J Korean Neurosurg Soc.  2025 Mar;68(2):202-212. 10.3340/jkns.2024.0149.

Comparative Analysis of Transcription Factors TWIST2, GATA3, and HES5 in Glioblastoma Multiforme : Evaluating Biomarker Potential and Therapeutic Targets Using in Silico Methods

• Chung S
• Kim C

• KMID: 2565277
• DOI: http://doi.org/10.3340/jkns.2024.0149

Abstract

Objective
: Glioblastoma multiforme (GBM) is characterized by substantial heterogeneity and limited therapeutic options. As molecular approaches to central nervous system tumors have gained prominence, this study examined the roles of three genes, TWIST2, GATA3, and HES5, known to be involved in oncogenesis, developmental processes, and maintenance of cancer stem cell properties, which have not yet been extensively studied in GBM. This study is the first to present gene expression data for TWIST2, GATA3, and HES5 specifically within the context of GBM patient survival.
Methods
: Gene expression data for TWIST2, GATA3, and HES5 were collected from GBM and normal brain tissues using datasets from The Cancer Genome Atlas via the Genomic Data Commons portal and the Genotype-Tissue Expression database. These data were rigorously analyzed using in silico methods.
Results
: All three genes were significantly more expressed in GBM tissues than in normal tissues. TWIST2 and GATA3 were linked to lower survival rates in GBM patients. Interestingly, higher HES5 levels were associated with better survival rates, suggesting a complex role that needs more investigation.
Conclusion
: This study shows that TWIST2, GATA3, and HES5 could help predict outcomes in GBM patients. Our multigene model offers a better understanding of GBM and points to new treatment options, bringing hope for improved therapies and patient outcomes. This research advances our knowledge of GBM and highlights the potential of molecular diagnostics in oncology.

Keyword

Glioblastoma; TWIST2; GATA3; HES5; Gene expression

Figure

• 

  Fig. 1. A : Comparison of TWIST2 gene expression in glioblastoma multiforme (GBM) and normal tissues. TWIST2 expression is markedly elevated in GBM tissues relative to normal counterparts. The y-axis depicts expression levels in log2 (FPKM+1), while the x-axis distinguishes between ‘normal’ and ‘GBM’ tissue categories. The blue box indicates expression levels in normal tissues, and the red box represents those in GBM tissues. Each box plot illustrates the median, interquartile range, and potential outliers, offering a comprehensive visual representation of expression distribution. B : Kaplan-Meier survival analysis for TWIST2 expression. The Kaplan-Meier curves illustrate survival probabilities over time for patients stratified by TWIST2 expression levels. The high-expression group (red line) exhibits significantly lower OS compared to the low-expression group (blue line). The steep decline in the high-expression group’s curve indicates a higher rate of adverse events, underscoring the prognostic impact of elevated TWIST2 expression on patient survival. C : Receiver operating characteristic (ROC) curve for TWIST2. The ROC curve demonstrates the trade-off between sensitivity (true positive rate) and 1-specificity (false positive rate) for various thresholds of TWIST2 expression. The area under the ROC curve (AUC) of 0.567 reflects moderate discriminative power in distinguishing between positive and negative cases, highlighting the predictive performance of TWIST2 in this model. FPKM : fragments per kilobase of transcript per million mapped reads. OS : overall survival.

• 

  Fig. 2. A : Comparative analysis of GATA3 gene expression in glioblastoma multiforme (GBM) and normal tissues. GATA3 expression is elevated in GBM tissues compared to normal brain tissues. The figure provides a visual representation of the differential expression levels between the two tissue types. B : Kaplan-Meier survival analysis for GATA3 expression. The Kaplan-Meier curve indicates that patients with higher GATA3 expression (red line) exhibit significantly lower overall survival compared to those with lower expression (blue line), with a statistically significant p-value of 0.0019. C : Receiver operating characteristic (ROC) curve for GATA3. The ROC curve illustrates the balance between sensitivity (true positive rate) and specificity (true negative rate) for GATA3 expression. With an area under the ROC curve (AUC) of 0.536, the figure reflects modest discriminative ability for GATA3 in distinguishing between positive and negative cases. FPKM : fragments per kilobase of transcript per million mapped reads.

• 

  Fig. 3. A : Comparative analysis of HES5 gene expression in glioblastoma multiforme (GBM) and normal tissues. HES5 expression is significantly higher in GBM tissues compared to normal brain tissues, highlighting its differential expression between the two tissue types. B : Kaplan-Meier survival analysis for HES5 expression. The Kaplan-Meier curve demonstrates that patients with higher HES5 expression (red line) had significantly greater overall survival compared to those with lower expression (blue line), with a statistically significant p-value of 0.0039. C : Receiver operating characteristic (ROC) curve for HES5. The ROC curve demonstrates the balance between sensitivity (true positive rate) and specificity (true negative rate) for HES5 expression. With an area under the ROC curve (AUC) of 0.538, the figure indicates modest discriminative ability for HES5 in distinguishing between positive and negative cases. FPKM : fragments per kilobase of transcript per million mapped reads.

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  Fig. 4. The predictive accuracy of GATA3, TWIST2, and HES5 as measured by Harrell’s C-index. The black squares represent the C-index values for each gene, with horizontal lines indicating the 95% confidence intervals (CIs). The p-values are displayed to the right of the plot. The combined model of TWIST2, GATA3, and HES5 yielded a C-index of 0.6260 (95% CI, 0.5620–0.6890), with a highly significant p-value of <0.001, demonstrating superior predictive performance.

Reference

References

1. Basak O, Taylor V. Identification of self-replicating multipotent progenitors in the embryonic nervous system by high Notch activity and Hes5 expression. Eur J Neurosci. 25:1006–1022. 2007.

2. Becker AP, Sells BE, Haque SJ, Chakravarti A. Tumor heterogeneity in glioblastomas: from light microscopy to molecular pathology. Cancers (Basel). 13:761. 2021.

3. Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C. Emerging landscape of oncogenic signatures across human cancers. Nat Genet. 45:1127–1133. 2013.

4. Debacker C, Catala M, Labastie MC. Embryonic expression of the human GATA-3 gene. Mech Dev. 85:183–187. 1999.

5. Gao D, Vahdat LT, Wong S, Chang JC, Mittal V. Microenvironmental regulation of epithelial-mesenchymal transitions in cancer. Cancer Res. 72:4883–4889. 2012.

6. Giachino C, Boulay JL, Ivanek R, Alvarado A, Tostado C, Lugert S, et al. A tumor suppressor function for notch signaling in forebrain tumor subtypes. Cancer Cell. 28:730–742. 2015.

7. Haim O, Agur A, Efrat OT, Valdes P, Ram Z, Grossman R. The clinical significance of radiological changes associated with gliadel implantation in patients with recurrent high grade glioma. Sci Rep. 13:11. 2023.

8. Han L, Yuan Y, Zheng S, Yang Y, Li J, Edgerton ME, et al. The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes. Nat Commun. 5:3963. 2014.

9. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 15:361–387. 1996.

10. Hatakeyama J, Bessho Y, Katoh K, Ookawara S, Fujioka M, Guillemot F, et al. Hes genes regulate size, shape and histogenesis of the nervous system by control of the timing of neural stem cell differentiation. Development. 131:5539–5550. 2004.

11. Ho IC, Tai TS, Pai SY. GATA3 and the T-cell lineage: essential functions before and after T-helper-2-cell differentiation. Nat Rev Immunol. 9:125–135. 2009.

12. Iser IC, Pereira MB, Lenz G, Wink MR. The epithelial-to-mesenchymal transition-like process in glioblastoma: an updated systematic review and in silico investigation. Med Res Rev. 37:271–313. 2017.

13. Iyer MK, Niknafs YS, Malik R, Singhal U, Sahu A, Hosono Y, et al. The landscape of long noncoding RNAs in the human transcriptome. Nat Genet. 47:199–208. 2015.

14. Kim Y, Varn FS, Park SH, Yoon BW, Park HR, Lee C, et al. Perspective of mesenchymal transformation in glioblastoma. Acta Neuropathol Commun. 9:50. 2021.

15. Kubelt C, Hattermann K, Sebens S, Mehdorn HM, Held-Feindt J. Epithelial-to-mesenchymal transition in paired human primary and recurrent glioblastomas. Int J Oncol. 46:2515–3625. 2015.

16. Lausen B, Schumacher M. Maximally selected rank statistics. Biometrics. 48:73–85. 1992.

17. Linker SB, Narvaiza I, Hsu JY, Wang M, Qiu F, Mendes APD, et al. Human-specific regulation of neural maturation identified by cross-primate transcriptomics. Curr Biol. 32:4797–4807.e5. 2022.

18. Luiken S, Fraas A, Bieg M, Sugiyanto R, Goeppert B, Singer S, et al. NOTCH target gene HES5 mediates oncogenic and tumor suppressive functions in hepatocarcinogenesis. Oncogene. 39:3128–3144. 2020.

19. Manning CS, Biga V, Boyd J, Kursawe J, Ymisson B, Spiller DG, et al. Quantitative single-cell live imaging links HES5 dynamics with cell-state and fate in murine neurogenesis. Nat Commun. 10:2835. 2019.

20. Mondragon-Soto M, Rodríguez-Hernández LA, Moreno Jiménez S, Gómez Amador JL, Gutierrez-Aceves A, Montano-Tello H, et al. Clinical, therapeutic, and prognostic experience in patients with glioblastoma. Cureus. 14:e29856. 2022.

21. Mouabbi JA, Meric-Bernstam F, Turova P, Chernyshov K, Kushnarev V, Kotlov N, et al. Genomic characterization of the GATA3 mutational landscape in breast cancer. J Clin Oncol. 41(16_suppl):1039. 2023.

22. Park YW, Vollmuth P, Foltyn-Dumitru M, Sahm F, Ahn SS, Chang JH, et al. The 2021 WHO classification for gliomas and implications on imaging diagnosis: part 1-key points of the fifth edition and summary of imaging findings on adult-type diffuse gliomas. J Magn Reson Imaging. 58:677–689. 2023.

23. Sabouri M, Dogonchi AF, Shafiei M, Tehrani DS. Survival rate of patient with glioblastoma: a population-based study. Egypt J Neurosurg. 39:42. 2024.

24. Scheel C, Weinberg RA. Cancer stem cells and epithelial-mesenchymal transition: concepts and molecular links. Semin Cancer Biol. 22:396–403. 2012.

25. Song Y, Zhang W, Zhang J, You Z, Hu T, Shao G, et al. TWIST2 inhibits EMT and induces oxidative stress in lung cancer cells by regulating the FGF21- mediated AMPK/mTOR pathway. Exp Cell Res. 405:112661. 2021.

26. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 352:987–996. 2005.

27. Takaya K, Sunohara A, Sakai S, Aramaki-Hattori N, Okabe K, Kishi K. Twist2 contributes to skin regeneration and hair follicle formation in mouse fetuses. Sci Rep. 14:10854. 2024.

28. The GTEx Consortium, Ardlie KG, Deluca DS, Segrè AV, Sullivan TJ, Young TR, et al. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 348:648–660. 2015.

29. Thiery JP. Epithelial-mesenchymal transitions in tumour progression. Nat Rev Cancer. 2:442–454. 2002.

30. Tsuji T, Ibaragi S, Hu GF. Epithelial-mesenchymal transition and cell cooperativity in metastasis. Cancer Res. 69:7135–7139. 2009.

31. Velásquez C, Mansouri S, Mora C, Nassiri F, Suppiah S, Martino J, et al. Molecular and clinical insights into the invasive capacity of glioblastoma cells. J Oncol. 2019:1740763. 2019.

32. Wan YY. GATA3: a master of many trades in immune regulation. Trends Immunol. 35:233–242. 2014.

33. Wright MN, Dankowski T, Ziegler A. Unbiased split variable selection for random survival forests using maximally selected rank statistics. Stat Med. 36:1272–1284. 2017.

34. Yan W, Cao QJ, Arenas RB, Bentley B, Shao R. GATA3 inhibits breast cancer metastasis through the reversal of epithelial-mesenchymal transition. J Biol Chem. 285:14042–14051. 2010.

35. Zaidan N, Nitsche L, Diamanti E, Hannah R, Fidanza A, Wilson NK, et al. Endothelial-specific Gata3 expression is required for hematopoietic stem cell generation. Stem Cell Rep. 17:1788–1798. 2022.

36. Zavadil J, Haley J, Kalluri R, Muthuswamy SK, Thompson E. Epithelial-mesenchymal transition. Cancer Res. 68:9574–9577. 2008.

37. Zeng W, Gu S, Yu Y, Feng Y, Xiao M, Feng XH. ZNF451 stabilizes TWIST2 through SUMOylation and promotes epithelial-mesenchymal transition. Am J Cancer Res. 11:898–915. 2021.

38. Zhou Y, Han D. GATA3 modulates neuronal survival through regulating TRPM2 in Parkinson’s disease. Int J Clin Exp Med. 10:15178–15186. 2017.