Basic research

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

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.

Cite this article

Ren-yan WU , Xiao-lin GUO , Deng-li HONG , Lei CHEN . Identification of potential therapeutic target genes in pediatric acute leukemia of ambiguous lineage based on bioinformatics analysis[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2021 , 41(3) : 320 -327 . DOI: 10.3969/j.issn.1674-8115.2021.03.006

References

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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