Identification of Hub Genes and Molecular Pathways in Age-Related Hearing Loss: An Integrated Bioinformatics and...

Area of Science:

• Genomics and Bioinformatics
• Otolaryngology
• Computational Biology

Background:

• Age-related hearing loss (ARHL), or presbycusis, is a common sensory impairment in older adults.
• It results from degenerative changes in cochlear structures and significantly impacts quality of life.
• The underlying molecular mechanisms of ARHL are not fully understood.

Purpose of the Study:

• To identify novel hub genes and molecular pathways associated with ARHL using bioinformatics and machine learning.
• To uncover potential therapeutic targets for mitigating presbycusis.

Main Methods:

• Integrated analysis of three mouse cochlear gene expression datasets (GEO: GSE121856, GSE197634, GSE98070).
• Identification of differentially expressed genes (DEGs) and functional enrichment analysis (GO, KEGG).
• Construction and analysis of a protein-protein interaction (PPI) network to identify hub genes, followed by machine learning (LASSO, SVM-RFE) for feature gene prioritization.

Main Results:

• Seventy-four DEGs were identified, enriched in extracellular matrix organization and ECM-receptor interaction pathways.
• Key hub genes identified include Cd68, Mmp12, Agt, Lgals3, Lyz2, and H2-Eb1.
• Machine learning prioritized Aplnr and Agt as critical hub genes, both found to be downregulated in presbycusis.

Conclusions:

• This study elucidates molecular alterations in ARHL.
• Aplnr and Agt are identified as potential novel biomarkers or therapeutic targets for presbycusis.