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Identification of Hub Genes and Molecular Pathways in Age-Related Hearing Loss: An Integrated Bioinformatics and
1Department of Otorhinolaryngology, Ankang Central Hospital, Ankang, China.
Background:
Age-related hearing loss (ARHL), or presbycusis, is a prevalent sensory impairment in the elderly, driven by multifactorial degenerative changes in cochlear structures such as the inner and outer hair cells, stria vascularis, and auditory nerve. Despite its significant impact on quality of life, including associations with social isolation, depression, and cognitive decline, the molecular mechanisms underlying ARHL remain incompletely understood. This study leverages bioinformatics and machine learning to identify hub genes and pathways from gene expression data, aiming to uncover novel therapeutic targets.
Methods:
Integrated analysis of 3 gene expression omnibus (GEO) datasets (GSE121856, GSE197634, GSE98070) from mouse cochlear tissues, specifically auditory nerve and cochlear lateral wall of young and old CBA/CaJ mice, was performed, with batch effects removed using the sva R package and validated via principal component analysis. Differentially expressed genes (DEGs) were identified using the limma package (|logFC| ≥ 1, P < .05). Functional enrichment was performed with gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses via clusterProfiler. A protein-protein interaction (PPI) network was constructed using STRING and analyzed in Cytoscape with cytoHubba and MCODE plugins for hub gene selection. Machine learning algorithms (LASSO and SVM-RFE) were applied to prioritize feature genes.
Results:
Seventy-four DEGs were identified (38 upregulated, 36 downregulated), enriched in GO terms like extracellular matrix organization, angiogenesis regulation, and collagen fibril organization, and KEGG pathways including ECM-receptor interaction and AGE-RAGE signaling. The PPI network highlighted hub genes such as Cd68, Mmp12, Agt, Lgals3, Lyz2, and H2-Eb1. Machine learning intersection yielded 2 key hub genes, Aplnr and Agt, both downregulated in the presbycusis group.
Conclusion:
This integrated analysis elucidates molecular alterations in ARHL, identifying Aplnr and Agt as potential hub genes that may serve as novel biomarkers or therapeutic targets to mitigate presbycusis progression.
