Genetic analysis of potential biomarkers and therapeutic targets in age-related hearing loss

Age-related hearing loss (ARHL) or presbycusis is the phenomenon of hearing loss due to the aging of auditory organs with age. It seriously affects the cognitive function and quality of life of the elderly. This study is based on comprehensive bioinformatic and machine learning methods to identify the critical genes of ARHL and explore its therapy targets and pathological mechanisms. The ARHL and normal samples were from GSE49543 datasets of the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to obtain significant modules. The Limma R-package was used to identify differentially expressed genes (DEGs). The 15 common genes of the practical module and DEGs were screened. Functional enrichment analysis suggested that these genes were mainly associated with inflammation, immune response, and infection. Cytoscape software created the protein-protein interaction (PPI) layouts and cytoHubba, support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) algorithms screened hub genes. After validating the hub gene expressions in GSE6045 and GSE154833 datasets, Clec4n, Mpeg1, and Fcgr3 are highly expressed in ARHL and have higher diagnostic efficacy for ARHL, so they were identified as hub genes. In conclusion, Clec4n, Mpeg1, and Fcgr3 play essential roles in developing ARHL, and they might become vital targets in ARHL diagnosis and anti-inflammatory therapy.