Relevanz von Protease-Netzwerken in Krebs und Entwicklung

Proteasen sind bereits klinisch anerkannte Zielstrukturen für eine Vielzahl von Krebserkrankungen. Die humane Threonin-Aspartase1 wird über die Interaktion mit Nukleophosmin1 über Importin-α in den Kern transportiert und besitzt das Spaltungsmotif (Q3[F,I,L,V]2D1↓G1'X2'D3'D4'). Zu ihren Substraten gehört der Transkriptionsfaktor TFIIA, der mittels Proteolyse durch Taspase1 translokalisiert und damit feinreguliert wird. Eine Erhöhung der Taspase1-Expression führt über die Regulation von TFIIA zu einer niedrigeren Expression von p16. Weiterführende Expressionsstatus und -veränderungen wurden über genomweite Microarray-Analysen aus zusammengehörendem Primärtumor, Lymphknotenmetastase und Normalgewebe aus 15 Kopf-Hals-Tumor-Patienten verglichen und bioinformatisch ausgewertet. Signalwege involviert in die Remodellierung der extrazellulären Matrix, die epithelial-mesenchymale Transition, den Metabolismus, die Immunreaktivität sowie Protease-steuernde Netzwerke waren dereguliert, und die Ergebnisse wurden mit Hochdurchsatzsequenzierungsdaten des Cancer Genome Atlas-Netzwerkes verglichen. Durch stringente Filterkriterien konnten differenziell exprimierte Gene ermittelt werden, und Zytokeratin24 als bisher unbekannter, potentiell prognostischer Biomarker identifiziert werden.Proteases are already clinically recognized targets for a variety of cancers. The human threonine aspartase1 is transported via the interaction with nucleophosmin1 via importin-α into the nucleus and possesses the cleavage motif (Q3[F,I,L,V]2D1↓G1'X2'D3'D4'). Their substrates include the transcription factor TFIIA, which is translocated by proteolysis by Taspase1 and thus fine-regulated. An increase in taspase1 expression leads to a lower expression of p16 via the regulation of TFIIA. Differential expression was evaluated bioinformatically by genome-wide microarray analyzes of associated primary tumor, lymph node metastasis and normal tissue from 15 head and neck tumor patients. Signaling pathways involved in extracellular matrix remodeling, epithelial-mesenchymal transition, metabolism, immunoreactivity, and protease-controlling networks were deregulated and the results compared to high-throughput sequencing data from the Cancer Genome Atlas network. By stringent filter criteria, differentially expressed genes could be identified, and cytokeratin24 could be identified as a previously unknown, potentially prognostic biomarker

Expressional analysis of disease-relevant signalling-pathways in primary tumours and metastasis of head and neck cancers

Head and neck squamous cell carcinoma (HNSCC) often metastasize to lymph nodes resulting in poor prognosis for patients. Unfortunately, the underlying molecular mechanisms contributing to tumour aggressiveness, recurrences, and metastasis are still not fully understood. However, such knowledge is key to identify biomarkers and drug targets to improve prognosis and treatments. Consequently, we performed genome-wide expression profiling of 15 primary HNSSCs compared to corresponding lymph node metastases and non-malignant tissue of the same patient. Differentially expressed genes were bioinformatically exploited applying stringent filter criteria, allowing the discrimination between normal mucosa, primary tumours, and metastases. Signalling networks involved in invasion contain remodelling of the extracellular matrix, hypoxia-induced transcriptional modulation, and the recruitment of cancer associated fibroblasts, ultimately converging into a broad activation of PI3K/AKT-signalling pathway in lymph node metastasis. Notably, when we compared the diagnostic and prognostic value of sequencing data with our expression analysis significant differences were uncovered concerning the expression of the receptor tyrosine kinases EGFR and ERBB2, as well as other oncogenic regulators. Particularly, upregulated receptor tyrosine kinase combinations for individual patients varied, implying potential compensatory and resistance mechanisms against specific targeted therapies. Collectively, we here provide unique transcriptional profiles for disease predictions and comprehensively analyse involved signalling pathways in advanced HNSCC