Role of microRNA deregulation in the pathogenesis of diffuse large B-cell lymphoma (DLBCL)

AbstractMicroRNAs (miRNAs) are small endogenous RNA molecules that regulate gene expression at the post-transcriptional level through its sequence complementation with target mRNAs. An individual miRNA species can simultaneously influence the expression of multiple genes and conversely, several miRNAs can synchronously control expression of specific gene product mRNA levels. Thus, miRNAs expression in cells has to be precisely regulated and alterations in miRNA levels may cause an aberrant expression of genes involved in oncogenic pathways and consequently result in cancer development. Indeed, miRNA expression is often deregulated in many cancers, including B-cell lymphomas. Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous group of B-cell lymphomas with different genetic backgrounds, morphologic features, and responses to therapy. Over the past decade, miRNAs emerged as a new tool for understanding DLBCL biology, and promising candidate molecular markers in DLBCL classification and treatment. In this review, we will focus on miRNAs aberrantly expressed in DLBCL and discuss the putative mechanisms of this deregulation. Additionally, we will summarize miRNAs’ involvement in the identification of DLBCL subgroups, and their potential role as diagnostic/prognostic biomarkers as well as specific therapeutic targets for DLBCL

MSRE-PCR for analysis of gene-specific DNA methylation

Abnormal DNA methylation is observed in certain promoters of neoplastic cells, although the likelihood of methylation for each individual promoter varies. Simultaneous analysis of many promoters in the same sample can allow use of statistical methods for identification of neoplasia. Here we describe an assay for such analysis, based on digestion of genomic DNA with methylation-sensitive restriction enzyme and multiplexed PCR with gene-specific primers (MSRE-PCR). MSRE-PCR includes extensive digestion of genomic DNA (uncut fragments cannot be identified by PCR), can be applied to dilute samples (<1 pg/μl), requires limited amount of starting material (42 pg or genomic equivalent of seven cells) and can identify methylation in a heterogeneous mix containing <2% of cells with methylated fragments. When applied to 53 promoters of breast cancer cell lines MCF-7, MDA-MB-231 and T47D, MSRE-PCR correctly identified the methylation status of genes analyzed by other techniques. For selected genes results of MSRE-PCR were confirmed by methylation-specific PCR and bisulfite sequencing. The assay can be configured for any number of desired targets in any user-defined set of genes

Gene and pathway identification with Lp penalized Bayesian logistic regression

<p>Abstract</p> <p>Background</p> <p>Identifying genes and pathways associated with diseases such as cancer has been a subject of considerable research in recent years in the area of bioinformatics and computational biology. It has been demonstrated that the magnitude of differential expression does not necessarily indicate biological significance. Even a very small change in the expression of particular gene may have dramatic physiological consequences if the protein encoded by this gene plays a catalytic role in a specific cell function. Moreover, highly correlated genes may function together on the same pathway biologically. Finally, in sparse logistic regression with <it>L</it>_{<it>p </it>}(<it>p </it>< 1) penalty, the degree of the sparsity obtained is determined by the value of the regularization parameter. Usually this parameter must be carefully tuned through cross-validation, which is time consuming.</p> <p>Results</p> <p>In this paper, we proposed a simple Bayesian approach to integrate the regularization parameter out analytically using a new prior. Therefore, there is no longer a need for parameter selection, as it is eliminated entirely from the model. The proposed algorithm (BLpLog) is typically two or three orders of magnitude faster than the original algorithm and free from bias in performance estimation. We also define a novel similarity measure and develop an integrated algorithm to hunt the regulatory genes with low expression changes but having high correlation with the selected genes. Pathways of those correlated genes were identified with DAVID <url>http://david.abcc.ncifcrf.gov/</url>.</p> <p>Conclusion</p> <p>Experimental results with gene expression data demonstrate that the proposed methods can be utilized to identify important genes and pathways that are related to cancer and build a parsimonious model for future patient predictions.</p

Kernel based methods for accelerated failure time model with ultra-high dimensional data

<p>Abstract</p> <p>Background</p> <p>Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with <it>L</it>₁and <it>L_p</it>penalty have been extensively studied in survival analysis with high-dimensional genomic data. However, when the sample size <it>n </it>≪ <it>m </it>(the number of genes), directly identifying a small subset of genes from ultra-high (<it>m </it>> 10, 000) dimensional data is time-consuming and not computationally efficient. In current microarray analysis, what people really do is select a couple of thousands (or hundreds) of genes using univariate analysis or statistical tests, and then apply the LASSO-type penalty to further reduce the number of disease associated genes. This two-step procedure may introduce bias and inaccuracy and lead us to miss biologically important genes.</p> <p>Results</p> <p>The accelerated failure time (AFT) model is a linear regression model and a useful alternative to the Cox model for survival analysis. In this paper, we propose a nonlinear kernel based AFT model and an efficient variable selection method with adaptive kernel ridge regression. Our proposed variable selection method is based on the kernel matrix and dual problem with a much smaller <it>n </it>× <it>n </it>matrix. It is very efficient when the number of unknown variables (genes) is much larger than the number of samples. Moreover, the primal variables are explicitly updated and the sparsity in the solution is exploited.</p> <p>Conclusions</p> <p>Our proposed methods can simultaneously identify survival associated prognostic factors and predict survival outcomes with ultra-high dimensional genomic data. We have demonstrated the performance of our methods with both simulation and real data. The proposed method performs superbly with limited computational studies.</p

Post-transcriptional regulation of androgen receptor mRNA by an ErbB3 binding protein 1 in prostate cancer

Androgen receptor (AR)-mediated pathways play a critical role in the development and progression of prostate cancer. However, little is known about the regulation of AR mRNA stability and translation, two central processes that control AR expression. The ErbB3 binding protein 1 (EBP1), an AR corepressor, negatively regulates crosstalk between ErbB3 ligand heregulin (HRG)-triggered signaling and the AR axis, affecting biological properties of prostate cancer cells. EBP1 protein expression is also decreased in clinical prostate cancer. We previously demonstrated that EBP1 overexpression results in decreased AR protein levels by affecting AR promoter activity. However, EBP1 has recently been demonstrated to be an RNA binding protein. We therefore examined the ability of EBP1 to regulate AR post-transcriptionally. Here we show that EBP1 promoted AR mRNA decay through physical interaction with a conserved UC-rich motif within the 3′-UTR of AR. The ability of EBP1 to accelerate AR mRNA decay was further enhanced by HRG treatment. EBP1 also bound to a CAG-formed stem-loop in the 5′ coding region of AR mRNA and was able to inhibit AR translation. Thus, decreases of EBP1 in prostate cancer could be important for the post-transcriptional up-regulation of AR contributing to aberrant AR expression and disease progression