lncRNA Pairs Research Articles

Identification of Immune-Related LncRNA Pairs for Predicting Prognosis and Immunotherapeutic Response in Head and Neck Squamous Cell Carcinoma.

Long noncoding RNAs (lncRNAs) have multiple functions with regard to the cancer immunity response and the tumor microenvironment. The prognosis of head and neck squamous cell carcinoma (HNSCC) is still poor currently, and it may be effective to predict the clinical outcome and immunotherapeutic response of HNSCC by immunogenic analysis. Therefore, by using univariate COX analysis and Lasso Cox regression, we identified a signature consisting of 21 immune-related lncRNA pairs (IRLPs) that predicted clinical outcome and Immunotherapeutic response in HNSCC. Specifically, it was associated with immune cell infiltration (i.e., T cells CD4 memory resting, CD8 T cells, macrophages M0, M2, and NK cells), and more importantly this signature was strongly related with immune checkpoint inhibitors (ICIs) [such as PDCD1 (r = -0.35, P < 0.001), CTLA4 (r = -0.26, P < 0.001), LAG3 (r = -0.22, P < 0.001) and HAVCR2 (r = -0.2, P < 0.001)] and immunotherapy-related biomarkers (MMR and HLA). The present study highlighted the value of the 21 IRLPs signature as a predictor of prognosis and immunotherapeutic response in HNSCC.

Long non-coding RNA pairs to assist in diagnosing sepsis

BackgroundSepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias.ResultsCohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNAi > lncRNAj in sepsis patients and lncRNAi < lncRNAj in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus.ConclusionOur study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures.

Altered expression of lncRNAs in autism spectrum disorder.

Long non-coding RNAs (lncRNAs) have been recognized as an important epigenetic factor in the evolution of neuropsychiatric conditions. We have selected five lncRNAs (DISC2, PRKAR2A-AS1, LOC105375675, LRRC2-AS1, and LOC101928237) to measure their expression in blood samples of children with autism spectrum disorder (ASD) versus children with normal development. Expressions of DISC2, PRKAR2A-AS1 and LOC101928237 have been enhanced in ASD cases compared with healthy children (Posterior Beta = 2.508, P value<0.0001; Posterior Beta = 2.793, P value = 0.014 and Posterior Beta = 1.646, P value <0.0001, respectively). On the other hand, expression of LRRC2-AS1 has been lower in ASD patients compared with controls (Posterior Beta = -3.781, P value<0.0001). Remarkably, expression of DISC2 and PRKAR2A-AS1 have been lower in girls compared with boys (Posterior Beta = -0.982, P value<0.0001 and Posterior Beta = -0.135, P value<0.0001, respectively). In addition, expression of DISC2 has been lower in ASD cases aged more than 6 compared with those aged less than 6years (Posterior Beta = -0.876, P value = 0.003). DISC2, LOC101928237, LRRC2-AS1, and PRKAR2A-AS1 had the area under curve (AUC) values of 0.76, 0.90, 0.92, and 0.79 in distinguishing between ASD and healthy children. Expression levels of none of DISC2, LOC101928237, LOC105375675, LRRC2-AS1, and PRKAR2A-AS1 were correlated with age of ASD cases or healthy controls. A significant correlation was detected between expressions of DISC2 and PRKAR2A-AS1. There were inverse correlations between the following pairs of lncRNAs: DISC2/LRRC2-AS1, DISC2/LOC101928237, LRRC2-AS1/PRKAR2A-AS1, LOC101928237/LRRC2-AS1, and LOC101928237 /LOC105375675. We conclude that DISC2, LOC101928237, LRRC2-AS1, and PRKAR2A-AS1 might be used as potential markers for this condition.

Comprehensive analysis to identify DNA damage response-related lncRNA pairs as a prognostic and therapeutic biomarker in gastric cancer.

Gastric cancer (GC) is the fifth most common malignancy and the fourth leading cause of cancer-related mortality worldwide. The identification of valuable predictive signatures to improve the prognosis of patients with GC is becoming a realistic prospect. DNA damage response-related long noncoding ribonucleic acids (drlncRNAs) play an important role in the development of cancers. However, their prognostic and therapeutic values remain sparse in gastric cancer (GC). We obtained the transcriptome data and clinical information from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort. Co-expression network analyses were performed to discover functional modules using the igaph package. Subsequently, lncRNA pairs were identified by bioinformation analysis, and prognostic pairs were determined by univariate analysis, respectively. In addition, we utilized least absolute shrinkage and selection operator (LASSO) cox regression analysis to construct the risk model based on lncRNA pairs. Then, we distinguished between the high- or low- risk groups from patients with GC based on the optimal model. Finally, we reevaluated the association between risk score and overall survival, tumor immune microenvironment, specific tumor-infiltrating immune cells related biomarkers, and the sensitivity of chemotherapeutic agents. 32 drlncRNA pairs were obtained, and a 17-drlncRNA pairs signature was constructed to predict the overall survival of patients with GC. The ROC was 0.797, 0.812 and 0.821 at 1, 2, 3 years, respectively. After reclassifying these patients into different risk-groups, we could differentiate between them based on negative overall survival outcome, specialized tumor immune infiltration status, higher expressed immune cell related biomarkers, and a lower chemotherapeutics sensitivity. Compared with previous models, our model showed better performance with a higher ROC value. The prognostic and therapeutic signature established by novel lncRNA pairs could provide promising prediction value, and guide individual treatment strategies in the future.

Construction of an immune-related lncRNA pairs model to predict prognosis and immune landscape of lung adenocarcinoma patients

ABSTRACT The model of immune-related lncRNA pairs (IRLPs) seems to be an available predictor in lung adenocarcinoma (LUAD) patients. The aim of our study was to construct a model with IRLPs to predict the survival status and immune landscape of LUAD patients. Based on TCGA-LUAD dataset, a risk assessment model with IRLPs was established. Then, ROC curves were used to assess the predictive accuracy and effectiveness of our model. Next, we identified the difference of survival, immune cell infiltration, immune checkpoint inhibitor-related (ICI-related) biomarkers, and chemotherapeutics between high-risk group and low-risk group. Finally, A nomogram was built for predicting the survival rates of LUAD patients. 464 LUAD samples were randomly and equally divided into a training set and a test set. Six IRLPs were screened out to construct a risk model. K-M analysis and risk-plot suggested the prognosis of high-risk group was worse than low-risk group (p < 0.001). Multivariate analysis shows risk score was independent risk factor of LUAD (p < 0.001). In addition, the expression of immune cell infiltration, ICI-related biomarkers, chemotherapeutics all demonstrate significant difference in two groups. A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients. Our immune-related lncRNA pairs risk model is expected to be a reliable model for predicting the prognosis and immune landscape of LUAD patients.

LncRNAs are regulated by chromatin states and affect the skeletal muscle cell differentiation.

ObjectiveThis study aims to clarify the mechanisms underlying transcriptional regulation and regulatory roles of lncRNAs in skeletal muscle cell differentiation.MethodsWe analysed the expression patterns of lncRNAs via time‐course RNA‐seq. Then, we further combined the ATAC‐seq and ChIP‐seq to investigate the governing mechanisms of transcriptional regulation of differentially expressed (DE) lncRNAs. Weighted correlation network analysis and GO analysis were conducted to identify the transcription factor (TF)‐lncRNA pairs related to skeletal muscle cell differentiation.ResultsWe identified 385 DE lncRNAs during C2C12 differentiation, the transcription of which is determined by chromatin states around their transcriptional start sites. The TF‐lncRNA correlation network showed substantially concordant changes in DE lncRNAs between C2C12 differentiation and satellite cell rapid growth stages. Moreover, the up‐regulated lncRNAs showed a significant decrease following the differentiation capacity of satellite cells, which gradually declines during skeletal muscle development. Notably, inhibition of the lncRNA Atcayos and Trp53cor1 led to the delayed differentiation of satellite cells. Those lncRNAs were significantly up‐regulated during the rapid growth stage of satellite cells (4‐6 weeks) and down‐regulated with reduced differentiation capacity (8‐12 weeks). It confirms that these lncRNAs are positively associated with myogenic differentiation of satellite cells during skeletal muscle development.ConclusionsThis study extends the understanding of mechanisms governing transcriptional regulation of lncRNAs and provides a foundation for exploring their functions in skeletal muscle cell differentiation.

DeepLGP: a novel deep learning method for prioritizing lncRNA target genes.

Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurrence and development of complex diseases. Recent wet-lab experiments have shown that lncRNAs function by regulating the expression of protein-coding genes (PCGs), which could also be the mechanism responsible for causing diseases. Currently, lncRNA-related biological data are increasing rapidly. Whereas, no computational methods have been designed for predicting the novel target genes of lncRNA. In this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing target PCGs of lncRNA. First, gene and lncRNA features were selected, these included their location in the genome, expression in 13 tissues and miRNA-mediated lncRNA-gene pairs. Next, GCN was applied to convolve a gene interaction network for encoding the features of genes and lncRNAs. Then, these features were used by the convolutional neural network for prioritizing target genes of lncRNAs. In 10-cross validations on two independent datasets, DeepLGP obtained high area under curves (0.90-0.98) and area under precision-recall curves (0.91-0.98). We found that lncRNA pairs with high similarity had more overlapped target genes. Further experiments showed that genes targeted by the same lncRNA sets had a strong likelihood of causing the same diseases, which could help in identifying disease-causing PCGs. https://github.com/zty2009/LncRNA-target-gene. Supplementary data are available at Bioinformatics online.

Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA-disease association prediction.

As the abnormalities of long non-coding RNAs (lncRNAs) are closely related to various human diseases, identifying disease-related lncRNAs is important for understanding the pathogenesis of complex diseases. Most of current data-driven methods for disease-related lncRNA candidate prediction are based on diseases and lncRNAs. Those methods, however, fail to consider the deeply embedded node attributes of lncRNA-disease pairs, which contain multiple relations and representations across lncRNAs, diseases and miRNAs. Moreover, the low-dimensional feature distribution at the pairwise level has not been taken into account. We propose a prediction model, VADLP, to extract, encode and adaptively integrate multi-level representations. Firstly, a triple-layer heterogeneous graph is constructed with weighted inter-layer and intra-layer edges to integrate the similarities and correlations among lncRNAs, diseases and miRNAs. We then define three representations including node attributes, pairwise topology and feature distribution. Node attributes are derived from the graph by an embedding strategy to represent the lncRNA-disease associations, which are inferred via their common lncRNAs, diseases and miRNAs. Pairwise topology is formulated by random walk algorithm and encoded by a convolutional autoencoder to represent the hidden topological structural relations between a pair of lncRNA and disease. The new feature distribution is modeled by a variance autoencoder to reveal the underlying lncRNA-disease relationship. Finally, an attentional representation-level integration module is constructed to adaptively fuse the three representations for lncRNA-disease association prediction. The proposed model is tested over a public dataset with a comprehensive list of evaluations. Our model outperforms six state-of-the-art lncRNA-disease prediction models with statistical significance. The ablation study showed the important contributions of three representations. In particular, the improved recall rates under different top $k$ values demonstrate that our model is powerful in discovering true disease-related lncRNAs in the top-ranked candidates. Case studies of three cancers further proved the capacity of our model to discover potential disease-related lncRNAs.

Prediction of Specific Subtypes and Common Markers of Non-Small Cell Lung Cancer Based on Competing Endogenous RNA Network.

BackgroundThere are various pathological types of lung cancer, including squamous cell carcinoma and adenocarcinoma. Although both of them are lung cancers, there are significant differences in diagnosis, pathogenesis, location, imaging, metastasis, and treatment. According to the competing endogenous RNA (ceRNA) theory, long non-coding RNAs (lncRNAs) compete with encoding protein genes (mRNAs) to connect with miRNAs, thus affecting the level of mRNA.Material/MethodsFirst, using the t test, we identified mRNAs and lncRNAs that have different expressions (fold change >2, P<0.01) in normal samples and in tumor samples. We calculated the significance of the shared miRNAs for mRNAs and lncRNAs by hypergeometric test (P<0.01). Further, mRNA and lncRNA pairs with co-expression relationships in cancer samples were used to establish ceRNA networks. Then, the random walk algorithm was used to optimize the specific ceRNA networks and identify potential prognostic markers of survival. Finally, we built a common ceRNA network to find markers of non-small-cell lung cancer.ResultsWe identified some potential key markers, such as PVT1, LINC00472, CDKN2A, and FAM83B, in LUSC and HOXA11-AS, HNF1A-AS1, LINC00511, and HOTAIR in LUAD by analyzing the ceRNA networks. Moreover, a number of common ceRNA pairs, such as CDC25C/CDK1/RRM2-LINC00355, have been found, and they are also significant markers for tumor survival and prognosis.ConclusionsIn summary, the present study provides a comparative analysis in 2 kinds of lung cancer ceRNA networks. Some specific and common markers we predicted that may be of great importance for clinical diagnosis and treatment.

Inferring lncRNA Functional Similarity Based on Integrating Heterogeneous Network Data

Although lncRNAs lack the potential to be translated into proteins directly, their complicated and diversiform functions make them as a window into decoding the mechanisms of human physiological activities. Accumulating experiment studies have identified associations between lncRNA dysfunction and many important complex diseases. However, known experimentally confirmed lncRNA functions are still very limited. It is urgent to build effective computational models for rapid predicting of unknown lncRNA functions on a large scale. To this end, valid similarity measure between known and unknown lncRNAs plays a vital role. In this paper, an original model was developed to calculate functional similarities between lncRNAs by integrating heterogeneous network data. In this model, a novel integrated network was constructed based on the data of four single lncRNA functional similarity networks (miRNA-based similarity network, disease-based similarity network, GTEx expression-based network and NONCODE expression-based network). Using the lncRNA pairs that share the target mRNAs as the benchmark, the results show that this integrated network is more effective than any single networks with an AUC of 0.736 in the cross validation, while the AUC of four single networks were 0.703, 0.733, 0.611, and 0.602. To implement our model, a web server named IHNLncSim was constructed for inferring lncRNA functional similarity based on integrating heterogeneous network data. Moreover, the modules of network visualization and disease-based lncRNA function enrichment analysis were added into IHNLncSim. It is anticipated that IHNLncSim could be an effective bioinformatics tool for the researches of lncRNA regulation function studies. IHNLncSim is freely available at http://www.lirmed.com/ihnlncsim.

Learning Multimodal Networks From Heterogeneous Data for Prediction of lncRNA-miRNA Interactions.

Long noncoding RNAs (lncRNAs) is an important class of non-protein coding RNAs. They have recently been found to potentially be able to act as a regulatory molecule in some important biological processes. MicroRNAs (miRNAs) have been confirmed to be closely related to the regulation of various human diseases. Recent studies have suggested that lncRNAs could interact with miRNAs to modulate their regulatory roles. Hence, predicting lncRNA-miRNA interactions are biologically significant due to their potential roles in determining the effectiveness of diagnostic biomarkers and therapeutic targets for various human diseases. For the details of the mechanisms to be better understood, it would be useful if some computational approaches are developed to allow for such investigations. As diverse heterogeneous datasets for describing lncRNA and miRNA have been made available, it becomes more feasible for us to develop a model to describe potential interactions between lncRNAs and miRNAs. In this work, we present a novel computational approach called LMNLMI for such purpose. LMNLMI works in several phases. First, it learns patterns from expression, sequences and functional data. Based on the patterns, it then constructs several networks including an expression-similarity network, a functional-similarity network, and a sequence-similarity network. Based on a measure of similarities between these networks, LMNLMI computes an interaction score for each pair of lncRNA and miRNA in the database. The novelty of LMNLMI lies in the use of a network fusion technique to combine the patterns inherent in multiple similarity networks and a matrix completion technique in predicting interaction relationships. Using a set of real data, we show that LMNLMI can be a very effective approach for the accurate prediction of lncRNA-miRNA interactions.

DIANA-LncBase v3: indexing experimentally supported miRNA targets on non-coding transcripts.

DIANA-LncBase v3.0 (www.microrna.gr/LncBase) is a reference repository with experimentally supported miRNA targets on non-coding transcripts. Its third version provides approximately half a million entries, corresponding to ∼240 000 unique tissue and cell type specific miRNA–lncRNA pairs. This compilation of interactions is derived from the manual curation of publications and the analysis of >300 high-throughput datasets. miRNA targets are supported by 14 experimental methodologies, applied to 243 distinct cell types and tissues in human and mouse. The largest part of the database is highly confident, AGO-CLIP-derived miRNA-binding events. LncBase v3.0 is the first relevant database to employ a robust CLIP-Seq-guided algorithm, microCLIP framework, to analyze 236 AGO-CLIP-Seq libraries and catalogue ∼370 000 miRNA binding events. The database was redesigned from the ground up, providing new functionalities. Known short variant information, on >67,000 experimentally supported target sites and lncRNA expression profiles in different cellular compartments are catered to users. Interactive visualization plots, portraying correlations of miRNA–lncRNA pairs, as well as lncRNA expression profiles in a wide range of cell types and tissues, are presented for the first time through a dedicated page. LncBase v3.0 constitutes a valuable asset for ncRNA research, providing new insights to the understanding of the still widely unexplored lncRNA functions.

High-Throughput RNA Sequencing Reveals NDUFC2-AS lncRNA Promotes Adipogenic Differentiation in Chinese Buffalo (Bubalus bubalis L).

The buffalo (Bubalus bubalis L.) is prevalent in China and the increasing demand for meat production has changed its role from being a beast of burden to a meat source. The low fat deposition level has become one of the main barriers for its use in meat production. It is urgent to reveal factors involved in fat deposition in buffalo. This study performed RNA sequencing to investigate both long noncoding RNAs (lncRNAs) and mRNAs of adipose tissues in young and adult buffalos. A total of 124 lncRNAs and 2008 mRNAs showed differential expression patterns between young and adult samples. Coexpression analysis and functional enrichment revealed 585 mRNA–lncRNA pairs with potential function in fat deposition. After validation by qRT-PCR, we focused on a lncRNA transcribed from the ubiquinone oxidoreductase subunit C2 (NDUFC2) antisense (AS) strand which showed high correlation with thyroid hormone responsive protein (THRSP). NDUFC2-AS lncRNA is highly expressed in adipose tissue and maturation adipocytes and mainly exists in the nucleus. Functional assays demonstrated that NDUFC2-AS lncRNA promotes adipogenic differentiation by upregulating the expression levels of THRSP and CCAAT enhancer binding protein alpha (C/EBPα) in buffalo. These results indicate that NDUFC2-AS lncRNA promotes fat deposition in buffalo.

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.

It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA–disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA–disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs.

Regulation of CCL2 expression in human vascular endothelial cells by a neighboring divergently transcribed long noncoding RNA

Atherosclerosis is a chronic inflammatory disease that is driven, in part, by activation of vascular endothelial cells (ECs). In response to inflammatory stimuli, the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway orchestrates the expression of a network of EC genes that contribute to monocyte recruitment and diapedesis across the endothelium. Although many long noncoding RNAs (lncRNAs) are dysregulated in atherosclerosis, they remain poorly characterized, especially in the context of human vascular inflammation. Prior studies have illustrated that lncRNAs can regulate their neighboring protein-coding genes via interaction with protein complexes. We therefore identified and characterized neighboring interleukin-1β (IL-1β)-regulated messenger RNA (mRNA)-lncRNA pairs in ECs. We found these pairs to be highly correlated in expression, especially when located within the same chromatin territory. Additionally, these pairs were predominantly divergently transcribed and shared common gene regulatory elements, characterized by active histone marks and NF-κB binding. Further analysis was performed on lncRNA-CCL2, which is transcribed divergently to the gene, CCL2, encoding a proatherosclerotic chemokine. LncRNA-CCL2 and CCL2 showed coordinate up-regulation in response to inflammatory stimuli, and their expression was correlated in unstable symptomatic human atherosclerotic plaques. Knock-down experiments revealed that lncRNA-CCL2 positively regulated CCL2 mRNA levels in multiple primary ECs and EC cell lines. This regulation appeared to involve the interaction of lncRNA-CCL2 with RNA binding proteins, including HNRNPU and IGF2BP2. Hence, our approach has uncovered a network of neighboring mRNA-lncRNA pairs in the setting of inflammation and identified the function of an lncRNA, lncRNA-CCL2, which may contribute to atherogenesis in humans.

Identification of a long non‑coding RNA‑mediated competitive endogenous RNA network in hepatocellular carcinoma.

The present study was designed to identify the endogenous RNA regulatory networks involved in hepatocellular carcinoma (HCC) by bioinformatic analysis. Both miRNA interaction network‑based correlation analysis and expression‑based Spearman correlation coefficients were utilized to identify potential mRNA‑lncRNA interactions. Then, a competitive endogenous (ce)RNA network was constructed from these interactions, and network topology and Gene Ontology enrichment analyses were conducted to mine potential functions of ceRNAs. In HCC samples, a ceRNA network was constructed. It was composed of 35,657 edges connecting 113 lncRNAs and 6,136 mRNAs which were differentially expressed in HCC and normal liver tissues. Meanwhile, a number of significantly positively correlated mRNA and lncRNA pairs in this ceRNA network were found to be consistently positively correlated in another independent dataset. To be noted, further analyses on the potential roles of ceRNAs demonstrated than various lncRNAs such as LINC00657, TUG1 and SNHG1 may play key roles in HCC by regulating protein phosphorylation or cell cycle pathways or influencing miRNAs. From the perspective that lncRNAs can function as ceRNAs, this study revealed that the interaction between lncRNAs, miRNAs and mRNAs may provide new insight for the diagnosis and treatment in the tumorigenesis of hepatocellular carcinoma.

Expression profiles of long noncoding RNAs associated with the NSUN2 gene in HepG2 cells.

NOP2/Sun domain family member 2 (NSUN2) is upregulated in numerous types of tumors and may be implicated in multiple biological processes, including cell proliferation, migration and human tumorigenesis. However, little is known about how NSUN2 serves a role in these processes. In the present study, expression profiles of long noncoding RNAs (lncRNAs) and mRNAs were developed in NSUN2-deficient HepG2 cells by RNA-sequencing analysis. A total of 757 lncRNAs were differentially expressed, 392 of which were upregulated, and 365 were downregulated compared with wild-type HepG2 cells. Moreover, 212 lncRNAs were co-expressed with 368 target mRNAs. It was also observed that 253 pairs of lncRNAs and mRNAs exhibited negative correlations and that 290 pairs had positive correlations. Bioinformatics analysis indicated that these lncRNAs regulated by NSUN2 were associated with ‘signal transduction’, ‘extracellular exosome’ and ‘calcium ion binding’, and were enriched in ‘pathways in cancer’, ‘PI3K-Akt signaling pathway’ and ‘ECM-receptor interaction pathway’. These results illustrate the landscape and co-expression network of lncRNAs regulated by NSUN2 and provide invaluable information for studying the molecular function of NSUN2.

Genomic analysis of lncRNA and mRNA profiles in circulating exosomes of patients with rheumatic heart disease.

ABSTRACTRheumatic heart disease (RHD) remains one of the most common cardiovascular conditions in developing countries. Accumulating evidence suggests that circulating exosomes and their cargoes, including mRNA and long noncoding RNA (lncRNA), play essential roles in many cardiovascular diseases. However, their specific roles in RHD remain unexplored. In the present study, we identified 231 lncRNAs and 179 mRNAs differentially expressed in the circulating exosomes harvested from RHD patients compared to healthy controls. We performed gene ontology (GO) and KEGG pathway analysis, and identified five pairs of lncRNAs and their flanking coding genes simultaneously dysregulated in the circulating exosomes. Collectively, we provide the first transcriptome analysis identifying differentially expressed lncRNAs and mRNAs in circulating exosomes of RHD patients, which may bring valuable insights for the discovery of potential biomarkers and therapeutic targets for RHD.

Aberrant Expression of Long Non-coding RNAs in Peripheral Blood of Autistic Patients.

Long non-coding RNAs (lncRNAs) constitute a large proportion of human transcriptome and are involved in fundamental aspects of neurodevelopment. In the present study, we evaluated expression levels of three lncRNAs, namely, Nuclear Paraspeckle Assembly Transcript 1 (NEAT1), p21-associated ncRNA DNA damage-activated (PANDA), and taurine-upregulated gene 1 (TUG1), in peripheral blood of autism spectrum disorder (ASD) patients compared with those in healthy subjects. We found a significant upregulation of NEAT1 and TUG1 in ASD patients. In addition, relative expressions of the pairs of lncRNAs were significantly correlated in both patients and healthy subjects. However, expressions of these lncRNAs were not correlated with age of study participants. The present study provides further evidences for dysregulation of lncRNAs in ASD patients.

Genome-wide analysis of lncRNAs in 3'-untranslated regions: CR933609 acts as a decoy to protect the INO80D gene.

Long non‑coding RNAs (lncRNAs) have various functions, including chromatin remodeling and the regulation of gene expression at the transcriptional and post-transcriptional levels. However, few lncRNAs have been investigated comprehensively, with the majority being uncharacterized. In the present study, a bioinformatics pipeline was established to identify novel lncRNA sequences similar to the 3'-untranslated regions (3'‑UTRs) of protein-coding genes. These pairs of lncRNAs and coding genes contained the same microRNA (miRNA) target sites; the lncRNA CR933609 matched the 3'‑UTR of INO80 complex subunitD (INO80D) mRNA. The expression levels of CR933609 and INO80D were significantly decreased in non‑small cell lung cancer (NSCLC) and other cancer tissues. The expression levels of CR933609 and INO80D were decreased in CR933609-knockdown NSCLC cells, but only expression levels of INO80D decreased in INO80D knockdown cells. It was shown that there are independent promoters in CR933609 and INO80D. It was also found that the expression levels of INO80D were downregulated by endogenous miRNA‑5096 in A549 cells, but not in CR933609-overexpressing A549 cells. Furthermore, the lncRNA CR933609 acted as a decoy to protect INO80D from downregulation by miRNA‑5096 in NSCLC cells. A protocol was established to identify novel lncRNAs in the 3'‑UTR and the existence of novel lncRNAs was confirmed.