Difference analysis of myeloid leukemia fusion oncogene expression network based on time series
-
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。 -
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
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. -
Key words:
- AML /
- Fusion oncogene /
- Hub gene /
- Network difference analysis /
- Time series gene expression data
-
图 1 各个时间点的基因表达数据构建的联合网络图
注:节点代表基因, 节点间的连接代表基因间的相互关系, 不同颜色的节点代表不同的网络, 一个节点上不同的颜色表示不同的网络共享这个节点。网络中几个节点密集的连接在一起形成一个小的社区, 即模块, 不同颜色的字体代表不同的社区。
Figure 1. Union network graph of gene expression data at different time points
图 2 基于邻居相似性指数(CNSI)的气泡图
注:气泡图两边轴标签对应不同融合基因不同时间点的网络:如PML-8 d.txt.net表示由融合基因PML-RARA体外转导人CD34+细胞后8 h对应的网络。
Figure 2. Bubble chart based on CompNet neighbor similarity index (CNSI)
图 3 前5个社区的累积社区分布图
注:社区累计分布图展示不同融合基因的不同时间点的网络中的社区分布。
Figure 3. Cumulative community distribution for the top 5 communities
图 4 联合网络中的中介中心性排名前10的节点(基因)
注:条块的颜色指示该节点所在的网络。
Figure 4. The top 10 nodes (genes) in the union network
表 1 不同时间点的扰动基因数目的筛选结果
Table 1. Screening results of the number of perturbed genes at different time points
时间 状态 AML1-ETO MLL-AF9 PML-RARA NUP98-HOXA9 6 h Up 624 55 220 111 down 1 325 38 490 103 3 d Up 110 136 42 280 Down 35 119 17 26 8 d Up 10 110 18 84 down 14 43 32 114 注:Up表示上调基因, Down表示下调基因。 
下载: 导出CSV
表 2 全局网络属性的统计分析结果
Table 2. Statistical analysis results of global network attributes
网络属性 属性值 1 000个随机网络的均值 P值 网络直径 12.000 11.606 0.088 12个网络的联合网络 网络密度 0.007 0.001 < 0.001 聚类系数 0.191 0.262 0.802 网络直径 11.000 8.009 0.106 AML1-ETO 网络密度 0.009 0.001 < 0.001 聚类系数 0.187 0.260 0.754 网络直径 4.000 1.159 < 0.001 MLL-AF9 网络密度 0.023 0.001 < 0.001 聚类系数 0.273 0.000 < 0.001 网络直径 8.000 2.369 < 0.001 PML-RARA 网络密度 0.023 0.001 < 0.001 聚类系数 0.242 0.000 < 0.001 网络直径 8.000 2.486 < 0.001 NUP98-HOXA9 网络密度 0.031 0.001 < 0.001 聚类系数 0.340 0.000 < 0.001
下载: 导出CSV
表 3 5个社区的富集分析结果
Table 3. Enrichment analysis results for the top 5 communities
社区 GO条目 C1 膜, 细胞质, 多细胞生物过程, 等离子膜 C2 信号转导, 信号传递过程, 信号传递, 代谢过程调控, 细胞代谢过程的调控, 信号通路 C3 细胞内细胞器, 核碱基, 核苷, 核苷酸和核酸代谢过程的调控, 细胞生物合成过程的调控, 氮化合物代谢过程的调控, 基因表达的调控, 核酸结合, DNA结合, RNA代谢过程的调控 C4 细胞内部分, 核, 转录因子活性, 转录调节因子活性, DNA依赖性, 转录调控 C5 多细胞生物过程, 生物过程的负调控, 转录调控, 大分子生物合成过程调控, 多细胞生物发育
下载: 导出CSV
-
[1] National Cancer Institute(NIH). Adult acute myeloid leukemia treatment-national cancer institute[M]. Bethesda: National Cancer Institute(US), 2013. [2] Dohner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia[J]. N Engl J Med, 2015, 373(12): 1136-1152. DOI: 10.1056/NEJMra1406184. [3] Zhang H, Song G, Song G, et al. Identification of DNA methylation prognostic signature of acute myelocytic leukemia[J]. PloS one, 2018, 13(6): e0199689. DOI: 10.1371/journal.pone.0199689. [4] Crispino JD, Horwitz MS. GATA factor mutations in hematologic disease[J]. Blood, 2017, 129(15): 2103-2110. DOI: 10.1182/blood-2016-09-687889. [5] Hirabayashi S, Wlodarski MW, Kozyra E, et al. Heterogeneity of GATA2-related myeloid neoplasms[J]. Int J Hematol, 2017, 106(2): 175-182. DOI: 10.1007/s12185-017-2285-2. [6] 李川, 梁佩淇, 刘卓刚, 等. AML1-ETO融合基因转阴对急性髓细胞白血病预后的影响[J].现代肿瘤医学, 2018, 26(19): 3106-3110. DOI: 10.3969/j.issn.1672-4992.2018.19.028.Li C, Liang PQ, Liu ZG, et al. The effect of AML1-ETO fusion gene switched on the prognosis of patients in acute myeloid leukemia[J]. Modern Oncology, 2018, 26(19): 3106-3110. DOI: 10.3969/j.issn.1672-4992.2018.19.028. [7] Lee JH, Lee JH. Seven co-occurring mutations in a patient with acute myeloid leukemia identified by next-generation sequencing[J]. Clin Lab, 2019, 65(6). DOI: 10.7754/Clin.Lab.2018.181223. [8] Iijima-Yamashita Y, Matsuo H. Multiplex fusion gene testing in pediatric acute myeloid leukemia[J]. Pediatr Int, 2018, 60(1): 47-51. DOI: 10.1111/ped.13451. [9] 林凡琳, 潘慧, 刘胜先, 等.急性髓系白血病发病机制的研究进展[J].中国细胞生物学学报, 2018, 40(5): 850-856. DOI: 10.11844/cjcb.2018.05.0319.Lin FL, Pan H, Liu SX, et al. Advances in the pathogenesis study of acute myeloid leukemia[J]. Chinese Journal of Cell Biology. 2018, 40(5): 850-856. DOI: 10.11844/cjcb.2018.05.0319. [10] 刘会娟, 侯艳, 李康. ARTIVA在时间序列基因表达数据网络构建中的应用[J].中国卫生统计, 2018, 35(5): 642-645. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgwstj201805001Liu HJ, Hou Y, Li K. The application of auto regressive time varying models to the network building of time series microarray data[J]. Chin J Health Statistics, 2018, 35(5): 642-645. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgwstj201805001 [11] 刘秉慈, 郑玉新.基因组学、蛋白质组学及其在预防医学中的应用[J].中华预防医学杂志, 2004, 38(2): 130-134. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zhyfyx200402020Liu BC, Zheng YX. Genomics, proteomics and their applications in preventive medicine[J]. Chin J Prev Med, 2004, 38(2): 130-134. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zhyfyx200402020 [12] Storey JD, Wenzhong X, Leek JT, et al. Significance analysis of time course microarray experiments[J]. Proc Natl Acad Sci U S A, 2005, 102(36): 12837-12842. DOI: 10.1073/pnas.0504609102. [13] Bringmann LF, Nathalie V, Marieke W, et al. A network approach to psychopathology: new insights into clinical longitudinal data[J]. PloS one, 2013, 8(4): e60188. DOI: 10.1371/journal.pone.0060188. [14] Kuntal BK, Dutta A, Mande SS. CompNet: a GUI based tool for comparison of multiple biological interaction networks[J]. BMC bioinformatics, 2016, 17(1): 185. DOI: 10.1186/s12859-016-1013-x. [15] Abdul-Nabi AM, Yassin ER, Varghese N, et al. In vitro transformation of primary human CD34+ cells by AML fusion oncogenes: early gene expression profiling reveals possible drug target in AML[J]. PloS one, 2010, 5(8): e12464. DOI: 10.1371/journal.pone.0012464. [16] Abos A, Segura B, Baggio HC, et al. Disrupted structural connectivity of fronto-deep gray matter pathways in progressive supranuclear palsy[J]. Neuroimage Clin, 2019, 23: 101899. DOI: 10.1016/j.nicl.2019.101899. [17] Leclerc RD. Survival of the sparsest: robust gene networks are parsimonious[J]. Mol Syst Biol, 2008, 4: 213. DOI: 10.1038/msb.2008.52. [18] Wang Y, Tao F, Zuo C, et al. Disrupted resting frontal-parietal attention network topology is associated with a clinical measure in children with attention-deficit/hyperactivity disorder[J]. Front psychiatry, 2019, 10: 300. DOI: 10.3389/fpsyt.2019.00300. [19] Clauset A, Newman ME, Moore C. Finding community structure in very large networks[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2004, 70(6 Pt 2): 066111. DOI: 10.1103/physreve.70.066111. [20] Liu W, Huang X, Liang X, et al. Identification of key modules and hub genes of Keloids with weighted gene coexpression network analysis[J]. Plast Reconstr Surg, 2017, 139(2): 376-390. DOI: 10.1097/PRS.0000000000003014. [21] Maere S, Heymans K, Kuiper M. BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks[J]. Bioinformatics, 2005, 21(16): 3448-3449. DOI: 10.1002/pssc.200778445. [22] Scardoni G, Laudanna C. Centralities based analysis of complex networks[C]. New Frontiers in Graph Theory, 2012: 323-348. DOI: 10.5772/35846. [23] Pavlopoulos GA, Secrier M, Moschopoulos CN, et al. Using graph theory to analyze biological networks[J]. BioData Min, 2011, 4(1): 10. DOI: 10.1186/1756-0381-4-10. [24] Jain A, Tiwari A, Verma A, et al. Vitamins for cancer prevention and treatment: An insight[J]. Curr Mol Med, 2017, 17(5): 321-340. DOI: 10.2174/1566524018666171205113329. [25] Locksley RM, Killeen N, Lenardo MJ. The TNF and TNF receptor superfamilies: integrating mammalian biology[J]. Cell, 2001, 104(4): 487-501. DOI: 10.1016/S0092-8674(01)00237-9. [26] Old LJ. Tumor necrosis factor(TNF)[J]. Science(New York), 1985, 230(4726): 630-632. DOI: 10.1126/science.2413547. [27] Ferrara N. VEGF: an update on biological and therapeutic aspects[J]. Curr Opin Biotechnol, 2000, 11(6): 617-624. DOI: 10.1016/S0958-1669(00)00153-1. [28] Park DJ, Seo AN, Yoon C, et al. Serum VEGF-A and tumor vessel VEGFR-2 levels predict survival in Caucasian but not Asian patients undergoing resection for gastric adenocarcinoma[J]. Ann Surg Oncol, 2015, 22(3): 1508-1515. DOI: 10.1245/s10434-015-4790-y. [29] Gayther SA, Batley SJ, Linger L, et al. Mutations truncating the EP300 acetylase in human cancers[J]. Nat Genet, 2000, 24(3): 300-303. DOI: 10.1038/73536. [30] Zhao Y, Adjei AA. The clinical development of MEK inhibitors[J]. Nat Rev Clin Oncol, 2014, 11(7): 385-400. DOI: 10.1038/nrclinonc.2014.83. [31] Neill T, Schaefer L, Iozzo RV. Decorin: a guardian from the matrix[J]. Am J Pathol, 2012, 181(2): 380-387. DOI: 10.1016/j.ajpath.2012.04.029. [32] Lee S, Chung YH, Lee C. US28, a virally-encoded GPCR as an antiviral target for human cytomegalovirus infection[J]. Biomol Ther(Seoul), 2017, 25(1): 69-79. DOI: 10.4062/biomolther.2016.208. [33] Qian Z, Zhang Q, Hu Y, et al. Investigating the mechanism by which SMAD3 induces PAX6 transcription to promote the development of non-small cell lung cancer[J]. Respir Res, 2018, 19(1): 262. DOI: 10.1186/s12931-018-0948-z. -
点击查看大图
图(4) / 表(3)
计量
- 文章访问数: 209
- HTML全文浏览量: 112
- PDF下载量: 26
- 被引次数: 0