ArticleMolecular biotechnology2024
Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma.
Article in Molecular biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 1 citations in OpenAlex.
- Identification of disulfidptosis-related molecular subtypes in pleural mesothelioma demonstrates a correlation with the immune infiltration, prognosis and therapy efficacy.Discover oncology · 2026Article
- Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms.BMC neurology · 2025Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and multivariate Cox analysis to determine a signature including five neurotrophin-related genes (NTGs), CLIC1, SULF2, TGIF1, TTF2, and WEE1. Two-sample Mendelian Randomization (MR) further explored whether these prognostic-related genes were genetic variants that increase the risk of glioma. A total of 1306 patients have been included in this study, and the results obtained from the training set can be verified by four independent validation sets. The low-risk subgroup had longer overall survival in five datasets, and its AUC values all reached above 0.7. The risk groups divided by the NTGs signature exhibited a distinct difference in targeted therapies from the copy-number variation, somatic mutation, LGG's surrounding microenvironment, and drug response. MR corroborated that TGIF1 was a potential causal target for increasing the risk of glioma. Our study identified a five-NTGs signature that presented an excellent survival prediction and potential biological function, providing new insight for the selection of LGGs therapy.
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