Identification of key modules and genes associated with breast cancer prognosis using WGCNA and ceRNA network analysis.
Identification of key modules and genes associated with breast cancer prognosis using WGCNA and ceRNA network analysis.
复制标题
使用 WGCNA 和 ceRNA 网络分析鉴定与乳腺癌预后相关的关键模块和基因
DOI：
10.18632/aging.202285
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发表时间：
2020-12-09
期刊：
影响因子：
--
通讯作者：
Yu H
中科院分区：
文献类型：
--
作者：
Yin X;Wang P;Yang T;Li G;Teng X;Huang W;Yu H
乳腺癌是全世界妇女癌症相关死亡率的主要原因之一，已成为一个主要的公共卫生问题。虽然乳腺癌的确切原因尚不清楚，但许多对乳腺癌敏感的基因已被先进技术检测到。我们的研究从癌症基因组图谱数据库中确定了3301个肿瘤和正常样本之间差异表达的lncRNA和mRNA。结合基因表达分析和临床特征以及加权基因共表达网络分析，发现共表达Brown模块是乳腺癌预后的关键。Brown模块中共有453个基因用于功能富集、蛋白质-蛋白质相互作用分析、lncRNA-miRNA-mRNA ceRNA网络和lncRNA-RNA结合蛋白-mRNA网络构建。根据蛋白质-蛋白质相互作用、lncRNA-miRNA-mRNA和lncRNA-RNA结合蛋白-mRNA网络，GRM 4、SSTR 2、PARD 6 B、PRR 15、COX 6C和lncRNA DSCAM-AS 1是中枢基因。根据多个数据库，发现它们的高表达与乳腺癌的发展相关。总之，本研究提供了乳腺癌共表达基因模块的框架，并确定了几个重要的生物标志物在乳腺癌的发展和预后。
Breast cancer is one of the leading causes of cancer-associated mortality in women worldwide and has become a major public health problem. Although the definitive cause of breast cancer is not known, many genes sensitive to breast cancer have been detected using advanced technologies. Our study identified 3301 differentially expressed lncRNAs and mRNAs between tumor and normal samples from The Cancer Genome Atlas database. Based on the gene expression analysis and clinical traits as well as weighted gene co-expression network analysis, the co-expression Brown module was found to be key for breast cancer prognosis. A total of 453 genes in the Brown module were used for functional enrichment, protein-protein interaction analysis, lncRNA-miRNA-mRNA ceRNA network, and lncRNA-RNA binding protein-mRNA network construction. GRM4, SSTR2, PARD6B, PRR15, COX6C, and lncRNA DSCAM-AS1 were the hub genes according to protein-protein interaction, lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA network. Their high expression was found to be correlated with breast cancer development, according to multiple databases. In conclusion, this study provides a framework of the co-expression gene modules of breast cancer and identifies several important biomarkers in breast cancer development and prognosis.
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影响因子：
37.3
作者：
Gibb EA;Brown CJ;Lam WL
通讯作者：
Lam WL
谷氨酸代谢受体在肾细胞癌风险和生存中的临床意义。
影响因子：
4
作者：
Huang CY;Hsueh YM;Chen LC;Cheng WC;Yu CC;Chen WJ;Lu TL;Lan KJ;Lee CH;Huang SP;Bao BY
通讯作者：
Bao BY
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
3.6
作者：
Liu, Rong;Guo, Cheng-Xian;Zhou, Hong-Hao
通讯作者：
Zhou, Hong-Hao