Identification and development of long non‐coding RNA‐associated regulatory network in colorectal cancer

Colorectal cancer (CRC) is one of the leading causes of cancer‐associated death globally. Long non‐coding RNAs (lncRNAs) have been identified as micro RNA (miRNA) sponges in a competing endogenous RNA (ceRNA) network and are involved in the regulation of mRNA expression. This study aims to construct a lncRNA‐associated ceRNA network and investigate the prognostic biomarkers in CRC. A total of 38 differentially expressed (DE) lncRNAs, 23 DEmiRNAs and 27 DEmRNAs were identified by analysing the expression profiles of CRC obtained from The Cancer Genome Atlas (TCGA). These RNAs were chosen to develop a ceRNA regulatory network of CRC, which comprised 125 edges. Survival analysis showed that four lncRNAs, six miRNAs and five mRNAs were significantly associated with overall survival. A potential regulatory axis of ADAMTS9‐AS2/miR‐32/PHLPP2 was identified from the network. Experimental validation was performed using clinical samples by quantitative real‐time PCR (qRT‐PCR), which showed that expression of the genes in the axis was associated with clinicopathological features and the correlation among them perfectly conformed to the ‘ceRNA theory’. Overexpression of ADAMTS9‐AS2 in colon cancer cell lines significantly inhibited the miR‐32 expression and promoted PHLPP2 expression, while ADAMTS9‐AS2 knockdown had the opposite effects. The constructed novel ceRNA network may provide a comprehensive understanding of the mechanisms of CRC carcinogenesis. The ADAMTS9‐AS2/miR‐32/PHLPP2 regulatory axis may serve as a potential therapeutic target for CRC.     

LNRLMI: Linear neighbour representation for predicting lncRNA‐miRNA interactions

LncRNA and miRNA are key molecules in mechanism of competing endogenous RNAs(ceRNA), and their interactions have been discovered with important roles in gene regulation. As supplementary to the identification of lncRNA‐miRNA interactions from CLIP‐seq experiments, in silico prediction can select the most potential candidates for experimental validation. Although developing computational tool for predicting lncRNA‐miRNA interaction is of great importance for deciphering the ceRNA mechanism, little effort has been made towards this direction. In this paper, we propose an approach based on linear neighbour representation to predict lncRNA‐miRNA interactions (LNRLMI). Specifically, we first constructed a bipartite network by combining the known interaction network and similarities based on expression profiles of lncRNAs and miRNAs. Based on such a data integration, linear neighbour representation method was introduced to construct a prediction model. To evaluate the prediction performance of the proposed model, k‐fold cross validations were implemented. As a result, LNRLMI yielded the average AUCs of 0.8475 ± 0.0032, 0.8960 ± 0.0015 and 0.9069 ± 0.0014 on 2‐fold, 5‐fold and 10‐fold cross validation, respectively. A series of comparison experiments with other methods were also conducted, and the results showed that our method was feasible and effective to predict lncRNA‐miRNA interactions via a combination of different types of useful side information. It is anticipated that LNRLMI could be a useful tool for predicting non‐coding RNA regulation network that lncRNA and miRNA are involved in.     

Analysing the relationship between lncRNA and protein‐coding gene and the role of lncRNA as ceRNA in pulmonary fibrosis

Long non‐coding RNAs (lncRNAs) are involved in various pathophysiologic processes and human diseases. However, their dynamics and corresponding functions in pulmonary fibrosis remain poorly understood. In this study, portions of lncRNAs adjacent or homologous to protein‐coding genes were determined by searching the UCSC genome bioinformatics database. This was found to be potentially useful for exploring lncRNA functions in disease progression. Previous studies showed that competing endogenous RNA (ceRNA) hypothesis is another method to predict lncRNA function. However, little is known about the function of ceRNA in pulmonary fibrosis. In this study, we selected two differentially expressed lncRNAs MRAK088388 and MRAK081523 to explore their regulatory mechanisms. MRAK088388 and MRAK081523 were analysed as long‐intergenic non‐coding RNAs (lincRNAs), and identified as orthologues of mouse lncRNAs AK088388 and AK081523, respectively. qRT‐PCR and in situ hybridization (ISH) showed that they were significantly up‐regulated, and located in the cytoplasm of interstitial lung cells. We also showed that MRAK088388 and N4bp2 had the same miRNA response elements (MREs) for miR‐200, miR‐429, miR‐29, and miR‐30, whereas MRAK081523 and Plxna4 had the same MREs for miR‐218, miR‐141, miR‐98, and let‐7. Moreover, the expression levels of N4bp2 and Plxna4 significantly increased in fibrotic rats, and were highly correlated with those of MRAK088388 and MRAK081523, respectively. Among their shared miRNAs, miR‐29b‐3p and let‐7i‐5p decreased in the model group, and were negatively correlated with the expression of MRAK088388 and MRAK081523, respectively. MRAK088388 and MRAK081523 could regulate N4bp2 and Plxna4 expression by sponging miR‐29b‐3p and let‐7i‐5p, respectively, and possessed regulatory functions as ceRNAs. Thus, our study may provide insights into the functional interactions of lncRNA, miRNA and mRNA, and lead to new theories for the pathogenesis and treatment of pulmonary fibrosis.     

To construct a ceRNA regulatory network as prognostic biomarkers for bladder cancer

Emerging evidence demonstrates that competing endogenous RNA (ceRNA) hypothesis has played a role in molecular biological mechanisms of cancer occurrence and development. But the effect of ceRNA network in bladder cancer (BC), especially lncRNA‐miRNA‐mRNA regulatory network of BC, was not completely expounded. By means of The Cancer Genome Atlas (TCGA) database, we compared the expression of RNA sequencing (RNA‐Seq) data between 19 normal bladder tissue and 414 primary bladder tumours. Then, weighted gene co‐expression network analysis (WGCNA) was conducted to analyse the correlation between two sets of genes with traits. Interactions between miRNAs, lncRNAs and target mRNAs were predicted by MiRcode, miRDB, starBase, miRTarBase and TargetScan. Next, by univariate Cox regression and LASSO regression analysis, the 86 mRNAs obtained by prediction were used to construct a prognostic model which contained 4 mRNAs (ACTC1 + FAM129A + OSBPL10 + EPHA2). Then, by the 4 mRNAs in the prognostic model, a ceRNA regulatory network with 48 lncRNAs, 14 miRNAs and 4 mRNAs was constructed. To sum up, the ceRNA network can further explore gene regulation and predict the prognosis of BC patients.     

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.     

An 11-lncRNA expression could be potential prognostic biomarkers in head and neck squamous cell carcinoma

The aim of our study is to construct the competing endogenous RNA (ceRNA) network of head and neck squamous cell carcinoma (HNSCC) and identify key long noncoding RNAs (lncRNAs) to predict prognosis. The genes whose expression were differentially in HNSCC and normal tissues were explored by the Cancer Genome Atlas database. The ceRNA network was constructed by the Cytoscape software. The lncRNAs which could estimate the overall survival were explored from Cox proportional hazards regression. There are 1997, 589, and 82 mRNAs, lncRNAs, and miRNAs whose expression were statistically significant different, respectively. Then, the network between miRNA and mRNA or miRNA and lncRNA was constructed by miRcode, miRDB, TargetScan, and miRanda. Five mRNAs, 10 lncRNAs, and 3 miRNAs were associated with overall survival. Then, 11-lncRNAs were found to be prognostic factors. Therefore, our research analyzed the potential signature of novel 11-lncRNA as candidate prognostic biomarker from the ceRNA network for patients with HNSCC.     

Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.     

Identification of circRNA and mRNA expression profiles and functional networks of vascular tissue in lipopolysaccharide‐induced sepsis

Sepsis is the most common cause of death in intensive care units. This study investigated the circular RNA (circRNA) and mRNA expression profiles and functional networks of the aortic tissue in sepsis. We established a lipopolysaccharide (LPS)‐induced rat sepsis model. High‐throughput sequencing was performed on the aorta tissue to identify differentially expressed (DE) circRNAs and mRNAs, which were validated by real‐time quantitative polymerase chain reaction (RT‐qPCR). Bioinformatic analysis was carried out and coding and non‐coding co‐expression (CNC) and competing endogenous RNA (ceRNA) regulatory networks were constructed to investigate the mechanisms. In total, 373 up‐regulated and 428 down‐regulated circRNAs and 2063 up‐regulated and 2903 down‐regulated mRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs showed that the down‐regulated genes were mainly enriched in the process of energy generation. CNC and ceRNA regulatory networks were constructed with seven DE circRNAs. The results of functional enrichment analysis of CNC target genes revealed the important role of circRNAs in inflammatory response. The ceRNA network also highlighted the significant enrichment in calcium signalling pathway. Significant alterations in circRNAs and mRNAs were observed in the aortic tissue of septic rats. In addition, CNC and ceRNA networks were established.     

Genome‐scale identification of miRNA–mRNA and miRNA–lncRNA interactions in domestic animals

Domestic animals show considerable genetic diversity. Previous studies suggested that animal phenotypes were affected by miRNA–mRNA interplay, but these studies focused mainly on the analysis of one or several miRNA–mRNA interactions. However, in this study, we investigated miRNA–mRNA and miRNA–lncRNA interactions on a genomic scale using miranda and targetscan algorithms. There has been strong directional artificial selection practiced during the domestication of animals. Thus, we investigated SNPs that were located in miRNAs and miRNA binding sites and found that several SNPs located in 3′‐UTRs of mRNAs had the potential to affect miRNA–mRNA interactions. In addition, a database, named miRBond, was developed to provide visualization, analysis and downloading of the resulting datasets. Our results open the way to further experimental verification of miRNA–mRNA and miRNA–lncRNA interactions as well as the influence of SNPs upon such interplay.     

A comprehensive study of construction and analysis of competitive endogenous RNA networks in lung adenocarcinoma

BackgroundLong noncoding RNAs (lncRNAs) have gain increasing attention in lung adenocarcinoma. In this study, we aimed at constructing and analyzing the lncRNAs and the related proteins based competitive endogenous RNA (ceRNA) network.MethodsRNA expression data of lung adenocarcinoma were extracted from the TCGA database. Differentially expressed (DE) lncRNAs, messenger RNAs (mRNAs) and microRNAs (miRNAs) were identified and then a DElncRNA-DEmiRNA-DEmRNA ceRNA network was constructed for lung adenocarcinoma. We also analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEgenes. Kaplan-Meier survival curves were also been further utilized for exploring the prognostic factors.ResultsAfter compared and calculated lncRNA, mRNA and miRNA expression profiles between lung adenocarcinoma and normal samples, 1709 differential expressed lncRNAs, 2554 differential expressed mRNAs and 116 differential expressed miRNAs were finally identified. Afterwards, a lncRNA mediated ceRNA network was constructed, according to the interactions among 544 pairs of DElncRNA-DEmiRNA relationships and 47 pairs of DEmiRNA-DEmRNA relationships. As for the survival analyses, we found 10 DElncRNAs, 25 DEmRNAs and 7 miRNAs have statistically prognostic significance for overall survival, respectively.ConclusionsThis study provides meaningful information for deeper understanding the underlying molecular mechanism of lung adenocarcinoma and for evaluating prognosis, which could monitor recurrence, guide clinical treatment drugs and subsequent related researches.     

lncRNA作为竞争性内源RNA在非小细胞肺癌中的作用

长链非编码RNA(long non-coding RNA,lncRNA)参与肿瘤的多种生理、病理进程.研究表明,lncRNA可通过与微小RNA (microRNA, mi RNA)反应元件相互作用,并与其他RNA分子形成竞争性内源RNA (competing endogenous RNA,ceRNA)的调控网络,参与基因的表达调控.lncRNA以ceRNA方式参与非小细胞肺癌(non-small cell lung cancer,NSCLC)的发生发展过程,为揭示NSCLC的分子机理开拓了新的思路,也为NSCLC的治疗提供新的靶点.本文在课题组前期发现NSCLC相关ceRNA基础上,主要讨论lncRNA作为ceRNA在NSCLC中高表达、低表达及治疗相关方面的作用.     

Identification of lncRNA‐associated differential subnetworks in oesophageal squamous cell carcinoma by differential co‐expression analysis

Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co‐expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co‐expression of lncRNAs and protein‐coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA‐PCG differential co‐expression network (DCN). DCN was characterized as a scale‐free, small‐world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA‐PCG subnetworks were identified from the DCN by integrating both differential expression and differential co‐expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.     

ceRNA network construction and comparison of gastric cancer with or without Helicobacter pylori infection

Gastric cancer (GC) is a lethal disease, and among its variety of etiological factors, Helicobacter pylori (H. pylori) infection is the strongest risk factor. However, the genetic and molecular mechanisms underlying H. pylori-related GC need further elucidation. We investigated the competing endogenous RNA (ceRNA) network differences between H. pylori (+) and H. pylori (−) GC. The long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) expression data from 32 adjacent noncancerous samples and 18 H. pylori (+) and 141 H. pylori (−) stomach adenocarcinoma samples were downloaded from the TCGA database. After construction of lncRNA–miRNA–mRNA ceRNA networks of H. pylori (+) and H. pylori (−) GC, Panther and Kobas databases were used to analyze the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Finally, survival analysis was used to discover the key genes. In H. pylori (+) GC, we identified a total of 1,419 lncRNAs, 82 miRNAs, and 2,501 mRNAs with differentially expressed profiles. In H. pylori (−) GC, 2,225 lncRNAs, 130 miRNAs, and 3,146 mRNAs were differentially expressed. Furthermore, three unique pathways (cytokine–cytokine receptor interaction, HIF-1 signaling pathway, and Wnt signaling pathway) were enriched in H. pylori (+) GC. According to the overall survival analysis, three lncRNAs (AP002478.1, LINC00111, and LINC00313) and two mRNAs (MYB and COL1A1) functioned as prognostic biomarkers for patients with H. pylori (+) GC. In conclusion, our study has identified the differences in ceRNA regulatory networks between H. pylori (+) and H. pylori (−) GC and provides a rich candidate reservoir for future studies.     

Long non‐coding RNA cardiac hypertrophy‐associated regulator governs cardiac hypertrophy via regulating miR‐20b and the downstream PTEN/AKT pathway

Pathological cardiac hypertrophy (CH) is a key factor leading to heart failure and ultimately sudden death. Long non‐coding RNAs (lncRNAs) are emerging as a new player in gene regulation relevant to a wide spectrum of human disease including cardiac disorders. Here, we characterize the role of a specific lncRNA named cardiac hypertrophy‐associated regulator (CHAR) in CH and delineate the underlying signalling pathway. CHAR was found markedly down‐regulated in both in vivo mouse model of cardiac hypertrophy induced by pressure overload and in vitro cellular model of cardiomyocyte hypertrophy induced by angiotensin II (AngII) insult. CHAR down‐regulation alone was sufficient to induce hypertrophic phenotypes in healthy mice and neonatal rat ventricular cells (NRVCs). Overexpression of CHAR reduced the hypertrophic responses. CHAR was found to act as a competitive endogenous RNA (ceRNA) to down‐regulate miR‐20b that we established as a pro‐hypertrophic miRNA. We experimentally established phosphatase and tensin homolog (PTEN), an anti‐hypertrophic signalling molecule, as a target gene for miR‐20b. We found that miR‐20b induced CH by directly repressing PTEN expression and indirectly increasing AKT activity. Moreover, CHAR overexpression mitigated the repression of PTEN and activation of AKT by miR‐20b, and as such, it abrogated the deleterious effects of miR‐20b on CH. Collectively, this study characterized a new lncRNA CHAR and unravelled a new pro‐hypertrophic signalling pathway: lncRNA‐CHAR/miR‐20b/PTEN/AKT. The findings therefore should improve our understanding of the cellular functionality and pathophysiological role of lncRNAs in the heart.