Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Eur Arch Otorhinolaryngol. 2022 Sep;279(9):4451-4460. doi: 10.1007/s00405-022-07404-9. Epub 2022 Apr 27.
Predicting the prognosis in laryngeal squamous cell carcinoma (LSCC) patients will improve clinical decision-making. Here, we aimed to identify a qualitative signature based on the within-sample relative expression orderings (REOs) of microRNA (miRNA) pairs to predict the overall survival (OS) of LSCC patients.
First, we constructed non-repeating miRNA pairs based on differentially expressed miRNAs (DEmiRNAs) between LSCC and normal tissues. Then, we applied a bootstrap-based feature selection method to identify a robust miRNA-pair signature. The bootstrap-based feature selection improved the stability of feature selection by an ensemble based on the data perturbation. Furthermore, a series of bioinformatics analyses were carried out to explore the potential mechanisms of the signature and potential drug targets for LSCC.
Based on the REOs of miRNA pairs, we identified a qualitative signature that consisted of 12 miRNA pairs. The constructed signature has good performance in predicting the OS of LSCC patients. It is robust against batch effects and more suitable for individual clinical applications. Furthermore, we identified several hub genes that may be potential drug targets for LSCC.
Overall, our findings provided a promising signature for predicting the OS of LSCC patients.
预测喉鳞状细胞癌（LSCC）患者的预后将改善临床决策。在这里，我们旨在基于 miRNA 对（miRNA）之间的样本内相对表达顺序（REO）确定一个定性特征，以预测 LSCC 患者的总生存期（OS）。
首先，我们基于 LSCC 和正常组织之间差异表达的 miRNA（DEmiRNA）构建非重复 miRNA 对。然后，我们应用基于引导的特征选择方法来识别稳健的 miRNA 对特征。基于引导的特征选择通过基于数据扰动的集成提高了特征选择的稳定性。此外，还进行了一系列生物信息学分析，以探索特征的潜在机制和 LSCC 的潜在药物靶点。
基于 miRNA 对的 REO，我们确定了一个由 12 个 miRNA 对组成的定性特征。所构建的特征在预测 LSCC 患者的 OS 方面具有良好的性能。它对批次效应具有鲁棒性，更适合个体临床应用。此外，我们还确定了几个可能是 LSCC 潜在药物靶点的枢纽基因。
总体而言，我们的研究结果为预测 LSCC 患者的 OS 提供了一个有前途的特征。
