A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study
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 limitat...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2020-05-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2020/5/e16084 |
| _version_ | 1819238838682255360 |
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| author | Pak, Kyoungjune Oh, Sae-Ock Goh, Tae Sik Heo, Hye Jin Han, Myoung-Eun Jeong, Dae Cheon Lee, Chi-Seung Sun, Hokeun Kang, Junho Choi, Suji Lee, Soohwan Kwon, Eun Jung Kang, Ji Wan Kim, Yun Hak |
| author_facet | Pak, Kyoungjune Oh, Sae-Ock Goh, Tae Sik Heo, Hye Jin Han, Myoung-Eun Jeong, Dae Cheon Lee, Chi-Seung Sun, Hokeun Kang, Junho Choi, Suji Lee, Soohwan Kwon, Eun Jung Kang, Ji Wan Kim, Yun Hak |
| author_sort | Pak, Kyoungjune |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T13:42:35Z |
| format | Article |
| id | doaj.art-5e1299771de24b9a9a2a1c10aa390fe8 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-12-23T13:42:35Z |
| publishDate | 2020-05-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-5e1299771de24b9a9a2a1c10aa390fe82022-12-21T17:44:49ZengJMIR PublicationsJournal of Medical Internet Research1438-88712020-05-01225e1608410.2196/16084A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation StudyPak, KyoungjuneOh, Sae-OckGoh, Tae SikHeo, Hye JinHan, Myoung-EunJeong, Dae CheonLee, Chi-SeungSun, HokeunKang, JunhoChoi, SujiLee, SoohwanKwon, Eun JungKang, Ji WanKim, Yun HakBackgroundPrognostic 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.https://www.jmir.org/2020/5/e16084 |
| spellingShingle | Pak, Kyoungjune Oh, Sae-Ock Goh, Tae Sik Heo, Hye Jin Han, Myoung-Eun Jeong, Dae Cheon Lee, Chi-Seung Sun, Hokeun Kang, Junho Choi, Suji Lee, Soohwan Kwon, Eun Jung Kang, Ji Wan Kim, Yun Hak A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study Journal of Medical Internet Research |
| title | A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study |
| title_full | A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study |
| title_fullStr | A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study |
| title_full_unstemmed | A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study |
| title_short | A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study |
| title_sort | user friendly web based integrative tool esurv for survival analysis development and validation study |
| url | https://www.jmir.org/2020/5/e16084 |
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