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Magnetic resonance imaging and deoxyribonucleic acid methylation–based radiogenomic models for survival risk stratification of glioblastoma

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Abstract

Glioblastoma multiforme (GBM) is one of the deadliest tumours. This study aimed to construct radiogenomic prognostic models of glioblastoma overall survival (OS) based on magnetic resonance imaging (MRI) Gd-T1WI images and deoxyribonucleic acid (DNA) methylation-seq and to understand the related biological pathways. The ResNet3D-18 model was used to extract radiomic features, and Lasso-Cox regression analysis was utilized to establish the prognostic models. A nomogram was constructed by combining the radiogenomic features and clinicopathological variables. The DeLong test was performed to compare the area under the curve (AUC) of the models. We screened differentially expressed genes (DEGs) with original ribonucleic acid (RNA)-seq in risk stratification and used Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotations for functional enrichment analysis. For the 1-year OS models, the AUCs of the radiogenomic set, methylation set and deep learning set in the training cohort were 0.864, 0.804 and 0.787, and those in the validation cohort were 0.835, 0.768 and 0.651, respectively. The AUCs of the 0.5-, 1- and 2-year nomograms in the training cohort were 0.943, 0.861 and 0.871, and those in the validation cohort were 0.864, 0.885 and 0.805, respectively. A total of 245 DEGs were screened; functional enrichment analysis showed that these DEGs were associated with cell immunity. The survival risk-stratifying radiogenomic models for glioblastoma OS had high predictability and were associated with biological pathways related to cell immunity.

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References

  1. Beig N, Bera K, Prasanna P, Antunes J, Correa R, Singh S et al (2020) Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma. Clin Cancer Res 26(8):1866–1876. https://doi.org/10.1158/1078-0432.CCR-19-2556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhang X, Lu H, Tian Q, Feng N, Yin L, Xu X et al (2019) A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival. Eur Radiol 29(10):5528–5538. https://doi.org/10.1007/s00330-019-06069-z

    Article  PubMed  Google Scholar 

  3. X. Jia, Y. Zhai, D. Song, Y. Wang, S. Wei, F. Yang, et al. (2022) A multiparametric MRI-based radiomics nomogram for preoperative prediction of survival stratification in glioblastoma patients with standard treatment. Frontiers in Oncology 12. https://doi.org/10.3389/fonc.2022.758622

  4. J. Lao, Y. Chen, Z.-C. Li, Q. Li, J. Zhang, J. Liu, et al. (2017) A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Scientific Reports 7(1). https://doi.org/10.1038/s41598-017-10649-8

  5. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D et al (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23(8):1231–1251. https://doi.org/10.1093/neuonc/noab106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Suelves M, Carrio E, Nunez-Alvarez Y, Peinado MA (2016) DNA methylation dynamics in cellular commitment and differentiation. Brief Funct Genomics 15(6):443–453. https://doi.org/10.1093/bfgp/elw017

    Article  CAS  PubMed  Google Scholar 

  7. Lu C, Wei Y, Wang X, Zhang Z, Yin J, Li W et al (2020) DNA-methylation-mediated activating of lncRNA SNHG12 promotes temozolomide resistance in glioblastoma. Mol Cancer 19(1):28. https://doi.org/10.1186/s12943-020-1137-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ji J, Zhao L, Zhao X, Li Q, An Y, Li L et al (2020) Genomewide DNA methylation regulation analysis of long noncoding RNAs in glioblastoma. Int J Mol Med 46(1):224–238. https://doi.org/10.3892/ijmm.2020.4579

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057. https://doi.org/10.1007/s10278-013-9622-7

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tomczak K, Czerwinska P, Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 19(1A):A68-77. https://doi.org/10.5114/wo.2014.47136

    Article  PubMed  Google Scholar 

  11. Zhang W, Peng J, Zhao S, Wu W, Yang J, Ye J et al (2022) Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes. J Cancer Res Clin Oncol 148(10):2773–2780. https://doi.org/10.1007/s00432-022-04047-5

    Article  PubMed  Google Scholar 

  12. Ellingson BM, Abrey LE, Nelson SJ, Kaufmann TJ, Garcia J, Chinot O et al (2018) Validation of postoperative residual contrast-enhancing tumor volume as an independent prognostic factor for overall survival in newly diagnosed glioblastoma. Neuro Oncol 20(9):1240–1250. https://doi.org/10.1093/neuonc/noy053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Li G, Li L, Li Y, Qian Z, Wu F, He Y et al (2022) An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas. Brain 145(3):1151–1161. https://doi.org/10.1093/brain/awab340

    Article  PubMed  PubMed Central  Google Scholar 

  14. Koch A, Joosten SC, Feng Z, de Ruijter TC, Draht MX, Melotte V et al (2018) Analysis of DNA methylation in cancer: location revisited. Nat Rev Clin Oncol 15(7):459–466. https://doi.org/10.1038/s41571-018-0004-4

    Article  CAS  PubMed  Google Scholar 

  15. Reis AH, Vargas FR, Lemos B (2016) Biomarkers of genome instability and cancer epigenetics. Tumour Biol 37(10):13029–13038. https://doi.org/10.1007/s13277-016-5278-5

    Article  CAS  PubMed  Google Scholar 

  16. Adeberg S, Knoll M, Koelsche C, Bernhardt D, Schrimpf D, Sahm F et al (2022) DNA-methylome-assisted classification of patients with poor prognostic subventricular zone associated IDH-wildtype glioblastoma. Acta Neuropathol 144(1):129–142. https://doi.org/10.1007/s00401-022-02443-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cui Q, Shi H, Ye P, Li L, Qu Q, Sun G et al (2017) m(6)A RNA methylation regulates the self-renewal and tumorigenesis of glioblastoma stem cells. Cell Rep 18(11):2622–2634. https://doi.org/10.1016/j.celrep.2017.02.059

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Huang W, Weng W, Wu B, Ye T, Lin Z, Zhang Z et al (2021) Development and validation of the trans-omics model for pancreatic adenocarcinoma. Epigenomics 13(1):15–30. https://doi.org/10.2217/epi-2020-0184

    Article  CAS  PubMed  Google Scholar 

  19. Chang S, Yim S, Park H (2019) The cancer driver genes IDH1/2, JARID1C/ KDM5C, and UTX/ KDM6A: crosstalk between histone demethylation and hypoxic reprogramming in cancer metabolism. Exp Mol Med 51(6):1–17. https://doi.org/10.1038/s12276-019-0230-6

    Article  CAS  PubMed  Google Scholar 

  20. L.B. Wang, A. Karpova, M.A. Gritsenko, J.E. Kyle, S. Cao, Y. Li, et al. (2021) Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Cell 39(4):509–528 e520. https://doi.org/10.1016/j.ccell.2021.01.006

  21. Sugita Y, Nakamura Y, Yamamoto M, Ogasawara S, Ohshima K, Shigemori M (2008) Expression of KIAA 0864 protein in neuroepithelial tumors: an analysis based on the presence of monoclonal antibody HFB-16. J Neurooncol 89(2):151–158. https://doi.org/10.1007/s11060-008-9610-9

    Article  CAS  PubMed  Google Scholar 

  22. She S, Bian S, Huo R, Chen K, Huang Z, Zhang J et al (2016) Degradable organically-derivatized polyoxometalate with enhanced activity against glioblastoma cell line. Sci Rep 6:33529. https://doi.org/10.1038/srep33529

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dao LM, Machule ML, Bacher P, Hoffmann J, Ly LT, Wegner F et al (2021) Decreased inflammatory cytokine production of antigen-specific CD4(+) T cells in NMDA receptor encephalitis. J Neurol 268(6):2123–2131. https://doi.org/10.1007/s00415-020-10371-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Niibori-Nambu A, Midorikawa U, Mizuguchi S, Hide T, Nagai M, Komohara Y et al (2013) Glioma initiating cells form a differentiation niche via the induction of extracellular matrices and integrin alphaV. PLoS ONE 8(5):e59558. https://doi.org/10.1371/journal.pone.0059558

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Liu X, Li Y, Qian Z, Sun Z, Xu K, Wang K et al (2018) A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. Neuroimage Clin 20:1070–1077. https://doi.org/10.1016/j.nicl.2018.10.014

    Article  PubMed  PubMed Central  Google Scholar 

  26. W. Meng, J.D. Palmer, M. Siedow, S.J. Haque, A. Chakravarti (2022) Overcoming radiation resistance in gliomas by targeting metabolism and DNA repair pathwayS. Int J Mol Sci 23(4). https://doi.org/10.3390/ijms23042246

  27. David BA, Kubes P (2019) Exploring the complex role of chemokines and chemoattractants in vivo on leukocyte dynamics. Immunol Rev 289(1):9–30. https://doi.org/10.1111/imr.12757

    Article  CAS  PubMed  Google Scholar 

  28. Li M, Chen H, Yin P, Song J, Jiang F, Tang Z et al (2021) Identification and clinical validation of key extracellular proteins as the potential biomarkers in relapsing-remitting multiple sclerosis. Front Immunol 12:753929. https://doi.org/10.3389/fimmu.2021.753929

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. J.Y. Cao, Q. Guo, G.F. Guan, C. Zhu, C.Y. Zou, L.Y. Zhang, et al. (2020) Elevated lymphocyte specific protein 1 expression is involved in the regulation of leukocyte migration and immunosuppressive microenvironment in glioblastoma. Aging (Albany NY) 12(2):1656–1684. https://doi.org/10.18632/aging.102706

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Author notes

  1. Wentao Zhang and Zikang Yan contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Zhang Wentao, Shan Zhao & Shengsheng Xu

  2. Department of Bioinformatics, the Basic Medical School of Chongqing Medical University, Chongqing, 400016, China

    Zikang Yan & Longke Ran

  3. The Center for Clinical Molecular Medical Detection, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Jian Peng

  4. Department of Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Haoyang Yin & Dong Zhong

  5. Key Laboratory of Optoelectronic Technologyand, Systems of the Ministry of Education, Chongqing University, Chongqing, 400044, China

    Junjun Yang & Junyong Ye

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  1. Zhang Wentao
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Corresponding authors

Correspondence to Junyong Ye or Shengsheng Xu.

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All data involved in this study were downloaded from TCGA, and data collection and application were conducted in accordance with TCGA’s publication guidelines and data access policies, with additional approval from the local ethics committee.

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Zhang, W., Yan, Z., Peng, J. et al. Magnetic resonance imaging and deoxyribonucleic acid methylation–based radiogenomic models for survival risk stratification of glioblastoma. Med Biol Eng Comput 62, 853–864 (2024). https://doi.org/10.1007/s11517-023-02971-3

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