Maybe a New Target for Gliomas: AQP1

Yıl 2025, Cilt: 14 Sayı: 1, 21 - 31, 26.03.2025

Saniye Elvan Öztürk

https://doi.org/10.46810/tdfd.1496982

Öz

Gliomas are the most common and aggressive tumors of the central nervous system. However, the prognosis of gliomas is poor. Therefore, studies that will form the basis for the diagnosis and treatment of the disease are very important for these patient groups. In this study, members of the Aquaporin family in gliomas were examined, and the relationships of AQP1 and AQP4 with gliomas were investigated.
In this study conducted using in silico methods, the expression levels of the AQP family in LGG and GBM-type gliomas were compared. As a result of the findings, the prognostic value and clinical importance of AQP1 and AQP4 were evaluated by using GEPIA, UCSC Xena, Gliovis, cBioPortal, and Ivy GAP analysis tools.
According to the data, AQP1 and AQP4 gene expressions were found as more expressed in tumor tissue than in normal tissue in the LGG and GBM datasets. The effects of these two genes on the survival and prognosis of patients were investigated using the expression patterns in the LGG and GBM datasets. When compared the expression levels of these two genes in the subtypes, AQP1 was found low in the Oligodendrogram subtype of LGG, and both AQP1 and AQP4 levels were high in all the remaining subtypes.
With these findings, both AQP4 and AQP1 from the aquaporin family are effective genes in the pathogenesis of gliomas and patient survival. AQP1 may be used as a new drug target or biomarker against aggressive gliomas. Therefore, it should be evaluated for future studies.

Anahtar Kelimeler

AQP4, AQP1, LGG, GBM, In silico

Kaynakça

• Ostrom, Q.T., et al., CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011-2015. Neuro Oncol, 2018. 20(suppl_4): p. iv1-iv86.
• Louis, D.N., et al., The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol, 2021. 23(8): p. 1231-1251.
• Molinaro, A.M., et al., Genetic and molecular epidemiology of adult diffuse glioma. Nat Rev Neurol, 2019. 15(7): p. 405-417.
• Ostrom, Q.T., et al., The epidemiology of glioma in adults: a "state of the science" review. Neuro Oncol, 2014. 16(7): p. 896-913.
• Claus, E.B. and P.M. Black, Survival rates and patterns of care for patients diagnosed with supratentorial low-grade gliomas: data from the SEER program, 1973-2001. Cancer, 2006. 106(6): p. 1358-63.
• Venneti, S. and J.T. Huse, The evolving molecular genetics of low-grade glioma. Adv Anat Pathol, 2015. 22(2): p. 94-101.
• Perry, J.R., et al., Short-Course Radiation plus Temozolomide in Elderly Patients with Glioblastoma. N Engl J Med, 2017. 376(11): p. 1027-1037.
• Ostrom, Q.T., et al., CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012-2016. Neuro Oncol, 2019. 21(Suppl 5): p. v1-v100.
• Sant, M., et al., Survival of European patients with central nervous system tumors. Int J Cancer, 2012. 131(1): p. 173-85.
• Dubois, L.G., et al., Gliomas and the vascular fragility of the blood brain barrier. Front Cell Neurosci, 2014. 8: p. 418.
• Dahlrot, R.H., et al., Prognostic value of O-6-methylguanine-DNA methyltransferase (MGMT) protein expression in glioblastoma excluding nontumour cells from the analysis. Neuropathol Appl Neurobiol, 2018. 44(2): p. 172-184.
• Eckel-Passow, J.E., et al., Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors. N Engl J Med, 2015. 372(26): p. 2499-508.
• Garcia-Escudero, R. and J.M. Paramio, Gene expression profiling as a tool for basic analysis and clinical application of human cancer. Mol Carcinog, 2008. 47(8): p. 573-9.
• Kim, Y.W., et al., Identification of novel synergistic targets for rational drug combinations with PI3 kinase inhibitors using siRNA synthetic lethality screening against GBM. Neuro Oncol, 2011. 13(4): p. 367-75.
• Weeraratna, A.T., Discovering causes and cures for cancer from gene expression analysis. Ageing Res Rev, 2005. 4(4): p. 548-63.
• Cheng, W., et al., Bioinformatic profiling identifies an immune-related risk signature for glioblastoma. Neurology, 2016. 86(24): p. 2226-34.
• Li, J., et al., TCPA: a resource for cancer functional proteomics data. Nat Methods, 2013. 10(11): p. 1046-7.
• Zhang, Y., et al., S100A gene family: immune-related prognostic biomarkers and therapeutic targets for low-grade glioma. Aging (Albany NY), 2021. 13(11): p. 15459-15478.
• Zhu, H., et al., TUBA1C is a Prognostic Marker in Low-grade Glioma and Correlates with Immune Cell Infiltration in the Tumor Microenvironment. Front Genet, 2021. 12: p. 759953.
• Charlestin, V., et al., Aquaporins: New players in breast cancer progression and treatment response. Front Oncol, 2022. 12: p. 988119.
• Moon, C.S., D. Moon, and S.K. Kang, Aquaporins in Cancer Biology. Front Oncol, 2022. 12: p. 782829.
• Varricchio, A. and A.J. Yool, Aquaporins and Ion Channels as Dual Targets in the Design of Novel Glioblastoma Therapeutics to Limit Invasiveness. Cancers (Basel), 2023. 15(3).
• Magni, F., et al., AQP1 expression analysis in human diseases: implications for proteomic characterization. Expert Rev Proteomics, 2008. 5(1): p. 29-43.
• Tang, Z., et al., GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res, 2017. 45(W1): p. W98-W102.
• Bowman, R.L., et al., GlioVis data portal for visualization and analysis of brain tumor expression datasets. Neuro Oncol, 2017. 19(1): p. 139-141.
• Goldman, M.J., et al., Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol, 2020. 38(6): p. 675-678.
• Puchalski, R.B., et al., An anatomic transcriptional atlas of human glioblastoma. Science, 2018. 360(6389): p. 660-663.
• Szklarczyk, D., et al., STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019. 47(D1): p. D607-D613.
• Dennis, G., Jr., et al., DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol, 2003. 4(5): p. P3.
• Werner, J.M., et al., Expression of FAS-L Differs from Primary to Relapsed Low-grade Gliomas and Predicts Progression-free Survival. Anticancer Res, 2017. 37(12): p. 6639-6648.
• Reon, B.J., et al., Expression of lncRNAs in Low-Grade Gliomas and Glioblastoma Multiforme: An In Silico Analysis. PLoS Med, 2016. 13(12): p. e1002192.
• Kourghi, M., et al., Bumetanide Derivatives AqB007 and AqB011 Selectively Block the Aquaporin-1 Ion Channel Conductance and Slow Cancer Cell Migration. Mol Pharmacol, 2016. 89(1): p. 133-40.
• Pei, J.V., et al., Differential Inhibition of Water and Ion Channel Activities of Mammalian Aquaporin-1 by Two Structurally Related Bacopaside Compounds Derived from the Medicinal Plant Bacopa monnieri. Mol Pharmacol, 2016. 90(4): p. 496-507.
• Salman, M.M., et al., Recent breakthroughs and future directions in drugging aquaporins. Trends Pharmacol Sci, 2022. 43(1): p. 30-42.
• Saadoun, S., et al., Aquaporin-4 expression is increased in oedematous human brain tumours. J Neurol Neurosurg Psychiatry, 2002. 72(2): p. 262-5.
• Oshio, K., et al., Expression of the aquaporin-1 water channel in human glial tumors. Neurosurgery, 2005. 56(2): p. 375-81; discussion 375-81.
• Boon, K., et al., Identification of astrocytoma associated genes including cell surface markers. BMC Cancer, 2004. 4: p. 39.
• Deen, P.M., et al., The human gene for water channel aquaporin 1 (AQP1) is localized on chromosome 7p15-->p14. Cytogenet Cell Genet, 1994. 65(4): p. 243-6.
• Brabet, P., et al., Localization of the human pituitary adenylate cyclase-activating polypeptide receptor (PACAP1-R) gene to 7p15-p14 by fluorescence in situ hybridization. Genomics, 1996. 38(1): p. 100-2.
• Gonzalez, P. and K.G. Martinez, The role of stress and fear in the development of mental disorders. Psychiatr Clin North Am, 2014. 37(4): p. 535-46.
• Oyola, M.G. and R.J. Handa, Hypothalamic-pituitary-adrenal and hypothalamic-pituitary-gonadal axes: sex differences in regulation of stress responsivity. Stress, 2017. 20(5): p. 476-494.
• Hu, G., et al., Prognostic Markers Identification in Glioma by Gene Expression Profile Analysis. J Comput Biol, 2020. 27(1): p. 81-90.
• Staruschenko, A., M.R. Hodges, and O. Palygin, Kir5.1 channels: potential role in epilepsy and seizure disorders. Am J Physiol Cell Physiol, 2022. 323(3): p. C706-C717.