Identification and validation of oncologic miRNA biomarkers for Luminal A-like breast cancer

Introduction: Breast cancer is a common disease with distinct tumor subtypes phenotypically characterized by ER and HER2/neu receptor status. MiRNAs play regulatory roles in tumor initiation and progression, and altered miRNA expression has been demonstrated in a variety of cancer states presenting the potential for exploitation as cancer biomarkers. Blood provides an excellent medium for biomarker discovery. This study investigated systemic miRNAs differentially expressed in Luminal A-like (ER+PR+HER2/neu-) breast cancer and their effectiveness as oncologic biomarkers in the clinical setting. Methods: Blood samples were prospectively collected from patients with Luminal A-like breast cancer (n=54) and controls (n=56). RNA was extracted, reverse transcribed and subjected to microarray analysis (n=10 Luminal A-like; n=10 Control). Differentially expressed miRNAs were identified by artificial neural network (ANN) data-mining algorithms. Expression of specific miRNAs was validated by RQ-PCR (n=44 Luminal A; n=46 Control) and potential relationships between circulating miRNA levels and clinicopathological features of breast cancer were investigated. Results: Microarray analysis identified 76 differentially expressed miRNAs. ANN revealed 10 miRNAs for further analysis ( miR-19b, miR-29a, miR-93, miR-181a, miR-182, miR-223, miR-301a, miR-423-5p, miR-486-5 and miR-652 ). The biomarker potential of 4 miRNAs ( miR-29a, miR-181a , miR-223 and miR-652 ) was confirmed by RQ-PCR, with significantly reduced expression in blood of women with Luminal A-like breast tumors compared to healthy controls (p=0.001, 0.004, 0.009 and 0.004 respectively). Binary logistic regression confirmed that combination of 3 of these miRNAs ( miR-29a, miR-181a and miR-652 ) could reliably differentiate between cancers and controls with an AUC of 0.80. Conclusion: This study provides insight into the underlying molecular portrait of Luminal A-like breast cancer subtype. From an initial 76 miRNAs, 4 were validated with altered expression in the blood of women with Luminal A-like breast cancer. The expression profiles of these 3 miRNAs, in combination with mammography, has potential to facilitate accurate subtype- specific breast tumor detection

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The fibroblast mitogen platelet-derived growth factor -BB (PDGF-BB) induces a transient expression of the orphan nuclear receptor NR4A1 (also named Nur77, TR3 or NGFIB). The aim of the present study was to investigate the pathways through which NR4A1 is induced by PDGF-BB and its functional role. We demonstrate that in PDGF-BB stimulated NIH3T3 cells, the MEK1/2 inhibitor CI-1040 strongly represses NR4A1 expression, whereas Erk5 downregulation delays the expression, but does not block it. Moreover, we report that treatment with the NF-κB inhibitor BAY11-7082 suppresses NR4A1 mRNA and protein expression. The majority of NR4A1 in NIH3T3 was found to be localized in the cytoplasm and only a fraction was translocated to the nucleus after continued PDGF-BB treatment. Silencing NR4A1 slightly increased the proliferation rate of NIH3T3 cells; however, it did not affect the chemotactic or survival abilities conferred by PDGF-BB. Moreover, overexpression of NR4A1 promoted anchorage-independent growth of NIH3T3 cells and the glioblastoma cell lines U-105MG and U-251MG. Thus, whereas NR4A1, induced by PDGF-BB, suppresses cell growth on a solid surface, it increases anchorage-independent growth

Estrogen-dependent dynamic profile of eNOS-DNA associations in prostate cancer

In previous work we have documented the nuclear translocation of endothelial NOS (eNOS) and its participation in combinatorial complexes with Estrogen Receptor Beta (ERβ) and Hypoxia Inducible Factors (HIFs) that determine localized chromatin remodeling in response to estrogen (E2) and hypoxia stimuli, resulting in transcriptional regulation of genes associated with adverse prognosis in prostate cancer (PCa). To explore the role of nuclear eNOS in the acquisition of aggressive phenotype in PCa, we performed ChIP-Sequencing on chromatin-associated eNOS from cells from a primary tumor with poor outcome and from metastatic LNCaP cells. We found that: 1. the eNOS-bound regions (peaks) are widely distributed across the genome encompassing multiple transcription factors binding sites, including Estrogen Response Elements. 2. E2 increased the number of peaks, indicating hormone-dependent eNOS re-localization. 3. Peak distribution was similar with/without E2 with ≈ 55% of them in extragenic DNA regions and an intriguing involvement of the 5′ domain of several miRs deregulated in PCa. Numerous potentially novel eNOS-targeted genes have been identified suggesting that eNOS participates in the regulation of large gene sets. The parallel finding of downregulation of a cluster of miRs, including miR-34a, in PCa cells associated with poor outcome led us to unveil a molecular link between eNOS and SIRT1, an epigenetic regulator of aging and tumorigenicity, negatively regulated by miR-34a and in turn activating eNOS. E2 potentiates miR-34a downregulation thus enhancing SIRT1 expression, depicting a novel eNOS/SIRT1 interplay fine-tuned by E2-activated ER signaling, and suggesting that eNOS may play an important role in aggressive PCa

A fresh look at the evolution and diversification of photochemical reaction centers

In this review, I reexamine the origin and diversification of photochemical reaction centers based on the known phylogenetic relations of the core subunits, and with the aid of sequence and structural alignments. I show, for example, that the protein folds at the C-terminus of the D1 and D2 subunits of Photosystem II, which are essential for the coordination of the water-oxidizing complex, were already in place in the most ancestral Type II reaction center subunit. I then evaluate the evolution of reaction centers in the context of the rise and expansion of the different groups of bacteria based on recent large-scale phylogenetic analyses. I find that the Heliobacteriaceae family of Firmicutes appears to be the earliest branching of the known groups of phototrophic bacteria; however, the origin of photochemical reaction centers and chlorophyll synthesis cannot be placed in this group. Moreover, it becomes evident that the Acidobacteria and the Proteobacteria shared a more recent common phototrophic ancestor, and this is also likely for the Chloroflexi and the Cyanobacteria. Finally, I argue that the discrepancies among the phylogenies of the reaction center proteins, chlorophyll synthesis enzymes, and the species tree of bacteria are best explained if both types of photochemical reaction centers evolved before the diversification of the known phyla of phototrophic bacteria. The primordial phototrophic ancestor must have had both Type I and Type II reaction centers

Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases

<p>Abstract</p> <p>Background</p> <p>Identifying quantitative trait loci (QTL) for both additive and epistatic effects raises the statistical issue of selecting variables from a large number of candidates using a small number of observations. Missing trait and/or marker values prevent one from directly applying the classical model selection criteria such as Akaike's information criterion (AIC) and Bayesian information criterion (BIC).</p> <p>Results</p> <p>We propose a two-step Bayesian variable selection method which deals with the sparse parameter space and the small sample size issues. The regression coefficient priors are flexible enough to incorporate the characteristic of "large <it>p </it>small <it>n</it>" data. Specifically, sparseness and possible asymmetry of the significant coefficients are dealt with by developing a Gibbs sampling algorithm to stochastically search through low-dimensional subspaces for significant variables. The superior performance of the approach is demonstrated via simulation study. We also applied it to real QTL mapping datasets.</p> <p>Conclusion</p> <p>The two-step procedure coupled with Bayesian classification offers flexibility in modeling "large p small n" data, especially for the sparse and asymmetric parameter space. This approach can be extended to other settings characterized by high dimension and low sample size.</p

REGγ is associated with multiple oncogenic pathways in human cancers

<p>Abstract</p> <p>Background</p> <p>Recent studies suggest a role of the proteasome activator, REGγ, in cancer progression. Since there are limited numbers of known REGγ targets, it is not known which cancers and pathways are associated with REGγ.</p> <p>Methods</p> <p>REGγ protein expressions in four different cancers were investigated by immunohistochemistry (IHC) analysis. Following NCBI Gene Expression Omnibus (GEO) database search, microarray platform validation, differential expressions of REGγ in corresponding cancers were statistically analyzed. Genes highly correlated with REGγ were defined based on Pearson's correlation coefficient. Functional links were estimated by Ingenuity Core analysis. Finally, validation was performed by RT-PCR analysis in established cancer cell lines and IHC in human colon cancer tissues</p> <p>Results</p> <p>Here, we demonstrate overexpression of REGγ in four different cancer types by micro-tissue array analysis. Using meta-analysis of publicly available microarray databases and biological studies, we verified elevated REGγ gene expression in the four types of cancers and identified genes significantly correlated with REGγ expression, including genes in p53, Myc pathways, and multiple other cancer-related pathways. The predicted correlations were largely consistent with quantitative RT-PCR analysis.</p> <p>Conclusions</p> <p>This study provides us novel insights in REGγ gene expression profiles and its link to multiple cancer-related pathways in cancers. Our results indicate potentially important pathogenic roles of REGγ in multiple cancer types and implicate REGγ as a putative cancer marker.</p