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Transcriptome Profiling Of Peripheral Blood Cells Identifies Potential Biomarkers For Doxorubicin Cardiotoxicity In A Rat Model

V. Todorova, M. Beggs, R. Delongchamp, I. Dhakal, I. Makhoul, J. Wei, V. Klimberg
Published 2012 · Medicine

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Aims Doxorubicin (DOX), a widely used anticancer agent, can cause an unpredictable cardiac toxicity which remains a major limitation in cancer chemotherapy. There is a need for noninvasive, sensitive and specific biomarkers which will allow identifying patients at risk for DOX-induced cardiotoxicity to prevent permanent cardiac damage. The aim of this study was to investigate whether the expression of specific genes in the peripheral blood can be used as surrogate marker(s) for DOX-induced cardiotoxicity. Methods/Results Rats were treated with a single dose of DOX similar to one single dose that is often administered in humans. The cardiac and peripheral blood mononuclear cells (PBMCs) genome-wide expression profiling were examined using Illumina microarrays. The results showed 4,409 differentially regulated genes (DRG) in the hearts and 4,120 DRG in PBMC. Of these 2411 genes were similarly DRG (SDRG) in both the heart and PBMC. Pathway analysis of the three datasets of DRG using Gene Ontology (GO) enrichment analysis and Ingenuity Pathways Analysis (IPA) showed that most of the genes in these datasets fell into pathways related to oxidative stress response and protein ubiquination. IPA search for potential eligible biomarkers for cardiovascular disease within the SDRG list revealed 188 molecules. Conclusions We report the first in-depth comparison of DOX-induced global gene expression profiles of hearts and PBMCs. The high similarity between the gene expression profiles of the heart and PBMC induced by DOX indicates that the PBMC transcriptome may serve as a surrogate marker of DOX-induced cardiotoxicity. Future directions of this research will include analysis of PBMC expression profiles of cancer patients treated with DOX-based chemotherapy to identify the cardiotoxicity risk, predict DOX-treatment response and ultimately to allow individualized anti-cancer therapy.
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