A Bioinformatics Approach to Defining Gene Signaling Pathways
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Date
2014-05
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The Ohio State University
Abstract
An improved understanding of gene signaling pathways and mechanisms involved in cancer continues to be a necessary feat to further cancer research and yield effective cancer treatments. Many cancers involve the misregulation of transcription factors E2f, c-Myc, FoxM1, and Stat family members, which are responsible for regulating many genes in the cell. While some gene targets of these transcription factors are known, many more are yet to be discovered. Identification of all the direct gene targets will provide not only the unidentified pathways, but also a better idea of the interconnectedness of the misregulated pathways in cancer.
The project employs an information retrieval and integration approach to identify all the potential direct targets of E2fs, c-Myc, FoxM1, and Stat in order to understand the relationship between these regulators as well as their respective roles in cancer. As the medical community delves further into researching cancer treatments, there is an increasing demand for an effective and reliable tool that assimilates massive amounts of existing information regarding relevant cancer pathways and provides easy access through a single portal. This project aims to mine data from existing public repositories and ongoing experiments to process and calibrate it to a certain standard, so that searching for details about transcription factors and potential targets will be easier.
The data is categorized in multiple ways to make queries and searches more efficient. Every data entry has information regarding the source of the experiments, type of target gene regulation, experimental variables, and confidence score. The confidence score assigned to each essentially classifies the gene as a high or low stringency target gene. Currently, manual retrieval of data has yielded entries corresponding to about 3000 target genes amassed from over 600 publications concerning E2fs, Myc, Stat, and FoxM1. We have also computationally extracted data from relevant supplementary microarray raw data files. Furthermore, we aim to improve the precision of the confidence score given to each target gene by utilizing the influx of all new relevant data. The collective data obtained will serve to provide researchers with comprehensive information on gene-signaling pathways in various cancers with the goal of improving current research, instigating new ideas, and identifying potential targets of cancer treatments.
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Gene signaling pathways in cancer