上海交通大学学报（医学版）

上海交通大学学报（医学版） >

2021 , Vol. 41 >Issue 3: 320 - 327

DOI: https://doi.org/10.3969/j.issn.1674-8115.2021.03.006

论著·基础研究

基于生物信息学方法的儿童急性不明确谱系白血病的潜在治疗靶基因筛选

  • 吴任燕 ,
  • 郭晓琳 ,
  • 洪登礼 ,
  • 陈磊
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  • 1.上海交通大学基础医学院病理生理学系，细胞分化与凋亡教育部重点实验室，上海 200025
    2.上海交通大学医学院上海市免疫学研究所，上海 200025
吴任燕（1995—），女，硕士生；电子信箱：skye12138@sjtu.edu.cn。

收稿日期: 2020-05-12

  网络出版日期: 2021-04-06

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Identification of potential therapeutic target genes in pediatric acute leukemia of ambiguous lineage based on bioinformatics analysis

  • Ren-yan WU ,
  • Xiao-lin GUO ,
  • Deng-li HONG ,
  • Lei CHEN
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  • 1.Key Library of Cell Differentiation and Apoptosis of Ministry of Education, Department of Pathophysiology, Shanghai Jiao Tong University College of Basic Medical Sciences, Shanghai 200025, China
    2.Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

Received date: 2020-05-12

  Online published: 2021-04-06

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摘要

目的·利用生物信息学分析方法探索儿童急性不明确谱系白血病（acute leukemia of ambiguous lineage，ALAL）致病通路、生存相关的独特基因表达谱及枢纽基因。方法·从TARGET （Therapeutically Applicable Research to Generate Effective Treatment）和GEO （Gene Expression Omnibus）数据库下载患者和健康人的表达数据，利用limma、clusterProfiler、survival等生信工具对基因表达量进行差异分析、功能分析及生存分析，最后通过构建蛋白-蛋白相互作用网络筛选得到与ALAL发病及生存相关的枢纽基因。结果·将ALAL组和健康对照组进行比较，共筛选得到4 053个显著差异基因（均P<0.05），其中表达上调基因1 844个，表达下调基因2 209个。利用GO（Gene Ontology）和KEGG（Kyoto Encyclopedia of Genes and Genomes）富集分析发现，上调基因参与细胞周期及剪接，下调基因参与免疫调节。通过与其他类型白血病的表达谱进行比较，发现ALAL中生存相关基因呈现独特的表达模式。蛋白质-蛋白质相互作网络显示生存基因网络的核心基因为CXCL8（C-X-C motif chemokine ligand 8）和LMNA（lamin A/C）。结论·ALAL的发病机制与细胞周期以及免疫相关，ALAL预后不良可能与其生存基因的独特表达谱有关；CXCL8和LMNA在ALAL中发挥重要作用，可能作为潜在的治疗靶点；这些结果对ALAL的机制研究和临床治疗具有提示作用。

关键词： 生物信息学; 数据挖掘; 白血病; 生存分析

本文引用格式

吴任燕 , 郭晓琳 , 洪登礼 , 陈磊 . 基于生物信息学方法的儿童急性不明确谱系白血病的潜在治疗靶基因筛选[J]. 上海交通大学学报（医学版）, 2021 , 41(3) : 320 -327 . DOI: 10.3969/j.issn.1674-8115.2021.03.006

Abstract

Objective

·To explore the pathogenetic pathways, unique gene expression profiles related to survival and hub genes in children with acute leukemia of ambiguous lineage (ALAL) by bioinformatics analysis.

Methods

·From TARGET (Therapeutically Applicable Research to Generate Effective Treatment) and GEO (Gene Expression Omnibus), the expression data of patients and healthy individuals were downloaded. The differential analysis of gene expression, as well as its function and survival analysis, were performed by bioinformatics tools, such as limma, clusterProfiler and survival. Finally, the hub genes of ALAL were screened by constructing protein-protein interaction network (PPI).

Results

·Four thousand and fifty-three significant differentially expressed genes were identified in the differential analysis between ALAL group and control group (all P<0.05), of which 1 844 were up-regulated genes and 2 209 were down-regulated genes. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis indicated that up-regulated genes were related to cell cycle and splicing, while the down-regulated genes were associated with immunity. By comparing the expression profiles with those of other types of leukemia, a unique expression pattern of survival-related genes in ALAL were found. Finally, PPI showed that CXCL8 (C-X-C motif chemokine ligand 8) and LMNA (lamin A/C) were the hub genes of the survival gene network.

Conclusion

·The pathogenesis of ALAL is related to cell cycle and immunity. The poor prognosis of ALAL may be related to the unique expression profile of survival-related genes. CXCL8 and LMNA play an important role in ALAL, and may act as potential therapeutic targets. These results have implications for the mechanism research and clinical treatment of ALAL.

Key words： bioinformatics; data mining; leukemia; survival analysis

参考文献

1 Arber DA, Orazi A, Hasserjian R, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia[J]. Blood, 2016, 127(20): 2391-2405.
2 Slany RK. The molecular mechanics of mixed lineage leukemia[J]. Oncogene, 2016, 35(40): 5215-5223.
3 Guru Murthy GS, Dhakal I, Lee JY, et al. Acute leukemia of ambiguous lineage in elderly patients-analysis of survival using surveillance epidemiology and end results-medicare database[J]. Clin Lymphoma Myeloma Leuk, 2017, 17(2): 100-107.
4 Hrusak O, de Haas V, Stancikova J, et al. International cooperative study identifies treatment strategy in childhood ambiguous lineage leukemia[J]. Blood, 2018, 132(3): 264-276.
5 Lee HG, Baek HJ, Kim HS, et al. Biphenotypic acute leukemia or acute leukemia of ambiguous lineage in childhood: clinical characteristics and outcome[J]. Blood Res, 2019, 54(1): 63-73.
6 Noronha EP, Marques LVC, Andrade FG, et al. T-lymphoid/myeloid mixed phenotype acute leukemia and early T-cell precursor lymphoblastic leukemia similarities with NOTCH1 mutation as a good prognostic factor[J]. Cancer Manag Res, 2019, 11: 3933-3943.
7 de Leeuw DC, van den Ancker W, Denkers F, et al. MicroRNA profiling can classify acute leukemias of ambiguous lineage as either acute myeloid leukemia or acute lymphoid leukemia[J]. Clin Cancer Res, 2013, 19(8): 2187-2196.
8 Ritchie ME, Phipson B, Wu D, et al. Limma Powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res, 2015, 43(7): e47.
9 Yu G, Wang LG, Yan GR, et al. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis[J]. Bioinformatics, 2015, 31(4): 608-609.
10 Schriml LM, Arze C, Nadendla S, et al. Disease Ontology: a backbone for disease semantic integration[J]. Nucleic Acids Res, 2012, 40(database issue): D940-D946.
11 Yu G, Wang LG, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287.
12 Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function[J]. Nucleic Acids Res, 2010, 38(web server issue): W214-W220.
13 Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks[J]. Genome Res, 2003, 13(11): 2498-2504.
14 Chin CH, Chen SH, Wu HH, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome[J]. BMC Syst Biol, 2014, 8(): S11.
15 Baggiolini M, Dewald B, Moser B. Human chemokines: an update [J]. Annu Rev Immunol, 1997, 15: 675-705.
16 Kittang AO, Hatfield K, Sand K, et al. The chemokine network in acute myelogenous leukemia: molecular mechanisms involved in leukemogenesis and therapeutic implications[J]. Curr Top Microbiol Immunol, 2010, 341: 149-172.
17 Li Y, Cheng J, Li Y, et al. CXCL8 is associated with the recurrence of patients with acute myeloid leukemia and cell proliferation in leukemia cell lines[J]. Biochem Biophys Res Commun, 2018, 499(3): 524-530.
18 Liu Q, Li A, Tian Y, et al. The CXCL8-CXCR1/2 pathways in cancer[J]. Cytokine Growth Factor Rev, 2016, 31: 61-71.
19 Kamohara H, Takahashi M, Ishiko T, et al. Induction of interleukin-8 (CXCL-8) by tumor necrosis factor-α and leukemia inhibitory factor in pancreatic carcinoma cells: impact of CXCL-8 as an autocrine growth factor[J]. Int J Oncol, 2007, 31(3): 627-632.
20 Bonne G, Di Barletta MR, Varnous S, et al. Mutations in the gene encoding lamin A/C cause autosomal dominant Emery-Dreifuss muscular dystrophy[J]. Nat Genet, 1999, 21(3): 285-288.
21 Raffaele Di Barletta M, Ricci E, Galluzzi G, et al. Different mutations in the LMNA gene cause autosomal dominant and autosomal recessive Emery-Dreifuss muscular dystrophy[J]. Am J Hum Genet, 2000, 66(4): 1407-1412.
22 Rocha-Perugini V, González-Granado JM. Nuclear envelope lamin-A as a coordinator of T cell activation[J]. Nucleus, 2014, 5(5): 396-401.
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