Downregulation of miR-1266-5P, miR-185-5P and miR-30c-2 in prostatic cancer tissue and cell lines

Over the latest decade, the role of microRNAs (miRNAs/miRs) has received more attention. miRNAs are small non-coding RNAs that may serve a role as oncogenes or tumor suppressor genes. Certain miRNAs regulate the apoptosis pathway by influencing pro- or anti-apoptotic genes. We hypothesized that increases in the expression of B cell lymphoma 2 (BCL2) and BCL2-like 1 (BCL2L1) genes, which have been reported in various types of cancer tissues, may be due to the downregulation of certain miRNAs. The present study aimed to identify miRNAs that target BCL2 and BCL2L1 anti-apoptotic genes in prostate cancer (PCa) clinical tissue samples. Certain candidate miRNAs were selected bioinfor-matically and their expression in PCa samples was analyzed and compared with that in benign prostatic hyperplasia (BPH) tissue samples. The candidate miRNAs that targeted BCL2 and BCL2L1 genes were searched in online databases (miRWalk, microRNA.org, miRDB and TargetScan). A total of 12 miRNAs that target the 3'-untranslated region of the aforementioned genes and/or for which downregulation of their expression has previously been reported in cancer tissues. A total of 30 tumor tissue samples from patients with PCa and 30 samples tissues from patients with BPH were obtained and were subjected to reverse transcription-quantitative polymerase chain reaction for expression analysis of 12 candidate miRNAs, and the BCL2 and BCL2L1 genes. Additionally, expression of 3 finally selected miRNAs and genes was evaluated in prostate cancer PC3 and DU145 cell lines and human umbilical vein endothelial cells. Among 12 miRNA candidates, the expression of miR-1266, miR-185 and miR-30c-2 was markedly downregulated in PCa tumor tissues and cell lines. Furthermore, downregulation of these miRNAs was associated with upregulation of the BCL2 and BCL2L1 genes. An inverse association between three miRNAs (miR-1266, miR-185 and miR-30c-2) and two anti-apoptotic genes (BCL2 and BCL2L1) may be considered for interventional miRNA therapy of PCa. © 2018, Spandidos Publications. All rights reserved

Downregulation of miR-1266-5P, miR-185-5P and miR-30c-2 in prostatic cancer tissue and cell lines

Over the latest decade, the role of microRNAs (miRNAs/miRs) has received more attention. miRNAs are small non-coding RNAs that may serve a role as oncogenes or tumor suppressor genes. Certain miRNAs regulate the apoptosis pathway by influencing pro- or anti-apoptotic genes. We hypothesized that increases in the expression of B cell lymphoma 2 (BCL2) and BCL2-like 1 (BCL2L1) genes, which have been reported in various types of cancer tissues, may be due to the downregulation of certain miRNAs. The present study aimed to identify miRNAs that target BCL2 and BCL2L1 anti-apoptotic genes in prostate cancer (PCa) clinical tissue samples. Certain candidate miRNAs were selected bioinfor-matically and their expression in PCa samples was analyzed and compared with that in benign prostatic hyperplasia (BPH) tissue samples. The candidate miRNAs that targeted BCL2 and BCL2L1 genes were searched in online databases (miRWalk, microRNA.org, miRDB and TargetScan). A total of 12 miRNAs that target the 3'-untranslated region of the aforementioned genes and/or for which downregulation of their expression has previously been reported in cancer tissues. A total of 30 tumor tissue samples from patients with PCa and 30 samples tissues from patients with BPH were obtained and were subjected to reverse transcription-quantitative polymerase chain reaction for expression analysis of 12 candidate miRNAs, and the BCL2 and BCL2L1 genes. Additionally, expression of 3 finally selected miRNAs and genes was evaluated in prostate cancer PC3 and DU145 cell lines and human umbilical vein endothelial cells. Among 12 miRNA candidates, the expression of miR-1266, miR-185 and miR-30c-2 was markedly downregulated in PCa tumor tissues and cell lines. Furthermore, downregulation of these miRNAs was associated with upregulation of the BCL2 and BCL2L1 genes. An inverse association between three miRNAs (miR-1266, miR-185 and miR-30c-2) and two anti-apoptotic genes (BCL2 and BCL2L1) may be considered for interventional miRNA therapy of PCa. © 2018, Spandidos Publications. All rights reserved

Probabilistic reconstruction of the tumor progression process in gene regulatory networks in the presence of uncertainty

<p>Abstract</p> <p>Background</p> <p>Accumulation of gene mutations in cells is known to be responsible for tumor progression, driving it from benign states to malignant states. However, previous studies have shown that the detailed sequence of gene mutations, or the steps in tumor progression, may vary from tumor to tumor, making it difficult to infer the exact path that a given type of tumor may have taken.</p> <p>Results</p> <p>In this paper, we propose an effective probabilistic algorithm for reconstructing the tumor progression process based on partial knowledge of the underlying gene regulatory network and the steady state distribution of the gene expression values in a given tumor. We take the BNp (Boolean networks with pertubation) framework to model the gene regulatory networks. We assume that the true network is not exactly known but we are given an uncertainty class of networks that contains the true network. This network uncertainty class arises from our partial knowledge of the true network, typically represented as a set of local pathways that are embedded in the global network. Given the SSD of the cancerous network, we aim to simultaneously identify the true normal (healthy) network and the set of gene mutations that drove the network into the cancerous state. This is achieved by analyzing the effect of gene mutation on the SSD of a gene regulatory network. At each step, the proposed algorithm reduces the uncertainty class by keeping only those networks whose SSDs get close enough to the cancerous SSD as a result of additional gene mutation. These steps are repeated until we can find the best candidate for the true network and the most probable path of tumor progression.</p> <p>Conclusions</p> <p>Simulation results based on both synthetic networks and networks constructed from actual pathway knowledge show that the proposed algorithm can identify the normal network and the actual path of tumor progression with high probability. The algorithm is also robust to model mismatch and allows us to control the trade-off between efficiency and accuracy.</p

Predicting the valence of a scene from observers’ eye movements

Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to determine the emotional category of images using eye movements, the existing methods often learn a classifier using several features that are extracted from eye movements. Although it has been shown that eye movement is potentially useful for recognition of scene valence, the contribution of each feature is not well-studied. To address the issue, we study the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories. We assess ten features and their fusion. The features are histogram of saccade orientation, histogram of saccade slope, histogram of saccade length, histogram of saccade duration, histogram of saccade velocity, histogram of fixation duration, fixation histogram, top-ten salient coordinates, and saliency map. We utilize machine learning approach to analyze the performance of features by learning a support vector machine and exploiting various feature fusion schemes. The experiments reveal that ‘saliency map’, ‘fixation histogram’, ‘histogram of fixation duration’, and ‘histogram of saccade slope’ are the most contributing features. The selected features signify the influence of fixation information and angular behavior of eye movements in the recognition of the valence of images

Inferring Gene Expression From Ribosomal Promoter Sequences, a Crowdsourcing Approach

The Gene Promoter Expression Prediction challenge consisted of predicting gene expression from promoter sequences in a previously unknown experimentally generated data set. The challenge was presented to the community in the framework of the sixth Dialogue for Reverse Engineering Assessments and Methods (DREAM6), a community effort to evaluate the status of systems biology modeling methodologies. Nucleotide-specific promoter activity was obtained by measuring fluorescence from promoter sequences fused upstream of a gene for yellow fluorescence protein and inserted in the same genomic site of yeast Saccharomyces cerevisiae. Twenty-one teams submitted results predicting the expression levels of 53 different promoters from yeast ribosomal protein genes. Analysis of participant predictions shows that accurate values for low-expressed and mutated promoters were difficult to obtain, although in the latter case, only when the mutation induced a large change in promoter activity compared to the wild-type sequence. As in previous DREAM challenges, we found that aggregation of participant predictions provided robust results, but did not fare better than the three best algorithms. Finally, this study not only provides a benchmark for the assessment of methods predicting activity of a specific set of promoters from their sequence, but it also shows that the top performing algorithm, which used machine-learning approaches, can be improved by the addition of biological features such as transcription factor binding sites