Abstract
BackgroundPrognostic 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.ObjectiveTaking 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.MethodsWe 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).ResultsThrough 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.ConclusionsUsing 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.
Highlights
The accumulation of large amounts of genomic data following the development of next-generation sequencing techniques is paving the way toward precision medicine [1,2,3,4]
Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve, 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 and select the same outputs as above
Summary
The accumulation of large amounts of genomic data following the development of next-generation sequencing techniques is paving the way toward precision medicine [1,2,3,4]. To efficiently link high-dimensional genomic and survival data, statisticians have developed grouped variable selection models, based on the Cox proportional hazards model, including the following: least absolute shrinkage and selection operator (lasso), elastic net regularization (elastic net), and network-regularized high-dimensional Cox-regression (Coxnet, hereon referred to as Net) [2,10,11,12,13] Among these methods, Net has been found to have the fewest overfitting problems and the highest prediction performance in these applications, as it takes into consideration the complexities of biological networks [2,6,8,10,14]. Identifying and verifying prognostic factors using big databases is essential in medical research, but this can be difficult for researchers who are unfamiliar with computer science To address this unmet clinical need, some web-based survival analysis tools have been developed. Some web-based survival analysis tools have been developed, they have several limitations
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