Role of lncRNAs in health and disease-size and shape matter

GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations

BACKGROUND

A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs.

METHOD

In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations.

RESULTS

The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer.

CONCLUSIONS

The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.

GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization

Non-coding RNAs (ncRNAs) draw much attention from studies widely in recent years because they play vital roles in life activities. As a good complement to wet experiment methods, computational prediction methods can greatly save experimental costs. However, high false-negative data and insufficient use of multi-source information can affect the performance of computational prediction methods. Furthermore, many computational methods do not have good robustness and generalization on different datasets. In this work, we propose an effective end-to-end computing framework, called GDCL-NcDA, of deep graph learning and deep matrix factorization (DMF) with contrastive learning, which identifies the latent ncRNA-disease association on diverse multi-source heterogeneous networks (MHNs). The diverse MHNs include different similarity networks and proven associations among ncRNAs (miRNAs, circRNAs, and lncRNAs), genes, and diseases. Firstly, GDCL-NcDA employs deep graph convolutional network and multiple attention mechanisms to adaptively integrate multi-source of MHNs and reconstruct the ncRNA-disease association graph. Then, GDCL-NcDA utilizes DMF to predict the latent disease-associated ncRNAs based on the reconstructed graphs to reduce the impact of the false-negatives from the original associations. Finally, GDCL-NcDA uses contrastive learning (CL) to generate a contrastive loss on the reconstructed graphs and the predicted graphs to improve the generalization and robustness of our GDCL-NcDA framework. The experimental results show that GDCL-NcDA outperforms highly related computational methods. Moreover, case studies demonstrate the effectiveness of GDCL-NcDA in identifying the associations among diversiform ncRNAs and diseases.

Long non-coding RNA PVT1 induces proliferation, inhibits apoptosis, and induces autophagy by up-regulating Atg5 in rectal cancer cells

BACKGROUND

As a long noncoding RNA (lncRNA), PVT1 has been proved to play a role in promoting cancer in many tumors, but there are few reports on its impact on the biological behavior of rectal cancer. Therefore, this study investigated the expression of lncRNA PVT1 in rectal cancer and its relationship with prognosis, as well as its effect on rectal cancer cell autophagy, proliferation, and apoptosis, so as to provide a reliable target for treatment of rectal cancer.

AIM

To investigate the expression of lncRNA PVT1 in rectal cancer and its effects on autophagy and proliferation of rectal cancer cells.

METHODS

The expression data of lncRNA PVT1 in 92 rectal cancer samples and 318 healthy control samples were obtained from the GEPIA database, and the expression levels of lncRNA PVT1 in rectal cancer cell lines SW837, HR8348, SW1463, and FHC were detected by quantitative real-time PCR. The relationship between the expression level of lncRNA PVT1 and the prognosis of rectal cancer was analyzed using the R packages (survival and survminer) based on the TCGA database. Overexpression of lncRNA PVT1 was then induced in SW837 and HR8348 cells. Transwell assay, CCK-8 assay, and flow cytometry were used to analyze the changes of cell invasion, proliferation, and apoptosis. Western blot analysis was performed to detect the expression of LC3-II/LC3-I, immunofluorescence was used to analyze the change of LC3 expression, and transmission electron microscopy was used to determine the change of autophagosomes. After co-transfection with si-Atg5, the changes of rectal cancer cell autophagy were analyzed.

RESULTS

The expression of lncRNA PVT1 in rectal cancer tissues and cells increased significantly. The expression of lncRNA PVT1 was related to the prognosis of rectal cancer. Overexpression of lncRNA PVT1 activated autophagy of rectal cancer cells and induced tumor cell proliferation, invasion, and apoptosis inhibition (P < 0.05).

CONCLUSION

LncRNA PVT1 is highly expressed in rectal cancer tissues and cells, and is significantly related to the prognosis of rectal cancer. Overexpression of lncRNA PVT1 induces rectal cancer cell proliferation and invasion, and inhibits their apoptosis. LncRNA PVT1 participates in the regulation of rectal cancer cell autophagy by regulating the expression of Atg5, which may be involved in the occurrence and development of rectal cancer.

Mettl3 downregulation in germinal vesicle oocytes inhibits mRNA decay and the 1st polar body extrusion during maturation

In oocytes, mRNA decay is essential for maturation and subsequent events, such as maternal-zygotic transition, zygotic genomic activation, and embryo development. Reversible N6-methyladenosine RNA methylation directly regulates transcription, pre-mRNA splicing, mRNA export, mRNA stability, and translation. Here, we identified that downregulation of N6-methyladenosine modification by microinjecting a methyltransferase-like 3 (Mettl3)-specific small interfering RNA into mouse germinal vesicle oocytes led to defects in meiotic spindles and the 1st polar body extrusion during maturation in vitro. By further quantitative real-time polymerase chain reaction and Poly(A)-tail assay analysis, we found that N6-methyladenosine methylation mainly acts by reducing deadenylation of mRNAs mediated by the Carbon catabolite repression 4 (CCR4)- negative on TATA less-(NOT) system, thereby causing mRNA accumulation in oocytes. Meanwhile, transcriptome analysis of germinal vesicle oocytes revealed the downregulation of transcripts of several genes encoding ribosomal subunits proteins in the Mettl3 small interfering RNA treated group, suggesting that N6-methyladenosine modification might affect translation. Together, our results indicate that RNA methylation accelerates mRNA decay, confirming the critical role of RNA clearance in oocyte maturation.

Lnc5926 is essential for early embryonic development in goats through regulation of ZSCAN4 and EIF1AX

Long noncoding RNAs (lncRNAs) are abundant in mammalian genomes and have been found to play important roles in many biological events. However, the mechanism by which lncRNAs regulate embryonic development remains to be fully elucidated. Here, we investigated the function of the lncRNA, TCONS_00135926 (referred to as lnc5926), through knockdown and overexpression experiments in goat early embryos. Lnc5926 expression at the eight-cell embryonic stage was significantly higher than that at other stages, which was consistent with the pattern of embryonic genome activation (EGA) gene expression. The blastocyst rate after lnc5926 knockdown in eight-cell embryos was significantly lower than that in the control group (0.2% vs. 17.1%, p < 0.05), whereas the cleavage rate was not affected (71.9% vs. 75.1%, p ˃ 0.05). After knockdown or overexpression of lnc5926 in embryos, we measured expression levels of the potential target genes, STAM, HACD1, UBL5, MIOX, ELF1, and the key EGA genes, ZSCAN4 and EIF1AX. Only ZSCAN4 and EIF1AX were significantly downregulated after lnc5926 knockdown, and this effect was reversed by lnc5926 overexpression. We conclude that lnc5926 plays an essential role in early embryonic development in goats by regulating expression of EGA-associated genes.

DNA methylation dynamics of long noncoding RNA during human fetal development

Aim: To determine whether the promoters of long noncoding RNAs (lncRNAs) undergo dynamic changes in DNA methylation during fetal development. Methods: ANOVA and the tissue specificity index were used to identify and validate tissue-specific methylation sites. Age-associated DNA methylation signatures were identified by applying the elastic net method. Results: The lncRNA methylome landscape was characterized in four types of fetal tissue and at three gestational time points, and specific characteristics relative to the tissue of origin and developmental age were identified. Higher levels of lncRNA methylation might be involved in tissue differentiation. LncRNAs harboring age-associated methylation signatures may participate in the fetal developmental process. Conclusion: This study provides novel insights into the role of lncRNA methylomes in fetal tissue specification and development.

IIMLP: integrated information-entropy-based method for LncRNA prediction

BACKGROUND

The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs.

RESULTS

We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%.

CONCLUSIONS

We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

Long noncoding RNA MALAT1 sponges miR-129-5p to regulate the development of bronchopulmonary dysplasia by increasing the expression of HMGB1

OBJECTIVE

To explore the function and mechanism of long noncoding RNA (lncRNA) metastasis associated lung adenocarcinoma transcript 1 (MALAT1) in bronchopulmonary dysplasia.

METHODS

Alveolar epithelial cell line BEAS-2B was used as the cell model. The role of MALAT1 and microRNA miR-129-5p in regulating cellular viability and migration were examined by using the CCK-8 and Transwell assays, respectively, in vitro. The luciferase reporter assay and real-time (RT)-PCR were performed to confirm that miR-129-5p was a target of MALAT1. ELISA was conducted to validate MALAT1 and show that miR-129-5p regulated the gene encoding high-mobility group protein 1 (HMGB1).

RESULTS

Overexpression of MALAT1 significantly promoted cellular viability, whereas miR-129-5p had the opposite effect. miR-129-5p was shown to be a target of MALAT1, and HMGB1 could be upregulated by MALAT1 overexpression or miR-129-5p inhibition.

CONCLUSION

MALAT1 reduced the expression of miR-129-5p, promoting the viability of cells and blocking the development of bronchopulmonary dysplasia. In addition, MALAT1 increased the expression of HMGB1, which contributed to inflammation as the disease progressed.

Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks

Objective

Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need.

Methods

The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape.

Results

Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions.

Conclusion

This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

LncRNA NEAT1 promotes apoptosis and inflammation in LPS-induced sepsis models by targeting miR-590-3p

Sepsis is a complication of infection caused by disease or trauma. Increasing evidence have shown that long noncoding RNAs (lncRNAs) are involved in the regulation of sepsis. However, the mechanism of lncRNA nuclear enriched abundant transcript 1 (NEAT1) in the regulation of sepsis progression remains to be elucidated. Lipopolysaccharide (LPS) was used to induce a sepsis cell model. The expression levels of NEAT1 and microRNA (miR)-590-3p were determined by reverse transcription-quantitative PCR. Cell viability and apoptosis were detected using Cell Counting Kit-8 (CCK-8) assay and flow cytometry, respectively. Western blot analysis was performed to evaluate the levels of apoptosis- and NF-κB signaling pathway-related proteins. The concentration of inflammatory cytokines was determined using ELISA. In addition, dual-luciferase reporter assay, RNA immunoprecipitation and biotin-labeled RNA pull-down assay were performed to verify the interaction between NEAT1 and miR-590-3p. The results showed that NEAT1 was highly expressed in patients with sepsis and LPS-induced H9c2 cells. Knockdown of NEAT1 decreased LPS-induced cell apoptosis and inflammation response in H9c2 cells. Meanwhile, miR-590-3p showed decreased expression in sepsis, and its overexpression could relieve LPS-induced H9c2 cell damage. Further experiments revealed that NEAT1 could sponge miR-590-3p. Knockdown of miR-590-3p reversed the inhibitory effect of NEAT1 knockdown on LPS-induced H9c2 cell damage. Additionally, the NEAT1/miR-590-3p axis could regulate the activity of the NF-κB signaling pathway. To conclude, lncRNA NEAT1 accelerated apoptosis and inflammation in LPS-stimulated H9c2 cells via sponging miR-590-3p. These findings may provide a new strategy for the treatment of sepsis.

LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse

Long non-coding RNAs (lncRNAs) play crucial roles in regulating gene expression, and a growing number of researchers have focused on the identification of target genes of lncRNAs. However, no online repository is available to collect the information on target genes regulated by lncRNAs. To make it convenient for researchers to know what genes are regulated by a lncRNA of interest, we developed a database named lncRNA2Target to provide a comprehensive resource of lncRNA target genes in 2015. To update the database this year, we retrieved all new lncRNA-target relationships from papers published from 1 August 2014 to 30 April 2018 and RNA-seq datasets before and after knockdown or overexpression of a specific lncRNA. LncRNA2Target database v2.0 provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene, and is freely accessible at http://123.59.132.21/lncrna2target.

Long non-coding RNA Bhmt-AS attenuates hepatic gluconeogenesis via modulation of Bhmt expression

Dysregulation of gluconeogenesis contributes to the pathogenesis of metabolic disease, such as type-2 diabetes. The role of long non-coding RNAs (lncRNAs) in the pathogenesis of diabetes has recently received increased attention. In the present study, we identified a novel lncRNA, betaine-homocysteine methyltransferase-antisense (Bhmt-AS), and examined its expression patterns under pathophysiological conditions. Our results revealed that the expression of Bhmt-AS was significantly increased in the livers of fasted and db/db mice and was induced by gluconeogenic hormonal stimuli. The Bhmt-AS was also shown to be a concordant regulator of Bhmt expression. Functionally, depletion of Bhmt-AS suppressed hepatic glucose production both in vivo and in vitro. Adenovirus-mediated hepatic knockdown of Bhmt-AS improved pyruvate tolerance, glucose tolerance, and insulin sensitivity. Furthermore, overexpression of Bhmt restored the decreased glucose production caused by knockdown of Bhmt-AS in primary hepatocytes. Taken together, we uncovered a novel antisense lncRNA (Bhmt-AS) that is co-expressed with Bhmt and concordantly and specifically regulates Bhmt expression both in vitro and in vivo to regulate hepatic gluconeogenesis.

A Novel Model for Predicting LncRNA-disease Associations based on the LncRNA-MiRNA-Disease Interactive Network

Background:

Accumulating experimental studies have manifested that long-non-coding RNAs (lncRNAs) play an important part in various biological process. It has been shown that their alterations and dysregulations are closely related to many critical complex diseases.

Objective:

It is of great importance to develop effective computational models for predicting potential lncRNA-disease associations.

Method:

Based on the hypothesis that there would be potential associations between a lncRNA and a disease if both of them have associations with the same group of microRNAs, and similar diseases tend to be in close association with functionally similar lncRNAs. A novel method for calculating similarities of both lncRNAs and diseases is proposed, and then a novel prediction model LDLMD for inferring potential lncRNA-disease associations is proposed.

Results:

LDLMD can achieve an AUC of 0.8925 in the Leave-One-Out Cross Validation (LOOCV), which demonstrated that the newly proposed model LDLMD significantly outperforms previous state-of-the-art methods and could be a great addition to the biomedical research field.

Conclusion:

Here, we present a new method for predicting lncRNA-disease associations, moreover, the method of our present decrease the time and cost of biological experiments.

RNA sequencing reveals a key role for the long non-coding RNA MIAT in regulating neuroblastoma and glioblastoma cell fate

Myocardial Infarction Associated Transcript (MIAT) is a subnuclear lncRNA that interferes with alternative splicing and is associated with increased risk of various heart conditions and nervous system tumours. The current study aims to elucidate the role of MIAT in cell survival, apoptosis and migration in neuroblastoma and glioblastoma multiforme. To this end, MIAT was silenced by MIAT-specific siRNAs in neuroblastoma and glioblastoma cell lines, and RNA sequencing together with a series of functional assays were performed. The RNA sequencing has revealed that the expression of an outstanding number of genes is altered, including genes involved in cancer-related processes, such as cell growth and survival, apoptosis, reactive oxygen species (ROS) production and migration. Furthermore, the functional studies have confirmed the RNA sequencing leads, with our key findings suggesting that MIAT knockdown eliminates long-term survival and migration and increases basal apoptosis in neuroblastoma and glioblastoma cell lines. Taken together with the recent demonstration of the involvement of MIAT in glioblastoma, our observations suggest that MIAT could possess tumour-promoting properties, thereby acting as an oncogene, and has the potential to be used as a reliable biomarker for neuroblastoma and glioblastoma and be employed for prognostic, predictive and, potentially, therapeutic purposes for these cancers.

A Probabilistic Matrix Factorization Method for Identifying lncRNA-disease Associations

Recently, an increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) can participate in various crucial biological processes and can also be used as the most promising biomarkers for the treatment of certain diseases such as coronary artery disease and various cancers. Due to costs and time complexity, the number of possible disease-related lncRNAs that can be verified by traditional biological experiments is very limited. Therefore, in recent years, it has been very popular to use computational models to predict potential disease-lncRNA associations. In this study, we constructed three kinds of association networks, namely the lncRNA-miRNA association network, the miRNA-disease association network, and the lncRNA-disease correlation network firstly. Then, through integrating these three newly constructed association networks, we constructed an lncRNA-disease weighted association network, which would be further updated by adopting the KNN algorithm based on the semantic similarity of diseases and the similarity of lncRNA functions. Thereafter, according to the updated lncRNA-disease weighted association network, a novel computational model called PMFILDA was proposed to infer potential lncRNA-disease associations based on the probability matrix decomposition. Finally, to evaluate the superiority of the new prediction model PMFILDA, we performed Leave One Out Cross-Validation (LOOCV) based on strongly validated data filtered from MNDR and the simulation results indicated that the performance of PMFILDA was better than some state-of-the-art methods. Moreover, case studies of breast cancer, lung cancer, and colorectal cancer were implemented to further estimate the performance of PMFILDA, and simulation results illustrated that PMFILDA could achieve satisfying prediction performance as well.

Upregulation of long noncoding RNA TUG1 by EGR1 promotes adenomyotic epithelial cell migration and invasion through recruiting EZH2 and suppressing TIMP2

Emerging studies showed that lncRNA taurine upregulated 1 (TUG1) plays important roles in diverse biological processes. However, there is no previously published research reporting the regulatory role of lncRNAs in the progression of adenomyosis. In the present study, we found that TUG1 is upregulated in human adenomyosis, and the overexpression of TUG1 is associated with the transcription factor early growth response 1 (EGR1). Functionally, the knockdown of TUG1 inhibited adenomyotic epithelial cell migration and invasion but not growth. The mechanistic experiments demonstrated that the function of TUG1 in adenomyotic epithelial cell invasion is, at least in part, through recruiting the enhancer of zeste homolog 2 (EZH2) to the promoter of tissue inhibitor of metalloproteinases 2 (TIMP2) and negatively regulating its expression. Our study demonstrated that TUG1 promotes the migration and invasion of human adenomyotic epithelial cells, and EGR1/TUG1/EZH2/TIMP2 may be a potential therapeutic target for adenomyosis.

A compilation of Web-based research tools for miRNA analysis

Since the discovery of microRNAs (miRNAs), a class of noncoding RNAs that regulate the gene expression posttranscriptionally in sequence-specific manner, there has been a release of number of tools useful for both basic and advanced applications. This is because of the significance of miRNAs in many pathophysiological conditions including cancer. Numerous bioinformatics tools that have been developed for miRNA analysis have their utility for detection, expression, function, target prediction and many other related features. This review provides a comprehensive assessment of web-based tools for the miRNA analysis that does not require prior knowledge of any computing languages.

IRWRLDA: improved random walk with restart for lncRNA-disease association prediction

In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures. The novelty of IRWRLDA lies in the incorporation of lncRNA expression similarity and disease semantic similarity to set the initial probability vector of the RWR. Therefore, IRWRLDA could be applied to diseases without any known related lncRNAs. IRWRLDA significantly improved previous classical models with reliable AUCs of 0.7242 and 0.7872 in two known lncRNA-disease association datasets downloaded from the lncRNADisease database, respectively. Further case studies of colon cancer and leukemia were implemented for IRWRLDA and 60% of lncRNAs in the top 10 prediction lists have been confirmed by recent experimental reports.

Long non-coding RNAs and complex diseases: from experimental results to computational models

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

Long non-coding RNA HULC promotes bladder cancer cells proliferation but inhibits apoptosis via regulation of ZIC2 and PI3K/AKT signaling pathway

BACKGROUND

Bladder cancer is the fourth most common malignancy among men urinary system and it is a complex disease caused by genetic and environmental factors.

OBJECTIVE

This study aimed to evaluate the effects of hepatocellular carcinoma up-regulated long non-coding RNA (lncRNA HULC) on bladder cancer and to reveal the potential mechanisms.

METHODS

The expression level of HULC in 276 bladder cancer patients was detected. The association of HULC level with patient recurrence was performed by Kaplan-Meier and log-rank test. Moreover, T24 and RT4 cells were transfected with HULC and ZIC2 targeted siRNAs, HULC expressing vector and corresponding controls. Subsequently, cell viability, apoptosis and tumorigenesis were examined.

RESULTS

The expression level of HULC was increased in bladder cancer tissues. High expression of HULC was correlated with advanced clinical stage and lower recurrence-free rate. HULC was remarkably promoted cell viability but inhibited apoptosis, meanwhile conspicuously increased the expression of Cyclin A/D1/E and Bcl-2. Xenograft tumor model showed that HULC promoted tumor weights in vivo.

CONCLUSIONS

LncRNA HULC promoted bladder cancer cells proliferation and inhibited apoptosis.

The N6-Methyladenosine RNA modification in pluripotency and reprogramming

Chemical modifications of RNA provide a direct and rapid way to manipulate the existing transcriptome, allowing rapid responses to the changing environment further enriching the regulatory capacity of RNA. N⁶-Methyladenosine (m⁶A) has been identified as the most abundant internal modification of messenger RNA in eukaryotes, linking external stimuli to an intricate network of transcriptional, post-transcriptional and translational processes. M⁶A modification affects a broad spectrum of cellular functions, including maintenance of the pluripotency of embryonic stem cells (ESCs) and the reprogramming of somatic cells into induced pluripotent stem cells (iPSCs). In this review, we summarize the most recent findings on m⁶A modification with special focus on the different studies describing how m⁶A is implicated in ESC self-renewal, cell fate specification and iPSC generation.

Non-coding RNA-mediated epigenetic regulation of liver fibrosis

Hepatic stellate cells (HSC) activation plays a key role in liver fibrosis. Numerous studies have indicated that non-coding RNAs (ncRNAs) control liver fibrosis and fibroblasts proliferation. Greater knowledge of the role of the ncRNAs-mediated epigenetic mechanism in liver fibrosis could improve understanding of the liver fibrosis pathogenesis. The aim of this review is to describe the present knowledge about the ncRNAs significantly participating in liver fibrosis and HSC activation, and look ahead on new perspectives of ncRNAs-mediated epigenetic mechanism research. Moreover, we will discuss examples of non-coding RNAs that interact with histone modification or DNA methylation to regulate gene expression in liver fibrosis. Diverse classes of ncRNAs, ranging from microRNAs (miRs) to long non-coding RNAs (LncRNAs), have emerged as key regulators of several important aspects of function, including cell proliferation, activation, etc. In addition, recent advances suggest the important role of ncRNAs transcripts in epigenetic gene regulation. Targeting the miRs and LncRNAs can be a promising direction in liver fibrosis treatment. We discuss new perspectives of miRs and LncRNAs in liver fibrosis and HSC activation, mainly including interaction with histone modification or DNA methylation to regulate gene expression. These epigenetic mechanisms form powerful ncRNAs surveillance systems that may represent new targets for liver fibrosis therapeutic intervention.

Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA

Accumulating experimental studies have indicated that lncRNAs play important roles in various critical biological process and their alterations and dysregulations have been associated with many important complex diseases. Developing effective computational models to predict potential disease-lncRNA association could benefit not only the understanding of disease mechanism at lncRNA level, but also the detection of disease biomarkers for disease diagnosis, treatment, prognosis and prevention. However, known experimentally confirmed disease-lncRNA associations are still very limited. In this study, a novel model of HyperGeometric distribution for LncRNA-Disease Association inference (HGLDA) was developed to predict lncRNA-disease associations by integrating miRNA-disease associations and lncRNA-miRNA interactions. Although HGLDA didn't rely on any known disease-lncRNA associations, it still obtained an AUC of 0.7621 in the leave-one-out cross validation. Furthermore, 19 predicted associations for breast cancer, lung cancer, and colorectal cancer were verified by biological experimental studies. Furthermore, the model of LncRNA Functional Similarity Calculation based on the information of MiRNA (LFSCM) was developed to calculate lncRNA functional similarity on a large scale by integrating disease semantic similarity, miRNA-disease associations, and miRNA-lncRNA interactions. It is anticipated that HGLDA and LFSCM could be effective biological tools for biomedical research.

Similarity computation strategies in the microRNA-disease network: a survey

Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.

Spatiotemporal-specific lncRNAs in the brain, colon, liver and lung of macaque during development

Identification of spatiotemporal-specific lncRNAs during the development of multiple tissues in rhesus.