摘要
由一名新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-肿瘤学的新研究-Osteosarcom AS是一篇报道的主题。据《来自湖南的消息》，NewsRx记者报道，“骨肉瘤是一种原发性恶性肿瘤，常累及儿童和青少年，预后差。肿瘤异质性的存在导致不同的分子亚型和生存结局。”本研究经费来自国家自然科学基金。我们的新闻记者引用了中南大学的一篇研究文章：“最近，脂质代谢被确定为肿瘤的一个关键特征，因此，我们的研究旨在识别骨肉瘤的脂质代谢分子亚型，并开发预测生存率的标志。”四个多中心队列-target-os、GSE21257、GSE39058和GSE16091-were融合为一个统一的荟萃队列。在荟萃队列患者中描述了新的分子亚型。随后的过程特征选择包括SU B型之间差异表达基因的分析、单变量Cox分析和StepAIC分析。本研究利用4种机器学习算法,将其重新配置为10种独特的组合,筛选出最有效的脂代谢相关信号(LMRS)的算法,并在GSE21257、GSE39058和GSE16091的测试队列中获得最高的一致性指数(Cindex),在骨肉瘤患者中鉴定出两种不同的脂代谢分子亚型。C1和C2具有明显的生存率差异。C1以胆固醇、脂肪酸合成和酮代谢增加为特征。C2则以类固醇激素合成、花生四烯酸、甘油脂和亚油酸代谢为特征。在靶OS中进行特征选择，确定了12个脂代谢基因，从而建立了骨肉瘤患者生存预测模型。基于12个已鉴定基因的LMRS，在TAR GET-OS、测试队列和元队列中一致准确地预测预后。结合12个已发表的特征，LMR显示出稳健且显著优越的预测能力。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Osteosarcom as is the subject of a report. According to news originating from Hunan, People' s Republic of China, by NewsRx correspondents, research stated, "Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecu lar subtypes and survival outcomes." Financial support for this research came from National Natural Science Foundatio n of China. Our news journalists obtained a quote from the research from Central South Unive rsity, "Recently, lipid metabolism has been identified as a critical characteris tic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabo lism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalga mated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selecti on processes, encompassing analyses of differentially expressed genes between su btypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarke rs related to lipid metabolism in TARGET-OS. We selected the most effective algo rithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (Cindex) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lip id metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with signi ficantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid meta bolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TAR GET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability."
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