Cancer Biomarker Discovery from Gene Co-expression Networks Using Community Detection Methods
Cancer Biomarker Discovery from Gene Co-expression Networks Using Community Detection Methods
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DOI：
10.1109/bibm47256.2019.8982960
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发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
R. Tanvir;A. Mondal
中科院分区：
文献类型：
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作者：
R. Tanvir;A. Mondal
寻找癌症的网络生物标记物，并分析参与这些生物标记物的癌症驱动基因，对于理解癌症的动态是必不可少的。共表达网络中的基因簇通常被称为功能单位。这项工作是基于一种假设，即癌症患者基因共表达网络中的致密簇或群落可能代表与癌症发生和进展有关的功能单位。本研究利用来自癌症基因组图谱(TCGA)的三种癌症--乳腺浸润性癌(BRCA)、结直肠腺癌(CoAD)和多形性胶质母细胞瘤(GBM)的RNA-seq基因表达数据，利用Pearson相关性构建基因共表达网络。在这些网络上应用了六种著名的社区检测算法来识别具有五个或更多基因的社区。为了进一步挖掘在其他癌症中保守的群落，进行了排列测试，从而将它们称为保守群落。然后使用保守的社区基因作为预后协变量，对三种癌症的临床资料进行生存分析。能够区分癌症患者高风险和低风险群体的社区被认为是癌症生物标记物。在本研究中，发现了16个这样的网络生物标记物。
Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in co-expression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene co-expression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNA-seq gene expression data of three cancers - Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM) - from The Cancer Genome Atlas (TCGA) are used to construct gene co-expression networks using Pearson Correlation. Six well-known community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic co-variates. The communities that could distinguish the cancer patients between high- and low-risk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered.