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      Differentially Expressed Transcripts and Dysregulated Signaling Pathways and Networks in African American Breast Cancer

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          Abstract

          African Americans (AAs) have higher mortality rate from breast cancer than that of Caucasian Americans (CAs) even when socioeconomic factors are accounted for. To better understand the driving biological factors of this health disparity, we performed a comprehensive differential gene expression analysis, including subtype- and stage-specific analysis, using the breast cancer data in the Cancer Genome Atlas (TCGA). In total, 674 unique genes and other transcripts were found differentially expressed between these two populations. The numbers of differentially expressed genes between AA and CA patients increased in each stage of tumor progression: there were 26 in stage I, 161 in stage II, and 223 in stage III. Resistin, a gene that is linked to obesity, insulin resistance, and breast cancer, was expressed more than four times higher in AA tumors. An uncharacterized, long, non-coding RNA, LOC90784, was down-regulated in AA tumors, and its expression was inversely related to cancer stage and was the lowest in triple negative AA breast tumors. Network analysis showed increased expression of a majority of components in p53 and BRCA1 subnetworks in AA breast tumor samples, and members of the aurora B and polo-like kinase signaling pathways were also highly expressed. Higher gene expression diversity was observed in more advanced stage breast tumors suggesting increased genomic instability during tumor progression. Amplified resistin expression may indicate insulin-resistant type II diabetes and obesity are associated with AA breast cancer. Expression of LOC90784 may have a protective effect on breast cancer patients, and its loss, particularly in triple negative breast cancer, could be having detrimental effects. This work helps elucidate molecular mechanisms of breast cancer health disparity and identifies putative biomarkers and therapeutic targets such as resistin, and the aurora B and polo-like kinase signaling pathways for treating AA breast cancer patients.

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          The transcriptional landscape of the yeast genome defined by RNA sequencing.

          The identification of untranslated regions, introns, and coding regions within an organism remains challenging. We developed a quantitative sequencing-based method called RNA-Seq for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome. We applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome and demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed. We confirmed many known and predicted introns and demonstrated that others are not actively used. Alternative initiation codons and upstream open reading frames also were identified for many yeast genes. We also found unexpected 3'-end heterogeneity and the presence of many overlapping genes. These results indicate that the yeast transcriptome is more complex than previously appreciated.
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            Moderated statistical tests for assessing differences in tag abundance.

            Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologies for measuring gene expression on a genomic scale, without the need for prior knowledge of the genome sequence. As the cost of sequencing DNA decreases, the number of DGE datasets is expected to grow dramatically. Various tests of differential expression have been proposed for replicated DGE data using binomial, Poisson, negative binomial or pseudo-likelihood (PL) models for the counts, but none of the these are usable when the number of replicates is very small. We develop tests using the negative binomial distribution to model overdispersion relative to the Poisson, and use conditional weighted likelihood to moderate the level of overdispersion across genes. Not only is our strategy applicable even with the smallest number of libraries, but it also proves to be more powerful than previous strategies when more libraries are available. The methodology is equally applicable to other counting technologies, such as proteomic spectral counts. An R package can be accessed from http://bioinf.wehi.edu.au/resources/
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              Aurora B couples chromosome alignment with anaphase by targeting BubR1, Mad2, and Cenp-E to kinetochores

              The Aurora/Ipl1 family of protein kinases plays multiple roles in mitosis and cytokinesis. Here, we describe ZM447439, a novel selective Aurora kinase inhibitor. Cells treated with ZM447439 progress through interphase, enter mitosis normally, and assemble bipolar spindles. However, chromosome alignment, segregation, and cytokinesis all fail. Despite the presence of maloriented chromosomes, ZM447439-treated cells exit mitosis with normal kinetics, indicating that the spindle checkpoint is compromised. Indeed, ZM447439 prevents mitotic arrest after exposure to paclitaxel. RNA interference experiments suggest that these phenotypes are due to inhibition of Aurora B, not Aurora A or some other kinase. In the absence of Aurora B function, kinetochore localization of the spindle checkpoint components BubR1, Mad2, and Cenp-E is diminished. Furthermore, inhibition of Aurora B kinase activity prevents the rebinding of BubR1 to metaphase kinetochores after a reduction in centromeric tension. Aurora B kinase activity is also required for phosphorylation of BubR1 on entry into mitosis. Finally, we show that BubR1 is not only required for spindle checkpoint function, but is also required for chromosome alignment. Together, these results suggest that by targeting checkpoint proteins to kinetochores, Aurora B couples chromosome alignment with anaphase onset.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                4 December 2013
                : 8
                : 12
                : e82460
                Affiliations
                [1 ]Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida, United States of America
                [2 ]Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, United States of America
                [3 ]Department of Statistics, Florida State University, Tallahassee, Florida, United States of America
                Stony Brook University, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PAS JL MDR QXS JZ. Performed the experiments: PAS JL MDR QXS JZ. Analyzed the data: PAS JL MDR QXS JZ. Contributed reagents/materials/analysis tools: PAS JL MDR QXS JZ. Wrote the manuscript: PAS JL MDR QXS JZ.

                Article
                PONE-D-13-40214
                10.1371/journal.pone.0082460
                3853650
                24324792
                76e32baf-ee35-4cf4-91bb-877e6dae773c
                Copyright @ 2013

                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.

                History
                : 30 September 2013
                : 23 October 2013
                Funding
                Salaries of QXS and JZ are paid by Florida State University. MDR's salary was paid by Elsa U. Pardee grant to Dr. Sang. PAS was supported by a graduate assistantship from Florida State University. JL is compensated hourly for teaching labs at Florida State University. This work was in part supported by Elsa U. Pardee Foundation ( http://www.pardeefoundation.org/) to Dr. Sang and the Florida State University ( http://www.fsu.edu/) to Dr. Sang and Dr. Zhang. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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