首页|Chongqing Medical University Reports Findings in Gliomas (Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma)
Chongqing Medical University Reports Findings in Gliomas (Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma)
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New research on Oncology - Gliomas is the subject of a report. According to news reporting from Chongqing, People's Republic of China, by NewsRx journalists, research stated, “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.” The news correspondents obtained a quote from the research from Chongqing Medical University, “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.”
ChongqingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesGeneticsGliomasHealth and MedicineMachine LearningOncologyRisk and Prevention
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