Prioritizing Cancer lncRNA Modulators via Integrated lncRNA-mRNA Network and Somatic Mutation Data

Abstract Background: Long noncoding RNAs (LncRNAs) represent a large category of functional RNA molecules that play a significant role in human cancers. lncRNAs can be genes modulators to affect the biological process of multiple cancers.Methods: Here, we developed a computational framework that uses lncRNA-mRNA network and mutations in individual genes of 9 cancers from TCGA to prioritize cancer lncRNA modulators. Our method screened risky cancer lncRNA regulators based on integrated multiple lncRNA functional networks and 3 calculation methods in network. Results: Validation analyses revealed that our method was more effective than prioritization based on a single lncRNA network. This method showed high predictive performance and the highest ROC score was 0.836 in breast cancer. It’s worth noting that we found that 5 lncRNAs scores were abnormally high and these lncRNAs appeared in 9 cancers. By consulting the literatures, these 5 lncRNAs were experimentally supported lncRNAs. Analyses of prioritizing lncRNAs reveal that these lncRNAs are enriched in various cancer-related biological processes and pathways.Conclusions: Together, these results demonstrated the ability of this method identifying candidate lncRNA molecules and improved insights into the pathogenesis of cancer.

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Studying RNA–DNA interactome by Red-C identifies noncoding RNAs associated with various chromatin types and reveals transcription dynamics

Nucleic Acids Research ◽

10.1093/nar/gkaa457 ◽

2020 ◽

Vol 48 (12) ◽

pp. 6699-6714 ◽

Cited By ~ 2

Author(s):

Alexey A Gavrilov ◽

Anastasiya A Zharikova ◽

Aleksandra A Galitsyna ◽

Artem V Luzhin ◽

Natalia M Rubanova ◽

...

Keyword(s):

Messenger Rna ◽

Noncoding Rnas ◽

K562 Cells ◽

Biological Processes ◽

Intron Splicing ◽

Rna Molecules ◽

Proximity Ligation ◽

Data Support ◽

Non Coding Rnas ◽

Kinetics Of

Abstract Non-coding RNAs (ncRNAs) participate in various biological processes, including regulating transcription and sustaining genome 3D organization. Here, we present a method termed Red-C that exploits proximity ligation to identify contacts with the genome for all RNA molecules present in the nucleus. Using Red-C, we uncovered the RNA–DNA interactome of human K562 cells and identified hundreds of ncRNAs enriched in active or repressed chromatin, including previously undescribed RNAs. Analysis of the RNA–DNA interactome also allowed us to trace the kinetics of messenger RNA production. Our data support the model of co-transcriptional intron splicing, but not the hypothesis of the circularization of actively transcribed genes.

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Structural relation matching: an algorithm to identify structural patterns into RNAs and their interactions

Journal of Integrative Bioinformatics ◽

10.1515/jib-2020-0039 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Michela Quadrini

Keyword(s):

Hydrogen Bonding ◽

Secondary Structure ◽

Nucleotide Sequence ◽

Thermus Thermophilus ◽

Three Dimensional ◽

Secondary Structures ◽

Structural Effect ◽

Biological Processes ◽

Structural Pattern ◽

Rna Molecules

Abstract RNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA–RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA–RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of Thermus thermophilus, and we quantify the structural effect of the inhibitors.

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The Role of Extracellular Vesicles as Shuttles of RNA and Their Clinical Significance as Biomarkers in Hepatocellular Carcinoma

Genes ◽

10.3390/genes12060902 ◽

2021 ◽

Vol 12 (6) ◽

pp. 902

Author(s):

Eva Costanzi ◽

Carolina Simioni ◽

Gabriele Varano ◽

Cinzia Brenna ◽

Ilaria Conti ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Extracellular Vesicles ◽

Tumor Metastasis ◽

Biological Processes ◽

Rna Molecules ◽

Expression Of Genes ◽

Non Coding Rna ◽

Donor Cells ◽

Relevant Role

Extracellular vesicles (EVs) have attracted interest as mediators of intercellular communication following the discovery that EVs contain RNA molecules, including non-coding RNA (ncRNA). Growing evidence for the enrichment of peculiar RNA species in specific EV subtypes has been demonstrated. ncRNAs, transferred from donor cells to recipient cells, confer to EVs the feature to regulate the expression of genes involved in differentiation, proliferation, apoptosis, and other biological processes. These multiple actions require accuracy in the isolation of RNA content from EVs and the methodologies used play a relevant role. In liver, EVs play a crucial role in regulating cell–cell communications and several pathophysiological events in the heterogeneous liver class of cells via horizontal transfer of their cargo. This review aims to discuss the rising role of EVs and their ncRNAs content in regulating specific aspects of hepatocellular carcinoma development, including tumorigenesis, angiogenesis, and tumor metastasis. We analyze the progress in EV-ncRNAs’ potential clinical applications as important diagnostic and prognostic biomarkers for liver conditions.

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Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer

BMC Bioinformatics ◽

10.1186/s12859-021-04296-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Erica Ponzi ◽

Magne Thoresen ◽

Therese Haugdahl Nøst ◽

Kajsa Møllersen

Keyword(s):

Lung Cancer ◽

Biological Process ◽

Case Control ◽

Integrative Analysis ◽

Data Sources ◽

Omics Data ◽

Biological Processes ◽

Blood Samples ◽

Model Predictions

Abstract Background Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific (“individual”) patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as “shared” or “joint”. In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case–control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case–control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. Results Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. Conclusions In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

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Long noncoding RNAs expression profile and functional networks in rheumatoid arthritis

Oncotarget ◽

10.18632/oncotarget.20036 ◽

2017 ◽

Vol 8 (56) ◽

pp. 95280-95292 ◽

Cited By ~ 7

Author(s):

Donghua Xu ◽

Ye Jiang ◽

Lu Yang ◽

Xixing Hou ◽

Jihong Wang ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Expression Profile ◽

Noncoding Rnas ◽

Long Noncoding Rnas ◽

Functional Networks

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MicroRNAs: Key Regulators in Lung Cancer

MicroRNA ◽

10.2174/2211536610666210527102522 ◽

2021 ◽

Vol 10 ◽

Author(s):

Younes El Founini ◽

Imane Chaoui ◽

Hind Dehbi ◽

Mohammed El Mzibri ◽

Roger Abounader ◽

...

Keyword(s):

Lung Cancer ◽

Messenger Rna ◽

Recent Literature ◽

Immune Surveillance ◽

Rna Stability ◽

Noncoding Rnas ◽

Therapy Resistance ◽

Biological Processes ◽

Genome Wide ◽

Stable Forms

: Noncoding RNAs have emerged as key regulators of the genome upon gene expression profiling and genome-wide sequencing. Among these noncoding RNAs, microRNAs are short noncoding RNAs that regulate a plethora of functions, biological processes and human diseases by targeting the messenger RNA stability through 3’UTR binding, leading to either mRNA cleavage or translation repression, depending on microRNA-mRNA complementarity degree. Additionally, strong evidence has suggested that dysregulation of miRNAs contribute to the etiology and progression of human cancers, such as lung cancer, the most common and deadliest cancer worldwide. Indeed, by acting as oncogenes or tumor suppressors, microRNAs control all aspects of lung cancer malignancy, including cell proliferation, survival, migration, invasion, angiogenesis, cancer stem cells, immune-surveillance escape, and therapy resistance; and their expressions are often associated with clinical parameters. Moreover, several deregulated microRNAs in lung cancer are carried by exosomes, microvesicles and secreted in body fluids, mainly the circulation where they conserve their stable forms. Subsequently, seminal efforts have been focused on extracellular microRNAs levels as noninvasive diagnostic and prognostic biomarkers in lung cancer. In this review, focusing on recent literature, we summarize the deregulation, mechanisms of action, functions and highlight clinical applications of miRNAs for better management and design of future lung cancer targeted therapies.

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Annotating proteins with generalized functional linkages

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.0809583105 ◽

2008 ◽

Vol 105 (46) ◽

pp. 17700-17705 ◽

Cited By ~ 11

Author(s):

Richard Llewellyn ◽

David S. Eisenberg

Keyword(s):

Protein Function ◽

Biological Process ◽

Computational Method ◽

Majority Voting ◽

Target Protein ◽

Biological Processes ◽

Gene Products ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks

As genome sequencing outstrips the rate of high-quality, low-throughput biochemical and genetic experimentation, accurate annotation of protein function becomes a bottleneck in the progress of the biomolecular sciences. Most gene products are now annotated by homology, in which an experimentally determined function is applied to a similar sequence. This procedure becomes error-prone between more divergent sequences and can contaminate biomolecular databases. Here, we propose a computational method of assignment of function, termed Generalized Functional Linkages (GFL), that combines nonhomology-based methods with other types of data. Functional linkages describe pairwise relationships between proteins that work together to perform a biological task. GFL provides a Bayesian framework that improves annotation by arbitrating a competition among biological process annotations to best describe the target protein. GFL addresses the unequal strengths of functional linkages among proteins, the quality of existing annotations, and the similarity among them while incorporating available knowledge about the cellular location or individual molecular function of the target protein. We demonstrate GFL with functional linkages defined by an algorithm known as zorch that quantifies connectivity in protein–protein interaction networks. Even when using proteins linked only by indirect or high-throughput interactions, GFL predicts the biological processes of many proteins in Saccharomyces cerevisiae, improving the accuracy of annotation by 20% over majority voting.

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Hidden in plain sight: What remains to be discovered in the eukaryotic proteome?

10.1101/469569 ◽

2018 ◽

Author(s):

Valerie Wood ◽

Antonia Lock ◽

Midori A. Harris ◽

Kim Rutherford ◽

Jürg Bähler ◽

...

Keyword(s):

Gene Ontology ◽

Fission Yeast ◽

Genome Sequencing ◽

Large Scale ◽

Biological Process ◽

Blind Spot ◽

Model Organisms ◽

Biological Processes ◽

Health And Disease

AbstractThe first decade of genome sequencing stimulated an explosion in the characterization of unknown proteins. More recently, the pace of functional discovery has slowed, leaving around 20% of the proteins even in well-studied model organisms without informative descriptions of their biological roles. Remarkably, many uncharacterized proteins are conserved from yeasts to human, suggesting that they contribute to fundamental biological processes. To fully understand biological systems in health and disease, we need to account for every part of the system. Unstudied proteins thus represent a collective blind spot that limits the progress of both basic and applied biosciences.We use a simple yet powerful metric based on Gene Ontology (GO) biological process terms to define characterized and uncharacterized proteins for human, budding yeast, and fission yeast. We then identify a set of conserved but unstudied proteins in S. pombe, and classify them based on a combination of orthogonal attributes determined by large-scale experimental and comparative methods. Finally, we explore possible reasons why these proteins remain neglected, and propose courses of action to raise their profile and thereby reap the benefits of completing the catalog of proteins’ biological roles.

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RNA Deregulation in Amyotrophic Lateral Sclerosis: The Noncoding Perspective

International Journal of Molecular Sciences ◽

10.3390/ijms221910285 ◽

2021 ◽

Vol 22 (19) ◽

pp. 10285

Author(s):

Pietro Laneve ◽

Paolo Tollis ◽

Elisa Caffarelli

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Potential Role ◽

Noncoding Rnas ◽

Rna Metabolism ◽

Biological Processes ◽

Diagnostic Biomarkers ◽

Defective Rna ◽

Almost All ◽

Lateral Sclerosis

RNA metabolism is central to cellular physiopathology. Almost all the molecular pathways underpinning biological processes are affected by the events governing the RNA life cycle, ranging from transcription to degradation. The deregulation of these processes contributes to the onset and progression of human diseases. In recent decades, considerable efforts have been devoted to the characterization of noncoding RNAs (ncRNAs) and to the study of their role in the homeostasis of the nervous system (NS), where they are highly enriched. Acting as major regulators of gene expression, ncRNAs orchestrate all the steps of the differentiation programs, participate in the mechanisms underlying neural functions, and are crucially implicated in the development of neuronal pathologies, among which are neurodegenerative diseases. This review aims to explore the link between ncRNA dysregulation and amyotrophic lateral sclerosis (ALS), the most frequent motoneuron (MN) disorder in adults. Notably, defective RNA metabolism is known to be largely associated with this pathology, which is often regarded as an RNA disease. We also discuss the potential role that these transcripts may play as diagnostic biomarkers and therapeutic targets.

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Long Noncoding RNA: Regulatory Mechanisms and Therapeutic Potential in Sepsis

Frontiers in Cellular and Infection Microbiology ◽

10.3389/fcimb.2021.563126 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wei Wang ◽

Ni Yang ◽

Ri Wen ◽

Chun-Feng Liu ◽

Tie-Ning Zhang

Keyword(s):

Host Response ◽

Noncoding Rna ◽

Therapeutic Potential ◽

Noncoding Rnas ◽

Specific Treatment ◽

Kidney Injury ◽

Biological Processes ◽

Immune Functions ◽

Life Threatening ◽

Organ Dysfunctions

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and is characterized by a hyperinflammatory state accompanied by immunosuppression. Long noncoding RNAs (lncRNAs) are noncoding RNAs longer than 200 nucleotides and have important roles in mediating various biological processes. Recently, lncRNAs were found to exert both promotive and inhibitory immune functions in sepsis, thus participating in sepsis regulation. Additionally, several studies have revealed that lncRNAs are involved in sepsis-induced organ dysfunctions, including cardiovascular dysfunction, acute lung injury, and acute kidney injury. Considering the lack of effective biomarkers for early identification and specific treatment for sepsis, lncRNAs may be promising biomarkers and even targets for sepsis therapies. This review systematically highlights the recent advances regarding the roles of lncRNAs in sepsis and sheds light on their use as potential biomarkers and treatment targets for sepsis.

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