Abstract
Differential gene expression analysis has emerged as a valuable tool in enhancing our understanding of the quantitative variations in gene expression levels across different groups of interest. The relationship between Alzheimer's disease (AD) and type 1 diabetes (T1D) has become a significant research interest. Researchers aim to uncover potential gene expression patterns between these two complex diseases, ultimately leading to a deeper comprehension of their underlying molecular mechanisms and potential therapeutic targets. Our study seeks to broaden the understanding of these genetic biomarkers by analyzing real patient data of individuals affected by AD and T1D. We utilized RNA-sequencing data from three distinct patient groups: AD, T1D, and wildtypes. Using the DESeq2 workflow in R, we identified genes with differential expression levels among the groups. The process involved data normalization, filtering, statistical analysis, and generating plots, enabling us to pinpoint genes of potential interest. Out of the 15,467 genes analyzed: there were 497 upregulated and 1452 downregulated genes. GO enrichment identified gene categories that suggest the immune system’s role in AD and T1D pathogenesis. GSEA identified gene sets that may contribute to cognition impairment in AD and loss of smell associated with diabetic neuropathy. The examination reveals a potential connection between the brain's immune system and the autoimmune system of T1D patients. Additionally, it indicates the existence of a genetic association between impaired cognition and diabetic neuropathy.
How to Cite:
, S., , K., , B. & , A.,
(2023) “Differential Gene Expression Analysis of Alzheimer & Type 1 Diabetic Patients ”,
Journal of Student-Scientists' Research 5.
doi: https://doi.org/10.13021/jssr2023.3978
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