(180ak) A Network-Based Study On the Progression of Astrocytoma in Human Beings

Many diseases, including cancer, are known to emerge from cumulative interactions between multiple genes and other molecular agents within biological networks. Examining the disease-perturbed networks specific to a certain malignant phenotype helps us to gain insights into the biology and understand the mechanisms of the disease. Astrocytomas account for roughly 75% of neuroepithelial tumors and are usually classified into four grades. While the lower (I and II) grades astrocytomas have lower occurrence rate, higher grades (III and IV) occur much more frequently and represent higher mortality rates. A lot of effort has been made to identify possible classifiers (both as subnetworks or as individual genes) to accurately classify the correct stage, so that clinicians could choose the optimal therapy for the patients. A network-based binary classification method-differential ranking conservation (DIRAC) has become available recently and promises to identify accurate pathway biomarkers for different cancer phenotypes. In this project, we applied DIRAC to identify the most differentially expressed sub-networks which appeared central to different stages of astrocytoma. We investigated how these pathways evolved over different stages and how they interact to bring about the known disease phenotype. From the hypothetical process network map we constructed, we conclude that in low grades astrocytomas, the most perturbed subnetworks are the signaling pathways or related to angiogenesis. As the disease progresses to higher grades, more and more cell cycle and cell apoptosis pathways become involved. This shift of central biological events may help us to infer the underlying mechanism of astrocytomas. We also applied the well-known gene set enrichment analysis (GSEA) to the same patients and compared the resulting perturbed networks. Ultimately we hope to design and develop novel strategies for therapeutic intervention.