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

Genome‑wide investigation of the clinical implications and molecular mechanism of long noncoding RNA LINC00668 and protein‑coding genes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of tumor‑related mortalities worldwide. Long noncoding RNAs have been reported to be associated with tumor initiation, progression and prognosis. The present study aimed to explore the association between long noncoding RNA LINC00668 and its co‑expression correlated protein‑coding genes (PCGs) in HCC. Data of 370 HCC patients from The Cancer Genome Atlas database were used for analysis. LINC00668 and its top 10 PCGs were selected to determine their diagnostic and prognostic value. Molecular mechanisms were explored to identify metabolic processes that LINC00668 and its PCGs are involved in. Prognosis‑related clinical factors and PCGs were used to construct a nomogram for predicting prognosis in HCC. A Connectivity Map was constructed to identify candidate target drugs for HCC. The top 10 PCGs identified were: Pyrimidineregic receptor P2Y4 (P2RY4), signal peptidase complex subunit 2 (SPCS2), family with sequence similarity 86 member C1 (FAM86C1), tudor domain containing 5 (TDRD5), ferritin light chain (FTL), stratifin (SFN), nucleolar complex associated 2 homolog (NOC2L), peroxiredoxin 1 (PRDX1), cancer/testis antigen 2 CTAG2 and leucine zipper and CTNNBIP1 domain containing (LZIC). FAM86C1, CTAG2 and SFN had significant diagnostic value for HCC (total area under the curve ≥0.7, P≤0.05); LINC00668, FAM86C1, TDRD5, FTL and SFN were of significant prognostic value for HCC (all P≤0.05). Investigation into the molecular mechanism indicated that LINC00668 affects cell division, cell cycle, mitotic nuclear division, and drug metabolism cytochrome P450 (all P≤0.05). The Connectivity Map identified seven candidate target drugs for the treatment of HCC, which were: Indolylheptylamine, mimosine, disopyramide, lidocaine, NU‑1025, bumetanide, and DQNLAOWBTJPFKL‑PKZXCIMASA‑N (all P≤0.05). Our findings indicated that LINC00668 may function as an oncogene and its overexpression indicates poor prognosis of HCC. FAM86C1, CTAG2 and SFN are of diagnostic significance, while FAM86C1, TDRD5, FTL and SFN are of prognostic significance for HCC.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

LINC01018 confers a novel tumor suppressor role in hepatocellular carcinoma through sponging microRNA-182-5p

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality. Emerging evidence has demonstrated that some long noncoding RNAs (lncRNAs) are involved in the development and progression of HCC. Herein, the current study aimed to explore the potential mechanism of LINC01018 in regulating the progression of HCC. Initially, the expression of LINC01018, microRNA-182-5p (miR-182-5p), and forkhead box protein O1 (FOXO1) was quantified in 72 paired HCC and adjacent normal tissue samples as well as HCC cells, followed by identification of the interaction among them. To define the contributory role of LINC01018 in the progression of HCC, the expression of LINC01018, miR-182-5p, or FOXO1 was altered in HCC cells, followed by evaluation of cell proliferation, cell cycle distribution, and cell apoptosis. Finally, in vivo tests were performed to further verify the role of LINC01018 in HCC. It was observed that LINC01018 and FOXO1 were poorly expressed but miR-182-5p was highly expressed in HCC tissues and cells. The upregulation of LINC01018 was shown to decrease proliferation while promoting apoptosis of HCC cells. LINC01018 acted as a sponge of miR-182-5p, which targeted FOXO1. Last, mice injected with Hep3B overexpressing FOXO1 displayed suppressed xenograft tumor formation. Collectively, overexpression of LINC01018 represses proliferation and promotes apoptosis of HCC cells via upregulation of FOXO1 by sponging miR-182-5p, which highlights overexpression of LINC01018 as a candidate suppressor of HCC.NEW & NOTEWORTHY This study provides evidence for understanding the molecular mechanism involved in the progression of hepatocellular carcinoma and identifies a novel network of LINC01018/miR-182-5p/FOXO1. We also conducted in vivo experiments in nude mice to validate the anti-tumor effect of LINC01018.

The number of titrated microRNA species dictates ceRNA regulation

microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts-our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were co-regulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor- and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent.

Large-scale inference of competing endogenous RNA networks with sparse partial correlation

MOTIVATION

MicroRNAs (miRNAs) are important non-coding post-transcriptional regulators that are involved in many biological processes and human diseases. Individual miRNAs may regulate hundreds of genes, giving rise to a complex gene regulatory network in which transcripts carrying miRNA binding sites act as competing endogenous RNAs (ceRNAs). Several methods for the analysis of ceRNA interactions exist, but these do often not adjust for statistical confounders or address the problem that more than one miRNA interacts with a target transcript.

RESULTS

We present SPONGE, a method for the fast construction of ceRNA networks. SPONGE uses 'multiple sensitivity correlation', a newly defined measure for which we can estimate a distribution under a null hypothesis. SPONGE can accurately quantify the contribution of multiple miRNAs to a ceRNA interaction with a probabilistic model that addresses previously neglected confounding factors and allows fast P-value calculation, thus outperforming existing approaches. We applied SPONGE to paired miRNA and gene expression data from The Cancer Genome Atlas for studying global effects of miRNA-mediated cross-talk. Our results highlight already established and novel protein-coding and non-coding ceRNAs which could serve as biomarkers in cancer.

AVAILABILITY AND IMPLEMENTATION

SPONGE is available as an R/Bioconductor package (doi: 10.18129/B9.bioc.SPONGE).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Network-Based Methods and Other Approaches for Predicting lncRNA Functions and Disease Associations

The discovery that a considerable portion of eukaryotic genomes is transcribed and gives rise to long noncoding RNAs (lncRNAs) provides an important new perspective on the transcriptome and raises questions about the centrality of these lncRNAs in gene-regulatory processes and diseases. The rapidly increasing number of mechanistically investigated lncRNAs has provided evidence for distinct functional classes, such as enhancer-like lncRNAs, which modulate gene expression via chromatin looping, and noncoding competing endogenous RNAs (ceRNAs), which act as microRNA decoys. Despite great progress in the last years, the majority of lncRNAs are functionally uncharacterized and their implication for disease biogenesis and progression is unknown. Here, we summarize recent developments in lncRNA function prediction in general and lncRNA-disease associations in particular, with emphasis on in silico methods based on network analysis and on ceRNA function prediction. We believe that such computational techniques provide a valuable aid to prioritize functional lncRNAs or disease-relevant lncRNAs for targeted, experimental follow-up studies.

PVT1 Signaling Is a Mediator of Cancer Progression

There is increasing evidence that PVT1 has oncogenic properties and regulates proliferation and growth of many cancers. Themolecular mechanisms of action of PVT1 are mediated, in part, by microRNAs (miRNAs). However, some well-established transcription factors involved in cancer cell proliferation share a common thread of microRNA associations with PVT1. Furthermore, these microRNAs are also involved in mechanisms that lead to the development of drug resistance in cancer cells. While several microRNAs have been implicated directly in PVT1-mediated tumorigenesis, significant steps need to be taken to elucidate these important relationships. We synthesize the current knowledge of the miRNAs and associated genes by which PVT1 contributes to tumorigenesis. Overall, the trend suggests a negative correlation of microRNA expression with PVT1. It is clear that future studies involving PVT1 should be carried out in conjunction with microRNA analysis and should include large scale lncRNA-miRNA-mRNA network analysis. Likewise, the relationship between established transcription factors such as p53 and MYC, and processes like epithelial-mesenchymal transition may offer valuable insight into the yet unknown mechanisms of PVTI-mediated cancer progression via microRNA-dependent signaling networks.

Knockdown of long non-coding RNA PVT1 inhibits the proliferation of Raji cells through cell cycle regulation

Long non-coding RNA plasmacytoma variant translocation 1 (PVT1) has been reported to be associated with oncogenesis. However, the functional role of PVT1 in Burkitt lymphoma has not yet been addressed. The purpose of the present study was to investigate the effect of PVT1 knockdown by small interfering RNA (siRNA) on the proliferation of Burkitt lymphoma Raji cells and to explore its possible mechanism of action. An effective siRNA targeting PVT1 was screened and the corresponding short hairpin RNA (shRNA) was reconstructed into a lentiviral vector. Cell proliferation and cell cycle distribution were assessed by Cell Counting kit-8 assay and flow cytometry, respectively. Protein expression levels of c-Myc, cyclin-dependent kinase inhibitor1A (CDKN1A, P21) and cyclin E1 (CCNE1) were detected by western blotting. A polymerase chain reaction (PCR) array was used to analyse the expression of genes associated with the cell cycle. PVT1 knockdown markedly suppressed proliferation, and induced cell cycle arrest at the G₀/G₁ phase in Raji cells. Protein expression levels of c-Myc and CCNE1 were reduced, whereas P21 protein expression was markedly increased following downregulation of PVT1 in Raji cells. The cell cycle PCR array revealed that 54 genes were upregulated and 26 genes were downregulated in Raji cells following PVT1 knockdown. Reverse transcription-quantitative PCR demonstrated that cyclin G2 (CCNG2), CDKN1A, Retinoblastoma-like 2 (RBL2, p130), HUS1 checkpoint homolog, cyclin dependent kinase inhibitor 3 (CDKN3) and cyclin dependent kinase inhibitor 1B (CDKN1B) expression were upregulated, whereas the expression levels of CCNE1, cyclin D1 (CCND1) and cell division cycle 20 (CDC20) were downregulated in Raji cells with PVT1 knockdown. In conclusion, PVT1 knockdown may inhibit the proliferation of Raji cells by arresting cells in G₀/G₁ phase. Furthermore, inhibition of cell proliferation may be associated with a reduction inc-Myc expression and alterations in the expression levels of cell cycle-associated genes.

Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer

In endometrial carcinoma, the clinical outcome directly correlates with the TNM stage, but the lack of sufficient information prevents accurate prediction. The molecular mechanism underlying the competing endogenous RNA (ceRNA) hypothesis has not been investigated in endometrial cancer. Multi-bioinformatic analyses, including differentially expressed gene analysis, ceRNA network construction, Cox regression analysis, function enrichment analysis, and protein-protein network analysis, were performed on the sequence data acquired from The Cancer Genome Atlas (TCGA) data bank. A ceRNA network comprising 366 mRNAs, 27 microRNAs (miRNAs), and 66 long non-coding RNAs (lncRNAs) was established. Survival analysis performed with the univariate Cox regression analysis revealed nine lncRNAs with prognostic power in endometrial carcinoma. In multivariate Cox regression analysis, a signature comprising LINC00491, LINC00483, ADARB2-AS1, and C8orf49 showed remarkable prognostic power. Risk score and neoplasm status, but not TNM stage, were independent prognostic factors of endometrial carcinoma. A ceRNA network comprising differentially expressed mRNAs, miRNAs, and lncRNAs may reveal the molecular events involved in the progression of endometrial carcinoma. In addition, the signature with prognostic value may discriminate patients with increased risk for poor outcome, which may allow physicians to take accurate decisions.

miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules

BACKGROUND

A microRNA (miRNA) sponge is an RNA molecule with multiple tandem miRNA response elements that can sequester miRNAs from their target mRNAs. Despite growing appreciation of the importance of miRNA sponges, our knowledge of their complex functions remains limited. Moreover, there is still a lack of miRNA sponge research tools that help researchers to quickly compare their proposed methods with other methods, apply existing methods to new datasets, or select appropriate methods for assisting in subsequent experimental design.

RESULTS

To fill the gap, we present an R/Bioconductor package, miRspongeR, for simplifying the procedure of identifying and analyzing miRNA sponge interaction networks and modules. It provides seven popular methods and an integrative method to identify miRNA sponge interactions. Moreover, it supports the validation of miRNA sponge interactions and the identification of miRNA sponge modules, as well as functional enrichment and survival analysis of miRNA sponge modules.

CONCLUSIONS

This package enables researchers to quickly evaluate their new methods, apply existing methods to new datasets, and consequently speed up miRNA sponge research.

Characteristics of the competition among RNAs for the binding of shared miRNAs

Competing endogenous RNAs (ceRNAs) are RNAs that share common miRNA binding sites and compete with each other for the miRNA association at these sites. The observation of this phenomenon in the cells altered the view of the miRNA target RNAs from molecules that are passively controlled by miRNAs to molecules that also modulate the miRNAs activity. In this review, we build a general profile of ceRNAS characteristics in order to facilitate ceRNAs identification by researchers. The information summarized here contains an actualized list of previously reported ceRNAs and classes of RNAs that can participate in this type of interaction, the expression behavior and characteristics of ceRNAs and miRNAs in the context of competition, the influence of the shared MREs/miRNAs numbers and the miRNA binding strength on the competition, reports on competition between RNAs in different subcellular localizations and the concept that ceRNAs may form a huge regulatory network in the cell.

Interplay Between Long Noncoding RNAs and MicroRNAs in Cancer

In the last decade noncoding RNAs (ncRNAs) have been extensively studied in several biological processes and human diseases including cancer. microRNAs (miRNAs) are the best-known class of ncRNAs. miRNAs are small ncRNAs of around 20-22 nucleotides (nt) and are crucial posttranscriptional regulators of protein coding genes. Recently, new classes of ncRNAs, longer than miRNAs have been discovered. Those include intergenic noncoding RNAs (lincRNAs) and circular RNAs (circRNAs). These novel types of ncRNAs opened a very exciting field in biology, leading researchers to discover new relationships between miRNAs and long noncoding RNAs (lncRNAs), which act together to control protein coding gene expression. One of these new discoveries led to the formulation of the "competing endogenous RNA (ceRNA) hypothesis." This hypothesis suggests that an lncRNA acts as a sponge for miRNAs reducing their expression and causing the upregulation of miRNA targets. In this chapter we first discuss some recent discoveries in this field showing the mutual regulation of miRNAs, lncRNAs, and protein-coding genes in cancer. We then discuss the general approaches for the study of ceRNAs and present in more detail a recent computational approach to explore the ability of lncRNAs to act as ceRNAs in human breast cancer that has been shown to be, among the others, the most precise and promising.

Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction

BACKGROUND

microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments.

RESULTS

In this research, we aim to find miRNA targets which are consistent across different breast cancer subtypes. Thus, first of all, we apply the Pam50 method to categorize BRCA samples into different "environment" groups based on different cancer subtypes. Then we use the invariant causal prediction method to find miRNA-mRNA regulatory relationships across subtypes. We validate the results with the miRNA-transfected experimental data and the results show that our method outperforms the state-of-the-art methods. In addition, we also integrate this new method with the Pearson correlation analysis method and Lasso in an ensemble method to take the advantages of these methods. We then validate the results of the ensemble method with the experimentally confirmed data and the ensemble method shows the best performance, even comparing to the proposed causal method.

CONCLUSIONS

This research found miRNA targets which are consistent across different breast cancer subtypes. Further functional enrichment analysis shows that miRNAs involved in the regulatory relationships predicated by the proposed methods tend to synergistically regulate target genes, indicating the usefulness of these methods, and the identified miRNA targets could be used in the design of wet-lab experiments to discover the causes of breast cancer.

Long Noncoding RNA Expression Signatures of Colon Cancer Based on the ceRNA Network and Their Prognostic Value

Background

The specific functional roles of long noncoding RNAs (lncRNAs) as ceRNAs in colon cancer and their potential implications for colon cancer prognosis remain unclear. In the present study, a genome-wide analysis was performed to investigate the potential lncRNA-mediated ceRNA interplay in colon cancer based on the “ceRNA hypothesis.” The prognostic value of the lncRNAs was evaluated.

Methods

A dysregulated lncRNA-associated ceRNA network was constructed based on the miRNA, lncRNA, and mRNA expression profiles in combination with the miRNA regulatory network by using an integrative computational method. Molecular biological techniques, including qPCR and gene knockdown techniques, were used to verify candidate targets in colon cancer. Survival analysis was performed to identify the candidate lncRNAs with prognostic value.

Results

Our network analysis uncovered several novel lncRNAs as functional ceRNAs through crosstalk with miRNAs. The QRT-PCR assays of patient tissues as well as gene knockdown colon cancer cells confirmed the expression of top lncRNAs and their correlation with target genes in the ceRNA network. Functional enrichment analysis predicted that differentially expressed lncRNAs might participate in broad biological functions associated with tumor progression. Moreover, these lncRNAs may be involved in a range of cellular pathways, including the apoptosis, PI3K-AKT, and EGFR signaling pathways. The survival analysis showed that the expression level of several lncRNAs in the network was correlated with the prognosis of patients with colon cancer.

Conclusions

This study uncovered a dysregulated lncRNA-associated ceRNA network in colon cancer. The function of the identified lncRNAs in colon cancer was preliminarily explored, and their potential prognostic value was evaluated. Our study demonstrated that lncRNAs could potentially serve as important regulators in the development and progression of colon cancer. Candidate prognostic lncRNA biomarkers in colon cancer were identified.

Down-regulation of long non-coding RNA ANRIL inhibits the proliferation, migration and invasion of cervical cancer cells

OBJECTIVES

Cervical cancer (CC) is a common malignant tumor in the female reproductive system that is characterized by a high metastatic potential. LncRNA ANRIL has been found to be a cancer oncogene in multiple tumors. In our study, we altered the expression of ANRIL in CC cells and evaluated its ability on influencing proliferation, migration and invasion of CC cells and associated mechanism.

METHODS

Differentially expressed lncRNAs in CC were identified by microarray and TCGA analyses. CC tissues and adjacent tissues were collected in order to extract CC cells. The expression of ANRIL was determined by RT-qPCR. The CC cells were transfected with siRNA or si-NC against ANRIL to find out whether ANRIL can influence the expression of Cyclin D1, CDK4, CDK6, E-cadherin, vimentin and N-cadherin, as well as affect cell proliferation, cell apoptosis, cell migration and cell invasion of CC cells.

RESULTS

Based on TCGA and microarray analyses, ANRIL was predicted to be highly expressed in CC and CC with migration. Then further verification was obtained by means of RT-qPCR that ANRIL was highly expressed in CC tissues. In addition, high expression of ANRIL was related to increased E-cadherin expression, high migration of CC as well as decreased cell apoptosis rate. On the other hand, inhibition of ANRIL expression led to decreased expressions of Cyclin D1, CDK4, CDK6, N-cadherin and Vimentin, along with attenuated cell proliferation, migration and invasion of CC cells.

CONCLUSION

The key findings of our study demonstrated that the inhibition of lncRNA ANRIL reduces the proliferation, migration and invasion capabilities of CC cells. Down-regulation of ANRIL may serve as a potential therapeutic target in the treatment of CC.

An eight-lncRNA signature predicts survival of breast cancer patients: a comprehensive study based on weighted gene co-expression network analysis and competing endogenous RNA network

PURPOSE

To identify a lncRNA signature to predict survival of breast cancer (BRCA) patients.

METHODS

A total of 1222 BRCA case and control datasets were downloaded from the TCGA database. The weighted gene co-expression network analysis of differentially expressed mRNAs was performed to generate the modules associated with BRCA overall survival status and further construct a hub on competing endogenous RNA (ceRNA) network. LncRNA signatures for predicting survival of BRCA patients were generated using univariate survival analyses and a multivariate Cox hazard model analysis and validated and characterized for prognostic performance measured using receiver operating characteristic (ROC) curves.

RESULTS

A prognostic score model of eight lncRNAs signature was identified as Prognostic score = (0.121 × EXP_AC007731.1) + (0.108 × EXP_AL513123.1) + (0.105 × EXP_C10orf126) + (0.065 × EXP_WT1-AS) + (- 0.126 × EXP_ADAMTS9-AS1) + (- 0.130 × EXP_SRGAP3-AS2) + (0.116 × EXP_TLR8-AS1) + (0.060 × EXP_HOTAIR) with median score 1.088. Higher scores predicted higher risk. The lncRNAs signature was an independent prognostic factor associated with overall survival. The area under the ROC curves (AUC) of the signature was 0.979, 0.844, 0.99 and 0.997 by logistic regression, support vector machine, decision tree and random forest models, respectively, and the AUCs in predicting 1- to 10-year survival were between 0.656 and 0.748 in the test dataset from TCGA database.

CONCLUSIONS

The eight-lncRNA signature could serve as an independent biomarker for prediction of overall survival of BRCA. The lncRNA-miRNA-mRNA ceRNA network is a good tool to identify lncRNAs that is correlated with overall survival of BRCA.

Identification of cancer-related potential biomarkers based on lncRNA-pseudogene-mRNA competitive networks

Accumulating evidence indicates that mRNAs and noncoding RNAs act as competitive endogenous RNAs (ceRNAs) and play a key role in tumorigenesis. However, the complex competitive relationship among genes remains unknown. In the present study, the long noncoding RNAs (lncRNAs), pseudogenes and mRNAs that compete with common microRNAs are defined as lncRNA-pseudogene-mRNA competitive triples. We find that some candidate ceRNAs, modules and triples are associated with cancers and can significantly divide patients into high-risk and low-risk groups; thus, they may serve as potential cancer biomarkers. In sum, the present study systematically analyzes the association between competitive triples and cancer, which provides a reference for a deeper understanding of cancer progression.

LINC00461 promotes cell migration and invasion in breast cancer through miR-30a-5p/integrin β3 axis

Mounting evidence has demonstrated that long noncoding RNAs (lncRNAs) are dysregulated and implicated in the occurrence and development of a wide range of human malignancies. LINC00461, a novel cancer-related lncRNA, has been reported to be highly expressed and serve as oncogene in glioma; however, its biological role in breast cancer (BC) remains obscure. This study aimed to explore the role of LINC00461 in BC and elucidate the potential molecular mechanisms involved. In the current study, LINC00461 was found to be significantly upregulated in both BC tissues and cell lines. Besides, we found that high LINC00461 expression was associated with TNM stage and differentiation. Furthermore, functional studies demonstrated that LINC00461 expedited BC cell migration and invasion. Notably, LINC00461 was observed to enhance the expression of vimentin and zinc-finger E-box binding homeobox factor 1, suppress the expression of E-cadherin, and promote the activation of extracellular signal-regulated kinase and AKT signaling pathways. Mechanical investigations revealed that LINC00461 positively modulated integrin β3 (ITGB3) expression as miR-30a-5p sponge in BC cells. Taken together, LINC00461 exerts an oncogenic role in BC through miR-30a-5p/ITGB3 axis. Our data indicate that LINC00461 may be used to be a novel candidate therapeutic target and a valuable diagnostic biomarker for BC.

Investigating miRNA-lncRNA Interactions: Computational Tools and Resources

In the last two decades noncoding RNAs have been the recipients of increasing scientific interest. In particular, miRNAs, short (~22 nts) noncoding transcripts, have been thoroughly investigated since their essential role in posttranscriptional gene expression regulation had been established in the early 2000s. With the advent and the advancements of high-throughput sequencing technologies in recent years, long noncoding RNAs have also started to emerge as important actors in cellular functions and processes. Such transcripts, on average longer than 200 nt, whose functions have yet to be fully characterized, have recently been identified as regulatory elements of the RNAi pathway, harboring several miRNA response elements, uncovering the phenomena of competing endogenous RNAs (ceRNAs), or "sponge RNAs." The present chapter aims to provide a brief update on the actual biomedical relevance of ceRNAs, together with a summary of resources, tools, and practical examples of their application to aid researchers in the discovery and further elucidation of lncRNA-miRNA interactions.

Prognostic values of long noncoding RNA PVT1 in various carcinomas: An updated systematic review and meta-analysis

Cancers have been a worldwide health problem with a high mortality rate, but ideal biomarkers are not available to effectively screen and diagnose patients. Currently, an increasing number of long noncoding RNAs have been reported to be abnormally expressed in human carcinomas and play a vital role in tumourigenesis. Plasmacytoma variant translocation 1 (PVT1) is upregulated in various carcinomas, and its overexpression is associated with poor survival in cancer patients. We conduct an updated meta-analysis to determine its potential in prognosis for tumours. In total, 14 studies comprising 2435 patients were enrolled according to Reporting Recommendations for Tumour Marker Prognostic Studies guidelines. High PVT1 expression indicated poor overall survival (hazard ratio [HR] = 1.98, 95% confidence interval [CI]: 1.62-2.42, P < 0.00001) and disease-free survival (HR = 1.63, 95% CI: 1.45-1.84, P < 0.00001). Additionally, increased PVT1 expression was positively associated with lymphatic node metastasis (odd ratio [OR] = 2.87, 95% CI: 1.66-4.96, P = 0.0002), distant metastasis (OR = 2.47, 95% CI: 1.74-3.50, P < 0.00001), advanced tumour-node-metastasis stages (OR = 2.59, 95% CI: 1.38-4.88, P = 0.003). New findings highlight that PVT1 acts as competing RNA to microRNAs to protect mRNAs from miRNAs repression. Therefore, we also discuss PVT1-related microRNAs and their interaction in tumourigenesis. In conclusion, PVT1 may be a potential biomarker of poor prognosis for patients with different cancer types.

Nanotopographical cues of electrospun PLLA efficiently modulate non-coding RNA network to osteogenic differentiation of mesenchymal stem cells during BMP signaling pathway

Application of stem cells in combination with nanofibrous substrates is an interesting biomimetic approach for enhanced regeneration of damaged tissues such as bone and cartilage. The investigation of the complex interplay between nanotopographical cues of niche and noncoding RNAs in stem cells fate is an effective tool to find a new strategy for enhancing the induction of osteogenesis. In this study, we investigated the effects of aligned and random orientations of nanofibers as a natural ECM-mimicking environment on the network of noncoding RNA in mesenchymal stem cells. Aligned and randomly oriented Ploy (L-lactide) PLLA scaffolds were fabricated via electrospinning. Human Adipose Tissue-Derived Mesenchymal Stem Cells (hASCs) were isolated from adipose tissue and were cultured on surfaces of these scaffolds. Their capacity to support hMSCs proliferation was also investigated by MTT assay and the expression of c-Myc gene. Then, after 7, 14 and 21 days, the osteogenic commitment of hMSCs and the miRNA regulatory network in BMP signaling pathway were evaluated by measuring alkaline phosphatase (ALP) activity, extracellular calcium deposition, and bone-related gene activation by Real-Time PCR. Furthermore, osteogenic differentiation was evaluated with regard to their noncoding RNA network. Our results for the first time showed an interaction between nanotopographical cues and miRNA activity in hMSCs. We found that the nanotopographical cues could be used to influence the osteogenic differentiation process of hMSCs through the modulation of lncRNAs and miR-125b as negative regulators of osteogenesis as well as the H19 modulator BMP signaling pathway that acts as a miRNA sponge. Moreover, we also demonstrated for the first time that MEG3 as a long noncoding RNA is controlled by miR-125b and microRNA-triggered lncRNA decay mechanism. This strategy seems to be an important tool for controlling stem cell fate in engineered tissues and provide new insights into most biocompatible scaffolds for bone-graft substitutes.

Identification of functional lncRNAs in atrial fibrillation by integrative analysis of the lncRNA-mRNA network based on competing endogenous RNAs hypothesis

A mounting body of evidence has suggested that long noncoding RNAs (lncRNAs) play critical roles in human diseases by acting as competing endogenous RNAs (ceRNAs). However, the functions and ceRNA mechanisms of lncRNAs in atrial fibrillation (AF) remain to date unclear. In this study, we constructed an AF-related lncRNA-mRNA network (AFLMN) based on ceRNA theory, by integrating probe reannotation pipeline and microRNA (miRNA)-target regulatory interactions. Two lncRNAs with central topological properties in the AFLMN were first obtained. By using bidirectional hierarchical clustering, we identified two modules containing four lncRNAs, which were significantly enriched in many known pathways of AF. To elucidate the ceRNA interactions in certain disease or normal condition, the dysregulated lncRNA-mRNA crosstalks in AF were further analyzed, and six hub lncRNAs were obtained from the network. Furthermore, random walk analysis of the AFLMN suggested that lncRNA RP11-296O14.3 may function importantly in the pathological process of AF. All these eight lncRNAs that were identified from previous steps (RP11-363E7.4, GAS5, RP11-410L14.2, HAGLR, RP11-421L21.3, RP11-111K18.2, HOTAIRM1, and RP11-296O14.3) exhibited a strong diagnostic power for AF. The results of our study provide new insights into the functional roles and regulatory mechanisms of lncRNAs in AF, and facilitate the discovery of novel diagnostic biomarkers or therapeutic targets.

Integrative analysis of competing endogenous RNA networks reveals the functional lncRNAs in heart failure

Heart failure has become one of the top causes of death worldwide. It is increasing evidence that lncRNAs play important roles in the pathology processes of multiple cardiovascular diseases. Additionally, lncRNAs can function as ceRNAs by sponging miRNAs to affect the expression level of mRNAs, implicating in numerous biological processes. However, the functional roles and regulatory mechanisms of lncRNAs in heart failure are still unclear. In our study, we constructed a heart failure-related lncRNA-mRNA network by integrating probe re-annotation pipeline and miRNA-target interactions. Firstly, some lncRNAs that had the central topological features were found in the heart failure-related lncRNA-mRNA network. Then, the lncRNA-associated functional modules were identified from the network, using bidirectional hierarchical clustering. Some lncRNAs that involved in modules were demonstrated to be enriched in many heart failure-related pathways. To investigate the role of lncRNA-associated ceRNA crosstalks in certain disease or physiological status, we further identified the lncRNA-associated dysregulated ceRNA interactions. And we also performed a random walk algorithm to identify more heart failure-related lncRNAs. All these lncRNAs were verified to show a strong diagnosis power for heart failure. These results will help us to understand the mechanism of lncRNAs in heart failure and provide novel lncRNAs as candidate diagnostic biomarkers or potential therapeutic targets.

Ept7, a quantitative trait locus that controls estrogen-induced pituitary lactotroph hyperplasia in rat, is orthologous to a locus in humans that has been associated with numerous cancer types and common diseases

Pituitary adenoma is a common intracranial neoplasm that is observed in approximately 10% of unselected individuals at autopsy. Prolactin-producing adenomas, i.e., prolactinomas, comprise approximately 50% of all pituitary adenomas and represent the most common class of pituitary tumor. Multiple observations suggest that estrogens may contribute to development of prolactinoma; however, direct evidence for a causal role of estrogens in prolactinoma etiology is lacking. Rat models of estrogen-induced prolactinoma have been utilized extensively to identify the factors, pathways and processes that are involved in pituitary tumor development. The objective of this study was to localize to high resolution Ept7 (Estrogen-induced pituitary tumor), a quantitative trait locus (QTL) that controls lactotroph responsiveness to estrogens and was mapped to rat chromosome 7 (RNO7) in an intercross between BN and ACI rats. Data presented and discussed herein localize the Ept7 causal variant(s) to a 1.91 Mb interval of RNO7 that contains two protein coding genes, A1bg and Myc, and Pvt1, which yields multiple non-protein coding transcripts of unknown function. The Ept7 orthologous region in humans is located at 8q24.21 and has been linked in genome wide association studies to risk of 8 distinct epithelial cancers, including breast, ovarian, and endometrial cancers; 3 distinct types of B cell lymphoma; multiple inflammatory and autoimmune diseases; and orofacial cleft defects. In addition, the Ept7 locus in humans has been associated with variation in normal hematologic and development phenotypes, including height. Functional characterization of Ept7 should ultimately enhance our understanding of the genetic etiology of prolactinoma and these other diseases.

Non-coding transcript variants of protein-coding genes - what are they good for?

The total number of protein-coding genes in the human genome is not significantly higher than those in much simpler eukaryotes, despite a general increase in genome size proportionate to the organismal complexity. The large non-coding transcriptome and extensive differential splicing, are increasingly being accepted as the factors contributing to the complex mammalian physiology and architecture. Recent studies reveal additional layers of functional complexity: some long non-coding RNAs have been re-defined as micropeptide or microprotein encoding transcripts, and in turn some protein-coding RNAs are bifunctional and display also non-coding functions. Moreover, several protein-coding genes express long non-coding RNA splice-forms and generate circular RNAs in addition to their canonical mRNA transcripts, revoking the strict definition of a gene as coding or non-coding. In this mini review, we discuss the current understanding of these hybrid genes and their possible roles and relevance.

Discovering lncRNA mediated sponge interactions in breast cancer molecular subtypes

BACKGROUND

Long non-coding RNAs (lncRNAs) can indirectly regulate mRNAs expression levels by sequestering microRNAs (miRNAs), and act as competing endogenous RNAs (ceRNAs) or as sponges. Previous studies identified lncRNA-mediated sponge interactions in various cancers including the breast cancer. However, breast cancer subtypes are quite distinct in terms of their molecular profiles; therefore, ceRNAs are expected to be subtype-specific as well.

RESULTS

To find lncRNA-mediated ceRNA interactions in breast cancer subtypes, we develop an integrative approach. We conduct partial correlation analysis and kernel independence tests on patient gene expression profiles and further refine the candidate interactions with miRNA target information. We find that although there are sponges common to multiple subtypes, there are also distinct subtype-specific interactions. Functional enrichment of mRNAs that participate in these interactions highlights distinct biological processes for different subtypes. Interestingly, some of the ceRNAs also reside in close proximity in the genome; for example, those involving HOX genes, HOTAIR, miR-196a-1 and miR-196a-2. We also discover subtype-specific sponge interactions with high prognostic potential. We found that patients differ significantly in their survival distributions if they are group based on the expression patterns of specific ceRNA interactions. However, it is not the case if the expression of individual RNAs participating in ceRNA is used.

CONCLUSION

These results can help shed light on subtype-specific mechanisms of breast cancer, and the methodology developed herein can help uncover sponges in other diseases.

Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.

Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks

Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better. In addition, testing on an alternative dataset proved that the key pathways we found were more accurate and reliable as biological markers of disease. These results show that our proposed method is effective.

Cancerin: A computational pipeline to infer cancer-associated ceRNA interaction networks

MicroRNAs (miRNAs) inhibit expression of target genes by binding to their RNA transcripts. It has been recently shown that RNA transcripts targeted by the same miRNA could “compete” for the miRNA molecules and thereby indirectly regulate each other. Experimental evidence has suggested that the aberration of such miRNA-mediated interaction between RNAs—called competing endogenous RNA (ceRNA) interaction—can play important roles in tumorigenesis. Given the difficulty of deciphering context-specific miRNA binding, and the existence of various gene regulatory factors such as DNA methylation and copy number alteration, inferring context-specific ceRNA interactions accurately is a computationally challenging task. Here we propose a computational method called Cancerin to identify cancer-associated ceRNA interactions. Cancerin incorporates DNA methylation, copy number alteration, gene and miRNA expression datasets to construct cancer-specific ceRNA networks. We applied Cancerin to three cancer datasets from the Cancer Genome Atlas (TCGA) project. Our results indicated that ceRNAs were enriched with cancer-related genes, and ceRNA modules in the inferred ceRNA networks were involved in cancer-associated biological processes. Using LINCS-L1000 shRNA-mediated gene knockdown experiment in breast cancer cell line to assess accuracy, Cancerin was able to predict expression outcome of ceRNA genes with high accuracy.

CeRNA interaction is a post-transcriptional gene regulation that involves interactions between RNAs competing for common miRNA regulators. Dysregulation of ceRNA interactions have been implicated in multiple diseases including cancer. Here we propose a computational pipeline called Cancerin that infers genome-wide ceRNA interactions in cancer. Unlike existing ceRNA inference tools that consider miRNAs as the only factor that regulate gene expression, Cancerin considers other types of gene regulators besides miRNAs, namely transcription factors, copy number alteration, and DNA methylation. To identify miRNA regulators for each gene, Cancerin incorporates a LASSO-based variable selection procedure that leverages both sequence-based and gene expression information. Then multiple expression-based filtering conditions are employed to select ceRNA interactions. Cancerin was applied to three cancer datasets from TCGA. Functional analysis indicated that the inferred ceRNAs were enriched with cancer-related genes, and ceRNAs within ceRNA modules (densely-connected ceRNAs) were involved in cancer-associated biological processes. Survival analysis showed that compared to non-ceRNAs, ceRNAs hold better prognostic power to predict survival outcomes. Our results showed that Cancerin can be used to identify genome-wide and functionally important ceRNA interactions, which makes it a valuable tool to better understand this recently discovered gene regulation mechanism and its role in cancer biology.

Long non-coding RNA PVT1 activates hepatic stellate cells through competitively binding microRNA-152

Epithelial-mesenchymal transition (EMT) process is considered as a key event in the activation of hepatic stellate cells (HSCs). Hedgehog (Hh) pathway is known to be required for EMT process. Long non-coding RNAs (lncRNAs) have been reported to be involved in a wide range of biological processes. Plasmacytoma variant translocation 1 (PVT1), a novel lncRNA, is often up-regulated in various human cancers. However, the role of PVT1 in liver fibrosis remains undefined. In this study, PVT1 was increased in fibrotic liver tissues and activated HSCs. Depletion of PVT1 attenuated collagen deposits in vivo. In vitro, PVT1 down-regulation inhibited HSC activation including the reduction of HSC proliferation, α-SMA and type I collagen. Further studies showed that PVT1 knockdown suppressed HSC activation was through inhibiting EMT process and Hh pathway. Patched1 (PTCH1), a negative regulator factor of Hh pathway, was enhanced by PVT1 knockdown. PTCH1 demethylation caused by miR-152 was responsible for the effects of PVT1 knockdown on PTCH1 expression. Notably, miR-152 inhibitor reversed the effects of PVT1 knockdown on HSC activation. Luciferase reporter assays and pull-down assays showed a direct interaction between miR-152 and PVT1. Collectively, we demonstrate that PVT1 epigenetically down-regulates PTCH1 expression via competitively binding miR-152, contributing to EMT process in liver fibrosis.

Knockdown of long non‑coding RNA PVT1 reverses multidrug resistance in colorectal cancer cells

Multidrug resistance (MDR) is one of the primary causes of chemotherapy failure in colorectal cancer (CRC), and extensive biological studies into MDR are required. The non-coding RNA plasmacytoma variant translocation 1 (PVT1) has been demonstrated to be associated with low survival rates in patients with CRC. However, whether PVT1 serves a critical function in the MDR of CRC remains to be determined. To determine the association between PVT1 expression and 5-fluorouracil (5-FU) resistance in CRC, the expression levels of PVT1 mRNA in 5-FU-resistant CRC tissues and cell lines (HCT-8/5-FU and HCT-116/5-FU) were assessed by a reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Cytotoxicity was evaluated using a Cell Counting Kit-8 assay and apoptosis rates were assessed via flow cytometry. In the present study, PVT1 mRNA was highly expressed in 5-FU-resistant CRC tissues and cell lines. HCT-8/5-FU and HCT-116/5-FU cells transfected with small interfering RNA PVT1 and treated with 5-FU exhibited higher apoptotic rates and lower survival rates. By contrast, overexpression of PVT1 in HCT-8 and HCT-116 cells transfected with lentiviral vector-PVT1-green fluorescent protein and treated with 5-FU exhibited lower apoptosis rates and higher survival rates. RT-qPCR and western blotting demonstrated that the overexpression of PVT1 increased the mRNA and protein expression levels of multidrug resistance-associated protein 1, P-glycoprotein, serine/threonine-protein kinase mTOR and apoptosis regulator Bcl2. The present study indicates that PVT1 overexpression may promote MDR in CRC cells, and suggested that inhibition of PVT1 expression may be an effective therapeutic strategy for reversing MDR in CRC.

Molecular mechanisms of long noncoding RNAs and their role in disease pathogenesis

LncRNAs are long non-coding regulatory RNAs that are longer than 200 nucleotides. One of the major functions of lncRNAs is the regulation of specific gene expression at multiple steps including, recruitment and expression of basal transcription machinery, post-transcriptional modifications and epigenetics [1]. Emerging evidence suggests that lncRNAs also play a critical role in maintaining tissue homeostasis during physiological and pathological conditions, lipid homeostasis, as well as epithelial and smooth muscle cell homeostasis, a topic that has been elegantly reviewed [2-5]. While aberrant expression of lncRNAs has been implicated in several disease conditions, there is paucity of information about their contribution to the etiology of diseases [6]. Several studies have compared the expression of lncRNAs under normal and cancerous conditions and found differential expression of several lncRNAs, suggesting thereby an involvement of lncRNAs in disease processes [7, 8]. Furthermore, the ability of lncRNAs to influence epigenetic changes also underlies their role in disease pathogenesis since epigenetic regulation is known to play a critical role in many human diseases [1]. LncRNAs thus are not only involved in homeostatic functioning but also play a vital role in the progression of many diseases, thereby underscoring their potential as novel therapeutic targets for the alleviation of a variety of human disease conditions.

lncRNA PVT1 promotes the angiogenesis of vascular endothelial cell by targeting miR‑26b to activate CTGF/ANGPT2

Angiogenesis is essential for various biological processes, including tumor blood supply delivery, cancer cell growth, invasion and metastasis. Plasmacytoma variant translocation 1 (PVT1) long noncoding RNA (lncRNA) has been previously reported to affect angiogenesis of glioma microvascular endothelial cells by regulating microRNA (miR)‑186 expression level. However, the specific underlying molecular mechanism of PVT1 regulation of angiogenesis in vascular endothelial cells remains to be elucidated. The present study investigated the role of PVT1 in cell proliferation, migration and vascular tube formation of human umbilical vein endothelial cells (HUVECs) using MTT assay, Transwell migration assay and in vitro vascular tube formation assay, respectively. In order to determine the effect of miR‑26b on cell proliferation, migration and vascular tube formation of HUVECs, miR‑26 mimic or miR‑26b inhibitor were transfected into HUVECs. Reverse transcription‑quantitative polymerase chain reaction and western blotting were conducted to quantify the mRNA and protein expression levels of target genes. The present study confirmed that miR‑26b bound 3'‑untranslated region (3'‑UTR) and subsequently influenced gene expression level using dual luciferase reporter assay. The current study observed that PVT1 affected cell proliferation, migration and in vitro vascular tube formation of HUVECs. In addition, it was determined that PVT1 was able to bind and degrade miR‑26b to promote connective tissue growth factor (CTGF) and angiopoietin 2 (ANGPT2) expression. miR‑26b was also identified to have a suppressive role in cell proliferation, migration and in vitro vascular tube formation of HUVECs via binding 3'‑UTR regions and downregulating CTGF and ANGPT2 expression levels. The current findings may improve the understanding of the underlying mechanism of PVT1 contributing to angiogenesis of vascular endothelial cells and offer rationale for targeting PVT1 to treat angiogenesis dysfunction‑associated diseases, including cancer metastasis.

The gain and loss of long noncoding RNA associated-competing endogenous RNAs in prostate cancer

Prostate cancer (PC) is one of the most common solid tumors in men. However, the molecular mechanism of PC remains unclear. Numerous studies have demonstrated that long noncoding RNA (lncRNA) can act as microRNA (miRNA) sponge, one type of competing endogenous RNAs (ceRNAs), which offers a novel viewpoint to elucidate the mechanisms of PC. Here, we proposed an integrative systems biology approach to infer the gain and loss of ceRNAs in PC. First, we re-annotated exon microarray data to obtain lncRNA expression profiles of PC. Second, by integrating mRNA and miRNA expression, as well as miRNA targets, we constructed lncRNA-miRNA-mRNA ceRNA networks in cancer and normal samples. The lncRNAs in these two ceRNA networks tended to have a longer transcript length and cover more exons than the lncRNAs not involved in ceRNA networks. Next, we further extracted the gain and loss ceRNA networks in PC. We found that the gain ceRNAs in PC participated in cell cycle, and the loss ceRNAs in PC were associated with metabolism. We also identified potential prognostic ceRNA pairs such as MALAT1-EGR2 and MEG3-AQP3. Finally, we inferred a novel mechanism of known drugs, such as cisplatin, for the treatment of PC through gain and loss ceRNA networks. The potential drugs such as 1,2,6-tri-O-galloyl-beta-D-glucopyranose (TGGP) could modulate lncRNA-mRNA competing relationships, which may uncover new strategy for treating PC. In summary, we systematically investigated the gain and loss of ceRNAs in PC, which may prove useful for identifying potential biomarkers and therapeutics for PC.

Characterization of dysregulated lncRNA-mRNA network based on ceRNA hypothesis to reveal the occurrence and recurrence of myocardial infarction

Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play important roles in initiation and development of human diseases. However, the mechanism of ceRNA regulated by lncRNA in myocardial infarction (MI) remained unclear. In this study, we performed a multi-step computational method to construct dysregulated lncRNA-mRNA networks for MI occurrence (DLMN_MI_OC) and recurrence (DLMN_MI_Re) based on “ceRNA hypothesis”. We systematically integrated lncRNA and mRNA expression profiles and miRNA-target regulatory interactions. The constructed DLMN_MI_OC and DLMN_MI_Re both exhibited biological network characteristics, and functional analysis demonstrated that the networks were specific for MI. Additionally, we identified some lncRNA-mRNA ceRNA modules involved in MI occurrence and recurrence. Finally, two new panel biomarkers defined by four lncRNAs (RP1-239B22.5, AC135048.13, RP11-4O1.2, RP11-285F7.2) from DLMN_MI_OC and three lncRNAs (RP11-363E7.4, CTA-29F11.1, RP5-894A10.6) from DLMN_MI_Re with high classification performance were, respectively, identified in distinguishing controls from patients, and patients with recurrent events from those without recurrent events. This study will provide us new insight into ceRNA-mediated regulatory mechanisms involved in MI occurrence and recurrence, and facilitate the discovery of candidate diagnostic and prognosis biomarkers for MI.

Construction of differential mRNA-lncRNA crosstalk networks based on ceRNA hypothesis uncover key roles of lncRNAs implicated in esophageal squamous cell carcinoma

Increasing evidence has indicated that lncRNAs acting as competing endogenous RNAs (ceRNAs) play crucial roles in tumorigenesis, metastasis and diagnosis of cancer. However, the function of lncRNAs as ceRNAs involved in esophageal squamous cell carcinoma (ESCC) is still largely unknown. In this study, clinical implications of two intrinsic subtypes of ESCC were identified based on expression profiles of lncRNA and mRNA. ESCC subtype-specific differential co-expression networks between mRNAs and lncRNAs were constructed to reveal dynamic changes of their crosstalks mediated by miRNAs during tumorigenesis. Several well-known cancer-associated lncRNAs as the hubs of the two networks were firstly proposed in ESCC. Based on the ceRNA mechanism, we illustrated that the"loss" of miR-186-mediated PVT1-mRNA and miR-26b-mediated LINC00240-mRNA crosstalks were related to the two ESCC subtypes respectively. In addition, crosstalks between LINC00152 and EGFR, LINC00240 and LOX gene family were identified, which were associated with the function of "response to wounding" and "extracellular matrix-receptor interaction". Furthermore, functional cooperation of multiple lncRNAs was discovered in the two differential mRNA-lncRNA crosstalk networks. These together systematically uncovered the roles of lncRNAs as ceRNAs implicated in ESCC.

Long non-coding RNA PVT1 predicts poor prognosis and induces radioresistance by regulating DNA repair and cell apoptosis in nasopharyngeal carcinoma

The long non-coding RNA, plasmacytoma variant translocation 1 (PVT1), is highly expressed in a variety of tumors, and is believed to be a potential oncogene. However, the role and mechanism of action of PVT1 in the carcinogenesis and progression of nasopharyngeal carcinomas (NPCs) remains unclear. In this study, for the first time, we have discovered that PVT1 shows higher expression in NPCs than in normal nasopharyngeal epithelial tissue, and patients with NPCs who show higher expression of PVT1 have worse progression-free and overall survivals. Additionally, we observed that the proliferation of NPC cells decreased, and their rate of apoptosis increased; these results indicated that the knockdown of PVT1 expression in the NPC cells induced radiosensitivity. Further, we have shown that the knockdown of PVT1 expression can induce apoptosis in the NPC cells by influencing the DNA damage repair pathway after radiotherapy. In general, our study shows that PVT1 may be a novel biomarker for prognosis and a new target for the treatment of NPCs. Additionally, targeting PVT1 may be a potential strategy for the clinical management of NPC and for the improvement of the curative effect of radiation in NPCs.

Comprehensive characterization of lncRNA-mRNA related ceRNA network across 12 major cancers

Recent studies indicate that long noncoding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) to indirectly regulate mRNAs through shared microRNAs, which represents a novel layer of RNA crosstalk and plays critical roles in the development of tumor. However, the global regulation landscape and characterization of these lncRNA related ceRNA crosstalk in cancers is still largely unknown. Here, we systematically characterized the lncRNA related ceRNA interactions across 12 major cancers and the normal physiological states by integrating multidimensional molecule profiles of more than 5000 samples. Our study suggest the large difference of ceRNA regulation between normal and tumor states and the higher similarity across similar tissue origin of tumors. The ceRNA related molecules have more conserved features in tumor networks and they play critical roles in both the normal and tumorigenesis processes. Besides, lncRNAs in the pan-cancer ceRNA network may be potential biomarkers of tumor. By exploring hub lncRNAs, we found that these conserved key lncRNAs dominate variable tumor hallmark processes across pan-cancers. Network dynamic analysis highlights the critical roles of ceRNA regulation in tumorigenesis. By analyzing conserved ceRNA interactions, we found that miRNA mediate ceRNA regulation showed different patterns across pan-cancer; while analyzing the cancer specific ceRNA interactions reveal that lncRNAs synergistically regulated tumor driver genes of cancer hallmarks. Finally, we found that ceRNA modules have the potential to predict patient survival. Overall, our study systematically dissected the lncRNA related ceRNA networks in pan-cancer that shed new light on understanding the molecular mechanism of tumorigenesis.

Interaction and cross-talk between non-coding RNAs

Non-coding RNA (ncRNA) has been shown to regulate diverse cellular processes and functions through controlling gene expression. Long non-coding RNAs (lncRNAs) act as a competing endogenous RNAs (ceRNAs) where microRNAs (miRNAs) and lncRNAs regulate each other through their biding sites. Interactions of miRNAs and lncRNAs have been reported to trigger decay of the targeted lncRNAs and have important roles in target gene regulation. These interactions form complicated and intertwined networks. Certain lncRNAs encode miRNAs and small nucleolar RNAs (snoRNAs), and may regulate expression of these small RNAs as precursors. SnoRNAs have also been reported to be precursors for PIWI-interacting RNAs (piRNAs) and thus may regulate the piRNAs as a precursor. These miRNAs and piRNAs target messenger RNAs (mRNAs) and regulate gene expression. In this review, we will present and discuss these interactions, cross-talk, and co-regulation of ncRNAs and gene regulation due to these interactions.

Comprehensive analysis of lncRNA expression profiles reveals a novel lncRNA signature to discriminate nonequivalent outcomes in patients with ovarian cancer

There is growing evidence of dysregulated long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic efforts of searching for an expression-based lncRNA signature for prognosis prediction in ovarian cancer (OvCa) have not been made yet. Here, we performed comprehensive analysis for lncRNA expression profiles and clinical data of 544 OvCa patients from The Cancer Genome Atlas (TCGA), and identified an eight-lncRNA signature with ability to classify patients of the training cohort into high-risk group showing poor outcome and low-risk group showing significantly improved outcome, which was further validated in the validation cohort and entire TCGA cohort. Multivariate Cox regression analysis and stratified analysis demonstrated that the prognostic value of this signature was independent of other clinicopathological factors. Associating the outcome prediction with BRCA1 and/or BRCA2 mutation revealed a superior prognosis performance both in BRCA1/2-mutated and BRCA1/2 wild-type tumors. Finally, a significantly correlation was found between the lncRNA signature and the complete response rate of chemotherapy, suggesting that this eight-lncRNA signature may be a measure to predict chemotherapy response and identify platinum-resistant patients who might benefit from other more efficacious therapies. With further prospective validation, this eight-lncRNA signature may have important implications for outcome prediction and therapy decisions.

Construction and analysis of dysregulated lncRNA-associated ceRNA network identified novel lncRNA biomarkers for early diagnosis of human pancreatic cancer

It is increasing evidence that ceRNA activity of long non-coding RNAs (lncRNAs) played critical roles in both normal physiology and tumorigenesis. However, functional roles and regulatory mechanisms of lncRNAs as ceRNAs in pancreatic ductal adenocarcinoma (PDAC), and their potential implications for early diagnosis remain unclear. In this study, we performed a genome-wide analysis to investigate potential lncRNA-mediated ceRNA interplay based on "ceRNA hypothesis". A dysregulated lncRNA-associated ceRNA network (DLCN) was constructed by utilizing sample-matched miRNA, lncRNA and mRNA expression profiles in PDAC and normal samples in combination with miRNA regulatory network. The results of network analysis uncovered seven novel lncRNAs as functional ceRNAs whose aberrant expression will result in the extensive variation in tumorigenic or tumor-suppressive gene expression through DLCN at the post-transcriptional level contributing to PDAC. Therefore, we developed a 7-lncRNA signature (termed LncRisk-7) based on the expression data of seven lncRNAs and SVM algorithm as a novel diagnostic tool to improve early diagnosis of PDAC. The LncRisk-7 achieved high performance in distinguishing PDAC patients from nonmalignant pancreas samples in the discovery cohort and was further confirmed in another two independent validation cohorts. Functional analysis demonstrated that seven lncRNA biomarkers act as ceRNAs involving the regulation of cell death, cell adhesion and cell cycle. This study will help to improve our understanding of the lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of PDAC and provide novel lncRNAs as candidate diagnostic biomarkers or potential therapeutic targets.

Retracted Article: SNHG3 promotes proliferation and invasion by regulating the miR-101/ZEB1 axis in breast cancer

Background: Dysregulated lncRNA expression contributes to the pathogenesis of human tumors via the lncRNAs functioning as oncogenes or tumor suppressors. Small nucleolar RNA host gene 3 (SNHG3) was demonstrated to be upregulated in breast cancer cells. However, the detailed roles and molecular mechanism of SNHG3 in breast cancer are largely unknown. Methods: The expression of SNHG3, miR-101, and zinc finger E-box-binding protein 1 (ZEB1) in breast cancer tissues and cells was detected using qRT-PCR. The effects of SNHG3 on cell proliferation and invasion were evaluated using MTT, EdU, and cell invasion assays. The protein levels of Ki-67, proliferating cell nuclear antigen (PCNA), matrix metalloproteinase MMP-2, and MMP-9 were analyzed using western blot analysis. A luciferase reporter assay and RNA immunoprecipitation (RIP) were performed to explore the interaction between SNHG3, ZEB1 and miR-101. A subcellular fractionation assay was used to detect the subcellular location of SNHG3. Xenograft tumor experiments were conducted to verify the role and mechanism of SNHG3 in breast cancer in vivo. Results: SNHG3 expression was upregulated in breast cancer tissues and correlated with poor prognosis. SNHG3 knockdown suppressed breast cancer cell proliferation and invasion, which was further demonstrated by high levels of proliferation marker proteins Ki-67/PCNA and metastasis-related proteins MMP-2/MMP-9. Additionally, SNHG3 was located in the cytoplasm of breast cancer cells. SNHG3 functioned as a molecular sponge for miR-101 in breast cancer cells. miR-101 was downregulated in breast cancer tissues and negatively correlated with SNHG3 expression. Moreover, ZEB1, a target of miR-101, was positively regulated by SNHG3 in breast cancer cells. ZEB1 mRNA expression was upregulated in breast cancer tissues and positively correlated with SNHG3 expression. Mechanistically, SNHG3 knockdown suppressed cell proliferation and invasion by upregulation of miR-101 and downregulation of ZEB1 expression in breast cancer cells in vitro and in vivo. Conclusion: SNHG3 promoted proliferation and invasion by regulating the miR-101/ZEB1 axis in breast cancer.

In the present study, we investigated the expression and functional roles of SNHG3 in breast cancer cells, as well as the underlying mechanism of SNHG3 involved in the progression of breast cancer in vitro and in vivo.

Competitive endogenous RNA in colorectal cancer: A systematic review

Colorectal cancer is one of the most common malignant tumours. Competitive endogenous RNA (ceRNA) networks have been hypothesized, in which various RNAs regulate each other's expression using microRNA response elements (MREs). Recent evidence has highlighted the crucial regulatory roles of ceRNA networks in colorectal cancer. In this review, we summarize the present research methods as well as the currently known ceRNA competitors and targets in colorectal cancer. In addition, we discuss the significance of ceRNA and shortcomings of current studies of colorectal cancer.

Competing endogenous RNA networks in human cancer: hypothesis, validation, and perspectives

Non-coding RNAs represent a majority of the human transcriptome. However, less is known about the functions and regulatory mechanisms of most non-coding species. Moreover, little is known about the potential non-coding functions of coding RNAs. The competing endogenous RNAs (ceRNAs) hypothesis is proposed recently. This hypothesis describes potential communication networks among all transcript RNA species mediated by miRNAs and miRNA-recognizing elements (MREs) within RNA transcripts. Here we review the evolution of the ceRNA hypothesis, summarize the validation experiments and discusses the significance and perspectives of this hypothesis in human cancer.

Long noncoding RNA expression profile reveals lncRNAs signature associated with extracellular matrix degradation in kashin-beck disease

Kashin-Beck disease (KBD) is a deformative, endemic osteochondropathy involving degeneration and necrosis of growth plates and articular cartilage. The pathogenesis of KBD is related to gene expression and regulation mechanisms, but long noncoding RNAs (lncRNAs) in KBD have not been investigated. In this study, we identified 316 up-regulated and 631 down-regulated lncRNAs (≥ 2-fold change) in KBD chondrocytes using microarray analysis, of which more than three-quarters were intergenic lncRNAs and antisense lncRNAs. We also identified 232 up-regulated and 427 down-regulated mRNAs (≥ 2-fold change). A lncRNA-mRNA correlation analysis combined 343 lncRNAs and 292 mRNAs to form 509 coding-noncoding gene co-expression networks (CNC networks). Eleven lncRNAs were predicted to have cis-regulated target genes, including NAV2 (neuron navigator 2), TOX (thymocyte selection-associated high mobility group box), LAMA4 (laminin, alpha 4), and DEPTOR (DEP domain containing mTOR-interacting protein). The differentially expressed mRNAs in KBD significantly contribute to biological events associated with the extracellular matrix. Meanwhile, 34 mRNAs and 55 co-expressed lncRNAs constituted a network that influences the extracellular matrix. In the network, FBLN1 and LAMA 4 were the core genes with the highest significance. These novel findings indicate that lncRNAs may play a role in extracellular matrix destruction in KBD.

Systematic review of computational methods for identifying miRNA-mediated RNA-RNA crosstalk

Posttranscriptional crosstalk and communication between RNAs yield large regulatory competing endogenous RNA (ceRNA) networks via shared microRNAs (miRNAs), as well as miRNA synergistic networks. The ceRNA crosstalk represents a novel layer of gene regulation that controls both physiological and pathological processes such as development and complex diseases. The rapidly expanding catalogue of ceRNA regulation has provided evidence for exploitation as a general model to predict the ceRNAs in silico. In this article, we first reviewed the current progress of RNA-RNA crosstalk in human complex diseases. Then, the widely used computational methods for modeling ceRNA-ceRNA interaction networks are further summarized into five types: two types of global ceRNA regulation prediction methods and three types of context-specific prediction methods, which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. To provide guidance in the computational prediction of ceRNA-ceRNA interactions, we finally performed a comparative study of different combinations of miRNA–target methods as well as five types of ceRNA identification methods by using literature-curated ceRNA regulation and gene perturbation. The results revealed that integration of different miRNA–target prediction methods and context-specific miRNA/gene expression profiles increased the performance for identifying ceRNA regulation. Moreover, different computational methods were complementary in identifying ceRNA regulation and captured different functional parts of similar pathways. We believe that the application of these computational techniques provides valuable functional insights into ceRNA regulation and is a crucial step for informing subsequent functional validation studies.