基因共表达网络
基因
生物
基因表达
卵巢癌
癌症研究
计算生物学
基因表达谱
癌症干细胞
干细胞标记物
干细胞
微阵列分析技术
表达式(计算机科学)
鉴定(生物学)
基因调控网络
作者
Meng Luo,Hao Zeng,Xin-Yu Ma,Xuelei Ma
出处
期刊:Journal of Sichuan University. Medical science edition
日期:2021-03-01
卷期号:52 (2): 248-258
被引量:1
摘要
Objective To investigate the significance of stemness-related genes in the diagnosis and treatment of ovarian cancer. Methods Key modules and genes were identified with weighted gene co-expression network analysis (WGCNA). The signal pathways of high expression of key genes were analyzed by gene set enrichment analysis (GSEA) and single cell sequencing data. The chemosensitivity of ovarian cancer to chemotherapy drugs was estimated with pRRophetic. Flow cytometry was used to examine the expression of CD44 +CD117 +in SKOV3 cells and cancer stem cells. The expression of key genes in ovarian cancer stem cells was confirmed by qRT-PCR. The core genes were identified by GeneMANIA analysis. Results According to the WGCNA results, 15 key genes were identified at the transcription level, all being highly expressed in many kinds of tumors. They were involved in the cell cycle, DNA repair, E2 target and G2M checkpoint pathway, and had significant correlation with chemosensitivity. The proportion of CD44 + CD117 + cells in SKOV3 cells and ovarian cancer stem cells were (1.20±0.34)% and (37.17±1.80)% respectively, with statistically significant difference ( P<0.05). qRT-PCR confirmed that seven key genes ( BUB1, CDC20, CCNB2, DLGAP5, KIF4 A, NEK2, NUSAP1) in the WGCNA results were highly expressed in ovarian cancer stem cells, and BUB1 might have played a core role. Conclusion Seven hub genes, especially BUB1, were identified by constructing gene co-expression network, which may become potential biomarkers of ovarian cancer gene.
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