加权基因共表达分析在粘液性卵巢癌分子表达谱特征分析中的应用（英文）

【作者】 张桂红； 卢仁泉； 郭林；

【机构】 复旦大学附属肿瘤医院检验科； 复旦大学肿瘤学系；

【摘要】 Objective: Primary invasive mucinous epithelial ovarian cancer(mEOC) has always been diagnostically and therapeutically challenging. To address this problem, here, the transcriptome profiling by weighted gene co-expression network analysis(WGCNA) has been proposed as an effective method to identify specific biomarkers for mEOC. Methods: The gene expression dataset GSE26193 was reanalyzed with a systematical approach, WGCNA. mEOC-related gene coexpression modules were detected and the functional enrichments of these modules were performed at GO and KEGG terms. Hub genes in mEOC-related modules were validated using an independent transcriptome expression dataset GSE44104. Results: 11 co-expressed gene modules were detected by WGCNA based on 4917 genes and 99 epithelial ovarian cancer samples. The MEturquoise was found to be significantly associated with the subtype of mEOC. KEGG pathway enrichment analysis showed genes in the MEturquoise significantly enriched in the metabolism of xenobiotics by cytochrome P450 and steroid hormone biosynthesis. Ten hub genes(LIPH, BCAS1, FUT3, ZG16 B, PTPRH, SLC4 A4, MUC13, TFF1, HNF4 G and TFF2) in the MEturquoise were validated to be highly expressed in m EOC using an independent gene expression dataset GSE44104. Conclusions: Our work proposed an applicable framework of molecular characteristics for patients with mEOC, which may help us to understand the biological mechanisms involved in the mEOC progression. The specific biomarkers of mEOC, as potential drug targets, may be applied in the treatment of m EOC in the furture.更多还原

【Abstract】 Objective: Primary invasive mucinous epithelial ovarian cancer(mEOC) has always been diagnostically and therapeutically challenging. To address this problem, here, the transcriptome profiling by weighted gene co-expression network analysis(WGCNA) has been proposed as an effective method to identify specific biomarkers for mEOC. Methods: The gene expression dataset GSE26193 was reanalyzed with a systematical approach, WGCNA. mEOC-related gene coexpression modules were detected and the functional enrichments of these modules were performed at GO and KEGG terms. Hub genes in mEOC-related modules were validated using an independent transcriptome expression dataset GSE44104. Results: 11 co-expressed gene modules were detected by WGCNA based on 4917 genes and 99 epithelial ovarian cancer samples. The MEturquoise was found to be significantly associated with the subtype of mEOC. KEGG pathway enrichment analysis showed genes in the MEturquoise significantly enriched in the metabolism of xenobiotics by cytochrome P450 and steroid hormone biosynthesis. Ten hub genes(LIPH, BCAS1, FUT3, ZG16 B, PTPRH, SLC4 A4, MUC13, TFF1, HNF4 G and TFF2) in the MEturquoise were validated to be highly expressed in m EOC using an independent gene expression dataset GSE44104. Conclusions: Our work proposed an applicable framework of molecular characteristics for patients with mEOC, which may help us to understand the biological mechanisms involved in the mEOC progression. The specific biomarkers of mEOC, as potential drug targets, may be applied in the treatment of m EOC in the furture.更多还原