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A network analysis of SNP-SNP associations in the antigen presentation pathway of hepatocellular carcinoma using extended pathways of distinction analysis

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Hepatocellular carcinoma (HCC) is the most common type of liver cancer and has been shown to have genetic factors that contribute to cancer susceptibility. These genetic factors can be studied using Genome-Wide association studies (GWAS), which allow for the assessment

Hepatocellular carcinoma (HCC) is the most common type of liver cancer and has been shown to have genetic factors that contribute to cancer susceptibility. These genetic factors can be studied using Genome-Wide association studies (GWAS), which allow for the assessment of associations between specific biologic markers. Through GWAS, associations can be analyzed to identify genetic components that contribute to the onset of HCC. This study uses an extended version of Pathways of Distinction analysis (PoDA) to identify the subset of SNPs within the Antigen Presentation and Processing Pathway that distinguish cases from controls. Further analysis was performed to explore SNP-SNP association differences between HCC cases and controls using R-squared values and p-values. Three SNPs show significant inter-SNP associations in both HCC cases and controls. Additionally, 4 SNPs showed significant SNP-SNP associations exclusively in the control data set, possibly suggesting that control pathways have a greater degree of genetic regulation and robustness that is lost in carcinogenesis. This result suggests that these SNP associations may contribute to HCC susceptibility.

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2016-05

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Isoform Variation across Triple Negative Breast Cancer and Prostate Cancer

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Cancer is a disease in which abnormal cells divide uncontrollably and destroy body tissue, and currently plagues today’s world. Carcinomas are cancers derived from epithelial cells and include breast and prostate cancer. Breast cancer is a type of carcinoma that

Cancer is a disease in which abnormal cells divide uncontrollably and destroy body tissue, and currently plagues today’s world. Carcinomas are cancers derived from epithelial cells and include breast and prostate cancer. Breast cancer is a type of carcinoma that forms in breast tissue cells. The tumor cells can be further categorized after testing the cells for the presence of certain molecules. Hormone receptor positive breast cancer includes the tumor cells with receptors that respond to the steroid hormones, estrogen and progesterone, or the peptide hormone, HER2. These forms of cancer respond well to chemotherapy and endocrine therapy. On the other hand, triple negative breast cancer (TNBC) is characterized by the lack of hormone receptor expression and tends to have a worse prognosis in women. Prostate cancer forms in the cells of the prostate gland and has been attributed to mutations in androgen receptor ligand specificity. In a subset of triple negative breast cancer, genetic expression profiling has found a luminal androgen receptor that is dependent on androgen signaling. TNBC has also been found to respond well to enzalutamide, a an androgen receptor inhibitor. As the gene of the androgen receptor, AR, is located on the X chromosome and expressed in a variety of tissues, the responsiveness of TNBC to androgen receptor inhibition could be due to the differential usage of isoforms - different gene mRNA transcripts that produce different proteins. Thus, this study analyzed differential gene expression and differential isoform usage between TNBC cancers – that do and do not express the androgen receptor – and prostate cancer in order to better understand the underlying mechanism behind the effectiveness of androgen receptor inhibition in TNBC. Through the analysis of differential gene expression between the TNBC AR+ and AR- conditions, it was found that seven genes are significantly differentially expressed between the two types of tissues. Genes of significance are AR and EN1, which was found to be a potential prognostic marker in a subtype of TNBC. While some genes are differentially expressed between the TNBC AR+ and AR- tissues, the differences in isoform expression between the two tissues do not reflect the difference in gene expression. We discovered 11 genes that exhibited significant isoform switching between AR+ and AR- TNBC and have been found to contribute to cancer characteristics. The genes CLIC1 and RGS5 have been found to help the rapid, uncontrolled growth of cancer cells. HSD11B2, IRAK1, and COL1Al have been found to contribute to general cancer characteristics and metastasis in breast cancer. PSMA7 has been found to play a role in androgen receptor activation. Finally, SIDT1 and GLYATL1 are both associated with breast and prostate cancers. Overall, through the analysis of differential isoform usage between AR+ and AR- samples, we uncovered differences that were not detected by a gene level differential expression analysis. Thus, future work will focus on analyzing differential gene and isoform expression across all types of breast cancer and prostate cancer to better understand the responsiveness of TNBC to androgen receptor inhibition.

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2019-05

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A review of pathway-based visualization and quantification analysis tools using microarray data

Description

Pathway analysis helps researchers gain insight into the biology behind gene expression-based data. By applying this data to known biological pathways, we can learn about mutations or other changes in cellular function, such as those seen in cancer. There are

Pathway analysis helps researchers gain insight into the biology behind gene expression-based data. By applying this data to known biological pathways, we can learn about mutations or other changes in cellular function, such as those seen in cancer. There are many tools that can be used to analyze pathways; however, it can be difficult to find and learn about the which tool is optimal for use in a certain experiment. This thesis aims to comprehensively review four tools, Cytoscape, PaxtoolsR, PathOlogist, and Reactome, and their role in pathway analysis. This is done by applying a known microarray data set to each tool and testing their different functions. The functions of these programs will then be analyzed to determine their roles in learning about biology and assisting new researchers with their experiments. It was found that each tools holds a very unique and important role in pathway analysis. Visualization pathways have the role of exploring individual pathways and interpreting genomic results. Quantification pathways use statistical tests to determine pathway significance. Together one can find pathways of interest and then explore areas of interest.

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2020-05

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Big Data Network Analysis of Genetic Variation and Gene Expression in Individuals with Breast Cancer

Description

The advent of big data analytics tools and frameworks has allowed for a plethora of new approaches to research and analysis, making data sets that were previously too large or complex more accessible and providing methods to collect, store, and

The advent of big data analytics tools and frameworks has allowed for a plethora of new approaches to research and analysis, making data sets that were previously too large or complex more accessible and providing methods to collect, store, and investigate non-traditional data. These tools are starting to be applied in more creative ways, and are being used to improve upon traditional computation methods through distributed computing. Statistical analysis of expression quantitative trait loci (eQTL) data has classically been performed using the open source tool PLINK - which runs on high performance computing (HPC) systems. However, progress has been made in running the statistical analysis in the ecosystem of the big data framework Hadoop, resulting in decreased run time, reduced storage footprint, reduced job micromanagement and increased data accessibility. Now that the data can be more readily manipulated, analyzed and accessed, there are opportunities to use the modularity and power of Hadoop to further process the data. This project focuses on adding a component to the data pipeline that will perform graph analysis on the data. This will provide more insight into the relation between various genetic differences in individuals with breast cancer, and the resulting variation - if any - in gene expression. Further, the investigation will look to see if there is anything to be garnered from a perspective shift; applying tools used in classical networking contexts (such as the Internet) to genetically derived networks.

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2016-12

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The Integration of Complementary and Alternative Medicine (CAM) into Western Biomedical Oncology Treatment

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Through a standpoint feminist perspective (Harding 2009) I conducted a situational analysis (Clarke, 2015) that examined academic literature and cancer support discussion boards (DBs) to identify how Western biomedicine, specifically oncology, can integrate complementary and alternative medicine (CAM) to improve

Through a standpoint feminist perspective (Harding 2009) I conducted a situational analysis (Clarke, 2015) that examined academic literature and cancer support discussion boards (DBs) to identify how Western biomedicine, specifically oncology, can integrate complementary and alternative medicine (CAM) to improve cancer treatment in children. The aims of this project were: 1) to identify the CAM treatments that are being used to alleviate the side effects from oncological treatments and/or treat pediatric cancers; 2) to compare the subjective experience of CAM to Western biomedicine of cancer patients who leave comments on Group Loop, Cancer Compass and Cancer Forums, which are online support groups (N=20). I used grounded theory and situational mapping to analyze discussion threads. The participants identified using the following CAM treatments: herbs, imagery, prayer, stinging nettle, meditation, mind-body therapies and supplements. The participants turned to CAM treatments when their cancer was late-stage or terminal, often as an integrative and not exclusively to treat their cancer. CAM was more "effective" than biomedical oncology treatment at improving their overall quality of life and functionality. We found that youth on discussion boards did not discuss CAM treatments like the adult participants, but all participants visited these sites for support and verification of their cancer treatments. My main integration recommendation is to combine mind-body CAM therapies with biomedical treatment. This project fills the gap in literature that ignores the ideas of vulnerable populations by providing the experiences of adult and pediatric cancer patients, and that of their families. It is applicable to areas of the social studies of medicine, patient care, and families suffering from cancer. KEYWORDS: Cancer; Complementary and Alternative Medicine; Situational Analysis; Standpoint Feminism

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2016-12