive regulation, activation, and blue for negative regulation, inhibition). The circles follow the same color scheme, except that whenever a gene is positively regulated by one TF and negatively regulated by another TF, it is colored gray. Cancers 2021, 13, 1860 18 of 24

Appendix B

Table A1. This table contains the 44 master regulators (MRs) inferred for network 1 with the signature obtained with human Mesenchymal Stem Cells. p-values were adjusted with Benjamini & Hochberg—BH.

MR p-Value Adjusted p-Value CREB3L1 1.80 × 10−11 1.20 × 10−8 AEBP1 4.80 × 10−9 1.50 × 10−6 SNAI2 8.00 × 10−9 1.70 × 10−6 PBX3 1.10 × 10−8 1.80 × 10−6 SMAD7 6.80 × 10−8 8.70 × 10−6 NR6A1 2.10 × 10−7 2.30 × 10−5 SOX11 5.10 × 10−7 4.30 × 10−5 HIC1 5.40 × 10−7 4.30 × 10−5 AHR 8.50 × 10−7 6.10 × 10−5 CTBP2 1.40 × 10−6 8.90 × 10−5 TFAP2B 2.60 × 10−5 0.0009 HHEX 2.70 × 10−5 0.0009 TRPS1 3.70 × 10−6 0.00022 HIF1A 6.00 × 10−6 0.00027 HOXB13 5.50 × 10−6 0.00027 SHOX2 5.50 × 10−6 0.00027 EPAS1 7.80 × 10−6 0.00033 KLF9 2.10 × 10−5 0.00083 ELK3 2.20 × 10−5 0.00083 KLF10 3.20 × 10−5 0.001 IRF2 3.30 × 10−5 0.001 FOSL2 3.80 × 10−5 0.0011 PAX7 6.90 × 10−5 0.0018 XBP1 6.90 × 10−5 0.0018 KLF11 6.70 × 10−5 0.0018 MEIS1 0.00012 0.0029 CREG1 0.00012 0.003 IRF7 0.00029 0.0067 ELF4 0.00038 0.0084 SIX2 0.00043 0.0093 RUNX3 0.0008 0.016 ESR1 0.00076 0.016 ZNF667 0.00094 0.018 MEF2C 0.0013 0.025 GAS7 0.0015 0.028 ZNF124 0.0016 0.029 CBFB 0.0017 0.03 GATA6 0.0017 0.03 GLI3 0.0019 0.032 RUNX2 0.0021 0.035 ARNT2 0.0026 0.042 ZNF34 0.0028 0.043 ETS1 0.003 0.045 ZNF195 0.0031 0.045 Cancers 2021, 13, 1860 19 of 24

Table A2. This table contains the 13 MRs inferred for network 1 with the signature obtained with human Neural Crest Cells. p-values were adjusted with Benjamini & Hochberg—BH.

MR p-Value Adjusted p-Value PAX7 5.00 × 10−5 6.50 × 10−3 AEBP1 1.50 × 10−4 1.40 × 10−2 ARNT2 8.20 × 10−6 3.80 × 10−3 PBX3 1.70 × 10−5 3.80 × 10−3 RUNX3 5.30 × 10−4 3.40 × 10−2 NKX2-2 2.20 × 10−5 3.80 × 10−3 MEF2C 2.40 × 10−5 3.80 × 10−3 NOTCH2 1.90 × 10−4 1.50 × 10−2 CREB3L1 1.30 × 10−4 1.40 × 10−2 ZNF423 6.80 × 10−4 3.70 × 10−2 TEF 6.00 × 10−4 0.035 GLI3 9.70 × 10−4 0.048 SOX1 4.70 × 10−4 0.034

Table A3. This table contains the 66 master regulators (MRs) inferred for network 2 with the signature obtained with human Mesenchymal Stem Cells. p-values were adjusted with Benjamini & Hochberg—BH.

MR p-Value Adjusted p-Value SNAI2 8.30 × 10−37 4.70 × 10−34 MEF2C 1.20 × 10−22 3.40 × 10−20 PAX7 5.00 × 10−17 9.50 × 10−15 RUNX1 1.40 × 10−16 2.00 × 10−14 CREB3L1 1.00 × 10−15 1.20 × 10−13 AEBP1 3.30 × 10−14 3.10 × 10−12 NKX2-2 9.00 × 10−14 7.30 × 10−12 PBX3 1.20 × 10−12 8.20 × 10−11 AHR 2.50 × 10−12 1.60 × 10−10 CTNNB1 6.50 × 10−12 3.70 × 10−10 XBP1 8.50 × 10−12 4.40 × 10−10 EPAS1 3.50 × 10−10 1.70 × 10−8 BACH2 1.30 × 10−9 5.60 × 10−8 GLI3 1.40 × 10−9 5.60 × 10−8 SOX11 1.80 × 10−9 6.90 × 10−8 ETV1 1.30 × 10−8 4.20 × 10−7 MAF 1.20 × 10−8 4.20 × 10−7 ETS1 1.60 × 10−8 4.90 × 10−7 TCF4 2.70 × 10−8 8.20 × 10−7 CREG1 3.60 × 10−8 1.00 × 10−6 MEOX2 5.20 × 10−8 1.40 × 10−6 RUNX3 1.90 × 10−7 4.80 × 10−6 ETV5 2.20 × 10−7 5.40 × 10−6 TRPS1 8.80 × 10−7 2.10 × 10−5 FOSL2 1.50 × 10−6 3.30 × 10−5 HEY2 1.70 × 10−6 3.70 × 10−5 CEBPB 7.30 × 10−6 0.00015 PRDM1 9.40 × 10−6 0.00019 PKNOX2 1.20 × 10−5 0.00023 ZNF74 1.30 × 10−5 0.00024 ARNTL2 1.80 × 10−5 0.00033 SOX1 2.00 × 10−5 0.00035 IRF2 2.30 × 10−5 0.00039 ETV4 4.50 × 10−5 0.00075 Cancers 2021, 13, 1860 20 of 24

Table A3. Cont.

MR p-Value Adjusted p-Value ELF1 5.30 × 10−5 0.00086 ZBTB25 7.40 × 10−5 0.0011 ARNT2 7.60 × 10−5 0.0011 NR0B1 7.50 × 10−5 0.0011 RUNX2 8.00 × 10−5 0.0012 ESRRB 0.00016 0.0023 ZBTB16 0.00017 0.0024 KLF9 0.0002 0.0027 TFEC 0.00021 0.0028 HEYL 0.00028 0.0036 HCLS1 0.00031 0.0039 HR 0.0005 0.0062 MEIS2 0.00064 0.0076 ZFP36L2 0.0007 0.0082 KLF4 0.00078 0.009 CREB3L2 0.00088 0.01 HIVEP1 0.0012 0.013 ZNF516 0.0012 0.013 ZNF235 0.0013 0.014 MYT1 0.0015 0.016 ZNF219 0.0016 0.017 AFF3 0.0021 0.021 HIVEP3 0.0024 0.024 CEBPD 0.0027 0.026 TEAD1 0.0028 0.027 SMAD5 0.0031 0.029 NR3C1 0.0041 0.038 NFE2L1 0.0043 0.039 NKX6-1 0.0049 0.043 ZNF550 0.0049 0.043 STAT1 0.0054 0.047 SATB2 0.0058 0.05

Table A4. This table contains the 42 MRs inferred for network 2 with the signature obtained with human Neural Crest Cells. p-values were adjusted with Benjamini & Hochberg—BH.

MR p-Value Adjusted p-Value PBX3 1.80 × 10−13 1.00 × 10−10 PKNOX2 3.90 × 10−13 1.10 × 10−10 PAX7 3.30 × 10−12 6.20 × 10−10 CEBPB 1.60 × 10−11 2.20 × 10−9 GLI3 2.30 × 10−9 2.60 × 10−7 AFF3 9.20 × 10−9 7.40 × 10−7 ZNF215 9.20 × 10−9 7.40 × 10−7 MYT1 1.20 × 10−8 7.60 × 10−7 RUNX3 1.20 × 10−8 7.60 × 10−7 ARNT2 5.00 × 10−8 2.80 × 10−6 ETV4 1.40 × 10−7 7.20 × 10−6 CREB3L1 6.30 × 10−7 3.00 × 10−5 MYT1L 1.60 × 10−6 7.00 × 10−5 ZBTB25 1.80 × 10−6 7.40 × 10−5 MEOX2 4.30 × 10−6 0.00016 SNAI2 8.70 × 10−6 0.00031 Cancers 2021, 13, 1860 21 of 24

Table A4. Cont.

MR p-Value Adjusted p-Value RUNX1 9.40 × 10−6 0.00031 NKX2-2 1.60 × 10−5 0.00051 MSC 3.50 × 10−5 0.001 ETV1 1.10 × 10−4 0.0031 MAF 1.10 × 10−4 0.0031 MYC 1.40 × 10−4 0.0036 HOXB9 1.60 × 10−4 0.0039 MEF2C 1.80 × 10−4 0.0044 LMO1 2.40 × 10−4 0.0055 ETS1 0.00029 0.0063 MEIS2 0.00041 0.0086 SOX17 0.00068 0.014 BACH2 0.00069 0.014 HEY2 0.00076 0.014 HIVEP3 0.00097 0.018 ARNTL2 0.001 0.018 SMAD9 0.001 0.018 TCF4 0.0011 0.019 HOXC11 0.0014 0.023 NR0B1 0.0019 0.029 ESRRB 0.0022 0.033 PRDM1 0.0024 0.035 SMAD6 0.0026 0.037 NR2F1 0.003 0.042 SALL1 0.0036 0.049 TCF7L2 0.0037 0.05

References 1. Moch, . (Ed.) Soft Tissue and Bone Tumours WHO Classification of Tumours, 5th ed.; International Agency for Research on Cancer: Lyon, France, 2020. 2. Tu, J.; Huo, Z.; Gingold, J.; Zhao, R.; Shen, J.; Lee, D.F. The histogenesis of Ewing Sarcoma. Cancer Rep. Rev. 2017, 1, 1–2. [CrossRef] 3. Kallen, M.E.; Hornick, J.. The 2020 WHO classification: What’s new in soft tissue tumor pathology? Am. J. Surg. Pathol. 2021, 45, 1–23. doi:10.1097/PAS.0000000000001552. [CrossRef] 4. Grünewald, T.G.P.; Cidre-Aranaz, F.; Surdez, D.; Tomazou, E.M.; de Álava, E.; Kovar, H.; Sorensen, P.H.; Delattre, O.; Dirksen, U. Ewing sarcoma. Nat. Rev. Dis. Prim. 2018, 4, 5. [CrossRef] 5. Patel, M.; Simon, J.M.; Iglesia, M.D.; Wu, S.B.; McFadden, A..; Lieb, J.D.; Davis, I.J. Tumor-specific retargeting of an oncogenic transcription factor chimera results in dysregulation of chromatin and transcription. Genome Res. 2012, 22, 259–270. [CrossRef] [PubMed] 6. Riggi, N.; Knoechel, B.; Gillespie, S.M.; Rheinbay, E.; Boulay, G.; Suvà, M.L.; Rossetti, N.E.; Boonseng, W.E.; Oksuz, O.; Cook, E.B.; et al. EWS-FLI1Utilizes Divergent Mechanisms to Directly Activate or Repress Elements in Ewing Sarcoma. Cancer Cell 2014, 26, 668–681. [CrossRef][PubMed] 7. Gaspar, N.; Hawkins, D.S.; Dirksen, U.; Lewis, I.J.; Ferrari, S.; Le Deley, M.C.; Kovar, H.; Grimer, R.; Whelan, J.; Claude, L.; et al. Ewing sarcoma: Current management and future approaches through collaboration. J. Clin. Oncol. 2015, 33, 3036–3046. [CrossRef][PubMed] 8. Cotterill, S.J.; Ahrens, S.; Paulussen, M.; Jürgens, H.F.; Voûte, P.A.; Gadner, H.; Craft, A.W. Prognostic factors in Ewing’s tumor of bone: Analysis of 975 patients from the European Intergroup Cooperative Ewing’s Sarcoma Study Group. J. Clin. Oncol. 2000, 18, 3108–3114. doi:10.1200/JCO.2000.18.17.3108. [CrossRef] 9. Cavazzana, A.O.; Miser, J.S.; Jefferson, J.; Triche, T.J. Experimental evidence for a neural origin of Ewing’s sarcoma of bone. Am. J. Pathol. 1987, 127, 507–18. 10. von Levetzow, C.; Jiang, .; Gwye, .; von Levetzow, G.; Hung, L.; Cooper, A.; Hsu, J.H.R.; Lawlor, E.R. Modeling initiation of ewing sarcoma in human neural crest cells. PLoS ONE 2011, 6, e19305. [CrossRef] 11. Carro, M.S.; Lim, W..; Alvarez, M.J.; Bollo, R.J.; Zhao, X.; Snyder, E.Y.; Sulman, E.P.; Anne, S.L.; Doetsch, F.; Colman, H.; et al. The transcriptional network for mesenchymal transformation of tumours. 2010, 463, 318–325. 12. Rooj, A.K.; Bronisz, A.; Godlewski, J. The role of octamer binding transcription factors in glioblastoma multiforme. Biochim. Biophys. Acta 2016, 1859, 805–811. [CrossRef] Cancers 2021, 13, 1860 22 of 24

13. Castro, M.A.; De Santiago, I.; Campbell, T.M.; Vaughn, C.; Hickey, T.E.; Ross, E.; Tilley, W.D.; Markowetz, F.; Ponder, B.A.; Meyer, K.B. Regulators of genetic risk of breast cancer identified by integrative network analysis. Nat. Genet. 2015, 48, 12–21. [CrossRef][PubMed] 14. Albanus, R.D.O.; Dalmolin, R.J.S.; Castro, M.A.A.; De Bittencourt Pasquali, M.A.; De Miranda Ramos, .; Gelain, D.P.; Moreira, J.C.F. Reverse engineering the neuroblastoma regulatory network uncovers as one of the master regulators of tumor progression. PLoS ONE 2013, 8, e82457. [CrossRef] 15. Fletcher, M.N.; Castro, M.A.; Wang, X.; De Santiago, I.; O’Reilly, M.; Chin, S.F.; Rueda, O.M.; Caldas, C.; Ponder, B.A.; Markowetz, F.; et al. Master regulators of FGFR2 signalling and breast cancer risk. Nat. Commun. 2013, 4, 2464. [Cross- Ref][PubMed] 16. Sartor, I.T.S.; Zeidán-Chuliá.; F.; Albanus, R.D.; Dalmolin, R.J.S.; Moreira, J.C.F. Computational analyses reveal a prognostic impact of TULP3 as a transcriptional master regulator in pancreatic ductal adenocarcinoma. Mol. BioSystems 2014, 10, 1461–1468. [CrossRef][PubMed] 17. Mattick, J.S.; Taft, R.J.; Faulkner, G.J. A global view of genomic information–moving beyond the gene and the master regulator. Trends Genet. 2010, 26, 21–8. [CrossRef] 18. Chan, S.S.K.; Kyba, M. What is a Master Regulator? J. Stem Cell Res. Ther. 2013, 3.[CrossRef] 19. Brohl, A.S.; Solomon, D.A.; Chang, W.; Wang, J.; Song, Y.; Sindiri, S.; Patidar, R.; Hurd, L.; Chen, L.; Shern, J.F.; et al. The Genomic Landscape of the Ewing Sarcoma Family of Tumors Reveals Recurrent STAG2 . PLoS Genet. 2014, 10, e1004475. [CrossRef] 20. Crompton, B.D.; Stewart, C.; Taylor-Weiner, A.; Alexe, G.; Kurek, K.C.; Calicchio, M.L.; Kiezun, A.; Carter, S.L.; Shukla, S.A.; Mehta, S.S.; et al. The genomic landscape of pediatric Ewing sarcoma. Cancer Discov. 2014, 4, 1326–1341. [CrossRef] 21. Tirode, F.; Surdez, D.; Ma, X.; Parker, M.; Le Deley, M.C.; Bahrami, A.; Zhang, Z.; Lapouble, E.; Grossetete-Lalami, S.; Rusch, M.; et al. Genomic landscape of ewing sarcoma defines an aggressive subtype with co-association of STAG2 and TP53 mutations. Cancer Discov. 2014, 4, 1342–1353. 22. Solomon, D.A.; Kim, T.; Diaz-Martinez, L.A.; Fair, J.; Elkahloun, A.G.; Harris, B.T.; Toretsky, J.A.; Rosenberg, S.A.; Shukla, N.; Ladanyi, M.; et al. Mutational inactivation of STAG2 causes in human cancer. Science 2011, 333, 1039–1043. [CrossRef] 23. Edgar, R. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30, 207–210. doi:10.1093/nar/30.1.207. [CrossRef][PubMed] 24. Postel-Vinay, S.; Véron, A.S.; Tirode, F.; Pierron, G.; Reynaud, S.; Kovar, H.; Oberlin, O.; Lapouble, E.; Ballet, S.; Lucchesi, C.; et al. Common variants near TARDBP and EGR2 are associated with susceptibility to Ewing sarcoma. Nat. Genet. 2012, 44, 323–327. [CrossRef][PubMed] 25. Volchenboum, S.L.; Andrade, J.; Huang, L.; Barkauskas, D.A.; Krailo, M.; Womer, R.B.; Ranft, A.; Potratz, J.; Dirksen, U.; Triche, T.J.; et al. Gene expression profiling of Ewing sarcoma tumours reveals the prognostic importance of tumour-stromal interactions: A report from the Children’s Oncology Group. J. Pathol. Clin. Res. 2015, 1, 83–94. [CrossRef][PubMed] 26. Zeltner, N.; Fattahi, F.; Dubois, N.C.; Saurat, N.; Lafaille, F.; Shang, L.; Zimmer, B.; Tchieu, J.; Soliman, M.A.; Lee, G.; et al. Capturing the biology of disease severity in a PSC-based model of familial dysautonomia. Nat. Med. 2016, 22, 1421–1427. [CrossRef] 27. Town, J.; Pais, H.; Harrison, S.; Stead, L.F.; Bataille, C.; Bunjobpol, W.; Zhang, J.; Rabbitts, T.H. Exploring the surfaceome of Ewing sarcoma identifies a new and unique therapeutic target. Proc. Natl. Acad. Sci. USA 2016, 113, 3603–3608. [CrossRef] 28. Gautier, L.; Cope, L.; Bolstad, B.M.; Irizarry, R.A. Affy—Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004, 20, 307–315. [CrossRef] 29. Carvalho, B.S.; Irizarry, R.A. A framework for oligonucleotide microarray preprocessing. Bioinformatics 2010, 26, 2363–2367. [CrossRef] 30. Savola, S.; Klami, A.; Myllykangas, S.; Manara, C.; Scotlandi, K.; Picci, P.; Knuutila, S.; Vakkila, J. High Expression of Complement Component 5 (C5) at Tumor Site Associates with Superior Survival in Ewing’s Sarcoma Family of Tumour Patients. Int. Sch. Res. Not. Oncol. 2011, 2011, 168712. [CrossRef] 31. Puerto-Camacho, P.; Teresa Amaral, A.; Lamhamedi-Cherradi, S.E.; Menegaz, B.A.; Castillo-Ecija, H.; Luis Ord, J.; Domínguez, S.; Jordan-Perez, C.; Diaz-Martin, J.; Romero-Perez, L.; et al. Preclinical Efficacy of -Targeting Antibody-Drug Conjugates for the Treatment of Ewing Sarcoma. Clin. Cancer Res. 2019, 25.[CrossRef] 32. Maksimovic, J.; Phipson, B.; Oshlack, A. A cross-package Bioconductor workflow for analysing methylation array data. F1000Research 2017, 5.[CrossRef] 33. Yu, G.; Wang, L.G.; Han, Y.; He, .Y. ClusterProfiler: An R package for comparing biological themes among gene clusters. OMICS A J. Integr. Biol. 2012, 16, 284–287. [CrossRef] 34. Castro, M.A.A.; Wang, X.; Fletcher, M.N.C.; Meyer, K.B.; Markowetz, F. RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations. Genome Biol. 2012, 13, R29. [CrossRef] 35. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-∆∆CT method. Methods 2001, 25, 402–408. [CrossRef] 36. Allen-Rhoades, W.; Whittle, S.B.; Rainusso, N. Pediatric Solid Tumors of Infancy: An Overview. Pediatr. Rev. 2018, 39, 57–67. [CrossRef][PubMed] Cancers 2021, 13, 1860 23 of 24

37. Riggi, N.; Suvà, M.L.; Suvà, D.; Cironi, L.; Provero, P.; Tercier, S.; Joseph, J.M.; Stehle, J.C.; Baumer, K.; Kindler, V.; et al. EWS-FLI-1 Expression Triggers a Ewing’s Sarcoma Initiation Program in Primary Human Mesenchymal Stem Cells. Cancer Res. 2008, 68, 2176–85. [CrossRef][PubMed] 38. Kinsey, M.; Smith, R.; Lessnick, S.L. NR0B1 Is Required for the Oncogenic Phenotype Mediated by EWS/FLI in Ewing’s Sarcoma. Mol. Cancer Res. 2006, 4, 851–859. [CrossRef] 39. Shi, X.; Zheng, Y.; Jiang, L.; Zhou, B.; Yang, W.; Li, L.; Ding, L.; Huang, M.; Gery, S.; Lin, D.C.; et al. EWS-FLI1 regulates and cooperates with core regulatory circuitry in Ewing sarcoma. Nucleic Acids Res. 2020, 48, 11434–11451. [CrossRef][PubMed] 40. Vidya Rani, P.S.; Shyamala, K.; Girish, H.C.; Murgod, S. Pathogenesis of Ewing sarcoma: A review. J. Adv. Clin. Res. Insights 2015, 2, 164–168. [CrossRef] 41. Riggi, N.; Suvá, M.L.; Stamenkovic, I. Ewing’s Sarcoma. N. Engl. J. Med. 2021, 384, 154–164. [CrossRef] 42. von Heyking, K.; Roth, L.; Ertl, M.; Schmidt, O.; Calzada-Wack, J.; Neff, F.; Lawlor, E.R.; Burdach, S.; Richter, G.H. The posterior HOXD : Its contribution to phenotype and malignancy of Ewing sarcoma. Oncotarget 2016, 7, 41767–41780. [CrossRef] 43. Selvanathan, S.P.; Graham, G.T.; Erkizan, H.V.; Dirksen, U.; Natarajan, T.G.; Dakic, A.; Yu, S.; Liu, X.; Paulsen, M.T.; Ljungman, M.E.; et al. Oncogenic fusion protein EWS-FLI1 is a network hub that regulates . Proc. Natl. Acad. Sci. USA 2015, 112, E1307–E1316. [CrossRef][PubMed] 44. Svoboda, L.K.; Harris, A.; Bailey, N.J.; Schwentner, R.; Tomazou, E.; von Levetzow, C.; Magnuson, B.; Ljungman, M.; Kovar, H.; Lawlor, E.R. Overexpression of HOX genes is prevalent in Ewing sarcoma and is associated with altered epigenetic regulation of developmental transcription programs. 2014, 9, 1613–1625. [CrossRef][PubMed] 45. Sheffield, N.C.; Pierron, G.; Klughammer, J.; Datlinger, P.; Schoenegger, A.; Schuster, M.; Hadler, J.; Surdez, D.; Guillemot, D.; Lapouble, E.; et al. DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma. Nat. Med. 2017, 23, 386–395. [CrossRef][PubMed] 46. Mellor, P.; Deibert, L.; Calvert, B.; Bonham, K.; Carlsen, S.A.; Anderson, D.H. CREB3L1 Is a Metastasis Suppressor That Represses Expression of Genes Regulating Metastasis, Invasion, and Angiogenesis. Mol. Cell. Biol. 2013, 33, 4985–4995. [CrossRef][PubMed] 47. Feng, Y.X.; Jin, D.X.; Sokol, E.S.; Reinhardt, F.; Miller, D.H.; Gupta, P.B. Cancer-specific PERK signaling drives invasion and metastasis through CREB3L1. Nat. Commun. 2017, 8, 1079. [CrossRef][PubMed] 48. Bogeas, A.; Morvan-Dubois, G.; El-Habr, E.A.; Lejeune, F.X.; Defrance, M.; Narayanan, A.; Kuranda, K.; Burel-Vandenbos, F.; Sayd, S.; Delaunay, V.; et al. Changes in chromatin state reveal ARNT2 at a node of a tumorigenic transcription factor signature driving glioblastoma cell aggressiveness. Acta Neuropathol. 2018, 135, 267–283. [CrossRef] 49. Li, H.; Sun, G.; Liu, C.; Wang, J.; Jing, R.; Wang, J.; Zhao, X.; Xu, X.; Yang, Y. PBX3 is associated with proliferation and poor prognosis in patients with cervical cancer. OncoTargets Ther. 2017, 10, 5685–5694. [CrossRef][PubMed] 50. Wang, F.; Wu, J.; Qiu, Z.; Ge, X.; Liu, X.; Zhang, C.; Xu, W.; Wang, F.; Hua, D.; Qi, X.; et al. ACOT1 expression is associated with poor prognosis in gastric adenocarcinoma. Hum. Pathol. 2018, 77, 35–44. [CrossRef] 51. Li, J.; Qiu, M.; An, Y.; Huang, J.; Gong, C. miR-7-5p acts as a tumor suppressor in bladder cancer by regulating the hedgehog pathway factor Gli3. Biochem. Biophys. Res. Commun. 2018, 503, 2101–2107. [CrossRef] 52. Wang, S.; Li, C.; Wang, W.; Xing, C. PBX3 promotes gastric cancer invasion and metastasis by inducing epithelial-mesenchymal transition. Oncol. Lett. 2016, 12, 3485–3491. [CrossRef][PubMed] 53. Sannino, G.; Marchetto, A.; Kirchner, T.; Grünewald, T.G. Epithelial-to-mesenchymal and mesenchymal-to-epithelial transition in mesenchymal tumors: A paradox in sarcomas? Cancer Res. 2017, 77, 4556–4561. [CrossRef] 54. Maltepe, E.; Keith, B.; Arsham, A.M.; Brorson, J.R.; Simon, M.C. The role of ARNT2 in tumor angiogenesis and the neural response to hypoxia. Biochem. Biophys. Res. Commun. 2000, 273, 231–238. [CrossRef][PubMed] 55. Rankin, E.B.; Giaccia, A.J. The role of hypoxia-inducible factors in tumorigenesis. Cell Death Differ. 2008, 15, 678–685. 56. Palmer, T.D.; Ashby, W.J.; Lewis, J.D.; Zijlstra, A. Targeting tumor cell motility to prevent metastasis. Adv. Drug Deliv. Rev. 2011, 63, 568–581. doi:10.1016/j.addr.2011.04.008. [CrossRef][PubMed] 57. Velez, D.O.; Tsui, B.; Goshia, T.; Chute, C.L.; Han, A.; Carter, H.; Fraley, S.I. 3D collagen architecture induces a conserved migratory and transcriptional response linked to vasculogenic mimicry. Nat. Commun. 2017, 8.[CrossRef] 58. Winkler, J.; Abisoye-Ogunniyan, A.; Metcalf, K.J.; Werb, Z. Concepts of extracellular matrix remodelling in tumour progression and metastasis. Nat. Commun. 2020, 11, 1–19. [CrossRef] 59. Saaristo, A.; Karpanen, T.; Alitalo, K. Mechanisms of angiogenesis and their use in the inhibition of tumor growth and metastasis. 2000, 19, 6122–6129. [CrossRef] 60. Yang, Y.; Zheng, H.; Zhan, Y.; Fan, S. An emerging tumor invasion mechanism about the collective cell migration. Am. J. Transl. Res. 2019, 11, 5301–5312. 61. Franzetti, G.A.; Laud-Duval, K.; Van Der Ent, W.; Brisac, A.; Irondelle, M.; Aubert, S.; Dirksen, U.; Bouvier, C.; De Pinieux, G.; Snaar-Jagalska, E.; et al. Cell-to-cell heterogeneity of EWSR1-FLI1 activity determines proliferation/migration choices in Ewing sarcoma cells. Oncogene 2017, 36, 3505–3514. [CrossRef] 62. Charville, G.W.; Wang, W.L.; Ingram, D.R.; Roy, A.; Thomas, D.; Patel, R.M.; Hornick, J.L.; Van De Rijn, M.; Lazar, A.J. EWSR1 fusion mediate PAX7 expression in Ewing sarcoma. Mod. Pathol. 2017, 30, 1312–1320. [CrossRef][PubMed] 63. Bledsoe, K.L.; Mcgee-Lawrence, M.E.; Camilleri, E.T.; Wang, X.; Riester, S.M.; van Wijnen, A.J.; Oliveira, A.M.; Westendorf, J.J. RUNX3 facilitates growth of Ewing sarcoma cells. J. Cell. Physiol. 2014, 229, 2049–2056. [CrossRef] Cancers 2021, 13, 1860 24 of 24

64. Basch, M.L.; Bronner-Fraser, M.; García-Castro, M.I. Specification of the neural crest occurs during and requires Pax7. Nature 2006, 441, 218–222. [CrossRef] 65. Charville, G.W.; Varma, S.; Forgó, E.; Dumont, S.N.; Zambrano, E.; Trent, J.C.; Lazar, A.J.; Van De Rijn, M. PAX7 expression in rhabdomyosarcoma, related soft tissue tumors, and small round blue cell . Am. J. Surg. Pathol. 2016, 40, 1305–1315. [CrossRef] 66. Toki, S.; Wakai, S.; Sekimizu, M.; Mori, T.; Ichikawa, H.; Kawai, A.; Yoshida, A. PAX7 immunohistochemical evaluation of Ewing sarcoma and other small round cell tumours. Histopathology 2018, 73, 645–652. [CrossRef][PubMed] 67. Baldauf, M.C.; Gerke, J.S.; Orth, M.F.; Dallmayer, M.; Baumhoer, D.; De Alava, E.; Hartmann, W.; Kirchner, T.; Grunewald, T.G. Are EWSR1-NFATc2-positive sarcomas really Ewing sarcomas? Mod. Pathol. 2018, 31, 997–999. [CrossRef][PubMed] 68. Ito, Y.; Bae, S.C.; Chuang, L.S.H. The RUNX family: Developmental regulators in cancer. Nat. Rev. Cancer 2015, 15, 81–95. [CrossRef] 69. Lau, Q.C.; Raja, E.; Salto-Tellez, M.; Liu, Q.; Ito, K.; Inoue, M.; Putti, T.C.; Loh, M.; Ko, T.K.; Huang, C.; et al. RUNX3 is frequently inactivated by dual mechanisms of protein mislocalization and promoter hypermethylation in breast cancer. Cancer Res. 2006, 66, 6512–6520. doi:10.1158/0008-5472.CAN-06-0369. [CrossRef] 70. Mei, P.J.; Bai, J.; Liu, H.; Li, C.; Wu, Y.P.; Yu, Z.Q.; Zheng, J.N. RUNX3 expression is lost in glioma and its restoration causes drastic suppression of tumor invasion and migration. J. Cancer Res. Clin. Oncol. 2011, 137, 1823–1830. [CrossRef] 71. Zhang, Z.; Chen, G.; Cheng, Y.; Martinka, M.; Li, G. Prognostic significance of RUNX3 expression in human melanoma. Cancer 2011, 117, 2719–2727. [CrossRef] 72. Ahlquist, T.; Lind, G.E.; Costa, V.L.; Meling, G.I.; Vatn, M.; Hoff, G.S.; Rognum, T.O.; Skotheim, R.I.; Thiis-Evensen, E.; Lothe, R.A. Gene methylation profiles of normal mucosa, and benign and malignant colorectal tumors identify early onset markers. Mol. Cancer 2008, 7, 94. [CrossRef][PubMed]