| Summary: | Longxiang Xie,1 Qiang Wang,1 Fangmei Nan,1 Linna Ge,1 Yifang Dang,1 Xiaoxiao Sun,1 Ning Li,1 Huan Dong,1 Yali Han,1 Guosen Zhang,1 Wan Zhu,2 Xiangqian Guo1 1Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People’s Republic of China; 2Department of Anesthesia, Stanford University, Stanford, CA, USACorrespondence: Xiangqian GuoBioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People’s Republic of ChinaTel +86-371-23880585Email xqguo@henu.edu.cnAbstract: Gene expression profiling data with long-term clinical follow-up information are great resources to screen, develop, evaluate and validate prognostic biomarkers in translational cancer research. However, an easy-to-use interactive online tool is needed to analyze these profiling and clinical data. In the current work, we developed OSacc (Online consensus Survival analysis of ACC), a web tool that provides rapid and user-friendly survival analysis based on seven independent transcriptomic profiles with long-term clinical follow-up information of 259 ACC patients gathered from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. OSacc allows researchers and clinicians to evaluate the prognostic value of genes of interest by Kaplan–Meier (KM) survival plot with hazard ratio (HR) and log-rank test in ACC. OSacc is freely available at http://bioinfo.henu.edu.cn/ACC/ACCList.jsp.Keywords: ACC, prognostic marker, over survival, web tool, KM plot
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