Event Title
Exploration of Cancer Genes Through Bioinformatics Analyses
Location
Hager-Lubbers Exhibition Hall
Description
PURPOSE: The main goal of this work was to design an interactive visualization tool to detail the genes potentially responsible for breast cancer as they are discovered through bioinformatics analysis. SUBJECTS: The miRNA expression profile of accession number GSE37210 was downloaded from the GEO database, which was collected by Johnson JK et al. The study attempted to use non-BRCA1/2 breast cancer families in order to identify additional high-risk breast cancer susceptibility genes. Expression profiles of miRNAs in 15 untreated patients with breast cancer and 15 healthy control subjects were available, all from one family. METHODS AND MATERIALS: The data were analyze using the statistical packages SAS and R. Furthermore, Prefuse, a Java-based toolkit for building interactive information visualization applications, was used to visualize the association pathways of the aberrant expression genes. ANALYSES: To identify the differentially expressed genes between the 15 cancer patients and the 15 healthy control subjects, we applied different multiple testing procedures. We chose the following parameters: P <.0001, False Discovery Rate <0.01 and Bonferroni <0.05 were considered to indicate a statistically significant difference. RESULTS: The results were compared using the databases DAVID and KEGG. 52 mRNA were found from almost 70 significant mutation genes with aberrant expression. DAVID was used to discover the function of genes within the modules and the Gene Ontology (GO) terms. CONCLUSIONS: Bioinformatics analysis can be useful to predict susceptible genes for different kinds of cancer, and it can contribute more efficiently to discover new methods to fight cancer.
Exploration of Cancer Genes Through Bioinformatics Analyses
Hager-Lubbers Exhibition Hall
PURPOSE: The main goal of this work was to design an interactive visualization tool to detail the genes potentially responsible for breast cancer as they are discovered through bioinformatics analysis. SUBJECTS: The miRNA expression profile of accession number GSE37210 was downloaded from the GEO database, which was collected by Johnson JK et al. The study attempted to use non-BRCA1/2 breast cancer families in order to identify additional high-risk breast cancer susceptibility genes. Expression profiles of miRNAs in 15 untreated patients with breast cancer and 15 healthy control subjects were available, all from one family. METHODS AND MATERIALS: The data were analyze using the statistical packages SAS and R. Furthermore, Prefuse, a Java-based toolkit for building interactive information visualization applications, was used to visualize the association pathways of the aberrant expression genes. ANALYSES: To identify the differentially expressed genes between the 15 cancer patients and the 15 healthy control subjects, we applied different multiple testing procedures. We chose the following parameters: P <.0001, False Discovery Rate <0.01 and Bonferroni <0.05 were considered to indicate a statistically significant difference. RESULTS: The results were compared using the databases DAVID and KEGG. 52 mRNA were found from almost 70 significant mutation genes with aberrant expression. DAVID was used to discover the function of genes within the modules and the Gene Ontology (GO) terms. CONCLUSIONS: Bioinformatics analysis can be useful to predict susceptible genes for different kinds of cancer, and it can contribute more efficiently to discover new methods to fight cancer.