Researcher ORCID Identifier

Graduation Year


Date of Submission


Document Type

Open Access Senior Thesis

Degree Name

Bachelor of Arts



Reader 1

Pete Chandrangsu

Reader 2

Matthew Faldyn

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© 2021 Michael W Madsen


The search for improvements to detection and treatment of cancers is a paramount goal for all of medicine. The most important step for oncological research is to expand the knowledge base of the genetic characteristics and abnormalities that give rise to cancer. In our present day, one of the most pressing and deadly forms of cancer is that of the lung, with lung adenocarcinomas being the most prevalent variation of the disease. Improving our cancer genomic insight can provide the seedings for improved cancer detection, novel cancer treatment, and serve as a guide for avenues to explore with future oncological research. Using The Cancer Genome Atlas (TCGA), a cancer genomics program from the National Cancer Institute, a data set of 512 lung adenocarcinoma samples was compiled for analysis into the interconnectivity of patient characteristics and their impact on gene expression profiles. For this sample population, patient sex, age, race, smoking history, and tumor stage were analyzed using a permutational multivariate analysis of variance (perMANOVA) modeled with non-metric multidimensional scaling (NMDS) to determine significant interactions between the variables on tumor genetic differentiation. Patient smoking history and tumor stage, sex and tumor stage, and sex and age were all found to have statistically significant impacts on gene expression, while race on its own was also found to have a significant impact. This analysis highlights that male and female cancers might differentiate quite differently and illuminates a need to explore sex related differences in cancer progression. Additionally, the research emphasizes the continued buildup of mutations throughout a cancer’s proliferation, showing the need for investigation into cancer development after it has been initially discovered. Lastly, this research demonstrates that patient variables interact significantly to impact gene expression profiles and accentuates the need for research about how external factors combine and interconnect to make each cancer unique.

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