ArticleBMC bioinformatics2018
BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
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- An immune-related prognostic signature for predicting breast cancer recurrence.Cancer medicine · 2020Article
- Identification of Modulated MicroRNAs Associated with Breast Cancer, Diet, and Physical Activity.Cancers · 2020Article
- RNA-Seq-Based Breast Cancer Subtypes Classification Using Machine Learning Approaches.Computational intelligence and neuroscience · 2020Article
- Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer.Frontiers in genetics · 2020Article
- Transcriptome analyses identify hub genes and potential mechanisms in adenoid cystic carcinoma.Medicine · 2020Observational
- Integrated multi-omics analysis of genomics, epigenomics, and transcriptomics in ovarian carcinoma.Aging · 2019Article
- Identification of a 4-mRNA metastasis-related prognostic signature for patients with breast cancer.Journal of cellular and molecular medicine · 2019Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data.
resultsBRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, 'Pathways summary' and 'Oncoprint' with mutual exclusivity sort can be generated dynamically with a request by the user. Data in BRCA-Pathway can be downloaded by REST API for further analysis.
conclusionsBRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.