RETRACTED ARTICLE: Construction and validation of a machine learning-based immune-related prognostic model for glioma
Abstract Background Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to de...
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| Format: | Article |
| Language: | English |
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Springer
2024-10-01
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| Series: | Journal of Cancer Research and Clinical Oncology |
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| Online Access: | https://doi.org/10.1007/s00432-024-05970-5 |
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| author | Qi Mao Zhi Qiao Qiang Wang Wei Zhao Haitao Ju |
| author_facet | Qi Mao Zhi Qiao Qiang Wang Wei Zhao Haitao Ju |
| author_sort | Qi Mao |
| collection | DOAJ |
| description | Abstract Background Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to develop and validate a prognostic model for glioma based on immune-related genes, to provide new tools for precision medicine. Methods Glioma samples were obtained from a database that includes the ImmPort database. Additionally, we incorporated ten machine learning algorithms to assess the model’s performance using evaluation metrics like the Harrell concordance index (C-index). The model genes were further studied using GSCA, TISCH2, and HPA databases to understand their role in glioma pathology at the genomic, molecular, and single-cell levels, and validate the biological function of IKBKE in vitro experiments. Results In this study, a total of 199 genes associated with prognosis were identified using univariate Cox analysis. Subsequently, a consensus prognostic model was developed through the application of machine learning algorithms. In which the Lasso + plsRcox algorithm demonstrated the best predictive performance. The model showed a good ability to distinguish two groups in both the training and test sets. Additionally, the model genes were closely related to immunity (oligodendrocytes and macrophages), and mutation burden. The results of in vitro experiments showed that the expression level of the IKBKE gene had a significant effect on the apoptosis and migration of GL261 glioma cells. Western blot analysis showed that down-regulation of IKBKE resulted in increased expression of pro-apoptotic protein Bax and decreased expression of anti-apoptotic protein Bcl-2, which was consistent with increased apoptosis rate. On the contrary, IKBKE overexpression caused a decrease in Bax expression an increase in Bcl-2 expression, and a decrease in apoptosis rate. Tunel results further confirmed that down-regulation of IKBKE promoted apoptosis, while overexpression of IKBKE reduced apoptosis. In addition, cells with down-regulated IKBKE had reduced migration in scratch experiments, while cells with overexpression of IKBKE had increased migration. Conclusion This study successfully constructed a glioma prognosis model based on immune-related genes. These findings provide new perspectives for glioma prognosis assessment and immunotherapy. |
| format | Article |
| id | doaj-art-f4e777cb849f4d68b78ec515f2d6b17d |
| institution | Kabale University |
| issn | 1432-1335 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of Cancer Research and Clinical Oncology |
| spelling | doaj-art-f4e777cb849f4d68b78ec515f2d6b17d2025-01-26T12:13:27ZengSpringerJournal of Cancer Research and Clinical Oncology1432-13352024-10-011501011910.1007/s00432-024-05970-5RETRACTED ARTICLE: Construction and validation of a machine learning-based immune-related prognostic model for gliomaQi Mao0Zhi Qiao1Qiang Wang2Wei Zhao3Haitao Ju4Department of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical UniversityDepartment of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical UniversityDepartment of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical UniversityDepartment of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical UniversityDepartment of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical UniversityAbstract Background Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to develop and validate a prognostic model for glioma based on immune-related genes, to provide new tools for precision medicine. Methods Glioma samples were obtained from a database that includes the ImmPort database. Additionally, we incorporated ten machine learning algorithms to assess the model’s performance using evaluation metrics like the Harrell concordance index (C-index). The model genes were further studied using GSCA, TISCH2, and HPA databases to understand their role in glioma pathology at the genomic, molecular, and single-cell levels, and validate the biological function of IKBKE in vitro experiments. Results In this study, a total of 199 genes associated with prognosis were identified using univariate Cox analysis. Subsequently, a consensus prognostic model was developed through the application of machine learning algorithms. In which the Lasso + plsRcox algorithm demonstrated the best predictive performance. The model showed a good ability to distinguish two groups in both the training and test sets. Additionally, the model genes were closely related to immunity (oligodendrocytes and macrophages), and mutation burden. The results of in vitro experiments showed that the expression level of the IKBKE gene had a significant effect on the apoptosis and migration of GL261 glioma cells. Western blot analysis showed that down-regulation of IKBKE resulted in increased expression of pro-apoptotic protein Bax and decreased expression of anti-apoptotic protein Bcl-2, which was consistent with increased apoptosis rate. On the contrary, IKBKE overexpression caused a decrease in Bax expression an increase in Bcl-2 expression, and a decrease in apoptosis rate. Tunel results further confirmed that down-regulation of IKBKE promoted apoptosis, while overexpression of IKBKE reduced apoptosis. In addition, cells with down-regulated IKBKE had reduced migration in scratch experiments, while cells with overexpression of IKBKE had increased migration. Conclusion This study successfully constructed a glioma prognosis model based on immune-related genes. These findings provide new perspectives for glioma prognosis assessment and immunotherapy.https://doi.org/10.1007/s00432-024-05970-5GliomaImmune microenvironmentPrognosis modelMachine learningImmune-related genes |
| spellingShingle | Qi Mao Zhi Qiao Qiang Wang Wei Zhao Haitao Ju RETRACTED ARTICLE: Construction and validation of a machine learning-based immune-related prognostic model for glioma Journal of Cancer Research and Clinical Oncology Glioma Immune microenvironment Prognosis model Machine learning Immune-related genes |
| title | RETRACTED ARTICLE: Construction and validation of a machine learning-based immune-related prognostic model for glioma |
| title_full | RETRACTED ARTICLE: Construction and validation of a machine learning-based immune-related prognostic model for glioma |
| title_fullStr | RETRACTED ARTICLE: Construction and validation of a machine learning-based immune-related prognostic model for glioma |
| title_full_unstemmed | RETRACTED ARTICLE: Construction and validation of a machine learning-based immune-related prognostic model for glioma |
| title_short | RETRACTED ARTICLE: Construction and validation of a machine learning-based immune-related prognostic model for glioma |
| title_sort | retracted article construction and validation of a machine learning based immune related prognostic model for glioma |
| topic | Glioma Immune microenvironment Prognosis model Machine learning Immune-related genes |
| url | https://doi.org/10.1007/s00432-024-05970-5 |
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