Network-based multi-task learning models for biomarker selection and cancer outcome prediction
Network-based multi-task learning models for biomarker selection and cancer outcome prediction
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DOI：
10.1093/bioinformatics/btz809
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发表时间：
2020-03-15
期刊：
影响因子：
5.8
通讯作者：
Zhang, Wei
中科院分区：
文献类型：
--
作者：
Wang, Zhibo;He, Zhezhi;Zhang, Wei
动机：利用mRNA测序或基于阵列的数据检测癌症基因表达和转录组变化对于理解癌症发生和癌症进展期间细胞事件的分子机制非常重要。在以前的研究中，在一种癌症类型的患者中检测到差异表达的基因。这些研究忽略了mRNA表达变化在驱动肿瘤发生机制中的作用，这些机制在不同肿瘤类型中是普遍的或特异性的。为了解决这个问题，我们引入了两个基于网络的多任务学习框架，NetML和NetSML，以发现不同癌症类型之间共享的共同差异表达基因以及每种癌症类型特有的差异表达基因。所提出的框架考虑了共同的潜在基因共表达模块和基因样本biclusters潜在的多个癌症datasets学习知识跨越不同的肿瘤type.Results：模拟和真实的癌症高通量数据集上的大规模实验验证，所提出的基于网络的多任务学习框架进行更好的样本分类相比，模型没有跨不同的癌症类型的知识共享。通过癌症基因组图谱卵巢癌、乳腺癌和前列腺癌数据集上的多任务学习框架检测到的常见和癌症特异性分子特征与已知标记基因相关，并在癌症相关的京都基因和基因组途径百科全书和基因本体术语中富集。
Motivation: Detecting cancer gene expression and transcriptome changes with mRNA-sequencing or array-based data are important for understanding the molecular mechanisms underlying carcinogenesis and cellular events during cancer progression. In previous studies, the differentially expressed genes were detected across patients in one cancer type. These studies ignored the role of mRNA expression changes in driving tumorigenic mechanisms that are either universal or specific in different tumor types. To address the problem, we introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type. The proposed frameworks consider the common latent gene co-expression modules and gene-sample biclusters underlying the multiple cancer datasets to learn the knowledge crossing different tumor types.Results: Large-scale experiments on simulations and real cancer high-throughput datasets validate that the proposed network-based multi-task learning frameworks perform better sample classification compared with the models without the knowledge sharing across different cancer types. The common and cancer-specific molecular signatures detected by multi-task learning frameworks on The Cancer Genome Atlas ovarian, breast and prostate cancer datasets are correlated with the known marker genes and enriched in cancer-relevant Kyoto Encyclopedia of Genes and Genome pathways and gene ontology terms.