Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier




Background: MicroRNAs (miRNAs) are small, non-coding RNA that regulate gene expression through post-transcriptional silencing. Differential expression observed in miRNAs, combined with advancements in deep learning (DL), have the potential to improve cancer classification by modelling non-linear miRNA-phenotype associations. We propose a novel miRNA-based deep cancer classifier (DCC) incorporating genomic and hierarchical tissue annotation, capable of accurately predicting the presence of cancer in wide range of human tissues. Methods: miRNA expression profiles were analyzed for 1746 neoplastic and 3871 normal samples, across 26 types of cancer involving six organ sub-structures and 68 cell types. miRNAs were ranked and filtered using a specificity score representing their information content in relation to neoplasticity, incorporating 3 levels of hierarchical biological annotation. A DL architecture composed of stacked autoencoders (AE) and a multi-layer perceptron (MLP) was trained to predict neoplasticity using 497 abundant and informative miRNAs. Additional DCCs were trained using expression of miRNA cistrons and sequence families, and combined as a diagnostic ensemble. Important miRNAs were identified using backpropagation, and analyzed in Cytoscape using iCTNet and BiNGO. Results: Nested four-fold cross-validation was used to assess the performance of the DL model. The model achieved an accuracy, AUC/ROC, sensitivity, and specificity of 94.73%, 98.6%, 95.1%, and 94.3%, respectively. Conclusion: Deep autoencoder networks are a powerful tool for modelling complex miRNA-phenotype associations in cancer. The proposed DCC improves classification accuracy by learning from the biological context of both samples and miRNAs, using anatomical and genomic annotation. Analyzing the deep structure of DCCs with backpropagation can also facilitate biological discovery, by performing gene ontology searches on the most highly significant features.
机译:背景:MicroRNA(miRNA)是小,非编码RNA,其通过转录后沉默调节基因表达。在miRNA中观察到的差异表达,结合深度学习(DL)的进步,通过建模非线性miRNA-表型关联来改善癌症分类。我们提出了一种新的基于miRNA的深癌症分类器(DCC),其包含基因组和分层组织注释,能够准确地预测癌症的存在范围广泛的人组织。方法:分析miRNA表达谱对涉及六种器官亚结构和68个细胞类型的26种癌症,分析了MiRNA表达谱。使用表示其与肿瘤族的信息含量的特异性分数进行排序和过滤,包括3级层次的生物注释。培训由堆叠的AutoEncoders(AE)和多层Perceptron(MLP)组成的DL架构以预测使用497丰富和信息性MiRNA预测肿瘤族性。使用MiRNA传感和序列系列的表达培训额外的DCC,并将其作为诊断集合组合。使用Extpropagation识别重要的miRNA,并使用ICTNET和宾果分析在Cytoscape中。结果:嵌套四折交叉验证用于评估DL模型的性能。该模型分别实现了精度,AUC / ROC,敏感性,94.73%,98.6%,95.1%和94.3%的特异性。结论:深度自动化器网络是一种强大的工具,用于在癌症中建模复杂的miRNA-表型关联。所提出的DCC通过使用解剖学和基因组注释来学习样品和miRNA的生物学背景来提高分类准确性。通过对最重要的特征进行基因本体研究,通过对最高显着特征进行基因本体研究,可以促进生物发现的DCCS的深度结构。



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