BACKGROUND/OBJECTIVES:Diagnostic metabolites measured in newborn screening, inherited metabolic disease, lysosomal storage disease, oncometabolite testing and routine clinical biochemistry are direct read-outs of human metabolic state. Their mechanistic interpretation requires linking measured metabolites to enzymes, pathways, regulatory context, disease knowledge and, increasingly, AI-assisted quantitative systems pharmacology (AI-QSP) workflows. We developed EHMN2026®T as a license-aware AI-QSP integration framework that connects the EHMN2026® metabolic backbone with licensed geneXplain knowledge resources while keeping ownership, licensing and redistribution constraints explicit. METHODS:EHMN2026®T integrates the SBML-encoded EHMN2026® metabolic backbone with licensed TRANSFAC® 2025.2, TRANSPATH® 2025.2 and HumanPSD™ 2025.2 resources. TRANSFAC® position weight matrices were used for promoter-level analysis of EHMN metabolic genes. The resulting transcription factor (TF)-gene connections were mapped to EHMN genes, TRANSPATH® signalling/molecular-state entries and HumanPSD™ disease/drug context. The framework is positioned as a controlled component of the IQANOVA AI-QSP environment, but only aggregate statistics, non-proprietary EHMN-derived summaries and manuscript-level examples are reported publicly unless separate permission is obtained from the relevant rightsholders. RESULTS:Promoter analysis of 1681 EHMN2026® metabolic genes using 1147 mapped TRANSFAC® matrices identified 291,387 ENSG-level TF-gene regulatory-potential connections involving 398 TFs and 1,107,264 predicted binding sites. The diagnostic panel contained 80 covered genes (63.5%), including complete coverage of oncometabolite enzymes and high coverage of organic acidaemia, steroidogenesis and fatty-acid oxidation categories. Mapping to TRANSPATH® expanded the EHMN genes into 144,529 molecular-state representations and 14,879 gene-pathway or gene-chain pairs. HumanPSD™ was used as a licensed translational context layer; EHMN-specific HumanPSD™ outputs are treated as license-controlled derived outputs and are therefore not redistributed as open detailed tables in this manuscript. CONCLUSIONS:EHMN2026®T provides a license-aware AI-QSP integration framework for tracing a diagnostic metabolite from a measured clinical value to candidate enzyme nodes, regulatory potential, signalling/molecular-state context and disease or therapeutic interpretation. PWM-derived TF-gene links are presented as regulatory hypotheses, not proof of active regulation. Public release should be limited to aggregate statistics and non-proprietary EHMN-derived components; detailed TRANSFAC®, TRANSPATH® and HumanPSD™-derived edges, mappings, annotations and SBML outputs remain subject to geneXplain ownership and licensing terms.

Myocardial fibrosis, a common feature of heart disease, remains an unsolved clinical challenge. Fibrosis resolution requires activation of cardiac fibroblasts exhibiting context-dependent beneficial and detrimental dichotomy. Here, we explored the hypothesis of fibroblast reversible transition between quiescence and activated myofibroblastic states as a manifestation of cell phenotypic switching in myocardial remodelling. In support, gene regulatory networks executing conversion of cardiac fibroblasts to myofibroblasts and vice versa in fibrosis resolution are reconstructed using TRANSPATH database. In a scenario of fibroblast activation triggered by transforming growth factor beta, a cardinal mediator of tissue fibrosis, signalling cascades governing entry into or exit from specific fibroblast statures in cardiac fibrotic remodelling were dissected. It is suggested that fibroblast phenotypic switching constitutes the central gait toward guiding cell state-gating strategies to counteract adverse cardiac fibrosis, a devastating disorder with no approved therapeutic option.

Aberrant activation of fibroblasts is a pivotal component of cardiac fibrosis predisposing to heart failure. However, the molecular regulation of the functional state of cardiac fibroblasts in fibrosis resolution remains largely unexplored, and therefore, effective antifibrosis therapies are still lacking. By translating mouse transcriptomics to humans, we unlocked common molecular denominators connecting the fibroblast phenotypic state and fibrogenic signaling pathways in cardiac fibrosis. Through the construction of a fibroblast-specific transcriptional gene regulatory network, we found ITGAL and DUSP9 as key druggable targets for human myocardial fibrosis. A computational drug repurposing approach predicted 367 antifibrotic candidate compounds for heart disease. In primary cardiac fibroblasts derived from patients with heart failure, we provided experimental validation of the top 2-ranked repositioned drug candidates and their combination. These innovative approaches facilitate the identification of potential targets and drug candidates for cardiac fibrosis, providing actionable opportunities for clinical translation.

Background/Objectives: The COVID-19 pandemic has posed a significant challenge to global healthcare systems and has prompted a need for a better understanding of the molecular mechanisms underlying SARS-CoV-2 infection. This study aims to analyze differential gene expression in COVID-19 patients to identify regulatory genes influencing key pathways involved in disease progression. Methods: We conducted a transcriptomic analysis of patients admitted to the Infectious Disease Department of City Hospital No. 40, confirmed with SARS-CoV-2 via PCR. The study received ethical approval (protocol No. 171, 18 May 2020), and all participants provided informed consent. Total RNA was extracted from blood samples, followed by RNA sequencing using the DNBSEQ-G400 platform. Differential gene expression was analyzed using the Mann–Whitney test, and Gene Ontology enrichment analysis was performed to identify relevant biological processes. Results: Our analysis revealed significant number of differentially expressed genes within studied groups (severity, outcome, cytokine storm and paired samples). These genes are involved in key regulatory and signal transduction pathways governing immune responses, intercellular communication, and the metabolism of various compounds. Furthermore, we identified genes ALOX15, PRL, FLT3, S100A8, S100A12, IL4, IL13, and a few others as master regulators within the studied pathways, which represent promising candidates for further investigation as potential therapeutic targets. Conclusions: This study highlights critical gene expression changes associated with COVID-19 severity and outcomes, identifying potential biomarkers. Our findings contribute to the understanding of the molecular drivers of COVID-19 and suggest new avenues for therapeutic interventions aimed at modulating immune responses.

Aim. To study the applicability of RNA (ribonucleic acid) sequencing with master regulator identification for predicting the effectiveness of targeted therapy in patients with colorectal cancer.Materials and methods. Tissue samples from three patients with colorectal cancer obtained from postoperative material were used. All patients received palliative antitumor therapy in standard regimens in accordance with the tumor location and the status of RAS and BRAF gene mutations. The transcriptome of tumor and healthy tissue of each patient was sequenced, and master regulators in the tumor tissue were analyzed.Results. A list of master regulators was found for each patient and possible therapeutic agents most suitable for suppressing the tumor process were predicted.Conclusion. The potential of computer analysis of the molecular profile of colorectal adenocarcinoma in predicting the effectiveness of therapy is shown. However, to determine the clinical potential of this technique, a study on a wider sample is required.

Abstract Background Standard biological therapy for moderate-severe cases of UC consists in the use of Infliximab (IFX), an anti-tumour necrosis factor α (anti-TNF-α) targeting agent. However, approximately 40% of the patients do not respond to IFX. While transcriptional alterations have been widely associated with non-response to IFX, the precise factors that regulates this altered gene expression remains poorly understood. The aim of this study was perform a robust in silico analysis to identify master transcriptional regulators (MTRs) which regulate gene expression changes in IFX non-responsive UC patients, followed by perturbation in vitro to establish their functional role in UC pathogenesis. Methods Differentially expressed genes (DEGs) identified from four independent public datasets were applied to the GeneXplain platform to identify transcription factors (TFs) enriched for the DEG’s. This was followed by an upstream analysis to identify MTR’s which regulate expression of these genes through the TFs. We next explored the role of these MTRs in driving UC-associated phenotype, using siRNA-mediated knock-down in a novel IBD-like in vitro system comprising of normal rectal epithelial cells treated with TNFa. Results Over 200 TFs were identified in both upregulated and downregulated genes in IFX non-responders across all data sets. RELA, NANOG, FOSJUN were the top 3 TF’ enriched for upregulated genes in three out of the four datasets. Using the TF data, the upstream analysis identified CXCL8, SELE, PTGS2, FCGR3, TFPI2 as the top five MTRs. Next, we showed that of the 5 MTRs, only TFPI2 (tissue factor pathway inhibitor 2) were significantly upregulated in an IBD-like in vitro model (normal rectal epithelial cell line: CRL1831 +TNFa). siRNA-mediated knock-down of TFPI2 in this model resulted in a significant decrease in genes including TNFAIP6, TNFAIP3, FCGR3B, BIRC3 and CXCL5, all of which were predicted in silico to be upregulated by TFPI2 during the upstream analysis. Intriguingly, we showed that downregulation of TFPI2 resulted in significant amelioration of pro-inflammatory cytokines including IL1β, IFNγ, IL10, IL13, IL6 and IL8. Conclusion Our study for the first time identifies key MTRs such TFPI2 which acts as a master regulator of genes whose upregulation drives poor response to IFX. Therefore, these results warrant further in vitro/ex vivo investigation to rationalise their role of TFPI2 as a potential therapeutic target, whose perturbation in UC patient would likely result in improved response to IFX.

Background Existing colorectal cancer subtyping methods were generated without much consideration of potential differences in expression profiles between colon and rectal tissues. Moreover, locally advanced rectal cancers at resection often have received neoadjuvant chemoradiotherapy which likely has a significant impact on gene expression. Methods We collected mRNA expression profiles for rectal and colon cancer samples ( n = 2121). We observed that (i) Consensus Molecular Subtyping (CMS) had a different prognosis in treatment-naïve rectal vs. colon cancers, and (ii) that neoadjuvant chemoradiotherapy exposure produced a strong shift in CMS subtypes in rectal cancers. We therefore clustered 182 untreated rectal cancers to find rectal cancer-specific subtypes (RSSs). Results We identified three robust subtypes. We observed that RSS1 had better, and RSS2 had worse disease-free survival. RSS1 showed high expression of MYC target genes and low activity of angiogenesis genes. RSS2 exhibited low regulatory T cell abundance, strong EMT and angiogenesis signalling, and high activation of TGF-β, NF-κB, and TNF-α signalling. RSS3 was characterised by the deactivation of EGFR, MAPK and WNT pathways. Conclusions We conclude that RSS subtyping allows for more accurate prognosis predictions in rectal cancers than CMS subtyping and provides new insight into targetable disease pathways within these subtypes.

Abstract New precision medicine therapies are urgently required for glioblastoma (GBM). Recently, we developed a novel GBM classification system, identifying three patient clusters uniquely characterized by tumor microenvironment (TME) composition: TMELow, TMEMedium, and TMEHigh1. Our objective now is to further investigate these subtypes employing transcriptomic analysis and network modelling approaches to identify novel subtype-specific targets of vulnerability. We analyzed transcriptomic data from >600 GBM samples from publicly available and in-house datasets. All samples underwent TME subtyping. Next, we performed differential gene expression (using DESeq2, edgeR) and pathway analysis (using PROGENy). The ‘TRANSFAC’ tool 2 was used to identify potential transcription factors enriched in each TME subtype. TMELow tumours manifested highly upregulated NLGN3 (P=5.777e-5) and HES5 (P=2.233e-3) expression compared to TMEHigh. Moreover, TMEHigh compared to TMELow tumours were enriched for genes associated with tissue remodelling, and showed elevated MMP7 (P=3.051e-6), CLCL13 (P= 3.843e-4), and CXCL5 expression (P= 1.592e-6). PROGENy analysis suggested that the WNT, TRAIL, and JAK/STAT pathways were significantly enriched in TMEHigh, while the PI3K pathway was significantly upregulated in TMEMedium. Additionally, VEGF pathway activity was significantly upregulated in TMELow. TRANSFAC analysis measured by regulatory score (a measure of a transcription factor effect on gene expression) revealed that in TMELow, key transcription factors includes ETS2, NANOG, and MECP2 (regulatory score: 1.97, 1.45, and 1.4, respectively). In TMEMedium, transcription factors HIF1A, PPARA, and CDX2 were identified (regulatory score: 2.52, 2.08 and 1.96, respectively). In TMEHigh, TRANSFAC indicated activiation of transcription factors SMAD3, ETS2, and NFKB1 regulatory score: 2.56, 2.41, 2.24, respectively). Overall these findings highlight distinct molecular signatures across TME subtypes, and provide a deeper understanding of associated molecular mechanisms. Ongoing work is focused on validating key subtype specific genes and associated pathways using spatial proteomics. Prioritised targets will ultimately be interrogated in vivo, as novel subtype-specific treatments. 1. White et al. Ann Oncol. 2023;34(3):300–314. 2. Matys et al. Nucleic Acids Res. 2003;31.

Background and Aims:Primary sclerosing cholangitis (PSC) is a progressive cholestatic disease with up to 80% of patients also suffering from ulcerative colitis (PSC-UC). The difficulty in the diagnosis along with the increased risk for developing cancer represents a clinical challenge. Furthermore, the precise molecular factors regulating the phenotype of this disease subtype remain unknown. Methods:We applied methyl-capture sequencing and mRNA sequencing to colonic mucosal biopsies from 3 groups of treatment-naïve children at diagnosis from the Determinants and Outcomes in CHildren and AdolescentS study: UC (n = 10), PSC-UC (n = 10), and healthy controls (n = 10). Results:Differential gene expression between UC and PSC-UC showed significantly higher gene expression changes in PSC-UC patients when compared to UC. Specifically, expression of these genes was regulated by master transcriptional regulators (NLRP3, DLL1) and transcription factors (RELA, Myogenin, and FOXO1), which are shown to regulate expression of inflammatory response and immune-associated genes in PSC-UC patients exclusively. Differential methylation analysis between PSC-UC and UC demonstrated >2000 differentially methylated regions with a large proportion of them enriched in gene promoter and enhancer regions. We further show no difference in epigenetic age between PSC-UC and UC. Finally, we identify KLHL17 as hypomethylated and upregulated in PSC-UC patients. Conclusion:Our study, for the first time, identifies distinct gene expression and DNA methylation alterations that differentiate UC from PSC-UC at diagnosis in treatment-naïve pediatric patients. We show the gene expression differences observed between PSC-UC and UC are modulated by intricate molecular mechanisms involving master transcriptional regulator-mediated signaling through transcription factors. These findings suggest the potential utility of these molecular markers as predictive biomarkers for PSC development in UC at an early stage of development. Further validation in larger patient cohorts is warranted.

Abstract Background Primary sclerosing cholangitis (PSC) is a progressive choleostatic disease and up to 80% of patients also have ulcerative colitis (PSC-UC). This presents a clinical challenge owing to difficulty in diagnosis and increased risk for developing cancer. While several multifactorial processes including inflammation and microbial dysbiosis have been associated with PSC-UC pathogenesis, the precise molecular factors that regulate the phenotype of this disease subtype remain unknown. Methods We applied methyl-capture sequencing and mRNA sequencing to colonic mucosal biopsies derived from the DOCHAS study (GEN-193/11), to identify transcriptomic and epigenetic differences between treatment naïve paediatric UC (n=10), PSC-UC (n=10) and healthy controls (n=10) samples. Results Differential gene expression between UC and PSC-UC identified 9 up-regulated genes - ADMTS14, PNCK, NLRP3, SLC6A19, DLL1, FCGR2C, KLHL17, APOB, EHBP1L1 and 5 downregulated - SLC37A2, SLC14A2, RPL27, RPS25, SLC38A4 in PSC-UC relative to UC. Importantly, we show that expression of these genes was intricately regulated by master transcriptional regulators (pro-caspases, IL7RA) and transcription factors (TFs) :AR, p53, JUND, CEBPA. Similarly, differential methylation analysis between PSC-UC and UC identified 22 differentially methylated regions (DMRs) relative to controls, where 5 were hypermethylated and 8 hypomethylated. Intriguingly, in general we show that these DMRs are largely localised in gene promoter regions as opposed to enhancers. Importantly, we show that these DMR’s identified between PSC-UC vs UC is enriched for binding sites for the TF: ASCL1, suggesting its activity likely is effected due to the altered methylation of its binding site in PSC-UC patients. Collectively, these results highlight the importance of TF’s in driving molecular differences between PSC-UC and UC paediatric patients in a treatment naïve setting. Conclusion In summary, for the first time this study provides a critical insight into the transcriptional differences between treatment naïve children with PSC-UC vs UC as well as highlights the intricate regulatory processes involving master transcriptional regulators, transcription factors and DNA methylation. These processes thus warrant further examination in larger cohorts to rationalise their role as diagnostic/therapeutic targets.

Glioblastoma (GBM) is an aggressive brain cancer that typically results in death in the first 15 months after diagnosis. There have been limited advances in finding new treatments for GBM. In this study, we investigated molecular differences between patients with extremely short (≤ 9 months, Short term survivors, STS) and long survival (≥ 36 months, Long term survivors, LTS).Patients were selected from an in-house cohort (GLIOTRAIN-cohort), using defined inclusion criteria (Karnofsky score > 70; age < 70 years old; Stupp protocol as first line treatment, IDH wild type), and a multi-omic analysis of LTS and STS GBM samples was performed.Transcriptomic analysis of tumour samples identified cilium gene signatures as enriched in LTS. Moreover, Immunohistochemical analysis confirmed the presence of cilia in the tumours of LTS. Notably, reverse phase protein array analysis (RPPA) demonstrated increased phosphorylated GAB1 (Y627), SRC (Y527), BCL2 (S70) and RAF (S338) protein expression in STS compared to LTS. Next, we identified 25 unique master regulators (MR) and 13 transcription factors (TFs) belonging to ontologies of integrin signalling and cell cycle to be upregulated in STS.Overall, comparison of STS and LTS GBM patients, identifies novel biomarkers and potential actionable therapeutic targets for the management of GBM.

Purpose The histone deacetylase inhibitor (HDACi), belinostat, has had limited therapeutic impact in solid tumors, such as colon cancer, due to its poor metabolic stability. Here we evaluated a novel belinostat prodrug, copper-bis-belinostat (Cubisbel), in vitro and ex vivo , designed to overcome the pharmacokinetic challenges of belinostat. Methods The in vitro metabolism of each HDACi was evaluated in human liver microsomes (HLMs) using mass spectrometry. Next, the effect of belinostat and Cubisbel on cell growth, HDAC activity, apoptosis and cell cycle was assessed in three colon cancer cell lines. Gene expression alterations induced by both HDACis were determined using RNA-Seq, followed by in silico analysis to identify master regulators (MRs) of differentially expressed genes (DEGs). The effect of both HDACis on the viability of colon cancer patient-derived tumor organoids (PDTOs) was also examined. Results Belinostat and Cubisbel significantly reduced colon cancer cell growth mediated through HDAC inhibition and apoptosis induction. Interestingly, the in vitro half-life of Cubisbel was significantly longer than belinostat. Belinostat and its Cu derivative commonly dysregulated numerous signalling and metabolic pathways while genes downregulated by Cubisbel were potentially controlled by VEGFA, ERBB2 and DUSP2 MRs. Treatment of colon cancer PDTOs with the HDACis resulted in a significant reduction in cell viability and downregulation of stem cell and proliferation markers. Conclusions Complexation of belinostat to Cu(II) does not alter the HDAC activity of belinostat, but instead significantly enhances its metabolic stability in vitro and targets anti-cancer pathways by perturbing key MRs in colon cancer. Complexation of HDACis to a metal ion might improve the efficacy of clinically used HDACis in patients with colon cancer.

Supplementary Figure 1D from Activation of TLX3 and NKX2-5 in t(5;14)(q35;q32) T-Cell Acute Lymphoblastic Leukemia by Remote 3′-BCL11B Enhancers and Coregulation by PU.1 and HMGA1

Epigenomic changes in the venous cells exerted by oscillatory shear stress towards the endothelium may result in consolidation of gene expression alterations upon vein wall remodeling during varicose transformation. We aimed to reveal such epigenome-wide methylation changes. Primary culture cells were obtained from non-varicose vein segments left after surgery of 3 patients by growing the cells in selective media after magnetic immunosorting. Endothelial cells were either exposed to oscillatory shear stress or left at the static condition. Then, other cell types were treated with preconditioned media from the adjacent layer's cells. DNA isolated from the harvested cells was subjected to epigenome-wide study using Illumina microarrays followed by data analysis with GenomeStudio (Illumina), Excel (Microsoft), and Genome Enhancer (geneXplain) software packages. Differential (hypo-/hyper-) methylation was revealed for each cell layer's DNA. The most targetable master regulators controlling the activity of certain transcription factors regulating the genes near the differentially methylated sites appeared to be the following: (1) HGS, PDGFB, and AR for endothelial cells; (2) HGS, CDH2, SPRY2, SMAD2, ZFYVE9, and P2RY1 for smooth muscle cells; and (3) WWOX, F8, IGF2R, NFKB1, RELA, SOCS1, and FXN for fibroblasts. Some of the identified master regulators may serve as promising druggable targets for treating varicose veins in the future.

Solid tumors resulting from oncogenic stimulation of neurotrophin receptors (TRK) by chimeric proteins are a group of rare tumors of various localization that respond to therapy with targeted drugs entrectinib and larotrectinib. The standard method for detecting chimeric TRK genes in tumor samples today is considered to be next generation sequencing with the determination of the prime structure of the chimeric transcripts. We hypothesized that expression of the chimeric tyrosine kinase proteins in tumors can determine the specific transcriptomic profile of tumor cells. We detected differentially expressed genes allowing distinguishing between TRK-dependent tumors papillary thyroid cancer (TC) from other molecular variants of tumors of this type. Using PCR with reverse transcription (RT-PCR), we identified 7 samples of papillary TC carrying a EVT6-NTRK3 rearrangement (7/215, 3.26%). Using machine learning and the data extracted from TCGA, we developed of a recognition function for predicting the presence of rearrangement in NTRK genes based on the expression of 10 key genes: AUTS2 , DTNA , ERBB4 , HDAC1 , IGF1 , KDR , NTRK1 , PASK , PPP2R5B , and PRSS1 . The recognition function was used to analyze the expression data of the above genes in 7 TRK-dependent and 10 TRK-independent thyroid tumors obtained by RT-PCR. On the test samples from TCGA, the sensitivity was 72.7%, the specificity — 99.6%. On our independent validation samples tested by RT-PCR, sensitivity was 100%, specificity — 70%. We proposed an mRNA profile of ten genes that can classify TC in relation to the presence of driver NTRK -chimeric TRK genes with acceptable sensitivity and specificity.

Glioblastoma (GBM) is a very aggressive malignant brain tumor with the vast majority of patients surviving less than 12 months (Short-term survivors [STS]). Only around 2% of patients survive more than 36 months (Long-term survivors [LTS]). Studying these extreme survival groups might help in better understanding GBM biology. This work aims at exploring application of machine learning methods in predicting survival groups(STS, LTS). We used age and gene expression profiles belonging to 249 samples from publicly available datasets. 10 Machine learning methods have been implemented and compared for their performances. Hyperparameter tuned random forest model performed best with accuracy of 80% (AUC of 74% and F1_score of 85%). The performance of this model is validated on external test data of 16 samples. The model predicted the true survival group for 15 samples achieving an accuracy of 93.75%. This classification model is deployed as a web tool GlioSurvML. The top 1500 features which retained classification efficiency (Accuracy of 80%, AUC of 74%) were studied for enriched pathways and disease-causal biomarker associations using the HumanPSDTM database. We identified 199 genes as possible biomarkers of GBM and/or similar diseases (like Glioma, astrocytoma, and others). 57 of these genes are shown to be differentially expressed across survival groups and/or have impact on survival. This work demonstrates the application of machine learning methods in predicting survival groups of GBM.

Varicose vein disease (VVD) undoubtedly has its genetic and epigenetic constituents. However, there is still an enigma around them. In order to observe systemic effects, we decided to shift paradigm at this time and to study possible gene expression differences that may reflect genetically predisposing determinants, between normal venous tissue samples from both conditionally healthy patients and those with VVD, and analyzed nonvaricose (!) vein segments in both cases. RNA sequencing was utilized for transcriptome profiling. The study included nonvaricose GSV samples (n = 3) adjacent to surgically removed varicose segments harvested from the patients with VVD (C3) and control GSV samples (n = 4) harvested from the patients subjected to coronary artery bypass surgery. Ribosomal RNA-depleted total venous RNAs used for cDNA libraries preparation were sequenced on the Illumina platform. The exported “fastq” files were uploaded to the "Genome Enhancer” software package—a multiomics analysis service of geneXplain platform. An automated pipeline incorporated in the Genome Enhancer included identification of differentially expressed genes (DEGs), analysis of enriched transcription factor (TF) binding sites in promoters and enhancers of DEGs, and finding master regulators in signal transduction pathways upstream of the revealed TFs. The latter may serve as potential therapeutic targets since they have a master effect on the regulation of molecular pathways implicated in VVD pathogenesis. From the next-generation sequencing dataset analyzed in this study, we found the following TFs to be potentially involved in the regulation of the DEGs: FOXO1, SREBF2, RXRA, SMAD3, ELK1, SP1, ESR1, NFE2L2, REST, GTF2I, and STAT1. The subsequent network analysis predicted for molecular mechanisms of VVD to be mainly based on the following key targets: Cdk4-isoform1:cyclinD1a, Cdk1, 26S proteasome, MKP-4, and suggested PSMA7 and DUSP9 (that control activity of TFs FOXO1, SMAD3, and ESR1 on promoters of DEGs) as the most promising for further research, drug development and drug repurposing initiatives. PSMA7 inhibits the transactivation function of HIF-1α (which upregulates vascular endothelial growth factor, erythropoietin, and glycolytic enzymes under hypoxic conditions) and its action is associated with the proteasome pathway. However, these candidates need independent validation. Reconstruction of the disease-specific regulatory networks can help identify potential master regulators of the respective pathological processes, which can point to ways how to block pathological regulatory cascades. Suppression of certain molecular targets as components of these cascades may stop the pathological process and cure the disease.

NTRK gene fusions are drivers of tumorigenesis events that specific Trk-inhibitors can target. Current knowledge of the downstream pathways activated has been previously limited to the pathways of regulator proteins phosphorylated directly by Trk receptors. Here, we aimed to detect genes whose expression is increased in response to the activation of these pathways. We identified and analyzed differentially expressed genes in thyroid cancer samples with NTRK1 or NTRK3 gene fusions, and without any NTRK fusions, versus normal thyroid gland tissues, using data from the Cancer Genome Atlas, the DESeq2 tool, and the Genome Enhancer and geneXplain platforms. Searching for the genes activated only in samples with an NTRK fusion as opposed to those without NTRK fusions, we identified 29 genes involved in nervous system development, including AUTS2, DTNA, ERBB4, FLRT2, FLRT3, RPH3A, and SCN4A. We found that genes regulating the expression of the upregulated genes (i.e., upstream regulators) were enriched in the “signaling by ERBB4” pathway. ERBB4 was also one of three genes encoding master regulators whose expression was increased only in samples with an NTRK fusion. Moreover, the algorithm searching for positive feedback loops for gene promoters and transcription factors (a so-called “walking pathways” algorithm) identified the ErbB4 protein as the key master regulator. ERBB4 upregulation (p-value = 0.004) was confirmed in an independent sample of ETV6-NTRK3-positive FFPE specimens. Thus, ErbB4 is the potential key regulator of the pathways activated by NTRK gene fusions in thyroid cancer. These results are preliminary and require additional biochemical validation.