Department of Oral Medicine, Guizhou Provincial People's Hospital, Guizhou, China.
Department of Orthodontics, Guizhou Provincial People's Hospital, Guizhou, China.
Biomed Res Int. 2020 Dec 9;2020:1686480. doi: 10.1155/2020/1686480. eCollection 2020.
Tumor mutation burden (TMB) is considered to be an independent genetic biomarker that can predict the tumor patient's response to immune checkpoint inhibitors (ICIs). Meanwhile, microRNA (miRNA) plays a key role in regulating the anticancer immune response. However, the correlation between miRNA expression patterns and TMB is not elucidated in HNSCC. In the HNSCC cohort of the TCGA dataset, miRNAs that were differentially expressed in high TMB and low TMB samples were screened. The least absolute contraction and selection operator (LASSO) method is used to construct a miRNA-based feature classifier to predict the TMB level in the training set. The test set is used to verify the classifier. The correlation between the miRNA-based classifier index and the expression of three immune checkpoints (PD1, PDL1, and CTLA4) was explored. We further perform functional enrichment analysis on the miRNA contained in the miRNA-based feature classifier. Twenty-five differentially expressed miRNAs are used to build miRNA-based feature classifiers to predict TMB levels. The accuracy of the 25-miRNA-based signature classifier is 0.822 in the training set, 0.702 in the test set, and 0.774 in the total set. The miRNA-based feature classifier index showed a low correlation with PD1 and PDL1, but no correlation with CTLA4. The enrichment analysis of these 25 miRNAs shows that they are involved in many immune-related biological processes and cancer-related pathways. The miRNA expression patterns are related to tumor mutation burden, and miRNA-based feature classifiers can be used as biomarkers to predict TMB levels in HNSCC.
肿瘤突变负担（TMB）被认为是一种独立的遗传生物标志物，可预测肿瘤患者对免疫检查点抑制剂（ICI）的反应。同时，microRNA（miRNA）在调节抗肿瘤免疫反应中起着关键作用。然而，miRNA 表达模式与 TMB 之间的相关性在 HNSCC 中尚未阐明。在 TCGA 数据集的 HNSCC 队列中，筛选了在高 TMB 和低 TMB 样本中差异表达的 miRNA。使用最小绝对收缩和选择算子（LASSO）方法构建基于 miRNA 的特征分类器，以预测训练集中的 TMB 水平。使用测试集验证分类器。探索基于 miRNA 的分类器指数与三种免疫检查点（PD1、PDL1 和 CTLA4）表达之间的相关性。我们进一步对基于 miRNA 的特征分类器中包含的 miRNA 进行功能富集分析。使用 25 个差异表达的 miRNA 构建基于 miRNA 的特征分类器，以预测 TMB 水平。在训练集中，25-miRNA 基于特征的分类器的准确性为 0.822，在测试集中为 0.702，在总集中为 0.774。基于 miRNA 的特征分类器指数与 PD1 和 PDL1 相关性较低，但与 CTLA4 无相关性。这些 25 个 miRNA 的富集分析表明，它们参与了许多免疫相关的生物学过程和癌症相关的途径。miRNA 表达模式与肿瘤突变负担有关，基于 miRNA 的特征分类器可用作预测 HNSCC 中 TMB 水平的生物标志物。
