Shradha Mukherjee
Arizona State University, College of Health Informatics
Title: Data-mining and Bioinformatic analysis of gene expression datasets from aging and cancer glioblastoma studies identifies common pathways
Biography
Biography: Shradha Mukherjee
Abstract
Regenrative and proliferative capacity of stem cells is lost in most species including humans with aging and is associated with brain neurodegenerative diseases. On the other hand, increased proliferation of stem cells causes cancer and tumors, such as glioblastoma in the brain. An integrated analysis of cell cycle gene expression changes associated with aging and cancer is missing and will help increase the molecular understanding of cell cycle regulation in aging and cancer. In the present study a bioinformatic pipeline was developed to compare gene expression between aged brain and glioblastoma brain cancer cells from human samples. The RNA-seq datasets of gene expression for aging and glioblastoma brain samples used in this study was obtained from Allen Brain repositories, aging.brain-map.org and glioblastoma.alleninstitute.org, respectively. The RNA-seq datasets were mapped on hg19 human genome. Next, differential gene expression cuffdiff analysis and GSEA (gene set enrichment analysis) was performed on the following pair of conditions: 1) aged vs young brains 2) glioblastoma vs non-glioblastoma 3) aged brain vs glioblastoma. This analysis produced a list of cell cycle genes enriched in aged brain only, glioblastoma only and in both aged and glioblastoma. Taken together, these results show that in the context of aging brain and glioblastoma brain cancer, both unique and common genes within the cell cycle gene network play a regulatory role. These results have relevance in healthcare as it identifies genes or potential drug targets for treatment of cancer in aged individuals using the common genes identified in this study