首页|Central South University Reports Findings in Glioblastomas (Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients)
Central South University Reports Findings in Glioblastomas (Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients)
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New research on Oncology - Glioblastomas is the subject of a report. According to news reporting originating from Changsha, People's Republic of China, by NewsRx correspondents, research stated, "Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment." Financial supporters for this research include National Natural Science Foundation of China, Special funds for innovation in Hunan Province, High talent project of Hunan Province. Our news editors obtained a quote from the research from Central South University, "The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR."
ChangshaPeople's Republic of ChinaAsiaCancerCyborgsDrugs and TherapiesEmerging TechnologiesGeneticsGlioblastomasGliomasHealth and MedicineImmunotherapyMachine LearningOncology
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