1,057
Views
3
CrossRef citations to date
0
Altmetric
Article

Weighted gene coexpression network analysis identifies critical genes in different subtypes of acute myeloid leukaemia

, , , , &
Pages 925-936 | Received 26 Apr 2020, Accepted 14 Aug 2020, Published online: 27 Aug 2020

References

  • Wang H, Naghavi M, Allen C, et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1459–1544.
  • Sites A. SEER cancer statistics review 1975-2011. Bethesda (MD): National Cancer Institute; 2014.
  • Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.
  • Li S, Garrett-Bakelman FE, Chung SS, et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nat Med. 2016;22(7):792–799.
  • Timms JA, Relton CL, Rankin J, et al. DNA methylation as a potential mediator of environmental risks in the development of childhood acute lymphoblastic leukemia. Epigenomics. 2016;8(4):519–536.
  • Iacobucci I, Mullighan CG. Genetic basis of acute lymphoblastic leukemia. J Clin Oncol. 2017;35(9):975–983.
  • Tadmor T, Polliack A. Epidemiology and environmental risk in hairy cell leukemia. Best Pract Res Clin Haematol. 2015;28(4):175–179.
  • Metayer C, Dahl G, Wiemels J, et al. Childhood leukemia: a preventable disease. Pediatrics. 2016;138(Suppl. 1):S45–S55.
  • Williams LA, Yang JJ, Hirsch BA, et al. Is there etiologic heterogeneity between subtypes of childhood acute lymphoblastic leukemia? A review of variation in risk by subtype. Cancer Epidemiol Biomarkers Prev. 2019;28(5):846–856.
  • Barrington-Trimis JL, Cockburn M, Metayer C, et al. Trends in childhood leukemia incidence over two decades from 1992 to 2013. Int J Cancer. 2017;140(5):1000–1008.
  • Bazyka D, Finch S, Ilienko I, et al. Buccal mucosa micronuclei counts in relation to exposure to low dose-rate radiation from the Chornobyl nuclear accident and other medical and occupational radiation exposures. Environ Health. 2017;16(1):23–70.
  • Sun CC, Li SJ, Chen ZL, et al. Expression and prognosis analyses of runt-related transcription factor family in human leukemia. Mol Ther Oncolyt. 2019;12:103–111.
  • Rabbitts TH. Translocations, master genes, and differences between the origins of acute and chronic leukemias. Cell. 1991;67(4):641–644.
  • Bonnet D, Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med. 1997;3(7):730–737.
  • Shallis RM, Wang R, Davidoff A, et al. Epidemiology of acute myeloid leukemia: recent progress and enduring challenges. Blood Rev. 2019;36:70–87.
  • Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66(1):7–30.
  • Tan TK, Zhang C, Sanda T. Oncogenic transcriptional program driven by TAL1 in T-cell acute lymphoblastic leukemia. Int J Hematol. 2019;109(1):5–17.
  • Mrózek K, Marcucci G, Paschka P, et al. Clinical relevance of mutations and gene-expression changes in adult acute myeloid leukemia with normal cytogenetics: are we ready for a prognostically prioritized molecular classification?. Blood. 2007;109(2):431–448.
  • Consoli ML, Romano A, Parrinello NL, et al. Unusual karyotype in acute myelomonocitic leukemia: a case report. Anticancer Res. 2019;39(8):4329–4332.
  • Li S, Bian H, Cao Y, et al. Identification of novel lncRNAs involved in the pathogenesis of childhood acute lymphoblastic leukemia. Oncol Lett. 2019;17(2):2081–2090.
  • Schlenk RF, Döhner K, Krauter J, et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. N Engl J Med. 2008;358(18):1909–1918.
  • Canaani J, Labopin M, Itälä-Remes M, et al. Prognostic significance of recurring chromosomal abnormalities in transplanted patients with acute myeloid leukemia. Leukemia. 2019;33(8):1944–1952.
  • Wang SY, Cheng WY, Mao YF, et al. Genetic alteration patterns and clinical outcomes of elderly and secondary acute myeloid leukemia. Hematol Oncol. 2019;37(4):456–463.
  • Patel JP, Gönen M, Figueroa ME, et al. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N Engl J Med. 2012;366(12):1079–1089.
  • Papaemmanuil E, Gerstung M, Bullinger L, et al. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med. 2016;374(23):2209–2221.
  • Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4(1):7.
  • Ruan J, Dean AK, Zhang W. A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst Biol. 2010;4(1):8.
  • Yang Y, Han L, Yuan Y, et al. Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nat Commun. 2014;5:3231.
  • Most D, Leiter C, Blednov YA, et al. Synaptic microRNAs coordinately regulate synaptic mRNAs: perturbation by chronic alcohol consumption. Neuropsychopharmacology. 2016;41(2):538–548.
  • Casero D, Sandoval S, Seet CS, et al. Long non-coding RNA profiling of human lymphoid progenitor cells reveals transcriptional divergence of B cell and T cell lineages. Nat Immunol. 2015;16(12):1282–1291.
  • Gonzalez-Jaramillo V, Portilla-Fernandez E, Glisic M, et al. The role of DNA methylation and histone modifications in blood pressure: a systematic review. J Hum Hypertens. 2019;33(10):703–715.
  • Pan JQ, Zhang YQ, Wang JH, et al. lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia. Int J Mol Med. 2017;39(3):663–671.
  • Luo M, Zhang Q, Xia M, et al. Differential co-expression and regulatory network analysis uncover the relapse factor and mechanism of t cell acute leukemia. Mol Ther Nucleic Acids. 2018;12:184–194.
  • Saadatpour A, Guo G, Orkin SH, et al. Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis. Genome Biol. 2014;15(12):525.
  • O'Neill K, Zhang F, Li H, et al. Thymidine kinase 1-a prognostic and diagnostic indicator in ALL and AML patients. Leukemia. 2007;21(3):560–563.
  • Ong YL, McMullin MF, Bailie KEM, et al. High bax expression is a good prognostic indicator in acute myeloid leukaemia. Br J Haematol. 2000;111(1):182–189.
  • Falini B, Nicoletti I, Martelli MF, et al. Acute myeloid leukemia carrying cytoplasmic/mutated nucleophosmin (NPMc + AML): biologic and clinical features. Blood. 2007;109(3):874–885.
  • Teng F, Meng X, Wang X, et al. Expressions of CD8 + TILs, PD-L1 and Foxp3 + TILs in stage I NSCLC guiding adjuvant chemotherapy decisions. Oncotarget. 2016;7(39):64318–64329.
  • Mandai M, Hamanishi J, Abiko K, et al. Immunology and immunotherapy in ovarian cancer. In: Frontiers in ovarian cancer science. Heidelberg, Germany: Springer; 2017. p. 225–242.
  • Zhao Q, Cao L, Guan L, et al. Immunotherapy for gastric cancer: dilemmas and prospect. Brief Funct Genomics. 2019;18(2):107–112.
  • Bedognetti D, Hendrickx W, Marincola FM, et al. Prognostic and predictive immune gene signatures in breast cancer. Curr Opin Oncol. 2015;27(6):433–444.
  • Ma X, Park Y, Mayne ST, et al. Diet, lifestyle, and acute myeloid leukemia in the NIH–AARP cohort. Am J Epidemiol. 2010;171(3):312–322.
  • Chyla BJ, Harb J, Mantis C, et al. Response to venetoclax in combination with low intensity therapy (LDAC or HMA) in untreated patients with acute myeloid leukemia patients with IDH, FLT3 and other mutations and correlations with BCL2 family expression. Blood. 2019;134(Supplement_1):546–546.
  • Pollyea DA, Stevens BM, Jones CL, et al. Venetoclax with azacitidine disrupts energy metabolism and targets leukemia stem cells in patients with acute myeloid leukemia. Nat Med. 2018;24(12):1859–1866.
  • Bryan TO, Neil O, Gary RF, et al. Follow the ATP: tumor energy production: a perspective. Anticancer Agents Med Chem. 2014;14(9):1187–1198.
  • Chen WL, Wang YY, Zhao A, et al. Enhanced fructose utilization mediated by SLC2A5 is a unique metabolic feature of acute myeloid leukemia with therapeutic potential. Cancer Cell. 2016;30(5):779–791.
  • Ignatz-Hoover JJ, Wang H, Moreton SA, et al. The role of TLR8 signaling in acute myeloid leukemia differentiation. Leukemia. 2015;29(4):918–926.
  • Payton JE, Grieselhuber NR, Chang LW, et al. High throughput digital quantification of mRNA abundance in primary human acute myeloid leukemia samples. J Clin Invest. 2009;119(6):1714–1726.
  • Nagata H, Kozaki K-I, Muramatsu T, et al. Genome-wide screening of DNA methylation associated with lymph node metastasis in esophageal squamous cell carcinoma. Oncotarget. 2017;8(23):37740–37750.
  • Pertesi M, Ekdahl L, Palm A, et al. Essential genes shape cancer genomes through linear limitation of homozygous deletions. Commun Biol. 2019;2:262.
  • Abe M, Hamada J-I, Takahashi O, et al. Disordered expression of HOX genes in human non-small cell lung cancer. Oncol Rep. 2006;15(4):797–802.
  • Hur H, Lee JY, Yun HJ, et al. Analysis of HOX gene expression patterns in human breast cancer. Mol Biotechnol. 2014;56(1):64–71.
  • Kuo TL, Cheng KH, Chen LT, et al. Deciphering the potential role of Hox genes in pancreatic cancer. Cancers. 2019;11(5):734.
  • Abo Elwafa R, Gamaleldin M, Ghallab O. The clinical and prognostic significance of FIS1, SPI1, PDCD7 and Ang2 expression levels in acute myeloid leukemia. Cancer Genet. 2019;233–234:84–95.