BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways

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.

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

“…The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general (Rappoport and Shamir, 2018). Accordingly, DNA copy number aberration (CNA) or mutations, resulting in genomic alteration, play vital roles in cancer occurrence and progression (Kim et al, 2018); meanwhile, DNA methylation (MET), resulting in epigenetic regulation of the cancer genome, is thought to make considerable contributions to the heterogeneity of cancer (Yang et al, 2019). Especially, with a highly heterogeneous disease like breast cancer (BRCA), it is clearly no exception (Luen et al, 2016;Karsli-Ceppioglu et al, 2017).…”

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

“…In recent years, detailed information regarding prognosis evaluation for cancer patients can be effectively provided by genome‐wide expression profiling detection . Numerous studies have evaluated the prognostic roles of array‐based gene expression signatures acquired from tumours .…”

“…In recent years, detailed information regarding prognosis evaluation for cancer patients can be effectively provided by genome-wide expression profiling detection. [14][15][16][17] Numerous studies have evaluated the prognostic roles of array-based gene expression signatures acquired from tumours. 14,18,19 Several gene signatures have also been established to distinguish the prognosis of patients beyond the BC clinicopathologic features; however, most of them are not used clinically.…”

An immune‐related prognostic signature for predicting breast cancer recurrence

“…Microarray analysis and whole-genome sequencing have granted the possibility to explore the genomic characteristics of high-risk cancers. 19,20 In particular, genomics and molecular characterization studies have revealed driving mechanisms of BC. 20,21 Several studies have shown that multi-gene signature models based on the analysis of tumor arrays can help predict cancer prognosis and recurrence more accurately than conventional methods.…”