首页|Peking Union Medical College Hospital Reports Findings in Glioblastomas (Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms)
Peking Union Medical College Hospital Reports Findings in Glioblastomas (Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - New research on Oncology - Glioblastomas is the s ubject of a report. According to news reportingoriginating from Beijing, People ’s Republic of China, by NewsRx correspondents, research stated, “Weaimed to de velop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma(GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293GB patients from CGGA and 169 from TCGA da tabase were assigned to training and validation cohort,respectively.”Our news editors obtained a quote from the research from Peking Union Medical Co llege Hospital,“The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape werecompared between LTS and short time surv ivor (STS) (OS <1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genesdifferentially expressed between LTS and STS. Three differen t machine learning algorithms were employedto select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram.The comparis on between LTS and STS revealed that STS exhibited an immune-resistant status, w ithhigher expression of ICGs (P <0.05) and greater infiltr ation of immune suppression cells compared to LTS(P <0.05) . Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorpo ratedinto the nomogram, which possessed good potential in predicting LTS probab ility among GB patientsboth in the training (C-index, 0.791; 0.772-0.817) and v alidation cohort (C-index, 0.770; 0.751-0.806).STS was found to be more likely to exhibit an immune-cold phenotype.”
BeijingPeople’s Republic of ChinaAsiaAlgorithmsCancerCyborgsEmerging TechnologiesGeneticsGlioblastomasHealth and MedicineMachine LearningOncology
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