| 基于时间序列的急性髓系白血病融合基因表达网络差异分析 |
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| 引用本文： | 王慧慧, 萨建, 曹红艳, 崔跃华. 基于时间序列的急性髓系白血病融合基因表达网络差异分析[J]. 中华疾病控制杂志, 2020, 24(3): 274-278. doi: 10.16462/j.cnki.zhjbkz.2020.03.006 |
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| 作者姓名： | 王慧慧 萨建 曹红艳 崔跃华 |
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| 作者单位： | 1.030001 太原, 山西医科大学卫生统计教研室;2.030001 太原, 重大疾病风险评估山西省重点实验室;3.MI48824 东兰辛, 美国密西根州立大学统计与概率系 |
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| 基金项目： | 云南省应用基础研究计划;国家自然科学基金;山西省回国留学人员科研项目 |
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| 摘 要： | 目的 探讨急性髓系白血病(acute myelocytic leukemia, AML)不同融合基因的时间序列基因表达数据的网络差异, 识别靶基因。 方法 采用网络差异分析的方法, 针对AML的不同融合基因相关的基因表达数据构建网络, 对联合网络的全局属性进行统计分析, 使用邻域相似性指数(CompNet neighbor similarity index, CNSI)分析不同融合基因对应的网络间的相似性, 使用“fast-greedy”算法检测联合网络中的社区, 最后识别枢纽基因。 结果 通过网络差异分析确定了网络的相似性:NUP98-HOXA9-3 d和NUP98-HOXA9-8 d间的CNSI值为0.73, AML1-ETO-6 h和PML-RARA-6 h间的CNSI值为0.25;并识别出了10个AML的枢纽基因, 其中有7个已在文献中报道:TNF, VEGFA, EP300, EGF, CD44, PTGS2, SMAD3。 结论 网络差异分析揭示了AML的基因表达的时间转化模式及网络中基因表达的异质性, 为不同融合基因类型的AML治疗提供了靶基因。
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| 关 键 词： | 急性髓系白血病 融合基因 枢纽基因 网络差异分析 时间序列基因表达数据 |
| 收稿时间： | 2019-09-30 |
| 修稿时间： | 2019-12-20 |
Difference analysis of myeloid leukemia fusion oncogene expression network based on time series |
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| WANG Hui-hui, SA Jian, CAO Hong-yan, CUI Yue-hua. Difference analysis of myeloid leukemia fusion oncogene expression network based on time series[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2020, 24(3): 274-278. doi: 10.16462/j.cnki.zhjbkz.2020.03.006 |
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| Authors: | WANG Hui-hui SA Jian CAO Hong-yan CUI Yue-hua |
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| Affiliation: | 1. Department of Health Statistics, Shanxi Medical University, Taiyuan 030001, China;2. Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan 030001, China;3. Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA |
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| Abstract: | Objective Focusing on four types acute myeloid leukemia(AML)fusion oncogenes, so as to explore the network difference with time series expression data and further identify important genes in networks. Methods Gene network difference analysis was conducted while focusing on the global attributes of the union network. The CompNet neighborhood similarity index(CNSI) was adopted to assess network similarity. "fast-greedy" algorithm was used to detect communities based on the union network, and further identify hub genes. Results The CNSI value between NUP98-HOXA9-3 d and NUP98-HOXA9-8 d was 0.73, while AML1-ETO-6 h and PML-RARA-6 h was 0.25. We identified ten AML associated genes and seven of them(TNF, VEGFA, EP300, EGF, CD44, PTGS2, SMAD3) were reported in the literature. Conclusions The network difference analysis revealed the pattern and heterogeneity of AML gene expression change across different time points, and further provided target genes for efficient treatment of AML with different types of fusion oncogenes. |
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| Keywords: | AML Fusion oncogene Hub gene Network difference analysis Time series gene expression data |
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