首页|Dalian Medical University Reports Findings in Head and Neck Cancer (Quantified pathway mutations associate epithelial- mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell ...)
Dalian Medical University Reports Findings in Head and Neck Cancer (Quantified pathway mutations associate epithelial- mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell ...)
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New research on Oncology - Head and Neck Cancer is the subject of a report. Ac- cording to news reporting from Dalian, People's Republic of China, by NewsRx journalists, research stated, "Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape." Financial support for this research came from National Natural Science Fundation of China. The news correspondents obtained a quote from the research from Dalian Medical University, "Yet, there is a lack of score to accurately quantify pathway mutations. Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB- related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response. We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures."
DalianPeople's Republic of ChinaAsiaBiomarkersCancerCarcinomasDiagnostics and ScreeningDrugs and TherapiesEmerging TechnologiesGeneticsHead and Neck CancerHealth and MedicineImmunotherapyMachine LearningOncologySquamous Cell CarcinomaSupport Vector MachinesVector Machines
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