Posters
Presenting Author Academic/Professional Position
Graduate Student
Academic Level (Author 1)
Graduate Student
Academic Level (Author 2)
Faculty
Presentation Type
Poster
Discipline Track
Biomedical Science
Abstract Type
Research/Clinical
Abstract
There are many cancers that are affecting the human population, with some being more common and studied than others. These cancers have mostly been studied individually until 2012 when scientists began studying through comparison analysis of different cancers to see possible connections on the genomic and molecular level and have resulted in the categorization of tumors into types. The result from molecular analysis has re-classified types of tumors into new clusters, which aid doctors in deciding the optimal way of treating tumors. In this study, we analyze a dataset comprising over 2,000 cancer samples, focusing on six cancer types: breast, colon, kidney, uterus, ovarian, and lung. Following data preprocessing, we explore descriptive statistics such as age, gender, tobacco and alcohol consumption, and family history of cancer. Our primary goal is to map probe IDs to gene expressions in the selected cancer samples, constructing a gene co-expression network by identifying correlations across samples. We are interested in seeing if any factors from our description statistics show any significant impact on certain gene expressions and identify possible patterns for further analysis. Additionally, the creation of a gene co-expression network would help in understanding gene to gene interactions and regulatory mechanisms. If a pattern is identified based off factors such as tobacco and alcohol consumption have an impact on gene expression, then we look towards creating a predictive model to classify samples based off these factors and evaluate the importance of these factors in the model. Additionally, we intend to identify prominent hub genes and modules within the co-expression network to explore their biological significance, examine associations between modules and cancer outcomes, and compare networks to evaluate similarities among cancer types. These findings may contribute to a deeper understanding of cancer biology and inform future therapeutic strategies.
Recommended Citation
Solis, Arely and Ayati, Marzieh, "Gene Co-Expression Networks and Descriptive Statistical Patterns in Cancer Subtypes" (2025). Research Symposium. 22.
https://scholarworks.utrgv.edu/somrs/2025/posters/22
Included in
Gene Co-Expression Networks and Descriptive Statistical Patterns in Cancer Subtypes
There are many cancers that are affecting the human population, with some being more common and studied than others. These cancers have mostly been studied individually until 2012 when scientists began studying through comparison analysis of different cancers to see possible connections on the genomic and molecular level and have resulted in the categorization of tumors into types. The result from molecular analysis has re-classified types of tumors into new clusters, which aid doctors in deciding the optimal way of treating tumors. In this study, we analyze a dataset comprising over 2,000 cancer samples, focusing on six cancer types: breast, colon, kidney, uterus, ovarian, and lung. Following data preprocessing, we explore descriptive statistics such as age, gender, tobacco and alcohol consumption, and family history of cancer. Our primary goal is to map probe IDs to gene expressions in the selected cancer samples, constructing a gene co-expression network by identifying correlations across samples. We are interested in seeing if any factors from our description statistics show any significant impact on certain gene expressions and identify possible patterns for further analysis. Additionally, the creation of a gene co-expression network would help in understanding gene to gene interactions and regulatory mechanisms. If a pattern is identified based off factors such as tobacco and alcohol consumption have an impact on gene expression, then we look towards creating a predictive model to classify samples based off these factors and evaluate the importance of these factors in the model. Additionally, we intend to identify prominent hub genes and modules within the co-expression network to explore their biological significance, examine associations between modules and cancer outcomes, and compare networks to evaluate similarities among cancer types. These findings may contribute to a deeper understanding of cancer biology and inform future therapeutic strategies.
