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Zhang G, Xu X, Zhu L, Li S, Chen R, Lv N, Li Z, Wang J, Li Q, Zhou W, Yang P, Liu J. A Novel Molecular Classification Method for Glioblastoma Based on Tumor Cell Differentiation Trajectories. Stem Cells Int 2023; 2023:2826815. [PMID: 37964983 PMCID: PMC10643041 DOI: 10.1155/2023/2826815] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/29/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2023] Open
Abstract
The latest 2021 WHO classification redefines glioblastoma (GBM) as the hierarchical reporting standard by eliminating glioblastoma, IDH-mutant and only retaining the tumor entity of "glioblastoma, IDH-wild type." Knowing that subclassification of tumors based on molecular features is supposed to facilitate the therapeutic choice and increase the response rate in cancer patients, it is necessary to carry out molecular classification of the newly defined GBM. Although differentiation trajectory inference based on single-cell sequencing (scRNA-seq) data holds great promise for identifying cell heterogeneity, it has not been used in the study of GBM molecular classification. Single-cell transcriptome sequencing data from 10 GBM samples were used to identify molecular classification based on differentiation trajectories. The expressions of identified features were validated by public bulk RNA-sequencing data. Clinical feasibility of the classification system was examined in tissue samples by immunohistochemical (IHC) staining and immunofluorescence, and their clinical significance was investigated in public cohorts and clinical samples with complete clinical follow-up information. By analyzing scRNA-seq data of 10 GBM samples, four differentiation trajectories from the glioblastoma stem cell-like (GSCL) cluster were identified, based on which malignant cells were classified into five characteristic subclusters. Each cluster exhibited different potential drug sensitivities, pathways, functions, and transcriptional modules. The classification model was further examined in TCGA and CGGA datasets. According to the different abundance of five characteristic cell clusters, the patients were classified into five groups which we named Ac-G, Class-G, Neo-G, Opc-G, and Undiff-G groups. It was found that the Undiff-G group exhibited the worst overall survival (OS) in both TCGA and CGGA cohorts. In addition, the classification model was verified by IHC staining in 137 GBM samples to further clarify the difference in OS between the five groups. Furthermore, the novel biomarkers of glioblastoma stem cells (GSCs) were also described. In summary, we identified five classifications of GBM and found that they exhibited distinct drug sensitivities and different prognoses, suggesting that the new grouping system may be able to provide important prognostic information and have certain guiding significance for the treatment of GBM, and identified the GSCL cluster in GBM tissues and described its characteristic program, which may help develop new potential therapeutic targets for GSCs in GBM.
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Affiliation(s)
- Guanghao Zhang
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xiaolong Xu
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Luojiang Zhu
- Neurosurgery Department, 922th Hospital of Joint Logistics Support Force, PLA, China
| | - Sisi Li
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Rundong Chen
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Nan Lv
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Zifu Li
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Jing Wang
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Qiang Li
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Wang Zhou
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Pengfei Yang
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Jianmin Liu
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai 200433, China
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Kwiatkowska I, Hermanowicz JM, Przybyszewska-Podstawka A, Pawlak D. Not Only Immune Escape-The Confusing Role of the TRP Metabolic Pathway in Carcinogenesis. Cancers (Basel) 2021; 13:2667. [PMID: 34071442 PMCID: PMC8198784 DOI: 10.3390/cancers13112667] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/20/2021] [Accepted: 05/26/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The recently discovered phenomenon that cancer cells can avoid immune response has gained scientists' interest. One of the pathways involved in this process is tryptophan (TRP) metabolism through the kynurenine pathway (KP). Individual components involved in TRP conversion seem to contribute to cancerogenesis both through a direct impact on cancer cells and the modulation of immune cell functionality. Due to this fact, this pathway may serve as a target for immunotherapy and attempts are being made to create novel compounds effective in cancer treatment. However, the results obtained from clinical trials are not satisfactory, which raises questions about the exact role of KP elements in tumorigenesis. An increasing number of experiments reveal that TRP metabolites may either be tumor promoters and suppressors and this is why further research in this field is highly needed. The aim of this study is to present KP as a modulator of cancer development through multiple mechanisms and to point to its ambiguity, which may be a reason for failures in treatment based on the inhibition of tryptophan metabolism.
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Affiliation(s)
- Iwona Kwiatkowska
- Department of Pharmacodynamics, Medical University of Bialystok, Mickiewicza 2C, 15-222 Bialystok, Poland; (J.M.H.); (D.P.)
| | - Justyna Magdalena Hermanowicz
- Department of Pharmacodynamics, Medical University of Bialystok, Mickiewicza 2C, 15-222 Bialystok, Poland; (J.M.H.); (D.P.)
- Department of Clinical Pharmacy, Medical University of Bialystok, Mickiewicza 2C, 15-222 Bialystok, Poland
| | | | - Dariusz Pawlak
- Department of Pharmacodynamics, Medical University of Bialystok, Mickiewicza 2C, 15-222 Bialystok, Poland; (J.M.H.); (D.P.)
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