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Network‐based integration method for potential breast cancer gene identification
Author(s) -
Zhang Yue,
Li Wan,
Zhang Yihua,
Hu Erqiang,
Rong Zherou,
Ge Luanfeng,
Deng Gui,
He Yuehan,
Lv Junjie,
Chen Lina,
He Weiming
Publication year - 2020
Publication title -
journal of cellular physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.529
H-Index - 174
eISSN - 1097-4652
pISSN - 0021-9541
DOI - 10.1002/jcp.29450
Subject(s) - breast cancer , gene , prioritization , robustness (evolution) , identification (biology) , computational biology , biology , gene regulatory network , disease , cancer , bioinformatics , genetics , medicine , gene expression , pathology , botany , management science , economics
Breast cancer is the most common female death‐causing cancer worldwide. A network‐based integration method was proposed to identify potential breast cancer genes. First, genes were prioritized using a gene prioritization algorithm by the strategy of disease risks transferred between genes in a network with weighted vertexes and edges. Our prioritization algorithm was effectives and robust for top‐ranked seed gene number and higher area under the curve values compared to ToppGene and ToppNet. Then, 20 potential breast cancer genes were identified as common genes of the top 50 candidate genes for their robustness in multiple prioritizations. These genes could accurately classify tumor and normal samples of all and paired sample sets and three independent datasets. Of potential breast cancer genes, 18 were verified by literature and 2 were novel genes that need further study. This study would contribute to the understanding of the genetic architecture for the diagnosis and treatment of breast cancer.