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基于 TARGET-osteosarcoma 数据库的 12 个与生存相关的差异表达基因。

12 Survival-related differentially expressed genes based on the TARGET-osteosarcoma database.

机构信息

School of Biomedical Sciences, The University of Western Australia, Perth, WA 6009, Australia.

Perron Institute for Neurological and Translational Science, QEII Medical Centre, Nedlands, WA 6009, Australia.

出版信息

Exp Biol Med (Maywood). 2021 Oct;246(19):2072-2081. doi: 10.1177/15353702211007410. Epub 2021 Apr 29.

Abstract

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project aims to determine molecular changes that drive childhood cancers, including osteosarcoma. The main purpose of the program is to use the open-source database to develop novel, effective, and less toxic therapies. We downloaded TARGET-OS RNA-Sequencing data through R studio and merged the mRNA expression of genes with clinical information (vital status, survival time and gender). Further, we analyzed differential gene expressions between dead and alive patients based on TARGET-OS project. By this study, we found 5758 differentially expressed genes between deceased and alive patients with a false discovery rate below 0.05; 4469 genes were upregulated in deceased patients compared to alive, whereas 1289 genes were downregulated. The survival-related genes were obtained using Kaplan-Meier survival analysis and Cox univariate regression (KM < 0.05 and Cox -value < 0.05). Out of 5758 differentially expressed genes, only 217 have been associated with overall survival. Eight survival-related downregulated genes (, , , , , , , and ) and four survival-related upregulated genes (, , and ) were selected for further analysis as potential independent prognostic candidate genes. This study may help to discover novel prognostic markers and potential therapeutic targets for osteosarcoma.

摘要

靶向治疗应用研究以产生有效疗法(TARGET)项目旨在确定驱动儿童癌症(包括骨肉瘤)的分子变化。该计划的主要目的是利用开源数据库开发新型、有效且毒性较小的疗法。我们通过 R 工作室下载了 TARGET-OS RNA 测序数据,并将基因的 mRNA 表达与临床信息(生死状态、生存时间和性别)合并。此外,我们根据 TARGET-OS 项目分析了死亡和存活患者之间的差异基因表达。通过这项研究,我们在死亡和存活患者之间发现了 5758 个差异表达基因,假发现率低于 0.05;与存活患者相比,死亡患者中有 4469 个基因上调,而 1289 个基因下调。使用 Kaplan-Meier 生存分析和 Cox 单变量回归(KM<0.05 和 Cox 值<0.05)获得了与生存相关的基因。在 5758 个差异表达基因中,只有 217 个与总生存相关。选择了 8 个与生存相关的下调基因(、、、、、、和)和 4 个与生存相关的上调基因(、、和)进行进一步分析,作为潜在的独立预后候选基因。这项研究可能有助于发现骨肉瘤的新预后标志物和潜在治疗靶点。

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