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Identification of prognostic genes in the acute myeloid leukemia immune microenvironment based on TCGA data analysis

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Abstract

Acute myeloid leukemia (AML) is a common and lethal hematopoietic malignancy that is highly dependent on the bone marrow (BM) microenvironment. Infiltrating immune and stromal cells are important components of the BM microenvironment and significantly influence the progression of AML. This study aimed to elucidate the value of immune/stromal cell-associated genes for AML prognosis by integrated bioinformatics analysis. We obtained expression profiles from The Cancer Genome Atlas (TCGA) database and used the ESTIMATE algorithm to calculate immune scores and stromal scores; we then identified differentially expressed genes (DEGs) based on these scores. Overall survival analysis was applied to reveal common DEGs of prognostic value. Subsequently, we conducted a functional enrichment analysis, generated a protein–protein interaction (PPI) network and performed an interrelation analysis of immune system processes, showing that these genes are mainly associated with the immune/inflammatory response. Finally, eight genes (CD163, CYP27A1, KCNA5, PPM1J, FOLR1, IL1R2, MYOF, VSIG2) were verified to be significantly associated with AML prognosis in the Gene Expression Omnibus (GEO) database. In summary, we identified key microenvironment-related genes that affect the outcomes of AML patients and might serve as therapeutic targets.

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Abbreviations

AML:

Acute myeloid leukemia

BM:

Bone marrow

BP:

Biological processes

CC:

Cellular component

DAVID:

Database for Annotation, Visualization and Integrated Discovery

DEGs:

Differentially expressed genes

ECM:

Extracellular matrix

EPCs:

Endothelial progenitor cells

ESTIMATE:

Estimation of STromal and Immune cells in Malignant Tumours using Expression data

GEO:

Gene Expression Omnibus

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

MF:

Molecular function

PPI:

Protein–protein interaction

TCGA:

The Cancer Genome Atlas

References

  1. Rowe JM, Tallman MS (2010) How I treat acute myeloid leukemia. Blood 116:3147–3156. https://doi.org/10.1182/blood-2010-05-260117

    Article  CAS  PubMed  Google Scholar 

  2. Weinberg OK, Sohani AR (2017) Diagnostic work-up of acute myeloid leukemia. 92:317–321. https://doi.org/10.1002/ajh.24648

    Article  Google Scholar 

  3. Buckley SA, Kirtane K, Walter RB, Lee SJ, Lyman GH (2018) Patient-reported outcomes in acute myeloid leukemia: Where are we now? Blood Rev 32:81–87. https://doi.org/10.1016/j.blre.2017.08.010

    Article  PubMed  Google Scholar 

  4. Zeng Z, Shi YX, Samudio IJ, Wang RY, Ling X, Frolova O, Levis M, Rubin JB, Negrin RR, Estey EH, Konoplev S, Andreeff M, Konopleva M (2009) Targeting the leukemia microenvironment by CXCR4 inhibition overcomes resistance to kinase inhibitors and chemotherapy in AML. Blood 113:6215–6224. https://doi.org/10.1182/blood-2008-05-158311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Abdul-Aziz AM, Shafat MS, Mehta TK, Di Palma F, Lawes MJ, Rushworth SA, Bowles KM (2017) MIF-induced stromal PKCbeta/IL8 is essential in human acute myeloid leukemia. Can Res 77:303–311. https://doi.org/10.1158/0008-5472.can-16-1095

    Article  CAS  Google Scholar 

  6. Dohner H, Estey EH, Amadori S, Appelbaum FR, Buchner T, Burnett AK, Dombret H, Fenaux P, Grimwade D, Larson RA, Lo-Coco F, Naoe T, Niederwieser D, Ossenkoppele GJ, Sanz MA, Sierra J, Tallman MS, Lowenberg B, Bloomfield CD (2010) Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood 115:453–474. https://doi.org/10.1182/blood-2009-07-235358

    Article  CAS  PubMed  Google Scholar 

  7. Kayser S, Levis MJ (2018) Advances in targeted therapy for acute myeloid leukaemia. 180:484–500. https://doi.org/10.1111/bjh.15032

    Article  Google Scholar 

  8. Ayala F, Dewar R, Kieran M, Kalluri R (2009) Contribution of bone microenvironment to leukemogenesis and leukemia progression. Leukemia 23:2233–2241. https://doi.org/10.1038/leu.2009.175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Austin R, Smyth MJ, Lane SW (2016) Harnessing the immune system in acute myeloid leukaemia. Critical reviews in oncology/hematology 103:62–77. https://doi.org/10.1016/j.critrevonc.2016.04.020

    Article  PubMed  Google Scholar 

  10. Yehudai-Resheff S, Attias-Turgeman S, Sabbah R, Gabay T, Musallam R, Fridman-Dror A, Zuckerman T (2019) Abnormal morphological and functional nature of bone marrow stromal cells provides preferential support for survival of acute myeloid leukemia cells. Int J Cancer 144:2279–2289. https://doi.org/10.1002/ijc.32063

    Article  CAS  PubMed  Google Scholar 

  11. Beyar-Katz O, Gill S (2018) Novel approaches to acute myeloid leukemia immunotherapy. Clin Cancer Res 24:5502–5515. https://doi.org/10.1158/1078-0432.ccr-17-3016

    Article  PubMed  Google Scholar 

  12. Vandsemb EN, Kim TK, Zeidan AM (2019) Will deeper characterization of the landscape of immune checkpoint molecules in acute myeloid leukemia bone marrow lead to improved therapeutic targeting? Cancer 125:1410–1413. https://doi.org/10.1002/cncr.32042

    Article  PubMed  Google Scholar 

  13. Herrmann M, Krupka C, Deiser K, Brauchle B, Marcinek A, Ogrinc Wagner A, Rataj F, Mocikat R, Metzeler KH, Spiekermann K, Kobold S, Fenn NC, Hopfner KP, Subklewe M (2018) Bifunctional PD-1 x alphaCD3 x alphaCD33 fusion protein reverses adaptive immune escape in acute myeloid leukemia. Blood 132:2484–2494. https://doi.org/10.1182/blood-2018-05-849802

    Article  CAS  PubMed  Google Scholar 

  14. Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, Trevino V, Shen H, Laird PW, Levine DA, Carter SL, Getz G, Stemke-Hale K, Mills GB, Verhaak RG (2013) Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4:2612. https://doi.org/10.1038/ncomms3612

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shah N, Wang P, Wongvipat J, Karthaus WR, Abida W, Armenia J, Rockowitz S, Drier Y, Bernstein BE, Long HW (2017) Regulation of the glucocorticoid receptor via a BET-dependent enhancer drives antiandrogen resistance in prostate cancer. eLife 6. https://doi.org/10.7554/eLife.27861

  16. Alonso MH, Aussó S, Lopez-Doriga A, Cordero D, Guinó E, Solé X, Barenys M, de Oca J, Capella G, Salazar R, Sanz-Pamplona R, Moreno V (2017) Comprehensive analysis of copy number aberrations in microsatellite stable colon cancer in view of stromal component. Br J Cancer 117:421–431. https://doi.org/10.1038/bjc.2017.208

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jia D, Li S, Li D, Xue H, Yang D, Liu Y (2018) Mining TCGA database for genes of prognostic value in glioblastoma microenvironment. Aging 10:592–605. https://doi.org/10.18632/aging.101415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lowenberg B, Downing JR, Burnett A (1999) Acute myeloid leukemia. New Engl J Med 341:1051–1062. https://doi.org/10.1056/nejm199909303411407

    Article  CAS  PubMed  Google Scholar 

  19. Slovak ML, Kopecky KJ, Cassileth PA, Harrington DH, Theil KS, Mohamed A, Paietta E, Willman CL, Head DR, Rowe JM, Forman SJ, Appelbaum FR (2000) Karyotypic analysis predicts outcome of preremission and postremission therapy in adult acute myeloid leukemia: a Southwest Oncology Group/Eastern Cooperative Oncology Group Study. Blood 96:4075–4083

    Article  CAS  Google Scholar 

  20. Abdul-Aziz AM, Sun Y, Hellmich C, Marlein CR, Mistry J, Forde E, Piddock RE, Shafat MS, Morfakis A, Mehta T, Di Palma F, Macaulay I, Ingham CJ, Haestier A, Collins A, Campisi J, Bowles KM, Rushworth SA (2019) Acute myeloid leukemia induces protumoral p16INK4a-driven senescence in the bone marrow microenvironment. Blood 133:446–456. https://doi.org/10.1182/blood-2018-04-845420

    Article  CAS  PubMed  Google Scholar 

  21. Tabe Y, Konopleva M (2015) Role of Microenvironment in Resistance to Therapy in AML. Curr Hematol Malig Rep 10:96–103. https://doi.org/10.1007/s11899-015-0253-6

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kogan AA, Lapidus RG, Baer MR, Rassool FV (2019) Exploiting epigenetically mediated changes: Acute myeloid leukemia, leukemia stem cells and the bone marrow microenvironment. Adv Cancer Res 141:213–253. https://doi.org/10.1016/bs.acr.2018.12.005

    Article  PubMed  Google Scholar 

  23. Shafat MS, Oellerich T, Mohr S, Robinson SD, Edwards DR, Marlein CR, Piddock RE, Fenech M, Zaitseva L, Abdul-Aziz A, Turner J, Watkins JA, Lawes M, Bowles KM, Rushworth SA (2017) Leukemic blasts program bone marrow adipocytes to generate a protumoral microenvironment. Blood 129:1320–1332. https://doi.org/10.1182/blood-2016-08-734798

    Article  CAS  PubMed  Google Scholar 

  24. Huang R, Liao X, Li Q (2018) Identification of key pathways and genes in TP53 mutation acute myeloid leukemia: evidence from bioinformatics analysis. OncoTargets Ther 11:163–173. https://doi.org/10.2147/ott.s156003

    Article  CAS  Google Scholar 

  25. Feng Y, Shen Y, Chen H, Wang X, Zhang R, Peng Y, Lei X, Liu T, Liu J, Gu L, Wang F, Yang Y, Bai J, Wang J, Zhao W, He A (2018) Expression profile analysis of long non-coding RNA in acute myeloid leukemia by microarray and bioinformatics. Cancer Sci 109:340–353. https://doi.org/10.1111/cas.13465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Nahas MR, Stroopinsky D (2019) Hypomethylating agent alters the immune microenvironment in acute myeloid leukaemia (AML) and enhances the immunogenicity of a dendritic cell/AML vaccine 185(4):679–690. https://doi.org/10.1111/bjh.15818

    Article  CAS  Google Scholar 

  27. Yang D, Zhang X, Zhang X, Xu Y (2017) The progress and current status of immunotherapy in acute myeloid leukemia. Ann Hematol 96:1965–1982. https://doi.org/10.1007/s00277-017-3148-x

    Article  CAS  PubMed  Google Scholar 

  28. Rotiroti MC, Arcangeli S, Casucci M, Perriello V, Bondanza A, Biondi A, Tettamanti S, Biagi E (2017) Acute myeloid leukemia targeting by chimeric antigen receptor T cells: bridging the gap from preclinical modeling to human studies. Hum Gene Ther 28:231–241. https://doi.org/10.1089/hum.2016.092

    Article  CAS  PubMed  Google Scholar 

  29. Nishioka C, Ikezoe T, Yang J, Nobumoto A, Kataoka S, Tsuda M, Udaka K, Yokoyama A (2014) CD82 regulates STAT5/IL-10 and supports survival of acute myelogenous leukemia cells. Int J Cancer 134:55–64. https://doi.org/10.1002/ijc.28348

    Article  CAS  PubMed  Google Scholar 

  30. Meazza R, Basso S, Gaggero A, Detotero D, Trentin L, Pereno R, Azzarone B, Ferrini S (1998) Interleukin (IL)-15 induces survival and proliferation of the growth factor-dependent acute myeloid leukemia M-07e through the IL-2 receptor beta/gamma. Int J Cancer 78:189–195

    Article  CAS  Google Scholar 

  31. Ignatz-Hoover JJ, Wang H, Moreton SA, Chakrabarti A, Agarwal MK, Sun K, Gupta K, Wald DN (2015) The role of TLR8 signaling in acute myeloid leukemia differentiation. Leukemia 29:918–926. https://doi.org/10.1038/leu.2014.293

    Article  CAS  PubMed  Google Scholar 

  32. Smits EL, Cools N, Lion E, Van Camp K, Ponsaerts P, Berneman ZN, Van Tendeloo VF (2010) The Toll-like receptor 7/8 agonist resiquimod greatly increases the immunostimulatory capacity of human acute myeloid leukemia cells. Cancer Immunol Immunother 59:35–46. https://doi.org/10.1007/s00262-009-0721-8

    Article  CAS  PubMed  Google Scholar 

  33. Walter RB, Bachli EB, Schaer DJ, Ruegg R, Schoedon G (2003) Expression of the hemoglobin scavenger receptor (CD163/HbSR) as immunophenotypic marker of monocytic lineage in acute myeloid leukemia. Blood 101:3755–3756. https://doi.org/10.1182/blood-2002-11-3414

    Article  CAS  PubMed  Google Scholar 

  34. Bachli EB, Schaer DJ, Walter RB, Fehr J, Schoedon G (2006) Functional expression of the CD163 scavenger receptor on acute myeloid leukemia cells of monocytic lineage. J Leukoc Biol 79:312–318. https://doi.org/10.1189/jlb.0605309

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank the TCGA and GEO databases for the availability of the data.

Funding

This work was supported by grants from the National Natural Science Foundation of China [Grant number 91742110) and Zhejiang Provincial Natural Science Foundation (Grant number LY19H080004).

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Author notes

  1. Haimeng Yan and Jianwei Qu contributed equally to this work.

Authors and Affiliations

  1. Bone Marrow Transplantation Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, No.79 Qingchun Rd, Hangzhou, 310006, Zhejiang, China

    Haimeng Yan, Jianwei Qu, Wen Cao, Yang Liu, Gaofeng Zheng, Enfan Zhang & Zhen Cai

  2. Institute of Hematology, Zhejiang University, Hangzhou, Zhejiang, China

    Zhen Cai

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  1. Haimeng Yan
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Contributions

HY and WC conceived and designed the study. JQ obtained the datasets. YL and GZ conducted data analysis. HY wrote the manuscript. EZ and ZC revised the manuscript. All authors reviewed and approved the final manuscript.

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Correspondence to Zhen Cai.

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The authors declare that they have no conflict of interest.

Ethical approval and ethical standards

The data used in our study were obtained from public databases TCGA and GEO, therefore, ethical approval was not required.

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Yan, H., Qu, J., Cao, W. et al. Identification of prognostic genes in the acute myeloid leukemia immune microenvironment based on TCGA data analysis. Cancer Immunol Immunother 68, 1971–1978 (2019). https://doi.org/10.1007/s00262-019-02408-7

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  • DOI: https://doi.org/10.1007/s00262-019-02408-7

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