Determination of Differential miRNA Expression Profile in People with Noise-Induced Hearing Loss

Abstract

Noise-induced hearing loss (NIHL) is a significant occupational health issue, characterized by permanent damage to the cochlea due to prolonged exposure to high-intensity noise. Circulating microRNAs (c-miRNAs) have emerged as promising non-invasive indicators of inner ear pathology and potential modulators of cellular stress responses. Nevertheless, their specific roles in NIHL remain inadequately characterized. This study evaluated miRNA expression in the peripheral blood of individuals with bilateral NIHL (n = 12) and matched healthy controls (n = 6) using GeneChip^® miRNA 4.0 arrays. The Transcriptome Analysis Console software was used for differential expression analysis, and bioinformatic predictions of gene targets and pathway enrichment were performed using TargetScan (version 8.0) and the Enrichr tool. Among the 72 differentially expressed miRNAs (FDR < 0.05), hsa-miR-486-2, hsa-miR-664b-3p, hsa-miR-4485, hsa-miR-501, and hsa-miR-663b were notably upregulated, while hsa-miR-6723, hsa-miR-194-2, hsa-miR-668-5p, hsa-miR-4722-3p, and hsa-miR-4716 showed significant downregulation. Enrichment analyses indicated involvement in apoptosis regulation, mitochondrial stability, and cell cycle control. Principal component analysis (PCA) and clustering methods revealed clear molecular distinctions between the patient and control groups. The observed alterations in c-miRNA profiles highlight their relevance to NIHL-related cellular stress and degeneration. These findings support their utility as candidate biomarkers for diagnosis and prognosis, warranting further validation in functional and longitudinal studies.

Keywords: miRNA array; miRNAs; noise-induced hearing loss; pathogenesis.

Figure 1

The volcano plot representing differentially expressed miRNAs and small RNAs obtained from the GeneChip^® miRNA 4.0 Array data, analyzed using the Transcriptome Analysis Console software version 4.0.3.14. The x-axis displays the log2 fold change (log2FC) in expression, calculated as patient vs. control, where negative values indicate downregulation in patients and positive values indicate upregulation. The y-axis shows the −log10 of the p-value, reflecting the statistical significance of the differential expression. Each dot represents an individual transcript (miRNA or small RNA). Red dots correspond to significantly upregulated transcripts in the patient group (log2FC > +2 and p < 0.05), while green dots denote significantly downregulated transcripts (log2FC < −2 and p < 0.05). Gray dots indicate transcripts that did not meet the defined thresholds for statistical significance. Transcripts farther from the center along the x-axis exhibit greater fold changes, while those higher on the y-axis are more statistically significant. Labeled points indicate the selected miRNAs of particular biological or diagnostic interest, primarily those identified as potential biomarker candidates in Table 4.

Figure 2

The hierarchical clustering and heatmap of miRNA expression profiles. Patient samples (red) and control samples (blue) are displayed. The clustering analysis revealed a clear separation between patients and controls, with Patient_12 clustering closer to the control group, suggesting individual variability within the patient cohort. Color intensity reflects relative expression levels, with red indicating higher expression and blue indicating lower expression.

Figure 3

PCA mapping of microRNA expression profiles. Patient samples (red) and control samples (blue) are shown. Patient_12 was positioned closer to the control group, suggesting a molecular profile distinct from other patients. PCA1, PCA2, and PCA3 accounted for 85.5% of the total variance.

Figure 4

The boxplot showing the distribution of cumulative weighted context++ scores for the top 10 predicted target genes of each miRNA.

Figure 5

Top enriched GO biological processes of predicted miRNA targets visualized by bar chart.