Skip to content
2000
Volume 13, Issue 3
  • ISSN: 2211-5366
  • E-ISSN: 2211-5374

Abstract

Background

Glioblastoma Multiforme (GBM) is a prevalent and deadly type of primary astrocytoma, constituting over 60% of adult brain tumors, and has a poor prognosis, with a high relapse rate within 7 months of diagnosis. Despite surgical, radiotherapy, and chemotherapy treatments, GBM remains challenging due to resistance. MicroRNA (miRNAs) control gene expression at transcriptional and post-transcriptional levels by targeting their messenger RNA (mRNA), and also contribute to the development of various neoplasms, including GBM.

Methods

The present study focuses on exploring the miRNAs-based pathogenesis of GBM and evaluating most potential plant-based therapeutic agents with analysis. Gene chips were retrieved from the Gene Expression Omnibus (GEO) database, followed by the Robust- RankAggereg algorithm to determine the Differentially Expressed miRNAs (DEMs). The predicted targets were intersected with the GBM-associated genes, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the overlapping genes was performed. At the same time, five phytochemicals were selected for the Connectivity map (CMap), and the most efficient ones were those that had undergone molecular docking analysis to obtain the potential therapeutic agents.

Results

The hsa-miR-10b, hsa-miR-21, and hsa-miR-15b were obtained, and eight genes were found to be associated with glioma pathways; VSIG4, PROCR, PLAT, and ITGB2 were upregulated while, CAMK2B, PDE1A, GABRA1, and KCNJ6 were downregulated. The drugs Resveratrol and Quercetin were identified as the most prominent drugs.

Conclusion

These miRNAs-based drugs can be used as a curative agent for the treatment of GBM. However, in vivo, experimental data, and clinical trials are necessary to provide an alternative to conventional GBM cancer chemotherapy.

Loading

Article metrics loading...

/content/journals/mirna/10.2174/0122115366302365240618122812
2024-11-01
2024-11-22
Loading full text...

Full text loading...

References

  1. AldoghachiA.F. AldoghachiA.F. BreyneK. LingK.H. CheahP.S. Recent advances in the therapeutic strategies of glioblastoma multiforme.Neuroscience202249124027010.1016/j.neuroscience.2022.03.030 35395355
    [Google Scholar]
  2. HanifF. MuzaffarK. PerveenK. MalhiS.M. SimjeeShU. Glioblastoma multiforme: A review of its epidemiology and pathogenesis through clinical presentation and treatment.Asian Pac. J. Cancer Prev.20171813910.22034/APJCP.2017.18.1.3 28239999
    [Google Scholar]
  3. AgnihotriS. BurrellK.E. WolfA. Glioblastoma, a brief review of history, molecular genetics, animal models and novel therapeutic strategies.Arch. Immunol. Ther. Exp. 2013611254110.1007/s00005‑012‑0203‑0 23224339
    [Google Scholar]
  4. MessaliA. VillacortaR. HayJ.W. A review of the economic burden of glioblastoma and the cost effectiveness of pharmacologic treatments.PharmacoEconomics201432121201121210.1007/s40273‑014‑0198‑y 25085219
    [Google Scholar]
  5. SchwartzbaumJ.A. FisherJ.L. AldapeK.D. WrenschM. Epidemiology and molecular pathology of glioma.Nat. Clin. Pract. Neurol.20062949450310.1038/ncpneuro0289 16932614
    [Google Scholar]
  6. ThakkarJ.P. DolecekT.A. HorbinskiC. Epidemiologic and molecular prognostic review of glioblastoma.Cancer Epidemiol. Biomarkers Prev.201423101985199610.1158/1055‑9965.EPI‑14‑0275 25053711
    [Google Scholar]
  7. HsuJ.F. ChuS.M. LiaoC.C. Nanotechnology and nanocarrier-based drug delivery as the potential therapeutic strategy for glioblastoma multiforme: An update.Cancers 202113219510.3390/cancers13020195 33430494
    [Google Scholar]
  8. AlifierisC. TrafalisD.T. Glioblastoma multiforme: Pathogenesis and treatment.Pharmacol. Ther.2015152638210.1016/j.pharmthera.2015.05.005 25944528
    [Google Scholar]
  9. XiongD.D. XuW.Q. HeR.Q. DangY.W. ChenG. LuoD.Z. In silico analysis identified miRNA based therapeutic agents against glioblastoma multiforme.Oncol. Rep.20194142194220810.3892/or.2019.7022 30816530
    [Google Scholar]
  10. JacksonC.M. ChoiJ. LimM. Mechanisms of immunotherapy resistance: Lessons from glioblastoma.Nat. Immunol.20192091100110910.1038/s41590‑019‑0433‑y 31358997
    [Google Scholar]
  11. ChenD. LeS.B. HutchinsonT.E. Tumor treating fields dually activate STING and AIM2 inflammasomes to induce adjuvant immunity in glioblastoma.J. Clin. Invest.20221328e14925810.1172/JCI149258 35199647
    [Google Scholar]
  12. ErthalL.C.S. GobboO.L. Ruiz-HernandezE. Biocompatible copolymer formulations to treat glioblastoma multiforme.Acta Biomater.20211218910210.1016/j.actbio.2020.11.030 33227487
    [Google Scholar]
  13. CloughesyT.F. CaveneeW.K. MischelP.S. Glioblastoma: From molecular pathology to targeted treatment.Annu. Rev. Pathol.20149112510.1146/annurev‑pathol‑011110‑130324 23937436
    [Google Scholar]
  14. LimM. XiaY. BettegowdaC. WellerM. Current state of immunotherapy for glioblastoma.Nat. Rev. Clin. Oncol.201815742244210.1038/s41571‑018‑0003‑5 29643471
    [Google Scholar]
  15. MahinfarP. MansooriB. RostamzadehD. BaradaranB. ChoW.C. MansooriB. The role of micrornas in multidrug resistance of glioblastoma.Cancers 20221413321710.3390/cancers14133217 35804989
    [Google Scholar]
  16. RattiM. LampisA. GhidiniM. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) as new tools for cancer therapy: First steps from bench to bedside.Target. Oncol.202015326127810.1007/s11523‑020‑00717‑x 32451752
    [Google Scholar]
  17. BrackenC.P. ScottH.S. GoodallG.J. A network-biology perspective of microRNA function and dysfunction in cancer.Nat. Rev. Genet.2016171271973210.1038/nrg.2016.134 27795564
    [Google Scholar]
  18. DongH. LeiJ. DingL. WenY. JuH. ZhangX. MicroRNA: Function, detection, and bioanalysis.Chem. Rev.201311386207623310.1021/cr300362f 23697835
    [Google Scholar]
  19. HuJ. SunT. WangH. MiR-215 is induced post-transcriptionally via hif-drosha complex and mediates glioma-initiating cell adaptation to hypoxia by targeting KDM1B.Cancer Cell2016291496010.1016/j.ccell.2015.12.005 26766590
    [Google Scholar]
  20. Berindan-NeagoeI. MonroigP.C. PasculliB. CalinG.A. MicroRNAome genome: A treasure for cancer diagnosis and therapy.CA Cancer J. Clin.201464531133610.3322/caac.21244 25104502
    [Google Scholar]
  21. SubramanianA. NarayanR. CorselloS.M. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles.Cell2017171614371452.e1710.1016/j.cell.2017.10.049 29195078
    [Google Scholar]
  22. ChienW. SunQ.Y. LeeK.L. Activation of protein phosphatase 2A tumor suppressor as potential treatment of pancreatic cancer.Mol. Oncol.20159488990510.1016/j.molonc.2015.01.002 25637283
    [Google Scholar]
  23. QuX.A. RajpalD.K. Applications of connectivity map in drug discovery and development.Drug Discov. Today20121723-241289129810.1016/j.drudis.2012.07.017 22889966
    [Google Scholar]
  24. RitchieM.E. PhipsonB. WuD. limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res.2015437e47e710.1093/nar/gkv007 25605792
    [Google Scholar]
  25. KoldeR. LaurS. AdlerP. ViloJ. Robust rank aggregation for gene list integration and meta-analysis.Bioinformatics201228457358010.1093/bioinformatics/btr709 22247279
    [Google Scholar]
  26. TangZ. LiC. KangB. GaoG. LiC. ZhangZ. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses.Nucleic Acids Res.201745W1W98W10210.1093/nar/gkx247 28407145
    [Google Scholar]
  27. StichtC. De La TorreC. ParveenA. GretzN. miRWalk: An online resource for prediction of microRNA binding sites.PLoS One20181310e020623910.1371/journal.pone.0206239 30335862
    [Google Scholar]
  28. WuT. HuE. XuS. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.Innovation20212310014110.1016/j.xinn.2021.100141 34557778
    [Google Scholar]
  29. SzklarczykD. GableA.L. NastouK.C. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets.Nucleic Acids Res.202149D1D605D61210.1093/nar/gkaa1074 33237311
    [Google Scholar]
  30. MusaA. GhoraieL.S. ZhangS.D. A review of connectivity map and computational approaches in pharmacogenomics.Brief. Bioinform.201718590310.1093/bib/bbx023 28334173
    [Google Scholar]
  31. EberhardtJ. Santos-MartinsD. TillackA.F. ForliS. AutoDock vina 1.2.0: New docking methods, expanded force field, and python bindings.J. Chem. Inf. Model.20216183891389810.1021/acs.jcim.1c00203 34278794
    [Google Scholar]
  32. BittrichS. BhikadiyaC. BiC. RCSB protein data bank: Efficient searching and simultaneous access to one million computed structure models alongside the pdb structures enabled by architectural advances.J. Mol. Biol.20234351416799410.1016/j.jmb.2023.167994 36738985
    [Google Scholar]
  33. KimS. Exploring chemical information in PubChem.Curr. Protoc.202118e21710.1002/cpz1.217 34370395
    [Google Scholar]
  34. RosignoliS. PaiardiniA. Boosting the full potential of PyMOL with structural biology plugins.Biomolecules20221212176410.3390/biom12121764 36551192
    [Google Scholar]
  35. ZhangW. ZhangJ. HoadleyK. miR-181d: A predictive glioblastoma biomarker that downregulates MGMT expression.Neuro-oncol.201214671271910.1093/neuonc/nos089 22570426
    [Google Scholar]
  36. JonesT.A. JeyapalanJ.N. ForshewT. Molecular analysis of pediatric brain tumors identifies microRNAs in pilocytic astrocytomas that target the MAPK and NF-κB pathways.Acta Neuropathol. Commun.2015318610.1186/s40478‑015‑0266‑3 26682910
    [Google Scholar]
  37. PiweckaM. RolleK. BelterA. Comprehensive analysis of microRNA expression profile in malignant glioma tissues.Mol. Oncol.2015971324134010.1016/j.molonc.2015.03.007 25864039
    [Google Scholar]
  38. WangZ.Q. ZhangM.Y. DengM.L. WengN.Q. WangH.Y. WuS.X. Low serum level of miR-485-3p predicts poor survival in patients with glioblastoma.PLoS One2017129e018496910.1371/journal.pone.0184969 28931080
    [Google Scholar]
  39. KongY.W. Ferland-McColloughD. JacksonT.J. BushellM. microRNAs in cancer management.Lancet Oncol.2012136e249e25810.1016/S1470‑2045(12)70073‑6 22652233
    [Google Scholar]
  40. ForterreA. KomuroH. AminovaS. HaradaM. A comprehensive review of cancer microrna therapeutic delivery strategies.Cancers2020127185210.3390/cancers12071852 32660045
    [Google Scholar]
  41. FuZ. WangL. LiS. ChenF. Au-YeungK.K.W. ShiC. MicroRNA as an important target for anticancer drug development.Front. Pharmacol.20211273632310.3389/fphar.2021.736323 34512363
    [Google Scholar]
  42. El FatimyR. SubramanianS. UhlmannE.J. KrichevskyA.M. Genome editing reveals glioblastoma addiction to MicroRNA-10b.Mol. Ther.201725236837810.1016/j.ymthe.2016.11.004 28153089
    [Google Scholar]
  43. GuessousF. Alvarado-VelezM. MarcinkiewiczL. Oncogenic effects of miR-10b in glioblastoma stem cells.J. Neurooncol.2013112215316310.1007/s11060‑013‑1047‑0 23307328
    [Google Scholar]
  44. AloizouA.M. PaterakiG. SiokasV. The role of MiRNA-21 in gliomas: Hope for a novel therapeutic intervention?Toxicol. Rep.202071514153010.1016/j.toxrep.2020.11.001 33251119
    [Google Scholar]
  45. YangC.H. YueJ. PfefferS.R. MicroRNA-21 promotes glioblastoma tumorigenesis by down-regulating insulin-like growth factor-binding protein-3 (IGFBP3).J. Biol. Chem.201428936250792508710.1074/jbc.M114.593863 25059666
    [Google Scholar]
  46. SunG. YanS. ShiL. Decreased expression of miR-15b in human gliomas is associated with poor prognosis.Cancer Biother. Radiopharm.201530416917310.1089/cbr.2014.1757 25811315
    [Google Scholar]
  47. WangJ. LiuH. TianL. miR-15b inhibits the progression of glioblastoma cells through targeting insulin-like growth factor receptor 1.Horm. Cancer201781495710.1007/s12672‑016‑0276‑z 27896672
    [Google Scholar]
  48. Karkon-ShayanS. AliashrafzadehH. Dianat-MoghadamH. Resveratrol as an antitumor agent for glioblastoma multiforme: Targeting resistance and promoting apoptotic cell deaths.Acta Histochem.2023125615205810.1016/j.acthis.2023.152058 37336070
    [Google Scholar]
  49. ArabzadehA. MortezazadehT. AryafarT. GharepapaghE. MajdaeenM. FarhoodB. Therapeutic potentials of resveratrol in combination with radiotherapy and chemotherapy during glioblastoma treatment: A mechanistic review.Cancer Cell Int.202121139110.1186/s12935‑021‑02099‑0 34289841
    [Google Scholar]
  50. ZhaiK. MazurakovaA. KoklesovaL. KubatkaP. BüsselbergD. Flavonoids synergistically enhance the anti-glioblastoma effects of chemotherapeutic drugs.Biomolecules20211112184110.3390/biom11121841 34944485
    [Google Scholar]
  51. WangW. YuanX. MuJ. Quercetin induces MGMT+ glioblastoma cells apoptosis via dual inhibition of Wnt3a/β-Catenin and Akt/NF-κB signaling pathways.Phytomedicine202311815493310.1016/j.phymed.2023.154933 37451151
    [Google Scholar]
  52. BappiM.H. ProttayA.A.S. KamliH. Quercetin antagonizes the sedative effects of linalool, possibly through the GABAergic interaction pathway.Molecules20232814561610.3390/molecules28145616 37513487
    [Google Scholar]
/content/journals/mirna/10.2174/0122115366302365240618122812
Loading
/content/journals/mirna/10.2174/0122115366302365240618122812
Loading

Data & Media loading...

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