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- Genre: Masters Thesis
Description
While only the sixth most common cancer globally, liver cancer is the third most deadly. Despite the importance of accurate diagnosis and effective treatment, standard diagnostic tests for most solid organ neoplasms are not required for the most common type of liver cancer, Hepatocellular Carcinoma (HCC). In addition, major discrepancies in the practices currently in place limits the ability to develop more precise oncological treatment and prognosis. This study aimed to identify biomarkers, with potential to more accurately diagnose how far cancer has advanced within a patient and determine prognosis. It is the hope that pathways provided by this study form the basis for future research into more standardized practices and potential treatment based on specific affected biological processes. The PathOlogist tool was utilized to calculate activity metrics for 1,324 biological pathways in 374 The Cancer Genome Atlas (TCGA) hepatocellular carcinoma donors. Further statistical analysis was done on two datasets, formed to identify grade or stage at time of diagnosis for the activity levels calculated by PathOlogist. The datasets were evaluated individually. Based on the variance and normality of each pathway’s activity levels in the respective data sets analysis of variance, Tukey-Kramer, Kruskal-Wallis, and Mann-Whitney-Wilcox tests were performed, when appropriate, to determine any statistically significant differences in pathway activity levels. Pathways were identified in both stage and grade data analyses that show significant differences in activity levels across designation. While some overlap is seen, there was a significant number of pathways unique to either stage or grade. These pathways are known to affect the cell cycle, cellular transport, disease, immune system, and metabolism regulation.
The biological pathways named by this research depict prospective biomarkers for progression of hepatocellular carcinoma per subdivision within both stage and grade. These findings may be instrumental to new methods of early and more accurate diagnosis. The distinct differences in identified pathways in grade and stage illustrate the need for these new methods to not only look at stage but also grade when determining prognosis. Furthermore, the pathways identified herein have potential to aid in the development of targeted treatment based on the affected biological processes.
ContributorsGarrison, Alyssa Cameron (Author) / Buetow, Kenneth (Thesis advisor) / Hinde, Katie (Committee member) / Wilson, Melissa (Committee member) / Arizona State University (Publisher)
Created2022
Description
This study evaluates the effects of feeding modes on the infant gut microbiome, especially focusing on the unique microbial composition provided by human milk. It analyzed the gut microbiome of 51 mother-infant dyads and identified significant differences in microbial diversity related to feeding practices. Alpha diversity results, measured using the Shannon diversity index (H = 38.134, p = 1.05e^-7) and Faith's Phylogenetic Diversity (H = 45.999, p = 2.45e^-9), showed that breastfeeding, in any form, supports microbial alpha diversity comparable to exclusive breastfeeding that was lower in infants receiving breast milk compared to formula and cow’s milk. In contrast, formula or cow's milk led to a distinctly different microbiome. This study utilized both unweighted and weighted UniFrac metrics to assess the impact of feeding modes on microbial community structure or beta diversity. Using these metrics, and PERMANOVA testing, significant differences were observed between several feeding modes. Cow’s milk and formula did not differ for gut microbiome community structure but all modes of feeding that included breastmilk were significantly different from both cow’s milk and formula (q < 0.005). Additionally, breastmilk fed at breast resulted in a significantly different community structure than in infants fed breastmilk at breast and pumped for bottle feeding. Multivariate models of beta diversity metrics, including both subject ID and time, suggested that individual differences accounted for 48% of the variance, while feeding mode accounted for 2%. Despite the smaller explained variance of feeding mode, the association between feeding mode and unweighted UniFrac was statistically significant (p = 0.01). Interestingly, while feeding mode was a significant factor in microbial community diversity, it did not significantly associate with the abundance of Bifidobacterium (p = 0.31) or Lactobacillus (p = 0.21). Covariate inclusion in models revealed that subject ID (individual baby) was the only substantial contributor (p < 0.0001) to the variance in Bifidobacterium abundance. These findings emphasize breast milk's critical role in the development of a healthy gut microbiome and highlight the complex interplay between diet, genetics, and microbial colonization. These insights suggest that while individual genetics are a driving force, breast milk consumption contributes significantly to the gut microbiome diversity and community composition, particularly when compared to formula or cow’s milk consumption. Further research into the mechanisms driving the establishment and maintenance of the infant gut microbiome are warranted.
ContributorsLiedike, Bethany Patricia (Author) / Whisner, Corrie (Thesis advisor) / Sears, Dorothy D. (Committee member) / Hinde, Katie (Committee member) / Arizona State University (Publisher)
Created2024