Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

Statistical Methods in Integrative Genomics

Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions

Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer

Publisher Copyright: © 2022The non-coding RNA (ncRNA) regulation appears to be associated to the diagnosis and targeted therapy of complex diseases. Motifs of non-coding RNAs and genes in the competing endogenous RNA (ceRNA) network would probably contribute to the accurate prediction of serous ovarian carcinoma (SOC). We conducted a microarray study profiling the whole transcriptomes of eight human SOCs and eight controls and constructed a ceRNA network including mRNAs, long ncRNAs, and circular RNAs (circRNAs). Novel form of motifs (mRNA-ncRNA-mRNA) were identified from the ceRNA network and defined as non-coding RNA's competing endogenous gene pairs (ceGPs), using a proposed method denoised individualized pair analysis of gene expression (deiPAGE). 18 cricRNA's ceGPs (cceGPs) were identified from multiple cohorts and were fused as an indicator (SOC index) for SOC discrimination, which carried a high predictive capacity in independent cohorts. SOC index was negatively correlated with the CD8+/CD4+ ratio in tumour-infiltration, reflecting the migration and growth of tumour cells in ovarian cancer progression. Moreover, most of the RNAs in SOC index were experimentally validated involved in ovarian cancer development. Our results elucidate the discriminative capability of SOC index and suggest that the novel competing endogenous motifs play important roles in expression regulation and could be potential target for investigating ovarian cancer mechanism or its therapy.Peer reviewe

Inference and Analysis of Multilayered Mirna-Mediated Networks in Cancer

MicroRNAs (miRNAs) are small noncoding transcripts that can regulate gene expression, thereby controlling diverse biological processes. Aberrant disruptions of miRNA expression and their interactions with other biological agents (e.g., coding and noncoding transcripts) have been associated with several types of cancer. The goal of this dissertation is to use multidimensional genomic data to model two different gene regulation mechanisms by miRNAs in cancer. This dissertation results from two research projects. The first project investigates a miRNA-mediated gene regulation mechanism called competing endogenous RNA (ceRNA) interactions, which suggests that some transcripts can indirectly regulate one another\u27s activity through their interactions with a common set of miRNAs. Identification of context-specific ceRNA interactions is a challenging task. To address that, we proposed a computational method called Cancerin to identify genome-wide cancer-associated ceRNA interactions. Cancerin incorporates DNA methylation (DM), copy number alteration (CNA), and gene and miRNA expression datasets to construct cancer-specific ceRNA networks. Cancerin was applied to three cancer datasets from the Cancer Genome Atlas (TCGA) project. We found that the RNAs involved in ceRNA interactions were enriched with cancer-related genes and have high prognostic power. Moreover, the ceRNA modules in the inferred ceRNA networks were involved in cancer-associated biological processes. The second project investigates what biological functions are regulated by both miRNAs and transcription factors (TFs). While it has been known that miRNAs and TFs can coregulate common target genes having similar biological functions, it is challenging to associate specific biological functions to specific miRNAs and TFs. In this project, we proposed a computational method called CanMod to identify gene regulatory modules. Each module consists of miRNAs, TFs and their coregulated target genes. CanMod was applied on the breast cancer dataset from TCGA. Many hub regulators (i.e., miRNAs and TFs) found in the inferred modules were known cancer genes, and CanMod was able to find experimentally validated regulator-target interactions. In addition, the modules were associated with distinguishable and cancer-related biological processes. Given the biological findings obtained from Cancerin and CanMod, we believe that the two computational methods are valuable tools to explore novel miRNA involvement in cancer

Characterizing the Huntington's disease, Parkinson's disease, and pan-neurodegenerative gene expression signature with RNA sequencing

Huntington's disease (HD) and Parkinson's disease (PD) are devastating neurodegenerative disorders that are characterized pathologically by degeneration of neurons in the brain and clinically by loss of motor function and cognitive decline in mid to late life. The cause of neuronal degeneration in these diseases is unclear, but both are histologically marked by aggregation of specific proteins in specific brain regions. In HD, fragments of a mutant Huntingtin protein aggregate and cause medium spiny interneurons of the striatum to degenerate. In contrast, PD brains exhibit aggregation of toxic fragments of the alpha synuclein protein throughout the central nervous system and trigger degeneration of dopaminergic neurons in the substantia nigra. Considering the commonalities and differences between these diseases, identifying common biological patterns across HD and PD as well as signatures unique to each may provide significant insight into the molecular mechanisms underlying neurodegeneration as a general process. State-of-the-art high-throughput sequencing technology allows for unbiased, whole genome quantification of RNA molecules within a biological sample that can be used to assess the level of activity, or expression, of thousands of genes simultaneously. In this thesis, I present three studies characterizing the RNA expression profiles of post-mortem HD and PD subjects using high-throughput mRNA sequencing data sets. The first study describes an analysis of differential expression between HD individuals and neurologically normal controls that indicates a widespread increase in immune, neuroinflammatory, and developmental gene expression. The second study expands upon the first study by making methodological improvements and extends the differential expression analysis to include PD subjects, with the goal of comparing and contrasting HD and PD gene expression profiles. This study was designed to identify common mechanisms underlying the neurodegenerative phenotype, transcending those of each unique disease, and has revealed specific biological processes, in particular those related to NFkB inflammation, common to HD and PD. The last study describes a novel methodology for combining mRNA and miRNA expression that seeks to identify associations between mRNA-miRNA modules and continuous clinical variables of interest, including CAG repeat length and clinical age of onset in HD

BAYESIAN INTEGRATIVE ANALYSIS OF OMICS DATA

Technological innovations have produced large multi-modal datasets that range in multiplatform genomic data, pathway data, proteomic data, imaging data and clinical data. Integrative analysis of such data sets have potentiality in revealing important biological and clinical insights into complex diseases like cancer. This dissertation focuses on Bayesian methodology establishment in integrative analysis of radiogenomics and pathway driver detection applied in cancer applications. We initially present Radio-iBAG that utilizes Bayesian approaches in analyzing radiological imaging and multi-platform genomic data, which we establish a multi-scale Bayesian hierarchical model that simultaneously identifies genomic and radiomic, i.e., radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. Our method is motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, wherein it identifies important magnetic resonance imaging features and the associated genomic platforms that are also significantly related with patient survival times. For another aspect of integrative analysis, we then present pathDrive that aims to detect key genetic and epigenetic upstream drivers that influence pathway activity. The method is applied into colorectal cancer incorporated with its four molecular subtypes. For each of the pathways that significantly differentiates subgroups, we detect important genomic drivers that can be viewed as “switches” for the pathway activity. To extend the analysis, finally, we develop proteomic based pathway driver analysis for multiple cancer types wherein we simultaneously detect genomic upstream factors that influence a specific pathway for each cancer type within the cancer group. With Bayesian hierarchical model, we detect signals borrowing strength from common cancer type to rare cancer type, and simultaneously estimate their selection similarity. Through simulation study, our method is demonstrated in providing many advantages, including increased power and lower false discovery rates. We then apply the method into the analysis of multiple cancer groups, wherein we detect key genomic upstream drivers with proper biological interpretation. The overall framework and methodologies established in this dissertation illustrate further investigation in the field of integrative analysis of omics data, provide more comprehensive insight into biological mechanisms and processes, cancer development and progression

The role of network science in glioblastoma

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.This work was partially supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018, CEECIND/00072/2018 and PD/BDE/143154/2019, UIDB/04516/2020, UIDB/00297/2020, UIDB/50021/2020, UIDB/50022/2020, UIDB/50026/2020, UIDP/50026/2020, NORTE-01-0145-FEDER-000013, and NORTE-01-0145-FEDER000023 and projects PTDC/CCI-BIO/4180/2020 and DSAIPA/DS/0026/2019. This project has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 951970 (OLISSIPO project)