Browsing by Subject "Colorectal Cancer"

Now showing 1 - 6 of 6
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    Drug-drug similarity networks for anti-cancer drug discovery – Case studies on Breast Cancer, Colorectal Cancer and Leukemia
    (Office of the Vice Chancellor for Research, 2013-04-05) Kugbe, Selom; Grinslade, Zachary; Momoh, Salamatu; Ibrahim, Sara
    Over the decades, the medical industry has been challenged with the issue of producing drugs that would avoid sidetracking to other targets (off-targets) and keep away from harmful side effects (drug adverse reactions). Many researches have shown that complex diseases, such as various cancers, make the “one disease - one target - one drug” strategy unsuccessful due to the intrinsic complexity of gene-gene interactions and gene-environment interactions. In order to discover efficient treatments for these diseases, we assume that optimal drugs should be capable of down-regulating the effects of over-expressed genes while activating the under-expressed genes, when necessary, to restore the patient’s status to a healthy one. In this MURI project, we constructed drug-drug similarity network models, as two drugs having similar side effects, targets or chemical structures may have compatible therapeutic effects on the diseases. First, we retrieved a list of top-ranking drugs for specific disease (using Breast Cancer, Colorectal Cancer and Leukemia as case studies) from different data sources of connection maps, including the CMaps webserver. Second, we identified the protein targets for each drugs using databases such as PubChem, MataDor, MetaDrug, the European Bioinformaics Institute, and DrugBank. Third, we calculated similarities between two drugs by using different types of definitions based on their characteristics, such as shared targets, chemical structures, ontology and side effects. Finally, a comprehensive drug-drug similarity network with multiple similarity definitions was created for each disease, through a molecular network visualization platform - Cytoscape. These drug-drug similarity networks for specific cancer phenotypes can be applied to the validation of therapeutic effect assessment for specific anti-cancer drugs based on drug-protein networks. Our research highlights the importance of drug similarity analysis, and will eventually help anti-cancer drug discovery in silico.
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    Exploring Graph Neural Networks for Clustering and Classification
    (2022-12) Tahabi, Fattah Muhammad; Luo, Xiao; King, Brian; Li, Lingxi
    Graph Neural Networks (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - clustering and classification. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.
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    A Longitudinal Analysis to Compare a Tailored Web-Based Intervention and Tailored Phone Counseling to Usual Care for Improving Beliefs of Colorectal Cancer Screening
    (2018-07) Dorman, Hannah Louise; Monahan, Patrick; Stump, Timothy; Bakoyannis, Giorgos; Lourens, Spencer
    An analysis of longitudinal data collected about beliefs regarding colorectal cancer (CRC) screenings at three-time points was analyzed to determine whether the beliefs improved from either the Web-Based, Phone-Based, or Web + Phone interventions compared to Usual Care. A mixed linear model adjusting for baseline and controlling for covariates was used to determine the effects of the intervention; Web-Based intervention was the most efficacious in improving beliefs, and phone intervention was also efficacious for several beliefs, compared to usual care.
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    A patient-oriented clinical decision support system for CRC risk assessment and preventative care
    (BioMed Central, 2018-12-07) Liu, Jiannan; Li, Chenyang; Xu, Jing; Wu, Huanmei; Biohealth Informatics, School of Informatics and Computing
    Colorectal Cancer (CRC) is the third leading cause of cancer death among men and women in the United States. Research has shown that the risk of CRC associates with genetic and lifestyle factors. It is possible to prevent or minimize certain CRC risks by adopting a healthy lifestyle. Existing Clinical Decision Support Systems (CDSS) mainly targeted physicians as the CDSS users. As a result, the availability of patient-oriented CDSS is limited. Our project is to develop patient-oriented CDSS for active CRC management.
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    Tailoring Colorectal Cancer Surveillance in Lynch Syndrome: More Is Not Always Better
    (Science Direct, 2021-08-01) Maratt, Jennifer K.; Rubenstein, Joel H.; Medicine, School of Medicine
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    Targeting Protein Arginine Methyltransferase 5 as a Novel Therapeutic Approach in Pancreatic & Colorectal Cancer
    (2018-12) Prabhu, Lakshmi Milind; Lu, Tao; Safa, Ahmad; Pollok, Karen; Skaar, Todd; Zhang, Jian-Ting
    Pancreatic ductal adenocarcinoma (PDAC) and colorectal cancer (CRC) are among the most commonly diagnosed forms of cancer in the United States. Due to their widespread prevalence and high mortality rate, it is vital to develop effective therapeutic drugs to combat these deadly diseases. In both PDAC and CRC, the multifunctional factor nuclear factor kappa B (NF-kB), a central coordinator of cellular immune responses, is activated abnormally, leading to tumorigenesis and cancer progression. Therefore, controlling NF-kB activity is critical in the treatment of these cancers. In a previous study, we identified a new mechanism by which NF-kB activity is regulated by an epigenetic enzyme known as protein arginine methyltransferase 5 (PRMT5). We showed that overexpression of PRMT5 not only activated NF-kB, but also significantly promoted several characteristics associated with cancer, including increased cell proliferation, migration, and anchorage-independent growth in both PDAC and CRC cells. Moreover, in order to examine the therapeutic potential of PRMT5 in these cancers, we adapted the state-of-the-art AlphaLISA technique into a high throughput screen (HTS) platform to screen for PRMT5 inhibitors. As a result, we successfully identified the small molecule PR5-LL-CM01 as our lead hit. Further validation experiments confirmed that PR5-LL-CM01 is a potent and specific PRMT5 inhibitor that exhibits significant anti-tumor efficacy in both in vitro and in vivo models of PDAC and CRC. Additionally, in a second screen, we discovered two natural compounds, P1608K04 and P1618J22, that can also function as the PRMT5 inhibitors. These findings further highlight the robustness of the PRMT5- specific AlphaLISA HTS technique. To conclude, we describe here for the first time a novel role of PRMT5 as a tumor-promoting factor in PDAC and CRC through NF-kB activation. By successfully developing and applying an innovative AlphaLISA HTS technique, we discovered PR5-LL-CM01, P1608K04, and P1618J22 as novel PRMT5 inhibitors, with PR5-LL-CM01 showing the strongest potency in both PDAC and CRC models. Therefore, we demonstrated that PRMT5 is a promising therapeutic target in PDAC and CRC, and the novel PRMT5 inhibitor PR5-LL-CM01 could serve as a promising basis for new drug development in PDAC and CRC.