加权基因共表达网络分析探索与卵巢癌病理分级相关的基因

A Co-expression Network Analysis Identified Genes in Association with Pathological Grading and Prognosis in Ovarian Cancer

目的卵巢癌(ovarian cancer,OC)是妇科最致命的恶性肿瘤,它的预后受到各种与卵巢癌不良进展相关基因的影响。本研究通过构建加权基因共表达网络筛选出与卵巢癌发病机制相关的hub模块和hub基因。方法我们使用数据集GSE63885通过加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)构建共表达网络。通过DAVID在线数据库对hub模块中的基因进行GO和KEGG富集分析。最后结合其他数据集中对hub基因进行验证。结果研究表明黄色基因模块与卵巢癌病理分级之间存在最高的相关性(r=0.35)。GO功能富集分析结果表明,黄色模块中的基因主要与细胞有丝分裂、细胞周期调节等功能相关。KEGG富集分析结果表明,黄色模块中的基因主要与细胞周期调节通路、P53信号通路等信号通路相关。共表达网络及PPI网络分析共筛选出12个hub基因,其中MELK与卵巢癌的病理分级相关性最高(r=0.91)。将这个基因放入到验证数据集中进行验证,结果表明,MELK与卵巢癌病理分级密切相关。ROC曲线结果表明MELK的表达能够有效地区分不同卵巢癌分级的卵巢癌患者。Kaplan-Meier在线数据库显示MELK高表达与卵巢癌不良预后相关。结论通过加权基因共表达网络分析筛选出与卵巢癌病理分级最相关的基因MELK,该基因可能成为卵巢癌的生物标志物及治疗靶点,为卵巢癌的精准治疗提供参考。

Objective Ovarian cancer is the most lethal gynecological malignancy,and its prognosis is influenced by the progression regulated by a complex network of gene interactions.In this study,weighted gene co-expression network was constructed to identify hub modules and hub genes related to the pathogenesis of ovarian cancer.Methods We used dataset GSE63885 to construct co-expression networks by the weighted gene co-expression network analysis.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed by the Database for Annotation,Visualization and Integrated Discovery.Hub genes were screened and validated by independent datasets.Results The result indicated that the highest association was found between the yellow module and pathological grading(r=0.35)by Pearson′s correlation analysis.GO functional enrichment analysis revealed that biological processes of yellow module was focused on cell division and cell cycle regulation,etc.KEGG pathway enrichment analysis revealed that the yellow module was focused on cell cycle and p53 signaling pathway,etc.In the significant module,a total of 12 network hub genes were identified,among which MELK exhibited the highest correlation(r=0.91)with ovarian cancer pathological grading.MELK was verified using the GSE30161 test set,and it was identified to be tightly linked to ovarian cancer pathological grading.The receiver operating characteristic(ROC)curve results showed that the MELK expression could effectively distinguish ovarian cancer patients with different pathological grading.The Kaplan-Meier online database indicated that high a MELK expression was associated with a poor ovarian cancer prognosis.Conclusion In conclusion,using weighted gene co-expression analysis,MELK is identified and validated in association with pathological grading and prognosis in ovarian cancer.Moreover,MELK could serve as a biomarker and therapeutic target for the precise diagnosis and treatment of ovarian cancer in the future.