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  • 贺海威,康争春,徐明娟.卵巢癌转移体细胞突变及其相关功能通路分析[J].第二军医大学学报,2019,40(11):1176-1182    [点击复制]
  • HE Hai-wei,KANG Zheng-chun,XU Ming-juan.Analysis of somatic mutations in metastatic ovarian carcinoma and related functional pathways[J].Acad J Sec Mil Med Univ,2019,40(11):1176-1182   [点击复制]
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卵巢癌转移体细胞突变及其相关功能通路分析
贺海威1△,康争春2△,徐明娟1*
0
(1. 海军军医大学(第二军医大学)长海医院妇产科, 上海 200433;
2. 海军军医大学(第二军医大学)长海医院肛肠外科, 上海 200433
共同第一作者
*通信作者)
摘要:
目的 借助癌症体细胞突变目录(COSMIC)数据库中原发性卵巢癌和转移性卵巢癌两类癌组织的全外显子测序数据,筛选转移性癌相对于原发性癌突变率差异具有统计学意义的体细胞基因群,并对其影响的功能和信号通路进行分析。方法 从COSMIC数据库下载全部肿瘤的全外显子测序数据,从中提取全部卵巢癌全外显子测序数据,在R 3.5.3环境下,对每个基因突变在原发性卵巢癌和转移性卵巢癌两类样本中的突变率行χ2检验或Fisher确切概率法分析,寻找突变率差异具有统计学意义的基因群,并进一步将差异突变基因群进行基因本体(GO)功能及京都基因与基因组百科全书(KEGG)通路富集分析,探寻其显著富集的GO功能和KEGG通路。结果 对比原发性卵巢癌与转移性卵巢癌两类组织样本共发现520个突变率差异具有统计学意义的体细胞突变基因,包括跨膜丝氨酸蛋白酶13(TMPRSS13)、高尔基蛋白转运抑制剂A抗性因子1(GBF1)、Fos样抗体2(FOSL2)、主导控制样蛋白3(MAML3)等。突变基因群GO功能富集分析发现显著富集的GO功能包括突触前组织、树突发育、通过质膜黏附分子的细胞黏附、肌动蛋白结合等,突变基因群KEGG通路富集分析发现显著富集通路包括肌动蛋白细胞骨架的调节、三磷酸腺苷结合盒运载体等。结论 探寻原发性卵巢癌与转移性卵巢癌之间差异体细胞突变基因群及其相关功能通路可为深入揭示卵巢癌的转移调控机制提供线索,显著突变基因群可能成为卵巢癌转移诊治的生物标志物。
关键词:  卵巢肿瘤  体细胞突变基因  卵巢转移  基因调控网络
DOI:10.16781/j.0258-879x.2019.11.1176
投稿时间:2019-09-11修订日期:2019-10-14
基金项目:国家重点研发计划(2016YFC1303100).
Analysis of somatic mutations in metastatic ovarian carcinoma and related functional pathways
HE Hai-wei1△,KANG Zheng-chun2△,XU Ming-juan1*
(1. Department of Obstetrics and Gynaecology, Changhai Hospital, Naval Medical University(Second Military Medical University), Shanghai 200433, China;
2. Department of Colorectal Surgery, Changhai Hospital, Naval Medical University(Second Military Medical University), Shanghai 200433, China
Co-first authors.
* Corresponding author)
Abstract:
Objective To screen the different mutated somatic genes between primary ovarian cancer and metastatic ovarian carcinoma using the whole exon sequencing data of catalogue of somatic mutations in cancer (COSMIC) database, and to analyze their function and signal pathway. Methods The whole exon sequencing data of all tumors were downloaded from the COSMIC database, and the whole exon sequencing data of all ovarian cancer were extracted. In the R 3.5.3 environment, mutation rate of each mutated gene in the primary and metastatic ovarian carcinoma samples were performed. The χ2 test or Fisher's exact probability method was used to identify the mutated gene groups which had statistically significant difference in mutation rate. The mutated gene groups were further analyzed for gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment. Results We found a total of 520 somatic mutations with statistically significant differences in mutation rate between primary ovarian cancer and metastatic ovarian carcinoma tissues, such as transmembrane protease serine 13 (TMPRSS13), Golgi brefeldin A resistance factor 1 (GBF1), Fos-like antigen 2 (FOSL2), mastermind-like 3 (MAML3), etc. Enriched GO function included presynapse organization, dendrite development, cell-cell adhesion via plasma membrane adhesion molecules, and actin binding, and so on. KEGG pathway included regulation of actin cytoskeleton, tricarboxylic acid carrier, and the like. Conclusion It can provide clues for revealing the metastasis regulation mechanism of ovarian cancer by exploring different mutated gene group between primary ovarian cancer and metastatic ovarian carcinoma and its related functional pathways. The significant mutated gene group may be used as biomarkers for the diagnosis and treatment of ovarian metastatic cancer.
Key words:  ovarian neoplasms  somatic mutation  neoplasm metastasis  gene regulatory networks