首页|Chongqing Medical University Reports Findings in Head and Neck Cancer (Mononuclear phagocyte system-related multi-omics features yield head and neck squamous cell carcinoma subtypes with distinct overall survival, drug, and immunotherapy ...)
Chongqing Medical University Reports Findings in Head and Neck Cancer (Mononuclear phagocyte system-related multi-omics features yield head and neck squamous cell carcinoma subtypes with distinct overall survival, drug, and immunotherapy ...)
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Springer Nature
New research on Oncology - Head and Neck Cancer is the subject of a report. According to news reporting from Chongqing, People's Republic of China, by NewsRx journalists, research stated, “Recent research reported that mononuclear phagocyte system (MPS) can contribute to immune defense but the classification of head and neck squamous cell carcinoma (HNSCC) patients based on MPS-related multi-omics features using machine learning lacked. In this study, we obtain marker genes for MPS through differential analysis at the single-cell level and utilize 'similarity network fusion' and 'MoCluster' algorithms to cluster patients' multi-omics features.” Financial supporters for this research include National Youth Science Foundation Project, Postdoctoral Fund project of Chongqing. The news correspondents obtained a quote from the research from Chongqing Medical University, “Subsequently, based on the corresponding clinical information, we investigate the prognosis, drugs, immunotherapy, and biological differences between the subtypes. A total of 848 patients have been included in this study, and the results obtained from the training set can be verified by two independent validation sets using 'the nearest template prediction'. We identified two subtypes of HNSCC based on MPS-related multi-omics features, with CS2 exhibiting better predictive prognosis and drug response. CS2 represented better xenobiotic metabolism and higher levels of T and B cell infiltration, while the biological functions of CS1 were mainly enriched in coagulation function, extracellular matrix, and the JAK-STAT signaling pathway. Furthermore, we established a novel and stable classifier called 'getMPsub' to classify HNSCC patients, demonstrating good consistency in the same training set. External validation sets classified by 'getMPsub' also illustrated similar differences between the two subtypes. Our study identified two HNSCC subtypes by machine learning and explored their biological difference.”
ChongqingPeople's Republic of ChinaAsiaCancerCarcinomasCellsCyborgsDrugs and TherapiesEmerging TechnologiesHead and Neck CancerHealth and MedicineImmune SystemImmunologyImmunotherapyMachine LearningMononuclear Phagocyte SystemOncologyPhagocytesSquamous Cell Carcinoma
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Springer Nature
