Zhang Ge, Peng Zhen, Yan Chaokun, Wang Jianlin, Luo Junwei, Luo Huimin
School of Computer and Information Engineering, Henan University, Kaifeng, China.
College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China.
Front Genet. 2022 Mar 21;13:855629. doi: 10.3389/fgene.2022.855629. eCollection 2022.
Cancer is one of the leading causes of death worldwide, which brings an urgent need for its effective treatment. However, cancer is highly heterogeneous, meaning that one cancer can be divided into several subtypes with distinct pathogenesis and outcomes. This is considered as the main problem which limits the precision treatment of cancer. Thus, cancer subtypes identification is of great importance for cancer diagnosis and treatment. In this work, we propose a deep learning method which is based on multi-omics and attention mechanism to effectively identify cancer subtypes. We first used similarity network fusion to integrate multi-omics data to construct a similarity graph. Then, the similarity graph and the feature matrix of the patient are input into a graph autoencoder composed of a graph attention network and omics-level attention mechanism to learn embedding representation. The K-means clustering method is applied to the embedding representation to identify cancer subtypes. The experiment on eight TCGA datasets confirmed that our proposed method performs better for cancer subtypes identification when compared with the other state-of-the-art methods. The source codes of our method are available at https://github.com/kataomoi7/multiGATAE.
癌症是全球主要死因之一，这使得对其进行有效治疗的需求迫在眉睫。然而，癌症具有高度异质性，这意味着一种癌症可分为几种具有不同发病机制和结果的亚型。这被认为是限制癌症精准治疗的主要问题。因此，癌症亚型识别对于癌症诊断和治疗至关重要。在这项工作中，我们提出了一种基于多组学和注意力机制的深度学习方法，以有效识别癌症亚型。我们首先使用相似性网络融合来整合多组学数据以构建相似性图。然后，将相似性图和患者的特征矩阵输入到由图注意力网络和组学级注意力机制组成的图自动编码器中，以学习嵌入表示。将K均值聚类方法应用于嵌入表示以识别癌症亚型。在八个TCGA数据集上的实验证实，与其他现有最先进方法相比，我们提出的方法在癌症亚型识别方面表现更好。我们方法的源代码可在https://github.com/kataomoi7/multiGATAE上获取。
