首页|Southern Medical University Reports Findings in Machine Learning (Machine learni ng-based identification of a cell death-related signature associated with progno sis and immune infiltration in glioma)
Southern Medical University Reports Findings in Machine Learning (Machine learni ng-based identification of a cell death-related signature associated with progno sis and immune infiltration in glioma)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Guangzhou, People's Re public of China, by NewsRx correspondents, research stated, "Accumulating eviden ce suggests that a wide variety of cell deaths are deeply involved in cancer imm unity. However, their roles in glioma have not been explored." Financial supporters for this research include National Natural Science Foundati on of China, China Postdoctoral Science Foundation, Natural Science Foundation o f Hunan Province. Our news journalists obtained a quote from the research from Southern Medical Un iversity, "We employed a logistic regression model with the shrinkage regulariza tion operator (LASSO) Cox combined with seven machine learning algorithms to ana lyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performan ce of the nomogram was assessed through the use of receiver operating characteri stic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known R NA transcripts (CIBERSORT) and single sample gene set enrichment analysis method s. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 w ere investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram p erformed well in predicting outcomes according to time-dependent ROC and calibra tion plots. In addition, a high-risk score was significantly related to high exp ression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregu lated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associate d with poor clinical outcomes and was positively related to CD163, PD-L1 and vim entin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma."
GuangzhouPeople's Republic of ChinaA siaCyborgsEmerging TechnologiesMachine Learning
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