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ORIGINAL RESEARCH article
Front. Immunol.
Sec. Multiple Sclerosis and Neuroimmunology
Volume 15 - 2024 |
doi: 10.3389/fimmu.2024.1452097
This article is part of the Research Topic Novel Therapeutic Strategies for Immuno-oncology in Cerebral Glioblastoma View all articles
Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning
Provisionally accepted- 1 Department of Neurosurgery, University Hospital rechts der Isar, Technical University of Munich, Munich, Germany
- 2 Department of Neurosurgery, Shanghai Changzheng Hospital, Shanghai, China
- 3 Department of Neurosurgery, Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, Anhui Province, China
- 4 Department of Neurosurgery, Changhai Hospital, Shanghai, China
Background: Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma.Methods: We consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model's performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents.Results: Our analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients.Conclusions: The prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.
Keywords: Glioma, Homologous recombination deficiency, prognosis, machine learning, Risk model
Received: 20 Jun 2024; Accepted: 13 Sep 2024.
Copyright: © 2024 Gong, Zhou, Shen, Ma, Wu, Hou, Wang and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Haotian Shen, Department of Neurosurgery, Shanghai Changzheng Hospital, Shanghai, China
Chao Ma, Department of Neurosurgery, Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, Anhui Province, China
Dejun Wu, Department of Neurosurgery, Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, Anhui Province, China
Lijun Hou, Department of Neurosurgery, Shanghai Changzheng Hospital, Shanghai, China
Hongxiang Wang, Department of Neurosurgery, Changhai Hospital, Shanghai, 07005897, China
Tao Xu, Department of Neurosurgery, Shanghai Changzheng Hospital, Shanghai, China
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