Objective: To explore the feasibility of constructing an objective tinnitus subtype model based on peripheral blood differentially expressed genes (DEGs) using a combination of Weighted Gene Co-expression Network Analysis (WGCNA) and Random Forest algorithm (RF). Methods: From October 2019 to June 2020, peripheral blood DEGs were obtained from 37 patients (from the Third Affiliated Hospital of Sun Yat-sen University)with chronic subjective high-frequency tinnitus (21 unbothersome type, 16 bothersome type) and 20 healthy volunteers through high-throughput sequencing. WGCNA was used to construct gene modules with different expression patterns and analyze their relationships with tinnitus characteristics. Subsequently, RF was employed to build subtype models, which were evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, and F1-score. Results: A total of 12 351 intergroup DEGs were divided into 9 gene modules. Among them, MEblue, MEgreen, and MEbrown showed significant negative correlations with the healthy volunteer group, while MEpink showed a significant positive correlation with the tinnitus distress group. The "Tinnitus vs. Normal" and "Compensatory vs. Decompensatory" subtype models, based on MEblue and MEpink respectively, both had AUCs greater than 0.80, accuracies above 90%, and F1-scores above 0.90, indicating good performance. Conclusions: Peripheral blood DEGs are potential biological indicators for objective classification of subjective tinnitus. The combined application of WGCNA and the Random Forest algorithm should be a viable approach to constructing an objective tinnitus subtype model. However, further exploration and refinement are needed to validate the model's generalizability, cross-dataset performance, and algorithm optimization.
目的： 探讨联合加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis，WGCNA）及随机森林算法构建基于外周血差异表达基因（differentially expressed genes，DEGs）的主观性耳鸣客观分型模型的可行性。 方法： 2019年10月至2020年6月期间，对中山大学附属第三医院37例慢性主观性高频耳鸣患者（代偿型21例，失代偿型16例）及20名健康志愿者通过高通量测序获得外周血DEGs。采用WGCNA构建不同表达模式的基因模块，并分析各自与耳鸣特征之间的关系。随后采用随机森林算法构建分型模型，并通过受试者工作特征曲线下面积（area under the curve，AUC）、准确度和F1-score对分型性能进行评价。 结果： 12 351个组间DEGs被分成9个基因模块，其中MEblue、MEgreen和MEbrown与健康志愿者组呈负相关，MEpink与耳鸣困扰组呈正相关。基于MEblue及MEpink分别构建“耳鸣-正常”及“代偿-失代偿”分型模型，AUC均>0.80，准确度均>90%，F1-score均>0.90，分型性能良好。 结论： 外周血DEGs是慢性主观性耳鸣客观分型的潜在生物学指标，而WGCNA和随机森林算法的联合应用是构建慢性主观性耳鸣客观分型模型的可行方案。但模型的外延、跨数据集性能的验证，以及模型算法的优化仍需进一步探索并完善。.