Diverse cell death signature based subtypes predict the prognosis and immune characteristics within glioma
Author:
Wang Lin1, Song Jia1, Xu Jing1, Qin Yidan1, Li Jia1, Sun Yajuan1, Jin Hui1, Chen Jiajun1, Wang Ziqian2
Affiliation:
1. China-Japan Union Hospital of Jilin University 2. The First Hospital of Jilin University
Abstract
Abstract
Background. Cell death plays an essential role in the pathogenesis, progression, drug resistance and recurrence of glioma. Although multiple cell death pathways are involved in glioma development, there is lack of a stratification and prognostic modelling for glioma based on the integration of diverse genes for cell deaths.
Methods. In this study, 1254 diverse cell death (DCD)-related genes were assessed using the ConsensusClusterPlus assessment to identify DCD patterns in glioma. CIBERSORT, ssGSEA, and ESTIMATE algorithms were applied to evaluate immune microenvironment differences between subtypes. LASSO Cox regression was used to screen prognosis-related DCD genes, and a risk scoremodel was constructed. TMB, TIDE, immune infiltration, and immunotherapy response was analyzed to evaluate the immune characteristics.
Results. Two DCD-related subgroups named Clusters 1 and 2, with distinct DCD levels, immune characteristics, and prognoses,were determined from glioma samples. A DCD-basedrisk scoremodel was developed to assess DCD levels in glioma patients and divide patients into high- and low-risk groups. We found this risk model can be used as an independent prognostic factor for glioma patients. Notably, glioma patients with low risk scoresexhibited subdued DCD activity, prolonged survival,and a favorable disposition towards benefiting from immune checkpoint blockade therapies.
Conclusions. This study established a novel signature classification and a risk model by comprehensively analyzing patterns of various DCDs to stratify glioma patients and to predict the prognosis and immune characteristics of glioma. We provided a theoretical basis for the clinical application of DCD-related genes in glioma prognosis and immunotherapy.
Publisher
Research Square Platform LLC
Reference27 articles.
1. Diffuse Glioma Heterogeneity and Its Therapeutic Implications;Nicholson JG;Cancer Discov,2021 2. Barthel FP, Johnson KC, Varn FS, Moskalik AD, Tanner G, Kocakavuk E, Anderson KJ, Abiola O, Aldape K, Alfaro KD, Alpar D, Amin SB, Ashley DM, Bandopadhayay P, Barnholtz-Sloan JS, Beroukhim R, Bock C, Brastianos PK, Brat DJ, Brodbelt AR, Bruns AF, Bulsara KR, Chakrabarty A, Chakravarti A, Chuang JH, Claus EB, Cochran EJ, Connelly J, Costello JF, Finocchiaro G, Fletcher MN, French PJ, Gan HK, Gilbert MR, Gould PV, Grimmer MR, Iavarone A, Ismail A, Jenkinson MD, Khasraw M, Kim H, Kouwenhoven MCM, LaViolette PS, Li M, Lichter P, Ligon KL, Lowman AK, Malta TM, Mazor T, McDonald KL, Molinaro AM, Nam DH, Nayyar N, Ng HK, Ngan CY, Niclou SP, Niers JM, Noushmehr H, Noorbakhsh J, Ormond DR, Park CK, Poisson LM, Rabadan R, Radlwimmer B, Rao G, Reifenberger G, Sa JK, Schuster M, Shaw BL, Short SC, Smitt PAS, Sloan AE, Smits M, Suzuki H, Tabatabai G, Van Meir EG, Watts C, Weller M, Wesseling P, Westerman BA, Widhalm G, Woehrer A, W.K.A., Yung G, Zadeh JT, Huse JF, De Groot. L.F. Stead, R.G.W. Verhaak, G. Consortium, Longitudinal molecular trajectories of diffuse glioma in adults, Nature, 576 (2019) 112–120. 3. Multigene signature for predicting prognosis of patients with 1p19q co-deletion diffuse glioma;Hu X;Neuro Oncol,2017 4. Cell Death in the Origin and Treatment of Cancer;Strasser A;Mol Cell,2020 5. Application of Regulatory Cell Death in Cancer: Based on Targeted Therapy and Immunotherapy;Qi X;Front Immunol,2022
|
|