TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery.
Simple Summary: TCGAnalyzeR provides a novel web site integrating 123 pre-computed pan-cancer cohorts (i.e., microsatellite instability, immune, metastasis, PAM50, Triple Negative breast cancer, idh1-mutated glioblastoma, etc.), along with our own iCluster+ subcohorts, computed based on pre-processe...
| Publicado en: | Cancers Vol. 16; no. 2; pp. 345 - 362 |
|---|---|
| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MDPI
Jan2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175048071&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175048071 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jan2024 vid: 16 iid: 2 pid: 97109 pub: MDPI artinfo: ui: 175048071 175048071 175048071 10.3390/cancers16020345 175048071 ppf: 345 ppct: 17 formats: tig: atl: TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery. aug: au: Zengin, Talip Masud, Başak Abak Önal-Süzek, Tuğba affil: Department of Molecular Biology and Genetics, Mugla Sitki Kocman University, Mugla 48000, Türkiye sug: subj: Cancer Patients World Wide Web Clinical Data Repository Data Analytics Mutation Gene Expression Profiling Research, Medical Human Turkiye Funding Source Prospective Studies Survival Analysis Neoplasm Metastasis MicroRNA Literature Searching Decision Making, Clinical ab: Simple Summary: TCGAnalyzeR provides a novel web site integrating 123 pre-computed pan-cancer cohorts (i.e., microsatellite instability, immune, metastasis, PAM50, Triple Negative breast cancer, idh1-mutated glioblastoma, etc.), along with our own iCluster+ subcohorts, computed based on pre-processed single-nucleotide variations, copy number variations, differential expression, miRNA, methylation, and clinical data. TCGAnalyzeR interface provides an optimized and fully customizable experience to each user, enabling the selection of their own "My patients" or "My genes" to a clipboard. Several use cases of the web site are presented as Help documents. For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database's size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|