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Transcriptome sequencing reveals regulatory genes associated with neurogenic hearing loss

BMC Medical Genomics volume 18, Article number: 11 (2025) Cite this article

Abstract

Hearing loss is a prevalent condition with a significant impact on individuals’ quality of life. However, comprehensive studies investigating the differential gene expression and regulatory mechanisms associated with hearing loss are lacking, particularly in the context of diverse patient samples. In this study, we integrated data from 10 patients across different regions, age groups, and genders, with their data retrieved from a public transcriptome database, to explore the molecular basis of hearing loss. These samples are mainly from fibroblasts and keratinocytes. Through differential gene expression analysis, we identified key genes, including ICAM1, SLC1A1, and CD24, which have already been shown to play important roles in neurogenic hearing loss. Furthermore, we predicted potential transcriptional regulatory factors that may modulate the expression of these genes. Enrichment analysis revealed biological processes and pathways associated with hearing loss, highlighting the involvement of circadian rhythm disruption and other neuro-related disorders. Although our study is limited by the sample size and the absence of larger-scale investigations, the identified genes and regulatory factors provide valuable insights into the molecular mechanisms underlying hearing loss. Further molecular and cellular experiments are necessary to validate these findings and elucidate the precise regulatory mechanisms involved. In conclusion, our study contributes to the understanding of hearing loss pathogenesis and offers potential targets for molecular diagnostics and gene-based therapies. This provides a foundation for further research into personalized approaches to diagnosing and treating hearing loss.

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Introduction

Hearing loss is a complex sensory disorder. Studies have shown that the 5-year incidence of hearing impairment (HI) was 14.1% (95% CI, 13.0–15.3%) [1], while the 10-year cumulative incidence reached 26.0% (95% CI, 24.6–27.6%) [1]. Additionally, the cumulative incidence of HI at 15 years was 56.8% [2]. Emerging research has shed light on the intricate interplay between hearing loss and various aspects of overall health, including sleep patterns [3, 4]. Specifically, the disruptive effects of tinnitus on sleep quality have garnered increasing attention [5]. Tinnitus, often experienced by individuals with hearing loss, can lead to disturbances in sleep initiation, maintenance, and overall sleep architecture [6, 7]. The incidence increases with age and can be classified into congenital and acquired hearing loss, depending on the timing of onset [8]. Congenital hearing loss involves a multifaceted etiology encompassing both genetic and environmental factors. Depending on the presence or absence of associated systemic diseases, genetic hearing loss can be further categorized as non-syndromic or syndromic. Single-gene mutations and hereditary factors play a major role in over 60% of cases of congenital hearing loss [9]. Recent large-scale molecular epidemiological studies in China have identified the GJB2 [10, 11], SLC26A4 [11], and MT-RNR1 [12] genes as the most common causes of non-syndromic hearing loss, collectively explaining approximately one-third of the mechanisms underlying the condition. The carrier rate of mutations in the GJB2, MT-RNR1, and SLC26A4 was estimated to be 26.21%, 1.86%, and 25.46% among Chinese patients with nonsyndromic hearing loss [13]. Furthermore, studies showed that the CD24 [14, 15], ICAM1 [16,17,18,19,20,21,22,23,24], and SLC1A1 [25] genes play important regulatory roles in the onset and development of hearing loss.

Transcriptional regulation plays a crucial role in modulating gene expression patterns, and dysregulation of transcriptional control can contribute to various diseases, including hearing loss and tinnitus. Numerous studies have investigated the impact of transcriptional regulatory factors on gene expression in hearing loss and tinnitus. Among them, C/EBPbeta can regulate the inflammatory response and cell apoptosis [26], probably related to the inflammatory response and apoptosis associated with hearing loss and tinnitus. GCF and GR are glucocorticoid receptors that may be related to the protective effects of glucocorticoids on hearing and tinnitus [27]. YY1 may be involved in regulating the transcription of genes related to hearing and tinnitus [28]. These factors, also known as transcription factors (TFs), bind to specific DNA sequences within the regulatory regions of genes, such as promoters and enhancers, and can activate or repress gene expression. By modulating the expression of target genes, transcription factors play a critical role in various cellular processes, including development, differentiation, and maintenance of normal physiological functions. After investigation, there are complex regulatory relationships between C / EBPbeta, glucocorticoid receptor nuclear factors GCF and GR, YY1,ER-alpha with CD24, ICAM1, SLC1A1, which can regulate the expression levels of these genes [29,30,31,32,33,34,35,36]. Specific regulatory mechanisms may involve interactions of transcription factors, binding to the promoter regions of the gene, and the involvement of other regulators. Integrating gene expression data with transcription factor binding predictions and functional studies, researchers can identify critical transcriptional regulatory pathways and potential therapeutic targets for intervention.

Despite recent progress, systematic cohort studies focusing on the molecular etiology of hearing impairment and effective genetic evaluation strategies remain lacking. The mechanisms underlying congenital hearing loss are not fully understood, which complicates the design of targeted treatments. Identifying genetic factors associated with hearing loss, along with understanding the role of TFs [37, 38] in its pathogenesis, could offer valuable insights for developing novel therapeutic strategies [39,40,41].

Here, we aimed to address these gaps in knowledge by utilizing whole transcriptome sequencing data available from the NCBI database, encompassing 10 patients with congenital hearing loss and utilizing six healthy wild-type individuals as controls. Through comprehensive analysis, we successfully identified three hearing loss-associated target genes (CD24, ICAM1, and SLC1A1) and predicted the corresponding TFs. Our investigation shed light on the regulatory mechanisms of these TFs within auditory tissues, offering new perspectives and directions for the exploration of innovative therapeutic strategies and personalized approaches for hearing loss management.

Methods

Acquisition of high-throughput sequencing data and clinical information

We obtained human whole transcriptome sequencing data from three independent research projects available in the National Center for Biotechnology Information (NCBI) database, namely PRJNA732455 [42], PRJNA480790 [43], and PRJNA544483 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA544483). The PRJNA732455 project, conducted in the United Kingdom, included two female cases (case1 and case2), both diagnosed with sensorineural hearing loss (m.3243 A > G) at ages 59 and 35, respectively. The control samples in this project (control1 and control2) were derived from the transcriptome data of fibroblast tissues from healthy individuals. In the PRJNA480790 project, which involved both Japan and Germany, two male cases (case3, 15 years old, and case4, 5 years old) exhibiting high-frequency deafness traits were included, along with four control samples (two males and two females), whose transcriptome data were obtained from fibroblast tissues. Additionally, the PRJNA544483 project, conducted in the United Kingdom, includes six cases diagnosed with keratitis-ichthyosis-deafness syndrome (c.148G > A), with tissues derived from keratinocytes. Since this project did not provide matched normal healthy samples, control data from the PRJNA732455 and PRJNA480790 projects were used as controls for this cohort.

Data preprocessing and quality control

Considering that the sequencing data were obtained from different projects and platforms, we implemented a consistent data filtering and quality control pipeline to minimize potential batch effects. Firstly, we performed an initial assessment of the raw data sequencing quality using default parameters in FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Subsequently, a unified sequencing quality report was generated using MultiQC [44]. Any samples with a Sequence Quality score below 30 were excluded from further analysis. Subsequently, a unified sequencing quality report was generated using MultiQC. Any samples with a Sequence Quality score below 30 were excluded from further analysis.

Following the initial quality check, we applied the fastp tool [45] with specific parameters for trimming and filtering (Supplementary Note).

Sequence alignment and gene expression quantification

For both case and control samples, we performed sequence alignment using the default parameters of the HISAT2 [46] aligner. The reads, whether single-end or paired-end, were aligned to the human reference genome GRCh38.p13(https://www.ncbi.nlm.nih.gov/grc) (obtained from the Ensembl database [47]). Subsequently, we used the samtools sort function to sort the resulting BAM files, and samtools stats was employed to obtain alignment statistics.

Next, we quantified gene expression levels using the htseq-count tool [48]. This tool takes BAM files as input and quantifies gene expression based on the annotation provided in a GTF (Gene Transfer Format) file. We used the GTF annotation file version 105, obtained from the Ensembl database [47]. During quantification, we set the parameters as follows: ‘-f bam’ for BAM file input and ‘-i gene_id’ to use the gene ID from the GTF file as the feature identifier.

Differential gene expression analysis

To investigate differential gene expression across all projects and samples, we combined the read count data into a single gene expression matrix. Genes with zero expression across all samples were removed from the analysis to focus on the expressed genes. For the differential gene expression analysis, we utilized the DESeq2 package [49] in R. Genes with read counts below 10 were considered low coverage and filtered out. To account for potential variations in library size and gene expression distribution, we applied the variance stabilizing transformations (VST) method to normalize the data. Statistical analysis was performed using DESeq2, which employs a negative binomial distribution model to identify significant differences in gene expression between groups. A p-value threshold of 0.05 was used to determine the significance level for differential expression.

We calculated the distance matrix between samples using the “dist” function in R. This distance matrix quantifies the dissimilarity between samples based on their normalized gene expression profiles. we utilized the pheatmap package [50] in R to visualize the distance matrix as a heatmap, providing an overview of the sample relationships and potential clusters.

To perform PCA, we used the “plotPCA” function, which decomposes the normalized gene expression data into principal components. This analysis allows for the identification of major sources of variation and potential confounding factors, such as covariates and batch effects. Additionally, we utilized ggplot2 [51], a widely used data visualization package in R, to further visualize the PCA results.

The three independent projects were analyzed separately for differential gene expression. To identify common differentially expressed genes across the projects, we performed a Venn analysis using the VennPainter [52].

Enrichment analysis

For the enrichment analysis, we utilized the clusterProfiler package [53] in R, which offers functionality for Gene Ontology (GO) [54] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [55] pathway enrichment analysis. GO enrichment analysis identifies overrepresented biological processes, cellular components, and molecular functions [56], while KEGG pathway analysis highlights enriched biological pathways [55]. In the enrichment analysis, we set the filtering thresholds for statistical significance as a p-value of 0.05 and a q-value of 0.2. These thresholds help identify significant enrichments while controlling for false discovery rates. The analysis was performed using appropriate statistical methods, such as hypergeometric testing, to assess the significance of gene set enrichments. To visualize the enrichment results, we employed barplot and dotplot functions in R. Barplots display the enriched GO terms or KEGG pathways along with their corresponding p-values or q-values, providing a concise summary of the enrichment analysis results. Dotplots present the enriched terms or pathways as individual data points, highlighting the magnitude and significance of the enrichment.

Transcription factor prediction

We extracted the upstream 2,000 bp and downstream 100 bp regions of the differentially expressed genes, considering these regions as putative promoter sequences. These promoter sequences were input into the PROMO database [57, 58], which provides a collection of known transcription factor binding sites. In the analysis, we specifically selected human transcription factors and human binding sites. We set the parameter for maximum matrix dissimilarity rate (a measure of similarity between predicted binding sites and known binding site matrices) to 0%, ensuring stringent prediction criteria. This parameter ensures that only highly likely transcription factor binding sites are identified, enhancing the reliability and specificity of the predictions.

Result

Study cohort

In our study, we obtained research data from three projects focusing on diseases associated with hearing loss. The cohort consisted of a total of 10 case samples and 6 control samples. The three projects described the patients as having sensorineural hearing loss, sensorineural hearing loss, and keratitis-ichthyosis-deafness syndrome, respectively (Fig. 1A and Table S1).

Fig. 1
figure 1

Study cohort. (A) Overview of the research projects, sample sizes, experimental groups, and clinical phenotypes. (B) Hierarchical clustering and gene expression heatmap of the 16 samples. The shades of blue indicate the expression levels from high to low. (C-E) PCA analysis of the three research projects (C: PRJNA732455, D: PRJNA480790, E: PRJNA544483)

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The first two projects revealed that de novo mutations played a major role in the pathogenesis of the patients. However, limited information was available regarding the transcriptional regulatory factors involved. To address this gap, we integrated these samples and obtained a study cohort comprising 2 males, 2 females, and 6 cases of unknown gender. Significant batch effects were observed between the different projects. However, the repeatability of the case and control samples within each project was satisfactory (Fig. 1B). To ensure a rigorous analysis, we opted not to blindly merge all samples for analysis. Instead, we performed separate analyses for each project using a unified analysis pipeline. This approach allowed us to account for the distinct characteristics and potential batch effects present in the individual projects.

PCA analysis was performed separately for each project to explore the variability and patterns within the data. In Project I, PC1 and PC2 explained 62.31% and 22.85% of the variability, respectively. Similarly, in Project II, PC1 and PC2 explained 49.46% and 23.41% of the variability, respectively. Since Project III lacked normal control samples, we included the normal samples from Projects I and II in the analysis. In Project III, PC1 accounted for 96.5% of the variability, indicating a high degree of consistency among the case samples in Project III (Fig. 1C-E). This high proportion of variability captured by PC1 suggests strong similarities within the case samples of Project III. The PCA analysis provided insights into the overall patterns and relationships among the samples within each project. The distinct patterns observed in the PCA plots reflected the unique characteristics and potential batch effects specific to each project. Additionally, the inclusion of normal control samples from Projects I and II in the analysis allowed for a comparative assessment of the case samples in Project III.

Identification of differentially expressed genes in hearing loss patients

In Project I, we identified 931 genes that were significantly upregulated or downregulated in the patients (two-tailed t-test, p < 0.05). In Project II, we found 151 differentially expressed genes, which was the smallest number among the three projects. Remarkably, in Project III, we discovered a substantial number of 16,648 differentially expressed genes (Fig. 2A).

Fig. 2
figure 2

Differential gene expression results. (A) Venn diagram illustrating the overlap of differentially expressed genes among the three projects. (B) Differential gene expression in neurogenic hearing loss. Genes displayed in red are upregulated, genes in blue are downregulated, and genes in gray show no significant differential expression. The 10 commonly shared genes are highlighted in the figure

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However, it is important to note that Project III lacked matched normal control samples, which may introduce potential false positives. To overcome this limitation, we sought to mitigate errors and narrow down the scope of the study by identifying the intersection of differentially expressed genes across the three projects. By extracting the common genes from the differentially expressed gene lists of the three projects, we minimized potential artifacts and focused our investigation. Ultimately, we identified 10 differentially expressed genes within this cohort of patients (Fig. 2A and Table S2).

Among the 10 differentially expressed genes, 3 were upregulated and 7 were downregulated. The most significantly differentially expressed gene was KRT16, which was markedly upregulated in the patients. CD24 was also significantly upregulated in the patients (p < 0.001). Additionally, the study found notable expression differences in ICAM1, suggesting its potential role in transcriptional regulation related to hearing loss. On the other hand, SLC1A1 was downregulated in the patients (Fig. 2B).

Functional annotation of differentially expressed genes

To further investigate the functional implications and signaling pathways associated with the differential gene expression, we integrated the differential gene expression data from the three projects and performed enrichment analysis. This analysis allowed us to identify significantly enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with the differentially expressed genes. Our analysis revealed a total of 1,412 enriched biological processes (BP), 283 enriched cellular components (CC), and 140 enriched molecular functions (MF) (Bonferroni correction, p < 0.05, q < 0.2) (Table S3). Moreover, we identified 113 significantly enriched KEGG signaling pathways (Bonferroni correction, p < 0.05, q < 0.2) (Table S4).

Among the top 10 enriched GO terms and KEGG pathways, The GO functional annotation analysis revealed that the target genes in the top 10 terms were mainly related to processes such as immune response, inflammatory pathways, cell death, neurotransmission, and dendritic development. KEGG pathway enrichment analysis revealed that target genes were functionally enriched in pathways such as ion channel function and neuronal signaling. Expressly, we observed a strong enrichment for genes involved in neurologically related disorders (Fig. 3A and B). This finding further supports the association between hearing loss and the nervous system, highlighting the intricate relationship between auditory impairment and neural processes.

Fig. 3
figure 3

Enrichment analysis results. (A) Top 10 results of gene ontology (GO) analysis, including biological processes (BP), cellular components (CC), and molecular functions (MF). The x-axis represents the gene count. (B) Top 10 results of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The x-axis represents the gene ratio. The color gradient from cool (blue) to warm (red) represents the adjusted p-values from low to high

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Transcriptional regulation mechanisms of hearing loss-related genes

Furthermore, we aimed to investigate the transcriptional regulation mechanisms underlying the expression of these hearing loss-related genes, which could have clinical therapeutic implications. To achieve this, we performed transcription factor prediction using the PROMO database for the aforementioned 10 genes and applied stringent filtering criteria. Our analysis predicted a range of 16 to 23 transcriptional regulatory factors for each gene, with Matrix Width values ranging from 4 to 11. These predicted transcriptional regulatory factors represent potential upstream regulators that may modulate the expression of the hearing loss-related genes (Fig. 4 and Table S5).

Fig. 4
figure 4

Transcription factor regulatory network of differentially expressed genes blue nodes represent differentially expressed genes, red nodes represent genes associated with neurogenic hearing loss, and orange nodes represent transcriptional regulatory factors (TFs). The arrows between nodes indicate the regulatory relationships, with the arrows pointing from the TFs to their target genes

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The hearing impairment genes ICAM1, SLC1A1, and CD24 have been extensively studied in terms of auditory function and have been shown to play key roles in various aspects of hearing impairment. Based on the established involvement of these genes in the pathogenesis of hearing loss and the supportive evidence from previous studies, we prioritized them for further study. We further investigated the transcriptional regulators shared between them to gain deeper insights into their regulatory mechanisms and potential therapeutic targets. Interestingly, we found a high degree of consistency in the transcriptional regulatory factors for these genes. Each gene was predicted to have 21 transcriptional regulatory factors, and remarkably, 13 of these factors were consistent among the three genes (Figure S1).

Discussion

Hearing loss, a global health issue, has significant impacts on individuals’ quality of life [59, 60]. Although previous studies have identified both genetic and environmental factors contributing to hearing loss, comprehensive research on the underlying molecular mechanisms remains limited. This study integrated patient samples from various regions, age groups, and genders to identify differentially expressed genes associated with hearing loss, while also investigating potential transcriptional regulatory mechanisms. Our findings highlight the key roles of genes such as ICAM1, SLC1A1, and CD24 in hearing loss and predict potential transcription factors, including GR, YY1, and ER-alpha. These results provide valuable data to support further research and clinical applications.

The cochlea is a critical organ for auditory perception, containing various cell types, including hair cells, supporting cells, cochlear neurons, and fibroblasts, all of which may be closely associated with the development of hearing loss [61]. In this study, we further explored the expression of ICAM1, SLC1A1, and CD24 in the cochlea and their potential functions.

ICAM1, a cell adhesion molecule, is prominently expressed in cochlear immune cells, particularly in hair cells and supporting cells. Its key role in hearing loss may be closely linked to its function in immune responses. Studies suggest that increased expression of ICAM1 could exacerbate cochlear inflammation by regulating the infiltration of immune cells [62]. Inflammatory responses are a major contributor to hearing loss, especially in cases of acute noise-induced damage and inflammation-related hearing loss [63, 64]. ICAM1 has been shown to play a crucial role in acute noise-induced hearing loss and other cochlear inflammatory reactions. Additionally, reduced ICAM1 expression has been associated with a slower deterioration of outer hair cell function [65], suggesting that ICAM1 not only participates in pro-inflammatory responses but may also play an important role in maintaining cochlear health. Our findings further indicate that ICAM1 could promote the progression of cochlear damage by modulating the local immune environment. These results provide new insights into the potential of ICAM1 as a therapeutic target for hearing loss.

The SLC1A1 gene encodes the glutamate transporter EAAC1, which plays a crucial role in cochlear neurons by reabsorbing glutamate from the synaptic cleft to maintain normal cochlear neural function [25]. Downregulation of SLC1A1 can lead to the accumulation of glutamate in the synaptic cleft, resulting in neurotoxicity and impairing neural conduction [66]. Studies have shown that the function of SLC1A1 in cochlear neurons is closely related to sensorineural hearing loss, and its dysfunction may affect the signal responsiveness of cochlear neurons, leading to auditory signal distortion or loss. These findings further emphasize that the role of SLC1A1 in cochlear neurons is essential for maintaining normal hearing, and its dysfunction may be a significant cause of hearing loss.

CD24 is a surface glycoprotein that is widely expressed in various immune cells and plays a crucial role in regulating immune responses and cell death. Although the specific function of CD24 in the cochlea remains unclear, its known immune regulatory roles in other tissues, along with its involvement in various immune processes, suggest that CD24 may be closely related to immune mechanisms underlying hearing loss [67, 68]. During cochlear inflammation or damage, CD24 may exacerbate local immune responses by modulating the aggregation and activation of immune cells. For instance, overexpression of CD24 could lead to the excessive activation of immune cells, thereby promoting inflammation in the cochlea, particularly in pathological conditions such as autoimmune inner ear disease (AIED). Moreover, CD24 may contribute to the persistence of inflammation and subsequent hearing loss through interactions with other immune molecules [15]. While direct evidence linking CD24 to cochlear cell death is lacking, its established role in other tissues leads us to hypothesize that CD24 may influence cochlear cell repair, regeneration, and immune regulation by modulating the local immune environment and apoptosis. Future research should focus on investigating whether CD24 is involved in cochlear cell repair and regeneration, as well as its potential mechanisms in hearing loss. Such studies will provide a clearer understanding of the feasibility of targeting CD24 for hearing loss therapies and open new avenues for future treatment strategies.

In our study, we identified ten differentially expressed genes in patients with hearing loss. Among these ten genes, ICAM1, SLC1A1, and CD24 have been previously associated with hearing loss. However, the other seven genes may also be biologically relevant and could potentially impact hearing loss. Their specific roles in auditory function, however, are not as well characterized or extensively studied as those of ICAM1, SLC1A1, and CD24. These seven genes may represent novel candidates that warrant further investigation in the context of auditory dysfunction. Therefore, we selected ICAM1, SLC1A1, and CD24 for further analysis. Interestingly, we found that there are 13 common transcriptional regulators for these three genes. This discovery highlights a potentially significant regulatory network that may play a crucial role in the pathogenesis of hearing loss. Identifying these shared transcriptional regulators provides valuable insights into the molecular mechanisms underlying auditory dysfunction and offers new avenues for future research and potential therapeutic interventions.

In this study, we identified 10 differentially expressed genes, among which ICAM1, SLC1A1, and CD24 are already known to be associated with hearing loss. However, the other seven genes may also play roles in hearing loss, although their specific functions have not been as fully characterized or extensively studied as those of ICAM1, SLC1A1, and CD24. These seven genes could represent emerging candidate genes that warrant further investigation in the context of hearing impairment. As a result, we chose to conduct a more detailed analysis of ICAM1, SLC1A1, and CD24. Interestingly, we found that these three genes share regulation by 13 common transcription factors, revealing a potentially important transcriptional regulatory network involved in the pathogenesis of hearing loss. By identifying these shared transcription factors, our study provides valuable insights into the molecular mechanisms of hearing impairment and suggests new directions for future research and potential therapeutic interventions.

While this study provides preliminary insights into the molecular mechanisms of hearing loss, several limitations remain. First, the relatively small sample size is a significant limitation of this study. Although we integrated samples from diverse regions and patient populations, the small sample size may affect the generalizability of our findings. Future studies should expand the sample size and include a larger number of control samples to improve the statistical reliability and representativeness of the results. Second, certain projects (particularly Project III) lacked matched control samples, limiting our ability to perform a comprehensive assessment of gene expression differences. The absence of control samples may introduce potential confounding factors, influencing the interpretation of the results. Thus, future research should ensure the use of matched control samples to obtain more accurate and comprehensive results. Furthermore, while we have made preliminary explorations of the mechanisms of hearing loss through differential gene expression and transcription factor prediction, these findings still require validation through more in-depth molecular and cellular experiments. The predicted transcription factors are likely to play important roles in regulating the expression of these genes, and future experimental studies should further confirm the specific functions of these genes in hearing loss.

In addition, hearing loss is often closely associated with insomnia or sleep disorders, creating a vicious cycle. Hearing loss can impact sleep quality, while sleep disturbances may exacerbate the negative effects of hearing loss. Future research should focus on the interplay between hearing loss, tinnitus, and sleep disorders, exploring integrated intervention strategies that improve the overall quality of life for patients. Customized treatment plans that address both auditory and sleep-related factors may offer a more comprehensive therapeutic approach for patients.

This study integrated samples from diverse patient groups to identify differentially expressed genes associated with hearing loss, predict potential transcription factors, and explore the functions and potential interactions of these genes within the cochlea. Despite limitations such as the small sample size and the lack of control samples, our research provides valuable insights into the molecular mechanisms of hearing loss. Future studies should further validate these findings and explore their potential applications in clinical diagnosis and treatment.

Data availability

The datasets analyzed during the current study are available in The National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/). The accession numbers are PRJNA732455, PRJNA480790 and PRJNA544483.

References

  1. Paulsen AJ, Fischer ME, Pinto A, Merten N, Dillard LK, Schubert CR, Huang GH, Klein BEK, Tweed TS, Cruickshanks KJ. Incidence of Hearing Impairment and Changes in Pure-Tone Average Across Generations. JAMA otolaryngology– head neck Surg. 2021;147(2):151–8.

    Article  Google Scholar 

  2. Cruickshanks KJ, Nondahl DM, Dalton DS, Fischer ME, Klein BE, Klein R, Nieto FJ, Schubert CR, Tweed TS. Smoking, central adiposity, and poor glycemic control increase risk of hearing impairment. J Am Geriatr Soc. 2015;63(5):918–24.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Yang C-H, Hwang C-F, Lin P-M, Chuang J-H, Hsu C-M, Lin S-F, Yang M-Y. Sleep Disturbance and Altered Expression of Circadian Clock Genes in Patients With Sudden Sensorineural Hearing Loss. Medicine 2015, 94(26).

  4. Chen C-K, Shen SC, Lee L-A, Sun M-H, Chen N-H, Chuang L-P, Li H-Y. Idiopathic Sudden Sensorineural Hearing Loss in Patients with Obstructive Sleep Apnea. Nat Sci Sleep. 2021;13:1877–85.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kayabasi S, Hizli O, Yildirim G. The association between obstructive sleep apnea and hearing loss: a cross-sectional analysis. Eur Arch Otorhinolaryngol. 2019;276(8):2215–21.

    Article  PubMed  Google Scholar 

  6. Hill CM, Bucks RS, Kennedy CR, Harrison D, Carroll A, Upton N, Hogan AM. Hearing loss mediates executive function impairment in sleep-disordered breathing. Sleep Med. 2017;34:18–23.

    Article  PubMed  Google Scholar 

  7. Kasemsuk N, Chayopasakul V, Banhiran W, Prakairungthong S, Rungmanee S, Suvarnsit K, Atipas S, Keskool P. Obstructive Sleep Apnea and Sensorineural Hearing Loss: A Systematic Review and Meta-analysis. Otolaryngology–Head Neck Surg 2022:01945998221120777.

  8. Figueroa-Ildefonso E, Bademci G, Rajabli F, Cornejo-Olivas M, Villanueva RDC, Badillo-Carrillo R, Inca-Martinez M, Neyra KM, Sineni C, Tekin M. Identification of Main Genetic Causes Responsible for Non-Syndromic Hearing Loss in a Peruvian Population. Genes (Basel) 2019, 10(8).

  9. Korver AM, Smith RJ, Van Camp G, Schleiss MR, Bitner-Glindzicz MA, Lustig LR, Usami SI, Boudewyns AN. Congenital hearing loss. Nat Rev Dis Primers. 2017;3:16094.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Mikstiene V, Jakaitiene A, Byckova J, Gradauskiene E, Preiksaitiene E, Burnyte B, Tumiene B, Matuleviciene A, Ambrozaityte L, Uktveryte I, et al. The high frequency of GJB2 gene mutation c.313_326del14 suggests its possible origin in ancestors of Lithuanian population. BMC Genet. 2016;17:45.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zhou Y, Li C, Li M, Zhao Z, Tian S, Xia H, Liu P, Han Y, Ren R, Chen J, et al. Mutation analysis of common deafness genes among 1,201 patients with non-syndromic hearing loss in Shanxi Province. Mol Genet Genomic Med. 2019;7(3):e537.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Li R, Greinwald JH Jr., Yang L, Choo DI, Wenstrup RJ, Guan MX. Molecular analysis of the mitochondrial 12S rRNA and tRNASer(UCN) genes in paediatric subjects with non-syndromic hearing loss. J Med Genet. 2004;41(8):615–20.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Du W, Cheng J, Ding H, Jiang Z, Guo Y, Yuan H. A rapid method for simultaneous multi-gene mutation screening in children with nonsyndromic hearing loss. Genomics. 2014;104(4):264–70.

    Article  PubMed  CAS  Google Scholar 

  14. Mutnal MB, Cheeran MC, Hu S, Lokensgard JR. Murine cytomegalovirus infection of neural stem cells alters neurogenesis in the developing brain. PLoS ONE. 2011;6(1):e16211.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Zhang J, Wang N, Xu A. Screening of genes associated with inflammatory responses in the endolymphatic sac reveals underlying mechanisms for autoimmune inner ear diseases. Experimental therapeutic Med. 2018;16(3):2460–70.

    Google Scholar 

  16. Schmutzhard J, Kositz CH, Glueckert R, Schmutzhard E, Schrott-Fischer A, Lackner P. Apoptosis of the fibrocytes type 1 in the spiral ligament and blood labyrinth barrier disturbance cause hearing impairment in murine cerebral malaria. Malar J. 2012;11:30.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Obasi CN, Cruickshanks KJ, Nondahl DM, Klein BE, Klein R, Nieto FJ, Shankar A, Fischer ME, Tsai MY, Chappell R. Association of Biomarkers for Inflammation, Endothelial Dysfunction and Oxidative Stress with Cognitive Impairment. The Epidemiology of Hearing Loss Study (EHLS). Oxid Antioxid Med Sci. 2012;1(3):169–73.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Chan J, Telang R, Kociszewska D, Thorne PR, Vlajkovic SM. A High-Fat Diet Induces Low-Grade Cochlear Inflammation in CD-1 Mice. Int J Mol Sci 2022, 23(9).

  19. Zhang J, Wang N, Xu A. Cmah deficiency may lead to age-related hearing loss by influencing miRNA-PPAR mediated signaling pathway. PeerJ. 2019;7:e6856.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Schmutzhard J, Kositz CH, Lackner P, Pritz C, Glueckert R, Fischer M, Schmutzhard E, Schrott-Fischer A. Murine cerebral malaria: histopathology and ICAM 1 immunohistochemistry of the inner ear. Trop Med Int Health. 2011;16(8):914–22.

    Article  PubMed  Google Scholar 

  21. Tian G, Zhang S, Yang J. Coexistence of IL-6 -572 C/G and ICAM-1 K469E Polymorphisms among Patients with Sudden Sensorineural Hearing Loss. Tohoku J Exp Med. 2018;245(1):7–12.

    Article  PubMed  CAS  Google Scholar 

  22. Shih CP, Kuo CY, Lin YY, Lin YC, Chen HK, Wang H, Chen HC, Wang CH. Inhibition of Cochlear HMGB1 Expression Attenuates Oxidative Stress and Inflammation in an Experimental Murine Model of Noise-Induced Hearing Loss. Cells 2021, 10(4).

  23. Miyao M, Firestein GS, Keithley EM. Acoustic trauma augments the cochlear immune response to antigen. Laryngoscope. 2008;118(10):1801–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Yamamoto H, Omelchenko I, Shi X, Nuttall AL. The influence of NF-kappaB signal-transduction pathways on the murine inner ear by acoustic overstimulation. J Neurosci Res. 2009;87(8):1832–40.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Chen Z, Kujawa SG, Sewell WF. Functional roles of high-affinity glutamate transporters in cochlear afferent synaptic transmission in the mouse. J Neurophysiol. 2010;103(5):2581–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Morales-Garcia JA, Gine E, Hernandez-Encinas E, Aguilar-Morante D, Sierra-Magro A, Sanz-SanCristobal M, Alonso-Gil S, Sanchez-Lanzas R, Castaño JG, Santos A, et al. CCAAT/Enhancer binding protein β silencing mitigates glial activation and neurodegeneration in a rat model of Parkinson’s disease. Sci Rep. 2017;7(1):13526.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Barnes CC, Yee KT, Vetter DE. Conditional Ablation of Glucocorticoid and Mineralocorticoid Receptors from Cochlear Supporting Cells Reveals Their Differential Roles for Hearing Sensitivity and Dynamics of Recovery from Noise-Induced Hearing Loss. Int J Mol Sci 2023, 24(4).

  28. Street VA, Li J, Robbins CA, Kallman JC. A DNA variant within the MYO7A promoter regulates YY1 transcription factor binding and gene expression serving as a potential dominant DFNA11 auditory genetic modifier. J Biol Chem. 2011;286(17):15278–86.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Hatzmann FM, Ejaz A, Wiegers GJ, Mandl M, Brucker C, Lechner S, Rauchenwald T, Zwierzina M, Baumgarten S, Wagner S et al. Quiescence, Stemness and Adipogenic Differentiation Capacity in Human DLK1(-)/CD34(+)/CD24(+) Adipose Stem/Progenitor Cells. Cells 2021, 10(2).

  30. Catron KM, Brickwood JR, Shang C, Li Y, Shannon MF, Parks TP. Cooperative binding and synergistic activation by RelA and C/EBPbeta on the intercellular adhesion molecule-1 promoter. Cell Growth Differ. 1998;9(11):949–59.

    PubMed  CAS  Google Scholar 

  31. Martinez SR, Elix CC, Ochoa PT, Sanchez-Hernandez ES, Alkashgari HR, Ortiz-Hernandez GL, Zhang L, Casiano CA. Glucocorticoid Receptor and β-Catenin Interact in Prostate Cancer Cells and Their Co-Inhibition Attenuates Tumorsphere Formation, Stemness, and Docetaxel Resistance. Int J Mol Sci 2023, 24(8).

  32. Lacal PM, Petrillo MG, Ruffini F, Muzi A, Bianchini R, Ronchetti S, Migliorati G, Riccardi C, Graziani G, Nocentini G. Glucocorticoid-induced tumor necrosis factor receptor family-related ligand triggering upregulates vascular cell adhesion molecule-1 and intercellular adhesion molecule-1 and promotes leukocyte adhesion. J Pharmacol Exp Ther. 2013;347(1):164–72.

    Article  PubMed  CAS  Google Scholar 

  33. Agarwal N, Dancik GM, Goodspeed A, Costello JC, Owens C, Duex JE, Theodorescu D. GON4L Drives Cancer Growth through a YY1-Androgen Receptor-CD24 Axis. Cancer Res. 2016;76(17):5175–85.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Islam T, Rahman MR, Aydin B, Beklen H, Arga KY, Shahjaman M. Integrative transcriptomics analysis of lung epithelial cells and identification of repurposable drug candidates for COVID-19. Eur J Pharmacol. 2020;887:173594.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Rosas S, Vargas MA, López-Bayghen E, Ortega A. Glutamate-dependent transcriptional regulation of GLAST/EAAT1: a role for YY1. J Neurochem. 2007;101(4):1134–44.

    Article  PubMed  CAS  Google Scholar 

  36. Barrett Mueller K, Lu Q, Mohammad NN, Luu V, McCurley A, Williams GH, Adler GK, Karas RH, Jaffe IZ. Estrogen receptor inhibits mineralocorticoid receptor transcriptional regulatory function. Endocrinology. 2014;155(11):4461–72.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Yusuf D, Butland SL, Swanson MI, Bolotin E, Ticoll A, Cheung WA, Cindy Zhang XY, Dickman CT, Fulton DL, Lim JS. The transcription factor encyclopedia. Genome Biol. 2012;13:1–25.

    Article  Google Scholar 

  38. Latchman DS. Transcription factors: An overview. Int J Biochem Cell Biol. 1997;29(12):1305–12.

    Article  PubMed  CAS  Google Scholar 

  39. Frydman M, Vreugde S, Nageris BI, Weiss S, Vahava O, Avraham KB. Clinical characterization of genetic hearing loss caused by a mutation in the POU4F3 transcription factor. Archives Otolaryngology–Head Neck Surg. 2000;126(5):633–7.

    Article  CAS  Google Scholar 

  40. Peters LM, Anderson DW, Griffith AJ, Grundfast KM, San Agustin TB, Madeo AC, Friedman TB, Morell RJ. Mutation of a transcription factor, TFCP2L3, causes progressive autosomal dominant hearing loss, DFNA28. Hum Mol Genet. 2002;11(23):2877–85.

    Article  PubMed  CAS  Google Scholar 

  41. Vahava O, Morell R, Lynch ED, Weiss S, Kagan ME, Ahituv N, Morrow JE, Lee MK, Skvorak AB, Morton CC. Mutation in transcription factor POU4F3 associated with inherited progressive hearing loss in humans. Science. 1998;279(5358):1950–4.

    Article  PubMed  CAS  Google Scholar 

  42. Chung C-Y, Singh K, Kotiadis VN, Valdebenito GE, Ahn JH, Topley E, Tan J, Andrews WD, Bilanges B, Pitceathly RDS, et al. Constitutive activation of the PI3K-Akt-mTORC1 pathway sustains the m.3243 A > G mtDNA mutation. Nat Commun. 2021;12(1):6409.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Noomi M, Takayuki S, Susanne M-G, Joanna S, Daniel JS, Dominik S, Ellen K, Werner S, Akio K, Markus S: De novo mutation in ELOVL1 causes ichthyosis, acanthosis nigricans, hypomyelination, spastic paraplegia, high frequency deafness and optic atrophy. Journal of Medical Genetics 2019, 56(3):164.

  44. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta. 2023;2(2):e107.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37(8):907–15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Cunningham F, Allen JE, Allen J, Alvarez-Jarreta J, Amode MR, Armean Irina M, Austine-Orimoloye O, Azov Andrey G, Barnes I, Bennett R, et al. Ensembl 2022. Nucleic Acids Res. 2022;50(D1):D988–95.

    Article  PubMed  CAS  Google Scholar 

  48. Putri GH, Anders S, Pyl PT, Pimanda JE, Zanini F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics. 2022;38(10):2943–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Kolde R. Pheatmap: pretty heatmaps. R package version. 2012;1(2):726.

    Google Scholar 

  51. Wickham H. ggplot2. Wiley interdisciplinary reviews: Comput Stat. 2011;3(2):180–5.

    Article  Google Scholar 

  52. Lin G, Chai J, Yuan S, Mai C, Cai L, Murphy RW, Zhou W, Luo J. VennPainter: A Tool for the Comparison and Identification of Candidate Genes Based on Venn Diagrams. PLoS ONE. 2016;11(4):e0154315.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov 2021, 2(3).

  54. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. PANTHER: Making genome-scale phylogenetics accessible to all. Protein Sci. 2022;31(1):8–22.

    Article  PubMed  CAS  Google Scholar 

  57. Farré Dn, Roset R, Huerta M, Adsuara JE, Roselló L, Albà MM, Messeguer X. Identification of patterns in biological sequences at the ALGGEN server: PROMO and MALGEN. Nucleic Acids Res. 2003;31(13):3651–3.

    Article  Google Scholar 

  58. Messeguer X, Escudero R, Farré D, Núñez O, Martı́nez J, Albà MM. PROMO: detection of known transcription regulatory elements using species-tailored searches. Bioinformatics. 2002;18(2):333–4.

    Article  PubMed  CAS  Google Scholar 

  59. Stevens G, Flaxman S, Brunskill E, Mascarenhas M, Mathers CD, Finucane M. Global and regional hearing impairment prevalence: an analysis of 42 studies in 29 countries. Eur J Public Health. 2013;23(1):146–52.

    Article  PubMed  Google Scholar 

  60. Hearing loss prevalence. and years lived with disability, 1990–2019: findings from the Global Burden of Disease Study 2019. Lancet. 2021;397(10278):996–1009.

    Article  Google Scholar 

  61. Driver EC, Kelley MW. Development of the cochlea. Development 2020, 147(12).

  62. Tornabene SV, Sato K, Pham L, Billings P, Keithley EM. Immune cell recruitment following acoustic trauma. Hear Res. 2006;222(1):115–24.

    Article  PubMed  CAS  Google Scholar 

  63. Lin YC, Shih CP, Lin YY, Lin HC, Kuo CY, Chen HK, Chen HC, Wang CH. C-Phycocyanin Attenuates Noise-Induced Cochlear Synaptopathy via the Inhibition of Oxidative Stress and Intercellular Adhesion Molecule-1 in the Cochlea. Int J Mol Sci 2024, 25(10).

  64. Tan WJ, Thorne PR, Vlajkovic SM. Characterisation of cochlear inflammation in mice following acute and chronic noise exposure. Histochem Cell Biol. 2016;146(2):219–30.

    Article  PubMed  CAS  Google Scholar 

  65. Syka J, Ouda L, Nachtigal P, Solichová D, Semecký V. Atorvastatin slows down the deterioration of inner ear function with age in mice. Neurosci Lett. 2007;411(2):112–6.

    Article  PubMed  CAS  Google Scholar 

  66. Bianchi MG, Bardelli D, Chiu M, Bussolati O. Changes in the expression of the glutamate transporter EAAT3/EAAC1 in health and disease. Cell Mol Life Sci. 2014;71(11):2001–15.

    Article  PubMed  CAS  Google Scholar 

  67. Suzuki T, Kiyokawa N, Taguchi T, Sekino T, Katagiri YU, Fujimoto J. CD24 induces apoptosis in human B cells via the glycolipid-enriched membrane domains/rafts-mediated signaling system. J Immunol (Baltimore Md: 1950). 2001;166(9):5567–77.

    Article  CAS  Google Scholar 

  68. Barkal AA, Brewer RE, Markovic M, Kowarsky M, Barkal SA, Zaro BW, Krishnan V, Hatakeyama J, Dorigo O, Barkal LJ, et al. CD24 signalling through macrophage Siglec-10 is a target for cancer immunotherapy. Nature. 2019;572(7769):392–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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  1. Department of Otolaryngology, First Affiliated Hospital of Kunming Medical University, 295 Xichang Road, WuHua District, Kunming City, Yunnan Province, China

    Fengfeng Jia, Fang Wang, Song Li, Yunhua Cui & Yongmei Yu

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Contributions

Y.Y. designed and led the project. F.W., S.L. and Y.C. collected and managed the sequencing data. F.J. performed data analysis and interpreted the results. F.J., F.W., S.L. and Y.C. developed the original manuscript. Y.Y. reviewed and edited the manuscript. All authors contributed and approved the final version of the manuscript.

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Supplementary Note

Supplementary Table S1 to S7

12920_2024_2067_MOESM3_ESM.png

Supplementary Figure S1: Venn Diagram of Transcriptional Regulatory Factors for Three Neurogenic Hearing Loss-Related Genes. The numbers in the figure represent the number of transcriptional regulators that regulate the genes

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Jia, F., Wang, F., Li, S. et al. Transcriptome sequencing reveals regulatory genes associated with neurogenic hearing loss. BMC Med Genomics 18, 11 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12920-024-02067-3

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BMC Medical Genomics

ISSN: 1755-8794

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