通过加权基因共表达网络分析构建共表达模块并识别癌症的潜在预后标志物。

Cancer biotherapy & radiopharmaceuticals Pub Date : 2022-10-01 Epub Date: 2020-10-14 DOI:10.1089/cbr.2020.3821
Yanyan Wang, Kang Cui, Mingzhi Zhu, Yuanting Gu
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引用次数: 1

摘要

背景:乳腺癌(BC)是女性发病率最高的恶性肿瘤,每年在世界范围内扰乱数百万人的生活。然而,其分子机制尚不清楚。材料与方法:采用加权基因共表达网络分析(WGCNA)对TCGA数据库中BC患者的rna测序和临床资料进行分析。此外,共表达模块被用来检测它们与BC临床特征的相关性。接下来,将最显著共表达模块的节点用于基因本体(GO)、京都基因与基因组百科全书(KEGG)途径、mRNA-lncRNA共表达网络和生存分析。结果:共鉴定出2056个差异表达mrna (DEmRNAs)和297个差异表达lncRNAs (DElncRNAs)并进行WGCNA分析,生成12个共表达模块。前5个显著模块(绿松石色、绿色、红色、棕色和蓝色模块)与BC的一个或多个临床特征相关。特别是,绿松石和绿色模块被选择用于进一步分析。接下来,通过绿松石和绿色模块的lncRNA-mRNA共表达分析,鉴定出12个demrna和2个delncrna为hub节点。这些网络中的lncrna相关mrna通常与几种癌症相关途径相关。此外,这些网络还揭示了RP11-389C8.2和TGFBR2在绿松石模块中的核心作用,MYLK、KIT和RP11-394O4.5在绿色模块中的核心作用。此外,这两个模块中的16个demrna和3个delncrna与BC患者的总生存率显著相关。结论:作者的研究确定了一些可能在BC的发展和治疗中发挥重要作用的预后生物标志物。特别是,lncRNAs AC016995.3、RP1-193H18.2和RP11-166D19.1是新的BC生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Coexpression Module Construction by Weighted Gene Coexpression Network Analysis and Identify Potential Prognostic Markers of Breast Cancer.

Background: Breast cancer (BC) is a malignant tumor with the highest morbidity among women, disrupting millions of their lives worldwide each year. However, the molecular mechanisms underlying remain unclear. Materials and Methods: The RNA-Sequencing and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene coexpression network analysis (WGCNA). Additionally, coexpressed modules were used to detect their correlation with the clinical traits of BC. Next, nodes of the most significant coexpression modules were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, mRNA-lncRNA coexpression network and survival analyses. Results: In total, 2056 differentially expressed mRNAs (DEmRNAs) and 297 differentially expressed lncRNAs (DElncRNAs) were identified and subjected to WGCNA analysis, and 12 coexpression modules were generated. The top five significant modules (turquoise, green, red, brown, and blue modules) were related to one or more clinical traits of BC. In particular, the turquoise and green modules were chosen for further analysis. Next, by lncRNA-mRNA coexpression analysis of the turquoise and green modules, 12 DEmRNAs and 2 DElncRNAs were identified as hub nodes. The lncRNA-associated mRNAs of the networks were commonly related to several cancer-related pathways. Moreover, these networks also revealed central roles for RP11-389C8.2 and TGFBR2 in the turquoise module and MYLK, KIT, and RP11-394O4.5 in the green module. Furthermore, 16 DEmRNAs and 3 DElncRNAs in these two modules were significantly correlated with the overall survival of BC patients. Conclusions: The authors' study identified some prognostic biomarkers that might play important roles in the development and treatment of BC. In particular, lncRNAs AC016995.3, RP1-193H18.2, and RP11-166D19.1 were novel biomarkers for BC.

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