Wang Qiang, Xie Longxiang, Dang Yifang, Sun Xiaoxiao, Xie Tiantian, Guo Jinshuai, Han Yali, Yan Zhongyi, Zhu Wan, Wang Yunlong, Li Wei, Guo Xiangqian
Department of Preventive Medicine, Institute of Biomedical Informatics, Joint National Laboratory for Antibody Drug Engineering, Cell Signal Transduction Laboratory, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
Department of Anesthesia, Stanford University, Stanford, CA, United States.
Front Oncol. 2019 Mar 29;9:190. doi: 10.3389/fonc.2019.00190. eCollection 2019.
The availability of transcriptome data and clinical annotation offers the opportunity to identify prognosis biomarkers in cancer. However, efficient online prognosis analysis tools are still lacking. Herein, we developed a user-friendly web server, namely nline consensus urvival analysis of eioyoarcoma (OSlms), to centralize published gene expression data and clinical datasets of leiomyosarcoma (LMS) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). OSlms comprises of a total of 268 samples from three independent datasets, and employs the Kaplan Meier survival plot with hazard ratio (HR) and log rank test to estimate the prognostic potency of genes of interests for LMS patients. Using OSlms, clinicians and basic researchers could determine the prognostic significance of genes of interests and get opportunities to identify novel potential important molecules for LMS. OSlms is free and publicly accessible at http://bioinfo.henu.edu.cn/LMS/LMSList.jsp.
转录组数据和临床注释的可用性为识别癌症预后生物标志物提供了机会。然而，高效的在线预后分析工具仍然匮乏。在此，我们开发了一个用户友好的网络服务器，即平滑肌肉瘤在线共识生存分析（OSlms），以集中来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的已发表的平滑肌肉瘤（LMS）患者基因表达数据和临床数据集。OSlms总共包含来自三个独立数据集的268个样本，并采用带有风险比（HR）的Kaplan Meier生存曲线和对数秩检验来估计LMS患者感兴趣基因的预后效力。使用OSlms，临床医生和基础研究人员可以确定感兴趣基因的预后意义，并有机会识别LMS新的潜在重要分子。OSlms免费且可通过http://bioinfo.henu.edu.cn/LMS/LMSList.jsp公开访问。
