ArticleMolecular therapy. Oncology2024
Comprehensive machine learning-based integration develops a novel prognostic model for glioblastoma.
Article in Molecular therapy. Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer.World journal of surgical oncology · 2026Article
- From Data to Decision: Integrating Bioinformatics into Glioma Patient Stratification and Immunotherapy Selection.International journal of molecular sciences · 2026Review
- Designing a web-based platform for dynamic estimation of individualized conditional survival in grade 3 gliomas.Discover oncology · 2025Article
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- Quantitative assessment of the associations between MTR and MTRR gene polymorphisms and glioma risk.Discover oncology · 2025Article
- Predicting survival in malignant glioma using artificial intelligence.European journal of medical research · 2025Review
- Article
- Impact of endogenous viral elements on glioma clinical phenotypes by inducing OCT4 in the host.Frontiers in cellular and infection microbiology · 2024Article
- TMEM2: A New Dimension in Hyaluronan Biology.Proteoglycan researchArticle
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Authors and funding
8 authors.
Funding
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Abstract
In this study, we developed a new prognostic model for glioblastoma (GBM) based on an integrated machine learning algorithm. We used univariate Cox regression analysis to identify prognostic genes by combining six GBM cohorts. Based on the prognostic genes, 10 machine learning algorithms were integrated into 117 algorithm combinations, and the artificial intelligence prognostic signature (AIPS) with the greatest average C-index was chosen. The AIPS was compared with 10 previously published models by univariate Cox analysis and the C-index. We compared the differences in prognosis, tumor immune microenvironment (TIME), and immunotherapy sensitivity between the high and low AIPS score groups. The AIPS based on the random survival forest algorithm with the highest average C-index (0.868) was selected. Compared with the previous 10 prognostic models, our AIPS has the highest C-index. The AIPS was closely linked to the clinical features of GBM. We discovered that patients in the low score group had improved prognoses, a more active TIME, and were more sensitive to immunotherapy. Finally, we verified the expression of several key genes by western blotting and immunohistochemistry. We identified an ideal prognostic signature for GBM, which might provide new insights into stratified treatment approaches for GBM patients.
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Registered trials
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