Glycosylation is an enzymatic process in which a carbohydrate is attached to a functional group from another molecule. Glycosylation is a crucial post translational process in protein modification. The tumor microenvironment produces altered glycans that contribute to cancer progression and aggressiveness. Abnormal glycosylation is widely observed in tumor angiogenesis. Despite many attempts to decipher the role of glycosylation in different aspects of cancer, little is known regarding the roles of glycans in angiogenesis. The blood vessels in tumors are often used to transport oxygen and nutrients for tumor progression and metastasis. The crosstalk within the tumor microenvironment can induce angiogenesis by manipulating these glycans to hijack the normal angiogenesis process, thus promoting tumor growth. Abnormal glycosylation has been shown to promote tumor angiogenesis by degrading the extracellular matrix to activate the angiogenic signaling pathways. This review highlights the latest update on how glycosylation can contribute to tumor angiogenesis that may affect treatment outcomes.
The development of new blood vessels from pre-existing vasculature is called angiogenesis. The growth of tumors depends on a network of supplying vessels that provide them with oxygen and nutrients. Pro-angiogenic factors that are secreted by tumors will trigger the sprouting of nearby existing blood vessels towards themselves and therefore researchers have developed targeted therapy towards these pro-angiogenic proteins to inhibit angiogenesis. However, certain pro-angiogenic proteins tend to bypass the inhibition. Thus, instead of targeting these expressed proteins, research towards angiogenesis inhibition had been focused on a deeper scale, epigenetic modifications. Epigenetic regulatory mechanisms are a heritable change in a sequence of stable but reversible gene function modification yet do not affect the DNA primary sequence directly. Methylation of DNA, modification of histone and silencing of micro-RNA (miRNA)-associated gene are currently considered to initiate and sustain epigenetic changes. Recent findings on the subject matter have provided an insight into the mechanism of epigenetic modifications, thus this review aims to present an update on the latest studies.
Renal cell carcinoma (RCC) is the most common type of kidney cancer and has the highest mortality rate among genitourinary cancers. Despite the advances in molecular targeted therapies to treat RCC, the inevitable emergence of resistance has delineated the need to uncover biomarkers to prospectively identify patient response to treatment and more accurately predict patient prognosis. Fringe is a fucose specific β1, 3N-acetylglucosaminyltransferase that modifies the Notch receptors. Given the link between its function and aberrant Notch activation in RCC, Fringe may be implicated in this disease. The Fringe homologs comprise of Lunatic fringe (LFng), Manic fringe (MFng) and Radical fringe (RFng). MFng has been reported to play a role in cancer. MFng is also essential in the development of B cells. However, the expression profile and clinical significance of MFng, and its association with B cells in RCC are unknown. CD20 is a clinically employed biomarker for B cells. This pilot study aimed to determine if MFng protein expression can be utilized as a prospective biomarker for therapeutics and prognosis in RCC, as well as to determine its association with CD20+ B cells. Analysis of publicly available MFng gene expression datasets on The Cancer Genome Atlas Netlwork (TCGA) identified MFng gene expression to be up-regulated in Kidney Clear Cell Renal Carcinoma (KIRC) patients. However there was no significant association between the patient survival probability and the level of MFng expression in this cohort. Immunohistochemistry performed on a tissue microarray containing cores from 64 patients revealed an elevated MFng protein expression in the epithelial and stromal tissues of RCC compared to the normal kidney, suggesting a possible role in tumorigenesis. Our study describes for the first time to our knowledge, the protein expression of MFng in the nuclear compartment of normal kidney and RCC, implicating a prospective involvement in gene transcription. At the cellular level, cytoplasmic MFng was also abundant in the normal kidney and RCC. However, MFng protein expression in the malignant epithelial and stromal tissue of RCC had no positive correlation with the patients' overall survival, progression-free survival and time to metastasis, as well as the gender, age, tumor stage and RCC subtype, indicating that MFng may not be an appropriate prognostic marker. The association between CD20+ B cells and epithelial MFng was found to approach borderline insignificance. Nonetheless, these preliminary findings may provide valuable information on the suitability of MFng as a potential therapeutic molecular marker for RCC, thus warrants further investigation using a larger cohort.
Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge-desire-intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users' beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.