Identification of Necroptosis-related Molecular Subtypes and Construction of Necroptosis-related Gene Signature for Glioblastoma Multiforme


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Abstract

Background:Necroptosis is a highly regulated and genetically controlled process, and therefore, attention has been paid to the exact effects of this disorder on a variety of diseases, including cancer. An in-depth understanding of the key regulatory factors and molecular events that trigger necroptosis can not only identify patients at risk of cancer development but can also help to develop new treatment strategies.

Aims:This study aimed to increase understanding of the complex role of necroptosis in glioblastoma multiforme (GBM) and provide a new perspective and reference for accurate prediction of clinical outcomes and gene-targeted therapy in patients with GBM. The objective of this study was to analyze the gene expression profile of necroptosis regulatory factors in glioblastoma multiforme (GBM) and establish a necroptosis regulatory factor-based GBM classification and prognostic gene signature to recognize the multifaceted impact of necroptosis on GBM.

Methods:The necroptosis score of the glioblastoma multiforme (GBM) sample in TCGA was calculated by ssGSEA, and the correlation between each gene and the necroptosis score was calculated. Based on necroptosis score-related genes, unsupervised consensus clustering was employed to classify patients. The prognosis, tumor microenvironment (TME), genomic changes, biological signal pathways and gene expression differences among clusters were analyzed. The gene signature of GBM was constructed by Cox and LASSO regression analysis of differentially expressed genes (DEGs).

Result:Based on 34 necroptosis score-related genes, GBM was divided into two clusters with different overall survival (OS) and TME. A necroptosis-related gene signature (NRGS) containing 8 genes was developed, which could stratify the risk of GBM in both the training set and verification set and had good prognostic value. NRGS and age were both independent prognostic indicators of GBM, and a nomogram developed by the integration of both of them showed a better predictive effect than traditional clinical features.

Conclusion:In this study, patients from public data sets were divided into two clusters and the unique TME and molecular characteristics of each cluster were described. Furthermore, an NRGS was constructed to effectively and independently predict the survival outcome of GBM, which provides some insights for the implementation of personalized precision medicine in clinical practice.

About the authors

Zhiyong Li

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Yinghui Jin

Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Tianshi Que

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Xi-An Zhang

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Guozhong Yi

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Haojie Zheng

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Xi Yuan

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Xiaoyan Wang

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Haiyan Xu

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Jing Nan

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Chao Chen

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Email: info@benthamscience.net

Yuankui Wu

Department of Medical Imaging, Nanfang Hospital, Southern Medical University

Author for correspondence.
Email: info@benthamscience.net

Guanglong Huang

Department of Neurosurgery, Nanfang Hospital, Southern Medical University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Stoyanov, G.S.; Dzhenkov, D.; Ghenev, P.; Iliev, B.; Enchev, Y.; Tonchev, A.B. Cell biology of glioblastoma multiforme: From basic science to diagnosis and treatment. Med. Oncol., 2018, 35(3), 27. doi: 10.1007/s12032-018-1083-x PMID: 29387965
  2. Vitovcova, B.; Skarkova, V.; Rudolf, K.; Rudolf, E. Biology of glioblastoma multiforme—exploration of mitotic catastrophe as a potential treatment modality. Int. J. Mol. Sci., 2020, 21(15), 5324. doi: 10.3390/ijms21155324 PMID: 32727112
  3. Batash, R.; Asna, N.; Schaffer, P.; Francis, N.; Schaffer, M. Glioblastoma multiforme, diagnosis and treatment; recent literature review. Curr. Med. Chem., 2017, 24(27), 3002-3009. PMID: 28521700
  4. Erthal, L.C.S.; Gobbo, O.L.; Ruiz-Hernandez, E. Biocompatible copolymer formulations to treat glioblastoma multiforme. Acta Biomater., 2021, 121, 89-102. doi: 10.1016/j.actbio.2020.11.030 PMID: 33227487
  5. Pearson, J.R.D.; Cuzzubbo, S.; McArthur, S.; Durrant, L.G.; Adhikaree, J.; Tinsley, C.J.; Pockley, A.G.; McArdle, S.E.B. Immune escape in glioblastoma multiforme and the adaptation of immunotherapies for treatment. Front. Immunol., 2020, 11, 582106. doi: 10.3389/fimmu.2020.582106 PMID: 33178210
  6. Carlsson, S.K.; Brothers, S.P.; Wahlestedt, C. Emerging treatment strategies for glioblastoma multiforme. EMBO Mol. Med., 2014, 6(11), 1359-1370. doi: 10.15252/emmm.201302627 PMID: 25312641
  7. Tong, X.; Tang, R.; Xiao, M.; Xu, J.; Wang, W.; Zhang, B.; Liu, J.; Yu, X.; Shi, S. Targeting cell death pathways for cancer therapy: Recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research. J. Hematol. Oncol., 2022, 15(1), 174. doi: 10.1186/s13045-022-01392-3 PMID: 36482419
  8. Shan, B.; Pan, H.; Najafov, A.; Yuan, J. Necroptosis in development and diseases. Genes Dev., 2018, 32(5-6), 327-340. doi: 10.1101/gad.312561.118 PMID: 29593066
  9. Zhang, G.; Wang, J.; Zhao, Z.; Xin, T.; Fan, X.; Shen, Q.; Raheem, A.; Lee, C.R.; Jiang, H.; Ding, J. Regulated necrosis, a proinflammatory cell death, potentially counteracts pathogenic infections. Cell Death Dis., 2022, 13(7), 637. doi: 10.1038/s41419-022-05066-3 PMID: 35869043
  10. Wang, T.; Jin, Y.; Yang, W.; Zhang, L.; Jin, X.; Liu, X.; He, Y.; Li, X. Necroptosis in cancer: An angel or a demon? Tumour Biol., 2017, 39(6) doi: 10.1177/1010428317711539 PMID: 28651499
  11. Gong, Y.; Fan, Z.; Luo, G.; Yang, C.; Huang, Q.; Fan, K.; Cheng, H.; Jin, K.; Ni, Q.; Yu, X.; Liu, C. The role of necroptosis in cancer biology and therapy. Mol. Cancer, 2019, 18(1), 100. doi: 10.1186/s12943-019-1029-8 PMID: 31122251
  12. Lalaoui, N.; Brumatti, G. Relevance of necroptosis in cancer. Immunol. Cell Biol., 2017, 95(2), 137-145. doi: 10.1038/icb.2016.120 PMID: 27922620
  13. Ru, B.; Wong, C.N.; Tong, Y.; Zhong, J.Y.; Zhong, S.S.W.; Wu, W.C.; Chu, K.C.; Wong, C.Y.; Lau, C.Y.; Chen, I.; Chan, N.W.; Zhang, J. TISIDB: An integrated repository portal for tumor–immune system interactions. Bioinformatics, 2019, 35(20), 4200-4202. doi: 10.1093/bioinformatics/btz210 PMID: 30903160
  14. Becht, E.; Giraldo, N.A.; Lacroix, L.; Buttard, B.; Elarouci, N.; Petitprez, F.; Selves, J.; Laurent-Puig, P.; Sautès-Fridman, C.; Fridman, W.H.; de Reyniès, A. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol., 2016, 17(1), 218. doi: 10.1186/s13059-016-1070-5 PMID: 27765066
  15. Geeleher, P.; Cox, N.; Huang, R.S. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One, 2014, 9(9), e107468. doi: 10.1371/journal.pone.0107468 PMID: 25229481
  16. Subramanian, A.; Kuehn, H.; Gould, J.; Tamayo, P.; Mesirov, J.P. GSEA-P : A desktop application for Gene Set Enrichment Analysis. Bioinformatics, 2007, 23(23), 3251-3253. doi: 10.1093/bioinformatics/btm369 PMID: 17644558
  17. Sprooten, J.; De Wijngaert, P.; Vanmeerbeek, I.; Martin, S.; Vangheluwe, P.; Schlenner, S.; Krysko, D.V.; Parys, J.B.; Bultynck, G.; Vandenabeele, P.; Garg, A.D. Necroptosis in immuno-oncology and cancer immunotherapy. Cells, 2020, 9(8), 1823. doi: 10.3390/cells9081823 PMID: 32752206
  18. Nutt, C.L.; Mani, D.R.; Betensky, R.A.; Tamayo, P.; Cairncross, J.G.; Ladd, C.; Pohl, U.; Hartmann, C.; McLaughlin, M.E.; Batchelor, T.T.; Black, P.M.; von Deimling, A.; Pomeroy, S.L.; Golub, T.R.; Louis, D.N. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res., 2003, 63(7), 1602-1607. PMID: 12670911
  19. Aldape, K.; Zadeh, G.; Mansouri, S.; Reifenberger, G.; von Deimling, A. Glioblastoma: Pathology, molecular mechanisms and markers. Acta Neuropathol., 2015, 129(6), 829-848. doi: 10.1007/s00401-015-1432-1 PMID: 25943888
  20. Verhaak, R.G.W.; Hoadley, K.A.; Purdom, E.; Wang, V.; Qi, Y.; Wilkerson, M.D.; Miller, C.R.; Ding, L.; Golub, T.; Mesirov, J.P.; Alexe, G.; Lawrence, M.; O’Kelly, M.; Tamayo, P.; Weir, B.A.; Gabriel, S.; Winckler, W.; Gupta, S.; Jakkula, L.; Feiler, H.S.; Hodgson, J.G.; James, C.D.; Sarkaria, J.N.; Brennan, C.; Kahn, A.; Spellman, P.T.; Wilson, R.K.; Speed, T.P.; Gray, J.W.; Meyerson, M.; Getz, G.; Perou, C.M.; Hayes, D.N. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 2010, 17(1), 98-110. doi: 10.1016/j.ccr.2009.12.020 PMID: 20129251
  21. Garofano, L.; Migliozzi, S.; Oh, Y.T.; D’Angelo, F.; Najac, R.D.; Ko, A.; Frangaj, B.; Caruso, F.P.; Yu, K.; Yuan, J.; Zhao, W.; Di Stefano, A.L.; Bielle, F.; Jiang, T.; Sims, P.; Suvà, M.L.; Tang, F.; Su, X.D.; Ceccarelli, M.; Sanson, M.; Lasorella, A.; Iavarone, A. Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities. Nat. Can., 2021, 2(2), 141-156. doi: 10.1038/s43018-020-00159-4 PMID: 33681822
  22. Dapash, M.; Hou, D.; Castro, B.; Lee-Chang, C.; Lesniak, M.S. The interplay between glioblastoma and its microenvironment. Cells, 2021, 10(9), 2257. doi: 10.3390/cells10092257 PMID: 34571905
  23. Yan, J.; Wan, P.; Choksi, S.; Liu, Z.G. Necroptosis and tumor progression. Trends Cancer, 2022, 8(1), 21-27. doi: 10.1016/j.trecan.2021.09.003 PMID: 34627742
  24. Aaes, T.L.; Kaczmarek, A.; Delvaeye, T.; De Craene, B.; De Koker, S.; Heyndrickx, L.; Delrue, I.; Taminau, J.; Wiernicki, B.; De Groote, P.; Garg, A.D.; Leybaert, L.; Grooten, J.; Bertrand, M.J.M.; Agostinis, P.; Berx, G.; Declercq, W.; Vandenabeele, P.; Krysko, D.V. Vaccination with necroptotic cancer cells induces efficient anti-tumor immunity. Cell Rep., 2016, 15(2), 274-287. doi: 10.1016/j.celrep.2016.03.037 PMID: 27050509
  25. Chen, Z.; Hambardzumyan, D. Immune microenvironment in glioblastoma subtypes. Front. Immunol., 2018, 9, 1004. doi: 10.3389/fimmu.2018.01004 PMID: 29867979
  26. Yaltirik, C.K.; Yilmaz, S.G.; Ozdogan, S.; Bilgin, E.Y.; Barut, Z.; Ture, U.; Isbir, T. Determination of IDH1, IDH2, MGMT, TERT and ATRX gene mutations in glial tumors. In Vivo, 2022, 36(4), 1694-1702. doi: 10.21873/invivo.12881 PMID: 35738587
  27. Crespo, I.; Vital, A.L.; Gonzalez-Tablas, M.; Patino, M.C.; Otero, A.; Lopes, M.C.; de Oliveira, C.; Domingues, P.; Orfao, A.; Tabernero, M.D. Molecular and genomic alterations in glioblastoma multiforme. Am. J. Pathol., 2015, 185(7), 1820-1833. doi: 10.1016/j.ajpath.2015.02.023 PMID: 25976245
  28. Koschmann, C.; Lowenstein, P.R.; Castro, M.G. ATRX mutations and glioblastoma: Impaired DNA damage repair, alternative lengthening of telomeres, and genetic instability. Mol. Cell. Oncol., 2016, 3(3), e1167158. doi: 10.1080/23723556.2016.1167158 PMID: 27314101
  29. Wong, Q.H.W.; Li, K.K.W.; Wang, W.W.; Malta, T.M.; Noushmehr, H.; Grabovska, Y.; Jones, C.; Chan, A.K.Y.; Kwan, J.S.H.; Huang, Q.J.Q.; Wong, G.C.H.; Li, W.C.; Liu, X.Z.; Chen, H.; Chan, D.T.M.; Mao, Y.; Zhang, Z.Y.; Shi, Z.F.; Ng, H.K. Molecular landscape of IDH-mutant primary astrocytoma Grade IV/glioblastomas. Mod. Pathol., 2021, 34(7), 1245-1260. doi: 10.1038/s41379-021-00778-x PMID: 33692446
  30. Yu, W.; Ma, Y.; Hou, W.; Wang, F.; Cheng, W.; Qiu, F.; Wu, P.; Zhang, G. Identification of immune-related lncRNA prognostic signature and mSubtypes for glioblastoma. Front. Immunol., 2021, 12, 706936. doi: 10.3389/fimmu.2021.706936 PMID: 34899682
  31. Vizcaíno, M.A.; Shah, S.; Eberhart, C.G.; Rodriguez, F.J. Clinicopathologic implications of NF1 gene alterations in diffuse gliomas. Hum. Pathol., 2015, 46(9), 1323-1330. doi: 10.1016/j.humpath.2015.05.014 PMID: 26190195
  32. Senhaji, N.; Squalli Houssaini, A.; Lamrabet, S.; Louati, S.; Bennis, S. Molecular and circulating biomarkers in patients with glioblastoma. Int. J. Mol. Sci., 2022, 23(13), 7474. doi: 10.3390/ijms23137474 PMID: 35806478
  33. Yang, Y.; Lv, W.; Xu, S.; Shi, F.; Shan, A.; Wang, J. Molecular and clinical characterization of LIGHT/TNFSF14 expression at tLevel via 998 samples with brain glioma. Front. Mol. Biosci., 2021, 8, 567327. doi: 10.3389/fmolb.2021.567327 PMID: 34513918
  34. Cao, J.Y.; Guo, Q.; Guan, G.F.; Zhu, C.; Zou, C.Y.; Zhang, L.Y.; Cheng, W.; Wang, G.; Cheng, P.; Wu, A.H.; Li, G.Y. Elevated lymphocyte specific protein 1 expression is involved in the regulation of leukocyte migration and immunosuppressive microenvironment in glioblastoma. Aging (Albany NY), 2020, 12(2), 1656-1684. doi: 10.18632/aging.102706 PMID: 32003759
  35. Jahani-Asl, A.; Yin, H.; Soleimani, V.D.; Haque, T.; Luchman, H.A.; Chang, N.C.; Sincennes, M.C.; Puram, S.V.; Scott, A.M.; Lorimer, I.A.J.; Perkins, T.J.; Ligon, K.L.; Weiss, S.; Rudnicki, M.A.; Bonni, A. Control of glioblastoma tumorigenesis by feed-forward cytokine signaling. Nat. Neurosci., 2016, 19(6), 798-806. doi: 10.1038/nn.4295 PMID: 27110918
  36. Oliva, C.R.; Halloran, B.; Hjelmeland, A.B.; Vazquez, A.; Bailey, S.M.; Sarkaria, J.N.; Griguer, C.E. IGFBP6 controls the expansion of chemoresistant glioblastoma through paracrine IGF2/IGF-1R signaling. Cell Commun. Signal., 2018, 16(1), 61. doi: 10.1186/s12964-018-0273-7 PMID: 30231881
  37. Wang, Y.; Hou, Y.; Zhang, W.; Alvarez, A.A.; Bai, Y.; Hu, B.; Cheng, S.Y.; Yang, K.; Li, Y.; Feng, H. Lipolytic inhibitor G0S2 modulates glioma stem-like cell radiation response. J. Exp. Clin. Cancer Res., 2019, 38(1), 147. doi: 10.1186/s13046-019-1151-x PMID: 30953555

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