Skip to main content

Advertisement

SpringerLink
Log in

miR-497 and 219 in blood aid meningioma classification

  • Research
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

Introduction

The current WHO classification and methylation status help predict meningioma recurrence and prognosis. However, up to date, there is no circulating biomarker showing clinical value in meningioma diagnosis or classification. Circulating miRNAs showed the potential to be used as cancer biomarkers in various tumours. This research evaluated specific miRNAs, miR-497 and miR-219, as convenient and efficient predictors of meningioma grades.

Methods

We studied serum and exosomal levels of miR-497 in 74 meningioma samples (WHO grade I = 25, WHO grade II = 25, and WHO grade III = 24) and 53 healthy controls. The serum level of miR-219 was studied in 56 meningioma samples WHO grade I = 22, WHO grade II = 14, and WHO grade III = 20). We used qPCR for miRNA quantification. We also tested two different normalisers, endogenous and external, and evaluated their impact on the diagnostic value of miR-497.

Results

The serum and exosomal levels of miR-497 distinguished meningioma from the control samples. Moreover, miR-497 was a suitable identifier for meningioma grade. When we combined miR-497 and miR-219, the efficacy of the combined biomarker was higher than miR-497 or miR-219 when used individually in meningioma classification. Both miR-497 and miR-219 showed a noticeable change with the methylation class of meningioma.

Conclusion

This study shows that serum miR-497 is an effective and easy-to-measure biomarker for meningioma diagnosis and classification. Moreover, when we combined miR-497 and miR-219, the combined biomarker showed enhanced accuracy in meningioma classification. Furthermore, this is the first study to evaluate the correlation between serum circulating miRNA and the methylation status in meningioma.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Zhao L, Zhao W, Hou Y, Wen C, Wang J, Wu P, Guo Z (2020) An overview of managements in meningiomas. Front Oncol 10:1523. https://doi.org/10.3389/fonc.2020.01523

    Article  PubMed  PubMed Central  Google Scholar 

  2. Goldbrunner R, Stavrinou P, Jenkinson MD, Sahm F, Mawrin C, Weber DC, Preusser M, Minniti G, Lund-Johansen M, Lefranc F et al (2021) EANO guideline on the diagnosis and management of meningiomas. Neuro-oncology 23:1821–1834. https://doi.org/10.1093/neuonc/noab150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Goldbrunner R, Minniti G, Preusser M, Jenkinson MD, Sallabanda K, Houdart E, von Deimling A, Stavrinou P, Lefranc F, Lund-Johansen M et al (2016) EANO guidelines for the diagnosis and treatment of meningiomas. Lancet Oncol 17:e383–e391. https://doi.org/10.1016/s1470-2045(16)30321-7

    Article  PubMed  Google Scholar 

  4. Islim AI, Mohan M, Moon RDC, Srikandarajah N, Mills SJ, Brodbelt AR, Jenkinson MD (2019) Incidental intracranial meningiomas: a systematic review and meta-analysis of prognostic factors and outcomes. J Neurooncol 142:211–221. https://doi.org/10.1007/s11060-019-03104-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nassiri F, Mamatjan Y, Suppiah S, Badhiwala JH, Mansouri S, Karimi S, Saarela O, Poisson L, Gepfner-Tuma I, Schittenhelm J et al (2019) DNA methylation profiling to predict recurrence risk in meningioma: development and validation of a nomogram to optimize clinical management. Neuro-oncology 21:901–910. https://doi.org/10.1093/neuonc/noz061

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sahm F, Schrimpf D, Stichel D, Jones DTW, Hielscher T, Schefzyk S, Okonechnikov K, Koelsche C, Reuss DE, Capper D et al (2017) DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol 18:682–694. https://doi.org/10.1016/S1470-2045(17)30155-9

    Article  CAS  PubMed  Google Scholar 

  7. Euskirchen P, Peyre M (2018) Management of meningioma. La Presse Médicale 47:e245–e252. https://doi.org/10.1016/j.lpm.2018.05.016

    Article  PubMed  Google Scholar 

  8. Cui M, Wang H, Yao X, Zhang D, Xie Y, Cui R, Zhang X (2019) Circulating microRNAs in cancer: potential and challenge. Front Genet 10:626

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Esquela-Kerscher A, Slack FJ (2006) Oncomirs—microRNAs with a role in cancer. Nat Rev Cancer 6:259–269. https://doi.org/10.1038/nrc1840

    Article  CAS  PubMed  Google Scholar 

  10. Chen X, Liang H, Zen K, Zhang C-Y (2016) Secreted microRNAs from tumor cells can suppress immune function. Oncoimmunology 5:e982407

    Article  PubMed  PubMed Central  Google Scholar 

  11. Chen X, Liang H, Zhang J, Zen K, Zhang C-Y (2012) Secreted microRNAs: a new form of intercellular communication. Trends Cell Biol 22:125–132

    Article  CAS  PubMed  Google Scholar 

  12. Rosenfeld N, Aharonov R, Meiri E, Rosenwald S, Spector Y, Zepeniuk M, Benjamin H, Shabes N, Tabak S, Levy A (2008) MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol 26:462

    Article  CAS  PubMed  Google Scholar 

  13. Nik Mohamed Kamal NNSB, Shahidan WNS (2020) Non-exosomal and exosomal circulatory microRNAs: which are more valid as biomarkers? Front Pharmacol 10:1500

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yang G, Xiong G, Cao Z, Zheng S, You L, Zhang T, Zhao Y (2016) miR-497 expression, function and clinical application in cancer. Oncotarget 7:55900–55911. https://doi.org/10.18632/oncotarget.10152

    Article  PubMed  PubMed Central  Google Scholar 

  15. Yan LX, Huang XF, Shao Q, Huang MY, Deng L, Wu QL, Zeng YX, Shao JY (2008) MicroRNA miR-21 overexpression in human breast cancer is associated with advanced clinical stage, lymph node metastasis and patient poor prognosis. RNA 14:2348–2360. https://doi.org/10.1261/rna.1034808

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liu Z, Wu S, Wang L, Kang S, Zhao B, He F, Liu X, Zeng Y, Liu J (2019) Prognostic value of microRNA-497 in various cancers: a systematic review and meta-analysis. Dis mark 2019:2491291–2491291. https://doi.org/10.1155/2019/2491291

    Article  CAS  Google Scholar 

  17. Özata DM, Caramuta S, Velázquez-Fernández D, Akçakaya P, Xie H, Höög A, Zedenius J, Bäckdahl M, Larsson C, Lui W-O (2011) The role of microRNA deregulation in the pathogenesis of adrenocortical carcinoma. Endocr Relat Cancer 18:643–655. https://doi.org/10.1530/ERC-11-0082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Xu J-W, Wang T-X, You L, Zheng L-F, Shu H, Zhang T-P, Zhao Y-P (2014) Insulin-like growth factor 1 receptor (IGF-1R) as a target of miR-497 and plasma IGF-1R levels associated with TNM stage of pancreatic cancer. PLoS ONE 9:e92847. https://doi.org/10.1371/journal.pone.0092847

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Itesako T, Seki N, Yoshino H, Chiyomaru T, Yamasaki T, Hidaka H, Yonezawa T, Nohata N, Kinoshita T, Nakagawa M et al (2014) The microRNA expression signature of bladder cancer by deep sequencing: the functional significance of the miR-195/497 cluster. PLoS ONE 9:e84311. https://doi.org/10.1371/journal.pone.0084311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wang S, Mo Y, Midorikawa K, Zhang Z, Huang G, Ma N, Zhao W, Hiraku Y, Oikawa S, Murata M (2015) The potent tumor suppressor miR-497 inhibits cancer phenotypes in nasopharyngeal carcinoma by targeting ANLN and HSPA4L. Oncotarget 6(2015):35893–35907. https://doi.org/10.18632/oncotarget.5651

    Article  PubMed  PubMed Central  Google Scholar 

  21. Shen L, Li J, Xu L, Ma J, Li H, Xiao X, Zhao J, Fang L (2012) miR-497 induces apoptosis of breast cancer cells by targeting Bcl-w. Exp Ther Med 3:475–480. https://doi.org/10.3892/etm.2011.428

    Article  CAS  PubMed  Google Scholar 

  22. Li W, Jin X, Deng X, Zhang G, Zhang B, Ma L (2014) The putative tumor suppressor microRNA-497 modulates gastric cancer cell proliferation and invasion by repressing eIF4E. Biochem Biophys Res Commun 449:235–240. https://doi.org/10.1016/j.bbrc.2014.05.011

    Article  CAS  PubMed  Google Scholar 

  23. Yan J-J, Zhang Y-N, Liao J-Z, Ke K-P, Chang Y, Li P-Y, Wang M, Lin J-S, He X-X (2015) miR-497 suppresses angiogenesis and metastasis of hepatocellular carcinoma by inhibiting VEGFA and AEG-1. Oncotarget 6:29527–29542. https://doi.org/10.18632/oncotarget.5012

    Article  PubMed  PubMed Central  Google Scholar 

  24. Wang W, Ren F, Wu Q, Jiang D, Li H, Shi H (2014) MicroRNA-497 suppresses angiogenesis by targeting vascular endothelial growth factor A through the Pi3k/Akt and MAPK/ERK pathways in ovarian cancer. Oncol Rep 32:2127–2133. https://doi.org/10.3892/or.2014.3439

    Article  CAS  PubMed  Google Scholar 

  25. Wei C, Luo Q, Sun X, Li D, Song H, Li X, Song J, Hua K, Fang L (2015) MicroRNA-497 induces cell apoptosis by negatively regulating Bcl-2 protein expression at the posttranscriptional level in human breast cancer. Int J Clin Exp Pathol 8:7729–7739

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Wang L, Li B, Li L, Wang T (2013) MicroRNA-497 suppresses proliferation and induces apoptosis in prostate cancer cells. Asian Pac J Cancer Prev 14:3499–3502. https://doi.org/10.7314/apjcp.2013.14.6.3499

    Article  PubMed  Google Scholar 

  27. Teplyuk NM, Mollenhauer B, Gabriely G, Giese A, Kim E, Smolsky M, Kim RY, Saria MG, Pastorino S, Kesari S (2012) MicroRNAs in cerebrospinal fluid identify glioblastoma and metastatic brain cancers and reflect disease activity. Neuro-oncology 14:689–700

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. López-Aguilar JE, Velázquez-Flores MA, Simón-Martínez LA, Ávila-Miranda R, Rodríguez-Florido MA, Garrido RR-E (2017) Circulating microRNAs as biomarkers for pediatric astrocytomas. Arch Med Res 48:323–332

    Article  PubMed  Google Scholar 

  29. Yue X, Lan F, Hu M, Pan Q, Wang Q, Wang J (2016) Downregulation of serum microRNA-205 as a potential diagnostic and prognostic biomarker for human glioma. J Neurosurg 124:122–128

    Article  CAS  PubMed  Google Scholar 

  30. Regazzo G, Terrenato I, Spagnuolo M, Carosi M, Cognetti G, Cicchillitti L, Sperati F, Villani V, Carapella C, Piaggio G et al (2016) A restricted signature of serum miRNAs distinguishes glioblastoma from lower grade gliomas. J Exp Clin Cancer Res 35:124. https://doi.org/10.1186/s13046-016-0393-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Negroni C, Hilton DA, Ercolano E, Adams CL, Kurian KM, Baiz D, Hanemann CO (2020) GATA-4, a potential novel therapeutic target for high-grade meningioma, regulates miR-497, a potential novel circulating biomarker for high-grade meningioma. EBioMedicine 59:102941. https://doi.org/10.1016/j.ebiom.2020.102941

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhi F, Shao N, Li B, Xue L, Deng D, Xu Y, Lan Q, Peng Y, Yang Y (2016) A serum 6-miRNA panel as a novel non-invasive biomarker for meningioma. Sci Rep 6:32067. https://doi.org/10.1038/srep32067

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Schageman J, Zeringer E, Li M, Barta T, Lea K, Gu J, Magdaleno S, Setterquist R, Vlassov AV (2013) The complete exosome workflow solution: from isolation to characterization of RNA cargo. Biomed Res Int 2013:253957. https://doi.org/10.1155/2013/253957

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Adams CL, Ercolano E, Ferluga S, Sofela A, Dave F, Negroni C, Kurian KM, Hilton DA, Hanemann CO (2020) A rapid robust method for subgrouping non-NF2 meningiomas according to genotype and detection of lower levels of M2 macrophages in AKT1 E17K mutated tumours. Int J Mol Sci. https://doi.org/10.3390/ijms21041273

    Article  PubMed  PubMed Central  Google Scholar 

  35. Madadi S, Soleimani M (2019) Comparison of miR-16 and cel-miR-39 as reference controls for serum miRNA normalization in colorectal cancer. J Cell Biochem 120:4802–4803. https://doi.org/10.1002/jcb.28174

    Article  CAS  PubMed  Google Scholar 

  36. Wang L, Chen S, Liu Y, Zhang H, Ren N, Ma R, He Z (2020) The biological and diagnostic roles of microRNAs in meningiomas. Rev Neurosci 31:771–778. https://doi.org/10.1515/revneuro-2020-0023

    Article  CAS  PubMed  Google Scholar 

  37. Tiberio P, Callari M, Angeloni V, Daidone MG, Appierto V (2015) Challenges in using circulating miRNAs as cancer biomarkers. BioMed Res Int 2015:731479. https://doi.org/10.1155/2015/731479

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bustin SA, Beaulieu JF, Huggett J, Jaggi R, Kibenge FS, Olsvik PA, Penning LC, Toegel S (2010) Miqe précis: practical implementation of minimum standard guidelines for fluorescence-based quantitative real-time PCR experiments. Bmc Mol Biol 11(1):74

    Article  PubMed  PubMed Central  Google Scholar 

  39. Benz F, Roderburg C, Vargas Cardenas D, Vucur M, Gautheron J, Koch A, Zimmermann H, Janssen J, Nieuwenhuijsen L, Luedde M et al (2013) U6 is unsuitable for normalization of serum miRNA levels in patients with sepsis or liver fibrosis. Exp Mol Med 45:e42. https://doi.org/10.1038/emm.2013.81

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang P, Yang D, Zhang H, Wei X, Ma T, Cheng Z, Hong Q, Hu J, Zhuo H, Song Y et al (2015) Early detection of lung cancer in serum by a panel of microRNA biomarkers. Clin Lung Cancer 16:313-319.e1. https://doi.org/10.1016/j.cllc.2014.12.006

    Article  CAS  PubMed  Google Scholar 

  41. Schwarzenbach H, da Silva AM, Calin G, Pantel K (2015) Data normalization strategies for microRNA quantification. Clin Chem 61:1333–1342. https://doi.org/10.1373/clinchem.2015.239459

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kim KM, Abdelmohsen K, Mustapic M, Kapogiannis D, Gorospe M (2017) RNA in extracellular vesicles. WIREs RNA 8:e1413. https://doi.org/10.1002/wrna.1413

    Article  CAS  Google Scholar 

  43. Mashayekhi F, Saberi A, Mashayekhi S (2018) Serum TIMP1 and TIMP2 concentration in patients with different grades of meningioma. Clin Neurol Neurosurg 170:84–87. https://doi.org/10.1016/j.clineuro.2018.05.001

    Article  PubMed  Google Scholar 

  44. Fouladseresht H, Ziaee SM, Erfani N, Doroudchi M (2019) Serum levels of APRIL increase in patients with glioma, meningioma and schwannoma. Asian Pac J Cancer Prev 20:751–756. https://doi.org/10.31557/apjcp.2019.20.3.751

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sofela AA, Hilton DA, Ammoun S, Baiz D, Adams CL, Ercolano E, Jenkinson MD, Kurian KM, Teo M, Whitfield PC et al (2021) Fibulin-2: a novel biomarker for differentiating grade II from grade I meningiomas. Int J Mol Sci. https://doi.org/10.3390/ijms22020560

    Article  PubMed  PubMed Central  Google Scholar 

  46. Carneiro V, Cirino M, Panepucci R, Peria F, Tirapelli D, Colli B, Carlotti CG Jr (2021) The role of microRNA 181d as a possible biomarker associated with tumor progression in meningiomas. Cureus 13:e19158. https://doi.org/10.7759/cureus.19158

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Tissue and serum samples were obtained from Graz biobank and Lancashire Teaching Hospital NHS Foundation Trust, which is part of the UK Brain Archive Information Network (BRAIN UK), which is funded by the Medical Research Council. We thank Brain Tumour Research for supporting this work. Ahmed Abdelrahman was supported by MSCA-ITN-ETN-European Training Networks [An Integrated Platform for Developing Brain Cancer Diagnostic Techniques (AiPBAND) Project ID 764281]. The funders had no role in study design, data collection, data analysis and interpretation, writing of the manuscript, or decision to publish.

Funding

This research was funded by MSCA-ITN-ETN-European Training Networks [An Integrated Platform for Developing Brain Cancer Diagnostic Techniques (AiPBAND) project ID 764281] and Brain Tumour Research (Hanemann).

Author information

Authors and Affiliations

  1. Peninsula Medical School, Faculty of Health, Medicine, Dentistry and Human Sciences, University of Plymouth, The John Bull Building, Plymouth Science Park, Research Way, Plymouth, PL6 8BU, UK

    Ahmed Abdelrahman, Caterina Negroni, Claire L. Adams & Clemens Oliver Hanemann

  2. Department of Neuropathology, Institute of Pathology, University Hospital Heidelberg, 69126, Heidelberg, Germany

    Felix Sahm

  3. Medical Trials Analysis, Via Ariosto 28, 44100, Ferrara, Italy

    Raffaellla Vergura & Cristina Morelli

  4. Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria

    Tadeja Urbanic-Purkart & Michael Khalil

Authors
  1. Ahmed Abdelrahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Caterina Negroni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Felix Sahm
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Claire L. Adams
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tadeja Urbanic-Purkart
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael Khalil
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Raffaellla Vergura
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Cristina Morelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Clemens Oliver Hanemann
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, AA and COH; methodology, AA and CN; software, AA; validation, AA; formal analysis, AA; investigation, COH; resources, AA, MK, TUP and CLA; data curation, AA; writing—original draft preparation, AA and COH; writing—review and editing, CN, CLA, MK, CM, RV and COH; visualization, COH; supervision, CM, RV and COH.; project administration, COH; funding acquisition, CM, RV and COH. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Clemens Oliver Hanemann.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of UNIVERSITY OF PLYMOUTH (protocol code 17/18-837and 14/15-450) and date of approval 27/ January 2021 for studies involving humans.

Informed consent

Informed consent was obtained from all subjects involved in the study. A copy of the written consent is available for review by the editor-in-chief of this journal on request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 221 kb)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdelrahman, A., Negroni, C., Sahm, F. et al. miR-497 and 219 in blood aid meningioma classification. J Neurooncol 160, 137–147 (2022). https://doi.org/10.1007/s11060-022-04126-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11060-022-04126-0

Keywords

Search

Navigation