Research Paper Volume 10, Issue 10 pp 2816—2831
Re-exploring the core genes and modules in the human frontal cortex during chronological aging: insights from network-based analysis of transcriptomic studies
- 1 Department of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, P.R. China
- 2 Department of Internal Medicine, University of Utah, Salt Lake City, Utah 84112, U.S.A
received: August 8, 2018 ; accepted: October 4, 2018 ; published: October 20, 2018 ;https://doi.org/10.18632/aging.101589
How to Cite
Copyright: Xu et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Frontal cortical dysfunction is a fundamental pathology contributing to age-associated behavioral and cognitive deficits that predispose older adults to neurodegenerative diseases. It is established that aging increases the risk of frontal cortical dysfunction; however, the underlying molecular mechanism remains elusive. Here, we used an integrative meta-analysis to combine five frontal cortex microarray studies with a combined sample population of 161 younger and 155 older individuals. A network-based analysis was used to describe an outline of human frontal cortical aging to identify core genes whose expression changes with age and to reveal the interrelationships among these genes. We found that histone deacetylase 1 (HDAC1) and YES proto-oncogene 1 (YES1) are the two most upregulated genes, while cell division cycle 42 (CDC42) is the central regulatory gene decreased in the aged human frontal cortex. Quantitative PCR assays revealed corresponding changes in frontal cortical Hdac1, Yes1 and Cdc42 mRNA levels in an established aging mouse model. Moreover, analysis of the GSE48350 dataset confirmed similar changes in HDAC1, CDC42 and YES1 expression in Alzheimer's disease, thereby providing a molecular connection between aging and Alzheimer's disease (AD). This framework of network-based analysis could provide novel strategies for detecting and monitoring aging in the brain.