Abstract：Objective To screen out important genes associated with poor prognosis of ovarian cancer based on comprehensive bioinformatics analysis. Methods The expression profiles were obtained from the gene expression compilation database, and the differential expression genes (DEG) with │logFC│> 1.5 and P < 0.05 were identified from the gene expression omnibus database. Functional enrichment, protein-protein interaction (PPI) network construction, functional modules analysis, survival analysis, and correlation analysis were performed. Results Firstly, 135 uniformly expressed genes were identified, and 33 up-regulated DEG genes were mainly concentrated in mitotic spindle assembly checkpoint and chromosome separation regulation; a total of 102 DEG downregulation was mainly concentrated in the regulation of neurotransmitter level and protein serine / threonine kinase activity. Then, a PPI network was constructed, and 20 hub genes were screened for survival analysis and expression correlation analysis. At the same time, suitable modules were screened and pathway enrichment analysis was conducted for genes. Finally, the intersection of UBE2C, TTK and CP were found to be highly expressed in ovarian cancer and lead to poor prognosis. Conclusion Three key candidate genes affecting the prognosis of ovarian cancer patients are screened out through comprehensive bioinformatics analysis based on database which can provide new ideas for treatment.
马惠涵1 秘嘉庆1 秦倩1 马梅杰1 冯勤梅2▲. 基于生物信息学分析UBE2C、TTK和CP对卵巢癌预后的影响[J]. 中国医药导报, 2022, 19(4): 22-27.
MA Huihan1 MI Jiaqing1 QIN Qian1 MA Meijie1 FENG Qinmei2▲. Analysis of the effect of UBE2C, TTK and CP on prognosis of ovrian cancer based on bioinformatics#br#. 中国医药导报, 2022, 19(4): 22-27.
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