A computational approach to identifying gene-microRNA modules in cancer

Integrative network analysis of miRNA-mRNA expression profiles during epileptogenesis in rats reveals therapeutic targets after emergence of first spontaneous seizure

Epileptogenesis is the process by which a normal brain becomes hyperexcitable and capable of generating spontaneous recurrent seizures. The extensive dysregulation of gene expression associated with epileptogenesis is shaped, in part, by microRNAs (miRNAs) - short, non-coding RNAs that negatively regulate protein levels. Functional miRNA-mediated regulation can, however, be difficult to elucidate due to the complexity of miRNA-mRNA interactions. Here, we integrated miRNA and mRNA expression profiles sampled over multiple time-points during and after epileptogenesis in rats, and applied bi-clustering and Bayesian modelling to construct temporal miRNA-mRNA-mRNA interaction networks. Network analysis and enrichment of network inference with sequence- and human disease-specific information identified key regulatory miRNAs with the strongest influence on the mRNA landscape, and miRNA-mRNA interactions closely associated with epileptogenesis and subsequent epilepsy. Our findings underscore the complexity of miRNA-mRNA regulation, can be used to prioritise miRNA targets in specific systems, and offer insights into key regulatory processes in epileptogenesis with therapeutic potential for further investigation.

Laryngeal cancer diagnosis via miRNA-based decision tree model

PURPOSE

Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the inaccessible nature of current diagnosis methods in some parts of the world. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers.

METHODS

In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a created series of miRNA attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways. This series of attributes is extracted from both differentially expressed blood-based miRNAs in laryngeal cancer and random, non-associated with cancer miRNAs.

RESULTS

Several machine-learning (ML) algorithms were tested in the ML model, and the Hoeffding Tree classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, our model is validated with the independent laryngeal cancer datasets and can accurately diagnose laryngeal cancer with 86% accuracy. We also explored the biological relationships of the attributes used in our model to understand their relationship with cancer proliferation or suppression pathways.

CONCLUSION

Our study demonstrates that the proposed model and an inexpensive miRNA testing strategy have the potential to serve as an additional method for diagnosing laryngeal cancer.

Identifying miRNA-Gene Common and Specific Regulatory Modules for Cancer Subtyping by a High-Order Graph Matching Model

Identifying regulatory modules between miRNAs and genes is crucial in cancer research. It promotes a comprehensive understanding of the molecular mechanisms of cancer. The genomic data collected from subjects usually relate to different cancer statuses, such as different TNM Classifications of Malignant Tumors (TNM) or histological subtypes. Simple integrated analyses generally identify the core of the tumorigenesis (common modules) but miss the subtype-specific regulatory mechanisms (specific modules). In contrast, separate analyses can only report the differences and ignore important common modules. Therefore, there is an urgent need to develop a novel method to jointly analyze miRNA and gene data of different cancer statuses to identify common and specific modules. To that end, we developed a High-Order Graph Matching model to identify Common and Specific modules (HOGMCS) between miRNA and gene data of different cancer statuses. We first demonstrate the superiority of HOGMCS through a comparison with four state-of-the-art techniques using a set of simulated data. Then, we apply HOGMCS on stomach adenocarcinoma data with four TNM stages and two histological types, and breast invasive carcinoma data with four PAM50 subtypes. The experimental results demonstrate that HOGMCS can accurately extract common and subtype-specific miRNA-gene regulatory modules, where many identified miRNA-gene interactions have been confirmed in several public databases.

Mesenchymal Stem Cell-Derived Exosomes as a Novel Strategy for the Treatment of Intervertebral Disc Degeneration

Intervertebral disc degeneration (IVDD) has been reported to be the most prevalent contributor to low back pain, posing a significant strain on the healthcare systems on a global scale. Currently, there are no approved therapies available for the prevention of the progressive degeneration of intervertebral disc (IVD); however, emerging regenerative strategies that aim to restore the normal structure of the disc have been fundamentally promising. In the last decade, mesenchymal stem cells (MSCs) have received a significant deal of interest for the treatment of IVDD due to their differentiation potential, immunoregulatory capabilities, and capability to be cultured and regulated in a favorable environment. Recent investigations show that the pleiotropic impacts of MSCs are regulated by the production of soluble paracrine factors. Exosomes play an important role in regulating such effects. In this review, we have summarized the current treatments for disc degenerative diseases and their limitations and highlighted the therapeutic role and its underlying mechanism of MSC-derived exosomes in IVDD, as well as the possible future developments for exosomes.

Network-based cancer genomic data integration for pattern discovery

BACKGROUND

Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted.

RESULTS

In this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network and gene expression data to identify underlying gene functional modules. The results on a set of simulated data show that SNSVD is more effective than the traditional SVD-based methods. The further experiment results on real cancer genomic data show that most co-expressed modules are not only significantly enriched on GO/KEGG pathways, but also correspond to dense sub-networks in the prior gene interaction network. Besides, we also use our method to identify ten differentially co-expressed miRNA-gene modules by integrating matched miRNA and mRNA expression data of breast cancer from The Cancer Genome Atlas (TCGA). Several important breast cancer related miRNA-gene modules are discovered.

CONCLUSIONS

All the results demonstrate that SNSVD can overcome the drawbacks of SSVD and capture more biologically relevant functional modules by incorporating a prior gene interaction network. These identified functional modules may provide a new perspective to understand the diagnostics, occurrence and progression of cancer.

Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis

Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers

Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer.

MicroRNAs (miRNAs) are a class of small non-coding RNAs. Previous studies have revealed that miRNA-gene regulatory modules play key roles in the occurrence and development of cancer. However, little has been done to discover miRNA-gene regulatory modules from a pan-cancer view. Thus, it is urgently needed to develop new methods to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data of multi-cancers. To build the connections between miRNA-gene regulatory modules across different cancer types, we propose a tensor sparse canonical correlation analysis (TSCCA) method. Our specific contributions are two-fold: (1) We propose a sparse statistical learning model TSCCA and an efficient block-coordinate descent algorithm to solve it. (2) We apply TSCCA to a multi-omics data set of 33 cancer types from TCGA and identify some cancer-related miRNA-gene modules with important biological functions and statistical significance.

Gene-based mediation analysis in epigenetic studies

Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.

RFCM3: Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer

Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Functional genomics data provides opportunities to study the aberrant microRNA-messenger RNA (miRNA-mRNA) interaction. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM³), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects set of miRNA-mRNA modules by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later, using the knowledge of the miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The prognostic value of the genes in a module with respect to cervical cancer is also demonstrated. The approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.

Evaluation of gene-drug common module identification methods using pharmacogenomics data

Accurately identifying the interactions between genomic factors and the response of cancer drugs plays important roles in drug discovery, drug repositioning and cancer treatment. A number of studies revealed that interactions between genes and drugs were 'many-genes-to-many drugs' interactions, i.e. common modules, opposed to 'one-gene-to-one-drug' interactions. Such modules fully explain the interactions between complex biological regulatory mechanisms and cancer drugs. However, strategies for effectively and robustly identifying the underlying common modules among pharmacogenomics data remain to be improved. In this paper, we aim to provide a detailed evaluation of three categories of state-of-the-art common module identification techniques from a machine learning perspective, including non-negative matrix factorization (NMF), partial least squares (PLS) and network analyses. We first evaluate the performance of six methods, namely SNMNMF, NetNMF, SNPLS, O2PLS, NSBM and HOGMMNC, using two series of simulated data sets with different noise levels and outlier ratios. Then, we conduct experiments using a real world data set of 2091 genes and 101 drugs in 392 cancer cell lines and compare the real experimental results from the aspect of biological process term enrichment, gene-drug and drug-drug interactions. Finally, we present interesting findings from our evaluation study and discuss the advantages and drawbacks of each method. Supplementary information: Supplementary file is available at Briefings in Bioinformatics online.

Dynamics of microRNA expression during mouse prenatal development

MicroRNAs (miRNAs) play a critical role as posttranscriptional regulators of gene expression. The ENCODE Project profiled the expression of miRNAs in an extensive set of organs during a time-course of mouse embryonic development and captured the expression dynamics of 785 miRNAs. We found distinct organ-specific and developmental stage-specific miRNA expression clusters, with an overall pattern of increasing organ-specific expression as embryonic development proceeds. Comparative analysis of conserved miRNAs in mouse and human revealed stronger clustering of expression patterns by organ type rather than by species. An analysis of messenger RNA expression clusters compared with miRNA expression clusters identifies the potential role of specific miRNA expression clusters in suppressing the expression of mRNAs specific to other developmental programs in the organ in which these miRNAs are expressed during embryonic development. Our results provide the most comprehensive time-course of miRNA expression as part of an integrated ENCODE reference data set for mouse embryonic development.

A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions

Detecting cancer-related genes and their interactions is a crucial task in cancer research. For this purpose, we proposed an efficient method, to detect coding genes, microRNAs (miRNAs), and their interactions related to a particular cancer or a cancer subtype using their expression data from the same set of samples. Firstly, biclusters specific to a particular type of cancer are detected based on rectified factor networks and ranked according to their associations with general cancers. Secondly, coding genes and miRNAs in each bicluster are prioritized by considering their differential expression and differential correlation values, protein-protein interaction data, and potential cancer markers. Finally, a rank fusion process is used to obtain the final comprehensive rank by combining multiple ranking results. We applied our proposed method on breast cancer datasets. Results show that our method outperforms other methods in detecting breast cancer-related coding genes and miRNAs. Furthermore, our method is very efficient in computing time, which can handle tens of thousands genes/miRNAs and hundreds of patients in hours on a desktop. This work may aid researchers in studying the genetic architecture of complex diseases, and improving the accuracy of diagnosis.

CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer

BACKGROUND

Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. However, the functional roles of vast majority of cancer specific ncRNAs and their combinational regulation patterns are still insufficiently understood.

RESULTS

Here we develop an integrative framework called CeModule to discover lncRNA, miRNA and mRNA-associated regulatory modules. We fully utilize the matched expression profiles of lncRNAs, miRNAs and mRNAs and establish a model based on joint orthogonality non-negative matrix factorization for identifying modules. Meanwhile, we impose the experimentally verified miRNA-lncRNA interactions, the validated miRNA-mRNA interactions and the weighted gene-gene network into this framework to improve the module accuracy through the network-based penalties. The sparse regularizations are also used to help this model obtain modular sparse solutions. Finally, an iterative multiplicative updating algorithm is adopted to solve the optimization problem.

CONCLUSIONS

We applied CeModule to two cancer datasets including ovarian cancer (OV) and uterine corpus endometrial carcinoma (UCEC) obtained from TCGA. The modular analysis indicated that the identified modules involving lncRNAs, miRNAs and mRNAs are significantly associated and functionally enriched in cancer-related biological processes and pathways, which may provide new insights into the complex regulatory mechanism of human diseases at the system level.

Application of Monte Carlo cross-validation to identify pathway cross-talk in neonatal sepsis

To explore genetic pathway cross-talk in neonates with sepsis, an integrated approach was used in this paper. To explore the potential relationships between differently expressed genes between normal uninfected neonates and neonates with sepsis and pathways, genetic profiling and biologic signaling pathway were first integrated. For different pathways, the score was obtained based upon the genetic expression by quantitatively analyzing the pathway cross-talk. The paired pathways with high cross-talk were identified by random forest classification. The purpose of the work was to find the best pairs of pathways able to discriminate sepsis samples versus normal samples. The results found 10 pairs of pathways, which were probably able to discriminate neonates with sepsis versus normal uninfected neonates. Among them, the best two paired pathways were identified according to analysis of extensive literature. Impact statement To find the best pairs of pathways able to discriminate sepsis samples versus normal samples, an RF classifier, the DS obtained by DEGs of paired pathways significantly associated, and Monte Carlo cross-validation were applied in this paper. Ten pairs of pathways were probably able to discriminate neonates with sepsis versus normal uninfected neonates. Among them, the best two paired pathways ((7) IL-6 Signaling and Phospholipase C Signaling (PLC); (8) Glucocorticoid Receptor (GR) Signaling and Dendritic Cell Maturation) were identified according to analysis of extensive literature.

A novel approach to identify the miRNA-mRNA causal regulatory modules in Cancer

MicroRNAs (miRNAs) play an essential role in many biological processes by regulating the target genes, especially in the initiation and development of cancers. Therefore, the identification of the miRNA-mRNA regulatory modules is important for understanding the regulatory mechanisms. Most computational methods only used statistical correlations in predicting miRNA-mRNA modules, and neglected the fact there are causal relationships between miRNAs and their target genes. In this paper, we propose a novel approach called CALM(the causal regulatory modules) to identify the miRNA-mRNA regulatory modules through integrating the causal interactions and statistical correlations between the miRNAs and their target genes. Our algorithm largely consists of three steps: it first forms the causal regulatory relationships of miRNAs and genes from gene expression profiles and detects the miRNA clusters according to the GO function information of their target genes, then expands each miRNA cluster by greedy adding(discarding) the target genes to maximize the modularity score. To show the performance of our method, we apply CALM on four datasets including EMT, breast, ovarian, thyroid cancer and validate our results. The experiment results show that our method can not only outperform the compared method, but also achieve ideal overall performance in terms of the functional enrichment.

FGMD: A novel approach for functional gene module detection in cancer

With the increasing availability of multi-dimensional biological datasets for the same samples (i.e., gene expression, microRNAs, copy numbers, mutations, methylations), it has now become possible to systematically understand the regulatory mechanisms operating in a cancer cell. For this task, it is important to discover a set of co-expressed genes with functions, representing a so-called functional gene module, because co-expressed genes tend to be co-regulated by the same regulators, including transcription factors, microRNAs, and copy number aberrations. Several algorithms have been used to identify such gene modules, including hierarchical clustering and non-negative matrix factorization. Although these algorithms have been applied to many microarray datasets, only a few systematic analyses of these algorithms have been performed for RNA-sequencing (RNA-Seq) data to date. Although gene expression levels determined based on microarray and RNA-Seq datasets tend to be highly correlated, the expression levels of some genes differ depending on the platforms used for analysis, which may result in the construction of different gene modules for the same samples. Here, we compare several module detection algorithms applied to both microarray and RNA-seq datasets. We further propose a new functional gene module detection algorithm (FGMD), which is based on a hierarchical clustering algorithm that was modified to reflect actual biological observations, including the fact that a single gene may be involved in multiple biological pathways. Application of existing algorithms and the new FGMD algorithm to breast cancer and ovarian cancer datasets from The Cancer Genome Atlas showed that the FGMD algorithm had the best performance for most of the functional pathway enrichment tests and in the transcription factor enrichment test. We expect that the FGMD algorithm will contribute to improving the identification of functional gene modules related to cancer.

Integration of MicroRNA, mRNA, and Protein Expression Data for the Identification of Cancer-Related MicroRNAs

MicroRNAs (miRNAs) are responsible for the regulation of target genes involved in various biological processes, and may play oncogenic or tumor suppressive roles. Many studies have investigated the relationships between miRNAs and their target genes, using mRNA and miRNA expression data. However, mRNA expression levels do not necessarily represent the exact gene expression profiles, since protein translation may be regulated in several different ways. Despite this, large-scale protein expression data have been integrated rarely when predicting gene-miRNA relationships. This study explores two approaches for the investigation of gene-miRNA relationships by integrating mRNA expression and protein expression data. First, miRNAs were ranked according to their effects on cancer development. We calculated influence scores for each miRNA, based on the number of significant mRNA-miRNA and protein-miRNA correlations. Furthermore, we constructed modules containing mRNAs, proteins, and miRNAs, in which these three molecular types are highly correlated. The regulatory interactions between miRNA and genes in these modules have been validated based on the direct regulations, indirect regulations, and co-regulations through transcription factors. We applied our approaches to glioblastomas (GBMs), ranked miRNAs depending on their effects on GBM, and obtained 52 GBM-related modules. Compared with the miRNA rankings and modules constructed using only mRNA expression data, the rankings and modules constructed using mRNA and protein expression data were shown to have better performance. Additionally, we experimentally verified that miR-504, highly ranked and included in the identified modules, plays a suppressive role in GBM development. We demonstrated that the integration of both expression profiles allows a more precise analysis of gene-miRNA interactions and the identification of a higher number of cancer-related miRNAs and regulatory mechanisms.

Prioritizing cancer-related microRNAs by integrating microRNA and mRNA datasets

MicroRNAs (miRNAs) are small non-coding RNAs regulating the expression of target genes, and they are involved in cancer initiation and progression. Even though many cancer-related miRNAs were identified, their functional impact may vary, depending on their effects on the regulation of other miRNAs and genes. In this study, we propose a novel method for the prioritization of candidate cancer-related miRNAs that may affect the expression of other miRNAs and genes across the entire biological network. For this, we propose three important features: the average expression of a miRNA in multiple cancer samples, the average of the absolute correlation values between the expression of a miRNA and expression of all genes, and the number of predicted miRNA target genes. These three features were integrated using order statistics. By applying the proposed approach to four cancer types, glioblastoma, ovarian cancer, prostate cancer, and breast cancer, we prioritized candidate cancer-related miRNAs and determined their functional roles in cancer-related pathways. The proposed approach can be used to identify miRNAs that play crucial roles in driving cancer development, and the elucidation of novel potential therapeutic targets for cancer treatment.

Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods

MicroRNAs (miRs) are small single-stranded noncoding RNA that function in RNA silencing and post-transcriptional regulation of gene expression. An increasing number of studies have shown that miRs play an important role in tumorigenesis, and understanding the regulatory mechanism of miRs in this gene regulatory network will help elucidate the complex biological processes at play during malignancy. Despite advances, determination of miR–target interactions (MTIs) and identification of functional modules composed of miRs and their specific targets remain a challenge. A large amount of data generated by high-throughput methods from various sources are available to investigate MTIs. The development of data-driven tools to harness these multi-dimensional data has resulted in significant progress over the past decade. In parallel, large-scale cancer genomic projects are allowing new insights into the commonalities and disparities of miR–target regulation across cancers. In the first half of this review, we explore methods for identification of pairwise MTIs, and in the second half, we explore computational tools for discovery of miR-regulatory modules in a cancer-specific and pan-cancer context. We highlight strengths and limitations of each of these tools as a practical guide for the computational biologists.

Therapeutic nucleic acids: current clinical status

Deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) are simple linear polymers that have been the subject of considerable research in the last two decades and have now moved into the realm of being stand-alone therapeutic agents. Much of this has stemmed from the appreciation that they carry out myriad functions that go beyond mere storage of genetic information and protein synthesis. Therapy with nucleic acids either uses unmodified DNA or RNA or closely related compounds. From both a development and regulatory perspective, they fall somewhere between small molecules and biologics. Several of these compounds are in clinical development and many have received regulatory approval for human use. This review addresses therapeutic uses of DNA based on antisense oligonucleotides, DNA aptamers and gene therapy; and therapeutic uses of RNA including micro RNAs, short interfering RNAs, ribozymes, RNA decoys and circular RNAs. With their specificity, functional diversity and limited toxicity, therapeutic nucleic acids hold enormous promise. However, challenges that need to be addressed include targeted delivery, mass production at low cost, sustaining efficacy and minimizing off-target toxicity. Technological developments will hold the key to this and help accelerate drug approvals in the years to come.

A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data

MOTIVATION

Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects.

RESULTS

We introduce a novel method of multi-modal data analysis that is designed for heterogeneous data based on non-negative matrix factorization. We provide an algorithm for jointly decomposing the data matrices involved that also includes a sparsity option for high-dimensional settings. The performance of the proposed method is evaluated on synthetic data and on real DNA methylation, gene expression and miRNA expression data from ovarian cancer samples obtained from The Cancer Genome Atlas. The results show the presence of common modules across patient samples linked to cancer-related pathways, as well as previously established ovarian cancer subtypes.

AVAILABILITY AND IMPLEMENTATION

The source code repository is publicly available at https://github.com/yangzi4/iNMF.

CONTACT

gmichail@umich.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Two-dimensional Enrichment Analysis for Mining High-level Imaging Genetic Associations

Enrichment analysis has been widely applied in the genome-wide association studies (GWAS), where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS-BC pair is enriched in a list of gene-QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer's Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 12 significant high level two dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases.

Integrative Analysis with Monte Carlo Cross-Validation Reveals miRNAs Regulating Pathways Cross-Talk in Aggressive Breast Cancer

In this work an integrated approach was used to identify functional miRNAs regulating gene pathway cross-talk in breast cancer (BC). We first integrated gene expression profiles and biological pathway information to explore the underlying associations between genes differently expressed among normal and BC samples and pathways enriched from these genes. For each pair of pathways, a score was derived from the distribution of gene expression levels by quantifying their pathway cross-talk. Random forest classification allowed the identification of pairs of pathways with high cross-talk. We assessed miRNAs regulating the identified gene pathways by a mutual information analysis. A Fisher test was applied to demonstrate their significance in the regulated pathways. Our results suggest interesting networks of pathways that could be key regulatory of target genes in BC, including stem cell pluripotency, coagulation, and hypoxia pathways and miRNAs that control these networks could be potential biomarkers for diagnostic, prognostic, and therapeutic development in BC. This work shows that standard methods of predicting normal and tumor classes such as differentially expressed miRNAs or transcription factors could lose intrinsic features; instead our approach revealed the responsible molecules of the disease.