Prioritizing cancer-related key miRNA-target interactions by integrative genomics

Microglia secrete miR-146a-5p-containing exosomes to regulate neurogenesis in depression

Enhancing neurogenesis within the hippocampal dentate gyrus (DG) is critical for maintaining brain development and function in many neurological diseases. However, the neural mechanisms underlying neurogenesis in depression remain unclear. Here, we show that microglia transfer a microglia-enriched microRNA, miR-146a-5p, via secreting exosomes to inhibit neurogenesis in depression. Overexpression of miR-146a-5p in hippocampal DG suppresses neurogenesis and spontaneous discharge of excitatory neurons by directly targeting Krüppel-like factor 4 (KLF4). Downregulation of miR-146a-5p expression ameliorates adult neurogenesis deficits in DG regions and depression-like behaviors in rats. Intriguingly, circular RNA ANKS1B acts as a miRNA sequester for miR-146a-5p to mediate post-transcriptional regulation of KLF4 expression. Collectively, these results indicate that miR-146a-5p can function as a critical factor regulating neurogenesis under conditions of pathological processes resulting from depression and suggest that microglial exosomes generate new crosstalk channels between glial cells and neurons.

Polymorphisms in xenobiotic metabolism-related genes in patients with hepatocellular carcinoma: a case-control study

This study was performed to investigate the relationship between polymorphisms in microsomal epoxide hydrolase (mEH; Tyr113His and His139Arg substitution) and glutathione S-transferase (GST; GSTM1 deletion, GSTT1 deletion, and GSTP1.Ala114Val substitution) and their correlation with clinico-histopathological features in hepatocellular carcinoma (HCC).We evaluated environmental risk factors and genetic alterations in 556 individuals (86 cases and 470 controls). PCR multiplex for GSTM1 and GSTT1, polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) for GSTP1, and real-time PCR for mEH were performed. Statistical analyses were performed using multiple logistic regression tests.Age over 48 years (p < 0.001) and alcohol consumption (p = 0.021) were the predictors of increased risk of developing HCC. GSTP1.Ala114Val for all regression models (p < 0.05), except the recessive model, and the GSTT1 null genotype (odds ratio [OR] = 0.43, 95% confidence interval [CI] = 0.21-0.87, p = 0.019) were predictors of an increased risk of developing HCC. Polymorphic GSTT1, GSTM1, GSTP1.Ala114Val, and mEH.His139Arg and wild-type mEH.Tyr113His (OR = 5.04; 95% CI = 1.59-16.04; p = 0.006) were associated with HCC.Age over 48 years, alcohol consumption, and the presence of polymorphic variants of GSTP1 and GSTT1 were associated with the risk of developing HCC.

Tanshinone IIA Ameliorates Inflammation Response in Osteoarthritis via Inhibition of miR-155/FOXO3 Axis

BACKGROUND

Osteoarthritis (OA) is the most common joint disorder characterized by degeneration of the articular cartilage and joint destruction with an associated risk of mobility disability in elderly people. Although a lot of achievements have been made, OA is still regarded as an incurable disease. Therefore, the pathological mechanisms and novel therapeutic strategies of OA need more investigation.

METHODS

MTT assay was conducted to measure the viability of chondrocytes after LPS treatment. Cell apoptosis was analyzed by annexin V/propidium iodide labeling. ELISA was used to determine the concentrations of interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α in the culture supernatant of chondrocytes. The expression level of miR-155, IL-1β, FOXO3, TNF-α, IL-6, caspase-3, and caspase-9 in chondrocytes was analyzed by RT-qPCR or Western blot.

RESULTS

We found that LPS led to inflammatory responses, cell apoptosis, and increased miR-155 expression in human articular chondrocytes. Tanshinone IIA could inhibit LPS-induced inflammation and cell apoptosis of chondrocytes via regulating the expression of miR-155 and FOXO3. miR-155 directly targeted the 3'-UTR of FOXO3 to regulate its expression.

CONCLUSIONS

Taken together, our data suggest tanshinone IIA ameliorates inflammation response in OA via inhibition of the miR-155/FOXO3 axis, and provide some evidences that tanshinone IIA could be designed and developed as a new promising clinical therapeutic drug for OA patients.

MiR-194 promotes hepatocellular carcinoma through negative regulation of CADM1

Aberrant expression of microRNAs may contribute to the initiation and progression of various types of human cancer and they may also constitute biomarkers for cancer diagnosis and treatment. However, the specific function of miR-194 in hepatocellular carcinoma (HCC), and the potential mechanism of its involvement in HCC were unclear. In the present study, we found that miR-194 inhibited CADM1 protein level expression by inhibiting mRNA translation of CADM1; the expression of CADM1 was low in liver cancer cells and tumor tissues, and the high expression of miR-194 was closely related to HCC. MiR-194 promoted proliferation, invasion, migration, and cell cycle progression of HCC cells, and such promotion effect was inhibited by CADM1. In addition, miR-194 may play a tumor-promoting action in a HCC xenograft tumor model. These results suggested that miR-194 may promote the occurrence and development of HCC by inhibiting CADM1. Therefore, miR-194 may be a promising novel therapy for diagnosis of hepatocellular carcinoma.

Identification of a 4-miRNA signature as a potential prognostic biomarker for pancreatic adenocarcinoma

An microRNA (miRNA) signature to predict the clinical outcome of pancreatic adenocarcinoma (PAAD) is still lacking. In the current study, we aimed at identifying and evaluating a prognostic miRNA signature for patients with PAAD. The miRNA expression profile and the clinical information regarding patients with PAAD were recruited from The Cancer Genome Atlas database. Differentially expressed miRNAs were identified between normal and tumor samples. By means of survival analysis, a 4-miRNA signature for predicting patients' with PAAD overall survival (OS) was constructed. Receiver operating characteristic (ROC) analysis was applied to determine the efficiency of survival prediction. Furthermore, the biological function of the predicted miRNAs was evaluated using a bioinformatics approach. Four (hsa-mir-126, hsa-mir-3613, hsa-mir-424, and hsa-mir-4772) out of 17 differentially expressed miRNAs were associated to the OS of patients with PAAD. Moreover, the area under the curve (AUC) of the constructed 4-miRNA signature associated to patients' with PAAD 2-year survival was 0.789. The multivariate Cox's proportional hazards regression model suggested that this 4-miRNA signature was an independent prognostic factor of other clinical parameters in patients with PAAD. Further pathway enrichment analyses revealed that the miRNAs in the 4-miRNA signature might regulate genes that affect focal adhesion, Wnt signaling pathway, and PI3K-Akt signaling pathway. Thus, these findings indicated that the 4-miRNA signature might be an effective independent prognostic biomarker in the prediction of PAAD patients' survival.

MicroRNAs and complex diseases: from experimental results to computational models

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.

Systematic Identification of Non-coding RNAs

Non-coding RNAs (ncRNAs) are biologically significant in variable ways. They modulate gene expression at the levels of transcription and post-transcription. MiRNAs and lncRNAs are two major classes of non-coding RNAs and have been extensively characterized. They are implicated in various biological processes and diseases. Thus, identification of miRNAs and lncRNAs are fundamental to further understand their roles and dissect their mechanisms. Here, we overviewed pipelines of identifying miRNAs and lncRNAs based on next-generation sequencing technologies. We applied the pipelines to identify miRNAs in multiple cell lines and perform expression quantification of mature, precursor and primary miRNAs. In addition, we provided an alternative way to re-annotate lncRNAs from microarray data. We summarized multiple resources and databases for lncRNA annotation and compared their annotation processes and specific parameters. Finally, we utilized RNA-seq and miRNA-seq data to construct a comprehensive transcriptome containing miRNAs, lncRNAs and protein-coding genes in heart failure.

Optimizing prognosis-related key miRNA-target interactions responsible for cancer metastasis

Increasing evidence suggests that the abnormality of microRNAs (miRNAs) and their downstream targets is frequently implicated in the pathogenesis of human cancers, however, the clinical benefit of causal miRNA-target interactions has been seldom studied. Here, we proposed a computational method to optimize prognosis-related key miRNA-target interactions by combining transcriptome and clinical data from thousands of TCGA tumors across 16 cancer types. We obtained a total of 1,956 prognosis-related key miRNA-target interactions between 112 miRNAs and 1,443 their targets. Interestingly, these key target genes are specifically involved in tumor progression-related functions, such as ‘cell adhesion’ and ‘cell migration’. Furthermore, they are most significantly correlated with ‘tissue invasion and metastasis’, a hallmark of metastasis, in ten distinct types of cancer through the hallmark analysis. These results implicated that the prognosis-related key miRNA-target interactions were highly associated with cancer metastasis. Finally, we observed that the combination of these key miRNA-target interactions allowed to distinguish patients with good prognosis from those with poor prognosis both in most TCGA cancer types and independent validation sets, highlighting their roles in cancer metastasis. We provided a user-friendly database named miRNATarget (freely available at http://biocc.hrbmu.edu.cn/miRNATar/), which provides an overview of the prognosis-related key miRNA-target interactions across 16 cancer types.

Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm

Identification of the associations between microRNA molecules and human diseases from large-scale heterogeneous biological data is an important step for understanding the pathogenesis of diseases in microRNA level. However, experimental verification of microRNA-disease associations is expensive and time-consuming. To overcome the drawbacks of conventional experimental methods, we presented a combinatorial prioritization algorithm to predict the microRNA-disease associations. Importantly, our method can be used to predict microRNAs (diseases) associated with the diseases (microRNAs) without the known associated microRNAs (diseases). The predictive performance of our proposed approach was evaluated and verified by the internal cross-validations and external independent validations based on standard association datasets. The results demonstrate that our proposed method achieves the impressive performance for predicting the microRNA-disease association with the Area Under receiver operation characteristic Curve (AUC), 86.93%, which is indeed outperform the previous prediction methods. Particularly, we observed that the ensemble-based method by integrating the predictions of multiple algorithms can give more reliable and robust prediction than the single algorithm, with the AUC score improved to 92.26%. We applied our combinatorial prioritization algorithm to lung neoplasms and breast neoplasms, and revealed their top 30 microRNA candidates, which are in consistent with the published literatures and databases.

Functional dissection of virus-human crosstalk mediated by miRNAs based on the VmiReg database

Recently, a number of viruses have been shown to encode microRNAs (miRNAs), and they play important roles in several biological processes, enhancing the intricacies of the virus-host crosstalk. However, systematically deciphering the characteristics of crosstalk mediated by viral and human miRNAs has been hampered by the lack of high-confidence targets. Here, a user-friendly platform is developed to provide experimentally validated and predicted target genes of viral miRNAs as well as their functions, named VmiReg. To explore the virus-human crosstalk meditated by miRNAs, validated human cellular targets of viral and cellular miRNAs are analyzed. As a result, target genes of viral miRNAs are prone to be silenced by human miRNAs. Two kinds of targets have globally significantly high functional similarities and are more often found simultaneously in many important biological functions, even in disease genes, particularly cancer genes, and essential genes. In addition, viral and human miRNA targets are in close proximity within the protein-protein interaction network, indicating frequent communication via physical interactions to participate in the same functions. Finally, multiple dense modules intuitively exhibit crosstalk between viral and cellular miRNAs. Furthermore, most co-regulated genes tend to be in important locations of modules. The lymphoma-related module is one of the typical examples. Our study suggests that the functional importance of cellular genes targeted by viral miRNAs and the intricate virus-host crosstalk mediated by miRNAs may be performed via the sharing of target genes or physical interactions, providing a new direction in further researching the roles of miRNAs in infection.

The Role of MicroRNAs in Kidney Disease

MicroRNAs (miRNAs) are short noncoding RNAs that regulate pathophysiological processes that suppress gene expression by binding to messenger RNAs. These biomolecules can be used to study gene regulation and protein expression, which will allow better understanding of many biological processes such as cell cycle progression and apoptosis that control the fate of cells. Several pathways have also been implicated to be involved in kidney diseases such as Transforming Growth Factor-β, Mitogen-Activated Protein Kinase signaling, and Wnt signaling pathways. The discovery of miRNAs has provided new insights into kidney pathologies and may provide new innovative and effective therapeutic strategies. Research has demonstrated the role of miRNAs in a variety of kidney diseases including renal cell carcinoma, diabetic nephropathy, nephritic syndrome, renal fibrosis, lupus nephritis and acute pyelonephritis. MiRNAs are implicated as playing a role in these diseases due to their role in apoptosis, cell proliferation, differentiation and development. As miRNAs have been detected in a stable condition in different biological fluids, they have the potential to be tools to study the pathogenesis of human diseases with a great potential to be used in disease prognosis and diagnosis. The purpose of this review is to examine the role of miRNA in kidney disease.

Genome-wide DNA methylome reveals the dysfunction of intronic microRNAs in major psychosis

BACKGROUND

DNA methylation is thought to be extensively involved in the pathogenesis of many diseases, including major psychosis. However, most studies focus on DNA methylation alteration at promoters of protein-coding genes, despite the poor correlation between DNA methylation and gene expression.

METHODS

We analyzed differentially methylated regions and differentially expressed genes in patients with schizophrenia and bipolar disorder and normal subjects. Gene expression and DNA methylation were analyzed with RNA-seq and MeDIP-seq of post-mortem brain tissue (brain region BA9) cohort in five schizophrenia, seven bipolar disorder cases and six controls, respectively.

RESULTS

Here, we performed a large-scale integrative analysis using MeDIP-seq, coupled with RNA-seq, on brain samples from major psychotic and normal subjects and observed obvious discrepancy between DNA methylation and gene expression. We found that differentially methylated regions (DMRs) were distributed across different types of genomic elements, especially introns. These intronic DMRs were significantly enriched for diverse regulatory elements, such as enhancers and binding sites of certain transcriptional factors (e.g., Pol3). Notably, we found that parts of intronic DMRs overlapped with some intragenic miRNAs, such as hsa-mir-7-3. These intronic DMR-related miRNAs were found to target many differentially expressed genes. Moreover, functional analysis demonstrated that differential target genes of intronic DMR-related miRNAs were sufficient to capture many important biological processes in major psychosis, such as neurogenesis, suggesting that miRNAs may function as important linkers mediating the relationships between DNA methylation alteration and gene expression changes.

CONCLUSIONS

Collectively, our study indicated that DNA methylation alteration could induce expression changes indirectly by affecting miRNAs and the exploration of DMR-related miRNAs and their targets enhanced understanding of the molecular mechanisms underlying major psychosis.

Systematically Prioritizing Functional Differentially Methylated Regions (fDMRs) by Integrating Multi-omics Data in Colorectal Cancer

While genome-wide differential DNA methylation regions (DMRs) have been extensively identified, the comprehensive prioritization of their functional importance is still poorly explored. Here, we aggregated multiple data resources rooted in the genome, epigenome and transcriptome to systematically prioritize functional DMRs (fDMRs) in colorectal cancer (CRC). As demonstrated, the top-ranked fDMRs from all of the data resources showed a strong enrichment for known methylated genes. Additionally, we analyzed those top 5% DMR-coupled coding genes using functional enrichment, which resulted in significant disease-related biological functions in contrast to the tail 5% genes. To further confirm the functional importance of the top-ranked fDMRs, we applied chromatin modification alterations of CRC cell lines to characterize their functional regulation. Specifically, we extended the utility of the top-ranked DMR-coupled genes to serve as classification and survival biomarkers, which showed a robust performance across diverse independent data sets. Collectively, our results established an integrative framework to prioritize fDMRs, which could help characterize aberrant DNA methylation-induced potential mechanisms underlying tumorigenesis and uncover epigenome-based biomarkers for clinical diagnosis and prognosis.

Chromatin states modify network motifs contributing to cell-specific functions

Epigenetic modification can affect many important biological processes, such as cell proliferation and apoptosis. It can alter chromatin conformation and contribute to gene regulation. To investigate how chromatin states associated with network motifs, we assembled chromatin state-modified regulatory networks by combining 269 ChIP-seq data and chromatin states in four cell types. We found that many chromatin states were significantly associated with network motifs, especially for feedforward loops (FFLs). These distinct chromatin state compositions contribute to different expression levels and translational control of targets in FFLs. Strikingly, the chromatin state-modified FFLs were highly cell-specific and, to a large extent, determined cell-selective functions, such as the embryonic stem cell-specific bivalent modification-related FFL with an important role in poising developmentally important genes for expression. Besides, comparisons of chromatin state-modified FFLs between cancerous/stem and primary cell lines revealed specific type of chromatin state alterations that may act together with motif structural changes cooperatively contribute to cell-to-cell functional differences. Combination of these alterations could be helpful in prioritizing candidate genes. Together, this work highlights that a dynamic epigenetic dimension can help network motifs to control cell-specific functions.

Prioritizing candidate disease miRNAs by integrating phenotype associations of multiple diseases with matched miRNA and mRNA expression profiles

MicroRNAs (miRNAs) have been validated to show widespread disruption of function in many cancers. However, despite concerted efforts to develop prioritization approaches based on a priori knowledge of disease-associated miRNAs, uncovering oncogene or tumor-suppressor miRNAs remains a challenge. Here, based on the assumption that diverse diseases with phenotype associations show similar molecular mechanisms, we present an approach for the systematic prioritization of disease-specific miRNAs by using known disease genes and context-dependent miRNA-target interactions derived from matched miRNA and mRNA expression data, independent of known disease miRNAs. After collecting matched miRNA and mRNA expression data for 11 cancer types, we applied this approach to systematically prioritize miRNAs involved in these cancers. Our approach yielded an average area under the ROC curve (AUC) of 75.84% according to known disease miRNAs from the miR2Disease database, with the highest AUC (80.93%) for pancreatic cancer. Moreover, we assessed the sensitivity and specificity as well as the integrative importance of this approach. Comparative analyses also showed that our method is comparable to previous methods. In summary, we provide a novel method for prioritization of disease-related miRNAs that can help researchers better understand the important roles of miRNAs in human disease.

Predicting the functions of long noncoding RNAs using RNA-seq based on Bayesian network

Long noncoding RNAs (lncRNAs) have been shown to play key roles in various biological processes. However, functions of most lncRNAs are poorly characterized. Here, we represent a framework to predict functions of lncRNAs through construction of a regulatory network between lncRNAs and protein-coding genes. Using RNA-seq data, the transcript profiles of lncRNAs and protein-coding genes are constructed. Using the Bayesian network method, a regulatory network, which implies dependency relations between lncRNAs and protein-coding genes, was built. In combining protein interaction network, highly connected coding genes linked by a given lncRNA were subsequently used to predict functions of the lncRNA through functional enrichment. Application of our method to prostate RNA-seq data showed that 762 lncRNAs in the constructed regulatory network were assigned functions. We found that lncRNAs are involved in diverse biological processes, such as tissue development or embryo development (e.g., nervous system development and mesoderm development). By comparison with functions inferred using the neighboring gene-based method and functions determined using lncRNA knockdown experiments, our method can provide comparable predicted functions of lncRNAs. Overall, our method can be applied to emerging RNA-seq data, which will help researchers identify complex relations between lncRNAs and coding genes and reveal important functions of lncRNAs.

IndividualizedPath: identifying genetic alterations contributing to the dysfunctional pathways in glioblastoma individuals

Due to the extensive complexity and high genetic heterogeneity of genetic alterations in cancer, comprehensively depicting the molecular mechanisms of cancer remains difficult. Characterizing personalized pathogenesis in cancer individuals can help to reveal new details of the complex mechanisms. In this study, we proposed an integrative method called IndividualizedPath to identify genetic alterations and their downstream risk pathways from the perspective of individuals through combining the DNA copy number, gene expression data and topological structures of biological pathways. By applying the method to TCGA glioblastoma multiforme (GBM) samples, we identified 394 gene-pathway pairs in 252 GBM individuals. We found that genes with copy number alterations showed high heterogeneity across GBM individuals, whereas they affected relatively consistent biological pathways. A global landscape of gene-pathway pairs showed that EGFR linked with multiple cancer-related biological pathways confers the highest risk of GBM. GBM individuals with MET-pathway pairs showed significantly shorter survival times than those with only MET amplification. Importantly, we found that the same risk pathways were affected by different genes in distinct groups of GBM individuals with a significant pattern of mutual exclusivity. Similarly, GBM subtype analysis revealed some subtype-specific gene-pathway pairs. In addition, we found that some rare copy number alterations had a large effect on contribution to numerous cancer-related pathways. In summary, our method offers the possibility to identify personalized cancer mechanisms, which can be applied to other types of cancer through the web server (http://bioinfo.hrbmu.edu.cn/IndividualizedPath/).

Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data

The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.

Spatiotemporal-specific lncRNAs in the brain, colon, liver and lung of macaque during development

Identification of spatiotemporal-specific lncRNAs during the development of multiple tissues in rhesus.

Identification of cancer-related lncRNAs through integrating genome, regulome and transcriptome features

LncRNAs have become rising stars in biology and medicine, due to their versatile functions in a wide range of important biological processes and active roles in various human cancers. Here, we developed a computational method based on the naïve Bayesian classifier method to identify cancer-related lncRNAs by integrating genome, regulome and transcriptome data, and identified 707 potential cancer-related lncRNAs. We demonstrated the performance of the method by ten-fold cross-validation, and found that integration of multi-omic data was necessary to identify cancer-related lncRNAs. We identified 707 potential cancer-related lncRNAs and our results showed that these lncRNAs tend to exhibit significant differential expression and differential DNA methylation in multiple cancer types, and prognosis effects in prostate cancer. We also found that these lncRNAs were more likely to be direct targets of TP53 family members than others. Moreover, based on 147 lncRNA knockdown data in mice, we validated that four of six mouse orthologous lncRNAs were significantly involved in many cancer-related processes, such as cell differentiation and the Wnt signaling pathway. Notably, one lncRNA, lnc-SNURF-1, which was found to be associated with TNF-mediated signaling pathways, was up-regulated in prostate cancer and the protein-coding genes affected by knockdown of the lncRNA were also significantly aberrant in prostate cancer patients, suggesting its probable importance in tumorigenesis. Taken together, our method underlines the power of integrating multi-omic data to uncover cancer-related lncRNAs.

Integrating multi-omics for uncovering the architecture of cross-talking pathways in breast cancer

Cross-talk among abnormal pathways widely occurs in human cancer and generally leads to insensitivity to cancer treatment. Moreover, alterations in the abnormal pathways are not limited to single molecular level. Therefore, we proposed a strategy that integrates a large number of biological sources at multiple levels for systematic identification of cross-talk among risk pathways in cancer by random walk on protein interaction network. We applied the method to multi-Omics breast cancer data from The Cancer Genome Atlas (TCGA), including somatic mutation, DNA copy number, DNA methylation and gene expression profiles. We identified close cross-talk among many known cancer-related pathways with complex change patterns. Furthermore, we identified key genes (linkers) bridging these cross-talks and showed that these genes carried out consistent biological functions with the linked cross-talking pathways. Through identification of leader genes in each pathway, the architecture of cross-talking pathways was built. Notably, we observed that linkers cooperated with leaders to form the fundamentation of cross-talk of pathways which play core roles in deterioration of breast cancer. As an example, we observed that KRAS showed a direct connection to numerous cancer-related pathways, such as MAPK signaling pathway, suggesting that it may be a central communication hub. In summary, we offer an effective way to characterize complex cross-talk among disease pathways, which can be applied to other diseases and provide useful information for the treatment of cancer.

Integrating epigenetic marks for identification of transcriptionally active miRNAs

MicroRNAs have been identified as important regulators involved in biological processes and human diseases. We proposed a computational approach to systematic identification of active promoters of miRNAs by active models using epigenetic characteristics at active promoters of protein-coding genes together with a genomic context-based filtering step in nine human cell types, which were validated to exhibit greater conservation, more overlap with CAGE-identified TSSs, more conserved TFBSs and higher transcription factor binding signal intensities. Furthermore, expression analysis showed discordance between transcriptional activation of miRNAs and expression of their precursor and mature forms, indicating that precursor and mature miRNA expression is insufficient to account for transcriptional activation of miRNAs. Compared to other methods, our approach identified higher percentages of active miRNAs with CAGE-detected TSS activity and primary transcript expression, further supporting the validity of our approach, which will be valuable to understand the biological roles of miRNAs in specific cell contexts.

Semi-supervised learning for potential human microRNA-disease associations inference

MicroRNAs play critical role in the development and progression of various diseases. Predicting potential miRNA-disease associations from vast amount of biological data is an important problem in the biomedical research. Considering the limitations in previous methods, we developed Regularized Least Squares for MiRNA-Disease Association (RLSMDA) to uncover the relationship between diseases and miRNAs. RLSMDA can work for diseases without known related miRNAs. Furthermore, it is a semi-supervised (does not need negative samples) and global method (prioritize associations for all the diseases simultaneously). Based on leave-one-out cross validation, reliable AUC have demonstrated the reliable performance of RLSMDA. We also applied RLSMDA to Hepatocellular cancer and Lung cancer and implemented global prediction for all the diseases simultaneously. As a result, 80% (Hepatocellular cancer) and 84% (Lung cancer) of top 50 predicted miRNAs and 75% of top 20 potential associations based on global prediction have been confirmed by biological experiments. We also applied RLSMDA to diseases without known related miRNAs in golden standard dataset. As a result, in the top 3 potential related miRNA list predicted by RLSMDA for 32 diseases, 34 disease-miRNA associations were successfully confirmed by experiments. It is anticipated that RLSMDA would be a useful bioinformatics resource for biomedical researches.

Protein-driven inference of miRNA-disease associations

MOTIVATION

MicroRNAs (miRNAs) are a highly abundant class of non-coding RNA genes involved in cellular regulation and thus also diseases. Despite miRNAs being important disease factors, miRNA-disease associations remain low in number and of variable reliability. Furthermore, existing databases and prediction methods do not explicitly facilitate forming hypotheses about the possible molecular causes of the association, thereby making the path to experimental follow-up longer.

RESULTS

Here we present miRPD in which miRNA-Protein-Disease associations are explicitly inferred. Besides linking miRNAs to diseases, it directly suggests the underlying proteins involved, which can be used to form hypotheses that can be experimentally tested. The inference of miRNAs and diseases is made by coupling known and predicted miRNA-protein associations with protein-disease associations text mined from the literature. We present scoring schemes that allow us to rank miRNA-disease associations inferred from both curated and predicted miRNA targets by reliability and thereby to create high- and medium-confidence sets of associations. Analyzing these, we find statistically significant enrichment for proteins involved in pathways related to cancer and type I diabetes mellitus, suggesting either a literature bias or a genuine biological trend. We show by example how the associations can be used to extract proteins for disease hypothesis.

AVAILABILITY AND IMPLEMENTATION

All datasets, software and a searchable Web site are available at http://mirpd.jensenlab.org.

Characterizing genes with distinct methylation patterns in the context of protein-protein interaction network: application to human brain tissues

BACKGROUND

DNA methylation is an essential epigenetic mechanism involved in transcriptional control. However, how genes with different methylation patterns are assembled in the protein-protein interaction network (PPIN) remains a mystery.

RESULTS

In the present study, we systematically dissected the characterization of genes with different methylation patterns in the PPIN. A negative association was detected between the methylation levels in the brain tissues and topological centralities. By focusing on two classes of genes with considerably different methylation levels in the brain tissues, namely the low methylated genes (LMGs) and high methylated genes (HMGs), we found that their organizing principles in the PPIN are distinct. The LMGs tend to be the center of the PPIN, and attacking them causes a more deleterious effect on the network integrity. Furthermore, the LMGs express their functions in a modular pattern and substantial differences in functions are observed between the two types of genes. The LMGs are enriched in the basic biological functions, such as binding activity and regulation of transcription. More importantly, cancer genes, especially recessive cancer genes, essential genes, and aging-related genes were all found more often in the LMGs. Additionally, our analysis presented that the intra-classes communications are enhanced, but inter-classes communications are repressed. Finally, a functional complementation was revealed between methylation and miRNA regulation in the human genome.

CONCLUSIONS

We have elucidated the assembling principles of genes with different methylation levels in the context of the PPIN, providing key insights into the complex epigenetic regulation mechanisms.