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UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses()

Genomics data from The Cancer Genome Atlas (TCGA) project has led to the comprehensive molecular characterization of multiple cancer types. The large sample numbers in TCGA offer an excellent opportunity to address questions associated with tumo heterogeneity. Exploration of the data by cancer resea...

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Autores principales: Chandrashekar, Darshan S., Bashel, Bhuwan, Balasubramanya, Sai Akshaya Hodigere, Creighton, Chad J., Ponce-Rodriguez, Israel, Chakravarthi, Balabhadrapatruni V.S.K., Varambally, Sooryanarayana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Neoplasia Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516091/
https://www.ncbi.nlm.nih.gov/pubmed/28732212
http://dx.doi.org/10.1016/j.neo.2017.05.002
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author Chandrashekar, Darshan S.
Bashel, Bhuwan
Balasubramanya, Sai Akshaya Hodigere
Creighton, Chad J.
Ponce-Rodriguez, Israel
Chakravarthi, Balabhadrapatruni V.S.K.
Varambally, Sooryanarayana
author_facet Chandrashekar, Darshan S.
Bashel, Bhuwan
Balasubramanya, Sai Akshaya Hodigere
Creighton, Chad J.
Ponce-Rodriguez, Israel
Chakravarthi, Balabhadrapatruni V.S.K.
Varambally, Sooryanarayana
author_sort Chandrashekar, Darshan S.
collection PubMed
description Genomics data from The Cancer Genome Atlas (TCGA) project has led to the comprehensive molecular characterization of multiple cancer types. The large sample numbers in TCGA offer an excellent opportunity to address questions associated with tumo heterogeneity. Exploration of the data by cancer researchers and clinicians is imperative to unearth novel therapeutic/diagnostic biomarkers. Various computational tools have been developed to aid researchers in carrying out specific TCGA data analyses; however there is need for resources to facilitate the study of gene expression variations and survival associations across tumors. Here, we report UALCAN, an easy to use, interactive web-portal to perform to in-depth analyses of TCGA gene expression data. UALCAN uses TCGA level 3 RNA-seq and clinical data from 31 cancer types. The portal's user-friendly features allow to perform: 1) analyze relative expression of a query gene(s) across tumor and normal samples, as well as in various tumor sub-groups based on individual cancer stages, tumor grade, race, body weight or other clinicopathologic features, 2) estimate the effect of gene expression level and clinicopathologic features on patient survival; and 3) identify the top over- and under-expressed (up and down-regulated) genes in individual cancer types. This resource serves as a platform for in silico validation of target genes and for identifying tumor sub-group specific candidate biomarkers. Thus, UALCAN web-portal could be extremely helpful in accelerating cancer research. UALCAN is publicly available at http://ualcan.path.uab.edu.
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spelling pubmed-55160912017-07-31 UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses() Chandrashekar, Darshan S. Bashel, Bhuwan Balasubramanya, Sai Akshaya Hodigere Creighton, Chad J. Ponce-Rodriguez, Israel Chakravarthi, Balabhadrapatruni V.S.K. Varambally, Sooryanarayana Neoplasia Original article Genomics data from The Cancer Genome Atlas (TCGA) project has led to the comprehensive molecular characterization of multiple cancer types. The large sample numbers in TCGA offer an excellent opportunity to address questions associated with tumo heterogeneity. Exploration of the data by cancer researchers and clinicians is imperative to unearth novel therapeutic/diagnostic biomarkers. Various computational tools have been developed to aid researchers in carrying out specific TCGA data analyses; however there is need for resources to facilitate the study of gene expression variations and survival associations across tumors. Here, we report UALCAN, an easy to use, interactive web-portal to perform to in-depth analyses of TCGA gene expression data. UALCAN uses TCGA level 3 RNA-seq and clinical data from 31 cancer types. The portal's user-friendly features allow to perform: 1) analyze relative expression of a query gene(s) across tumor and normal samples, as well as in various tumor sub-groups based on individual cancer stages, tumor grade, race, body weight or other clinicopathologic features, 2) estimate the effect of gene expression level and clinicopathologic features on patient survival; and 3) identify the top over- and under-expressed (up and down-regulated) genes in individual cancer types. This resource serves as a platform for in silico validation of target genes and for identifying tumor sub-group specific candidate biomarkers. Thus, UALCAN web-portal could be extremely helpful in accelerating cancer research. UALCAN is publicly available at http://ualcan.path.uab.edu. Neoplasia Press 2017-07-18 /pmc/articles/PMC5516091/ /pubmed/28732212 http://dx.doi.org/10.1016/j.neo.2017.05.002 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original article
Chandrashekar, Darshan S.
Bashel, Bhuwan
Balasubramanya, Sai Akshaya Hodigere
Creighton, Chad J.
Ponce-Rodriguez, Israel
Chakravarthi, Balabhadrapatruni V.S.K.
Varambally, Sooryanarayana
UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses()
title UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses()
title_full UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses()
title_fullStr UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses()
title_full_unstemmed UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses()
title_short UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses()
title_sort ualcan: a portal for facilitating tumor subgroup gene expression and survival analyses()
topic Original article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516091/
https://www.ncbi.nlm.nih.gov/pubmed/28732212
http://dx.doi.org/10.1016/j.neo.2017.05.002
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