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Identification of potential genes associated with ALDH1A1 overexpression and cyclophosphamide resistance in chronic myelogenous leukemia using network analysis

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Abstract

Cyclophosphamide (CP), an important alkylating agent which is used in the treatment therapy for chronic myeloid leukemia (CML). However, acquired drug resistance owing to the inactivation of its active metabolite aldophosphamide via tumoral-overexpressing aldehyde dehydrogenase (ALDH1A1) is one of the major issues with the CP therapy. However, the underlying mechanism of ALDH1A1 overexpression in cancer cells remains poorly defined. Therefore, the current study focused on analyzing the ALDH1A1-overexpressing microarray data for CP resistance and CP-sensitive CML cell lines. In this study, the microarray dataset was obtained from Gene Expression Omnibus GEO. The GEO2R tool was used to identify Differentially Expressing Genes (DEGs). Further, protein–protein interaction (PPI) network of DEGs were constructed using STRING database. Finally, Hub gene-miRNA-TFs interaction were constructed using miRNet tool. A total of 749 DEGs including 387 upregulated and 225 downregulated genes were identified from this pool of microarray data. The construction of DEGs network resulted in identification of three genes including ZEB2, EZH2, and MUC1 were found to be majorly responsible for ALDH1A1 overexpression. miRNA analysis identified that, hsa-mir-16-5p and hsa-mir-26a-5p as hub miRNA which are commonly interacting with maximum target genes. Additionally, drug-gene interaction analysis was performed to identify drugs which are responsible for ALDH1A1 expression. The entire study may provide a deeper understanding about ALDH1A1 regulatory genes responsible for its overexpression in CP resistance cancer. This understanding may be further explore for developing possible co-therapy to avoid the ALDH1A1-mediated CP resistance.

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References

  1. Arrigoni E, Del Re M, Galimberti S, Restante G, Rofi E, Crucitta S, Baratè C, Petrini M, Danesi R, Di Paolo A. Concise review: chronic myeloid leukemia: stem cell niche and response to pharmacologic treatment. Stem Cells Transl Med. 2018. https://doi.org/10.1002/sctm.17-0175.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Jabbour E, Kantarjian H. Chronic myeloid leukemia: 2020 update on diagnosis, therapy and monitoring. Am J Hematol. 2020. https://doi.org/10.1002/ajh.25792.

    Article  PubMed  Google Scholar 

  3. Cortes JE, Talpaz M, Kantarjian H. Chronic myelogenous leukemia: a review. Am J Med. 1996. https://doi.org/10.1016/s0002-9343(96)00061-7.

    Article  PubMed  Google Scholar 

  4. Palejwala AH, O’Connor KP, Shi H, Villeneuve L, Scordino T, Glenn CA. Chronic myeloid leukemia manifested as myeloid sarcoma: review of literature and case report. J Clin Neurosci. 2019. https://doi.org/10.1016/j.jocn.2019.04.011.

    Article  PubMed  Google Scholar 

  5. Hochhaus A, Breccia M, Saglio G, García-Gutiérrez V, Réa D, Janssen J, Apperley J. Expert opinion-management of chronic myeloid leukemia after resistance to second-generation tyrosine kinase inhibitors. Leukemia. 2020. https://doi.org/10.1038/s41375-020-0842-9.

    Article  PubMed  PubMed Central  Google Scholar 

  6. García-Gutiérrez V, Hernández-Boluda JC. Tyrosine kinase inhibitors available for chronic myeloid leukemia: efficacy and safety. Front Oncol. 2019. https://doi.org/10.3389/fonc.2019.00603.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Carofiglio F, Lopalco A, Lopedota A, Cutrignelli A, Nicolotti O, Denora N, Stefanachi A, Leonetti F. Bcr-Abl tyrosine kinase inhibitors in the treatment of pediatric CML. Int J Mol Sci. 2020. https://doi.org/10.3390/ijms21124469.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kong JH, Winton EF, Heffner LT, Gaddh M, Hill B, Neely J, Hatcher A, Joseph M, Arellano M, El-Rassi F, Kim A. Outcomes of chronic phase chronic myeloid leukemia after treatment with multiple tyrosine kinase inhibitors. J Clin Med. 2020. https://doi.org/10.3390/jcm9051542.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Biggs JC, Szer J, Crilley P, Atkinson K, Downs K, Dodds A, Concannon AJ, Avalos B, Tutschka P, Kapoor N. Treatment of chronic myeloid leukemia with allogeneic bone marrow transplantation after preparation with BuCy2. Blood. 1992;80:1352–7.

    Article  CAS  Google Scholar 

  10. Mueller MC, Cervantes F, Hjorth-Hansen H, Janssen JJ, Milojkovic D, Rea D, Rosti G. Ponatinib in chronic myeloid leukemia (CML): Consensus on patient treatment and management from a European expert panel. Crit Rev Oncol Hematol. 2017. https://doi.org/10.1016/j.critrevonc.2017.10.002.

    Article  Google Scholar 

  11. Andersson BS, Mroue M, Britten RA, Farquhar D, Murray D. Mechanisms of cyclophosphamide resistance in a human myeloid leukemia cell line. Acta Oncol. 1995. https://doi.org/10.3109/02841869509093963.

    Article  PubMed  Google Scholar 

  12. Mansoori B, Mohammadi A, Davudian S, Shirjang S, Baradaran B. The different mechanisms of cancer drug resistance: a brief review. Adv Pharm Bull. 2017. https://doi.org/10.15171/apb.2017.041.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhang J, Tian Q, Yung Chan S, Chuen Li S, Zhou S, Duan W, Zhu YZ. Metabolism and transport of oxazaphosphorines and the clinical implications. Drug Metab Rev. 2005. https://doi.org/10.1080/03602530500364023.

    Article  PubMed  Google Scholar 

  14. Gerber JM, Qin L, Kowalski J, Smith BD, Griffin CA, Vala MS, Collector MI, Perkins B, Zahurak M, Matsui W, Gocke CD. Characterization of chronic myeloid leukemia stem cells. Am J Hematol. 2011. https://doi.org/10.1002/ajh.21915.

    Article  PubMed  PubMed Central  Google Scholar 

  15. He X, Deng Y, Yue W. Investigating critical genes and gene interaction networks that mediate cyclophosphamide sensitivity in chronic myelogenous leukemia. Mol Med Rep. 2017. https://doi.org/10.3892/mmr.2017.6636.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Verma H, Silakari O. Investigating the Role of Missense SNPs on ALDH 1A1 mediated pharmacokinetic resistance to cyclophosphamide. Biol Med. 2020. https://doi.org/10.1016/j.compbiomed.2020.103979.

    Article  Google Scholar 

  17. Hobert O. Gene regulation by transcription factors and microRNAs. Science. 2008. https://doi.org/10.1126/science.1151651.

    Article  PubMed  Google Scholar 

  18. Liu L, Cai S, Han C, Banerjee A, Wu D, Cui T, Xie G, Zhang J, Zhang X, McLaughlin E, Yin M. ALDH1A1 contributes to PARP inhibitor resistance via enhancing DNA repair in BRCA2−/− ovarian cancer cells. Mol Cancer Ther. 2020. https://doi.org/10.1158/1535-7163.MCT-19-0242.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Clough E, Barrett T. The gene expression omnibus database. 2016. Stat Genom. https://doi.org/10.1007/978-1-4939-3578-9_5.

    Article  Google Scholar 

  20. Bao F, Polk P, Nordberg ML, Veillon DM, Sun A, Deininger M, Murray D, Andersson BS, Munker R. Comparative gene expression analysis of a chronic myelogenous leukemia cell line resistant to cyclophosphamide using oligonucleotide arrays and response to tyrosine kinase inhibitors. Leuk Res. 2007. https://doi.org/10.1016/j.leukres.2007.03.002.

    Article  PubMed  Google Scholar 

  21. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res. 2012;41:D991–5.

    Article  Google Scholar 

  22. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995. https://doi.org/10.2307/2346101.

    Article  Google Scholar 

  23. Xu Z, Zhou Y, Cao Y, Dinh TLA, Wan J, Zhao M. Identification of candidate biomarkers and analysis of prognostic values in ovarian cancer by integrated bioinformatics analysis. J R Stat Soc B. 1995. https://doi.org/10.2307/2346101.

    Article  Google Scholar 

  24. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 2016. https://doi.org/10.1093/nar/gkw937.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Otasek D, Morris JH, Bouças J, Pico AR, Demchak B. Cytoscape automation: empowering workflow-based network analysis. Genome Biol. 2019. https://doi.org/10.1186/s13059-019-1758-4.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Chang L, Zhou G, Soufan O, Xia J. miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 2020. https://doi.org/10.1093/nar/gkaa467.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Davis AP, Grondin CJ, Johnson RJ, Sciaky D, Wiegers J, Mattingly WTC, CJ. . Comparative toxicogenomics database (CTD): update. Nucleic Acids Res. 2021. https://doi.org/10.1093/nar/gkaa891.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Fleming RA. An overview of cyclophosphamide and ifosfamide pharmacology. Pharmacotherapy. 1997. https://doi.org/10.1002/j.1875-9114.1997.tb03817.x.

    Article  PubMed  Google Scholar 

  29. Verma H, Singh Bahia M, Choudhary S, Kumar Singh P, Silakari O. Drug metabolizing enzymes-associated chemo resistance and strategies to overcome it. Drug Metab Rev. 2019. https://doi.org/10.1080/03602532.2019.1632886.

    Article  PubMed  Google Scholar 

  30. Alam M, Rajabi H, Ahmad R, Jin C, Kufe D. Targeting the MUC1-C oncoprotein inhibits self-renewal capacity of breast cancer cells. Oncotarget. 2014. https://doi.org/10.18632/oncotarget.1848.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wang R, Yang L, Li S, Ye D, Yang L, Liu Q, Zhao Z, Cai Q, Tan J, Li X. Quercetin inhibits breast cancer stem cells via downregulation of aldehyde dehydrogenase 1A1 (ALDH1A1), chemokine receptor type 4 (CXCR4), mucin 1 (MUC1), and epithelial cell adhesion molecule (EpCAM). Med Sci Monit. 2018;24:412–20. https://doi.org/10.12659/msm.908022.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li H, Bitler BG, Vathipadiekal V, Maradeo ME, Slifker M, Creasy CL, Tummino PJ, Cairns P, Birrer MJ, Zhang R. ALDH1A1 is a novel EZH2 target gene in epithelial ovarian cancer identified by genome-wide approaches. Cancer Prev Res. 2012. https://doi.org/10.1158/1940-6207.CAPR-11-0414.

    Article  Google Scholar 

  33. Gorodetska I, Lukiyanchuk V, Peitzsch C, Kozeretska I, Dubrovska A. BRCA1 and EZH2 cooperate in regulation of prostate cancer stem cell phenotype. Int J Cancer. 2019. https://doi.org/10.1002/ijc.32323.

    Article  PubMed  Google Scholar 

  34. Peitzsch C, Cojoc M, Hein L, Kurth I, Mäbert K, Trautmann F, Klink B, Schröck E, Wirth MP, Krause M, Stakhovsky EA. An epigenetic reprogramming strategy to resensitize radioresistant prostate cancer cells. Cancer Res. 2016. https://doi.org/10.1158/0008-5472.CAN-15-2116.

    Article  PubMed  Google Scholar 

  35. Li Q, Liu KY, Liu Q, Wang G, Jiang W, Meng Q, Yi Y, Yang Y, Wang R, Zhu S, Li C. Antihistamine drug ebastine inhibits cancer growth by targeting polycomb group protein EZH2. Mol Cancer Ther. 2020. https://doi.org/10.1158/1535-7163.MCT-20-0250.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Momparler RL, Côté S. Targeting of cancer stem cells by inhibitors of DNA and histone methylation. Expert Opin Investig Drugs. 2015. https://doi.org/10.1517/13543784.2015.1051220.

    Article  PubMed  Google Scholar 

  37. Balasubramanian V, Iyer P, Arora S, Troyer P, Normant E. CPI-169, a novel and potent EZH2 inhibitor, synergizes with CHOP in vivo and achieves complete regression in lymphoma xenograft models. AACR. 2014. https://doi.org/10.1158/1538-7445.AM2014-1697.

    Article  Google Scholar 

  38. Yan Y, He M, Yu Z, Sun M, Zhao L, Zhao H, Yao W, Wei M. Combined expression of ZEB2 and ALDH1A1 is correlated with poor prognosis of breast cancer patients. Int J Clin Exp Med. 2018;11:1994–2003.

    Google Scholar 

  39. Colacino JA, Azizi E, Brooks MD, Harouaka R, Fouladdel S, McDermott SP, Lee M, Hill D, Madden J, Boerner J, Cote ML. Heterogeneity of human breast stem and progenitor cells as revealed by transcriptional profiling. BioRxiv. 2017. https://doi.org/10.1101/109751.

    Article  Google Scholar 

  40. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97.

    Article  CAS  Google Scholar 

  41. Liu T, Cai J, Cai J, Wang Z, Cai L. EZH2-miRNA positive feedback promotes tumor growth in ovarian cancer. Front Oncol. 2021. https://doi.org/10.3389/fonc.2020.608393.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Vishnubalaji R, Hamam R, Abdulla MH, Mohammed MAV, Kassem M, Al-Obeed O, Aldahmash A, Alajez NM. Genome-wide mRNA and miRNA expression profiling reveal multiple regulatory networks in colorectal cancer. Cell Death Dis. 2015. https://doi.org/10.1038/cddis.2014.556.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Reza G, Neda M, Jahanbakhsh A, Nazila N, Seyed Javad M, Yaghoub Y, Ashraf M, Hossein P, Sakari K, Reza M. Downregulation of plasma MiR-142–3p and MiR-26a-5p in patients with colorectal carcinoma. Iran J Cancer Prev. 2015. https://doi.org/10.17795/ijcp2329.

    Article  Google Scholar 

  44. Navid F, Baker SD, McCarville MB, Stewart CF, Billups CA, Wu J, Davidoff AM, Spunt SL, Furman WL, McGregor LM, Hu S. Phase I and clinical pharmacology study of bevacizumab, sorafenib, and low-dose cyclophosphamide in children and young adults with refractory/recurrent solid tumors. Clin Cancer Res. 2013. https://doi.org/10.1158/1078-0432.CCR-12-1897.

    Article  PubMed  Google Scholar 

  45. Ye Y, Zhang S, Chen Y, Wang X, Wang P. High ALDH1A1 expression indicates a poor prognosis in gastric neuroendocrine carcinoma. Pathol Res Pract. 2018. https://doi.org/10.1016/j.prp.2017.10.015.

    Article  PubMed  Google Scholar 

  46. Fukuda M, Yamaguchi S, Ohta T, Nakayama Y, Ogata H, Shimizu K, Nishikawa T, Adachi Y, Fukuma E. Combination therapy for advanced breast cancer: cyclophosphamide, doxorubicin, UFT, and tamoxifen. Oncology. 1999;13:77–81.

    CAS  PubMed  Google Scholar 

  47. Vishnubalaji R, Manikandan M, Fahad M, Hamam R, Alfayez M, Kassem M, Aldahmash A, Alajez NM. Molecular profiling of ALDH1+ colorectal cancer stem cells reveals preferential activation of MAPK, FAK, and oxidative stress pro-survival signalling pathways. Oncotarget. 2018. https://doi.org/10.18632/oncotarget.24420.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This work was supported by the Indian Council of Medical Research (ICMR), New Delhi; under sanction No. ISRM/12(10)/2019.

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Correspondence to Om Silakari.

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Narendra, G., Raju, B., Verma, H. et al. Identification of potential genes associated with ALDH1A1 overexpression and cyclophosphamide resistance in chronic myelogenous leukemia using network analysis. Med Oncol 38, 123 (2021). https://doi.org/10.1007/s12032-021-01569-9

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