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TRGAted:一种利用癌症基因组图谱中的蛋白质数据进行生存分析的网络工具。

TRGAted: A web tool for survival analysis using protein data in the Cancer Genome Atlas.

作者信息

Borcherding Nicholas, Bormann Nicholas L, Voigt Andrew P, Zhang Weizhou

机构信息

Holden Comprehensive Cancer Center, University of Iowa, Iowa City, Iowa, 52245, USA.

Department of Pathology, University of Iowa, Iowa City, Iowa, 52245, USA.

出版信息

F1000Res. 2018 Aug 10;7:1235. doi: 10.12688/f1000research.15789.2. eCollection 2018.

DOI:10.12688/f1000research.15789.2
PMID:30345029
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6173115/
Abstract

Reverse-phase protein arrays (RPPAs) are a highthroughput approach to protein quantification utilizing antibody-based micro-to-nano scale dot blot. Within the Cancer Genome Atlas (TCGA), RPPAs were used to quantify over 200 proteins in 8,167 tumor and metastatic samples. Protein-level data has particular advantages in assessing putative prognostic or therapeutic targets in tumors. However, many of the available pipelines do not allow for the partitioning of clinical and RPPA information to make meaningful conclusions. We developed a cloud-based application, TRGAted to enable researchers to better examine patient survival based on single or multiple proteins across 31 cancer types in the TCGA. TRGAted contains up-to-date overall survival, disease-specific survival, disease-free interval and progression-free interval information. Furthermore, survival information for primary tumor samples can be stratified based on gender, age, tumor stage, histological type, and subtype, allowing for highly adaptive and intuitive user experience. The code and processed data are open sourced and available on github and contains a tutorial built into the application for assisting users.

摘要

反相蛋白质阵列(RPPA)是一种利用基于抗体的微米到纳米尺度斑点印迹进行蛋白质定量的高通量方法。在癌症基因组图谱(TCGA)中,RPPA被用于对8167个肿瘤和转移样本中的200多种蛋白质进行定量。蛋白质水平数据在评估肿瘤中假定的预后或治疗靶点方面具有特殊优势。然而,许多现有的流程不允许对临床信息和RPPA信息进行划分以得出有意义的结论。我们开发了一个基于云的应用程序TRGAted,使研究人员能够更好地根据TCGA中31种癌症类型的单一或多种蛋白质来检查患者的生存率。TRGAted包含最新的总生存期、疾病特异性生存期、无病间期和无进展间期信息。此外,原发性肿瘤样本的生存信息可以根据性别、年龄、肿瘤分期、组织学类型和亚型进行分层,从而提供高度自适应和直观的用户体验。代码和处理后的数据是开源的,可在github上获取,并且应用程序中内置了一个教程来帮助用户。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/055c/6173116/ead7149b33fa/f1000research-7-17991-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/055c/6173116/853a9c7331d7/f1000research-7-17991-g0000.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/055c/6173116/f5cd3ec97743/f1000research-7-17991-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/055c/6173116/55e40649c096/f1000research-7-17991-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/055c/6173116/ead7149b33fa/f1000research-7-17991-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/055c/6173116/853a9c7331d7/f1000research-7-17991-g0000.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/055c/6173116/f5cd3ec97743/f1000research-7-17991-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/055c/6173116/55e40649c096/f1000research-7-17991-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/055c/6173116/ead7149b33fa/f1000research-7-17991-g0003.jpg

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