摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。据《中国人民日报广州消息》报道，NewsRx记者的研究表明：“积累的证据表明，多种细胞死亡与恶性肿瘤的发生密切相关，但它们在胶质瘤中的作用尚未被探讨。”国家自然科学基金、中国博士后基金、湖南省自然科学基金资助本研究。我们的新闻记者从南方医科大学的研究中获得了一句话：“我们使用了一个具有收缩正则化算子(LASSO)Cox的logistic回归模型，并结合七种机器学习算法来分析细胞死亡的模式(包括倒垂、铁垂、焦垂、黑垂、在肿瘤基因组图谱(TCGA)队列中，通过受试者操作特征(ROC)曲线和校准曲线评估列线图的表现，通过估计已知RNA转录本(CIBERSORT)的相对子集和单样本基因集富集分析方法估计细胞类型，通过机器筛选与预后模型相关的Hub基因。通过免疫组织化学(IHC)研究MYD88的表达模式及其临床意义。细胞死亡评分是胶质瘤患者预后不良的独立预后因子,其准确性明显高于已发表的10个标记。根据时间依赖性ROC图和校准图,列线图能很好地预测预后。高危评分与免疫检查点分子的高表达和肿瘤前细胞的密集浸润显著相关，这些发现与基于细胞死亡的预后模型相关，MYD88表达上调与恶性表型和预后不良有关，MYD88高表达与临床预后不良有关，CD163、CD163马内队列中PD-β1和Vim entin的表达。细胞死亡评分为胶质瘤提供了精确的分层和免疫状态。
Abstract
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."
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