背景噪声和药物引起的听力损失(HL)日益严重,但是缺乏基于转录组学和蛋白质组学的整合和分析。一方面,本研究旨在整合现有的关于噪声和庆大霉素诱导HL的公共转录组数据。另一方面,本研究旨在建立庆大霉素和噪声诱导的豚鼠HL模型,然后进行转录组学和蛋白质组学分析。通过对以上数据的综合分析,我们的目标是筛选,预测,初步验证了与HL密切相关的生物标志物。材料和方法我们筛选了基因表达综合数据库,以获得由噪声和庆大霉素引起的HL的转录组数据表达谱,然后构建豚鼠HL模型并进行转录组学和蛋白质组学分析。对公开和自我测序的数据进行差异表达和富集分析,并获得了常见的差异表达基因(DEGs)和信号通路。最后,我们使用蛋白质组数据筛选常见差异蛋白,并验证HL的常见差异表达基因.结果通过将公共数据集与自建模型数据集进行整合,我们最终获得了HL的两个核心生物标志物,分别为RSAD2和基质金属蛋白酶-3(MMP3)。它们的主要功能是调节内耳感觉器官的发育,主要参与丝裂原活化蛋白激酶和磷酸肌醇-3激酶/蛋白激酶B信号通路。最后,通过整合自我构建模型的蛋白质组数据,我们还发现了MMP3蛋白的差异表达。这也初步和部分地验证了上述核心生物标志物。结论和意义在本研究中,整合公共数据库和自建模型的转录组数据,我们通过差异分析筛选出HL的两个核心基因和各种信号通路,富集分析,等分析方法。然后,通过蛋白质组学分析对MMP3进行了初步验证。本研究为进一步验证HL的关键生物标志物指明了方向,这对于揭示HL的核心致病机制具有重要意义。
Background Noise and drug-induced hearing loss (HL) is becoming more and more serious, but the integration and analysis based on transcriptomics and proteomics are lacking. On the one hand, this
study aims to integrate existing public transcriptomic data on noise and
gentamicin-induced HL. On the other hand, the
study aims to establish the
gentamicin and noise-induced HL model of guinea pigs, then to perform the transcriptomic and proteomic analyses. Through comprehensive analysis of the above data, we aim to screen, predict, and preliminarily verify biomarkers closely related to HL. Material and Methods We screened the Gene Expression Omnibus database to obtain transcriptome data expression profiles of HL caused by noise and
gentamicin, then constructed the guinea pig HL model and perform the transcriptomic and proteomic analyses. Differential expression and enrichment analysis were performed on public and self-sequenced data, and common differentially expressed genes (DEGs) and signaling pathways were obtained. Finally, we used proteomic data to screen for common differential proteins and validate common differential expression genes for HL. Results By integrating the public data set with self-constructed model data set, we eventually obtained two core biomarkers of HL, which were RSAD2 and matrix metalloproteinase-3 (MMP3). Their main function is to regulate the development of sense organ in the inner ear and they are mainly involved in mitogen-activated protein kinase and phosphoinositol-3 kinase/protein kinase B signaling pathways. Finally, by integrating the proteomic data of the self-constructed model, we also found differential expression of MMP3 protein. This also preliminarily and partially verified the above-mentioned core biomarkers. Conclusion and Significance In this
study, public database and transcriptomic data of self-constructed model were integrated, and we screened out two core genes and various signal pathways of HL through differential analysis, enrichment analysis, and other analysis methods. Then, we preliminarily validated the MMP3 by proteomic analysis of self-constructed model. This
study pointed out the direction for further laboratory verification of key biomarkers of HL, which is of great significance for revealing the core pathogenic mechanism of HL.