OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma.
Longxiang XieQiang WangFangmei NanLinna GeYifang DangXiaoxiao SunNing LiHuan DongYali HanGuosen ZhangWan ZhuXiangqian GuoPublished in: Cancer management and research (2019)
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.
Keyphrases
- gene expression
- genome wide
- health information
- dna methylation
- papillary thyroid
- single cell
- free survival
- end stage renal disease
- big data
- electronic health record
- chronic kidney disease
- newly diagnosed
- squamous cell carcinoma
- healthcare
- palliative care
- lymph node metastasis
- copy number
- squamous cell
- high throughput
- machine learning
- prognostic factors
- young adults
- rna seq
- deep learning
- bioinformatics analysis