Tranilast (N-3,4-dimethoxycinnamoyl anthranilic acid) is an antiallergic agent with inhibitory effects on cell proliferation and extracellular matrix production. Here we assess the effect of tranilast on the expression of miR-29c and genes functionally involved in cell proliferation, fibrosis, and epigenetic regulation in isolated leiomyoma smooth muscle cells (LSMC). Tranilast significantly inhibited the rate of LSMC proliferation, which was associated with downregulation of cell cycle progression genes cyclin D1 (CCND1) and cyclin-dependent kinase 2 (CDK2) expression at messenger RNA and protein levels ( P < .05). Tranilast also suppressed the expression of collagen type I (COL1), collagen type III alpha 1 chain (COL3A1), the profibrotic cytokine, transforming growth factor β-3 (TGF-β3), DNA (cytosine-5)-methyltransferase 1 (DNMT1), and enhancer of zeste homolog 2 (EZH2), which regulate epigenetic status of gene promoters ( P < .05). Tranilast also significantly induced the expression of cellular and secreted miR-29c through downregulation of methylation status of miR-29c promoter ( P < .05). In addition, tranilast suppressed the activity of luciferase reporter containing 3'UTR of COL3A1 and CDK2, which are downstream targets of miR-29c ( P < .05). Knockdown of miR-29c expression attenuated the inhibitory effects of tranilast on COL3A1 and CDK2 protein expression ( P < .05). Collectively, these findings suggest that tranilast could have therapeutic potential as an inhibitory agent for leiomyoma growth and its associated symptoms.

Oral squamous cell carcinoma has been characterized as a complex disease which involves dynamic genomic changes at the molecular level. These changes indicate the worth to explore the interactions of the molecules and especially of differentially expressed genes that contribute to cancer progression. Moreover, based on this knowledge the identification of differentially expressed genes and related molecular pathways is of great importance. In the present study, we exploit differentially expressed genes in order to further perform pathway enrichment analysis. According to our results we found significant pathways in which the disease associated genes have been identified as strongly enriched. Furthermore, based on the results of the pathway enrichment analysis we propose a methodology for predicting oral cancer recurrence using dynamic Bayesian networks. The methodology takes into consideration time series gene expression data in order to predict a disease recurrence. Subsequently, we are able to conjecture about the causal interactions between genes in consecutive time intervals. Concerning the performance of the predictive models, the overall accuracy of the algorithm is 81.8% and the area under the ROC curve 89.2% regarding the knowledge from the overrepresented pre-NOTCH Expression and processing pathway.

Identification of combinatorial markers from multiple data sources is a challenging task in bioinformatics. Here, we propose a novel computational framework for identifying significant combinatorial markers ( s) using both gene expression and methylation data. The gene expression and methylation data are integrated into a single continuous data as well as a (post-discretized) boolean data based on their intrinsic (i.e., inverse) relationship. A novel combined score of methylation and expression data (viz., ) is introduced which is computed on the integrated continuous data for identifying initial non-redundant set of genes. Thereafter, (maximal) frequent closed homogeneous genesets are identified using a well-known biclustering algorithm applied on the integrated boolean data of the determined non-redundant set of genes. A novel sample-based weighted support ( ) is then proposed that is consecutively calculated on the integrated boolean data of the determined non-redundant set of genes in order to identify the non-redundant significant genesets. The top few resulting genesets are identified as potential s. Since our proposed method generates a smaller number of significant non-redundant genesets than those by other popular methods, the method is much faster than the others. Application of the proposed technique on an expression and a methylation data for Uterine tumor or Prostate Carcinoma produces a set of significant combination of markers. We expect that such a combination of markers will produce lower false positives than individual markers.

Gene regulatory networks are a global representation of complex interactions between molecules that dictate cellular behavior. Study of a regulatory network modulated by single or multiple modulators' expression levels, including microRNAs (miRNAs) and transcription factors (TFs), in different conditions can further reveal the modulators' roles in diseases such as cancers. Existing computational methods for identifying such modulated regulatory networks are typically carried out by comparing groups of samples dichotomized with respect to the modulator status, ignoring the fact that most biological features are intrinsically continuous variables. Here, we devised a sliding window-based regression scheme and proposed the Regression-based Inference of Modulation (RIM) algorithm to infer the dynamic gene regulation modulated by continuous-state modulators. We demonstrated the improvement in performance as well as computation efficiency achieved by RIM. Applying RIM to genome-wide expression profiles of 520 glioblastoma multiforme (GBM) tumors, we investigated miRNA- and TF-modulated gene regulatory networks and showed their association with dynamic cellular processes and brain-related functions in GBM. Overall, the proposed algorithm provides an efficient and robust scheme for comprehensively studying modulated gene regulatory networks.

Microarray data often contain missing values which significantly affect subsequent analysis. Existing LLSimpute-based imputation methods for dealing with missing data have been shown to be generally efficient. However, all of the LLSimpute-based methods do not consider the different importance of different neighbors of the target gene in the missing value estimation process and treat all the neighbors equally. In this paper, a locally auto-weighted least squares imputation (LAW-LSimpute) method is proposed for missing value estimation, which can automatically weight the neighboring genes based on the importance of the genes. Then, an accelerating strategy is added to the LAW-LSimpute method in order to improve the convergence. Furthermore, an iterative missing value estimation framework of LAW-LSimpute (ILAW-LSimpute) is designed. Experimental results show that the ILAW-LSimpute method is able to reduce the estimation error.

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .

One major goal of large-scale cancer omics study is to understand molecular mechanisms of cancer and find new biomedical targets. To deal with the high-dimensional multidimensional cancer omics data (DNA methylation, mRNA expression, etc.), which can be used to discover new insight on identifying cancer subtypes, clustering methods are usually used to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrate these data and map them into an effective low-dimensional subspace. In this paper, we develop a dimension-reduction and data-integration method for indentifying cancer subtypes, named Scluster. First, Scluster, respectively, projects the different original data into the principal subspaces by an adaptive sparse reduced-rank regression method. Then, a fused patient-by-patient network is obtained for these subgroups through a scaled exponential similarity kernel method. Finally, candidate cancer subtypes are identified using spectral clustering method. We demonstrate the efficiency of our Scluster method using three cancers by jointly analyzing mRNA expression, miRNA expression, and DNA methylation data. The evaluation results and analyses show that Scluster is effective for predicting survival and identifies novel cancer subtypes of large-scale multi-omics data.

Genome-wide association study (GWAS) has been widely witnessed as a powerful tool for revealing suspicious loci from various diseases. However, real world GWAS tasks always suffer from the data imbalance problem of sufficient control samples and limited case samples. This imbalance issue can cause serious biases to the result and thus leads to losses of significance for true causal markers. To tackle this problem, we proposed a computational framework to perform association correction for imbalanced data (ACID) that could potentially improve the performance of GWAS under the imbalance condition. ACID is inspired by the imbalance learning theory but is particularly modified to address the task of association discovery from sequential genomic data. Simulation studies demonstrate ACID can dramatically improve the power of traditional GWAS method on the dataset with severe imbalances. We further applied ACID to two imbalanced datasets (gastric cancer and bladder cancer) to conduct genome wide association analysis. Experimental results indicate that our method has better abilities in identifying suspicious loci than the regression approach and shows consistencies with existing discoveries.

In this study, in order to take advantage of complementary information from different types of data for better disease status diagnosis, we combined gene expression with DNA methylation data and generated a fused network, based on which the stages of Kidney Renal Cell Carcinoma (KIRC) can be better identified. It is well recognized that a network is important for investigating the connectivity of disease groups. We exploited the potential of the network's features to identify the KIRC stage. We first constructed a patient network from each type of data. We then built a fused network based on network fusion method. Based on the link weights of patients, we used a generalized linear model to predict the group of KIRC subjects. Finally, the group prediction method was applied to test the power of network-based features. The performance (e.g., the accuracy of identifying cancer stages) when using the fused network from two types of data is shown to be superior to that when using two patient networks from only one data type. The work provides a good example for using network based features from multiple data types for a more comprehensive diagnosis.

High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. The purpose of feature selection is to not only identify a feature subset from an original set of features [without reducing the predictive accuracy of classification algorithm] but also reduce the computation overhead in data mining. In this paper, we present our improved shuffled frog leaping algorithm which introduces a chaos memory weight factor, an absolute balance group strategy, and an adaptive transfer factor. Our proposed approach explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. To evaluate the effectiveness of our proposed method, we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. Experimental results show that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.

One of the most significant research issues in functional genomics is insilico identification of disease related genes. In this regard, the paper presents a new gene selection algorithm, termed as SiFS, for identification of disease genes. It integrates the information obtained from interaction network of proteins and gene expression profiles. The proposed SiFS algorithm culls out a subset of genes from microarray data as disease genes by maximizing both significance and functional similarity of the selected gene subset. Based on the gene expression profiles, the significance of a gene with respect to another gene is computed using mutual information. On the other hand, a new measure of similarity is introduced to compute the functional similarity between two genes. Information derived from the protein-protein interaction network forms the basis of the proposed SiFS algorithm. The performance of the proposed gene selection algorithm and new similarity measure, is compared with that of other related methods and similarity measures, using several cancer microarray data sets.

The recent development of microarray gene expression techniques have made it possible to offer phenotype classification of many diseases. However, in gene expression data analysis, each sample is represented by quite a large number of genes, and many of them are redundant or insignificant to clarify the disease problem. Therefore, how to efficiently select the most useful genes has been becoming one of the most hot research topics in the gene expression data analysis. In this paper, a novel unsupervised two-stage coarse-fine gene selection method is proposed. In the first stage, we apply the kmeans algorithm to over-cluster the genes and discard some redundant genes. In the second stage, we select the most representative genes from the remaining ones based on matrix factorization. Finally the experimental results on several data sets are presented to show the effectiveness of our method.

Hajirasouliha and Raphael (WABI 2014) proposed a model for deconvoluting mixed tumor samples measured from a collection of high-throughput sequencing reads. This is related to understanding tumor evolution and critical cancer mutations. In short, their formulation asks to split each row of a binary matrix so that the resulting matrix corresponds to a perfect phylogeny and has the minimum number of rows among all matrices with this property. In this paper, we disprove several claims about this problem, including an NP-hardness proof of it. However, we show that the problem is indeed NP-hard, by providing a different proof. We also prove NP-completeness of a variant of this problem proposed in the same paper. On the positive side, we propose an efficient (though not necessarily optimal) heuristic algorithm based on coloring co-comparability graphs, and a polynomial time algorithm for solving the problem optimally on matrix instances in which no column is contained in both columns of a pair of conflicting columns. Implementations of these algorithms are freely available at https://github.com/alexandrutomescu/MixedPerfectPhylogeny.

The pathogenesis of cancer in human is still poorly understood. With the rapid development of high-throughput sequencing technologies, huge volumes of cancer genomics data have been generated. Deciphering that data poses great opportunities and challenges to computational biologists. One of such key challenges is to distinguish driver mutations, genes as well as pathways from passenger ones. Mutual exclusivity of gene mutations (each patient has no more than one mutation in the gene set) has been observed in various cancer types and thus has been used as an important property of a driver gene set or pathway. In this article, we aim to review the recent development of computational models and algorithms for discovering driver pathways or modules in cancer with the focus on mutual exclusivity-based ones.

An unmet need exists for patients with high-risk non-muscle-invasive bladder cancer for whom bacille Calmette-Guérin (BCG) has failed and who seek further bladder-sparing approaches. This shortcoming poses difficult management dilemmas. This review explores previously investigated first-line intravesical therapies and discusses emerging second-line treatments for the heterogeneous group of patients for whom BCG has failed. The myriad of recently published and ongoing trials assessing novel salvage intravesical treatments offer promise to patients who both seek an effective cure and want to avoid radical surgery. However, these trials must carefully be contextualized by specific patient, tumor, and recurrence characteristics. As data continue to accumulate, there will potentially be a role for these agents as second-line or even first-line intravesical therapies. Cancer 2017;123:390-400. © 2016 American Cancer Society.

Historically, metastatic melanoma has had extremely poor survival outcomes. The outlook, however, is rapidly changing as new molecularly targeted therapies have vastly improved patient outcomes. One such therapy is the potent mitogen-activated protein kinase kinase (MEK) 1/2 inhibitor cobimetinib. Recently, cobimetinib was approved for the treatment of metastatic or unresectable melanoma with serine/threonine-protein kinase B-raf (BRAF) V600E or V600K mutations when used in combination with the BRAF inhibitor vemurafenib. Currently, multiple clinical trials are investigating this drug combination for the treatment of various cancer types (e.g., breast, melanoma, colorectal). In the phase III coBRIM trial, this combination therapy showed improved melanoma response rates and patient progression-free survival when compared to vemurafenib alone. Additionally, toxicities were generally found to be manageable with dose modification or interruption. However, tumor response to BRAF/MEK inhibition, though rapid, is often short-lived as tumors develop resistance to this combination therapy. Therefore, new trials are beginning to investigate the addition of a third targeted agent or immunotherapy in order to increase the durability of treatment response. These trials are already showing promising preliminary results.

Epithelial ovarian cancer has the highest mortality rate of all gynaecological malignancies. Most women present with advanced disease and develop a recurrence after radical surgery and chemotherapy. Improving the results of first- or subsequent-line chemotherapy has been slow, and novel approaches to systemic treatment are needed. Ovarian cancer is a heterogeneous disease with complex molecular and genetic changes. Understanding these better will provide information on the mechanisms of resistance and opportunities to target therapy more rationally, exploiting specific changes in the tumour. Here we reviewed targeted approaches to therapy, focussing on targeting angiogenesis and inhibition of DNA repair, 2 areas that show promising activity. Additionally, we reviewed studies that are underway, targeting the cell cycle, signalling pathways and immunotherapeutic strategies. Many of these innovative approaches already demonstrate promising activity in ovarian cancer and have the potential to improve the outcome in women with ovarian cancer.

Economic analyses of upcoming treatments for lung cancer benefit from real-world health utility scores (HUSs) in an era of targeted therapy.

Fibrolamellar carcinoma is a unique type of hepatocellular carcinoma with a distinctive predilection for young patients without underlying liver disease, characteristic large neoplastic cells with intervening, dense fibrosis, co-expression of keratin 7 and CD68 and activation of protein kinase A (most often by formation of DNAJB1-PRKACA). Fibrolamellar carcinoma has a similar prognosis to conventional hepatocellular carcinomas arising in non-cirrhotic livers. The current American Joint Cancer Committee staging system does not provide optimal stratification of patients with fibrolamellar carcinoma and an alternate systems should be considered in the future. The only effective treatment for fibrolamellar carcinoma is complete resection. Novel therapies may be on the horizon as investigation into the molecular biology of fibrolamellar carcinoma continues.