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Survival Genie, a web platform for survival analysis across pediatric and adult cancers

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outc...

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Autores principales: Dwivedi, Bhakti, Mumme, Hope, Satpathy, Sarthak, Bhasin, Swati S., Bhasin, Manoj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866543/
https://www.ncbi.nlm.nih.gov/pubmed/35197510
http://dx.doi.org/10.1038/s41598-022-06841-0
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author Dwivedi, Bhakti
Mumme, Hope
Satpathy, Sarthak
Bhasin, Swati S.
Bhasin, Manoj
author_facet Dwivedi, Bhakti
Mumme, Hope
Satpathy, Sarthak
Bhasin, Swati S.
Bhasin, Manoj
author_sort Dwivedi, Bhakti
collection PubMed
description The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan–Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/.
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spelling pubmed-88665432022-02-25 Survival Genie, a web platform for survival analysis across pediatric and adult cancers Dwivedi, Bhakti Mumme, Hope Satpathy, Sarthak Bhasin, Swati S. Bhasin, Manoj Sci Rep Article The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan–Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/. Nature Publishing Group UK 2022-02-23 /pmc/articles/PMC8866543/ /pubmed/35197510 http://dx.doi.org/10.1038/s41598-022-06841-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Dwivedi, Bhakti
Mumme, Hope
Satpathy, Sarthak
Bhasin, Swati S.
Bhasin, Manoj
Survival Genie, a web platform for survival analysis across pediatric and adult cancers
title Survival Genie, a web platform for survival analysis across pediatric and adult cancers
title_full Survival Genie, a web platform for survival analysis across pediatric and adult cancers
title_fullStr Survival Genie, a web platform for survival analysis across pediatric and adult cancers
title_full_unstemmed Survival Genie, a web platform for survival analysis across pediatric and adult cancers
title_short Survival Genie, a web platform for survival analysis across pediatric and adult cancers
title_sort survival genie, a web platform for survival analysis across pediatric and adult cancers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866543/
https://www.ncbi.nlm.nih.gov/pubmed/35197510
http://dx.doi.org/10.1038/s41598-022-06841-0
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