Skip to content
2000
Volume 32, Issue 27
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Aim

We focused on the FOXN3 gene and selected its antisense transcripts (FOXN3-AS1) to investigate its potential involvement in acute myeloid leukemia (AML).

Background

Several integrated multi-omics datasets have expanded the horizons of the cancer landscape. With the emergence of new high-throughput technologies, a large number of non-coding RNAs have been confirmed to be involved in the pathogenesis of different types of hematological malignancies.

Methods

We conducted experimental validation using quantitative polymerase chain reaction (qPCR) with bone marrow specimens from AML patients. Then, Kaplan-Meier (KM) and Receiver Operating Characteristic (ROC) curves were used to substantiate the prognostic association between FOXN3-AS1 and AML patients within the TCGA database. Correlation between FOXN3-AS1 expression and gene mutation, immune, and immune function using Spearman correlation analysis. To explore the physical and functional interaction between FOXN3-AS1 and the DNMT1 protein, we utilized the RPISeq web tool from Iowa State University. Subsequently, we performed qPCR experiments to test the effect of 5AzaC (DNMT1 inhibitor) on FOXN3-AS1 expression AML cell lines (THP1 and OCI-AML3). We leveraged the “OncoPredict” R package in conjunction with the Genomics of Drug Sensitivity (GDSC) database to predict drug response in AML patients expressing FOXN3-AS1.

Results

We observed a significant upregulation of FOXN3-AS1 expression in AML patients compared to healthy controls using clinical samples. The TCGA database revealed an association between high FOXN3-AS1 expression and adverse prognosis. In our subsequent analysis, genes with poor prognostic implications in AML patients were exclusively identified in the FOXN3-AS1 high-expression group, further corroborating this relationship. AML patients with higher FOXN3-AS1 expression levels may respond less optimally to immunotherapy than patients with lower levels. Besides, we computationally predicted the interaction of FOXN3-AS1 and DNMT1 protein and experimentally confirmed that DNMT1i (GSK-3484862) affects the expression level of FOXN3-AS1. We also found that the chemotherapy drugs (5-Fluorouralic, Cisplatin, Dactolisib, Sapitinib, Temozolomide, Ulixertinib, Vinorelbine, Ruxolitinib, Osimertinib and Cisplatin) showed favorable responses in AML patients with high FOXN3-AS1 expression levels.

Conclusion

Our candidate approach identifies FOXN3-AS1 as a prognostic indicator of survival in AML with a potential immune-related role. The preliminary observations we made on FOXN3-AS1/DNMT1 crosstalk warrant more in-depth invested immunotherapeutic approaches in AML.

Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673311108240926062214
2024-10-11
2025-09-06
Loading full text...

Full text loading...

References

  1. DöhnerH. WeisdorfD.J. BloomfieldC.D. Acute myeloid leukemia.N. Engl. J. Med.2015373121136115210.1056/NEJMra140618426376137
    [Google Scholar]
  2. O’DonnellM.R. TallmanM.S. AbboudC.N. AltmanJ.K. AppelbaumF.R. ArberD.A. BhattV. BixbyD. BlumW. CoutreS.E. De LimaM. FathiA.T. FiorellaM. ForanJ.M. GoreS.D. HallA.C. KropfP. LancetJ. ManessL.J. MarcucciG. MartinM.G. MooreJ.O. OlinR. PekerD. PollyeaD.A. PratzK. RavandiF. ShamiP.J. StoneR.M. StricklandS.A. WangE.S. WieduwiltM. GregoryK. OgbaN. acute myeloid leukemia, version 3.2017, NCCN clinical practice guidelines in oncology.J. Natl. Compr. Canc. Netw.201715792695710.6004/jnccn.2017.011628687581
    [Google Scholar]
  3. PapaemmanuilE. GerstungM. BullingerL. GaidzikV.I. PaschkaP. RobertsN.D. PotterN.E. HeuserM. TholF. BolliN. GundemG. Van LooP. MartincorenaI. GanlyP. MudieL. McLarenS. O’MearaS. RaineK. JonesD.R. TeagueJ.W. ButlerA.P. GreavesM.F. GanserA. DöhnerK. SchlenkR.F. DöhnerH. CampbellP.J. Genomic classification and prognosis in acute myeloid leukemia.N. Engl. J. Med.2016374232209222110.1056/NEJMoa151619227276561
    [Google Scholar]
  4. LevineR.L. ValkP.J.M. Next-generation sequencing in the diagnosis and minimal residual disease assessment of acute myeloid leukemia.Haematologica2019104586887110.3324/haematol.2018.20595530923100
    [Google Scholar]
  5. LiH. SharmaA. LuoK. QinZ.S. SunX. LiuH. DeconPeaker, a deconvolution model to identify cell types based on chromatin accessibility in ATAC-Seq data of mixture samples.Front. Genet.20201139210.3389/fgene.2020.0039232547592
    [Google Scholar]
  6. LiH. SharmaA. MingW. SunX. LiuH. A deconvolution method and its application in analyzing the cellular fractions in acute myeloid leukemia samples.BMC Genomics202021165210.1186/s12864‑020‑06888‑132967610
    [Google Scholar]
  7. El AchiH. Kanagal-ShamannaR. Biomarkers in acute myeloid leukemia: Leveraging next generation sequencing data for optimal therapeutic strategies.Front. Oncol.20211174825010.3389/fonc.2021.74825034660311
    [Google Scholar]
  8. KimY. ThanendrarajanS. Schmidt-WolfI.G.H. Wnt/ß- catenin: a new therapeutic approach to acute myeloid leukemia.Leukemia Res. Treat.201120111410.4061/2011/42896023213543
    [Google Scholar]
  9. GaidzikV.I. BullingerL. SchlenkR.F. ZimmermannA.S. RöckJ. PaschkaP. CorbaciogluA. KrauterJ. SchlegelbergerB. GanserA. SpäthD. KündgenA. Schmidt-WolfI.G.H. GötzeK. NachbaurD. PfreundschuhM. HorstH.A. DöhnerH. DöhnerK. RUNX1 mutations in acute myeloid leukemia: results from a comprehensive genetic and clinical analysis from the AML study group.J. Clin. Oncol.201129101364137210.1200/JCO.2010.30.792621343560
    [Google Scholar]
  10. SippelC. KimY. WallauA. BrossartP. Schmidt- WolfI. WalgerP. AML versus ICU: outcome of septic AML patients in an intensive care setting.J. Cancer Res. Clin. Oncol.201514191645165110.1007/s00432‑015‑1955‑925788431
    [Google Scholar]
  11. MayerK. Hahn-AstC. SchwabK. Schmidt-WolfI.G.H. BrossartP. GlasmacherA. von Lilienfeld-ToalM. Long-term follow-up of Cladribine, high-dose Cytarabine, and Idarubicin as salvage treatment for relapsed acute myeloid leukemia and literature review.Eur. J. Haematol.2020104653854510.1111/ejh.1339532049382
    [Google Scholar]
  12. ZhaoC. WangY. SharmaA. WangZ. ZhengC. WeiY. WuY. LiuP. LiuJ. ZhanX. Schmidt-WolfI. TuF. Identification of the integrated prognostic signature associated with immuno-relevant genes and long non- coding RNAs in acute myeloid leukemia.Cancer Invest.202240866367410.1080/07357907.2022.209623035770858
    [Google Scholar]
  13. BachD.H. LongN.P. LuuT.T.T. AnhN.H. KwonS.W. LeeS.K. The dominant role of forkhead box proteins in cancer.Int. J. Mol. Sci.20181910327910.3390/ijms1910327930360388
    [Google Scholar]
  14. CastanedaM. HollanderP. ManiS.A. Forkhead box transcription factors: Double-edged swords in cancer.Cancer Res.202282112057206510.1158/0008‑5472.CAN‑21‑337135315926
    [Google Scholar]
  15. MaharatiA. MoghbeliM. Forkhead box proteins as the critical regulators of cisplatin response in tumor cells.Eur. J. Pharmacol.202395617593710.1016/j.ejphar.2023.17593737541368
    [Google Scholar]
  16. MoghbeliM. TaghehchianN. AkhlaghipourI. SamsamiY. MaharatiA. Role of forkhead box proteins in regulation of doxorubicin and paclitaxel responses in tumor cells: A comprehensive review.Int. J. Biol. Macromol.202324812599510.1016/j.ijbiomac.2023.12599537499722
    [Google Scholar]
  17. DaiS. QuL. LiJ. ChenY. Toward a mechanistic understanding of DNA binding by forkhead transcription factors and its perturbation by pathogenic mutations.Nucleic Acids Res.20214918102351024910.1093/nar/gkab807
    [Google Scholar]
  18. ShiM.Y. BangI.H. HanC.Y. LeeD.H. ParkB.H. BaeE.J. Statin suppresses sirtuin 6 through miR-495, increasing FoxO1-dependent hepatic gluconeogenesis.Theranostics20201025114161142710.7150/thno.4977033052223
    [Google Scholar]
  19. NakamuraS. HiranoI. OkinakaK. TakemuraT. YokotaD. OnoT. ShigenoK. ShibataK. FujisawaS. OhnishiK. The FOXM1 transcriptional factor promotes the proliferation of leukemia cells through modulation of cell cycle progression in acute myeloid leukemia.Carcinogenesis201031112012202110.1093/carcin/bgq18520823107
    [Google Scholar]
  20. ZhangX. ZengJ. ZhouM. LiB. ZhangY. HuangT. WangL. JiaJ. ChenC. The tumor suppressive role of miRNA-370 by targeting FoxM1 in acute myeloid leukemia.Mol. Cancer20121115610.1186/1476‑4598‑11‑5622900969
    [Google Scholar]
  21. SomervilleT.D.D. WisemanD.H. SpencerG.J. HuangX. LynchJ.T. LeongH.S. WilliamsE.L. CheesmanE. SomervailleT.C.P. Frequent derepression of the mesenchymal transcription factor gene FOXC1 in acute myeloid leukemia.Cancer Cell201528332934210.1016/j.ccell.2015.07.01726373280
    [Google Scholar]
  22. KhanI. HalasiM. PatelA. SchultzR. KalakotaN. ChenY.-H. AardsmaN. LiuL. CrispinoJ.D. MahmudN. FOXM1 contributes to treatment failure in acute myeloid leukemia.JCI Insight.2018315e12158310.1172/jci.insight.121583
    [Google Scholar]
  23. GurnariC. FalconiG. De BellisE. VosoM.T. FabianiE. The role of forkhead box proteins in acute myeloid leukemia.Cancers (Basel)201911686510.3390/cancers1106086531234353
    [Google Scholar]
  24. HeH. ZhangJ. QuY. WangY. ZhangY. YanX. LiY. ZhangR. Novel tumor-suppressor FOXN3 is downregulated in adult acute myeloid leukemia.Oncol. Lett.20191821521152910.3892/ol.2019.1042431423219
    [Google Scholar]
  25. ZhangJ. WangY. MoW. ZhangR. LiY. The clinical and prognostic significance of FOXN3 downregulation in acute myeloid leukaemia.Int. J. Lab. Hematol.202042327027610.1111/ijlh.1316232078244
    [Google Scholar]
  26. Molaei RamsheS. GhaediH. OmraniM.D. GeranpayehL. AlipourB. Ghafouri-FardS. Up-regulation of FOXN3-AS1 in invasive ductal carcinoma of breast cancer patients.Heliyon2021710e0817910.1016/j.heliyon.2021.e0817934703931
    [Google Scholar]
  27. YuH. XuQ. LiuF. YeX. WangJ. MengX. Identification and validation of long noncoding RNA biomarkers in human non-small-cell lung carcinomas.J. Thorac. Oncol.201510464565410.1097/JTO.000000000000047025590602
    [Google Scholar]
  28. GengR. ChenT. ZhongZ. NiS. BaiJ. LiuJ. The m6A-related long noncoding rna signature predicts prognosis and indicates tumor immune infiltration in ovarian cancer.Cancers (Basel)20221416405610.3390/cancers1416405636011053
    [Google Scholar]
  29. BatesS.E. Epigenetic therapies for cancer.N. Engl. J. Med.2020383765066310.1056/NEJMra180503532786190
    [Google Scholar]
  30. TajimaS. SuetakeI. TakeshitaK. NakagawaA. KimuraH. SongJ. Domain structure of the Dnmt1, Dnmt3a, and Dnmt3b DNA Methyltransferases.Adv. Exp. Med. Biol.20221389456810.1007/978‑3‑031‑11454‑0_336350506
    [Google Scholar]
  31. ZhangZ. WangG. LiY. LeiD. XiangJ. OuyangL. WangY. YangJ. Recent progress in DNA methyltransferase inhibitors as anticancer agents.Front. Pharmacol.202213107265110.3389/fphar.2022.107265137077808
    [Google Scholar]
  32. ManX. LiQ. WangB. ZhangH. ZhangS. LiZ. DNMT3A and DNMT3B in breast tumorigenesis and potential therapy.Front. Cell Dev. Biol.20221091672510.3389/fcell.2022.91672535620052
    [Google Scholar]
  33. WongK.K. LawrieC.H. GreenT.M. Oncogenic roles and inhibitors of DNMT1, DNMT3A, and DNMT3B in acute myeloid leukaemia.Biomark. Insights20191410.1177/117727191984645431105426
    [Google Scholar]
  34. PonnusamyL. MahalingaiahP.K.S. SinghK.P. Epigenetic reprogramming and potential application of epigenetic-modifying drugs in acquired chemotherapeutic resistance.Adv. Clin. Chem.20209421925910.1016/bs.acc.2019.07.01131952572
    [Google Scholar]
  35. ContieriB. DuarteB.K.L. LazariniM. Updates on DNA methylation modifiers in acute myeloid leukemia.Ann. Hematol.202099469370110.1007/s00277‑020‑03938‑232025842
    [Google Scholar]
  36. Abou NajemS. KhawajaG. HodrojM.H. BabikianP. RizkS. Adjuvant epigenetic therapy of decitabine and suberoylanilide hydroxamic acid exerts anti-neoplastic effects in acute myeloid leukemia cells.Cells2019812148010.3390/cells812148031766421
    [Google Scholar]
  37. ChungW. KellyA.D. KropfP. FungH. JelinekJ. SuX.Y. RobozG.J. KantarjianH.M. AzabM. IssaJ.P.J. Genomic and epigenomic predictors of response to guadecitabine in relapsed/refractory acute myelogenous leukemia.Clin. Epigenetics201911110610.1186/s13148‑019‑0704‑331331399
    [Google Scholar]
  38. Blagitko-DorfsN. SchlosserP. GreveG. PfeiferD. MeierR. BaudeA. BrocksD. PlassC. LübbertM. Combination treatment of acute myeloid leukemia cells with DNMT and HDAC inhibitors: predominant synergistic gene downregulation associated with gene body demethylation.Leukemia201933494595610.1038/s41375‑018‑0293‑830470836
    [Google Scholar]
  39. ChangE. GangulyS. RajkhowaT. GockeCD. LevisM. KonigH. The combination of FLT3 and DNA methyltransferase inhibition is synergistically cytotoxic to FLT3/ITD acute myeloid leukemia cells.Leukemia.2016305102532
    [Google Scholar]
  40. BarmanP. ReddyD. BhaumikS.R. Mechanisms of antisense transcription initiation with implications in gene expression, genomic integrity and disease pathogenesis.Noncoding RNA2019511110.3390/ncrna501001130669611
    [Google Scholar]
  41. NajafiS. TanS.C. RaeeP. RahmatiY. AsemaniY. LeeE.H.C. HushmandiK. ZarrabiA. ArefA.R. AshrafizadehM. KumarA.P. ErtasY.N. GhaniS. AghamiriS. Gene regulation by antisense transcription: A focus on neurological and cancer diseases.Biomed. Pharmacother.202214511226510.1016/j.biopha.2021.11226534749054
    [Google Scholar]
  42. HuaL. YangN. LiY. HuangK. JiangX. LiuF. YuZ. ChenJ. LaiJ. DuJ. ZengH. Metformin sensitizes AML cells to venetoclax through endoplasmic reticulum stress-CHOP pathway.British J. Haematol.20232025971984
    [Google Scholar]
  43. GuS. HouY. DovatK. DovatS. SongC. GeZ. Synergistic effect of HDAC inhibitor Chidamide with Cladribine on cell cycle arrest and apoptosis by targeting HDAC2/c-Myc/RCC1 axis in acute myeloid leukemia.Exp. Hematol. Oncol.20231212310.1186/s40164‑023‑00383‑536849955
    [Google Scholar]
  44. ChenC. LiF. MaM.M. ZhangS. LiuY. YanZ.L. ChenW. CaoJ. ZengL.Y. WangX.Y. XuK.L. WuQ.Y. Roles of T875N somatic mutation in the activity, structural stability of JAK2 and the transformation of OCI-AML3 cells.Int. J. Biol. Macromol.20191371030104010.1016/j.ijbiomac.2019.07.06531299252
    [Google Scholar]
  45. LiuJ. MinS. KimD. ParkJ. ParkE. PeiS. KohY. ShinD.Y. ByunJ.M. KoM. YoonS.S. HongJ. Pharmacological GLUT3 salvage augments the efficacy of vitamin C-induced TET2 restoration in acute myeloid leukemia.Leukemia20233781638164810.1038/s41375‑023‑01954‑537393342
    [Google Scholar]
  46. GaoZ. XuJ. FanY. QiY. WangS. ZhaoS. GuoX. XueH. DengL. ZhaoR. SunC. ZhangP. LiG. PDIA3P1 promotes Temozolomide resistance in glioblastoma by inhibiting C/EBPβ degradation to facilitate proneural-to-mesenchymal transition.J. Exp. Clin. Cancer Res.202241122310.1186/s13046‑022‑02431‑035836243
    [Google Scholar]
  47. WangH. LiuJ. YangJ. WangZ. ZhangZ. PengJ. WangY. HongL. A novel tumor mutational burden-based risk model predicts prognosis and correlates with immune infiltration in ovarian cancer.Front. Immunol.20221394338910.3389/fimmu.2022.94338936003381
    [Google Scholar]
  48. ZhaoP. ZhenH. ZhaoH. HuangY. CaoB. Identification of hub genes and potential molecular mechanisms related to radiotherapy sensitivity in rectal cancer based on multiple datasets.J. Transl. Med.202321117610.1186/s12967‑023‑04029‑236879254
    [Google Scholar]
  49. LiuH. Emerging agents and regimens for AML.J. Hematol. Oncol.20211414910.1186/s13045‑021‑01062‑w33757574
    [Google Scholar]
  50. VagoL. GojoI. Immune escape and immunotherapy of acute myeloid leukemia.J. Clin. Invest.202013041552156410.1172/JCI12920432235097
    [Google Scholar]
  51. DöhnerH. EsteyE. GrimwadeD. AmadoriS. AppelbaumF.R. BüchnerT. DombretH. EbertB.L. FenauxP. LarsonR.A. LevineR.L. Lo-CocoF. NaoeT. NiederwieserD. OssenkoppeleG.J. SanzM. SierraJ. TallmanM.S. TienH.F. WeiA.H. LöwenbergB. BloomfieldC.D. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel.Blood2017129442444710.1182/blood‑2016‑08‑73319627895058
    [Google Scholar]
  52. BurchertA. Maintenance therapy for FLT3-ITD-mutated acute myeloid leukemia.Haematologica2021106366467010.3324/haematol.2019.24074733472354
    [Google Scholar]
  53. GrobT. SandersM.A. VonkC.M. KavelaarsF.G. RijkenM. HanekampD.W. GradowskaP.L. CloosJ. FløisandY. van Marwijk KooyM. ManzM.G. OssenkoppeleG.J. TickL.W. HavelangeV. LöwenbergB. Jongen-LavrencicM. ValkP.J.M. Prognostic value of FLT3 -internal tandem duplication residual disease in acute myeloid leukemia.J. Clin. Oncol.202341475676510.1200/JCO.22.0071536315929
    [Google Scholar]
  54. FaliniB. DillonR. Criteria for Diagnosis and Molecular Monitoring of NPM1 -Mutated AML.Blood Cancer Discov.20245182010.1158/2643‑3230.BCD‑23‑014437917833
    [Google Scholar]
  55. FaliniB. NPM1-mutated acute myeloid leukemia: New pathogenetic and therapeutic insights and open questions.Am. J. Hematol.20239891452146410.1002/ajh.2698937317978
    [Google Scholar]
  56. ShinD.Y. TP53 mutation in acute myeloid leukemia: An old foe revisited.Cancers (Basel)20231519481610.3390/cancers1519481637835510
    [Google Scholar]
  57. MuppiralaU.K. HonavarV.G. DobbsD. Predicting RNA-protein interactions using only sequence information.BMC Bioinformatics201112148910.1186/1471‑2105‑12‑48922192482
    [Google Scholar]
  58. YangW. SoaresJ. GreningerP. EdelmanE.J. LightfootH. ForbesS. BindalN. BeareD. SmithJ.A. ThompsonI.R. RamaswamyS. FutrealP.A. HaberD.A. StrattonM.R. BenesC. McDermottU. GarnettM.J. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.Nucleic Acids Res.201241D1D955D96110.1093/nar/gks111123180760
    [Google Scholar]
  59. YueC. XieS. ZhongJ. ZhaoH. LinZ. ZhangL. XuB. LuoY. SCAMP2/5 as diagnostic and prognostic markers for acute myeloid leukemia.Sci. Rep.20211111701210.1038/s41598‑021‑96440‑234426610
    [Google Scholar]
  60. XiaL. GuoH. WuX. XuY. ZhaoP. YanB. ZengY. HeY. ChenD. GaleR.P. ZhangY. ZhangX. Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia.Theranostics20231341289130110.7150/thno.8005436923527
    [Google Scholar]
  61. SunH. XieY. WuX. HuW. ChenX. WuK. WangH. ZhaoS. ShiQ. WangX. CuiB. WuW. FanR. RaoJ. WangR. WangY. ZhongY. YuH. ZhouB.S. ShenS. LiuY. circRNAs as prognostic markers in pediatric acute myeloid leukemia.Cancer Lett.202459121688010.1016/j.canlet.2024.21688038621457
    [Google Scholar]
  62. SunJ. LiH. HuoQ. CuiM. GeC. ZhaoF. TianH. ChenT. YaoM. LiJ. The transcription factor FOXN3 inhibits cell proliferation by downregulating E2F5 expression in hepatocellular carcinoma cells.Oncotarget2016728435344354510.18632/oncotarget.978027259277
    [Google Scholar]
  63. WangC. TuH. YangL. MaC. HuJ. LuoJ. WangH. FOXN3 inhibits cell proliferation and invasion via modulating the AKT/MDM2/p53 axis in human glioma.Aging (Albany NY)20211317215872159810.18632/aging.20349934511432
    [Google Scholar]
  64. ZhaoC. MoL. LiC. HanS. ZhaoW. LiuL. FOXN3 suppresses the growth and invasion of papillary thyroid cancer through the inactivation of Wnt/β-catenin pathway.Mol. Cell. Endocrinol.202051511092510.1016/j.mce.2020.11092532619584
    [Google Scholar]
  65. RobertsonE. PerryC. DohertyR. MadhusudanS. Transcriptomic profiling of forkhead box transcription factors in adult glioblastoma multiforme.Cancer Genomics Proteomics201512310311225977169
    [Google Scholar]
  66. LvC. SunL. GuoZ. LiH. KongD. XuB. LinL. LiuT. GuoD. ZhouJ. LiY. Circular RNA regulatory network reveals cell–cell crosstalk in acute myeloid leukemia extramedullary infiltration.J. Transl. Med.201816136110.1186/s12967‑018‑1726‑x30558617
    [Google Scholar]
  67. LiuP. MaQ. ChenH. ZhangL. ZhangX. Identification of RHOBTB2 aberration as an independent prognostic indicator in acute myeloid leukemia.Aging (Albany NY)20211311152691528410.18632/aging.20308734074803
    [Google Scholar]
  68. DunneJ. CullmannC. RitterM. SoriaN.M. DrescherB. DebernardiS. SkoulakisS. HartmannO. KrauseM. KrauterJ. NeubauerA. YoungB.D. HeidenreichO. siRNA-mediated AML1/MTG8 depletion affects differentiation and proliferation-associated gene expression in t(8;21)-positive cell lines and primary AML blasts.Oncogene200625456067607810.1038/sj.onc.120963816652140
    [Google Scholar]
  69. ZhangJ. ZhangL. CuiH. ZhangX. ZhangG. YangX. YangS. ZhangZ. WangJ. HuK. ShiJ. KeX. FuL. High expression levels of SMAD3 and SMAD7 at diagnosis predict poor prognosis in acute myeloid leukemia patients undergoing chemotherapy.Cancer Gene Ther.2019265-611912710.1038/s41417‑018‑0044‑z30177817
    [Google Scholar]
  70. MetzelerK.H. HeroldT. Rothenberg-ThurleyM. AmlerS. SauerlandM.C. GörlichD. SchneiderS. KonstandinN.P. DufourA. BräundlK. KsienzykB. ZellmeierE. HartmannL. GreifP.A. FieglM. SubkleweM. BohlanderS.K. KrugU. FaldumA. BerdelW.E. WörmannB. BüchnerT. HiddemannW. BraessJ. SpiekermannK. Spectrum and prognostic relevance of driver gene mutations in acute myeloid leukemia.Blood2016128568669810.1182/blood‑2016‑01‑69387927288520
    [Google Scholar]
  71. YangF. AnekpuritanangT. PressR.D. Clinical utility of next-generation sequencing in acute myeloid leukemia.Mol. Diagn. Ther.202024111310.1007/s40291‑019‑00443‑931848884
    [Google Scholar]
  72. Al-MataryY.S. BotezatuL. OpalkaB. HönesJ.M. LamsR.F. ThivakaranA. SchütteJ. KösterR. LennartzK. SchroederT. HaasR. DührsenU. KhandanpourC. Acute myeloid leukemia cells polarize macrophages towards a leukemia supporting state in a Growth factor independence 1 dependent manner.Haematologica2016101101216122710.3324/haematol.2016.14318027390361
    [Google Scholar]
  73. JiaM. ZhangH. WangL. ZhaoL. FanS. XiY. Identification of mast cells as a candidate significant target of immunotherapy for acute myeloid leukemia.Hematology202126128429410.1080/16078454.2021.188915833648435
    [Google Scholar]
  74. TettamantiS. PievaniA. BiondiA. DottiG. SerafiniM. Catch me if you can: how AML and its niche escape immunotherapy.Leukemia2022361132210.1038/s41375‑021‑01350‑x34302116
    [Google Scholar]
  75. VadakekolathuJ. MindenM.D. HoodT. ChurchS.E. ReederS. AltmannH. SullivanA.H. VibochE.J. PatelT. IbrahimovaN. WarrenS.E. ArrudaA. LiangY. SmithT.H. FouldsG.A. BaileyM.D. Gowen-MacDonaldJ. MuthJ. SchmitzM. CesanoA. PockleyA.G. ValkP.J.M. LöwenbergB. BornhäuserM. TasianS.K. RettigM.P. Davidson-MoncadaJ.K. DiPersioJ.F. RutellaS. Immune landscapes predict chemotherapy resistance and immunotherapy response in acute myeloid leukemia.Sci. Transl. Med.202012546eaaz046310.1126/scitranslmed.aaz046332493790
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673311108240926062214
Loading
/content/journals/cmc/10.2174/0109298673311108240926062214
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.


  • Article Type:
    Research Article
Keyword(s): acute myeloid leukemia; antisense transcripts; DNMT1; FOXN3; FOXN3-AS1; prognostic
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test