Skip to main content

Advertisement

SpringerLink
Log in

Bioinformatics Analysis and Experimental Validation for Exploring Key Molecular Markers for Glioblastoma

  • Original Article
  • Published:
Applied Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

Glioblastoma (GBM) is the most common primary intracranial malignancy with a very low survival rate. Exploring key molecular markers for GBM can help with early diagnosis, prognostic prediction, and recurrence monitoring. This study aims to explore novel biomarkers for GBM via bioinformatics analysis and experimental verification. Dataset GSE103229 was obtained from the GEO database to search differentially expressed lncRNA (DELs), mRNAs (DEMs), and miRNAs (DEMis). Hub genes were selected to establish competing endogenous RNA (ceRNA) networks. The GEPIA database was employed for the survival analysis and expression detection of hub genes. Hub gene expression in GBM tissue samples and cell lines was validated using RT-qPCR. Western blotting was employed for protein expression evaluation. SYT1 overexpression vector was transfected in GBM cells. CCK-8 assay and flow cytometry were performed to detect the malignant phenotypes of GBM cells. There were 901 upregulated and 1086 downregulated DEMs identified, which were prominently enriched in various malignancy-related functions and pathways. Twenty-two hub genes were selected from PPI networks. Survival analysis and experimental validation revealed that four hub genes were tightly associated with GBM prognosis and progression, including SYT1, GRIN2A, KCNA1, and SYNPR. The four genes were significantly downregulated in GBM tissues and cell lines. Overexpressing SYT1 alleviated the proliferation and promoted the apoptosis of GBM cells in vitro. We identify four genes that may be potential molecular markers of GBM, which may provide new ideas for improving early diagnosis and prediction of the disease.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Shi, Z., et al. (2023). Comprehensive analysis of oxidative stress-related lncRNA signatures in glioma reveals the discrepancy of prognostic and immune infiltration. Science and Reports, 13(1), 7731.

    Article  ADS  CAS  Google Scholar 

  2. Pirlog, B. O., et al. (2023). New perspective on DNA response pathway (DDR) in glioblastoma, focus on classic biomarkers and emerging roles of ncRNAs. Expert Reviews in Molecular Medicine, 25, e18.

    Article  CAS  PubMed  Google Scholar 

  3. Jafari, M., & Hasanzadeh, M. (2020). Cell-specific frequency as a new hallmark to early detection of cancer and efficient therapy: Recording of cancer voice as a new horizon. Biomedicine & Pharmacotherapy, 122, 109770.

    Article  Google Scholar 

  4. Huang, X., et al. (2018). High throughput single cell RNA sequencing, bioinformatics analysis and applications. Advances in Experimental Medicine and Biology, 1068, 33–43.

    Article  CAS  PubMed  Google Scholar 

  5. Yadav, D.K., et al. (2023). Identification of hub genes associated with prognosis of lung cancer via integrated bioinformatics and in vitro approach. Journal of Biomology Structure and Dynamics, 41(20), 11204–11218.

  6. Yin, X., et al. (2022). Identification of novel prognostic targets in glioblastoma using bioinformatics analysis. Biomedical Engineering Online, 21(1), 26.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kong, Y., et al. (2020). Identification of immune-related genes contributing to the development of glioblastoma using weighted gene co-expression network analysis. Frontiers in Immunology, 11, 1281.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Geng, R. X., et al. (2018). Identification of core biomarkers associated with outcome in glioma: evidence from bioinformatics analysis. Disease Markers, 2018, 3215958.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Qi, X., et al. (2015). ceRNA in cancer: Possible functions and clinical implications. Journal of Medical Genetics, 52(10), 710–718.

    Article  PubMed  Google Scholar 

  10. Xiao, H., Liang, S., & Wang, L. (2020). Competing endogenous RNA regulation in hematologic malignancies. Clinica Chimica Acta, 509, 108–116.

    Article  CAS  Google Scholar 

  11. Rahnama, S., et al. (2021). Identification of dysregulated competing endogenous RNA networks in glioblastoma: A way toward improved therapeutic opportunities. Life Sciences, 277, 119488.

    Article  CAS  PubMed  Google Scholar 

  12. Peng, Q., et al. (2020). Prediction of a competing endogenous RNA co-expression network as a prognostic marker in glioblastoma. Journal of Cellular and Molecular Medicine, 24(22), 13346–13355.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Barrett, T., et al. (2013). NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids, 41(Database issue), D991-5.

    CAS  Google Scholar 

  14. Sherman, B. T., et al. (2022). DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Research, 50, W216–W221.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. da Huang, W., Sherman, B. T., & Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1), 44–57.

    Article  CAS  PubMed  Google Scholar 

  16. Szklarczyk, D., et al. (2019). STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607-d613.

    Article  CAS  PubMed  Google Scholar 

  17. Li, J. H., et al. (2014). starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res, 42(Database issue), D92-7.

    Article  CAS  PubMed  Google Scholar 

  18. Tang, Z., et al. (2017). GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Research, 45(W1), W98-w102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Feng, S.W., et al. (2023). Exploring the functional roles of telomere maintenance 2 in the tumorigenesis of glioblastoma multiforme and drug responsiveness to temozolomide. International Journal of Molecular Sciences, 24(11), 9256.

  20. Quddusi, D.M. & Bajcinca, N. (2023). Identification of genomic biomarkers and their pathway crosstalks for deciphering mechanistic links in glioblastoma. IET Systems Biology, 17(4), 143–161.

  21. Zhuang, R., et al. (2023). Rab26 restricts insulin secretion via sequestering Synaptotagmin-1. PLoS Biology, 21(6), e3002142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Riggs, E., et al. (2022). SYT1-associated neurodevelopmental disorder: A narrative review. Children (Basel), 9(10), 1439.

  23. Lu, H., et al. (2019). miRNA-34a suppresses colon carcinoma proliferation and induces cell apoptosis by targeting SYT1. International Journal of Clinical and Experimental Pathology, 12(8), 2887–2897.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhang, J., et al. (2024). miRNA-363–3p Hinders proliferation, migration, invasion and autophagy of thyroid cancer cells by controlling SYT1 transcription to affect NF-κB. Endocrine Metabolic & Immune Disorders Drug Targets, 24(1), 153–162

  25. Yang, J., & Yang, Q. (2020). Identification of core genes and screening of potential targets in glioblastoma multiforme by integrated bioinformatic analysis. Frontiers in Oncology, 10, 615976.

    Article  PubMed  Google Scholar 

  26. Zhou, Y., et al. (2019). Identification of potential biomarkers in glioblastoma through bioinformatic analysis and evaluating their prognostic value. BioMed Research International, 2019, 6581576.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Myers, S.J., et al. (2019). Distinct roles of GRIN2A and GRIN2B variants in neurological conditions. F1000Res, 8, F1000.

  28. D’Mello, S. A., et al. (2014). Evidence that GRIN2A mutations in melanoma correlate with decreased survival. Frontiers Oncology, 3, 333.

    Google Scholar 

  29. Prickett, T. D., et al. (2014). Somatic mutation of GRIN2A in malignant melanoma results in loss of tumor suppressor activity via aberrant NMDAR complex formation. The Journal of Investigative Dermatology, 134(9), 2390–2398.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. D'Adamo, M.C., et al. (2020). Kv1.1 Channelopathies: Pathophysiological mechanisms and therapeutic approaches. International Journal of Molecular Science, 21(8), 2935.

  31. Liu, L., et al. (2019). Silencing of KCNA1 suppresses the cervical cancer development via mitochondria damage. Channels (Austin), 13(1), 321–330.

    Article  PubMed  Google Scholar 

  32. Uhan, S., et al. (2020). Hypermethylated promoters of genes UNC5D and KCNA1 as potential novel diagnostic biomarkers in colorectal cancer. Epigenomics, 12(19), 1677–1688.

    Article  CAS  PubMed  Google Scholar 

  33. Sun, T., et al. (2006). Differential expression of synaptoporin and synaptophysin in primary sensory neurons and up-regulation of synaptoporin after peripheral nerve injury. Neuroscience, 141(3), 1233–1245.

    Article  CAS  PubMed  Google Scholar 

  34. Su, K., et al. (2021). The role of a ceRNA regulatory network based on lncRNA MALAT1 site in cancer progression. Biomedicine & Pharmacotherapy, 137, 111389.

    Article  CAS  Google Scholar 

  35. Chen, X., et al. (2015). miR-372 regulates glioma cell proliferation and invasion by directly targeting PHLPP2. Journal of Cellular Biochemistry, 116(2), 225–232.

    Article  MathSciNet  CAS  PubMed  Google Scholar 

Download references

Funding

None.

Author information

Author notes

  1. Zhenchao Huang and Zhijie Chen contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosugery, Lingnan Hospital, branch of The Third Affiliated Hospital of Sun Yat-Sen University, No 2693Kaichuang AvenueHuangpu District, Guangzhou City, Guangdong Province, 510530, People’s Republic of China

    Zhenchao Huang, Zhijie Chen, En’peng Song, Peng Yu & Weiwen Chen

  2. Guangzhou BiDa Biological Technology CO., LTD, Guangzhou City, Guangdong Province, 510000, People’s Republic of China

    Huiqin Lin

Authors
  1. Zhenchao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhijie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. En’peng Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weiwen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huiqin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZCH and ZJC were main designers of this study, ZCH, ZJC, EPS and PY performed the experiments and analyzed the data, ZCH, ZJC, WWC and HQL drafted the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhenchao Huang.

Ethics declarations

Ethics Approval and Consent to Participate

The study was approved by the Ethics Committee of Lingnan Hospital, branch of The Third Affiliated Hospital of Sun Yat-sen University. Informed consents were obtained from the participants.

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

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 25 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Huang, Z., Chen, Z., Song, E. et al. Bioinformatics Analysis and Experimental Validation for Exploring Key Molecular Markers for Glioblastoma. Appl Biochem Biotechnol (2024). https://doi.org/10.1007/s12010-024-04894-7

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12010-024-04894-7

Keywords

Search

Navigation