Analysis of tumor immune-related differentially expressed genes in adults and children with acute myeloid leukemia

doi:10.3969/j.issn.1674-8115.2021.05.004

Abstract

Abstract: Objective

·To analyze tumor immune-related differentially expressed genes (DEGs) in the bone marrow of adults and children with acute myeloid leukemia (AML).

Methods

·The GSE134589 data set was downloaded from the GEO (Gene Expression Omnibus) database, and the patients in initial diagnosis/relapse were selected and divided into children group (0?16 years old, 34 cases), young and middle-aged group (17?59 years old, 62 cases) and elders group (60?80 years old, 62 cases). The tumor immune-related DEGs in the bone marrow samples of different groups of patients were screened by R language packages. The common DEGs of young and middle-aged group vs children group and elders group vs children group were compared with the DEGs of adults group vs children group in complete remission, and the functional enrichment analysis was conducted with the common DEGs. The Kaplan-Meier method was used to screen the DEGs that were significantly related to prognosis, and the core regulatory DEGs were screened by constructing a protein-protein interaction (PPI) network. The genes screened by the above two methods were regarded as key genes. The expression levels of the key genes in AML, diffuse large B cell lymphoma (DLBCL) and thymoma tumor samples and normal human samples were compared by GEPIA server, and the mRNA expression levels of key genes in the human AML tumor stem cells and the original hematopoietic cells of healthy people were analyzed by GEXC website.

Results

·In the GSE134589 data set, there were 51 DEGs up-regulated and 21 down-regulated between the young and middle-aged group and the children group; 47 DEGs were up-regulated and 20 down-regulated between the elders group and the children group; no DEG was found between the young and middle-aged group and the elders group. Fifty-seven tumor immune-related DEGs were screened in both the young and middle-aged group and the elders group, of which 39 were up-regulated and 18 were down-regulated; in these DEGs only 3 genes showed statistically significant difference in expression level when the disease was incomplete remission. The 57 common DEGs were mainly enriched in leukocyte migration and cytokine-related signal pathways. The patients with high expression of interleukin-2 receptor subunit alpha (IL2RA) and FMS-like tyrosine kinase 3 (FLT3) had significantly shorter overall survival than those with low expression (both P<0.05), and complement component 3a receptor 1 (C3AR1) was the core regulatory gene of the PPI network. The three DEGs were selected as key genes. They were all specifically highly expressed in the AML tumor samples (all P<0.05), and IL2RA was also significantly highly expressed in the DLBCL samples (P<0.05). The expression level of IL2RA was low both in the AML tumor stem cells and the original group of hematopoietic cells, but FLT3 was highly expressed in these cells. The expression level of C3AR1 was specifically high in the AML tumor stem cells.

Conclusion

·The difference in the prognosis between adults and children with AML may be related to the differences in the expression of tumor immune-related genes in bone marrow, in which IL2RA, FLT3 and C3AR1 may be the key genes.

Key words: acute myeloid leukemia (AML), tumor immunity, differentially expressed gene (DEG), interleukin-2 receptor subunit alpha (IL2RA), FMS-like tyrosine kinase 3 (FLT3), complement component 3a receptor 1 (C3AR1)

CLC Number:

R733.71

Ling-ling LI, Qian LI, Ming-yu LI, Zheng LIU, Qian-cheng SHEN. Analysis of tumor immune-related differentially expressed genes in adults and children with acute myeloid leukemia[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(5): 579-587.

Figures/Tables 7

Fig 1 Heatmap analysis of tumor immune-related genes expression in the AML patients of different age groups

Fig 2 Volcano map analysis of tumor immune-related genes in the AML patients of different age groups

Fig 3 Venn map and enrichment analysis of tumor immune-related DEGs in the AML patients of different age groups

Tab 1 Comparison of tumor immune-related DEGs between the AML patients in initial diagnosis/relapse and in complete remission

Disease status	Up-regulated DEG	Down-regulated DEG
Initial diagnosis/relapse^①	CD63, *CD53, CD44, CD58, CD74, CD99, CD97, CD244, CD27, CD22, CD3E, CD200, MAPK14, IL1RAP, CASP1, REL, C3AR1, FLT3, RUNX1, BCL2L1, TNFAIP3, TLR1, TNFSF13B, ITGA2B, CD275, PDGFC, TPSAB1, IL2RA, ABCB1, IL32, CCL25, CXCL16, CCL2, CCL23, CXCL2, CCL4, CCL1, CXCL12, KIT*	CD55, MAPK11, CCL24, SPACA3, CD79A, CASP10, CD40LG, CD6, IL34, C3, IL1RAPL2, CD290, CD274, CD9, CD49b, CXCL13, LCN2, LTF
Complete remission^②	IL8, CD83, CD87, *REL*, CDKN1A, *CXCL2*, CD141, CD54, CD354, *CD53*	BLNK, IKBKB

Fig 4 Overall survival analysis of the AML patients with different levels of IL2RA and FLT3 mRNA expression

Fig 5 PPI network and the most significant modules of up-regulated DEGs-corresponding proteins

Fig 6 mRNA expression of key genes in the tumor tissues of the three diseases and in the tumor stem cells of the AML patients and the hematopoietic cells of normal people

TrendMD

References 29

1	Vago L, Gojo I. Immune escape and immunotherapy of acute myeloid leukemia[J]. J Clin Invest, 2020, 130(4): 1552-1564.
2	Döhner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia[J]. N Engl J Med, 2015, 373(12): 1136-1152.
3	Swartz MA, Iida N, Roberts EW, et al. Tumor microenvironment complexity: emerging roles in cancer therapy[J]. Cancer Res, 2012, 72(10): 2473-2480.
4	Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository[J]. Nucleic Acids Res, 2002, 30(1): 207-210.
5	Vadakekolathu J, Minden MD, Hood T, et al. Immune landscapes predict chemotherapy resistance and immunotherapy response in acute myeloid leukemia[J]. Sci Transl Med, 2020, 12(546): eaaz0463.
6	Raney BJ, Dreszer TR, Barber GP, et al. Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser[J]. Bioinformatics, 2014, 30(7): 1003-1005.
7	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.
8	Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets[J]. Nat Commun, 2019, 10(1): 1523.
9	Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses[J]. Nucleic Acids Res, 2017, 45(W1): W98-W102.
10	Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life[J]. Nucleic Acids Res, 2015, 43(Database issue): D447-D452.
11	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.
12	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.
13	Bandettini WP, Kellman P, Mancini C, et al. MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study[J]. J Cardiovasc Magn Reson, 2012, 14: 83.
14	Gentles AJ, Plevritis SK, Majeti R, et al. Association of a leukemic stem cell gene expression signature with clinical outcomes in acute myeloid leukemia[J]. JAMA, 2010, 304(24): 2706-2715.
15	Klemm F, Joyce JA. Microenvironmental regulation of therapeutic response in cancer[J]. Trends Cell Biol, 2015, 25(4): 198-213.
16	Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response[J]. Nature, 2013, 501(7467): 346-354.
17	Bennett JM, Catovsky D, Daniel MT, et al. Proposals for the classification of the acute leukaemias[J]. Br J Haematol, 1976, 33(4): 451-458.
18	Olkhanud PB, Damdinsuren B, Bodogai M, et al. Tumor-evoked regulatory B cells promote breast cancer metastasis by converting resting CD4⁺ T cells to T-regulatory cells[J]. Cancer Res, 2011, 71(10): 3505-3515.
19	Wejksza K, Lee-Chang C, Bodogai M, et al. Cancer-produced metabolites of 5-lipoxygenase induce tumor-evoked regulatory B cells via peroxisome proliferator-activated receptor α[J]. J Immunol, 2013, 190(6): 2575-2584.
20	Yamamoto Y, Kiyoi H, Nakano Y, et al. Activating mutation of D835 within the activation loop of FLT3 in human hematologic malignancies[J]. Blood, 2001, 97(8): 2434-2439.
21	Reca R, Mastellos D, Majka M, et al. Functional receptor for C3a anaphylatoxin is expressed by normal hematopoietic stem/progenitor cells, and C3a enhances their homing-related responses to SDF-1[J]. Blood, 2003, 101(10): 3784-3793.
22	Möhle R, Bautz F, Rafii S, et al. The chemokine receptor CXCR-4 is expressed on CD34⁺ hematopoietic progenitors and leukemic cells and mediates transendothelial migration induced by stromal cell-derived factor-1[J]. Blood, 1998, 91(12): 4523-4530.
23	Lin Y, Ma Q, Li L, et al. The CXCL12-CXCR4 axis promotes migration, invasiveness, and EMT in human papillary thyroid carcinoma B-CPAP cells via NF-κB signaling[J]. Biochem Cell Biol, 2018, 96(5): 619-626.
24	Martin GH, Roy N, Chakraborty S, et al. CD97 is a critical regulator of acute myeloid leukemia stem cell function[J]. J Exp Med, 2019, 216(10): 2362-2377.
25	Zhao K, Yin LL, Zhao DM, et al. IL1RAP as a surface marker for leukemia stem cells is related to clinical phase of chronic myeloid leukemia patients[J]. Int J Clin Exp Med, 2014, 7(12): 4787-4798.
26	Warren CFA, Wong-Brown MW, Bowden NA. BCL-2 family isoforms in apoptosis and cancer[J]. Cell Death Dis, 2019, 10(3): 177.
27	Ferlin M, Noraz N, Hertogh C, et al. Insulin-like growth factor induces the survival and proliferation of myeloma cells through an interleukin-6-independent transduction pathway[J]. Br J Haematol, 2000, 111(2): 626-634.
28	Di Pietro A, Conseil G, Pérez-Victoria JM, et al. Modulation by flavonoids of cell multidrug resistance mediated by P-glycoprotein and related ABC transporters[J]. Cell Mol Life Sci, 2002, 59(2): 307-322.
29	Chiu HY, Sun KH, Chen SY, et al. Autocrine CCL2 promotes cell migration and invasion via PKC activation and tyrosine phosphorylation of paxillin in bladder cancer cells[J]. Cytokine, 2012, 59(2): 423-432.

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