Issue
Copyright (c) 2024 Kanglun Jiang, Tan Wang, Xinsheng Huang, Li Gao
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Identification of mRNA expression profiles and their characterization in age-related hearing loss
Corresponding Author(s) : Li Gao
Cellular and Molecular Biology,
Vol. 70 No. 4: Issue 4
Abstract
Age-related hearing loss (ARHL), is a pervasive health problem worldwide. ARHL seriously affects the quality of life and reportedly leads to social isolation and dementia in the elderly. ARHL is caused by the degeneration or disorders of cochlear hair cells and auditory neurons. Numerous studies have verified that genetic factors contributed to this impairment, however, the mechanism behind remains unclear. In this study, we analyzed an mRNA expression dataset (GSE49543) from the GEO database. Differentially expressed genes (DEGs) between young control mice and presbycusis mice were analyzed using limma in R and weighted gene co-expression network analysis (WGCNA) methods. Functional enrichment analyses of the DEGs were conducted with the clusterProfiler R package and the results were visualized using ggplot2 R package. The STRING database was used for the construction of the protein-protein interaction (PPI) network of the screened DEGs. Two machine learning algorithms LASSO and SVM-RFE were used to screen the hub genes. We identified 54 DEGs in presbycusis using limma and WGCNA. DEGs were associated with the synaptic vesicle cycle, distal axon, neurotransmitter transmembrane transporter activity in GO analysis, and alcoholic liver disease, pertussis, lysosome pathway according to KEGG analyses. PPI network analysis identified three significant modules. Five hub genes (CLEC4D, MS4A7, CTSS, LAPTM5, ALOX5AP) were screened by LASSO and SVM-RFE. These hub genes were highly expressed in presbycusis mice compared with young control mice. We screened DEGs and identified hub genes involved in ARHL development, which might provide novel clues to understanding the molecular basis of ARHL.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
