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Graph clustering techniques applied to the glycomic response in glioblastoma cells to treatments with STAT3 phosphorylation inhibition and fetal bovine serum

Cancer stem cells (CSC) represent a very small percentage of the total tumor population however they pose a big challenge in treating cancer. Glycans play a key role in cancer therapeutics since overexpression of them depending on the glycan type can lead either to cell death or more invasive metastasis. Two major components, fetal bovine serum (FBS) and STAT3, are known to up- or down-regulate certain glycolipid or phospholipid compositions found in glioblastoma CSCs. The analysis and the understanding of the global interactional behavior of lipidomic networks remains a challenging task and can not be accomplished solely based on intuitive reasoning. The present contribution aims at applying graph clustering networks to analyze the functional aspects of certain activators or inhibitors at the molecular level in glioblastoma stem cells (GSCs). This research enhances our understanding of the differences in phenotype changes and determining the responses of glycans to certain treatments for the aggressive GSCs, and represents together with a quantitative phosphoproteomic study1 the most detailed systems biology study of GSCs differentiation known so far. Thus, these new paradigms are providing unique understanding of the mechanisms involved in GSC maintenance and tumorigenicity and are thus opening a new window to biomedical frontiers.