Araştırma Makalesi
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Exploring Bladder, Prostate, and Endometrial Cancer Risk in RRMS Patients: ATM, CREB1, and miR-19b-3p are Shared Biomarkers

Yıl 2025, Cilt: 4 Sayı: 3, 247 - 264, 30.09.2025

Tuba Denkçeken , Elif Onur

https://doi.org/10.59312/ebshealth.1722327

Öz

Aim: Relapsing-remitting multiple sclerosis (RRMS) is the most common phenotype of MS. Bladder urothelial cancer (BLCA) is a highly prevalent malignancy of the urinary system. Prostate adenocarcinoma (PRAD) is the leading cause of cancer-related morbidity and mortality in males. Uterine corpus endometrial carcinoma (UCEC) is a prevalent malignancy in females. Identifying the risk of BLCA, PRAD, and UCEC in RRMS patients is crucial. This study aims to identify potential biomarkers that pose a risk for BLCA, PRAD, and UCEC in RRMS patients and have a common role.
Materials and Methods: Expression profiles of RRMS patients were obtained from the GEO and ArrayExpress databases. Differentially expressed miRNAs (DEMs) and mRNAs (DEGs) were identified using the Principal Component Analysis (PCA)-based Unsupervised-Feature-Extraction (UFE) method. GEO2R was applied to analyze datasets, and DEGs and DEMs were classified based on fold change. Target genes of up/downregulated DEMs were identified, and common gene clusters with corresponding up/downregulated DEGs were determined. Further bioinformatics analyses were conducted to identify hub-miRNAs and hub-genes.
Results: 321 control and 293 RRMS samples were analyzed. DEMs and DEGs were identified using both the PCA-based UFE and GEO2R, and their intersections were determined. Target genes of DEMs were selected based on validation and prediction in at least two databases. Negatively correlated target genes of up/downregulated DEMs were identified, and common gene clusters were established. STRING analysis was performed, and a negative regulatory network was constructed using Cytoscape. Validation of hub-genes and hub-miRNAs in BLCA, PRAD, and UCEC was conducted using UALCAN and OncomiR.
Discussion: Decreased ATM and CREB1 have been identified as direct targets of hsa-miR-19b-3p. They were identified as potential biomarkers in RRMS and further validated in BLCA, PRAD, and UCEC. This study highlights biomarkers in RRMS patients that may contribute to an increased risk of these cancers.

Anahtar Kelimeler

RRMS BLCA PRAD UCEC Bioinformatics ATM CREB1 hsa-miR-19b-3p

Etik Beyan

Not required

Destekleyen Kurum

TÜBİTAK (Türkiye Bilimsel ve Teknolojik Araştırma Kurumu)

Proje Numarası

223S246

Kaynakça

  • Angèle, S., Falconer, A., Edwards, S. M., Dörk, T., Bremer, M., Moullan, N., & The Cancer Research Uk/British Prostate Group/Association of Urological Surgeons, S. o. O. C. (2004). ATM polymorphisms as risk factors for prostate cancer development. British Journal of Cancer, 91(4), 783-787. doi:https://doi.org/10.1038/sj.bjc.6602007
  • Ascherio, A., & Munger, K. L. (2007). Environmental risk factors for multiple sclerosis. Part I: the role of infection. Ann Neurol, 61(4), 288-299. doi:https://doi.org/10.1002/ana.21117
  • Bahmanyar, S., Montgomery, S. M., Hillert, J., Ekbom, A., & Olsson, T. (2009). Cancer risk among patients with multiple sclerosis and their parents. Neurology, 72(13), 1170-1177. doi:https://doi.org/10.1212/01.wnl.0000345366.10455.62
  • Bosco-Lévy, P., Foch, C., Grelaud, A., Sabidó, M., Lacueille, C., Jové, J., & Blin, P. (2022). Incidence and risk of cancer among multiple sclerosis patients: A matched population-based cohort study. Eur J Neurol, 29(4), 1091-1099. doi:https://doi.org/10.1111/ene.15226
  • Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., & Varambally, S. (2017). UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia, 19(8), 649-658. doi:https://doi.org/https://doi.org/10.1016/j.neo.2017.05.002
  • Clarke, R. A., Fang, Z. M., Lee, C. S., Sarris, M., Murrell, D., & Kearsley, J. H. (2002). Multiple sclerosis in a radiosensitive family with low levels of the ATM protein. Australas Radiol, 46(3), 267-274. doi:https://doi.org/10.1046/j.1440-1673.2002.01058.x
  • Davis, S., & Meltzer, P. S. (2007). GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics, 23(14), 1846-1847. doi:https://doi.org/10.1093/bioinformatics/btm254 %J Bioinformatics
  • De Santis, G., Ferracin, M., Biondani, A., Caniatti, L., Rosaria Tola, M., Castellazzi, M., & Granieri, E. (2010). Altered miRNA expression in T regulatory cells in course of multiple sclerosis. J Neuroimmunol, 226(1-2), 165-171. doi:https://doi.org/10.1016/j.jneuroim.2010.06.009
  • Deng, X., Ljunggren-Rose, A., Maas, K., & Sriram, S. (2005). Defective ATM-p53-mediated apoptotic pathway in multiple sclerosis. Ann Neurol, 58(4), 577-584. doi:https://doi.org/10.1002/ana.20600
  • Denkçeken, T. P., E.; Benlier, N. (2020). Cisplatin treatment in pulmonary sarcoidosis: An In silico approach. Natl J Physiol Pharm Pharmacol, 10(10), 900-904. doi:https://doi.org/10.5455/njppp.2020.10.07193202024072020
  • Duca, R. B., Massillo, C., Dalton, G. N., Farré, P. L., Graña, K. D., Gardner, K., & De Siervi, A. (2021). MiR-19b-3p and miR-101-3p as potential biomarkers for prostate cancer diagnosis and prognosis. Am J Cancer Res, 11(6), 2802-2820.
  • Edgunlu, T. G., Yilmaz, S. G., Emre, U., Tasdelen, B., Kuru, O., Kutlu, G., & Erdal, M. E. (2022). miR-181a-5p is a potential candidate epigenetic biomarker in multiple sclerosis. Genome, 65(11), 547-561. doi:https://doi.org/10.1139/gen-2022-0040
  • Feldman, D., Krishnan, A. V., Swami, S., Giovannucci, E., & Feldman, B. J. (2014). The role of vitamin D in reducing cancer risk and progression. Nature Reviews Cancer, 14(5), 342-357. doi:https://doi.org/10.1038/nrc3691
  • Geng, G., Yu, X., Jiang, J., & Yu, X. (2020). Aetiology and pathogenesis of paraneoplastic autoimmune disorders. Autoimmunity Reviews, 19(1), 102422. doi:https://doi.org/https://doi.org/10.1016/j.autrev.2019.102422
  • Ghajarzadeh, M., Keshtkar, A. A., Azimi, A., Sahraian, M. A., Mohammadifar, M., & Ramagopalan, S. V. (2019). The Effect of Vitamin D Supplements on Clinical and Para-Clinical Outcomes in Patients With Multiple Sclerosis: Protocol for a Systematic Review. JMIR Res Protoc, 8(4), e12045. doi:https://doi.org/10.2196/12045
  • Ghajarzadeh, M., Mohammadi, A., & Sahraian, M. A. (2020). Risk of cancer in multiple sclerosis (MS): A systematic review and meta-analysis. Autoimmunity Reviews, 19(10), 102650. doi:https://doi.org/https://doi.org/10.1016/j.autrev.2020.102650
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RRMS Hastalarında Mesane, Prostat ve Endometrial Kanser Riskinin Araştırılması: ATM, CREB1 ve miR-19b-3p Ortak Biyobelirteçler

Yıl 2025, Cilt: 4 Sayı: 3, 247 - 264, 30.09.2025

Tuba Denkçeken , Elif Onur

https://doi.org/10.59312/ebshealth.1722327

Öz

Amaç: Relapsing-remitting multipl skleroz (RRMS), MS'in en yaygın fenotipidir. Mesane ürotelyal kanseri (BLCA), üriner sistemin oldukça yaygın bir malignitesidir. Prostat adenokarsinomu (PRAD), erkeklerde kanserle ilişkili morbidite ve mortalitenin önde gelen nedenidir. Uterin korpus endometriyal karsinomu (UCEC), kadınlarda yaygın bir malignitedir. RRMS hastalarında BLCA, PRAD ve UCEC riskini belirlemek çok önemlidir. Bu çalışma, RRMS hastalarında BLCA, PRAD ve UCEC için risk oluşturan ve ortak bir role sahip olan potansiyel biyobelirteçleri belirlemeyi amaçlamaktadır.
Yöntem: RRMS hastalarının ekspresyon profilleri GEO ve ArrayExpress veritabanlarından elde edildi. Diferansiyel ekprese miRNA'lar (DEM'ler) ve mRNA'lar (DEG'ler), Temel Bileşen Analizi (PCA) tabanlı Denetimsiz Özellik Çıkarımı (UFE) yöntemi kullanılarak belirlendi. GEO2R veri kümelerini analiz etmek için uygulandı ve DEG'ler ve DEM'ler kat değişimine göre sınıflandırıldı. Yukarı/aşağı düzenlenmiş DEM'lerin hedef genleri tanımlandı ve karşılık gelen yukarı/aşağı düzenlenmiş DEG'lere sahip ortak gen kümeleri belirlendi. Hub-miRNA'ları ve hub-genleri tanımlamak için ileri biyoenformatik analizler yapıldı.
Bulgular: 321 kontrol ve 293 RRMS örneği analiz edildi. DEM'ler ve DEG'ler hem PCA tabanlı UFE hem de GEO2R kullanılarak tanımlandı ve kesişimleri belirlendi. DEM'lerin hedef genleri en az iki veritabanındaki doğrulama ve tahmine göre seçildi. Yukarı/aşağı düzenlenmiş DEM'lerin negatif korelasyonlu hedef genleri belirlendi ve ortak gen kümeleri oluşturuldu. STRING analizi yapıldı ve Cytoscape kullanılarak negatif düzenleyici bir ağ oluşturuldu. BLCA, PRAD ve UCEC'deki hub-genlerin ve hub-miRNA'ların doğrulaması UALCAN ve OncomiR kullanılarak gerçekleştirildi.
Sonuç: Azalmış ATM ve CREB1, hsa-miR-19b-3p'nin doğrudan hedefleri olarak tanımlanmıştır. Bunlar RRMS'de potansiyel biyobelirteçler olarak tanımlanmış ve BLCA, PRAD ve UCEC'de valide edilmiştir. Bu çalışma, RRMS hastalarında bu kanserlerin artma riskine katkıda bulunabilecek biyobelirteçleri vurgulamaktadır.

Anahtar Kelimeler

RRMS BLCA PRAD UCEC Biyoinformatik ATM CREB1 hsa-miR-19b-3p

Proje Numarası

223S246

Kaynakça

  • Angèle, S., Falconer, A., Edwards, S. M., Dörk, T., Bremer, M., Moullan, N., & The Cancer Research Uk/British Prostate Group/Association of Urological Surgeons, S. o. O. C. (2004). ATM polymorphisms as risk factors for prostate cancer development. British Journal of Cancer, 91(4), 783-787. doi:https://doi.org/10.1038/sj.bjc.6602007
  • Ascherio, A., & Munger, K. L. (2007). Environmental risk factors for multiple sclerosis. Part I: the role of infection. Ann Neurol, 61(4), 288-299. doi:https://doi.org/10.1002/ana.21117
  • Bahmanyar, S., Montgomery, S. M., Hillert, J., Ekbom, A., & Olsson, T. (2009). Cancer risk among patients with multiple sclerosis and their parents. Neurology, 72(13), 1170-1177. doi:https://doi.org/10.1212/01.wnl.0000345366.10455.62
  • Bosco-Lévy, P., Foch, C., Grelaud, A., Sabidó, M., Lacueille, C., Jové, J., & Blin, P. (2022). Incidence and risk of cancer among multiple sclerosis patients: A matched population-based cohort study. Eur J Neurol, 29(4), 1091-1099. doi:https://doi.org/10.1111/ene.15226
  • Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., & Varambally, S. (2017). UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia, 19(8), 649-658. doi:https://doi.org/https://doi.org/10.1016/j.neo.2017.05.002
  • Clarke, R. A., Fang, Z. M., Lee, C. S., Sarris, M., Murrell, D., & Kearsley, J. H. (2002). Multiple sclerosis in a radiosensitive family with low levels of the ATM protein. Australas Radiol, 46(3), 267-274. doi:https://doi.org/10.1046/j.1440-1673.2002.01058.x
  • Davis, S., & Meltzer, P. S. (2007). GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics, 23(14), 1846-1847. doi:https://doi.org/10.1093/bioinformatics/btm254 %J Bioinformatics
  • De Santis, G., Ferracin, M., Biondani, A., Caniatti, L., Rosaria Tola, M., Castellazzi, M., & Granieri, E. (2010). Altered miRNA expression in T regulatory cells in course of multiple sclerosis. J Neuroimmunol, 226(1-2), 165-171. doi:https://doi.org/10.1016/j.jneuroim.2010.06.009
  • Deng, X., Ljunggren-Rose, A., Maas, K., & Sriram, S. (2005). Defective ATM-p53-mediated apoptotic pathway in multiple sclerosis. Ann Neurol, 58(4), 577-584. doi:https://doi.org/10.1002/ana.20600
  • Denkçeken, T. P., E.; Benlier, N. (2020). Cisplatin treatment in pulmonary sarcoidosis: An In silico approach. Natl J Physiol Pharm Pharmacol, 10(10), 900-904. doi:https://doi.org/10.5455/njppp.2020.10.07193202024072020
  • Duca, R. B., Massillo, C., Dalton, G. N., Farré, P. L., Graña, K. D., Gardner, K., & De Siervi, A. (2021). MiR-19b-3p and miR-101-3p as potential biomarkers for prostate cancer diagnosis and prognosis. Am J Cancer Res, 11(6), 2802-2820.
  • Edgunlu, T. G., Yilmaz, S. G., Emre, U., Tasdelen, B., Kuru, O., Kutlu, G., & Erdal, M. E. (2022). miR-181a-5p is a potential candidate epigenetic biomarker in multiple sclerosis. Genome, 65(11), 547-561. doi:https://doi.org/10.1139/gen-2022-0040
  • Feldman, D., Krishnan, A. V., Swami, S., Giovannucci, E., & Feldman, B. J. (2014). The role of vitamin D in reducing cancer risk and progression. Nature Reviews Cancer, 14(5), 342-357. doi:https://doi.org/10.1038/nrc3691
  • Geng, G., Yu, X., Jiang, J., & Yu, X. (2020). Aetiology and pathogenesis of paraneoplastic autoimmune disorders. Autoimmunity Reviews, 19(1), 102422. doi:https://doi.org/https://doi.org/10.1016/j.autrev.2019.102422
  • Ghajarzadeh, M., Keshtkar, A. A., Azimi, A., Sahraian, M. A., Mohammadifar, M., & Ramagopalan, S. V. (2019). The Effect of Vitamin D Supplements on Clinical and Para-Clinical Outcomes in Patients With Multiple Sclerosis: Protocol for a Systematic Review. JMIR Res Protoc, 8(4), e12045. doi:https://doi.org/10.2196/12045
  • Ghajarzadeh, M., Mohammadi, A., & Sahraian, M. A. (2020). Risk of cancer in multiple sclerosis (MS): A systematic review and meta-analysis. Autoimmunity Reviews, 19(10), 102650. doi:https://doi.org/https://doi.org/10.1016/j.autrev.2020.102650
  • Guo, L., Yin, M., & Wang, Y. (2018). CREB1, a direct target of miR-122, promotes cell proliferation and invasion in bladder cancer. Oncol Lett, 16(3), 3842-3848. doi:https://doi.org/10.3892/ol.2018.9118
  • Haunsberger, S. J., Connolly, N. M., & Prehn, J. H. (2017). miRNAmeConverter: an R/bioconductor package for translating mature miRNA names to different miRBase versions. Bioinformatics, 33(4), 592-593. doi:https://doi.org/10.1093/bioinformatics/btw660
  • Hongell, K., Kurki, S., Sumelahti, M. L., & Soilu-Hanninen, M. (2019). Risk of cancer among Finnish multiple sclerosis patients. Mult Scler Relat Disord, 35, 221-227. doi:https://doi.org/10.1016/j.msard.2019.08.005
  • Hu, Z., Fu, Y., Wang, J., Li, Y., & Jiang, Q. (2023). Association between multiple sclerosis and prostate cancer risk: A systematic review and meta‑analysis. Oncol Lett, 25(2), 83. doi:https://doi.org/10.3892/ol.2023.13669
  • International Multiple Sclerosis Genetics, C., Wellcome Trust Case Control, C., Sawcer, S., Hellenthal, G., Pirinen, M., Spencer, C. C., & Compston, A. (2011). Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature, 476(7359), 214-219. doi:https://doi.org/10.1038/nature10251
  • Jick, S. S., Li, L., Falcone, G. J., Vassilev, Z. P., & Wallander, M. A. (2014). Mortality of patients with multiple sclerosis: a cohort study in UK primary care. Journal of Neurology, 261(8), 1508-1517. doi:https://doi.org/10.1007/s00415-014-7370-3
  • Kingwell, E., Bajdik, C., Phillips, N., Zhu, F., Oger, J., Hashimoto, S., & Tremlett, H. (2012). Cancer risk in multiple sclerosis: findings from British Columbia, Canada. Brain, 135(Pt 10), 2973-2979. doi:https://doi.org/10.1093/brain/aws148
  • Kular, L., Needhamsen, M., Adzemovic, M. Z., Kramarova, T., Gomez-Cabrero, D., Ewing, E., & Jagodic, M. (2019). Neuronal methylome reveals CREB-associated neuro-axonal impairment in multiple sclerosis. Clin Epigenetics, 11(1), 86. doi:https://doi.org/10.1186/s13148-019-0678-1
  • Lalmohamed, A., Bazelier, M. T., Van Staa, T. P., Uitdehaag, B. M. J., Leufkens, H. G. M., De Boer, A., & De Vries, F. (2012). Causes of death in patients with multiple sclerosis and matched referent subjects: a population-based cohort study. European Journal of Neurology, 19(7), 1007-1014. doi:https://doi.org/https://doi.org/10.1111/j.1468-1331.2012.03668.x
  • Liu, Z., Fan, T., Mo, X., Kan, J., & Zhang, B. (2024). Association between multiple sclerosis and cancer risk: A two-sample Mendelian randomization study. PLoS One, 19(3), e0298271. doi:https://doi.org/10.1371/journal.pone.0298271
  • Liu, Z., Yang, Y., Yang, Z., Xia, S., Lin, D., Xiao, B., & Xiu, Y. (2020). Novel circRNA_0071196/miRNA‑19b‑3p/CIT axis is associated with proliferation and migration of bladder cancer. Int J Oncol, 57(3), 767-779. doi:https://doi.org/10.3892/ijo.2020.5093
  • Marinaccio, M., Christopher, C., Valeria, P., Zaza, C., Falcicchio, G., Pellicciari, R., & Cicinelli, E. (2023). Single brain metastasis as onset of stage I endometrial carcinoma in patient affected by multiple sclerosis: the first case in literature. Archives of Surgery and Clinical Research, 7(1), 012-015. doi:https://doi.org/10.29328/journal.ascr.1001068
  • Marrie, R. A., Maxwell, C., Mahar, A., Ekuma, O., McClintock, C., Seitz, D., & Groome, P. A. (2021). Cancer Incidence and Mortality Rates in Multiple Sclerosis: A Matched Cohort Study. Neurology, 96(4), e501-e512. doi:https://doi.org/10.1212/wnl.0000000000011219
  • Marrie, R. A., Reider, N., Cohen, J., Stuve, O., Trojano, M., Sorensen, P. S., & Cutter, G. (2015). A systematic review of the incidence and prevalence of cancer in multiple sclerosis. Mult Scler, 21(3), 294-304. doi:https://doi.org/10.1177/1352458514564489
  • Menkhorst, E., So, T., Rainczuk, K., Barton, S., Zhou, W., Edgell, T., & Dimitriadis, E. (2023). Endometrial stromal cell miR-19b-3p release is reduced during decidualization implying a role in decidual-trophoblast cross-talk. Frontiers in Endocrinology, 14. doi:https://doi.org/10.3389/fendo.2023.1149786
  • Pala, E., & Denkceken, T. (2020). Evaluation of miRNA Expression Profiles in Schizophrenia Using Principal-Component Analysis-Based Unsupervised Feature Extraction Method. J Comput Biol, 27(8), 1253-1263. doi:https://doi.org/10.1089/cmb.2019.0412
  • Pala, E. D., T. (2020). Identification of chemoresistance-associated miRNAs in hypopharyngeal squamous cell carcinoma. Med-Science, 9(1), 160-163. doi:https://doi.org/10.5455/medscience.2019.08.9158
  • Pierret, C., Mulliez, A., Le Bihan-Benjamin, C., Moisset, X., Bousquet, P.-J., & Leray, E. (2024). Cancer Risk Among Patients With Multiple Sclerosis. Neurology, 103(9), e209885. doi:https://doi.org/doi:10.1212/WNL.0000000000209885
  • Sagir, F., Ersoy Tunali, N., Tombul, T., Koral, G., Cirak, S., Yilmaz, V., & Tuzun, E. (2021). miR-132-3p, miR-106b-5p, and miR-19b-3p Are Associated with Brain-Derived Neurotrophic Factor Production and Clinical Activity in Multiple Sclerosis: A Pilot Study. Genet Test Mol Biomarkers, 25(11), 720-726. doi:https://doi.org/10.1089/gtmb.2021.0183
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  • Shriwash, N., Aiman, A., Singh, P., Basir, S. F., Shamsi, A., Shahid, M., & Islam, A. (2024). Understanding the role of potential biomarkers in attenuating multiple sclerosis progression via multiomics and network-based approach. PLoS One, 19(12), e0314428. doi:https://doi.org/10.1371/journal.pone.0314428
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  • Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., & Mering, C. V. (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 47(D1), D607-D613. doi:https://doi.org/10.1093/nar/gky1131
  • Taguchi, Y. H. (2016). Identification of More Feasible MicroRNA-mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction. Int J Mol Sci, 17(5). doi:https://doi.org/10.3390/ijms17050696
  • Taguchi, Y. H. (2017). Principal Components Analysis Based Unsupervised Feature Extraction Applied to Gene Expression Analysis of Blood from Dengue Haemorrhagic Fever Patients. Sci Rep, 7, 44016. doi:https://doi.org/10.1038/srep44016
  • Taguchi, Y. H., & Wang, H. (2017). Genetic Association between Amyotrophic Lateral Sclerosis and Cancer. Genes (Basel), 8(10). doi:https://doi.org/10.3390/genes8100243
  • Team, R. D. C. (2010). R: A language and environment for statistical computing. In (pp. 1-409). Vienna, Austria: R Foundation for Statistical Computing.
  • Watson, M. J., Berger, P. L., Banerjee, K., Frank, S. B., Tang, L., Ganguly, S. S., & Miranti, C. K. (2021). Aberrant CREB1 activation in prostate cancer disrupts normal prostate luminal cell differentiation. Oncogene, 40(18), 3260-3272. doi:https://doi.org/10.1038/s41388-021-01772-y
  • Wong, N. W., Chen, Y., Chen, S., & Wang, X. (2017). OncomiR: an online resource for exploring pan-cancer microRNA dysregulation. Bioinformatics, 34(4), 713-715. doi:https://doi.org/10.1093/bioinformatics/btx627
  • Yin, M., Grivas, P., Ali, S. M., Hsu, J., Vasekar, M. K., Emamekhoo, H., & Joshi, M. (2017). ATM/RB1 mutations to predict shorter overall survival (OS) in bladder cancer. Journal of Clinical Oncology, 35(6_suppl), 393-393. doi:https://doi.org/10.1200/JCO.2017.35.6_suppl.393
  • Zhou, Y., Börcsök, J., Adib, E., Kamran, S. C., Neil, A. J., Stawiski, K., & Mouw, K. W. (2023). ATM deficiency confers specific therapeutic vulnerabilities in bladder cancer. Sci Adv, 9(47), eadg2263. doi:https://doi.org/10.1126/sciadv.adg2263
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kanser Genetiği
Bölüm Araştırma Makaleleri
Yazarlar

Tuba Denkçeken 0000-0002-4663-5410

Elif Onur 0000-0002-1690-3170

Proje Numarası 223S246
Yayımlanma Tarihi 30 Eylül 2025
Gönderilme Tarihi 18 Haziran 2025
Kabul Tarihi 11 Ağustos 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 4 Sayı: 3

Kaynak Göster

APA Denkçeken, T., & Onur, E. (2025). Exploring Bladder, Prostate, and Endometrial Cancer Risk in RRMS Patients: ATM, CREB1, and miR-19b-3p are Shared Biomarkers. Doğu Karadeniz Sağlık Bilimleri Dergisi, 4(3), 247-264. https://doi.org/10.59312/ebshealth.1722327

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