Dataset Information

OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma.

ABSTRACT: 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.

SUBMITTER: Xie L

PROVIDER: S-EPMC6817837 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma.

Xie Longxiang L Wang Qiang Q Nan Fangmei F Ge Linna L Dang Yifang Y Sun Xiaoxiao X Li Ning N Dong Huan H Han Yali Y Zhang Guosen G Zhu Wan W Guo Xiangqian X

Cancer management and research 20191024

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 profile ...[more]

PMID: 31749633

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Project description:BACKGROUND:Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. OBJECTIVE:Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. METHODS:We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). RESULTS:Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. CONCLUSIONS:Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.

Dataset Information

OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma.

Publications

OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma.

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