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死活?TCGA 数据集生存分析的陷阱。

Dead or alive? Pitfall of survival analysis with TCGA datasets.

机构信息

Department of Medical Genome Sciences, Research Institute for Frontier Medicine, Sapporo Medical University School of Medicine, Sapporo, Japan.

Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan.

出版信息

Cancer Biol Ther. 2021 Dec 2;22(10-12):527-528. doi: 10.1080/15384047.2021.1979845. Epub 2021 Sep 16.

DOI:10.1080/15384047.2021.1979845
PMID:34530682
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8726696/
Abstract

We often encounter situations in which data from the TCGA that have been analyzed in papers we read or reviewed cannot be reproduced, even when TCGA datasets are used, especially in survival analyses. Therefore, we attempted to confirm the data source for TCGA survival analysis and found that several websites used to analyze the survival data of TCGA datasets inappropriately handle the survival data, causing differences in statistical analyses. This causes the misinterpretation of results because figures of survival analysis results in several papers are sometimes exactly as generated by these sites, and the results depend on only the tools provided by these sites. We would like to make this situation widely known and raise the problem for scientific soundness.

摘要

我们经常遇到这样的情况,即使使用 TCGA 数据集,我们也无法重现我们在阅读或评审的论文中分析过的 TCGA 数据,尤其是在生存分析中。因此,我们试图确认 TCGA 生存分析的数据来源,发现有几个网站在分析 TCGA 数据集的生存数据时处理不当,导致统计分析存在差异。这导致结果被误解,因为几篇论文中的生存分析结果图有时正是这些网站生成的,并且结果仅取决于这些网站提供的工具。我们希望让这种情况广为人知,并提出这个问题以确保科学的严谨性。

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Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.起源细胞模式主导了 33 种癌症类型的 10000 个肿瘤的分子分类。
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