ArticleJournal of medical Internet research2020
A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
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- PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing.BMC bioinformatics · 2023Article
- Exploring the prognostic function of TMB-related prognostic signature in patients with colon cancer.BMC medical genomics · 2023Article
- Role of reactive oxygen species in regulating 27-hydroxycholesterol-induced apoptosis of hematopoietic progenitor cells and myeloid cell lines.Cell death & disease · 2022Article
- Inhibition of USP1 enhances anticancer drugs-induced cancer cell death through downregulation of survivin and miR-216a-5p-mediated upregulation of DR5.Cell death & disease · 2022Article
- Association of Elevated Expression Levels ofJournal of personalized medicine · 2022Article
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- Survival Genie, a web platform for survival analysis across pediatric and adult cancers.Scientific reports · 2022Article
- m6A-Related Genes Contribute to Poor Prognosis of Hepatocellular Carcinoma.Computational and mathematical methods in medicine · 2022Article
- A metabolism-related gene signature for predicting the prognosis in thyroid carcinoma.Frontiers in genetics · 2022Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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