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. 2025 Jan 21;15(1):2687.
doi: 10.1038/s41598-024-83428-x.

Bioinformatics approach reveals the critical role of inflammation-related genes in age-related hearing loss

Xi Gu #  1   2   3 , Chenyu Chen #  4   5 , Yuqing Chen #  1   2   3 , Chaojun Zeng  1   2   3 , Yanchun Lin  1   2   3 , Ruosi Guo  6 , Shujin Xu  6 , Chang Lin  7   8   9
Affiliations

Bioinformatics approach reveals the critical role of inflammation-related genes in age-related hearing loss

Xi Gu et al. Sci Rep. .

Abstract

Age-related hearing loss (ARHL) is the most prevalent sensory impairment in the elderly. However, the pathogenesis of ARHL remains unclear. This study was aimed to explore the potential inflammation-related genes of ARHL and suggest novel therapeutic targets for this condition. Initially, a total of 105 Inflammatory related differentially expressed genes (IRDEGs) were obtained by overlapping the differentially expressed genes from the GSE49522 and GSE49543 datasets with Inflammatory related genes. The IRDEGs were mainly enriched in MAPK, PI3K-Akt, Hippo and JAK-STAT pathways by analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. We then identified 10 key IRDEGs including Alox5ap, Chil1, Clec7a, Dysf, Fcgr3, etc. using Least absolute shrinkage and selection operator regression analysis and converted them into human genes. The ROC curve indicated that Alox5ap expression presented a high accuracy in distinguishing between different groups. By CIBERSORT algorithm, 8 humanized key IRDEGs were correlated with the infiltration abundance of 3 immune cells. Finally, it showed that the Alox5ap expression was significantly more effective compared to other variables in the diagnostic model of ARHL. This study suggests that inflammation might play a role in the development of ARHL, providing a deeper understanding of the underlying causes of this disease.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
ARHL: age-related hearing loss; GSEA: Gene Set Enrichment Analysis; GSVA: Gene Set Variation Analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; ROC: receiver operating characteristic curve; PCA: Principal Component Analysis; IRDEGs: Inflammatory related differentially expressed genes; DEGs: differentially expressed genes; IRGs: Inflammatory related genes; TF: transcription factor; RBP: RNA-binding protein.
Fig. 2
Fig. 2
A–B. The boxplot graph before (A) and after (B) normalization of the merged dataset. C–D. The PCA graph of merged dataset before (C) and after (D) the process of removing the batch effect. (E) Distribution box plot of gene expression among samples in the GSE6045 data set before correction. (F) Box plot of the distribution of gene expression among samples in the corrected dataset GSE6045. PCA: Principal Component Analysis.
Fig. 3
Fig. 3
(A) Volcano map of the differentially expressed genes in merged dataset. (B) Venn diagram of DEGs and IRGs in the merged dataset. (C) Heat map of 105 IRDEGs in different groups. DEGs: differentially expressed genes. IRGs: Inflammatory related genes. IRDEGs: Inflammatory related differentially expressed genes.
Fig. 4
Fig. 4
A–B. Results of GO functional enrichment analysis (A) and KEGG pathway enrichment analysis (B) of IRDEGs are displayed in bubble diagrams. C–D. Results of GO function enrichment analysis (C) and KEGG pathway enrichment analysis (D) of IRDEGs are displayed in a circular network diagram. E–F. Results of GO functional enrichment analysis (E) and KEGG pathway enrichment analysis (F) of IRDEGs are shown in histograms. GO: Gene Ontology. KEGG: Kyoto Encyclopedia of Genes and Genomes. IRDEGs: Inflammatory related differentially expressed genes. BP: biological process. CC: cellular component. MF: molecular function. The screening criteria for GO and KEGG enrichment items were P.value < 0.05 and FDR value (q.value) < 0.05.
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