基于高通量芯片对小儿急性髓系白血病的生物信息学分析

doi:10.13481/j.1671-587x.20200522

Abstract

Abstract: Objective: To screen the differentially expressed genes (DEGs) related to pediatric acute myeloid leukemia (AML) with bioinformatics tools, and to explore the core genes of AML and clarify its pathogenesis. Methods: The transcriptional data of pediatric AML met the requirement were obtained from Gene Expression Omnibus (GEO) database. The DEGs were further screened out using GEO2R web tool, and the functional annotation of Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) were used to analyze the function and pathway enrichment of the DEGs. STRING database was used to construct protein-protein interaction (PPI) network, and the relative Hub genes and transcription factors were screened with Cytoscape software and iRegulon. The top five core genes were analyzed through Gene-Cloud of Biotechnology Information (GCBI) online database. Results: A total of 600 DEGs were identified, of which 407 genes were up-regulated and 193 genes were down-regulated. The GO analysis results showed that most of DEGs were associated with cellular components including nucleoplasm, cytosol, membrane and nuclear speck. The KEGG analysis demonstrated that the DEGs were mainly enriched in the tumor necrosis factor(TNF) signaling pathway, cytokine-cytokine receptor interaction, and Jak-STAT signaling pathway. Through STRING database and Cytoscape software, a total of the top 20 core genes with the highest connection were screened out, including FPR2, PIK3R1, EP300, HSP90AA1, and NRAS. Among them, Ep300, HSP90AA1,and NRAS were associated with the development of leukemia. Moreover, iRegulon tool screened 55 transcription factors which targeted to the Hub genes such as TP63, NFE2L1 and TBX. Conclusion: The selected Hub genes and transcription factors maybe participate in the occurrence and development of pediatric AML and may be used as new therapeutic target for the disease.

Key words: bioinformatics, acute myeloid leukemia, differential genes, gene chip

CLC Number:

R733.7

ZHANG Xi, ZHAI Li, SUN Yunyan, YANG Wei, GAO Yanzhang, LEI Ming, PAN Yuqing. Bioinformatics analysis of pediatric acute myeloid leukemia based on high-throughput microarray[J].Journal of Jilin University(Medicine Edition), 2020, 46(05): 1036-1042.

References

[1] CREUTZIG U,KUTNY M A,BARR R,et al. Acute myelogenous leukemia in adolescents and young adults[J]. Pediatr Blood Cancer,2018,65(9):e27089.
[2] KAHN J M,KEEGAN T H,TAO L,et al. Racial disparities in the survival of American children,adolescents,and young adults with acute lymphoblastic leukemia,acute myelogenous leukemia,and Hodgkin lymphoma[J]. Cancer,2016,122(17):2723-2730.
[3] DUGGAN M A,ANDERSON W F,ALTEKRUSE S,et al. The Surveillance,epidemiology,and end results (SEER) program and pathology:Toward strengthening the critical relationship[J]. Am J Surg Pathol,2016,40(12):e94-e102.
[4] MÖRICKE A,ZIMMERMANN M,REITER A,et al. Long-term results of five consecutive trials in childhood acute lymphoblastic leukemia performed by the ALL-BFM study group from 1981 to 2000[J]. Leukemia,2010,24(2):265-284.
[5] RASCHE M,ZIMMERMANN M,BORSCHEL L,et al. Successes and challenges in the treatment of pediatric acute myeloid leukemia:a retrospective analysis of the AML-BFM trials from 1987 to 2012[J]. Leukemia,2018,32(10):2167-2177.
[6] LANDRIGAN P J. Childhood leukemias[J]. N Engl J Med,1995,333(19):1286.
[7] SANZ M A,MONTESINOS P. How we prevent and treat differentiation syndrome in patients with acute promyelocytic leukemia[J]. Blood,2014,123(18):2777-2782.
[8] HUDSON M M,LINK M P,SIMONE J V. Milestones in the curability of pediatric cancers[J]. J Clin Oncol,2014,32(23):2391-2397.
[9] DAVIS S,MELTZER P S. GEOquery:a bridge between the gene expression omnibus (GEO) and BioConductor[J]. Bioinformatics,2007,23(14):1846-1847.
[10] HUANG D W,SHERMAN B T,LEMPICKI R A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[J]. Nat Protoc,2009,4(1):44-57.
[11] SZKLARCZYK D,GABLE A L,LYON D,et al. STRING v11:protein-protein association networks with increased coverage,supporting functional discovery in genome-wide experimental datasets[J]. Nucleic Acids Res,2019,47(D1):D607-D613.
[12] 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.
[13] JANKY R,VERFAILLIE A,IMRICHOVÁ H,et al. iRegulon:from a gene list to a gene regulatory network using large motif and track collections[J]. PLoS Comput Biol,2014,10(7):e1003731.
[14] SIEGEL R,DESANTIS C,VIRGO K,et al. Cancer treatment and survivorship statistics,2012[J]. CA Cancer J Clin,2012,62(4):220-241.
[15] KAYSER S,LEVIS M J. Advances in targeted therapy for acute myeloid leukaemia[J]. Br J Haematol,2018,180(4):484-500.
[16] NTZIACHRISTOS P,MULLENDERS J,TRIMARCHI T,et al. Mechanisms of epigenetic regulation of leukemia onset and progression[J]. Adv Immunol,2013,117:1-38.
[17] TSAI W H,CHIEN H Y,SHIH C H,et al. Annexin A1 mediates the anti-inflammatory effects during the granulocytic differentiation process in all-trans retinoic acid-treated acute promyelocytic leukemic cells[J]. J Cell Physiol,2012,227(11):3661-3669.
[18] MCNEER N A,PHILIP J,GEIGER H,et al. Genetic mechanisms of primary chemotherapy resistance in pediatric acute myeloid leukemia[J]. Leukemia,2019,33(8):1934-1943.
[19] WONG R W J,NGOC P C T,LEONG W Z,et al. Enhancer profiling identifies critical cancer genes and characterizes cell identity in adult T-cell leukemia[J]. Blood,2017,130(21):2326-2338.
[20] XIAO P F,TAO Y F,HU S Y,et al. mRNA expression profiling of histone modifying enzymes in pediatric acute monoblastic leukemia[J]. Pharmazie,2017,72(3):177-186.
[21] CHENG G,LIU F,ASAI T,et al. Loss of p300 accelerates MDS-associated leukemogenesis[J]. Leukemia,2017,31(6):1382-1390.
[22] WALSBY E J,LAZENBY M,PEPPER C J,et al. The HSP90 inhibitor NVP-AUY922-AG inhibits the PI3K and IKK signalling pathways and synergizes with cytarabine in acute myeloid leukaemia cells[J]. Br J Haematol,2013,161(1):57-67.
[23] ZHAO J,LIANG J W,XUE H L,et al. The genetics and clinical characteristics of children morphologically diagnosed as acute promyelocytic leukemia[J]. Leukemia,2019,33(6):1387-1399.
[24] ANDRADE F G,NORONHA E P,BRISSON G D,et al. Molecular characterization of pediatric acute myeloid leukemia:results of a multicentric study in Brazil[J]. Arch Med Res,2016,47(8):656-667.
[25] TAKAHASHI H,HATTA Y,IRIYAMA N,et al. Induced differentiation of human myeloid leukemia cells into M2 macrophages by combined treatment with retinoic acid and 1α,25-dihydroxyvitamin D3[J]. PLoS One,2014,9(11):e113722.
[26] YAN H,ZHANG D Y,LI X,et al. Long non-coding RNA GAS5 polymorphism predicts a poor prognosis of acute myeloid leukemia in Chinese patients via affecting hematopoietic reconstitution[J]. Leuk Lymphoma,2017,58(8):1948-1957.
[27] AMODIO N,STAMATO M A,JULI G,et al. Drugging the lncRNA MALAT1 via LNA gapmeR ASO inhibits gene expression of proteasome subunits and triggers anti-multiple myeloma activity[J]. Leukemia,2018,32(9):1948-1957.

Bioinformatics analysis of pediatric acute myeloid leukemia based on high-throughput microarray

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 11

Metrics

Comments

Recommended 0

[1]	WANG Zhi, HONG Li, LI Suting, ZENG Wanling. Analysis on endometrial cancer-related genes and candidate pathways based on GEO database bioinformatics methods [J]. Journal of Jilin University(Medicine Edition), 2020, 46(04): 804-809.
[2]	RAN Nan, MA Mingxing, PANG Zhiqiang, WANG Zeyu, LIU Yue, ZHENG Ruipeng, LU Junying, ZHANG Chao, CHEN Guang, ZHANG Hong, WANG Fang. Development and evaluation of thrombosis susceptibility gene chip [J]. Journal of Jilin University(Medicine Edition), 2020, 46(01): 182-187.
[3]	SU Long, ZHANG Yunwei, WANG Zhuo, SONG Fei, QIN Tianxue, GAO Sujun. Proliferation inhibition and pro-apoptotic effects of shikonin on human leukemia MV4-11 cells [J]. Journal of Jilin University(Medicine Edition), 2020, 46(01): 96-101.
[4]	YUAN Yuze, MA Mingxing, WANG Guoqiang, GUAN Xuewa, WANG Ziyan, GUO Yingqiao, ZHENG Jingtong, ZHANG Hong, CHEN Guang, WANG Fang. Preparation of gene chip detection kit for individualized treatment of thrombotic diseases and evaluation on its effectiveness [J]. Journal of Jilin University(Medicine Edition), 2019, 45(06): 1267-1274.
[5]	TAN Qi, REN Liqun, ZHANG Yibing, WANG Yadi, GU Zehui, HUANG Peng, CHEN Suxian. Application of bioinformatics in screening of miRNA biomarkers in triple-negative breast cancer [J]. Journal of Jilin University(Medicine Edition), 2019, 45(05): 1098-1105.
[6]	DU Yayan, LIU Yang, LU Mu, HUAI Shitao, LYU Zuoli, WEI Yutao. Expressions of adiponectin-related miR-371b-5p in epicardial adipose tissue and plasma of patients with coronary heart disease and its effect on secretion of adipocytokines [J]. Journal of Jilin University(Medicine Edition), 2019, 45(03): 643-650.
[7]	YANG Wei, LI Mingcheng. Analysis on gene profile of over-expression WWOX gene in human oral squamous cell carcinoma Tca8113 cells and tumor inhibition mechanism of WWOX gene [J]. Journal of Jilin University(Medicine Edition), 2018, 44(06): 1169-1173.
[8]	HUO Yanwei, XIE Bing, JIANG Lei, ZHANG Rui, SONG Mei, WANG Lan, WANG Xueyi, XU Shunjiang. Expressions of miRNAs related to accelerating senescence in serum of patients with amnestic mild cognitive impairment and analysison their biological information [J]. Journal of Jilin University Medicine Edition, 2017, 43(02): 322-327.
[9]	LI Qian, LI Jingao, ZHOU Maohua, LIAO Pengjun, PENG Qi, CHEN Jing, CHEN Shaoxian, WEI Shanshan, HUANG Huiting, SHE Miaorong. Relationship between CD56 antigen expression in leukemia cells and prognosis of patients with acute myeloid leukemia [J]. Journal of Jilin University Medicine Edition, 2016, 42(02): 283-289.
[10]	WEI Shanshan, ZHOU Maohua, LI Jingao, CHEN Jing, CHEN Shaoxian, LI Qian, XIA Pingfang, PENG Qi, SHE Miaorong. Expressions of CD34 and CD38 antigens in leukemia cells of patients with acute myeloid leukemia and their clinical significances [J]. Journal of Jilin University Medicine Edition, 2015, 41(02): 362-368.
[11]	CHEN Yi-Yang, HUANG Hai-Yan, YANG Shu-Yuan. Effects of 125I on programmed cell death and expressions of genes related to programmed cell death in glioma cell line SHG-44 [J]. J4, 2010, 36(3): 456-460.