Zhang Boxi, Kochetkova Elena, Norberg Erik
Department of Physiology and Pharmacology, Biomedicum, Karolinska Institutet, Stockholm, Sweden.
Institute of Cytology, Russian Academy of Sciences, Saint-Petersburg, Russia.
Methods Mol Biol. 2022;2445:275-288. doi: 10.1007/978-1-0716-2071-7_17.
The identification of novel biomarkers in cancer patients often requires both survival and gene expression analyses. The Kaplan-Meier survival analysis is one of the most common methods to assess the fraction of subjects living for a certain amount of time.Here, we describe a method for researchers to identify potential prognostic markers across distinct tumor types. We utilize The Cancer Genome Atlas (TCGA) as this is one of the most extensive and successful cancer genomics programs to date that includes expression data and clinical follow-up information for up to 33 distinct tumor types. Nevertheless, the method described here can also be applied to any open-source dataset where the RNA expression and clinical outcome are provided.We provide detailed practical instructions and advices for investigators to be able to successfully identify prognostic markers in cancer patients.
在癌症患者中鉴定新型生物标志物通常需要生存分析和基因表达分析。Kaplan-Meier生存分析是评估在一定时间内存活的受试者比例的最常用方法之一。在此,我们描述了一种方法,供研究人员识别不同肿瘤类型中的潜在预后标志物。我们利用癌症基因组图谱(TCGA),因为它是迄今为止最广泛且最成功的癌症基因组学项目之一,其中包含多达33种不同肿瘤类型的表达数据和临床随访信息。尽管如此,这里描述的方法也可应用于任何提供RNA表达和临床结果的开源数据集。我们为研究人员提供详细的实用指南和建议,以便他们能够成功识别癌症患者中的预后标志物。