Molecular profiling of mucinous epithelial ovarian cancer by weighted gene co-expression network analysis
Molecular profiling of mucinous epithelial ovarian cancer by weighted gene co-expression network analysis
复制标题
通过加权基因共表达网络分析对粘液性上皮性卵巢癌进行分子谱分析
DOI：
10.1016/j.gene.2019.05.034
复制
发表时间：
2019-08-15
期刊：
影响因子：
3.5
通讯作者：
Guo, Lin
中科院分区：
文献类型：
--
作者：
Zhang, Gui Hong;Chen, Miao Miao;Guo, Lin
目的：为了识别黏液上皮性卵巢癌（mucinous epithelial ovarian cancer, mEOC）的分子特征，提高其早期诊断的有效性，本文提出了加权基因共表达网络分析（weighted gene co-expression network analysis， WGCNA）的转录组谱分析作为一种有效的方法。方法：采用系统方法WGCNA重新分析基因表达数据集GSE26193。检测meoc相关基因共表达模块，并在GO和KEGG条件下对这些模块进行功能富集。使用两个独立的数据集GSE44104和GSE30274对meoc相关模块中的10个hub基因进行了验证。结果：基于4917个基因和99份上皮性卵巢癌样本，WGCNA共鉴定出11个共表达基因模块。绿松石模块被发现与mEOC亚型显著相关。KEGG通路富集分析显示，绿松石模块基因通过细胞色素P450和类固醇激素的生物合成显著富集外源代谢。使用两个独立的基因表达数据集GSE44104和GSE30274，验证了绿石模块中的10个枢纽基因（LIPH、BCAS1、FUT3、ZG16B、PTPRH、SLC4A4、MUC13、TFF1、HNF4G和TFF2）在mEOC中的高表达。结论：我们的工作为mEOC患者提供了一个适用的分子特征框架，有助于我们准确、全面地了解mEOC的分子复杂性。本研究发现的枢纽基因作为mEOC的潜在特异性生物标志物，可能在未来用于mEOC的早期诊断。
Purpose: In order to identify the molecular characteristics and improve the efficacy of early diagnosis of mucinous epithelial ovarian cancer (mEOC), here, the transcriptome profiling by weighted gene co-expression network analysis (WGCNA) has been proposed as an effective method.Methods: The gene expression dataset GSE26193 was reanalyzed with a systematical approach, WGCNA. mEOC-related gene co-expression modules were detected and the functional enrichments of these modules were performed at GO and KEGG terms. Ten hub genes in the mEOC-related modules were validated using two independent datasets GSE44104 and GSE30274.Results: 11 co-expressed gene modules were identified by WGCNA based on 4917 genes and 99 epithelial ovarian cancer samples. The turquoise module was found to be significantly associated with the subtype of mEOC. KEGG pathway enrichment analysis showed genes in the turquoise module significantly enriched in metabolism of xenobiotics by cytochrome P450 and steroid hormone biosynthesis. Ten hub genes (LIPH, BCAS1, FUT3, ZG16B, PTPRH, SLC4A4, MUC13, TFF1, HNF4G and TFF2) in the turquoise module were validated to be highly expressed in mEOC using two independent gene expression datasets GSE44104 and GSE30274.Conclusion: Our work proposed an applicable framework of molecular characteristics for patients with mEOC, which may help us to obtain a precise and comprehensive understanding on the molecular complexities of mEOC. The hub genes identified in our study, as potential specific biomarkers of mEOC, may be applied in the early diagnosis of mEOC in the future.