A Standardized Wedelia chinensis Extract Overcomes the Feedback Activation of HER2/3 Signaling upon Androgen-Ablation in Prostate Cancer

Crosstalk between the androgen receptor (AR) and other signaling pathways in prostate cancer (PCa) severely affects the therapeutic outcome of hormonal therapy. Although anti-androgen therapy prolongs overall survival in PCa patients, resistance rapidly develops and is often associated with increased AR expression and upregulation of the HER2/3-AKT signaling pathway. However, single agent therapy targeting AR, HER2/3 or AKT usually fails due to the reciprocal feedback loop. Previously, we reported that wedelolactone, apigenin, and luteolin are the active compounds in Wedelia chinensis herbal extract, and act synergistically to inhibit the AR activity in PCa. Here, we further demonstrated that an herbal extract of W. chinensis (WCE) effectively disrupted the AR, HER2/3, and AKT signaling networks and therefore enhanced the therapeutic efficacy of androgen ablation in PCa. Furthermore, WCE remained effective in suppressing AR and HER2/3 signaling in an in vivo adapted castration-resistant PCa (CRPC) LNCaP cell model that was insensitive to androgen withdrawal and second-line antiandrogen, enzalutamide. This study provides preclinical evidence that the use of a defined, single plant-derived extract can augment the therapeutic efficacy of castration with significantly prolonged progression-free survival. These data also establish a solid basis for using WCE as a candidate agent in clinical studies

EBV-positive Hodgkin lymphoma is associated with suppression of p21cip1/waf1 and a worse prognosis

<p>Abstract</p> <p>Background</p> <p>About 30-50% of Hodgkin lymphomas (HLs) harbor the Epstein-Barr virus (EBV), but the impact of EBV infection on clinical outcomes has been unclear. EBV-encoded small RNAs (<it>EBER</it>s) are presented in all EBV-infected cells, but their functions are still less understood.</p> <p>Results</p> <p><it>EBER1 </it>was transfected into two HL cell lines, KMH2 and L428, and microarrays were used to screen for <it>EBER1</it>-induced changes. We found that <it>EBER1 </it>suppressed <it>p21</it>^cip1/waf1transcription in HL cell lines. In addition, positive regulators of <it>p21</it>^cip1/waf1transcription, such as p53, EGR1, and STAT1, were decreased. Suppression of <it>p21</it>^cip1/waf1in the <it>EBER1</it>⁺HL cell lines was associated with increased resistance to histone deacetylase inhibitors or proteasome inhibitors, drugs known to cause apoptosis by increasing p21^cip1/waf1levels. On biopsy specimens, EBV⁺HLs had weaker expression of both p21^cip1/waf1and active caspase 3. Clinically, suppression of p21^cip1/waf1in EBV⁺HLs was associated with a worse 2-year disease-free survival rate (45% for EBV⁺HLs <it>vs</it>. 77% for EBV^-HLs, <it>p </it>= 0.002).</p> <p>Conclusion</p> <p>Although the underlying mechanisms are still relatively unclear, <it>EBER1 </it>inhibits <it>p21</it>^cip1/waf1transcription and prevents apoptosis through down-regulation of p53, EGR1, and STAT1. The anti-apoptotic activity of <it>EBER1 </it>may be important in the rescue of Reed-Sternberg cells from drug-induced apoptosis and in the clinical behaviors of EBV⁺HLs.</p

A model-based circular binary segmentation algorithm for the analysis of array CGH data

<p>Abstract</p> <p>Background</p> <p>Circular Binary Segmentation (CBS) is a permutation-based algorithm for array Comparative Genomic Hybridization (aCGH) data analysis. CBS accurately segments data by detecting change-points using a maximal-<it>t </it>test; but extensive computational burden is involved for evaluating the significance of change-points using permutations. A recent implementation utilizing a hybrid method and early stopping rules (hybrid CBS) to improve the performance in speed was subsequently proposed. However, a time analysis revealed that a major portion of computation time of the hybrid CBS was still spent on permutation. In addition, what the hybrid method provides is an approximation of the significance upper bound or lower bound, not an approximation of the significance of change-points itself.</p> <p>Results</p> <p>We developed a novel model-based algorithm, extreme-value based CBS (eCBS), which limits permutations and provides robust results without loss of accuracy. Thousands of aCGH data under null hypothesis were simulated in advance based on a variety of non-normal assumptions, and the corresponding maximal-<it>t </it>distribution was modeled by the Generalized Extreme Value (GEV) distribution. The modeling results, which associate characteristics of aCGH data to the GEV parameters, constitute lookup tables (eXtreme model). Using the eXtreme model, the significance of change-points could be evaluated in a constant time complexity through a table lookup process.</p> <p>Conclusions</p> <p>A novel algorithm, eCBS, was developed in this study. The current implementation of eCBS consistently outperforms the hybrid CBS 4× to 20× in computation time without loss of accuracy. Source codes, supplementary materials, supplementary figures, and supplementary tables can be found at <url>http://ntumaps.cgm.ntu.edu.tw/eCBSsupplementary</url>.</p

Transcriptome Changes in Relation to Manic Episode

Bipolar disorder (BD) is highly heritable and well known for its recurrent manic and depressive episodes. The present study focused on manic episode in BD patients and aimed to investigate state-specific transcriptome alterations between acute episode and remission, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and micro-RNAs (miRNAs), using microarray and RNA sequencing (RNA-Seq) platforms. BD patients were enrolled with clinical information, and peripheral blood samples collected at both acute and remission status spanning for at least 2 months were confirmed by follow-ups. Symptom severity was assessed by Young Mania Rating Scale. We enrolled six BD patients as the discovery samples and used the Affymetrix Human Transcriptome Array 2.0 to capture transcriptome data at the two time points. For replication, expression data from Gene Expression Omnibus that consisted of 11 BD patients were downloaded, and we performed a mega-analysis for microarray data of 17 patients. Moreover, we conducted RNA sequencing (RNA-Seq) in additional samples of 7 BD patients. To identify intraindividual differentially expressed genes (DEGs), we analyzed data using a linear model controlling for symptom severity. We found that noncoding genes were of majority among the top DEGs in microarray data. The expression fold change of coding genes among DEGs showed moderate to high correlations (∼0.5) across platforms. A number of lncRNAs and two miRNAs (MIR181B1 and MIR103A1) exhibited high levels of gene expression in the manic state. For coding genes, we reported that the taste function-related genes, including TAS2R5 and TAS2R3, may be mania state-specific markers. Additionally, four genes showed a nominal p-value of less than 0.05 in all our microarray data, mega-analysis, and RNA-Seq analysis. They were upregulated in the manic state and consisted of MS4A14, PYHIN1, UTRN, and DMXL2, and their gene expression patterns were further validated by quantitative real-time polymerase chain reaction (PCR) (qRT-PCR). We also performed weight gene coexpression network analysis to identify gene modules for manic episode. Genes in the mania-related modules were different from the susceptible loci of BD obtained from genome-wide association studies, and biological pathways in relation to these modules were mainly related to immune function, especially cytokine–cytokine receptor interaction. Results of the present study elucidated potential molecular targets and genomic networks that are involved in manic episode. Future studies are needed to further validate these biomarkers for their roles in the etiology of bipolar illness

Identification of Prognostic Genes for Recurrent Risk Prediction in Triple Negative Breast Cancer Patients in Taiwan

Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Hierarchical cluster analysis showed that triple negative breast cancers from different races were in separate sub-clusters but grouped in a bigger cluster. Two pathways, cAMP-mediated signaling and ephrin receptor signaling, were significantly associated with the recurrence of triple negative breast cancer. After using stepwise model selection from the combination of the initial filtered genes, we developed a prediction model based on the genes SLC22A23, PRKAG3, DPEP3, MORC2, GRB7, and FAM43A. The model had 91.7% accuracy, 81.8% sensitivity, and 94.6% specificity under leave-one-out support vector regression. In this study, we identified pathways related to triple negative breast cancer and developed a model to predict its recurrence. These results could be used for assisting with clinical prognosis and warrant further investigation into the possibility of targeted therapy of triple negative breast cancer in Taiwanese patients

Down-Regulation of NDRG1 Promotes Migration of Cancer Cells during Reoxygenation

One characteristic of tumor microenvironment is oxygen fluctuation, which results from hyper-proliferation and abnormal metabolism of tumor cells as well as disorganized neo-vasculature. Reoxygenation of tumors can induce oxidative stress, which leads to DNA damage and genomic instability. Although the cellular responses to hypoxia are well known, little is known about the dynamic response upon reoxygenation. In order to investigate the transcriptional responses of tumor adaptation to reoxygenation, breast cancer MCF-7 cells were cultured under 0.5% oxygen for 24 h followed by 24 h of reoxygenation in normoxia. Cells were harvested at 0, 1, 4, 8, 12, and 24 h during reoxygenation. The transcriptional profile of MCF-7 cells upon reoxygenation was examined using Illumina Human-6 v3 BeadChips. We identified 127 differentially expressed genes, of which 53.1% were up-regulated and 46.9% were down-regulated upon reoxygenation. Pathway analysis revealed that the HIF-1-alpha transcription factor network and validated targets of C-MYC transcriptional activation were significantly enriched in these differentially expressed genes. Among these genes, a subset of interest genes was further validated by quantitative reverse-transcription PCR. In particular, human N-MYC down-regulated gene 1 (NDRG1) was highly suppressed upon reoxygenation. NDRG1 is associated with a variety of stress and cell growth-regulatory conditions. To determine whether NDRG1 plays a role in reoxygenation, NDRG1 protein was overexpressed in MCF-7 cells. Upon reoxygenation, overexpression of NDRG1 significantly inhibited cell migration. Our results revealed the dynamic nature of gene expression in MCF-7 cells upon reoxygenation and demonstrated that NDRG1 is involved in tumor adaptation to reoxygenation

Integrated Analyses of Copy Number Variations and Gene Expression in Lung Adenocarcinoma

Numerous efforts have been made to elucidate the etiology and improve the treatment of lung cancer, but the overall five-year survival rate is still only 15%. Identification of prognostic biomarkers for lung cancer using gene expression microarrays poses a major challenge in that very few overlapping genes have been reported among different studies. To address this issue, we have performed concurrent genome-wide analyses of copy number variation and gene expression to identify genes reproducibly associated with tumorigenesis and survival in non-smoking female lung adenocarcinoma. The genomic landscape of frequent copy number variable regions (CNVRs) in at least 30% of samples was revealed, and their aberration patterns were highly similar to several studies reported previously. Further statistical analysis for genes located in the CNVRs identified 475 genes differentially expressed between tumor and normal tissues (p<10−5). We demonstrated the reproducibility of these genes in another lung cancer study (p = 0.0034, Fisher's exact test), and showed the concordance between copy number variations and gene expression changes by elevated Pearson correlation coefficients. Pathway analysis revealed two major dysregulated functions in lung tumorigenesis: survival regulation via AKT signaling and cytoskeleton reorganization. Further validation of these enriched pathways using three independent cohorts demonstrated effective prediction of survival. In conclusion, by integrating gene expression profiles and copy number variations, we identified genes/pathways that may serve as prognostic biomarkers for lung tumorigenesis