Pharmaceutical Sciences Faculty Publications
Document Type
Article
Publication Date
12-3-2018
Journal Title
Genome Med
ISSN
1756-994X
Volume
10
Issue
1
First Page
94
Last Page
94
DOI
10.1186/s13073-018-0602-x
PubMed ID
30501643
PubMed Central® ID
PMC6276229
Abstract
BACKGROUND: Transcriptome analysis of breast cancer discovered distinct disease subtypes of clinical significance. However, it remains a challenge to define disease biology solely based on gene expression because tumor biology is often the result of protein function. Here, we measured global proteome and transcriptome expression in human breast tumors and adjacent non-cancerous tissue and performed an integrated proteotranscriptomic analysis.
METHODS: We applied a quantitative liquid chromatography/mass spectrometry-based proteome analysis using an untargeted approach and analyzed protein extracts from 65 breast tumors and 53 adjacent non-cancerous tissues. Additional gene expression data from Affymetrix Gene Chip Human Gene ST Arrays were available for 59 tumors and 38 non-cancerous tissues in our study. We then applied an integrated analysis of the proteomic and transcriptomic data to examine relationships between them, disease characteristics, and patient survival. Findings were validated in a second dataset using proteome and transcriptome data from "The Cancer Genome Atlas" and the Clinical Proteomic Tumor Analysis Consortium.
RESULTS: We found that the proteome describes differences between cancerous and non-cancerous tissues that are not revealed by the transcriptome. The proteome, but not the transcriptome, revealed an activation of infection-related signal pathways in basal-like and triple-negative tumors. We also observed that proteins rather than mRNAs are increased in tumors and show that this observation could be related to shortening of the 3' untranslated region of mRNAs in tumors. The integrated analysis of the two technologies further revealed a global increase in protein-mRNA concordance in tumors. Highly correlated protein-gene pairs were enriched in protein processing and disease metabolic pathways. The increased concordance between transcript and protein levels was additionally associated with aggressive disease, including basal-like/triple-negative tumors, and decreased patient survival. We also uncovered a strong positive association between protein-mRNA concordance and proliferation of tumors. Finally, we observed that protein expression profiles co-segregate with a Myc activation signature and separate breast tumors into two subgroups with different survival outcomes.
CONCLUSIONS: Our study provides new insights into the relationship between protein and mRNA expression in breast cancer and shows that an integrated analysis of the proteome and transcriptome has the potential of uncovering novel disease characteristics.
Keywords
Breast Neoplasms, Chromatography, Liquid, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Mass Spectrometry, Oligonucleotide Array Sequence Analysis, Proteomics, Signal Transduction
Recommended Citation
Tang, Wei; Zhou, Ming; Dorsey, Tiffany H; Prieto, DaRue A; Wang, Xin W; Ruppin, Eytan; Veenstra, Timothy; and Ambs, Stefan, "Integrated Proteotranscriptomics of Breast Cancer Reveals Globally Increased Protein-mRNA Concordance Associated with Subtypes and Survival" (2018). Pharmaceutical Sciences Faculty Publications. 188.
https://digitalcommons.cedarville.edu/pharmaceutical_sciences_publications/188
