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

An ensemble of the iCluster method to analyze longitudinal lncRNA expression data for psoriasis patients

Background

Psoriasis is an immune-mediated, inflammatory disorder of the skin with chronic inflammation and hyper-proliferation of the epidermis. Since psoriasis has genetic components and the diseased tissue of psoriasis is very easily accessible, it is natural to use high-throughput technologies to characterize psoriasis and thus seek targeted therapies. Transcriptional profiles change correspondingly after an intervention. Unlike cross-sectional gene expression data, longitudinal gene expression data can capture the dynamic changes and thus facilitate causal inference.

Methods

Using the iCluster method as a building block, an ensemble method was proposed and applied to a longitudinal gene expression dataset for psoriasis, with the objective of identifying key lncRNAs that can discriminate the responders from the non-responders to two immune treatments of psoriasis.

Results

Using support vector machine models, the leave-one-out predictive accuracy of the 20-lncRNA signature identified by this ensemble was estimated as 80%, which outperforms several competing methods. Furthermore, pathway enrichment analysis was performed on the target mRNAs of the identified lncRNAs. Of the enriched GO terms or KEGG pathways, proteasome, and protein deubiquitination is included. The ubiquitination-proteasome system is regarded as a key player in psoriasis, and a proteasome inhibitor to target ubiquitination pathway holds promises for treating psoriasis.

Conclusions

An integrative method such as iCluster for multiple data integration can be adopted directly to analyze longitudinal gene expression data, which offers more promising options for longitudinal big data analysis. A comprehensive evaluation and validation of the resulting 20-lncRNA signature is highly desirable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00323-6.

Construction of liver hepatocellular carcinoma-specific lncRNA-miRNA-mRNA network based on bioinformatics analysis

Liver hepatocellular carcinoma (LIHC) is one of the major causes of cancer-related death worldwide with increasing incidences, however there are very few studies about the underlying mechanisms and pathways in the development of LIHC. We obtained LIHC samples from The Cancer Genome Atlas (TCGA) to screen differentially expressed mRNAs, lncRNAs, miRNAs and driver mutations. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses and protein–protein interaction (PPI) network were performed. Moreover, we constructed a competing endogenous lncRNAs-miRNAs-mRNAs network. Finally, cox proportional hazards regression analysis was used to identify important prognostic differentially expressed genes. Total of 1284 mRNAs, 123 lncRNAs, 47 miRNAs were identified within different tissues of LIHC patients. GO analysis indicated that upregulated and downregulated differentially expressed mRNAs (DEmRNAs) were mainly associated with cell division, DNA replication, mitotic sister chromatid segregation and complement activation respectively. Meanwhile, KEGG terms revealed that upregulated and downregulated DEmRNAs were primarily involved in DNA replication, Metabolic pathways, cell cycle and Metabolic pathways, chemical carcinogenesis, retinol metabolism pathway respectively. Among the DERNAs, 542 lncRNAs-miRNAs-mRNAs pairs were predicted to construct a ceRNA regulatory network including 35 DElncRNAs, 26 DEmiRNAs and 112 DEmRNAs. In the Kaplan‐Meier analysis, total of 43 mRNAs, 14 lncRNAs and 3 miRNAs were screened out to be significantly correlated with overall survival of LIHC. The mutation signatures were analyzed and its correlation with immune infiltrates were evaluated using the TIMER in LIHC. Among the mutation genes, TTN mutation is often associated with poor immune infiltration and a worse prognosis in LIHC. This work conducted a novel lncRNAs-miRNAs-mRNAs network and mutation signatures for finding potential molecular mechanisms underlying the development of LIHC. The biomarkers also can be used for predicting prognosis of LIHC.

GEE-TGDR: A Longitudinal Feature Selection Algorithm and Its Application to lncRNA Expression Profiles for Psoriasis Patients Treated with Immune Therapies

With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method-threshold gradient descent regularization (TGDR)-stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. We proposed a new novel feature selection algorithm for longitudinal outcomes-GEE-TGDR. In the GEE-TGDR method, the corresponding quasilikelihood function of a GEE model is the objective function to be optimized, and the optimization and feature selection are accomplished by the TGDR method. Long noncoding RNAs (lncRNAs) are posttranscriptional and epigenetic regulators and have lower expression levels and are more tissue-specific compared with protein-coding genes. So far, the implication of lncRNAs in psoriasis remains largely unexplored and poorly understood even though some evidence in the literature supports that lncRNAs and psoriasis are highly associated. In this study, we applied the GEE-TGDR method to a lncRNA expression dataset that examined the response of psoriasis patients to immune treatments. As a result, a list including 10 relevant lncRNAs was identified with a predictive accuracy of 70% that is superior to the accuracies achieved by two competitive methods and meaningful biological interpretation. A widespread application of the GEE-TGDR method in omics longitudinal data analysis is anticipated.

lncRNA SNHG9 Promotes Cell Proliferation, Migration, and Invasion in Human Hepatocellular Carcinoma Cells by Increasing GSTP1 Methylation, as Revealed by CRISPR-dCas9

Hepatocellular carcinoma (HCC) is among the major causes of cancer-related mortalities globally. Long non-coding RNAs (LncRNAs), as epigenetic molecules, contribute to malignant tumor incidences and development, including HCC. Although LncRNA SNHG9 is considered an oncogene in many cancers, the biological function and molecular mechanism of SNHG9 in HCC are still unclear. We investigated the effects of lncRNA SNHG9 on the methylation of glutathione S-transferase P1 (GSTP1) and the progression of HCC. Histological data analysis, CRISPR-dCas9, and cytological function experiment were used to study the expression level and biological function of SNHG9 in HCC. There was an upregulated expression of SNHG9 in HCC, which was associated with shorter disease-free survival. Knockdown of SNHG9 can inhibit cell proliferation, block cell cycle progression, and inhibit cell migration and invasion by upregulating GSTP1. LncRNA SNHG9 recruits methylated enzymes (DNMT1, DNMT3A, and DNMT3B) to increase GSTP1 promoter methylation, a common event in the development of HCC. Inhibition of lncRNA SNHG9 demethylates GSTP1, which prevents HCC progression, presents a promising therapeutic approach for HCC patients.

A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

Background

Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately.

Results

We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach.

Conclusion

Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available at https://github.com/zhanglabNKU/VGAELDA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04073-z.

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction

Background

Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions.

Results

In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully.

Conclusions

In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04069-9.

Computational screening of potential glioma-related genes and drugs based on analysis of GEO dataset and text mining

BACKGROUND

Considering the high invasiveness and mortality of glioma as well as the unclear key genes and signaling pathways involved in the development of gliomas, there is a strong need to find potential gene biomarkers and available drugs.

METHODS

Eight glioma samples and twelve control samples were analyzed on the GSE31095 datasets, and differentially expressed genes (DEGs) were obtained via the R software. The related glioma genes were further acquired from the text mining. Additionally, Venny program was used to screen out the common genes of the two gene sets and DAVID analysis was used to conduct the corresponding gene ontology analysis and cell signal pathway enrichment. We also constructed the protein interaction network of common genes through STRING, and selected the important modules for further drug-gene analysis. The existing antitumor drugs that targeted these module genes were screened to explore their efficacy in glioma treatment.

RESULTS

The gene set obtained from text mining was intersected with the previously obtained DEGs, and 128 common genes were obtained. Through the functional enrichment analysis of the identified 128 DEGs, a hub gene module containing 25 genes was obtained. Combined with the functional terms in GSE109857 dataset, some overlap of the enriched function terms are both in GSE31095 and GSE109857. Finally, 4 antitumor drugs were identified through drug-gene interaction analysis.

CONCLUSIONS

In this study, we identified that two potential genes and their corresponding four antitumor agents could be used as targets and drugs for glioma exploration.

RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation

An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.

LncRNAs and Immunity: Coding the Immune System with Noncoding Oligonucleotides

Long noncoding RNAs (lncRNAs) represent key regulators of gene transcription during the inflammatory response. Recent findings showed lncRNAs to be dysregulated in human diseases, such as inflammatory bowel disease, diabetes, allergies, asthma, and cancer. These noncoding RNAs are crucial for immune mechanism, as they are involved in differentiation, cell migration and in the production of inflammatory mediators through regulating protein–protein interactions or their ability to assemble with RNA and DNA. The last interaction can occur in cis or trans and is responsible for all the possible lncRNAs biological effects. Our proposal is to provide an overview on lncRNAs roles and functions related to immunity and immune mediated diseases, since these elucidations could be beneficial to untangle the complex bond between them.

An edge-based statistical analysis of long non-coding RNA expression profiles reveals a negative association between Parkinson's disease and colon cancer

BACKGROUND

Colon cancer (CC) is one of the most common malignant tumors, while Parkinson's disease (PD) is the second most common neurodegenerative disorder. Recent accumulating evidence indicates that these two diseases are associated with each other. Also, from the perspective of long non-coding RNAs, some well-known genes such as H19 and PVT1 can link these two diseases together. Several studies have shown that patients with PD had a decreased risk of developing CC compared with patients without PD. However, controversies surround the relationship between PD and CC, and to date, no concordant conclusion has been drawn.

METHODS

In this study, we aimed to assess the association between these two diseases based on lncRNA-to-lncRNA interactions. Motivated by the weighted gene co-expression network analysis method, a customized procedure was proposed and used to identify differentially correlated edges (DCEs) in the respective interaction networks for PD and CC and explore how these two diseases are linked.

RESULTS

Of the two sets of DCEs for PD and CC, 16 pairs overlapped. Among them, 15 edges had opposite signs, with positive signs for CC indicating a gain of connectivity, whereas negative signs for PD indicating a loss of connectivity.

CONCLUSIONS

By using the lncRNA expression profiles, and a customized procedure, an answer to the question about how PD and CC are associated is provided.

LincRNA-immunity landscape analysis identifies EPIC1 as a regulator of tumor immune evasion and immunotherapy resistance

Through an integrative analysis of the lincRNA expression and tumor immune response in 9,626 tumor samples across 32 cancer types, we developed a lincRNA-based immune response (LIMER) score that can predict the immune cells infiltration and patient prognosis in multiple cancer types. Our analysis also identified tumor-specific lincRNAs, including EPIC1, that potentially regulate tumor immune response in multiple cancer types. Immunocompetent mouse models and in vitro co-culture assays demonstrated that EPIC1 induces tumor immune evasion and resistance to immunotherapy by suppressing tumor cell antigen presentation. Mechanistically, lincRNA EPIC1 interacts with the histone methyltransferase EZH2, leading to the epigenetic silencing of IFNGR1, TAP1/2, ERAP1/2, and MHC-I genes. Genetic and pharmacological inhibition of EZH2 abolish EPIC1's immune-related oncogenic effect and its suppression of interferon-γ signaling. The EPIC1-EZH2 axis emerges as a potential mechanism for tumor immune evasion that can serve as therapeutic targets for immunotherapy.

Sea Urchin as a Universal Model for Studies of Gene Networks

The purple sea urchin Strongylocentrotus purpuratus has been used for over 150 years as a model organism in developmental biology. Using this model species, scientists have been able to describe, in detail, the mechanisms of cell cycle control and cell adhesion, fertilization, calcium signaling, cell differentiation, and death. Massive parallel sequencing of the sea urchin genome enabled the deciphering of the main components of gene regulatory networks during the activation of embryonic signaling pathways. This knowledge helped to extrapolate aberrations in somatic cells that may lead to diseases, including cancer in humans. Furthermore, since many, if not all, developmental signaling pathways were shown to be controlled by non-coding RNAs (ncRNAs), the sea urchin organism represents an attractive experimental model. In this review, we discuss the main discoveries in the genetics, genomics, and transcriptomics of sea urchins during embryogenesis with the main focus on the role of ncRNAs. This information may be useful for comparative studies between different organisms, and may help identify new regulatory networks controlled by ncRNAs.

Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs

There is accumulating evidence that long noncoding RNAs (lncRNAs) play crucial roles in biological processes and diseases. In recent years, computational models have been widely used to predict potential lncRNA-disease relations. In this chapter, we systematically describe various computational algorithms and prediction tools that have been developed to elucidate the roles of lncRNAs in diseases, coding potential/functional characterization, or ascertaining their involvement in critical biological processes as well as provide a comprehensive summary of these applications.

LogSum + L2 penalized logistic regression model for biomarker selection and cancer classification

Biomarker selection and cancer classification play an important role in knowledge discovery using genomic data. Successful identification of gene biomarkers and biological pathways can significantly improve the accuracy of diagnosis and help machine learning models have better performance on classification of different types of cancer. In this paper, we proposed a LogSum + L₂ penalized logistic regression model, and furthermore used a coordinate decent algorithm to solve it. The results of simulations and real experiments indicate that the proposed method is highly competitive among several state-of-the-art methods. Our proposed model achieves the excellent performance in group feature selection and classification problems.

The top 100 most-cited papers in long non-coding RNAs: a bibliometric study

Up to 90% of the human genome is transcribed into Long-noncoding RNAs (lncRNAs) that longer than 200 nucleotides but do not code for proteins. LncRNAs play a vital role in a broad range of biological process, it's dysregulations and mutations are linked to the development and progression of various complex human diseases. Given the dramatic changes and growing scientific outputs in lncRNAs field, using a quantitative measurement to analyze and characterize the existing studies has become imperative.Bibliometric analysis is a widely used tool to assess the academic influence of a publication or a country in a specific field. However, a bibliometric analysis of the top 100 most-cited papers in lncRNAs area has not been conducted. Thus, we executed a bibliometric study to identify the authors, journals, countries and institutions that contributed most to the top 100 lncRNAs list, characterize the key words and focus of top 100 most-cited papers, and detect the factors related to their successful citation. This study provides a comprehensive list of the most influential papers on lncRNAs research and demonstrates the important advances in this field, which might be benefit to researchers in their paper publication and scientific cooperation.

Identification of long non-coding RNA signatures for squamous cell carcinomas and adenocarcinomas

Studies have demonstrated that both squamous cell carcinomas (SCCs) and adenocarcinomas (ACs) possess some common molecular characteristics. Evidence has accumulated to support the theory that long non-coding RNAs (lncRNAs) serve as novel biomarkers and therapeutic targets in complex diseases such as cancer.

In this study, we aimed to identify pan lncRNA signatures that are common to squamous cell carcinomas or adenocarcinomas with different tissues of origin. With the aid of elastic-net regularized regression models, a 35-lncRNA pan discriminative signature and an 11-lncRNA pan prognostic signature were identified for squamous cell carcinomas, whereas a 6-lncRNA pan discriminative signature and a 5-lncRNA pan prognostic signature were identified for adenocarcinomas. Among them, many well-known cancer relevant genes such as MALAT1 and PVT1 were included.

The identified pan lncRNA lists can help experimental biologists generate research hypotheses and adopt existing treatments for less prevalent cancers. Therefore, these signatures warrant further investigation.

A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

Background

Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.

Results

In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.

Conclusion

The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

Comprehensive evaluation of differential long non-coding RNA and gene expression in patients with cartilaginous endplate degeneration of cervical vertebra

Long non-coding RNAs (lncRNAs) are emerging as key regulators in gene expression; however, little is currently known regarding their role in cartilaginous endplate (CE) degeneration (CED) of cervical vertebra. The present study aimed to investigate the expression levels of lncRNAs and analyze their potential functions in CED of cervical vertebra in patients with cervical fracture and cervical spondylosis. Human competitive endogenous RNA (ceRNA) array was used to analyze lncRNA and mRNA expression levels in CE samples from patients with cervical fracture and cervical spondylosis, who received anterior cervical discectomy and fusion. Differentially expressed lncRNAs (DELs) or differentially expressed genes (DEGs) were identified and functionally analyzed, using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. An lncRNA-microRNA(miRNA)-mRNA ceRNA regulatory network was constructed based on the DELs and DEGs, and the ceRNA network was visualized using Cytoscape 3.7.2 software. In total, one downregulated mRNA, one upregulated miRNA and five downstream regulated lncRNAs were identified using reverse transcription-quantitative PCR in CED and healthy CE samples. A total of 369 lncRNAs and 246 mRNAs were identified as differentially expressed in CE. The GO and KEGG analyses demonstrated that the majority of GO and KEGG enrichments were associated with CED. Furthermore, a ceRNA network was established, including 168 putative miRNA response elements, 189 upregulated and 37 downregulated lncRNAs and 47 upregulated and 10dow regulated DEGs. The present study analyzed the function of DEGs in the ceRNA network and filtered out the same items as in DEG-function enrichment analysis. These results provide a new perspective for an improved understanding of ceRNA-mediated gene regulation in cervical spondylosis, and provide a novel theoretical basis for further studies on the function of lncRNA in cervical spondylosis. However, further experiments are required to validate the results of the present study.

Long non-coding RNA SNHG15 in various cancers: a meta and bioinformatic analysis

Background

The snoRNA host gene SNHG15 produces a long non-coding RNA (lncRNA) with a short half-life and has been reported to be dysregulated in multiple cancers and has recently been found to be correlated with tumour progression. Therefore, this meta-analysis was performed to evaluate the generalised prognostic role of small nucleolar RNA host gene 15 (SNHG15) in malignancies, based on variable data from different studies.

Methods

Four public databases were used to identify eligible studies. The association between prognostic indicators and clinical features was extracted and pooled to estimate the hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs). Publication bias was measured using Begg’s test and Egger’s test, and the stability of pooled results were measured using sensitivity analysis. Additionally, an online database based on The Cancer Genome Atlas (TCGA) was screened to further validate our results. Ultimately, we predicted the molecular regulation of SNHG15 based on the public databases.

Results

In total, 11 studies including 1087 patients were ultimately enrolled in our meta-analysis. We found that SNHG15 overexpression was associated with worse overall survival (OS) and disease-free survival (DFS), and this was validated in the Gene Expression Profiling Interactive Analysis (GEPIA) cohort. Moreover, increased SNHG15 expression suggested advanced TNM stage and LNM, but was not associated with age, gender, or tumour size. No publication bias or instability of the results was observed. SNHG15 was significantly upregulated in seven cancers and elevated expression of SNHG15 indicated shorter OS and DFS in five malignancies based on the validation using the GEPIA cohort. Further functional prediction indicated that SNHG15 may participate in some cancer-related pathways.

Conclusions

Upregulation of lncRNA SNHG15 was notably associated with worse prognosis and clinical features, suggesting that SNHG15 might serve as a novel prognostic factor in various cancers.

lncRNA uc.48+ regulates immune and inflammatory reactions mediated by the P2X7 receptor in type 2 diabetic mice

Diabetes and non-coding RNAs are receiving increasing attention in contemporary medical research. The present study aimed to explore the role of the long non-coding RNA uc.48+ in the pathological changes of type 2 diabetes mellitus (T2DM) by observing the effects of uc.48+ small interfering RNA (siRNA) on the abdominal cells of a mouse model of T2DM. Mice with T2DM (DM group) were established by feeding with a high-sugar and -fat diet combined with intraperitoneal injections of low-dose streptozotocin. An intraperitoneal injection of uc.48+ siRNA was administered to the diabetic mice, and the serum levels of cytokines together with other clinical parameters, namely blood pressure, heart rate, mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL) were examined. Following the collection and identification of abdominal cells from the mice, the mRNA levels of uc.48+, mRNA and protein levels of the P2X₇ receptor, and phosphorylation levels of ERK1/2 were evaluated by reverse transcription-PCR and western blotting, respectively. The MWT and TWL were significantly decreased in the DM group compared with the non-diabetic control group. However, the reductions in MWT and TWL were significantly attenuated following uc.48+ siRNA injection. The systolic and diastolic blood pressure, as well as the serum levels of tumor necrosis factor α and interleukin 1β of mice in the DM group were significantly increased compared with those in the control group, whereas these changes were significantly attenuated following the injection of uc.48+ siRNA. In addition, the expression levels of P2X₇ receptor mRNA and protein, and the degree of phosphorylation of ERK1/2 in the abdominal cells were significantly increased in the DM group compared with the control group. These changes were also significantly attenuated following transfection with uc.48+ siRNA in vivo. In conclusion, these data suggest that uc.48+ may play an important role in the pathological changes of blood pressure, neurology and abdominal cell function in T2DM via interaction with the P2X₇ receptor.

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.

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features

Background

Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear.

Results

In this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lncRNAs. Both developmental brain gene expression data and transcript sequence were found to contain relevant information for ASD risk gene prediction. During the pre-training phase of model construction, an autoencoder network was implemented for a representation learning of the gene expression data, and a random-forest-based feature selection was applied to the transcript-sequence-derived k-mers. Our models, including logistic regression, support vector machine and random forest, showed robust performance based on tenfold cross-validations as well as candidate prioritization with hypothetical loci. We then utilized the models to predict and prioritize a list of candidate lncRNAs, including some reported to be cis-regulators of known ASD risk genes, for further investigation.

Conclusions

Our results suggest that ASD risk genes can be accurately predicted using developmental brain gene expression data and transcript sequence features, and the models may provide useful information for functional characterization of the candidate lncRNAs associated with ASD.

Genome-wide association study identifies candidate markers related to lincRNAs associated with male infertility in the Greek population

BACKGROUND

Male infertility is currently one of the most common problems faced by couples worldwide. We performed a GWAS on Greek population and gathered statistically significant SNPs in order to investigate whether they lie within or near lncRNA regions.

OBJECTIVES

The aim of this study was to investigate whether polymorphisms on or near lncRNAs affect interactions with miRNAs and can cause male infertility.

MATERIALS AND METHODS

In the present study, a GWAS was conducted, using samples from 159 individuals (83 normozoospermic individuals and 76 patients of known fertility issues). Standard procedures for quality controls and association testing were followed, based on case-control testing.

RESULTS

We detected six lncRNAs (LINC02231, LINC00347, LINC02134, NCRNA00157, LINC02493, Lnc-CASK-1) that are associated with male infertility through their interaction with miRNAs. Furthermore, we identified the genes targeted by those miRNAs and highlighted their functions in spermatogenesis and the fertilization process.

DISCUSSION AND CONCLUSION

lncRNAs are involved in spermatogenesis through their interaction with miRNAs. Thus, their study is very important, and it may contribute to the understanding of the molecular mechanisms underlying male infertility.

Recent advances on the machine learning methods in predicting ncRNA-protein interactions

Recent transcriptomics and bioinformatics studies have shown that ncRNAs can affect chromosome structure and gene transcription, participate in the epigenetic regulation, and take part in diseases such as tumorigenesis. Biologists have found that most ncRNAs usually work by interacting with the corresponding RNA-binding proteins. Therefore, ncRNA-protein interaction is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, machine-learning methods for predicting ncRNA-protein interactions are increasingly favored by the researchers. In this review, we summarize several machine learning predictive models of ncRNA-protein interactions over the past few years, and briefly describe the characteristics of these machine learning models. In order to optimize the performance of machine learning models to better predict ncRNA-protein interactions, we give some promising future computational directions at the end.

NAMS webserver: coding potential assessment and functional annotation of plant transcripts

Recent advances in transcriptomics have uncovered lots of novel transcripts in plants. To annotate such transcripts, dissecting their coding potential is a critical step. Computational approaches have been proven fruitful in this task; however, most current tools are designed/optimized for mammals and only a few of them have been tested on a limited number of plant species. In this work, we present NAMS webserver, which contains a novel coding potential classifier, NAMS, specifically optimized for plants. We have evaluated the performance of NAMS using a comprehensive dataset containing more than 3 million transcripts from various plant species, where NAMS demonstrates high accuracy and remarkable performance improvements over state-of-the-art software. Moreover, our webserver also furnishes functional annotations, aiming to provide users informative clues to the functions of their transcripts. Considering that most plant species are poorly characterized, our NAMS webserver could serve as a valuable resource to facilitate the transcriptomic studies. The webserver with testing dataset is freely available at http://sunlab.cpy.cuhk.edu.hk/NAMS/.

Identification and Functional Analysis of Long Non-coding RNAs in Autism Spectrum Disorders

Genetic and environmental factors, alone or in combination, contribute to the pathogenesis of autism spectrum disorder (ASD). Although many protein-coding genes have now been identified as disease risk genes for ASD, a detailed illustration of long non-coding RNAs (lncRNAs) associated with ASD remains elusive. In this study, we first identified ASD-related lncRNAs based on genomic variant data of individuals with ASD from a twin study. In total, 532 ASD-related lncRNAs were identified, and 86.7% of these ASD-related lncRNAs were further validated by an independent copy number variant (CNV) dataset. Then, the functions and associated biological pathways of ASD-related lncRNAs were explored by enrichment analysis of their three different types of functional neighbor genes (i.e., genomic neighbors, competing endogenous RNA (ceRNA) neighbors, and gene co-expression neighbors in the cortex). The results have shown that most of the functional neighbor genes of ASD-related lncRNAs were enriched in nervous system development, inflammatory responses, and transcriptional regulation. Moreover, we explored the differential functions of ASD-related lncRNAs in distinct brain regions by using gene co-expression network analysis based on tissue-specific gene expression profiles. As a set, ASD-related lncRNAs were mainly associated with nervous system development and dopaminergic synapse in the cortex, but associated with transcriptional regulation in the cerebellum. In addition, a functional network analysis was conducted for the highly reliable functional neighbor genes of ASD-related lncRNAs. We found that all the highly reliable functional neighbor genes were connected in a single functional network, which provided additional clues for the action mechanisms of ASD-related lncRNAs. Finally, we predicted several potential drugs based on the enrichment of drug-induced pathway sets in the ASD-altered biological pathway list. Among these drugs, several (e.g., amoxapine, piperine, and diflunisal) were partly supported by the previous reports. In conclusion, ASD-related lncRNAs participated in the pathogenesis of ASD through various known biological pathways, which may be differential in distinct brain regions. Detailed investigation into ASD-related lncRNAs can provide clues for developing potential ASD diagnosis biomarkers and therapy.

LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property

Discovering new long non-coding RNAs (lncRNAs) has been a fundamental step in lncRNA-related research. Nowadays, many machine learning-based tools have been developed for lncRNA identification. However, many methods predict lncRNAs using sequence-derived features alone, which tend to display unstable performances on different species. Moreover, the majority of tools cannot be re-trained or tailored by users and neither can the features be customized or integrated to meet researchers’ requirements. In this study, features extracted from sequence-intrinsic composition, secondary structure and physicochemical property are comprehensively reviewed and evaluated. An integrated platform named LncFinder is also developed to enhance the performance and promote the research of lncRNA identification. LncFinder includes a novel lncRNA predictor using the heterologous features we designed. Experimental results show that our method outperforms several state-of-the-art tools on multiple species with more robust and satisfactory results. Researchers can additionally employ LncFinder to extract various classic features, build classifier with numerous machine learning algorithms and evaluate classifier performance effectively and efficiently. LncFinder can reveal the properties of lncRNA and mRNA from various perspectives and further inspire lncRNA–protein interaction prediction and lncRNA evolution analysis. It is anticipated that LncFinder can significantly facilitate lncRNA-related research, especially for the poorly explored species. LncFinder is released as R package (https://CRAN.R-project.org/package=LncFinder). A web server (http://bmbl.sdstate.edu/lncfinder/) is also developed to maximize its availability.

LncRNA and mRNA expression profiles and functional networks of hyposalivation of the submandibular gland in hypertension

Hyposalivation is a complication of hypertension. However, little is known about the role of long non-coding RNAs (lncRNAs) in salivary glands in hypertension. This study aimed to compare the lncRNA and mRNA expression profiles between spontaneous hypertension rats (SHRs) and Wistar-Kyoto (WKY) rats through microarray analysis and apple bioinformatics methods to analyse their potential roles in hyposalivation. The differentially expressed (DE) lncRNAs and mRNAs were confirmed by quantitative real-time PCR (qRT-PCR). Compared with WKY rats, 225 DE lncRNAs and 473 DE mRNAs were identified in the SMG of SHRs. The pathway analyses of DE mRNAs showed that inflammatory mediator regulation of transient receptor potential channels was involved in hyposalivation in SHRs. Ten DE lncRNAs were chosen for further research. A coding-non-coding gene co-expression (CNC) network and competing endogenous RNA (ceRNA) network analysis revealed that the potential functions of these 10 DE lncRNAs were closely connected with the processes of the immune response. This study showed abundant DE lncRNAs and mRNAs in hypertensive SMGs. Furthermore, our results indicated strong associations between the immune response and hyposalivation and showed the potential of immune-related genes as novel and therapeutic targets for hyposalivation.

IDSSIM: an lncRNA functional similarity calculation model based on an improved disease semantic similarity method

Background

It has been widely accepted that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human diseases. Many association prediction models have been proposed for predicting lncRNA functions and identifying potential lncRNA-disease associations. Nevertheless, among them, little effort has been attempted to measure lncRNA functional similarity, which is an essential part of association prediction models.

Results

In this study, we presented an lncRNA functional similarity calculation model, IDSSIM for short, based on an improved disease semantic similarity method, highlight of which is the introduction of information content contribution factor into the semantic value calculation to take into account both the hierarchical structures of disease directed acyclic graphs and the disease specificities. IDSSIM and three state-of-the-art models, i.e., LNCSIM1, LNCSIM2, and ILNCSIM, were evaluated by applying their disease semantic similarity matrices and the lncRNA functional similarity matrices, as well as corresponding matrices of human lncRNA-disease associations coming from either lncRNADisease database or MNDR database, into an association prediction method WKNKN for lncRNA-disease association prediction. In addition, case studies of breast cancer and adenocarcinoma were also performed to validate the effectiveness of IDSSIM.

Conclusions

Results demonstrated that in terms of ROC curves and AUC values, IDSSIM is superior to compared models, and can improve accuracy of disease semantic similarity effectively, leading to increase the association prediction ability of the IDSSIM-WKNKN model; in terms of case studies, most of potential disease-associated lncRNAs predicted by IDSSIM can be confirmed by databases and literatures, implying that IDSSIM can serve as a promising tool for predicting lncRNA functions, identifying potential lncRNA-disease associations, and pre-screening candidate lncRNAs to perform biological experiments. The IDSSIM code, all experimental data and prediction results are available online at https://github.com/CDMB-lab/IDSSIM.

Identifying potential functional lncRNAs in metabolic syndrome by constructing a lncRNA-miRNA-mRNA network

The metabolic syndrome (MS) is a cluster of interrelated risk factors including diabetes mellitus, abdominal obesity, high cholesterol, and hypertension, which can significantly increase mortality and disability. Accumulating evidence suggest that long non-coding RNAs (lncRNAs) are involved in the pathogenesis of human metabolic diseases. However, little is known about the regulatory role of lncRNAs in MS. In this work, we proposed a method for identifying potential MS-associated lncRNAs by constructing an lncRNA-miRNA-mRNA network (LMMN). Firstly, we constructed LMMN by integrating MS-associated genes, miRNA-mRNA interactions, miRNA-lncRNA interactions and mRNA/miRNA expression profiles in patients with MS. Then, we predicted potential MS-associated lncRNAs based on the topological properties of LMMN. As a result, we identified XIST as the most important lncRNA in LMMN. Furthermore, we focused on XIST/miR-214-3p and mir-181a-5p/PTEN axis and validated their expression in MS using real-time quantitative polymerase chain reaction (RT-qPCR). The RT-qPCR results showed that the expression of XIST and PTEN was significantly decreased (P < 0.05) while the expression of miR-214-3p was significantly increased (P < 0.05) in peripheral blood mononuclear cells (PBMCs) of patients with MS, compared with healthy controls. In addition, correlation analysis showed that XIST was negatively correlated with serum C peptide and PTEN was positively correlated with BMI of MS patients. Our findings provided new evidence for further exploring the regulatory role of XIST and other lncRNAs in MS.

MutEx: a multifaceted gateway for exploring integrative pan-cancer genomic data

Somatic mutation and gene expression dysregulation are considered two major tumorigenesis factors. While independent investigations of either factor pervade, studies of associations between somatic mutations and gene expression changes have been sporadic and nonsystematic. Utilizing genomic data collected from 11 315 subjects of 33 distinct cancer types, we constructed MutEx, a pan-cancer integrative genomic database. This database records the relationships among gene expression, somatic mutation and survival data for cancer patients. MutEx can be used to swiftly explore the relationship between these genomic/clinic features within and across cancer types and, more importantly, search for corroborating evidence for hypothesis inception. Our database also incorporated Gene Ontology and several pathway databases to enhance functional annotation, and elastic net and a gene expression composite score to aid in survival analysis. To demonstrate the usability of MutEx, we provide several application examples, including top somatic mutations associated with the most extensive expression dysregulation in breast cancer, differential mutational burden downstream of DNA mismatch repair gene mutations and composite gene expression score-based survival difference in breast cancer. MutEx can be accessed at http://www.innovebioinfo.com/Databases/Mutationdb_About.php.

Non-coding RNAs in cardiovascular cell biology and atherosclerosis

Atherosclerosis underlies the predominant number of cardiovascular diseases and remains a leading cause of morbidity and mortality worldwide. The development, progression and formation of clinically relevant atherosclerotic plaques involves the interaction of distinct and over-lapping mechanisms which dictate the roles and actions of multiple resident and recruited cell types including endothelial cells, vascular smooth muscle cells, and monocyte/macrophages. The discovery of non-coding RNAs (ncRNAs) including microRNAs, long non-coding RNAs, and circular RNAs, and their identification as key mechanistic regulators of mRNA and protein expression has piqued interest in their potential contribution to atherosclerosis. Accruing evidence has revealed ncRNAs regulate pivotal cellular and molecular processes during all stages of atherosclerosis including cell invasion, growth, and survival; cellular uptake and efflux of lipids, expression and release of pro- and anti-inflammatory intermediaries, and proteolytic balance. The expression profile of ncRNAs within atherosclerotic lesions and the circulation have been determined with the aim of identifying individual or clusters of ncRNAs which may be viable therapeutic targets alongside deployment as biomarkers of atherosclerotic plaque progression. Consequently, numerous in vivo studies have been convened to determine the effects of moderating the function or expression of select ncRNAs in well-characterized animal models of atherosclerosis. Together, clinicopathological findings and studies in animal models have elucidated the multifaceted and frequently divergent effects ncRNAs impose both directly and indirectly on the formation and progression of atherosclerosis. From these findings' potential novel therapeutic targets and strategies have been discovered which may pave the way for further translational studies and possibly taken forward for clinical application.

ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs

Noncoding RNAs (ncRNAs) play critical roles in many critical biological processes and have become a novel class of potential targets and bio-markers for disease diagnosis, therapy, and prognosis. Annotating and analysing ncRNA-disease association data are essential but challenging. Current computational resources lack comprehensive database platforms to consistently interpret and prioritize ncRNA-disease association data for biomedical investigation and application. Here, we present the ncRPheno database platform (http://lilab2.sysu.edu.cn/ncrpheno), which comprehensively integrates and annotates ncRNA-disease association data and provides novel searches, visualizations, and utilities for association identification and validation. ncRPheno contains 482,751 non-redundant associations between 14,494 ncRNAs and 3,210 disease phenotypes across 11 species with supporting evidence in the literature. A scoring model was refined to prioritize the associations based on evidential metrics. Moreover, ncRPheno provides user-friendly web interfaces, novel visualizations, and programmatic access to enable easy exploration, analysis, and utilization of the association data. A case study through ncRPheno demonstrated a comprehensive landscape of ncRNAs dysregulation associated with 22 cancers and uncovered 821 cancer-associated common ncRNAs. As a unique database platform, ncRPheno outperforms the existing similar databases in terms of data coverage and utilities, and it will assist studies in encoding ncRNAs associated with phenotypes ranging from genetic disorders to complex diseases.

ABBREVIATIONS

APIs: application programming interfaces; circRNA: circular RNA; ECO: Evidence & Conclusion Ontology; EFO: Experimental Factor Ontology; FDR: false discovery rate; GO: Gene Ontology; GWAS: genome wide association studies; HPO: Human Phenotype Ontology; ICGC: International Cancer Genome Consortium; lncRNA: long noncoding RNA; miRNA: micro RNA; ncRNA: noncoding RNA; NGS: next generation sequencing; OMIM: Online Mendelian Inheritance in Man; piRNA: piwi-interacting RNA; snoRNA: small nucleolar RNA; TCGA: The Cancer Genome Atlas.

STAT1-induced upregulation of lncRNA LINC01123 predicts poor prognosis and promotes the progression of endometrial cancer through miR-516b/KIF4A

Long non-coding RNAs (lncRNAs) have been proposed as suppressors or promoters in many tumor processes. LncRNA LINC01123 (LINC01123) was a newly identified lncRNA which was firstly functionally analyzed in lung cancer. However, its expression and function in other tumor types were rarely reported. In this study, we firstly confirmed that LINC01123 was highly expressed in both endometrial cancer (EC) tissues and cell lines using bioinformatics analysis and RT-CPR. Then, we preliminarily analyzed the mechanisms involved in overexpression of LINC01123 in EC, finding that STAT1 could bind directly to the LINC01123 promoter region and activate its transcription. Clinical research with 106 patients indicated that high expression of LINC01123 was associated with advanced clinical progression and poor clinical outcome of EC patients. Functionally, knockdown of LINC01123 suppressed the proliferation, migration and invasion of EC cells, and promoted apoptosis. Mechanistically, we observed that LINC01123 may act as an endogenous sponge by competing for miR-516b, thereby regulating KIF4A. Overall, our study revealed a novel LINC01123/miR-516b/KIF4A pathway regulatory axis in EC pathogenesis. LINC01123 may be a novel prognostic biomarker and therapeutic target in EC.Abbreviations: EC: Endometrial cancer; LncRNA: Long non-coding RNA; EMT: epithelial-mesenchymal transition; miRNA: microRNA; qRT-PCR: Quantitative real-time polymerase chain reaction; SPSS: Statistical Package for Social Sciences; Chip: chromatin-immunoprecipitation, TCGA: The Cancer Genome Atlas; CCK-8: Cell Counting Kit-8; KIF4A: Chromosome-associated kinesin KIF4A.

Integrative Analysis of the Doxorubicin-Associated LncRNA-mRNA Network Identifies Chemoresistance-Associated lnc-TRDMT1-5 as a Biomarker of Breast Cancer Progression

Increasing evidence has revealed close relationships between long non-coding RNAs (lncRNAs) and chemoresistance in multiple types of tumors; however, functional lncRNAs in breast cancer (BC) have not been completely identified. In this study, we aimed to identify novel lncRNAs that might play critical roles in doxorubicn resistance, which could reveal potential biomarkers of BC. Using a BC dataset (GSE81971), we identified 452 lncRNAs that were upregulated and 659 that were downregulated; furthermore, there were 1896 differentially expressed mRNAs, of which 1137 were upregulated and 758 were downregulated in MCF-7/ADR cells compared with the expression in MCF-7 cells. We constructed an lncRNA–mRNA network by integrating probe reannotation and regulatory interactions. To elucidate the key lncRNAs in BC, we further analyzed dysregulated lncRNA–mRNA crosstalk, and six candidate lncRNAs (lnc-TRDMT1-5, ZNF667-AS1, lnc-MPPE1-13, DSCAM-AS1:5, DSCAM-AS1:2, and lnc-CFI-3) were identified. Notably, the expression level of lnc-TRDMT1-5 was significantly upregulated in resistant cells compared with sensitive cells, and its levels were increased in BC tissues compared with adjacent tissues. Levels were positively associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) expression levels. High expression of lnc-TRDMT1-5 predicted poor prognosis in ER-positve and HER2-positive BC patients, especially in patients with chemoresistance. Bioinformatic and functional analysis revealed that lnc-TRDMT1-5 was involved in many crucial pathways in cancer, such as the PI3K/AKT and Wnt signaling pathways. Subcellular localization predicted that lnc-TRDMT1-5 was located in the cytoplasm, and the lncRNA–miRNA–mRNA network showed that lnc-TRDMT1-5 might serve as a regulator in BC. Here, our results demonstrated a dysregulated lncRNA–mRNA network that might provide new treatment strategies for chemoresistant BC, and the results identified a new lncRNA, lnc-TRDMT1-5, with oncogenic and prognostic functions in human BC.

LncRNAMORT is upregulated in myocardial infarction and promotes the apoptosis of cardiomyocyte by downregulating miR-93

Background

Myocardial infarction (MI) affects the expression of a large number of lncRNAs, while the functions of those dysregulated lncRNAs are mostly unclear.

Materials and methods

Expression of MORT and miR-93 in hearth tissues and plasma of both MI mice and Sham mice and both MI patients and healthy controls was detected by RT-qPCR. Correlations of expression levels of MORT and miR-93 between hear tissues and plasma of MI mice were explored by performing linear regression.

Results

In the present study we found that MORT expression levels were higher, while expression levels of miR-93 were lower in both plasma and heart tissues of mice MI mice models compared with Sham mice. Plasma levels of MORT and miR-93 were largely consistent with expression levels of MORT and miR-93 in heart tissue of MI mice. MORT expression levels were also higher, while levels of miR-93 were also lower in plasma of MI patients compared with healthy controls. MORT and miR-93 were inversely correlated in MI patients but not in healthy controls. MORT overexpression resulted in inhibited miR-93 expression in cardiomyocytes (AC16 cell line), while miR-93 overexpression did not significantly affect MORT expression. MORT overexpression promoted cardiomyocyte apoptosis, while miR-93 overexpression played and opposite role and attenuated the effects of MORT overexpression.

Conclusion

Therefore, lncRNA MORT is upregulated in myocardial infarction and promotes the apoptosis of cardiomyocyte by downregulating miR-93.

LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases

Mounting evidence suggested that dysfunction of long non-coding RNAs (lncRNAs) is involved in a wide variety of diseases. A knowledgebase with systematic collection and curation of lncRNA-disease associations is critically important for further examining their underlying molecular mechanisms. In 2013, we presented the first release of LncRNADisease, representing a database for collection of experimental supported lncRNA-disease associations. Here, we describe an update of the database. The new developments in LncRNADisease 2.0 include (i) an over 40-fold lncRNA-disease association enhancement compared with the previous version; (ii) providing the transcriptional regulatory relationships among lncRNA, mRNA and miRNA; (iii) providing a confidence score for each lncRNA-disease association; (iv) integrating experimentally supported circular RNA disease associations. LncRNADisease 2.0 documents more than 200 000 lncRNA-disease associations. We expect that this database will continue to serve as a valuable source for potential clinical application related to lncRNAs. LncRNADisease 2.0 is freely available at http://www.rnanut.net/lncrnadisease/.

Inflammation and DNA methylation coregulate the CtBP-PCAF-c-MYC transcriptional complex to activate the expression of a long non-coding RNA CASC2 in acute pancreatitis

Long non-coding RNAs (lncRNAs) are emerging as important regulators involved in the pathogenesis of many diseases. However, it is still unknown if they contribute to the occurrence of acute pancreatitis (AP). Here, we identified a lncRNA CASC2 (Cancer Susceptibility Candidate 2) was significantly upregulated in the pancreatic tissues from AP patients. Knockdown or overexpression of CASC2 in vitro could specifically repress or induce the expression of two proinflammatory cytokines including IL6 (Interleukin 6) and IL17, respectively. Changing the expression levels of several transcription factors that were predicted to bind to the promoter of CASC2, we found c-MYC could specifically regulate the expression of CASC2. Using immunoprecipitation, mass spectrometry, and co-immunoprecipitation assays, we proved that c-MYC assembled a transcriptional complex with PCAF (p300/CBP-associated Factor) and CtBP1/2 (C-terminal Binding Protein 1 and 2), terming as the CtBP-PCAF-c-MYC (CPM) complex. Further investigation revealed that CtBPs were amplified in the pancreatic tissues from AP patients and they functioned as coactivators to induce the expression of CASC2 and thus led to the upregulation of IL6 and IL17. Moreover, we identified that decreased DNA methylation levels in the promoters of CtBPs and inflammatory stimuli coactivated the expression of CtBPs. Collectively, we identified a new signaling pathway in which DNA methylation and inflammatory stimuli coregulate the CPM complex to activate CASC2 expression, whose induction further activates the expression of IL6 and IL17, eventually aggravating inflammation response and causing the pathology of AP.

Deep belief network-Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the representation of miRNAs and diseases, and the final prediction label was obtained according to the feature matrices. The experimental results show that the proposed model outperforms the state-of-the-art approaches in miRNA-disease association prediction based on the 10-fold cross-validation. Besides, the effectiveness of our model was further demonstrated by case studies.

LncBook: a curated knowledgebase of human long non-coding RNAs

Long non-coding RNAs (lncRNAs) have significant functions in a wide range of important biological processes. Although the number of known human lncRNAs has dramatically increased, they are poorly annotated, posing great challenges for better understanding their functional significance and elucidating their complex functioning molecular mechanisms. Here, we present LncBook (http://bigd.big.ac.cn/lncbook), a curated knowledgebase of human lncRNAs that features a comprehensive collection of human lncRNAs and systematic curation of lncRNAs by multi-omics data integration, functional annotation and disease association. In the present version, LncBook houses a large number of 270 044 lncRNAs and includes 1867 featured lncRNAs with 3762 lncRNA–function associations. It also integrates an abundance of multi-omics data from expression, methylation, genome variation and lncRNA–miRNA interaction. Also, LncBook incorporates 3772 experimentally validated lncRNA-disease associations and further identifies a total of 97 998 lncRNAs that are putatively disease-associated. Collectively, LncBook is dedicated to the integration and curation of human lncRNAs as well as their associated data and thus bears great promise to serve as a valuable knowledgebase for worldwide research communities.

Cluster correlation based method for lncRNA-disease association prediction

BACKGROUND

In recent years, increasing evidences have indicated that long non-coding RNAs (lncRNAs) are deeply involved in a wide range of human biological pathways. The mutations and disorders of lncRNAs are closely associated with many human diseases. Therefore, it is of great importance to predict potential associations between lncRNAs and complex diseases for the diagnosis and cure of complex diseases. However, the functional mechanisms of the majority of lncRNAs are still remain unclear. As a result, it remains a great challenge to predict potential associations between lncRNAs and diseases.

RESULTS

Here, we proposed a new method to predict potential lncRNA-disease associations. First, we constructed a bipartite network based on known associations between diseases and lncRNAs/protein coding genes. Then the cluster association scores were calculated to evaluate the strength of the inner relationships between disease clusters and gene clusters. Finally, the gene-disease association scores are defined based on disease-gene cluster association scores and used to measure the strength for potential gene-disease associations.

CONCLUSIONS

Leave-One Out Cross Validation (LOOCV) and 5-fold cross validation tests were implemented to evaluate the performance of our method. As a result, our method achieved reliable performance in the LOOCV (AUCs of 0.8169 and 0.8410 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), and 5-fold cross validation (AUCs of 0.7573 and 0.8198 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), which were significantly higher than the other three comparative methods. Furthermore, our method is simple and efficient. Only the known gene-disease associations are exploited in a graph manner and further new gene-disease associations can be easily incorporated in our model. The results for melanoma and ovarian cancer have been verified by other researches. The case studies indicated that our method can provide informative clues for further investigation.

NEAT1 knockdown suppresses endothelial cell proliferation and induces apoptosis by regulating miR‑638/AKT/mTOR signaling in atherosclerosis

Long non-coding RNAs (lncRNAs) have been validated to mediate the development of atherosclerosis (AS). In the present study, the molecular mechanisms and functions of lncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) in the advancement of human aortic endothelial cells (HAECs) were investigated. The levels of lncRNA-NEAT1 and miR-638 expression in clinical samples and cells were explored via quantitative reverse transcription polymerase chain reaction. Colony formation and CCK-8 assays were performed to determine the proliferative capacity of cells, and the apoptotic capacity of cells was analyzed on the basis of apoptotic cell proportion and caspase-3 activity. Then, the proportion of cells and correlations among phosphoglycerate kinase 1 (PGK1), NEAT1, and miR-638 were determined through RNA immunoprecipitation and luciferase assays and bioinformatics analysis. Moreover, the expression levels of Ki-67, proliferating cell nuclear antigen, PGK1, Bax, Bcl-2, (p)-mTOR, (p)-AKT, and β-catenin were analyzed via western blot analysis. In the serum of patients with AS and HAECs induced by oxidized low-density lipoprotein (ox-LDL), the expression level of miR-638 was decreased, whereas that of NEAT1 was increased. After ox-LDL therapy, NEAT1 knockdown suppressed HAEC proliferation and stimulated HAEC apoptosis, which could be reversed by the miR-638 inhibitor. NEAT1 inhibited miR-638 expression through direct mutual action. The following mechanical investigations revealed that PGK1 was a miR-638 target, whose expression was increased by NEAT1, a competing endogenous RNA of miR-638. Additionally, the miR-638 inhibitor contributed to proliferation and suppressed apoptosis through the activation of the AKT/mTOR signaling pathway in ox-LDL-induced HAECs. NEAT1 adjusted the AKT/mTOR signaling pathway via miR-638 in ox-LDL-induced HAECs to accelerate their proliferation and impede their apoptosis. This result revealed that NEAT1 may be valuable in the treatment of AS.

Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions

Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological experiments provide a lot of valuable information for the initial identification of RNA-protein interactions (RPIs), but with the increasing complexity of RPIs networks, this method gradually falls into expensive and time-consuming situations. Therefore, there is an urgent need for high speed and reliable methods to predict RNA-protein interactions. In this study, we propose a computational method for predicting the RNA-protein interactions using sequence information. The deep learning convolution neural network (CNN) algorithm is utilized to mine the hidden high-level discriminative features from the RNA and protein sequences and feed it into the extreme learning machine (ELM) classifier. The experimental results with 5-fold cross-validation indicate that the proposed method achieves superior performance on benchmark datasets (RPI1807, RPI2241, and RPI369) with the accuracy of 98.83, 90.83, and 85.63 percent, respectively. We further evaluate the performance of the proposed model by comparing it with the state-of-the-art SVM classifier and other existing methods on the same benchmark data set. In addition, we predicted the independent NPInter v2.0 data set using the model trained on RPI369. The experimental results show that our model can serve as a useful tool for predicting RNA-protein interactions.

Altered levels of MALAT1 and H19 derived from serum or serum exosomes associated with type-2 diabetes

Environmental, genetic and epigenetic risk factors have been closely related to the development of type-2 diabetes (T2D). It has been reported that the expression in H19 and MALAT1 are related to metabolic diseases. To analyze the relationship between the expression of H19 and MALAT1 lncRNAs with diabetic patients. A study was conducted in subjects with T2D and nondiabetic controls, residents of Mexico City. Anthropometric measurements were made, and serum concentrations of glucose, glycosylated hemoglobin, total cholesterol, triglycerides, high- and low-density lipoprotein cholesterol were analyzed. Total RNA was extracted from serum and serum exosomes. The H19 and MALAT1 expression levels were quantified by RT-qPCR. A significant reduction in the expression of MALAT1 from serum or serum exosomes were found in patients with T2D, metabolic syndrome and low levels of HDL-c. Significant increase in H19 levels was found in diabetic subjects with poor glycemic control. Additionally, the principal component analyzes showed that serum MALAT1 expression was associated with total cholesterol and HDL-c levels, and the exosomes H19 expression was associated with waist circumference. The results obtained suggest that MALAT1 expression levels could be an epigenetic biomarker of diabetes risk or of its comorbidities.

Long noncoding RNA atlas of the inflammation caused by asthma in mice

There is little evidence regarding the roles of long noncoding RNAs (lncRNAs) in inflammation caused by asthma. In this study, we successfully generated an asthma mouse model that was induced by ovalbumin (OVA). The effects of dexamethasone (Dex) treatment on lung tissue were investigated using pathological and biochemical methods, including Diff-Quik staining, enzyme-linked immunosorbent assay (ELISA), hematoxylin-eosin (H&E) staining, and western blotting (WB). The inflammation was effectively relieved with Dex treatment. High-throughput sequencing revealed that a total of 1490 lncRNAs were detected in lung tissue samples. Differential expression analysis revealed that the Dex group had 20 upregulated and 15 downregulated lncRNAs compared with those in the Model group. Moreover, nine differentially expressed and inflammation-related lncRNAs were verified by quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, the regulation networks of these nine lncRNAs, their potential binding microRNA (miRNAs), and the putative target genes showed that these lncRNAs play important roles in the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway. We further identified the expression levels of three potential binding miRNAs by qRT-PCR. The results of this study contribute to a better understanding of the functions of lncRNAs in inflammation caused by asthma.

A random forest based computational model for predicting novel lncRNA-disease associations

BACKGROUND

Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources.

RESULTS

To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models.

CONCLUSIONS

Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

LncRNA H19 gene rs2839698 polymorphism is associated with a decreased risk of colorectal cancer in a Chinese Han population: A case-control study

Background

Long non‐coding RNA (lncRNA) H19 is involved in the carcinogenesis, progression, and metastasis of colorectal cancer (CRC). Recently, a few studies explored the relationship between lncRNA H19 gene rs2839698 polymorphism and CRC risk, but with conflicting findings.

Materials and methods

A case‐control study with 315 CRC cases and 441 controls was designed in a Chinese population. Genotyping was performed using PCR‐RFLP.

Results

It was found rs2839698 polymorphism was associated with a decreased risk of CRC (AA vs GG: OR, 0.73; 95% CI, 0.54‐0.98; P = .037; A vs G: OR, 0.78; 95% CI, 0.63‐0.96; P = .021). Stratified analyses indicated this positive association was also significant in the non‐smokers (AA vs GG: OR, 0.49; 95% CI, 0.25‐0.93; P = .029), non‐drinkers, those aged ≥ 60 years, and overweight individuals (BMI ≥ 24). In addition, rs2839698 polymorphism was also related to the lymph node metastasis (AA vs GG: OR, 0.43; 95% CI, 0.21‐0.88; P = .019) and tumor size (AA vs GG: OR, 0.42; 95% CI, 0.20‐0.88; P = .020) for patients with CRC.

Conclusion

To sum up, the lncRNA H19 gene rs2839698 polymorphism decreases the risk of CRC in Chinese individuals, especially among the non‐smokers, non‐drinkers, individuals aged ≥ 60 years, and overweight individuals (BMI ≥ 24). Thus, the lncRNA H19 gene rs2839698 polymorphism might be an important biomarker and diagnostic marker for predicting the susceptibility to CRC in Chinese Han population.

Genome-Wide Analysis and Function Prediction of Long Noncoding RNAs in Sheep Pituitary Gland Associated with Sexual Maturation

Long noncoding RNA (lncRNA) plays a crucial role in the hypothalamic-pituitary-testis (HPT) axis associated with sheep reproduction. The pituitary plays a connecting role in the HPT axis. However, little is known of their expression pattern and potential roles in the pituitary gland. To explore the potential lncRNAs that regulate the male sheep pituitary development and sexual maturation, we constructed immature and mature sheep pituitary cDNA libraries (three-month-old, TM, and nine-month-old, NM, respectively, n = 3) for lncRNA and mRNA high-throughput sequencing. Firstly, the expression of lncRNA and mRNA were comparatively analyzed. 2417 known lncRNAs and 1256 new lncRNAs were identified. Then, 193 differentially expressed (DE) lncRNAs and 1407 DE mRNAs were found in the pituitary between the two groups. Moreover, mRNA-lncRNA interaction network was constructed according to the target gene prediction of lncRNA and functional enrichment analysis. Five candidate lncRNAs and their targeted genes HSD17B12, DCBLD2, PDPK1, GPX3 and DLL1 that enriched in growth and reproduction related pathways were further filtered. Lastly, the interaction of candidate lncRNA TCONS_00066406 and its targeted gene HSD17B12 were validated in in vitro of sheep pituitary cells. Our study provided a systematic presentation of lncRNAs and mRNAs in male sheep pituitary, which revealed the potential role of lncRNA in male reproduction.

MicroRNAs Associated With Colon Cancer: New Potential Prognostic Markers and Targets for Therapy

MicroRNAs (miRNAs) are a kind of non-coding RNA (ncRNA) that regulate the expression of target genes and play a role in the occurrence and development of cancers. Colon cancer (COAD) is the second most common cause of cancer-related mortality. However, the prognostic value of miRNAs in COAD is still confusing. In this study, we obtain miRNAs and messenger RNAs (mRNAs) expression profiles of COAD from the Cancer Genome Atlas (TCGA) database. After preliminary data screening and preprocessing, we acquire the expression data of 894 miRNAs and 17,019 mRNAs. Then, compared with the normal samples, 39 upregulated miRNAs and 54 downregulated miRNAs are identified by differential expression analysis. Furthermore, we obtain 1,487 upregulated mRNAs and 2,847 downregulated mRNAs. We confirm nine key miRNAs related to the survival rate of COAD patients. Moreover, by using bioinformatics methods, we get 461 common genes from both the target genes of these nine key miRNAs and differentially expressed mRNAs. Through analyzing the protein-protein interaction (PPI) network of these 461 common genes and survival analysis, we confirm five hub genes as promising biomarkers for COAD prognosis. It is worth mentioning that no previous reports have found that PGR and KCNB1 are related to COAD. We expect these key miRNAs and hub genes will provide a new way for the study of COAD.