Nilubon Kurubanjerdjit

Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks

BackgroundMolecular networks are the basis of biological processes. Such networks can be decomposed into smaller modules, also known as network motifs. These motifs show interesting dynamical behaviors, in which co-operativity effects between the motif components play a critical role in human diseases. We have developed a motif-searching algorithm, which is able to identify common motif types from the cancer networks and signal transduction networks (STNs). Some of the network motifs are interconnected which can be merged together and form more complex structures, the so-called coupled motif structures (CMS). These structures exhibit mixed dynamical behavior, which may lead biological organisms to perform specific functions.ResultsIn this study, we integrate transcription factors (TFs), microRNAs (miRNAs), miRNA targets and network motifs information to build the cancer-related TF-miRNA-motif networks (TMMN). This allows us to examine the role of network motifs in cancer formation at different levels of regulation, i.e. transcription initiation (TF → miRNA), gene-gene interaction (CMS), and post-transcriptional regulation (miRNA → target genes). Among the cancer networks and STNs we considered, it is found that there is a substantial amount of crosstalking through motif interconnections, in particular, the crosstalk between prostate cancer network and PI3K-Akt STN.ConclusionsTo validate the role of network motifs in cancer formation, several examples are presented which demonstrated the effectiveness of the present approach. A web-based platform has been set up which can be accessed at: http://ppi.bioinfo.asia.edu.tw/pathway/. It is very likely that our results can supply very specific CMS missing information for certain cancer types, it is an indispensable tool for cancer biology research.

Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms

MicroRNAs are small, endogenous RNAs found in many different species and are known to have an influence on diverse biological phenomena. They also play crucial roles in plant biological processes, such as metabolism, leaf sidedness and flower development. However, the functional roles of most microRNAs are still unknown. The identification of closely related microRNAs and target genes can be an essential first step towards the discovery of their combinatorial effects on different cellular states. A lot of research has tried to discover microRNAs and target gene interactions by implementing machine learning classifiers with target prediction algorithms. However, high rates of false positives have been reported as a result of undetermined factors which will affect recognition. Therefore, integrating diverse techniques could improve the prediction. In this paper we propose identifying microRNAs target of Arabidopsis thaliana by integrating prediction scores from PITA, miRanda and RNAHybrid algorithms used as a feature vector of microRNA-target interactions, and then implementing SVM, random forest tree and neural network machine learning algorithms to make final predictions by majority voting. Furthermore, microRNA target genes are linked with their protein-protein interaction (PPI) partners. We focus on plant resistance genes and transcription factor information to provide new insights into plant pathogen interaction networks. Downstream pathways are characterized by the Jaccard coefficient, which is implemented based on Gene Ontology. The database is freely accessible at http://ppi.bioinfo.asia.edu.tw/At_miRNA/.

DNA methylation-regulated microRNA pathways in ovarian serous cystadenocarcinoma

Epigenetic regulation has been linked to the initiation and progression of cancer. Aberrant expression of microRNAs (miRNAs) is one such mechanism that can activate or silence oncogenes (OCGs) and tumor suppressor genes (TSGs) in cells. A growing number of studies suggest that miRNA expression can be regulated by methylation modification, thus triggering cancer development. However, there is no comprehensive in silico study concerning miRNA regulation by direct DNA methylation in cancer. Ovarian serous cystadenocarcinoma (OSC) was therefore chosen as a tumor model for the present work. Twelve batches of OSC data, with at least 35 patient samples in each batch, were obtained from The Cancer Genome Atlas (TCGA) database. The Spearman rank correlation coefficient (SRCC) was used to quantify the correlation between the CpG DNA methylation level and miRNA expression level. Meta-analysis was performed to reduce the effects of biological heterogeneity among different batches. MiRNA-target interactions were also inferred by computing SRCC and meta-analysis to assess the correlation between miRNA expression and cancer-associated gene expression and the interactions were further validated by a query against the miRTarBase database. A total of 26 potential epigenetic-regulated miRNA genes that can target OCGs or TSGs in OSC were found to show biological relevance between DNA methylation and miRNA gene expression. Furthermore, some of the identified DNA-methylated miRNA genes; for instance, the miR-200 family, were previously identified as epigenetic-regulated miRNAs and correlated with poor survival of ovarian cancer. We also found that several miRNA target genes, BTG3, NDN, HTRA3, CDC25A, and HMGA2 were also related to the poor outcomes in ovarian cancer. The present study proposed a systematic strategy to construct highly confident epigenetic-regulated miRNA pathways for OSC. The findings are validated and are in line with the literature. The inclusion of direct DNA methylated miRNA events may offer another layer of explanation that along with genetics can give a better understanding of the carcinogenesis process.

FARE-CAFE: a database of functional and regulatory elements of cancer-associated fusion events

Chromosomal translocation (CT) is of enormous clinical interest because this disorder is associated with various major solid tumors and leukemia. A tumor-specific fusion gene event may occur when a translocation joins two separate genes. Currently, various CT databases provide information about fusion genes and their genomic elements. However, no database of the roles of fusion genes, in terms of essential functional and regulatory elements in oncogenesis, is available. FARE-CAFE is a unique combination of CTs, fusion proteins, protein domains, domain–domain interactions, protein–protein interactions, transcription factors and microRNAs, with subsequent experimental information, which cannot be found in any other CT database. Genomic DNA information including, for example, manually collected exact locations of the first and second break points, sequences and karyotypes of fusion genes are included. FARE-CAFE will substantially facilitate the cancer biologist’s mission of elucidating the pathogenesis of various types of cancer. This database will ultimately help to develop ‘novel’ therapeutic approaches. Database URL: http://ppi.bioinfo.asia.edu.tw/FARE-CAFE

Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells

Background Abnormal proliferation of vascular smooth muscle cells (VSMC) is a major cause of cardiovascular diseases (CVDs). Many studies suggest that vascular injury triggers VSMC dedifferentiation, which results in VSMC changes from a contractile to a synthetic phenotype; however, the underlying molecular mechanisms are still unclear. Methods In this study, we examined how VSMC responds under mechanical stress by using time-course microarray data. A three-phase study was proposed to investigate the stress-induced differentially expressed genes (DEGs) in VSMC. First, DEGs were identified by using the moderated t-statistics test. Second, more DEGs were inferred by using the Gaussian Graphical Model (GGM). Finally, the topological parameters-based method and cluster analysis approach were employed to predict the last batch of DEGs. To identify the potential drugs for vascular diseases involve VSMC proliferation, the drug-gene interaction database, Connectivity Map (cMap) was employed. Success of the predictions were determined using in-vitro data, i.e. MTT and clonogenic assay. Results Based on the differential expression calculation, at least 23 DEGs were found, and the findings were qualified by previous studies on VSMC. The results of gene set enrichment analysis indicated that the most often found enriched biological processes are cell-cycle-related processes. Furthermore, more stress-induced genes, well supported by literature, were found by applying graph theory to the gene association network (GAN). Finally, we showed that by processing the cMap input queries with a cluster algorithm, we achieved a substantial increase in the number of potential drugs with experimental IC50 measurements. With this novel approach, we have not only successfully identified the DEGs, but also improved the DEGs prediction by performing the topological and cluster analysis. Moreover, the findings are remarkably validated and in line with the literature. Furthermore, the cMap and DrugBank resources were used to identify potential drugs and targeted genes for vascular diseases involve VSMC proliferation. Our findings are supported by in-vitro experimental IC50, binding activity data and clinical trials. Conclusion This study provides a systematic strategy to discover potential drugs and target genes, by which we hope to shed light on the treatments of VSMC proliferation associated diseases.

MicroRNA-Regulated Network Motifs with Drug Association in Lung Cancer

The value of microRNAs as therapeutic targets is now widely recognized. The regulation of microRNAs has an important role in cancer progression, and increasing importance of microRNA is the use of microRNA signatures in the diagnosis, prognosis and also drug treatment of many kinds of cancer such as lung cancer, breast cancer, and colon cancer. This study aim to understand the role of microRNA associated in cancer therapies and drug discovery, we identified microRNAs and their down-stream protein-protein interaction motifs for lung cancer based on a network topology analysis approach which is Clique Percolation Clustering Method (CPM). Then, target drugs of each significant motif were investigated by drug-gene interaction databases. It is expected that this study may insight explore the role of microRNA in cancer therapy and associated with drug response.

The surgical patient mortality rate prediction by machine learning algorithms

Malnutrition is a common problem in critical illness patients which is observed in patients who is undergoing for surgery and hospital mortality rate. The study found that patients undergone surgery who have malnutrition problem result in high death risk. In this research, we aim to predict the mortality rate of undergone surgery patient by using Chiang Rai Nutrition Assessment information (CNA) with various data mining models; J48, ADTree and KNN. Results from this study will help doctor to plan for patient health preparation before undergo surgery such as consumption behavior of patient. Besides, the approach developed in this study should be of value for future studies into understanding the effect of malnutrition in patient surgery result.