Skip to content
2000
image of The ER Stress-related Gene Prognostic Signature for Predicting 
Chemosensitivity and Prognosis in AML

Abstract

Introduction

Acute myeloid leukemia is characterized by high heterogeneity, and the current European Leukemia Net (ELN) risk stratification system is not universally applicable to all AML patients, requiring approximately three weeks for testing.

Aim

This study aimed to develop an applicable prognostic tool capable of addressing the limitations of current methods. We selected AML patients from the clinic and TCGA database to explore the role of ER stress in response to chemotherapy.

Methods

Patients from the TCGA database were employed as the training cohort, and two GEO datasets were used as external validation cohorts. Univariate/multivariate COX and LASSO regression were exemplified to establish the prognostic model. Kaplan-Meier and time-dependent ROC were used to assess and compare the efficiency of the model with ELN stratification and other models. In the training cohort, we selected 5 ER stress-related genes to predict chemosensitivity and establish the ERS-5 prognostic model.

Results

The model successfully predicted the overall survival of patients (p < 0.0001, HR = 4.86 (2.79-8.44); AUC = 0.83). It was verified in validation cohorts and could further stratify the risk of various AML subgroups. It also enhanced the ability of ELN to predict the response of patients with AML to main chemotherapeutic drugs. Finally, an “ERS-5” risk score was constructed by the nomogram based on the ERS-5 model and age.

Conclusion

Consequently, in this study, the ERS-5 model was constructed, which allowed more rapid (about 3 hours) and accurate risk stratification and complemented the ability of ELN to assess chemosensitivity.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/pra/10.2174/0115748928349165250202172440
2025-02-07
2025-11-13

  • From This Site

    /content/journals/pra/10.2174/0115748928349165250202172440
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Kantarjian H.M. Kadia T.M. DiNardo C.D. Welch M.A. Ravandi F. Acute myeloid leukemia: Treatment and research outlook for 2021 and the MD Anderson approach. Cancer 2021 127 8 1186 1207 10.1002/cncr.33477 33734442
    [Google Scholar]
  2. El Chaer F. Hourigan C.S. Zeidan A.M. How I. How I treat AML incorporating the updated classifications and guidelines. Blood 2023 141 23 2813 2823 36758209
    [Google Scholar]
  3. Döhner H. Estey E. Grimwade D. Amadori S. Appelbaum F.R. Büchner T. Dombret H. Ebert B.L. Fenaux P. Larson R.A. Levine R.L. Lo-Coco F. Naoe T. Niederwieser D. Ossenkoppele G.J. Sanz M. Sierra J. Tallman M.S. Tien H.F. Wei A.H. Löwenberg B. Bloomfield C.D. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 2017 129 4 424 447 10.1182/blood‑2016‑08‑733196 27895058
    [Google Scholar]
  4. Lachowiez CA Long N Saultz JN Gandhi A Newell LF Hayes-Lattin B Comparison and validation of the 2022 European LeukemiaNet guidelines in acute myeloid leukemia. Blood Advances 2023 7 9 1899 1909 2022
    [Google Scholar]
  5. Wang M. Lindberg J. Klevebring D. Nilsson C. Mer A.S. Rantalainen M. Lehmann S. Grönberg H. Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling. Leukemia 2017 31 10 2029 2036 10.1038/leu.2017.48 28167833
    [Google Scholar]
  6. Nimer S.D. Is it important to decipher the heterogeneity of “normal karyotype AML”? Best Pract. Res. Clin. Haematol. 2008 21 1 43 52 10.1016/j.beha.2007.11.010 18342811
    [Google Scholar]
  7. Bataller A. Garrido A. Guijarro F. Oñate G. Diaz-Beyá M. Arnan M. Tormo M. Vives S. de Llano M.P.Q. Coll R. Gallardo D. Vall-Llovera F. Escoda L. Garcia-Guiñon A. Salamero O. Sampol A. Merchan B.M. Bargay J. Castaño-Díez S. Esteban D. Oliver-Caldés A. Rivero A. Mozas P. López-Guerra M. Pratcorona M. Zamora L. Costa D. Rozman M. Nomdedéu J.F. Colomer D. Brunet S. Sierra J. Esteve J. European LeukemiaNet 2017 risk stratification for acute myeloid leukemia: validation in a risk-adapted protocol. Blood Adv. 2022 6 4 1193 1206 10.1182/bloodadvances.2021005585 34911079
    [Google Scholar]
  8. Walter R.B. Estey E.H. Selection of initial therapy for newly-diagnosed adult acute myeloid leukemia: Limitations of predictive models. Blood Rev. 2020 44 100679 10.1016/j.blre.2020.100679 32249005
    [Google Scholar]
  9. Cellworks Group Cellworks CBM biosimulation identifies genomic causes for induction failure in AML patients and suggests alternative therapies. Paper presented at the all-virtual 62nd American Society of Hematology (ASH) Annual Meeting SOUTH SAN FRANCISCO, Calif., 2020
    [Google Scholar]
  10. Long L. Assaraf Y.G. Lei Z.N. Peng H. Yang L. Chen Z.S. Ren S. Genetic biomarkers of drug resistance: A compass of prognosis and targeted therapy in acute myeloid leukemia. Drug Resist. Updat. 2020 52 100703 10.1016/j.drup.2020.100703 32599434
    [Google Scholar]
  11. Chen X. Cubillos-Ruiz J.R. Endoplasmic reticulum stress signals in the tumour and its microenvironment. Nat. Rev. Cancer 2021 21 2 71 88 10.1038/s41568‑020‑00312‑2 33214692
    [Google Scholar]
  12. Khateb A. Ronai Z.A. Unfolded Protein Response in Leukemia: From Basic Understanding to Therapeutic Opportunities. Trends Cancer 2020 6 11 960 973 10.1016/j.trecan.2020.05.012 32540455
    [Google Scholar]
  13. Hetz C. Zhang K. Kaufman R.J. Mechanisms, regulation and functions of the unfolded protein response. Nat. Rev. Mol. Cell Biol. 2020 21 8 421 438 10.1038/s41580‑020‑0250‑z 32457508
    [Google Scholar]
  14. Hetz C. Papa F.R. The Unfolded Protein Response and Cell Fate Control. Mol. Cell 2018 69 2 169 181 10.1016/j.molcel.2017.06.017 29107536
    [Google Scholar]
  15. Śniegocka M. Liccardo F. Fazi F. Masciarelli S. Understanding ER homeostasis and the UPR to enhance treatment efficacy of acute myeloid leukemia. Drug Resist. Updat. 2022 64 100853 10.1016/j.drup.2022.100853 35870226
    [Google Scholar]
  16. Szczęśniak P.P. Heidelberger J.B. Serve H. Beli P. Wagner S.A. VCP inhibition induces an unfolded protein response and apoptosis in human acute myeloid leukemia cells. PLoS One 2022 17 4 e0266478 10.1371/journal.pone.0266478 35385564
    [Google Scholar]
  17. Masciarelli S. Capuano E. Ottone T. Divona M. Lavorgna S. Liccardo F. Śniegocka M. Travaglini S. Noguera N.I. Picardi A. Petrozza V. Fatica A. Tamagnone L. Voso M.T. Lo Coco F. Fazi F. Retinoic acid synergizes with the unfolded protein response and oxidative stress to induce cell death in FLT3-ITD+ AML. Blood Adv. 2019 3 24 4155 4160 10.1182/bloodadvances.2019000540 31834935
    [Google Scholar]
  18. Sun Y. Ma Y. Jia X. Yao Q. Chen J. Li H. Inducement of ER Stress by PAD Inhibitor BB-Cl-Amidine to Effectively Kill AML Cells. Curr. Med. Sci. 2022 42 5 958 965 10.1007/s11596‑022‑2637‑x 36245030
    [Google Scholar]
  19. Shimizu T. Kamel W.A. Yamaguchi-Iwai S. Fukuchi Y. Muto A. Saya H. Calcitriol exerts an anti‐tumor effect in osteosarcoma by inducing the endoplasmic reticulum stress response. Cancer Sci. 2017 108 9 1793 1802 10.1111/cas.13304 28643892
    [Google Scholar]
  20. Nishimura N. Radwan M.O. Amano M. Endo S. Fujii E. Hayashi H. Ueno S. Ueno N. Tatetsu H. Hata H. Okamoto Y. Otsuka M. Mitsuya H. Matsuoka M. Okuno Y. Novel p97/ VCP inhibitor induces endoplasmic reticulum stress and apoptosis in both bortezomib‐sensitive and ‐resistant multiple myeloma cells. Cancer Sci. 2019 110 10 3275 3287 10.1111/cas.14154 31368616
    [Google Scholar]
  21. Schardt J.A. Weber D. Eyholzer M. Mueller B.U. Pabst T. Activation of the unfolded protein response is associated with favorable prognosis in acute myeloid leukemia. Clin. Cancer Res. 2009 15 11 3834 3841 10.1158/1078‑0432.CCR‑08‑2870 19470730
    [Google Scholar]
  22. Geeleher P. Cox N. Huang R.S. 2014
  23. Mihăilă R.G. Mihaila. Venetoclax in acute myeloid leukemia. Recent Patents Anticancer Drug Discov. 2023 18 1 11 28 [J]. 10.2174/1574892817666220429105338
    [Google Scholar]
  24. Zeng T. Cui L. Huang W. Liu Y. Si C. Qian T. Deng C. Fu L. The establishment of a prognostic scoring model based on the new tumor immune microenvironment classification in acute myeloid leukemia. BMC Med. 2021 19 1 176 10.1186/s12916‑021‑02047‑9 34348737
    [Google Scholar]
  25. Yang Z. Shang J. Li N. Zhang L. Tang T. Tian G. Chen X. Development and validation of a 10‐gene prognostic signature for acute myeloid leukaemia. J. Cell. Mol. Med. 2020 24 8 4510 4523 10.1111/jcmm.15109 32150667
    [Google Scholar]
  26. Li Z. Herold T. He C. Valk P.J.M. Chen P. Jurinovic V. Mansmann U. Radmacher M.D. Maharry K.S. Sun M. Yang X. Huang H. Jiang X. Sauerland M.C. Büchner T. Hiddemann W. Elkahloun A. Neilly M.B. Zhang Y. Larson R.A. Le Beau M.M. Caligiuri M.A. Döhner K. Bullinger L. Liu P.P. Delwel R. Marcucci G. Lowenberg B. Bloomfield C.D. Rowley J.D. Bohlander S.K. Chen J. Identification of a 24-gene prognostic signature that improves the European LeukemiaNet risk classification of acute myeloid leukemia: an international collaborative study. J. Clin. Oncol. 2013 31 9 1172 1181 10.1200/JCO.2012.44.3184 23382473
    [Google Scholar]
  27. Ng S.W.K. Mitchell A. Kennedy J.A. Chen W.C. McLeod J. Ibrahimova N. Arruda A. Popescu A. Gupta V. Schimmer A.D. Schuh A.C. Yee K.W. Bullinger L. Herold T. Görlich D. Büchner T. Hiddemann W. Berdel W.E. Wörmann B. Cheok M. Preudhomme C. Dombret H. Metzeler K. Buske C. Löwenberg B. Valk P.J.M. Zandstra P.W. Minden M.D. Dick J.E. Wang J.C.Y. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 2016 540 7633 433 437 10.1038/nature20598 27926740
    [Google Scholar]
  28. Pan M. Zhou J. Jiao C. Ge J. Bioinformatics analysis of the endoplasmic reticulum stress-related prognostic model and immune cell infiltration in acute myeloid leukemia. Hematology 2023 28 1 2221101 10.1080/16078454.2023.2221101 37294065
    [Google Scholar]
  29. Shoukier M. Kadia T. Konopleva M. Alotaibi A.S. Alfayez M. Loghavi S. Patel K.P. Kanagal-Shamanna R. Cortes J. Samra B. Jabbour E. Garcia-Manero G. Takahashi K. Pierce S. Short N.J. Yilmaz M. Sasaki K. Masarova L. Pemmaraju N. Borthakur G. Kantarjian H.M. Ravandi F. DiNardo C.D. Daver N. Clinical characteristics and outcomes in patients with acute myeloid leukemia with concurrent FLT3 ‐ITD and IDH mutations. Cancer 2021 127 3 381 390 10.1002/cncr.33293 33119202
    [Google Scholar]
  30. Martelli M.P. Sportoletti P. Tiacci E. Martelli M.F. Falini B. Mutational landscape of AML with normal cytogenetics: Biological and clinical implications. Blood Rev. 2013 27 1 13 22 10.1016/j.blre.2012.11.001 23261068
    [Google Scholar]
  31. Wang M. Yang C. Zhang L. Schaar D.G. Molecular Mutations and Their Cooccurrences in Cytogenetically Normal Acute Myeloid Leukemia. Stem Cells Int. 2017 2017 1 11 10.1155/2017/6962379 28197208
    [Google Scholar]
  32. Kiyoi H. Kawashima N. Ishikawa Y. FLT3 mutations in acute myeloid leukemia: Therapeutic paradigm beyond inhibitor development. Cancer Sci. 2020 111 2 312 322 10.1111/cas.14274 31821677
    [Google Scholar]
  33. Short N.J. Rytting M.E. Cortes J.E. Acute myeloid leukaemia. Lancet 2018 392 10147 593 606 10.1016/S0140‑6736(18)31041‑9 30078459
    [Google Scholar]
  34. Arellano M. Carlisle J.W. How I treat older patients with acute myeloid leukemia. Cancer 2018 124 12 2472 2483 10.1002/cncr.31347 29809277
    [Google Scholar]
  35. Medeiros B.C. Pandya B.J. Hadfield A. Pike J. Wilson S. Mueller C. Bui C.N. Flanders S.C. Rider A. Horvath Walsh L.E. Treatment patterns in patients with acute myeloid leukemia in the United States: a cross-sectional, real-world survey. Curr. Med. Res. Opin. 2019 35 5 927 935 10.1080/03007995.2019.1578152 30712406
    [Google Scholar]
  36. Russell NH Hills RK Thomas A Thomas I Kjeldsen L Dennis M Outcomes of older patients aged 60 to 70 years undergoing reduced intensity transplant for acute myeloblastic leukemia: results of the NCRI acute myeloid leukemia 16 trial. Haematologica 2022 107 7 1518 1527 2021
    [Google Scholar]
  37. Vey N. Low-intensity regimens versus standard-intensity induction strategies in acute myeloid leukemia. Ther. Adv. Hematol. 2020 11 2040620720913010 10.1177/2040620720913010 32215195
    [Google Scholar]
  38. Pastore F. Pastore A. Rothenberg-Thurley M. Metzeler K.H. Ksienzyk B. Schneider S. Bohlander S.K. Braess J. Sauerland M.C. Görlich D. Berdel W.E. Wörmann B. von Bergwelt-Baildon M.S. Hiddemann W. Spiekermann K. Molecular profiling of patients with cytogenetically normal acute myeloid leukemia and hyperleukocytosis. Cancer 2022 128 24 4213 4222 10.1002/cncr.34495 36271776
    [Google Scholar]
  39. Döhner H. Wei A.H. Appelbaum F.R. Craddock C. DiNardo C.D. Dombret H. Ebert B.L. Fenaux P. Godley L.A. Hasserjian R.P. Larson R.A. Levine R.L. Miyazaki Y. Niederwieser D. Ossenkoppele G. Röllig C. Sierra J. Stein E.M. Tallman M.S. Tien H.F. Wang J. Wierzbowska A. Löwenberg B. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood 2022 140 12 1345 1377 10.1182/blood.2022016867 35797463
    [Google Scholar]
  40. Wei H. Wang Y. Gale R.P. Lin D. Zhou C. Liu B. Qiu S. Gu R. Li Y. Zhao X. Wei S. Gong B. Liu K. Gong X. Liu Y. Zhang G. Song Z. Wang Y. Li W. Mi Y. Wang J. Randomized Trial of Intermediate-dose Cytarabine in Induction and Consolidation Therapy in Adults with Acute Myeloid Leukemia. Clin. Cancer Res. 2020 26 13 3154 3161 10.1158/1078‑0432.CCR‑19‑3433 32029439
    [Google Scholar]
  41. Weigert N. Rowe J.M. Lazarus H.M. Salman M.Y. Consolidation in AML: Abundant opinion and much unknown. Blood Rev. 2022 51 100873 10.1016/j.blre.2021.100873 34483002
    [Google Scholar]
  42. Radha G. Raghavan S.C. BCL2: A promising cancer therapeutic target. Biochimica et Biophysica Acta (BBA) -. Rev. Can. 2017 1868 309 314
    [Google Scholar]
  43. Wei Y. Xiong X. Li X. Lu W. He X. Jin X. Sun R. Lyu H. Yuan T. Sun T. Zhao M. Low‐dose decitabine plus venetoclax is safe and effective as post‐transplant maintenance therapy for high‐risk acute myeloid leukemia and myelodysplastic syndrome. Cancer Sci. 2021 112 9 3636 3644 10.1111/cas.15048 34185931
    [Google Scholar]
  44. Mistry J.J. Hellmich C. Lambert A. Moore J.A. Jibril A. Collins A. Bowles K.M. Rushworth S.A. Venetoclax and Daratumumab combination treatment demonstrates pre-clinical efficacy in mouse models of Acute Myeloid Leukemia. Biomark. Res. 2021 9 1 35 10.1186/s40364‑021‑00291‑y 33985565
    [Google Scholar]
  45. Li X Zhang F Ke Y Liu H Hu Z Wei L. Differentiation of acute leukemia cells including cells with MLL-AF4 rearrangements induced by Jiyuan Oridonin A. Recent Pat Anticancer Drug Discov 2024 10.2174/0115748928263141231204112640
    [Google Scholar]
  46. Li T. Gao R. Xu K. Pan P. Chen C. Wang D. Zhang K. Qiao J. Gu Y. BCL7A inhibits the progression and drug-resistance in acute myeloid leukemia. Drug Resist. Updat. 2024 76 101120 10.1016/j.drup.2024.101120 39053383
    [Google Scholar]
/content/journals/pra/10.2174/0115748928349165250202172440
Loading
The ER Stress-related Gene Prognostic Signature for Predicting 
Chemosensitivity and Prognosis in AML
Bentham Science Publishers ; https://doi.org/10.2174/0115748928349165250202172440
/content/journals/pra/10.2174/0115748928349165250202172440
/content/journals/pra/10.2174/0115748928349165250202172440
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: prognosis ; chemosensitivity ; gene panel ; ER stress ; AML

Most Cited Most Cited RSS feed

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