Identification of Differential Gene Expression in Peritumoral and Tumoral Glioma Cells
Poster Number
37
Faculty Mentor Name
Julia Olivieri
Research or Creativity Area
Engineering & Computer Science
Abstract
During the translation phase of the cell cycle, the exons, or coding regions, of the mRNA transcript are joined together to create the relevant proteins for the cell, while the introns, or non-coding regions, are cut out of the transcript. The amount of produced protein can be measured using RNA sequencing techniques, namely differential gene expression analysis. This study focuses on performing RNA sequencing analysis using publicly available genomic data for gliomas, separated by tumoral and peritumoral (area surrounding the tumor) cells. The data was analyzed using RStudio software with PCA, correlation, and logistic regression testing in order to determine the statistical variances between cell types. A statistical model was also created using R in order to predict whether a given gene is a tumoral or peritumoral gene. The survival rate for glioma patients is very low and this type of brain cancer is especially aggressive and resistant to treatment. Therefore, applications of this study include identification of specific proteins present in tumoral RNA that aid in glioma progression for gene therapy targeting.
Location
University of the Pacific, DeRosa University Center
Start Date
26-4-2025 10:00 AM
End Date
26-4-2025 1:00 PM
Identification of Differential Gene Expression in Peritumoral and Tumoral Glioma Cells
University of the Pacific, DeRosa University Center
During the translation phase of the cell cycle, the exons, or coding regions, of the mRNA transcript are joined together to create the relevant proteins for the cell, while the introns, or non-coding regions, are cut out of the transcript. The amount of produced protein can be measured using RNA sequencing techniques, namely differential gene expression analysis. This study focuses on performing RNA sequencing analysis using publicly available genomic data for gliomas, separated by tumoral and peritumoral (area surrounding the tumor) cells. The data was analyzed using RStudio software with PCA, correlation, and logistic regression testing in order to determine the statistical variances between cell types. A statistical model was also created using R in order to predict whether a given gene is a tumoral or peritumoral gene. The survival rate for glioma patients is very low and this type of brain cancer is especially aggressive and resistant to treatment. Therefore, applications of this study include identification of specific proteins present in tumoral RNA that aid in glioma progression for gene therapy targeting.
