Bottom-up, integrated -omics analysis identifies broadly dosage-sensitive genes in breast cancer samples from TCGA
Genre
Journal ArticleDate
2019-01-01Author
Kechavarzi, BDWu, H
Doman, TN
Subject
AneuploidyBig Data
Breast Neoplasms
DNA Copy Number Variations
DNA, Neoplasm
Databases, Genetic
Female
Gene Dosage
Gene Expression Profiling
Gene Knockdown Techniques
Genes, erbB-2
Genomics
Humans
Neoplasm Proteins
Oncogenes
Proteomics
RNA, Messenger
RNA, Neoplasm
Systems Biology
Permanent link to this record
http://hdl.handle.net/20.500.12613/4599
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10.1371/journal.pone.0210910Abstract
© 2019 Kechavarzi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The massive genomic data from The Cancer Genome Atlas (TCGA), including proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC), provides a unique opportunity to study cancer systematically. While most observations are made from a single type of genomics data, we apply big data analytics and systems biology approaches by simultaneously analyzing DNA amplification, mRNA and protein abundance. Using multiple genomic profiles, we have discovered widespread dosage compensation for the extensive aneuploidy observed in TCGA breast cancer samples. We do identify 11 genes that show strong correlation across all features (DNA/mRNA/protein) analogous to that of the well-known oncogene HER2 (ERBB2). These genes are generally less well-characterized regarding their role in cancer and we advocate their further study. We also discover that shRNA knockdown of these genes has an impact on cancer cell growth, suggesting a vulnerability that could be used for cancer therapy. Our study shows the advantages of systematic big data methodologies and also provides future research directions.Citation to related work
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http://dx.doi.org/10.34944/dspace/4581