Wu Hao-Wei, Wu Jian-De, Yeh Yen-Ping, Wu Timothy H, Chao Chi-Hong, Wang Weijing, Chen Ting-Wen
Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan.
Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei 10617, Taiwan.
iScience. 2023 Jul 4;26(8):107269. doi: 10.1016/j.isci.2023.107269. eCollection 2023 Aug 18.
We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.e. overall survival, disease-specific survival, disease-free interval, and progression-free interval, in 33 cancer types. Notably, the implemented AFT model provides an alternative method for genes/features which failed the proportional hazard assumption in Cox regression. With the unprecedented number of survival models implemented and high flexibility in analysis, DoSurvive is a unique platform for the identification of clinically relevant targets for cancer researcher and practitioners. DoSurvive is freely available at http://dosurvive.lab.nycu.edu.tw/.
我们展示了DoSurvive,这是一个用户友好的生存分析网络工具以及一个以癌症预后生物标志物为中心的数据库。DoSurvive是首个允许用户使用定制的基因/患者列表对癌症进行多变量生存分析的数据库。DoSurvive为用户提供了三种生存分析方法,即对数秩检验、Cox回归和加速失效时间模型(AFT),用于分析33种癌症类型中的五种定量特征(mRNA、miRNA、lncRNA、蛋白质和CpG岛甲基化)以及四种生存类型,即总生存、疾病特异性生存、无病间期和无进展间期。值得注意的是,所实施的AFT模型为在Cox回归中未满足比例风险假设的基因/特征提供了一种替代方法。凭借前所未有的大量生存模型以及高度灵活的分析功能,DoSurvive是癌症研究人员和从业者识别临床相关靶点的独特平台。可通过http://dosurvive.lab.nycu.edu.tw/免费访问DoSurvive。