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Clustering Alzheimer’s Disease Gene Expression Dataset Reveals Underlying Sexually Dimorphic and Disease Status Profiles by Levy, Sigal (author);Guttmann-Beck, Nili (author);Shweiki, Dorit (author) is a Medicine article available to read on EtoBox.

What is Clustering Alzheimer’s Disease Gene Expression Dataset Reveals Underlying Sexually Dimorphic and Disease Status Profiles about?

## Background: The multiple appearance phenotypes in Alzheimer’s disease (AD) are manifested in epidemiologic sexual dimorphism, variation in age of onset, progress, and severity of the disease. ## Objective: In this study, we focused on sexual dimorphism, aiming to untie some of the complex interconnections in AD between sex, disease status, and gene expression profiles. Two strategic decisions guided our study: 1) to value transcriptomic multi-layered profiles over alterations in single genes expression; and 2) to embrace a sexual dimorphism centered approach, as we suspect that transcriptomic profiles may dramatically differ not only between healthy and sick individuals but between men and women as well. ## Methods: Microarray dataset GSE15222, fulfilling our strict criteria, was retrieved from the GEO repository. We performed cluster analysis for each sex separately, comparing the proportion of healthy and AD individuals in each cluster. ## Results: We were able to identify a biased, female, AD-typified cluster. Furthermore, we showed that this female AD-typified cluster is highly similar to one of the male clusters. While the female cluster constitutes mostly sick individuals,

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Author

Levy, Sigal (author);Guttmann-Beck, Nili (author);Shweiki, Dorit (author)

Publisher

SAGE Publications

Published

2021

Language

EN

Field

Medicine (Health Sciences)