# ErbB4 Is a Potential Key Regulator of the Pathways Activated by NTRK-Fusions in Thyroid Cancer
## 4 Discussion  
In the current study, analyzing TCGA gene expression data, we suggested genes that may participate in the tumorigenesis induced by NTRK gene fusions: _ERBB4_, _EGFR_, _BMX_, _BMPR1B_, _AUTS2_, _DTNA_, _ERBB4_, _FLRT2_, _FLRT3_, _RPH3A_, _DKK1_, _NOG_, _ARHGEF7_, and _SCN4A_. All but _BMX_ and _ERBB4_ were statistically significantly upregulated in thyroid cancers, both with _NTRK1_ or _NTRK3_ gene fusions compared to normal thyroid gland tissue. However, _ERBB4_ was statistically significantly upregulated only when comparing both NTRK sample subtypes taken together versus normal tissue samples (log2 FC = 4.7; _p_-value = 0.009). Based on the results of the comparison of differential gene expression in different tumor subtypes, pathway analysis, STRING analysis, and master regulator searches, we focused most of our attention on seven of those genes that may be considered as the more likely participants of malignancy pathways activated by TrkA or TrkC: _ERBB4_, _EGFR_, _BMX_, _AUTS2_, _DKK1_, _ARHGEF7_, and _FLRT3_ (Table 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Gene**} & \multirow{2}{*}{\begin{tabular}{c} **DEG with** \\ **\(p\)-Value \(<\) 0.05** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Identifified as** \\ **Regulator** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Expression More than in** \\ **Samples without** \\ **NTRK-Fusions** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Interactions with Trk** \\ **Found in STRING** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Known Engagement** \\ **in Malignancy** \\ \end{tabular} } \\ \hline _ERBB4_ & yes/no & yes & yes & yes & yes \\ _EGFR_ & yes & yes & no & yes & yes \\ _BMX_ & no & yes & yes/no & yes & yes \\ _AUTS2_ & yes & no & yes & no & yes \\ _DKK1_ & yes & yes & yes & yes & yes \\ _ARHGEF7_ & yes & yes & yes & yes & yes \\ _FLRT3_ & yes & no & yes & no & no \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of genes suggested as participants of pathways activated by fused Trk. DEG is a differentially expressed gene. Figure 8: _ERBB4_ gene expression values in samples with (NTRK+) and without (NTRK\(-\)) NTRK gene rearrangements. In this work, in addition to the standard methods of pathway mapping and STRING analysis, we applied one of the most advanced approaches to the bioinformatic analysis of gene regulatory networks--an upstream analysis and master regulator search implemented in the set of tools provided by the geneXplain platform and the Genome Enhancer tool. The upstream analysis approach tries to reconstruct the causal interactions in the signal transduction and gene regulatory networks that are heavily rewired in the tumor cells, and cannot be properly mapped to canonical pathways that are acting in a non-tumor cellular context. To do such causal reconstruction, the most comprehensive collection of TF binding site motifs provided by the TRANSFAC database was necessary to minimize the chances of missing an important TF-DNA interaction playing a vital role in the tumorigenic process. We also needed to understand which particular and rather novel combination of transcription factors is responsible for such very specific gene regulation as was observed in the studied subtypes of tumors. The consequent use of the Match, F-Match and CMA tools provided us this possibility, which is hardly possible with other tools, such as \"MatrixCatch\" [26], which searches only known composite elements but cannot find new combinations, and \"PC-TraFF\" [27; 28] which searches for TF site pairs in the whole promoter length but not more complex combinations of co-localized TF sites. At the last step of the causal reconstruction of the rewired networks, we searched for the most probable regulators and key master-regulators.