Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia

Li,Guilan; Gao,Yang; Li,Kun; Lin,Anqi; Jiang,Zujun

doi:10.3892/ol.2020.11700

Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia

Authors:
- Guilan Li
- Yang Gao
- Kun Li
- Anqi Lin
- Zujun Jiang
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Affiliations: Department of Hematology, General Hospital of Southern Theatre Command of PLA, Guangzhou, Guangdong 510010, P.R. China, Department of Oncology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, P.R. China
Published online on: June 5, 2020 https://doi.org/10.3892/ol.2020.11700
Pages: 1824-1834
Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Acute myeloid leukemia (AML) is the most common childhood cancer and is a major cause of morbidity among adults with hematologic malignancies. Several novel genetic alterations, which target critical cellular pathways, including alterations in lymphoid development‑regulating genes, tumor suppressors and oncogenes that contribute to leukemogenesis, have been identified. The present study aimed to identify molecular markers associated with the occurrence and poor prognosis of AML. Information on these molecular markers may facilitate prediction of clinical outcomes. Clinical data and RNA expression profiles of AML specimens from The Cancer Genome Atlas database were assessed. Mutation data were analyzed and mapped using the maftools package in R software. Kyoto Encyclopedia of Genes and Genomes, Reactome and Gene Ontology analyses were performed using the clusterProfiler package in R software. Furthermore, Kaplan‑Meier survival analysis was performed using the survminer package in R software. The expression data of RNAs were subjected to univariate Cox regression analysis, which demonstrated that the mutation loads varied considerably among patients with AML. Subsequently, the expression data of mRNAs, microRNAs (miRNAs/miR) and long non‑coding RNAs (lncRNAs) were subjected to univariate Cox regression analysis to determine the the 100 genes most associated with the survival of patients with AML, which revealed 48 mRNAs and 52 miRNAs. The top 1,900 mRNAs (P<0.05) were selected through enrichment analysis to determine their functional role in AML prognosis. The results demonstrated that these molecules were involved in the transforming growth factor‑β, SMAD and fibroblast growth factor receptor‑1 fusion mutant signaling pathways. Survival analysis indicated that patients with AML, with high MYH15, TREML2, ATP13A2, MMP7, hsa‑let‑7a‑2‑3p, hsa‑miR‑362‑3p, hsa‑miR‑500a‑5p, hsa‑miR‑500b‑5p, hsa‑miR‑362‑5p, LINC00987, LACAT143, THCAT393, THCAT531 and KHCAT230 expression levels had a shorter survival time compared with those without these factors. Conversely, a high KANSL1L expression level in patients was associated with a longer survival time. The present study determined genetic mutations, mRNAs, miRNAs, lncRNAs and signaling pathways involved in AML, in order to elucidate the underlying molecular mechanisms of the development and recurrence of this disease.

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1	Tallman MS, Wang ES, Altman JK, Appelbaum FR, Bhatt VR, Bixby D, Coutre SE, De Lima M, Fathi AT, Fiorella M, et al: Acute myeloid leukemia, version 3.2019, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 17:721–749. 2019. View Article : Google Scholar : PubMed/NCBI
2	National Cancer Institute, . SEER cancer statistics review, 1975–2015: Overview, median age at diagnosis. September 10–2018
3	Juliusson G: Older patients with acute myeloid leukemia benefit from intensive chemotherapy: An update from the Swedish acute leukemia registry. Clin Lymphoma Myeloma Leuk. 11 (Suppl 1):S54–S59. 2011. View Article : Google Scholar : PubMed/NCBI
4	DiNardo C and Lachowiez C: Acute myeloid leukemia: From mutation profiling to treatment decisions. Curr Hematol Malig Rep. 14:386–394. 2019.PubMed/NCBI
5	Stuani L, Sabatier M and Sarry JE: Exploiting metabolic vulnerabilities for personalized therapy in acute myeloid leukemia. BMC Biol. 17:572019. View Article : Google Scholar : PubMed/NCBI
6	Pagana L, Pulsoni A, Tosti ME, Avvisati G, Mele L, Mele M, Martino B, Visani G, Cerri R, Di Bona E, et al: Clinical and biological features of acute myeloid leukaemia occurring as second malignancy: GIMEMA archive of adult acute leukaemia. Br J Haematol. 112:109–117. 2001. View Article : Google Scholar : PubMed/NCBI
7	Pulsoni A, Pagano L, Lo Coco F, Avvisati G, Mele L, Di Bona E, Invernizzi R, Leoni F, Marmont F, Mele A, et al: Clinicobiological features and outcome of acute promyelocytic leukemia occurring as a second tumor: The GIMEMA experience. Blood. 100:1972–1976. 2002. View Article : Google Scholar : PubMed/NCBI
8	Tomczak K, Czerwińska P and Wiznerowicz M: The cancer genome atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol (Pozn). 19:A68–A77. 2015.PubMed/NCBI
9	Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 6:pl12013. View Article : Google Scholar : PubMed/NCBI
10	Alboukadel K, Marcin K and Przemyslaw B: Drawing Survival Curves using ‘ggplot2’. R package version 0.4.6. September 9–2019
11	Chambers J: Programming with R. Software for data analysis. Springer-Verlag. (New York, NY). 2008. View Article : Google Scholar
12	Yu G, Wang LG, Han Y and He QY: clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS. 16:284–287. 2012. View Article : Google Scholar : PubMed/NCBI
13	Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C and Chanda SK: Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 10:15232019. View Article : Google Scholar : PubMed/NCBI
14	Wickham H: ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics. 3:180–185. 2011. View Article : Google Scholar
15	Gu Z, Eils R and Schlesner M: Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 32:2847–2849. 2016. View Article : Google Scholar : PubMed/NCBI
16	Ilyas AM, Ahmad S, Faheem M, Naseer MI, Kumosani TA, Al-Qahtani MH, Gari M and Ahmed F: Next generation sequencing of acute myeloid leukemia: Influencing prognosis. BMC Genomics. 16 (Suppl 1):S52015. View Article : Google Scholar : PubMed/NCBI
17	Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS, Ritchey JK, Young MA, Lamprecht T, McLellan MD, et al: Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature. 481:506–510. 2012. View Article : Google Scholar : PubMed/NCBI
18	Martelli MP, Sportoletti P, Tiacci E, Martelli MF and Falini B: Mutational landscape of AML with normal cytogenetics: Biological and clinical implications. Blood Rev. 27:13–22. 2013. View Article : Google Scholar : PubMed/NCBI
19	Tomasson MH, Xiang Z, Walgren R, Zhao Y, Kasai Y, Miner T, Ries RE, Lubman O, Fremont DH, McLellan MD, et al: Somatic mutations and germline sequence variants in the expressed tyrosine kinase genes of patients with de novo acute myeloid leukemia. Blood. 111:4797–4808. 2008. View Article : Google Scholar : PubMed/NCBI
20	Lindsley RC, Mar BG, Mazzola E, Grauman PV, Shareef S, Allen SL, Pigneux A, Wetzler M, Stuart RK, Erba HP, et al: Acute myeloid leukemia ontogeny is defined by distinct somatic mutations. Blood. 125:1367–1376. 2015. View Article : Google Scholar : PubMed/NCBI
21	Cancer Genome Atlas Research Network, . Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A, Hoadley K, Triche TJ Jr, Laird PW, et al: Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 368:2059–2074. 2013. View Article : Google Scholar : PubMed/NCBI
22	Marcucci G, Haferlach T and Döhner H: Molecular genetics of adult acute myeloid leukemia: Prognostic and therapeutic implications. J Clin Oncol. 29:475–486. 2011. View Article : Google Scholar : PubMed/NCBI
23	Wu H, Li P, Shao N, Ma J, Ji M, Sun X, Ma D and Ji C: Aberrant expression of Treg-associated cytokine IL-35 along with IL-10 and TGF-β in acute myeloid leukemia. Oncol Lett. 3:1119–1123. 2012. View Article : Google Scholar : PubMed/NCBI
24	Chen J, Mu Q, Li X, Yin X, Yu M, Jin J, Li C, Zhou Y, Zhou J, Suo S, et al: Homoharringtonine targets Smad3 and TGF-β pathway to inhibit the proliferation of acute myeloid leukemia cells. Oncotarget. 8:40318–40326. 2017. View Article : Google Scholar : PubMed/NCBI
25	De Visser KE and Kast WM: Effects of TGF-beta on the immune system: Implications for cancer immunotherapy. Leukemia. 13:1188–1199. 1999. View Article : Google Scholar : PubMed/NCBI
26	Lin HK, Bergmann S and Pandolfi PP: Deregulated TGF-beta signaling in leukemogenesis. Oncogene. 24:5693–5700. 2005. View Article : Google Scholar : PubMed/NCBI
27	Massagué J: TGFβ signalling in context. Nat Rev Mol Cell Biol. 13:616–630. 2012. View Article : Google Scholar : PubMed/NCBI
28	Massagué J: TGFbeta in cancer. Cell. 134:215–230. 2008. View Article : Google Scholar : PubMed/NCBI
29	Ruscetti FW, Akel S and Bartelmez SH: Autocrine transforming growth factor-beta regulation of hematopoiesis: Many outcomes that depend on the context. Oncogene. 24:5751–5763. 2005. View Article : Google Scholar : PubMed/NCBI
30	Matsunawa M, Ishii Y, Kasukabe T, Tomoyasu S, Ota H and Honma Y: Cotylenin A-induced differentiation is independent of the transforming growth factor-beta signaling system in human myeloid leukemia HL-60 cells. Leuk Lymphoma. 47:733–740. 2006. View Article : Google Scholar : PubMed/NCBI
31	Macdonald D, Reiter A and Cross NC: The 8p11 myeloproliferative syndrome: A distinct clinical entity caused by constitutive activation of FGFR1. Acta Haematol. 107:101–107. 2002. View Article : Google Scholar : PubMed/NCBI
32	Ren M, Li X and Cowell JK: Genetic fingerprinting of the development and progression of T-cell lymphoma in a murine model of atypical myeloproliferative disorder initiated by the ZNF198-fibroblast growth factor receptor-1 chimeric tyrosine kinase. Blood. 114:1576–8154. 2009. View Article : Google Scholar : PubMed/NCBI
33	Jackson CC, Medeiros LJ and Miranda RN: 8p11 myeloproliferative syndrome: A review. Hum Pathol. 41:461–476. 2010. View Article : Google Scholar : PubMed/NCBI
34	Yu J, Chen L, Cui B, Widhopf GF II, Shen Z, Wu R, Zhang L, Zhang S, Briggs SP and Kipps TJ: Wnt5a induces ROR1/ROR2 heterooligomerization to enhance leukemia chemotaxis and proliferation. J Clin Invest. 126:585–598. 2016. View Article : Google Scholar : PubMed/NCBI
35	Agarwal A, Das K, Lerner N, Sathe S, Cicek M, Casey G and Sizemore N: The AKT/IκB kinase pathway promotes angiogenic/metastatic gene expression in colorectal cancer by activating nuclear factor-κB and β-catenin. Oncogene. 24:1021–1031. 2005. View Article : Google Scholar : PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI PubMed/NCBI