College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China.
Translational Medicine Research Institute, Zhejiang University, Hangzhou, China.
BMC Cancer. 2019 Feb 7;19(1):127. doi: 10.1186/s12885-019-5340-y.
The miRNA isoforms (isomiRs) have been suggested to regulate the same pathways as the canonical miRNA and play an important biological role in miRNA-mediated gene regulation. Recently, a study has demonstrated that the presence or absence of all isomiRs could efficiently discriminate amongst 32 TCGA cancer types. Besides, an effective reduction of distinguishing isomiR features for multiclass tumor discrimination must have a major impact on our understanding of the disease and treatment of cancer.
In this study, we have constructed a combination of the genetic algorithms (GA) with Random Forest (RF) algorithms to detect reliable sets of cancer-associated 5'isomiRs from TCGA isomiR expression data for multiclass tumor classification.
We obtained 100 sets of the optimal predictive features, each of which comprised of 50-5'isomiRs that could effectively classify with an average sensitivity of 92% samples from 32 different tumor types. We calculated the frequency with which a 5'isomiR found in these sets as measuring its importance for tumor classification. Many highly frequent 5'isomiRs with different 5' loci from canonical miRNAs were detected in these sets, supporting that the isomiRs play a significant role in the multiclass tumor classification. The further functional enrichment analysis showed that the target genes of the 10 most frequently appearing 5'isomiRs were involved in the activity of transcription activator and protein kinase and cell-cell adhesion.
The findings of the present study indicated that the 5'isomiRs might be employed for multiclass tumor classification and the suggested that GA/RF model could perform effective tumor classification by a series of largely independent optimal predictor 5' isomiR sets.
miRNA 同型异构体（isomiRs）被认为可以调节与经典 miRNA 相同的通路，并在 miRNA 介导的基因调控中发挥重要的生物学作用。最近的一项研究表明，所有 isomiRs 的存在或缺失能够有效地区分 32 种 TCGA 癌症类型。此外，有效地减少区分多类肿瘤的 isomiR 特征对于我们理解疾病和癌症治疗具有重大影响。
在这项研究中，我们构建了遗传算法（GA）和随机森林（RF）算法的组合，从 TCGA isomiR 表达数据中检测用于多类肿瘤分类的可靠的癌症相关 5' isomiR 集合。
我们获得了 100 组最佳预测特征，每组由 50-5'isomiR 组成，可以有效地对 32 种不同肿瘤类型的 92%的样本进行分类。我们计算了这些集合中出现的 5'isomiR 的频率，以衡量其对肿瘤分类的重要性。在这些集合中检测到了许多具有不同 5' 位置的高频 5'isomiR，这些高频 5'isomiR 来自于经典 miRNA，支持了 isomiRs 在多类肿瘤分类中的重要作用。进一步的功能富集分析表明，10 个最常出现的 5'isomiR 的靶基因参与了转录激活因子和蛋白激酶以及细胞-细胞黏附的活性。
本研究的结果表明，5'isomiRs 可用于多类肿瘤分类，并且建议 GA/RF 模型可以通过一系列独立的最优预测 5' isomiR 集合来有效地进行肿瘤分类。
