Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer

MicroRNA-1205 Regulation of FRYL in Prostate Cancer

High mortality rates of prostate cancer (PCa) are associated with metastatic castration-resistant prostate cancer (CRPC) due to the maintenance of androgen receptor (AR) signaling despite androgen deprivation therapies (ADTs). The 8q24 chromosomal locus is a region of very high PCa susceptibility that carries genetic variants associated with high risk of PCa incidence. This region also carries frequent amplifications of the PVT1 gene, a non-protein coding gene that encodes a cluster of microRNAs including, microRNA-1205 (miR-1205), which are largely understudied. Herein, we demonstrate that miR-1205 is underexpressed in PCa cells and tissues and suppresses CRPC tumors in vivo. To characterize the molecular pathway, we identified and validated fry-like (FRYL) as a direct molecular target of miR-1205 and observed its overexpression in PCa cells and tissues. FRYL is predicted to regulate dendritic branching, which led to the investigation of FRYL in neuroendocrine PCa (NEPC). Resistance toward ADT leads to the progression of treatment related NEPC often characterized by PCa neuroendocrine differentiation (NED), however, this mechanism is poorly understood. Underexpression of miR-1205 is observed when NED is induced in vitro and inhibition of miR-1205 leads to increased expression of NED markers. However, while FRYL is overexpressed during NED, FRYL knockdown did not reduce NED, therefore revealing that miR-1205 induces NED independently of FRYL.

Long Non-Coding RNA LINC01410 Promoted Tumor Progression via the ErbB Signaling Pathway by Targeting STAT5 in Gallbladder Cancer

Objectives

Long non-coding RNAs (lncRNAs) have been recently emerging as crucial molecules in multiple human cancers. However, their expression patterns, roles as well as the underlying mechanisms in gallbladder cancer (GBC) remain largely unclear.

Materials and Methods

The expression of lncRNAs in GBC was downloaded from GEO database. Quantitative real-time polymerase chain reaction (qRT-PCR) and RNA in situ hybridization (ISH) were used to detect the expression of lncRNAs in GBC tissues. The full-sequence of LINC01410 was determined by RACE assay. Subcellular distribution of LINC01410 was examined by nuclear/cytoplasmic RNA fractionation analysis. Loss- and gain-of-function experiments were conducted to explore the biological functions of LINC01410 in vitro and in vivo. RNA pull-down, RNA immune-precipitation (RIP), and Western blot assay were conducted to investigate the mechanisms underlying the biological function of LINC01410 in GBC.

Results

LINC01410 was significantly upregulated in the GBC tissues compared to adjacent non-tumor tissues. High LINC01410 expression was significantly associated with poor prognosis of GBC patients. We identified LINC01410 to be 2,877 bp in length and mainly localized in the cytoplasm of GBC cells. Overexpression of LINC01410 promoted GBC cell proliferation, migration, and invasion in vitro and GBC progression in vivo, whereas LINC01410 downregulation rescued these effects in vitro. From RNA pull-down and RIP assay, we identified that STAT5 was a critical downstream target of LINC01410. Furthermore, ErbB signaling pathway was involved in the malignant phenotypes of GBC mediated by LINC01410.

Conclusions

Our results suggested that LINC01410 was an important lncRNA that promoted GBC progression via targeting STAT5 and activating ErbB signaling pathway.

The Functional Characterization of Epigenetically Related lncRNAs Involved in Dysregulated CeRNA-CeRNA Networks Across Eight Cancer Types

Numerous studies have demonstrated that lncRNAs could compete with other RNAs to bind miRNAs, as competing endogenous RNAs (ceRNAs), to regulate each other. On the other hand, ceRNAs were found to be recurrently dysregulated in cancer status. However, limited studies considered the upstream epigenetic regulatory factors that disrupted the normal competing mechanism. In the present study, we constructed the lncRNA-associated dysregulated ceRNA networks across eight cancer types. lncRNAs in the individual dysregulated network and pan-cancer core dysregulated ceRNA subnetwork were found to play more important roles than mRNAs. Integrating lncRNA methylation profiles, we identified 49 epigenetically related (ER) lncRNAs involved in the dysregulated ceRNA networks, including 18 epigenetically activated (EA) lncRNAs, 18 epigenetically silenced (ES) lncRNAs, and 13 rewired ER lncRNAs across eight cancer types. Furthermore, we evaluated the epigenetic regulating patterns of these lncRNAs and screened nine pan-cancer ER lncRNAs (six EA and three ES lncRNAs). The nine lncRNAs were found to regulate the cancer hallmarks by competing with mRNAs. Moreover, we found that integrating the expression and methylation profiles of the nine lncRNAs could predict cancer incidence in eight cancer types robustly and the cancer outcome of several cancer types. These results provide an improved understanding of methylation regulation to ceRNA and offer novel potential molecular therapeutic targets for the diagnosis and prognosis across different cancer types.

Clinicopathological-Associated Regulatory Network of Deregulated circRNAs in Hepatocellular Carcinoma

Simple Summary

Here, we present a novel strategy to identify key signatures of clinically-relevant co-expressed circRNA-mRNA networks in pertinent cancer-pathways that modulate the prognosis of HCC patients, by integrating clinicopathological features, circRNA and mRNA expression profiles. Five master circRNAs were identified and experimentally demonstrated to upregulate proliferate and promote transformation. Through further integration with miRNA-expression profiles, clinically-relevant competing-endogenous-RNA (ceRNA) networks of circRNA-miRNA-mRNAs were constructed. The most up-regulated nodal-circRNA, circGPC3 was experimentally demonstrated to up-regulate cell-cycle, migration and invasion. circGPC3 was found to act as a sponge of miR-378a-3p to regulate ASPM expression and modulate cell transformation. These 5 nodal circRNAs has potential to be good prognostic biomarkers with good prognostic performance. circGPC3 has great potential to be a promising non-invasive prognostic biomarker for early HCC. We have thus demonstrated the robustness of bioinformatically-predicted master circRNAs in clinically-relevant, circRNA-mRNA networks, underscoring the important roles that these identified deregulated key/master circRNAs play in HCC.

Abstract

Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers worldwide. Here, we present a novel strategy to identify key circRNA signatures of clinically relevant co-expressed circRNA-mRNA networks in pertinent cancer-pathways that modulate prognosis of HCC patients, by integrating clinic-pathological features, circRNA and mRNA expression profiles. Through further integration with miRNA expression profiles, clinically relevant competing-endogenous-RNA (ceRNA) networks of circRNA-miRNA-mRNAs were constructed. At least five clinically relevant nodal-circRNAs, co-expressed with numerous genes, were identified from the circRNA-mRNA networks. These nodal circRNAs upregulated proliferation (except circRaly) and transformation in cells. The most upregulated nodal-circRNA, circGPC3, associated with higher-grade tumors and co-expressed with 33 genes, competes with 11 mRNAs for two shared miRNAs. circGPC3 was experimentally demonstrated to upregulate cell-cycle and migration/invasion in both transformed and non-transformed liver cell-lines. circGPC3 was further shown to act as a sponge of miR-378a-3p to regulate APSM (Abnormal spindle-like microcephaly associated) expression and modulate cell transformation. This study identifies 5 key nodal master circRNAs in a clinically relevant circRNA-centric network that are significantly associated with poorer prognosis of HCC patients and promotes tumorigenesis in cell-lines. The identification and characterization of these key circRNAs in clinically relevant circRNA-mRNA and ceRNA networks may facilitate the design of novel strategies targeting these important regulators for better HCC prognosis.

Exploring miRNA Sponge Networks of Breast Cancer by Combining miRNA-disease-lncRNA and miRNA-target Networks

Background:

Recently, ample researches show that microRNAs (miRNAs) not only interact with coding genes but interact with a pool of different RNAs. Those RNAs are called miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help researchers to study the mechanisms of breast cancers.

Objective:

We develop a new method to explore miRNA sponge networks of breast cancer by combining miRNAdisease-lncRNA and miRNA-target networks (MSNMDL).

Method:

Firstly, MSNMDL infers miRNA-lncRNA functional similarity networks from miRNAdisease- lncRNA networks. Secondly, MSNMDL forms lncRNA-target networks by using lncRNA to replace the role of matched miRNA in miRNA-target networks according to the lncRNA-miRNA pair of miRNA-lncRNA functional similarity networks. And MSNMDL only retains the genes of breast cancer in lncRNA-target networks to construct candidate miRNA sponge networks. Thirdly, MSNMDL merges these candidate miRNA sponge networks with other miRNA sponge interactions and then selects top-hub lncRNA and its interactions to construct miRNA sponge networks.

Results:

MSNMDL is superior to other methods in terms of biological significance and its identified modules might act as module signatures for prognostication of breast cancer.

Conclusion:

MiRNA sponge networks identified by MSNMDL are biologically significant and are closely associated with breast cancer, which makes MSNMDL a promising way for researchers to study the pathogenesis of breast cancer.

Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions

To understand driving biological factors for complex diseases like cancer, regulatory circuity of genes needs to be discovered. Recently, a new gene regulation mechanism called competing endogenous RNA (ceRNA) interactions has been discovered. Certain genes targeted by common microRNAs (miRNAs) "compete" for these miRNAs, thereby regulate each other by making others free from miRNA regulation. Several computational tools have been published to infer ceRNA networks. In most existing tools, however, expression abundance sufficiency, collective regulation, and groupwise effect of ceRNAs are not considered. In this study, we developed a computational tool named Crinet to infer genome-wide ceRNA networks addressing critical drawbacks. Crinet considers all mRNAs, lncRNAs, and pseudogenes as potential ceRNAs and incorporates a network deconvolution method to exclude the spurious ceRNA pairs. We tested Crinet on breast cancer data in TCGA. Crinet inferred reproducible ceRNA interactions and groups, which were significantly enriched in the cancer-related genes and processes. We validated the selected miRNA-target interactions with the protein expression-based benchmarks and also evaluated the inferred ceRNA interactions predicting gene expression change in knockdown assays. The hub genes in the inferred ceRNA network included known suppressor/oncogene lncRNAs in breast cancer showing the importance of non-coding RNA's inclusion for ceRNA inference. Crinet-inferred ceRNA groups that were consistently involved in the immune system related processes could be important assets in the light of the studies confirming the relation between immunotherapy and cancer. The source code of Crinet is in R and available at https://github.com/bozdaglab/crinet.

LncRNA H19 promotes inflammatory response induced by cerebral ischemia-reperfusion injury through regulating the miR-138-5p-p65 axis

Recent studies have shown that long non-coding RNA(LncRNA) H19 is up-regulated in the brain of rats suffering from cerebral ischemia-reperfusion (I/R) injury, inducing severe disability and mortality. Little was known about the molecular mechanisms underlying the involvement of H19 in cerebral I/R injury. In this study, a rat model of I/R was induced by transient middle cerebral artery occlusion (tMCAO). PC-12 cells exposed to oxygen and glucose deprivation/reoxygenation (OGD/R) were used as an in vitro model. Our results show that H19 is up-regulated in both in vivo and in our in vitro model. Further study indicated that knockdown of H19 promotes cell proliferation, decreases the rate of cell apoptosis, and ameliorates inflammation after OGD/R simulation. Our in vivo study shows that H19 knockdown ameliorates inflammation and improves neurological function in our rat model of tMCAO. Remarkably, the results from our luciferase reporter assays suggest that H19 negatively regulates the expression of miR-138-5p, and p65 was identified as a target of miR-138-5p. To sum up, this study demonstrated that H19 promotes an inflammatory response and improves neurological function in a rat model of tMCAO by regulating the expression of miR-138-5p and p65. This study reveals the important role and underlying mechanism of H19 in the progress of cerebral I/R injury, which could serve as a potential target for further treatment.

Identification and Functional Prediction of Long Non-Coding RNAs in Dilated Cardiomyopathy by Bioinformatics Analysis

Dilated cardiomyopathy (DCM) is a relatively common cause of heart failure and the leading cause of heart transplantation. Aberrant changes in long non-coding RNAs (lncRNAs) are involved in DCM disorder; however, the detailed mechanisms underlying DCM initiation and progression require further investigation, and new molecular targets are needed. Here, we obtained lncRNA-expression profiles associated with DCM and non-failing hearts through microarray probe-sequence re-annotation. Weighted gene co-expression network analysis revealed a module highly associated with DCM status. Then eight hub lncRNAs in this module (FGD5-AS1, AC009113.1, WDFY3-AS2, NIFK-AS1, ZNF571-AS1, MIR100HG, AC079089.1, and EIF3J-AS1) were identified. All hub lncRNAs except ZNF571-AS1 were predicted as localizing to the cytoplasm. As a possible mechanism of DCM pathogenesis, we predicted that these hub lncRNAs might exert functions by acting as competing endogenous RNAs (ceRNAs). Furthermore, we found that the above results can be essentially reproduced in an independent external dataset. We observed the localization of hub lncRNAs by RNA-FISH in human aortic smooth muscle cells and confirmed the upregulation of the hub lncRNAs in DCM patients through quantitative RT-PCR. In conclusion, these findings identified eight candidate lncRNAs associated with DCM disease and revealed their potential involvement in DCM partly through ceRNA crosstalk. Our results facilitate the discovery of therapeutic targets and enhance the understanding of DCM pathogenesis.

miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data

In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.

Long Noncoding RNA H19 Overexpression Protects against Hypoxic-Ischemic Brain Damage by Inhibiting miR-107 and Up-Regulating Vascular Endothelial Growth Factor

Long noncoding RNAs play critical roles in cellular homeostasis, and long noncoding RNA H19 (H19) is implicated in several pathologic conditions. The putative role of H19 in the pathogenesis and progression of hypoxic-ischemic brain damage (HIBD) is not yet understood. Therefore, a series of in vivo and in vitro experiments were designed to investigate the potential roles of H19 in neuronal apoptosis and cognitive dysfunction in HIBD. H19 expression was decreased in HIBD rat models established by partial occlusion of carotid artery. H19 bound to and decreased the expression of miR-107, which also increased VEGF expression. H19 overexpression reduced neuronal apoptosis and alleviated cognitive dysfunction in HIBD rats. The up-regulation of miR-107 reversed the protective effects conferred by H19. In addition, the cell model of HIBD was established by oxygen-glucose deprivation in neuronal cells used. H19 overexpression in oxygen-glucose deprivation neurons increased B-cell lymphoma-2 and decreased B-cell lymphoma-2-associated X, total and cleaved caspase-3 expressions. Taken together, the results showed that H19 expresses at a low level in HIBD. H19 overexpression decreased miR-107 and increased VEGF expression, which resulted in repressed neuronal apoptosis and alleviated cognitive dysfunction. Thus, H19 may serve as a molecular target for translational research for HIBD therapy.

Targeting PVT1 Exon 9 Re-Expresses Claudin 4 Protein and Inhibits Migration by Claudin-Low Triple Negative Breast Cancer Cells

PVT1 is a long non-coding RNA transcribed from a gene located at the 8q24 chromosomal region that has been implicated in multiple cancers including breast cancer (BC). Amplification of the 8q24 chromosomal region is a common event in BC and is associated with poor clinical outcomes. Claudin-low (CL) triple negative breast cancer (TNBC) is a subtype of BC with a particularly dismal outcome. We assessed PVT1 exon 9 expression in the T47D estrogen receptor positive BC cell line, and in the MDA MB 468 and MDA MB 231 TNBC cell lines, followed by the assessment of the expression of claudins 1, 3, 4 and 7, in MDA MB 468 and MDA MB 231 (TNBC) cells. We found that MDA MB 231 TNBC cells significantly express less claudin 1, 3, 4, and 7 than MDA MB 468 TNBC cells. PVT1 exon 9 is significantly upregulated in MDA MB 231 CL TNBC cells, and significantly downregulated in MDA MB 468 claudin high (CH) TNBC cells, in comparison to T47D estrogen receptor positive BC cells. We then analyzed the functional consequences of siRNA targeting of PVT1 exon 9 expression in the MDA MB 231 CL TNBC cells. Notably, siRNA targeting of PVT1 exon 9 expression in the MDA MB 231 CL TNBC cells led to a significant reduction in migration and the re-expression of claudin 4. Taken together, our data indicate that PVT1 exon 9 regulates claudin 4 expression and migration in CL TNBC cells, and may have clinical implications in CL TNBC.

CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network

Background

The competing endogenous RNA (ceRNA) regulation is a newly discovered post-transcriptional regulation mechanism and plays significant roles in physiological and pathological progress. CeRNA networks provide global views to help understand the regulation of ceRNAs. CeRNA networks have been widely used to detect survival biomarkers, select candidate regulators of disease genes, and predict long noncoding RNA functions. However, there is no software platform to provide overall functions from the construction to analysis of ceRNA networks.

Results

To fill this gap, we introduce CeNet Omnibus, an R/Shiny application, which provides a unified framework for the construction and analysis of ceRNA network. CeNet Omnibus enables users to select multiple measurements, such as Pearson correlation coefficient (PCC), mutual information (MI), and liquid association (LA), to identify ceRNA pairs and construct ceRNA networks. Furthermore, CeNet Omnibus provides a one-stop solution to analyze the topological properties of ceRNA networks, detect modules, and perform gene enrichment analysis and survival analysis. CeNet Omnibus intends to cover comprehensiveness, high efficiency, high expandability, and user customizability, and it also offers a web-based user-friendly interface to users to obtain the output intuitionally.

Conclusion

CeNet Omnibus is a comprehensive platform for the construction and analysis of ceRNA networks. It is highly customizable and outputs the results in intuitive and interactive. We expect that CeNet Omnibus will assist researchers to understand the property of ceRNA networks and associated biological phenomena. CeNet Omnibus is an R/Shiny application based on the Shiny framework developed by RStudio. The R package and detailed tutorial are available on our GitHub page with the URL https://github.com/GaoLabXDU/CeNetOmnibus.

Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

Epigenetics: New Insights into Mammary Gland Biology

The mammary gland undergoes important anatomical and physiological changes from embryogenesis through puberty, pregnancy, lactation and involution. These steps are under the control of a complex network of molecular factors, in which epigenetic mechanisms play a role that is increasingly well described. Recently, studies investigating epigenetic modifications and their impacts on gene expression in the mammary gland have been performed at different physiological stages and in different mammary cell types. This has led to the establishment of a role for epigenetic marks in milk component biosynthesis. This review aims to summarize the available knowledge regarding the involvement of the four main molecular mechanisms in epigenetics: DNA methylation, histone modifications, polycomb protein activity and non-coding RNA functions.

A Signature of Five Long Non-Coding RNAs for Predicting the Prognosis of Alzheimer's Disease Based on Competing Endogenous RNA Networks

Long non-coding RNAs (lncRNAs) play important roles in the pathogenesis of Alzheimer's disease (AD). However, the functions and regulatory mechanisms of lncRNA are largely unclear. Herein, we obtained 3,158 lncRNAs by microarray re-annotation. A global network of competing endogenous RNAs (ceRNAs) was developed for AD and normal samples were based on the gene expressions profiles. A total of 255 AD-deficient messenger RNA (mRNA)-lncRNAs were identified by the expression correlation analysis. Genes in the dysregulated ceRNAs were found to be mainly enriched in transcription factors and micro RNAs (miRNAs). Analysis of the disordered miRNA in the lncRNA-mRNA network revealed that 40 pairs of lncRNA shared more than one disordered miRNA. Among them, nine lncRNAs were closely associated with AD, Parkinson's disease, and other neurodegenerative diseases. Of note, five lncRNAs were found to be potential biomarkers for AD. Real-time quantitative reverse transcription PCR (qRT-PCR) assay revealed that PART1 was downregulated, while SNHG14 was upregulated in AD serum samples when compared to normal samples. This study elucidates the role of lncRNAs in the pathogenesis of AD and presents new lncRNAs that can be exploited to design diagnostic and therapeutic agents for AD.

Identification of Potential Prognostic Competing Triplets in High-Grade Serous Ovarian Cancer

Increasing lncRNA-associated competing triplets were found to play important roles in cancers. With the accumulation of high-throughput sequencing data in public databases, the size of available tumor samples is becoming larger and larger, which introduces new challenges to identify competing triplets. Here, we developed a novel method, called LncMiM, to detect the lncRNA–miRNA–mRNA competing triplets in ovarian cancer with tumor samples from the TCGA database. In LncMiM, non-linear correlation analysis is used to cover the problem of weak correlations between miRNA–target pairs, which is mainly due to the difference in the magnitude of the expression level. In addition, besides the miRNA, the impact of lncRNA and mRNA on the interactions in triplets is also considered to improve the identification sensitivity of LncMiM without reducing its accuracy. By using LncMiM, a total of 847 lncRNA-associated competing triplets were found. All the competing triplets form a miRNA–lncRNA pair centered regulatory network, in which ZFAS1, SNHG29, GAS5, AC112491.1, and AC099850.4 are the top five lncRNAs with most connections. The results of biological process and KEGG pathway enrichment analysis indicates that the competing triplets are mainly associated with cell division, cell proliferation, cell cycle, oocyte meiosis, oxidative phosphorylation, ribosome, and p53 signaling pathway. Through survival analysis, 107 potential prognostic biomarkers are found in the competing triplets, including FGD5-AS1, HCP5, HMGN4, TACC3, and so on. LncMiM is available at https://github.com/xiaofengsong/LncMiM.

SPONGEdb: a pan-cancer resource for competing endogenous RNA interactions

microRNAs (miRNAs) are post-transcriptional regulators involved in many biological processes and human diseases, including cancer. The majority of transcripts compete over a limited pool of miRNAs, giving rise to a complex network of competing endogenous RNA (ceRNA) interactions. Currently, gene-regulatory networks focus mostly on transcription factor-mediated regulation, and dedicated efforts for charting ceRNA regulatory networks are scarce. Recently, it became possible to infer ceRNA interactions genome-wide from matched gene and miRNA expression data. Here, we inferred ceRNA regulatory networks for 22 cancer types and a pan-cancer ceRNA network based on data from The Cancer Genome Atlas. To make these networks accessible to the biomedical community, we present SPONGEdb, a database offering a user-friendly web interface to browse and visualize ceRNA interactions and an application programming interface accessible by accompanying R and Python packages. SPONGEdb allows researchers to identify potent ceRNA regulators via network centrality measures and to assess their potential as cancer biomarkers through survival, cancer hallmark and gene set enrichment analysis. In summary, SPONGEdb is a feature-rich web resource supporting the community in studying ceRNA regulation within and across cancer types.

Computational Identification of ceRNA and Reconstruction of ceRNA Regulatory Network Based on RNA-seq and Small RNA-seq Data in Plants

Competing endogenous RNAs (ceRNAs) are transcripts with the ability to competitively titrate microRNAs (miRNAs) against miRNA repressing target genes to post-transcriptionally regulate the expression of corresponding miRNAs. It is a newly discovered gene regulation pattern between longer RNA and miRNA molecules. Recent research has gradually revealed the functional significance of ceRNAs in regulating normal development and stress response processes in plants and animals, as well as in cancer genesis and metastasis. Therefore, ceRNA identification is an important and necessary step to deepen our understanding of the regulation mechanisms of various biological processes. Here, we provide a pipeline used to computationally identify plant ceRNAs and reconstruct ceRNA regulatory networks based on RNA-seq and small RNA-seq data.

An Overview of the Computational Models Dealing with the Regulatory ceRNA Mechanism and ceRNA Deregulation in Cancer

Pools of RNA molecules can act as competing endogenous RNAs (ceRNAs) and indirectly alter their expression levels by competitively binding shared microRNAs. This ceRNA cross talk yields an additional posttranscriptional regulatory layer, which plays key roles in both physiological and pathological processes. MicroRNAs can act as decoys by binding multiple RNAs, as well as RNAs can act as ceRNAs by competing for binding multiple microRNAs, leading to many cross talk interactions that could favor significant large-scale effects in spite of the weakness of single interactions. Identifying and studying these extended ceRNA interaction networks could provide a global view of the fine-tuning gene regulation in a wide range of biological processes and tumor progressions. In this chapter, we review current progress of predicting ceRNA cross talk, by summarizing the most up-to-date databases, which collect computationally predicted and/or experimentally validated miRNA-target and ceRNA-ceRNA interactions, as well as the widespread computational methods for discovering and modeling possible evidences of ceRNA-ceRNA interaction networks. These methods can be grouped in two categories: statistics-based methods exploit multivariate analysis to build ceRNA networks, by considering the miRNA expression levels when evaluating miRNA sponging relationships; mathematical methods build deterministic or stochastic models to analyze and predict the behavior of ceRNA cross talk.

ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare de novo or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA-target interactions predicted by each method, we choose the best method for identifying miRNA-target regulatory network. Based on the miRNA-target interaction network identified by the best method, we further infer miRNA-target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA-target interactions and gene expression data, we identify three types of networks, including lncRNA-lncRNA, lncRNA-mRNA, and mRNA-mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR.

LINC00174 is a novel prognostic factor in thymic epithelial tumors involved in cell migration and lipid metabolism

Long non-coding RNAs are emerging as new molecular players involved in many biological processes, such as proliferation, apoptosis, cell cycle, migration, and differentiation. Their aberrant expression has been reported in variety of diseases. The aim of this study is the identification and functional characterization of clinically relevant lncRNAs responsible for the inhibition of miR-145-5p, a key tumor suppressor in thymic epithelial tumors (TETs). Starting from gene expression analysis by microarray in a cohort of fresh frozen thymic tumors and normal tissues, we identified LINC00174 as upregulated in TET. Interestingly, LINC00174 expression is positively correlated with a 5-genes signature in TETs. Survival analyses, performed on the TCGA dataset, showed that LINC00174 and its associated 5-genes signature are prognostic in TETs. Specifically, we show that LINC00174 favors the expression of SYBU, FEM1B, and SCD5 genes by sponging miR-145-5p, a well-known tumor suppressor microRNA downregulated in a variety of tumors, included TETs. Functionally, LINC00174 impacts on cell migration and lipid metabolism. Specifically, SCD5, one of the LINC00174-associated genes, is implicated in the control of lipid metabolism and promotes thymic cancer cells migration. Our study highlights that LINC00174 and its associated gene signature are relevant prognostic indicators in TETs. Of note, we here show that a key controller of lipid metabolism, SCD5, augments the migration ability of TET cells, creating a link between lipids and motility, and highlighting these pathways as relevant targets for the development of novel therapeutic approaches for TET.

Towards a comprehensive pipeline to identify and functionally annotate long noncoding RNA (lncRNA)

Long noncoding RNAs (lncRNAs) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. They are a divergent group of RNAs and are easily differentiated from other types with unique characteristics, functions, and mechanisms of action. In this review, we provide a list of some of the prominent data repositories containing lncRNAs, their interactome, and predicted and validated disease associations. Next, we discuss various wet-lab experiments formulated to obtain the data for these repositories. We also provide a critical review of in silico methods available for the identification purpose and suggest techniques to further improve their performance. The bulk of the methods currently focus on distinguishing lncRNA transcripts from the coding ones. Functional annotation of these transcripts still remains a grey area and more efforts are needed in that space. Finally, we provide details of current progress, discuss impediments, and illustrate a roadmap for developing a generalized computational pipeline for comprehensive annotation of lncRNAs, which is essential to accelerate research in this area.

miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology

miRNet is an easy-to-use, web-based platform designed to help elucidate microRNA (miRNA) functions by integrating users' data with existing knowledge via network-based visual analytics. Since its first release in 2016, miRNet has been accessed by >20 000 researchers worldwide, with ∼100 users on a daily basis. While version 1.0 was focused primarily on miRNA-target gene interactions, it has become clear that in order to obtain a global view of miRNA functions, it is necessary to bring other important players into the context during analysis. Driven by this concept, in miRNet version 2.0, we have (i) added support for transcription factors (TFs) and single nucleotide polymorphisms (SNPs) that affect miRNAs, miRNA-binding sites or target genes, whilst also greatly increased (>5-fold) the underlying knowledgebases of miRNAs, ncRNAs and disease associations; (ii) implemented new functions to allow creation and visual exploration of multipartite networks, with enhanced support for in situ functional analysis and (iii) revamped the web interface, optimized the workflow, and introduced microservices and web application programming interface (API) to sustain high-performance, real-time data analysis. The underlying R package is also released in tandem with version 2.0 to allow more flexible data analysis for R programmers. The miRNet 2.0 website is freely available at https://www.mirnet.ca.

miRTissue ce: extending miRTissue web service with the analysis of ceRNA-ceRNA interactions

BACKGROUND

Non-coding RNAs include different classes of molecules with regulatory functions. The most studied are microRNAs (miRNAs) that act directly inhibiting mRNA expression or protein translation through the interaction with a miRNAs-response element. Other RNA molecules participate in the complex network of gene regulation. They behave as competitive endogenous RNA (ceRNA), acting as natural miRNA sponges to inhibit miRNA functions and modulate the expression of RNA messenger (mRNA). It became evident that understanding the ceRNA-miRNA-mRNA crosstalk would increase the functional information across the transcriptome, contributing to identify new potential biomarkers for translational medicine.

RESULTS

We present miRTissue ce, an improvement of our original miRTissue web service. By introducing a novel computational pipeline, miRTissue ce provides an easy way to search for ceRNA interactions in several cancer tissue types. Moreover it extends the functionalities of previous miRTissue release about miRNA-target interaction in order to provide a complete insight about miRNA mediated regulation processes. miRTissue ce is freely available at http://tblab.pa.icar.cnr.it/mirtissue.html .

CONCLUSIONS

The study of ceRNA networks and its dynamics in cancer tissue could be applied in many fields of translational biology, as the investigation of new cancer biomarker, both diagnostic and prognostic, and also in the investigation of new therapeutic strategies of intervention. In this scenario, miRTissue ce can offer a powerful instrument for the analysis and characterization of ceRNA-ceRNA interactions in different tissue types, representing a fundamental step in order to understand more complex regulation mechanisms.

Characterization of super-enhancer-associated functional lncRNAs acting as ceRNAs in ESCC

Long noncoding RNAs (lncRNAs) have important regulatory roles in cancer biology. Although some lncRNAs have well-characterized functions, the vast majority of this class of molecules remains functionally uncharacterized. To systematically pinpoint functional lncRNAs, a computational approach was proposed for identification of lncRNA-mediated competing endogenous RNAs (ceRNAs) through combining global and local regulatory direction consistency of expression. Using esophageal squamous cell carcinoma (ESCC) as model, we further identified many known and novel functional lncRNAs acting as ceRNAs (ce-lncRNAs). We found that most of them significantly regulated the expression of cancer-related hallmark genes. These ce-lncRNAs were significantly regulated by enhancers, especially super-enhancers (SEs). Landscape analyses for lncRNAs further identified SE-associated functional ce-lncRNAs in ESCC, such as HOTAIR, XIST, SNHG5, and LINC00094. THZ1, a specific CDK7 inhibitor, can result in global transcriptional downregulation of SE-associated ce-lncRNAs. We further demonstrate that a SE-associated ce-lncRNA, LINC00094 can be activated by transcription factors TCF3 and KLF5 through binding to SE regions and promoted ESCC cancer cell growth. THZ1 downregulated expression of LINC00094 through inhibiting TCF3 and KLF5. Our data demonstrated the important roles of SE-associated ce-lncRNAs in ESCC oncogenesis and might serve as targets for ESCC diagnosis and therapy.

Clinical significance of long non-coding RNA DUXAP8 and its protein coding genes in hepatocellular carcinoma

Backgrounds: Hepatocellular carcinoma (HCC) is a lethal malignancy worldwide that is difficult to diagnose during the early stages and its tumors are recurrent. Long non-coding RNAs (lncRNAs) have increasingly been associated with tumor biomarkers for diagnosis and prognosis. This study attempts to explore the potential clinical significance of lncRNA DUXAP8 and its co-expression related protein coding genes (PCGs) for HCC.

Method: Data from a total of 370 HCC patients from The Cancer Genome Atlas were utilized for the analysis. DUXAP8 and its top 10 PCGs were explored for their diagnostic and prognostic implications for HCC. A risk score model and nomogram were constructed for prognosis prediction using prognosis-related genes and DUXAP8. Molecular mechanisms of DUXAP8 and its PCGs involved in HCC initiation and progression were investigated. Then, potential target drugs were identified using genome-wide DUXAP8-related differentially expressed genes in a Connectivity Map database.

Results: The top 10 PCGs were identified as: RNF2, MAGEA1, GABRA3, MKRN3, FAM133A, MAGEA3, CNTNAP4, MAGEA6, MALRD1, and DGKI. Diagnostic analysis indicated that DUXAP8, MEGEA1, MKRN3, and DGKI show diagnostic implications (all area under curves ≥0.7, p≤0.05). Prognostic analysis indicated that DUXAP8 and RNF2 had prognostic implications for HCC (adjusted p=0.014 and 0.008, respectively). The risk score model and nomogram showed an advantage for prognosis prediction. A total of 3 target drugs were determined: cinchonine, bumetanide and amiprilose and they may serve as potential therapeutic targets for HCC.

Conclusion: Functioning as an oncogene, DUXAP8 is overexpressed in tumor tissue and may serve as both a diagnostic and prognosis biomarker for HCC. MEGEA1, MKRN3, and DGKI maybe potential diagnostic biomarkers and DGKI may also be potentially prognostic biomarkers for HCC.

Construction and Comprehensive Analysis of a ceRNA Network to Reveal Potential Novel Biomarkers for Triple-Negative Breast Cancer

Background

Triple-negative breast cancer (TNBC) is the most common and aggressive type of breast cancer with an unfavourable outcome worldwide. Novel therapeutic targets are urgently required to explore this malignancy. This study explored the ceRNA network and the important genes for predicting the therapeutic targets.

Methods

It identified the differentially expressed genes of mRNAs, lncRNAs and miRNAs between TNBC and non-TNBC samples in four cohorts (TCGA, GSE38959, GSE45827 and GSE65194) to explore the novel therapeutic targets for TNBC. Downstream analyses, including functional enrichment analysis, ceRNA network, protein–protein interaction and survival analysis, were then conducted by bioinformatics analysis. Finally, the potential core protein of the ceRNA network in TNBC was validated by immunohistochemistry.

Results

A total of 1,045 lncRNAs and 28 miRNAs were differentially expressed in the TCGA TNBC samples, and the intersections of 282 mRNAs (176 upregulations and 106 downregulations) between the GEO and TCGA databases were identified. A ceRNA network composed of 7 lncRNAs, 62 mRNAs, 12 miRNAs and 244 edges specific to TNBC was established. The functional assay showed dysregulated genes, and GO, DO and KEGG enrichment analysis were performed. Survival analysis showed that mRNA LIFR and lncRNA AC124312.3 were significantly correlated with the overall survival of patients with TNBC in the TCGA databases (P < 0.05). Finally, the LIFR protein was validated, and immunohistochemical results showed the upregulated expression of LIFR in TNBC tissues.

Conclusion

Thus, our study presents an enhanced understanding of the ceRNA network in TNBC, where the key gene LIFR may be a new promising potential therapeutic target for patients with TNBC.

Construction and Analysis of the Tumor-Specific mRNA-miRNA-lncRNA Network in Gastric Cancer

Weighted correlation network analysis (WGCNA) is a statistical method that has been widely used in recent years to explore gene co-expression modules. Competing endogenous RNA (ceRNA) is commonly involved in the cancer gene expression regulation mechanism. Some ceRNA networks are recognized in gastric cancer; however, the prognosis-associated ceRNA network has not been fully identified using WGCNA. We performed WGCNA using datasets from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) to identify cancer-associated modules. The criteria of differentially expressed RNAs between normal stomach samples and gastric cancer samples were set at the false discovery rate (FDR) < 0.01 and |fold change (FC)| > 1.3. The ceRNA relationships obtained from the RNAinter database were examined by both the Pearson correlation test and hypergeometric test to confirm the mRNA-lncRNA regulation. Overlapped genes were recognized at the intersections of genes predicted by ceRNA relationships, differentially expressed genes, and genes in cancer-specific modules. These were then used for univariate and multivariate Cox analyses to construct a risk score model. The ceRNA network was constructed based on the genes in this model. WGCNA-uncovered genes in the green and turquoise modules are those most associated with gastric cancer. Eighty differentially expressed genes were observed to have potential prognostic value, which led to the identification of 12 prognosis-related mRNAs (KIF15, FEN1, ZFP69B, SP6, SPARC, TTF2, MSI2, KYNU, ACLY, KIF21B, SLC12A7, and ZNF823) to construct a risk score model. The risk genes were validated using the GSE62254 and GSE84433 datasets, with 0.82 as the universal cutoff value. 12 genes, 12 lncRNAs, and 35 miRNAs were used to build a ceRNA network with 86 dysregulated lncRNA-mRNA ceRNA pairs. Finally, we developed a 12-gene signature from both prognosis-related and tumor-specific genes, and then constructed a ceRNA network in gastric cancer. Our findings may provide novel insights into the treatment of gastric cancer.

Competing Endogenous RNAs, Non-Coding RNAs and Diseases: An Intertwined Story

MicroRNAs (miRNAs), a class of small non-coding RNA molecules, are responsible for RNA silencing and post-transcriptional regulation of gene expression. They can mediate a fine-tuned crosstalk among coding and non-coding RNA molecules sharing miRNA response elements (MREs). In a suitable environment, both coding and non-coding RNA molecules can be targeted by the same miRNAs and can indirectly regulate each other by competing for them. These RNAs, otherwise known as competing endogenous RNAs (ceRNAs), lead to an additional post-transcriptional regulatory layer, where non-coding RNAs can find new significance. The miRNA-mediated interplay among different types of RNA molecules has been observed in many different contexts. The analyses of ceRNA networks in cancer and other pathologies, as well as in other physiological conditions, provide new opportunities for interpreting omics data for the field of personalized medicine. The development of novel computational tools, providing putative predictions of ceRNA interactions, is a rapidly growing field of interest. In this review, I discuss and present the current knowledge of the ceRNA mechanism and its implications in a broad spectrum of different pathologies, such as cardiovascular or autoimmune diseases, cancers and neurodegenerative disorders.

Exploring the potential microRNA sponge interactions of breast cancer based on some known interactions

MicroRNA (miRNA) sponges' regulatory mechanisms play an important role in developing human cancer. Herein, we develop a new method to explore potential miRNA sponge interactions (EPMSIs) for breast cancer. Based on some known interactions, and a matching gene expression profile, EPMSIs explored other potential miRNA sponge interactions for breast cancer. Every interaction is inferred with a value representing interaction intensity. Then, we apply a clustering algorithm called BCPlaid to potential interactions. Ten modules are identified; nine of them are closely associated with biological enrichments. When we employ a classification algorithm to separate normal and tumor samples in each module, each module demonstrates powerful classification performance. Furthermore, EPMSI illustrates a new method to explore the miRNA sponge regulatory network for breast cancer by applying its superior performance.

Emerging roles of long non-coding RNAs in the p53 network

Transcription factor p53 is activated in response to numerous stress stimuli in order to promote repair and survival or death of abnormal cells. For decades, regulatory mechanisms and downstream targets that execute the many biological functions of tumour suppressor p53 largely focused on the products of protein-coding genes. Recently, an entirely new class of molecules, termed long non-coding RNAs (lncRNAs), were discovered as key regulatory players in shaping p53 activity and biological outcomes. Many p53-regulated lncRNAs are now reported to either directly or indirectly intervene in p53-regulatory networks, generally in fine-tuning p53's tumour surveillance programme. Recent studies reveal that signals that converge upon p53 to regulate its activity, and molecules that implement downstream p53-response include both proteins and lncRNAs. In this review, we discuss the non-proteomic component of p53-regulatory networks, focusing on lncRNAs regulated by p53 and/or that regulate p53 activity, and their impact on biological outcomes.

Two way network construction and analysis of mRNA, miRNA and lncRNA reveals critical regulators and regulatory modules in cardiovascular diseases

BACKGROUND

Cardiovascular diseases contribute to the leading cause of deaths (31%) in the world population.

OBJECTIVE

The objective of this study is to compile non-coding RNA-gene interaction into a core regulatory network whose dysregulation might play an important role in disease progression.

METHOD

We applied a structured approach to reconstruct the interaction network of lncRNAs, miRNAs and genes involved in cardiovascular diseases. For network construction, we used 'diseasome to interactome' and 'interactome to diseasome' approaches and developed two regulatory networks in heart disorders. In diseasome to interactome approach, starting from a disease-centric network we, expanded the data into an interaction network. However in interactome to diseasome, we used a set of guide genes, miRNAs and lncRNAs to arrive at the common diseases. The disease-centric network in combination with the interaction network will shed light on the interconnected components in a huge diseasome network implicated in heart disorders and manifested through small sub-networks while progressing. Using the above networks we created a sub-networks consisting only of hub genes, miRNAs and lncRNAs on both approaches. The dysregulation of any one of the hubs can lead to a disease condition.

RESULTS

The top ranking hubs common in both the sub-networks were found to be VEGFA, MALAT1, HOTAIR, H19 and hsa-miR-15a. Our network based study reveals an entanglement of regulatory sub-network of miRNAs, lncRNAs and genes in multiple conditions.

CONCLUSION

The identification of hubs in the core triple node network of elements in disease development and progression demonstrates a promising role for network based approaches in targeting critical molecules for drug development.

Comprehensive analysis of aberrantly expressed long non‑coding RNAs, microRNAs, and mRNAs associated with the competitive endogenous RNA network in cervical cancer

Cervical cancer is a common malignant disease that poses a serious health threat to women worldwide. Growing research efforts have focused on protein‑coding and non‑coding RNAs involved in the tumorigenesis and prognosis of various types of cancer. The potential molecular mechanisms and the interaction among long non‑coding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs require further investigation in cervical cancer. In the present study, lncRNA, miRNA, and mRNA expression profiles of 304 primary tumor tissues from patients with cervical cancer and 3 solid normal tissues from The Cancer Genome Atlas (TCGA) dataset were studied via RNA sequencing (RNA‑seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using R package clusterProfiler to annotate the principal functions of differentially expressed (DE) mRNAs. Kaplan‑Meier analysis was also conducted to investigate the effects of DElncRNAs, DEmiRNAs, and DEmRNAs on overall survival. A total of 2,255 mRNAs, 133 miRNAs, and 150 lncRNAs that were differentially expressed were identified with a threshold of P<0.05 and |fold change (FC)|>2. Functional enrichment analysis indicated that DEmRNAs were enriched in cancer‑associated KEGG pathways. Furthermore, 255 mRNAs, 15 miRNAs, and 12 lncRNAs that were significantly associated with overall survival in cervical carcinoma were also identified. Importantly, an miRNA‑mediated competitive endogenous RNA (ceRNA) network was successfully constructed based on the expression profiles of DElncRNAs and DEmRNAs. More importantly, it was found that the lncRNA EPB41L4A‑AS1 may function as a pivotal regulator in carcinoma of the uterine cervix. Taken together, the present study has provided novel insights into investigating the potential mechanisms underlying tumorigenesis, development, and prognosis of cervical cancer, and presented new potential avenues for cancer therapeutics.

LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer

Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline’s performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.

Previous studies have revealed that long non-coding RNAs (lncRNAs), as microRNA (miRNA) sponges or competing endogenous RNAs (ceRNAs), can regulate the expression levels of messenger RNAs (mRNAs) by decreasing the amount of miRNAs interacting with mRNAs. In this work, we hypothesize that the “tug-of-war” between RNA transcripts for attracting miRNAs is across groups or modules. Based on the hypothesis, we propose a framework called LMSM, to identify LncRNA related MiRNA Sponge Modules. Based on the two miRNA sponge modular competition principles, significant sharing of miRNAs and high canonical correlation between the sponge lncRNAs and mRNAs, LMSM is also capable of predicting miRNA targets. LMSM not only extends the ceRNA hypothesis, but also provides a novel way to investigate the biological functions and modular mechanism of lncRNAs in breast cancer.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.

Identification of potential prognostic long non-coding RNA for predicting survival in intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is an aggressive biliary epithelial tumor with poor prognosis. There are increasing evidences that long non-coding RNAs (lncRNAs) are dysregulated in multifarious tumors, revealing potential significant role of lncRNAs in tumorigenesis.We used the ICC dataset retrieved from The Cancer Genome Atlas and the Gene Expression Omnibus database to obtain the lncRNAs expression profiles and identify potential prognostic lncRNAs for predicting the prognosis in ICC. Univariate and multivariate Cox regression analyses were performed to construct a prognostic index (PI). Furthermore, coexpression analysis and functional assessment were performed to initially investigate the function of these prognostic lncRNAs.A total of 255 differentially expressed lncRNAs (DElncRNAs) were identified among two RNA sequencing dataset of a total 63 ICC patients with 98 samples using R platform. Thirteen of 255 DElncRNAs were identified as prognostic lncRNAs and used for a PI. Patients with high PI were associated with poor prognostic (P = .0064), and the Cox regression showed consistent result (P = .042). The time-dependent receiver operating characteristic analysis showed the PI performed well in ICC survival prediction with an area under curve of 0.921, 0.801, and 0.717 for 1-, 3-, and 5-year survival, respectively.In conclusion, we included 13 identified prognostic DElncRNAs and constructed a prognostic signature/PI. ICC patient with higher PI was associated with poorer prognosis. However, the clinical role as well as biological functions of constructed PI and these prognostic DElncRNAs need to be verified in future study.

The Role of microRNAs, Long Non-coding RNAs, and Circular RNAs in Cervical Cancer

Prolonged infection of uterine cervix epithelium with human papillomavirus (HPV) and constitutive expression of viral oncogenes have been recognized as the main cause of the complex molecular changes leading to transformation of cervical epithelial cells. Deregulated expression of microRNAs (miRNA), long non-coding RNAs (lncRNA), and circular RNAs (circRNA) is involved in the initiation and promotion processes of cervical cancer development. Expression profiling of small RNAs in cervical neoplasia revealed up-regulated "oncogenic" miRNAs, such as miR-10a, miR-21, miR-19, and miR-146a, and down regulated "tumor suppressive" miRNAs, including miR-29a, miR-372, miR-214, and miR-218, associated with cell growth, malignant transformation, cell migration, and invasion. Also several lncRNAs, comprising among others HOTAIR, MALAT1, GAS5, and MEG3, have shown to be associated with various pathogenic processes such as tumor progression, invasion as well as therapeutic resistance and emerged as new diagnostic and prognostic biomarkers in cervical cancer. Moreover, human genes encoded circular RNAs, such as has_circ-0018289, have shown to sponge specific miRNAs and to concur to the deregulation of target genes. Viral encoded circE7 has also demonstrated to overexpress E7 oncoprotein thus contributing to cell transformation. In this review, we summarize current literature on the complex interplay between miRNAs, lncRNAs, and circRNAs and their role in cervical neoplasia.

Oncogenic Role of PVT1 and Therapeutic Implications

PVT1, a long non-coding RNA has been implicated in a variety of human cancers. Recent advancements have led to increasing discovery of the critical roles of PVT1 in cancer initiation and progression. Novel insight is emerging about PVT1's mechanism of action in different cancers. Identifying and understanding the variety of activities of PVT1 involved in cancers is a necessity for the development of PVT1 as a diagnostic biomarker or therapeutic target in cancers where PVT1 is dysregulated. PVT1's varied activities include overexpression, modulation of miRNA expression, protein interactions, targeting of regulatory genes, formation of fusion genes, functioning as a competing endogenous RNA (ceRNA), and interactions with MYC, among many others. Furthermore, bioinformatic analysis of PVT1 interactions in cancers has aided understanding of the numerous pathways involved in PVT1 contribution to carcinogenesis in a cancer type-specific manner. However, these recent findings show that there is much more to be learned to be able to fully exploit PVT1 for cancer prognostication and therapy. In this review, we summarize some of the latest findings on PVT1's oncogenic activities, signaling networks and how targeting these networks can be a strategy for cancer therapy.

Systematic analysis of lncRNA and microRNA dynamic features reveals diagnostic and prognostic biomarkers of myocardial infarction

Analyses of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) implicated in myocardial infarction (MI) have increased our understanding of gene regulatory mechanisms in MI. However, it is not known how their expression fluctuates over the different stages of MI progression. In this study, we used time-series gene expression data to examine global lncRNA and miRNA expression patterns during the acute phase of MI and at three different time points thereafter. We observed that the largest expression peak for mRNAs, lncRNAs, and miRNAs occurred during the acute phase of MI and involved mainly protein-coding, rather than non-coding RNAs. Functional analysis indicated that the lncRNAs and miRNAs most sensitive to MI and most unstable during MI progression were usually related to fewer biological functions. Additionally, we developed a novel computational method for identifying dysregulated competing endogenous lncRNA-miRNA-mRNA triplets (LmiRM-CTs) during MI onset and progression. As a result, a new panel of candidate diagnostic biomarkers defined by seven lncRNAs was suggested to have high classification performance for patients with or without MI, and a new panel of prognostic biomarkers defined by two lncRNAs evidenced high discriminatory capability for MI patients who developed heart failure from those who did not.

LncRNA FEZF1-AS1 Modulates Cancer Stem Cell Properties of Human Gastric Cancer Through miR-363-3p/HMGA2

Gastric cancer (GC) is a leading cause of cancer-related death with poor prognosis. Growing evidence has shown that long noncoding ribonucleic acid (lncRNA) FEZ family zinc finger 1 antisense RNA 1(FEZF1-AS1), an "oncogene," regulates tumor progression and supports cancer stem cell. However, the tumorigenic mechanism of FEZF1-AS1 on gastric cancer stem cell (GCSC) is yet to be investigated. Here, we discovered that FEZF1-AS1 was upregulated in GC tissues and cell lines. Knockdown of FEZF1-AS1 inhibited sphere formation and decreased expression of stem factors and markers. Moreover, FEZF1-AS1 silence also suppressed cell proliferation, viability, invasion, and migration of GCSCs. MiR-363-3p is used as a target of FEZF1-AS1, because its expression was suppressed by FEZF1-AS1 in GCSCs. FEZF1-AS1 could sponge miR-363-3p and increased the expression of high-mobility group AT-hook 2 (HMGA2). The expression of FEZF1-AS1 and miR-363-3p, as well as that of miR-363-3p and HMGA2, was negatively correlated in GC tissues. Finally, FEZF1-AS1 contributed to promotion of GCSCs progression partially through inhibition of miR-363-3p. Subcutaneous xenotransplanted tumor model revealed that silence of FEZF1-AS1 suppressed in vivo tumorigenic ability of GSCS via downregulation of HMGA2. In general, our findings clarified the critical regulatory role of FEZF1-AS1/miR-363-3p/HMGA2 axis in GCSC progression, providing a potential therapeutic target for GC.

Unravelling the role of long non-coding RNA - LINC01087 in breast cancer

Apoptosis is a 'programmed fate' of all cells participating in diverse physiological and pathological conditions. The role of critical regulators and their involvement in this complex multi-stage process of apoptosis weaved around non-coding RNAs (ncRNAs) is poorly deciphered in breast carcinoma (BC). Aberrant expression patterns of the ncRNAs and their interacting partners, either ncRNAs or coding RNAs or proteins at any point along these pathways, may lead to the malignant transformation of the affected cells, tumour metastasis and resistance to anticancer drugs. Longest non-coding type of ncRNAs (lncRNAs) have been considered as critical factors for the development and progression of breast cancer. The aim of our study was to identify set of novel lncRNAs interacting with microRNAs (miRNAs) or proteins that were significantly dysregulated in breast cancer using RNA-Sequencing (RNA-Seq) technique in different samples acting as oncogenic drivers contributing to cancerous phenotype involved in post-transcriptional processing of RNAs. Four lncRNAs; LINC01087, lnc-CLSTN2-1:1, lnc-c7orf65-3:3 and LINC01559:2 were selected for further analysis. Gene expression analysis of over-expressed LINC01087 in vitro reduced both cell viability and apoptosis. We integrated miRNA and mRNA (hsa-miR-548 and AKT1) expression profiles with curated regulations with lncRNA (LINC01087) which has not been previously associated with any breast cancer type, using different computational tools. The network (lncRNA→ miRNA→ mRNA) is promising for the identification of carcinoma associated genes and apoptosis signaling path highlighting the potential roles of LINC01087, hsa-miR548n, AKT1 gene which may play crucial role in proliferation.

Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information

BACKGROUND

A long noncoding RNA (lncRNA) can act as a competing endogenous RNA (ceRNA) to compete with an mRNA for binding to the same miRNA. Such an interplay between the lncRNA, miRNA, and mRNA is called a ceRNA crosstalk. As an miRNA may have multiple lncRNA targets and multiple mRNA targets, connecting all the ceRNA crosstalks mediated by the same miRNA forms a ceRNA network. Methods have been developed to construct ceRNA networks in the literature. However, these methods have limits because they have not explored the expression characteristics of total RNAs.

RESULTS

We proposed a novel method for constructing ceRNA networks and applied it to a paired RNA-seq data set. The first step of the method takes a competition regulation mechanism to derive candidate ceRNA crosstalks. Second, the method combines a competition rule and pointwise mutual information to compute a competition score for each candidate ceRNA crosstalk. Then, ceRNA crosstalks which have significant competition scores are selected to construct the ceRNA network. The key idea, pointwise mutual information, is ideally suitable for measuring the complex point-to-point relationships embedded in the ceRNA networks.

CONCLUSION

Computational experiments and results demonstrate that the ceRNA networks can capture important regulatory mechanism of breast cancer, and have also revealed new insights into the treatment of breast cancer. The proposed method can be directly applied to other RNA-seq data sets for deeper disease understanding.

Long noncoding RNAs in cancer: From discovery to therapeutic targets

Long noncoding RNAs (lncRNAs) have recently gained considerable attention as key players in biological regulation; however, the mechanisms by which lncRNAs govern various disease processes remain mysterious and are just beginning to be understood. The ease of next-generation sequencing technologies has led to an explosion of genomic information, especially for the lncRNA class of noncoding RNAs. LncRNAs exhibit the characteristics of mRNAs, such as polyadenylation, 5' methyl capping, RNA polymerase II-dependent transcription, and splicing. These transcripts comprise more than 200 nucleotides (nt) and are not translated into proteins. Directed interrogation of annotated lncRNAs from RNA-Seq datasets has revealed dramatic differences in their expression, largely driven by alterations in transcription, the cell cycle, and RNA metabolism. The fact that lncRNAs are expressed cell- and tissue-specifically makes them excellent biomarkers for ongoing biological events. Notably, lncRNAs are differentially expressed in several cancers and show a distinct association with clinical outcomes. Novel methods and strategies are being developed to study lncRNA function and will provide researchers with the tools and opportunities to develop lncRNA-based therapeutics for cancer.

Comprehensive analysis of the lncRNA‑associated competing endogenous RNA network in breast cancer

Long noncoding RNAs (lncRNAs) have been confirmed to be potential prognostic markers in a variety of cancers and to interact with microRNAs (miRNAs) as competing endogenous RNAs (ceRNAs) to regulate target gene expression. However, the role of lncRNA‑mediated ceRNAs in breast cancer (BC) remains unclear. In the present study, a ceRNA network was generated to explore their role in BC. The expression profiles of mRNAs, miRNAs and lncRNAs in 1,109 BC tissues and 113 normal breast tissues were obtained from The Cancer Genome Atlas database (TCGA). A total of 3,198 differentially expressed (DE) mRNAs, 150 differentially DEmiRNAs and 1,043 DElncRNAs were identified between BC and normal tissues. A lncRNA‑miRNA‑mRNA network associated with BC was successfully constructed based on the combined data obtained from RNA databases, and comprised 97 lncRNA nodes, 24 miRNA nodes and 74 mRNA nodes. The biological functions of the 74 DEmRNAs were further investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results demonstrated that the DEmRNAs were significantly enriched in two GO biological process categories; the main biological process enriched term was 'positive regulation of GTPase activity'. By KEGG analysis, four key enriched pathways were obtained, including the 'MAPK signaling pathway', the 'Ras signaling pathway', 'prostate cancer', and the 'FoxO signaling pathway'. Kaplan‑Meier survival analysis revealed that six DElncRNAs (INC AC112721.1, LINC00536, MIR7‑3HG, ADAMTS9‑AS1, AL356479.1 and LINC00466), nine DEmRNAs (KPNA2, RACGAP1, SHCBP1, ZNF367, NTRK2, ORS1, PTGS2, RASGRP1 and SFRP1) and two DEmiRNAs (hsa‑miR‑301b and hsa‑miR‑204) had significant effects on overall survival in BC. The present results demonstrated the aberrant expression of INC AC112721.1, AL356479.1, LINC00466 and MIR7‑3HG in BC, indicating their potential prognostic role in patients with BC.

LncmiRSRN: identification and analysis of long non-coding RNA related miRNA sponge regulatory network in human cancer

Motivation

MicroRNAs (miRNAs) are small non-coding RNAs with the length of ∼22 nucleotides. miRNAs are involved in many biological processes including cancers. Recent studies show that long non-coding RNAs (lncRNAs) are emerging as miRNA sponges, playing important roles in cancer physiology and development. Despite accumulating appreciation of the importance of lncRNAs, the study of their complex functions is still in its preliminary stage. Based on the hypothesis of competing endogenous RNAs (ceRNAs), several computational methods have been proposed for investigating the competitive relationships between lncRNAs and miRNA target messenger RNAs (mRNAs). However, when the mRNAs are released from the control of miRNAs, it remains largely unknown as to how the sponge lncRNAs influence the expression levels of the endogenous miRNA targets.

Results

We propose a novel method to construct lncRNA related miRNA sponge regulatory networks (LncmiRSRNs) by integrating matched lncRNA and mRNA expression profiles with clinical information and putative miRNA-target interactions. Using the method, we have constructed the LncmiRSRNs for four human cancers (glioblastoma multiforme, lung cancer, ovarian cancer and prostate cancer). Based on the networks, we discover that after being released from miRNA control, the target mRNAs are normally up-regulated by the sponge lncRNAs, and only a fraction of sponge lncRNA-mRNA regulatory relationships and hub lncRNAs are shared by the four cancers. Moreover, most sponge lncRNA-mRNA regulatory relationships show a rewired mode between different cancers, and a minority of sponge lncRNA-mRNA regulatory relationships conserved (appearing) in different cancers may act as a common pivot across cancers. Besides, differential and conserved hub lncRNAs may act as potential cancer drivers to influence the cancerous state in cancers. Functional enrichment and survival analysis indicate that the identified differential and conserved LncmiRSRN network modules work as functional units in biological processes, and can distinguish metastasis risks of cancers. Our analysis demonstrates the potential of integrating expression profiles, clinical information and miRNA-target interactions for investigating lncRNA regulatory mechanism.

Availability and implementation

LncmiRSRN is freely available (https://github.com/zhangjunpeng411/LncmiRSRN).

Supplementary information

Supplementary data are available at Bioinformatics online.

LncRNA PVT1 up-regulation is a poor prognosticator and serves as a therapeutic target in esophageal adenocarcinoma

Background

PVT1 has emerged as an oncogene in many tumor types. However, its role in Barrett’s esophagus (BE) and esophageal adenocarcinoma (EAC) is unknown. The aim of this study was to assess the role of PVT1 in BE/EAC progression and uncover its therapeutic value against EAC.

Methods

PVT1 expression was assessed by qPCR in normal, BE, and EAC tissues and statistical analysis was performed to determine the association of PVT1 expression and EAC (stage, metastases, and survival). PVT1 antisense oligonucleotides (ASOs) were tested for their antitumor activity in vitro and in vivo.

Results

PVT1 expression was up-regulated in EACs compared with paired BEs, and normal esophageal tissues. High expression of PVT1 was associated with poor differentiation, lymph node metastases, and shorter survival. Effective knockdown of PVT1 in EAC cells using PVT1 ASOs resulted in decreased cell proliferation, invasion, colony formation, tumor sphere formation, and reduced proportion of ALDH1A1⁺ cells. Mechanistically, we discovered mutual regulation of PVT1 and YAP1 in EAC cells. Inhibition of PVT1 by PVT1 ASOs suppressed YAP1 expression through increased phosphor-LATS1and phosphor-YAP1 while knockout of YAP1 in EAC cells significantly suppressed PVT1 levels indicating a positive regulation of PVT1 by YAP1. Most importantly, we found that targeting both PVT1 and YAP1 using their specific ASOs led to better antitumor activity in vitro and in vivo.

Conclusions

Our results provide strong evidence that PVT1 confers an aggressive phenotype to EAC and is a poor prognosticator. Combined targeting of PVT1 and YAP1 provided the highest therapeutic index and represents a novel therapeutic strategy.

Electronic supplementary material

The online version of this article (10.1186/s12943-019-1064-5) contains supplementary material, which is available to authorized users.

JAMI: fast computation of conditional mutual information for ceRNA network analysis

Motivation

Genome-wide measurements of paired miRNA and gene expression data have enabled the prediction of competing endogenous RNAs (ceRNAs). It has been shown that the sponge effect mediated by protein-coding as well as non-coding ceRNAs can play an important regulatory role in the cell in health and disease. Therefore, many computational methods for the computational identification of ceRNAs have been suggested. In particular, methods based on Conditional Mutual Information (CMI) have shown promising results. However, the currently available implementation is slow and cannot be used to perform computations on a large scale.

Results

Here, we present JAMI, a Java tool that uses a non-parametric estimator for CMI values from gene and miRNA expression data. We show that JAMI speeds up the computation of ceRNA networks by a factor of ∼70 compared to currently available implementations. Further, JAMI supports multi-threading to make use of common multi-core architectures for further performance gain.

Requirements

Java 8.

Availability and implementation

JAMI is available as open-source software from https://github.com/SchulzLab/JAMI.

Supplementary information

Supplementary data are available at Bioinformatics online.

Non-coding RNA regulatory networks

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder

The interaction of miRNA and lncRNA is known to be important for gene regulations. However, the number of known lncRNA-miRNA interactions is still very limited and there are limited computational tools available for predicting new ones. Considering that lncRNAs and miRNAs share internal patterns in the partnership between each other, the underlying lncRNA-miRNA interactions could be predicted by utilizing the known ones, which could be considered as a semi-supervised learning problem. It is shown that the attributes of lncRNA and miRNA have a close relationship with the interaction between each other. Effective use of side information could be helpful for improving the performance especially when the training samples are limited. In view of this, we proposed an end-to-end prediction model called GCLMI (Graph Convolution for novel lncRNA-miRNA Interactions) by combining the techniques of graph convolution and auto-encoder. Without any preprocessing process on the feature information, our method can incorporate raw data of node attributes with the topology of the interaction network. Based on a real dataset collected from a public database, the results of experiments conducted on k-fold cross validations illustrate the robustness and effectiveness of the prediction performance of the proposed prediction model. We prove the graph convolution layer as designed in the proposed model able to effectively integrate the input data by filtering the graph with node features. The proposed model is anticipated to yield highly potential lncRNA-miRNA interactions in the scenario that different types of numerical features describing lncRNA or miRNA are provided by users, serving as a useful computational tool.