Interactive gene identification for cancer subtyping based on multi-omics clustering
Interactive gene identification for cancer subtyping based on multi-omics clustering
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DOI：
10.1016/j.ymeth.2023.02.005
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发表时间：
2023-02-20
期刊：
影响因子：
4.8
通讯作者：
Sakurai，Tetsuya
中科院分区：
文献类型：
--
作者：
Ye，Xiucai;Shi，Tianyi;Sakurai，Tetsuya
多组学数据库的最新进展为探索跨层次生物学水平的复杂癌症系统提供了机会。通过整合多组学，已经提出了一些方法来鉴定在疾病发展中起重要作用的基因。然而，现有的方法都是将相关基因分开识别，忽略了与多基因疾病相关的基因间的相互作用。在这项研究中，我们开发了一个学习框架，以确定基于多组学数据，包括基因表达的相互作用的基因。首先，我们整合不同的组学的基础上，他们的相似性和光谱聚类癌症亚型识别。然后，为每个癌症亚型构建基因共表达网络。最后，我们利用模块度矩阵中特征向量的L1性质，通过学习稠密子图来检测共表达网络中的交互基因。我们将所提出的学习框架应用于多组学癌症数据集，以识别每个癌症亚型的相互作用基因。利用大卫和KEGG工具对检测到的基因进行系统的基因本体富集分析。分析结果表明，检测到的基因与肿瘤发生发展有关，不同肿瘤亚型中的基因与不同的生物学过程和途径有关，有望为了解肿瘤异质性和提高患者生存率提供重要参考。
Recent advances in multi-omics databases offer the opportunity to explore complex systems of cancers across hierarchical biological levels. Some methods have been proposed to identify the genes that play a vital role in disease development by integrating multi-omics. However, the existing methods identify the related genes separately, neglecting the gene interactions that are related to the multigenic disease. In this study, we develop a learning framework to identify the interactive genes based on multi-omics data including gene expression. Firstly, we integrate different omics based on their similarities and apply spectral clustering for cancer subtype identification. Then, a gene co-expression network is construct for each cancer subtype. Finally, we detect the interactive genes in the co-expression network by learning the dense subgraphs based on theL1prosperities of eigenvectors in the modularity matrix. We apply the proposed learning framework on a multi-omics cancer dataset to identify the interactive genes for each cancer subtype. The detected genes are examined by DAVID and KEGG tools for systematic gene ontology enrichment analysis. The analysis results show that the detected genes have relationships to cancer development and the genes in different cancer subtypes are related to different biological processes and pathways, which are expected to yield important references for understanding tumor heterogeneity and improving patient survival.