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

LDAI-ISPS: LncRNA-Disease Associations Inference Based on Integrated Space Projection Scores

Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA-disease associations. In this research, we proposed a lncRNA-disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA-disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA-disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA-disease associations and isolated diseases.

Multiview Consensus Graph Learning for lncRNA-Disease Association Prediction

Long noncoding RNAs (lncRNAs) are a class of noncoding RNA molecules longer than 200 nucleotides. Recent studies have uncovered their functional roles in diverse cellular processes and tumorigenesis. Therefore, identifying novel disease-related lncRNAs might deepen our understanding of disease etiology. However, due to the relatively small number of verified associations between lncRNAs and diseases, it remains a challenging task to reliably and effectively predict the associated lncRNAs for given diseases. In this paper, we propose a novel multiview consensus graph learning method to infer potential disease-related lncRNAs. Specifically, we first construct a set of similarity matrices for lncRNAs and diseases by taking advantage of the known associations. We then iteratively learn a consensus graph from the multiple input matrices and simultaneously optimize the predicted association probability based on a multi-label learning framework. To convey the utility of our method, three state-of-the-art methods are compared with our method on three widely used datasets. The experiment results illustrate that our method could obtain the best prediction performance under different cross validation schemes. The case study analysis implemented for uterine cervical neoplasms further confirmed the utility of our method in identifying lncRNAs as potential prognostic biomarkers in practice.

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression

As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model’s average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.

Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms

Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.

Prognostic and clinicopathological significance of SNHG6 in human cancers: a meta-analysis

Background

Recently, accumulating evidence has suggested that the aberrant expression of SNHG6 exists in a variety of tumors and has a correlation with poor clinical outcomes across cancer patients. Considering the inconsistent data among published studies, we aim to assess the prognostic effect of SNHG6 on malignancies.

Methods

We retrieved relevant publications in Web of Science, Embase, MEDLINE, PubMed and Cochrane Library based on predefined selection criteria, up to October 1, 2019. Pooled hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were utilized to evaluate the correlation between SNHG6 and overall survival (OS), recurrence-free survival (RFS) and progression-free survival (PFS) as well as clinicopathology.

Results

In total, 999 patients from 14 articles were enrolled in our meta-analysis. The results revealed that augmented SNHG6 expression was significantly correlated with poor OS (HR = 2.20, 95% CI = 1.76–2.75, P < 0.001) and RFS (HR = 3.10, 95% CI = 1.90–5.07, P < 0.001), but not with PFS (HR = 2.11, 95% CI = 0.82–5.39, P = 0.120). In addition to lung cancer and ovarian cancer, subgroup analysis showed that the prognostic value of SNHG6 across multiple tumors was constant as the tumor type, sample size, and methods of data extraction changed. Moreover, cancer patients with enhanced SNHG6 expression were prone to advanced TNM stage (OR = 3.31, 95% CI = 2.46–4.45, P < 0.001), distant metastasis (OR = 4.67, 95% CI = 2.98–7.31, P < 0.001), lymph node metastasis (OR = 2.59, 95% CI = 1.41–4.77, P = 0.002) and deep tumor invasion (OR = 3.75, 95% CI = 2.10–6.69, P < 0.001), but not associated with gender, histological grade and tumor size.

Conclusions

SNHG6 may serve as a promising indicator in the prediction of prognosis and clinicopathological features in patients with different kinds of tumors.

Identification of the Key Factors Related to Bladder Cancer by lncRNA-miRNA-mRNA Three-Layer Network

Bladder cancer is the most common malignant tumor of the urinary system, and it has high incidence, high degree of malignancy, and easy recurrence after surgery. The etiology and pathogenesis of bladder cancer are not fully understood, but more and more studies have shown that its development may be regulated by some core molecules. To identify key molecules in bladder cancer, we constructed a three-layer network by merging lncRNA-miRNA regulatory network, miRNA-mRNA regulatory network, and lncRNA-mRNA coexpression network, and further analyzed the topology attributes of the network including the degree, betweenness centrality and closeness centrality of nodes. We found that miRNA-93 and miRNA-195 are controllers for a three-layer network and regulators of numerous target genes associated with bladder cancer. Functional enrichment analysis of their target mRNAs revealed that miRNA-93 and miRNA-195 may be closely related to bladder cancer by disturbing the homeostasis of the cell cycle or HTLV-I infection. In addition, since E2F1 and E2F2 are enriched in various KEGG signaling pathways, we conclude that they are important target genes of miRNA-93, and participate in the apoptotic process by forming a complex with a certain protein or transcription factor activity, sequence-specific DNA binding in bladder cancer. Similarly, AKT3 is an important target gene of miRNA-195, its expression is associated with PI3K-Akt-mTOR signaling pathway and AMPK-mTOR signaling pathway. Therefore, we speculate that AKT3 may participate in proliferation and apoptosis of bladder cancer cells through these pathways, and ultimately affect the biological behavior of tumor cells. Furthermore, through survival analysis, we found that miRNA-195 and miRNA-93 are associated with poor prognosis of bladder cancer. And the Kaplan-Meier curve showed that 24 mRNAs and nine lncRNAs are closely related to overall survival of bladder cancer.

Idiopathic Pulmonary Fibrosis: Pathogenesis and the Emerging Role of Long Non-Coding RNAs

Idiopathic pulmonary fibrosis (IPF) is a progressive chronic disease characterized by excessing scarring of the lungs leading to irreversible decline in lung function. The aetiology and pathogenesis of the disease are still unclear, although lung fibroblast and epithelial cell activation, as well as the secretion of fibrotic and inflammatory mediators, have been strongly associated with the development and progression of IPF. Significantly, long non-coding RNAs (lncRNAs) are emerging as modulators of multiple biological processes, although their function and mechanism of action in IPF is poorly understood. LncRNAs have been shown to be important regulators of several diseases and their aberrant expression has been linked to the pathophysiology of fibrosis including IPF. This review will provide an overview of this emerging role of lncRNAs in the development of IPF.

Identification of candidate RNA signatures in triple-negative breast cancer by the construction of a competing endogenous RNA network with integrative analyses of Gene Expression Omnibus and The Cancer Genome Atlas data

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is characterized by aggressive and metastatic clinical characteristics and generally leads to earlier distant recurrence and poorer prognosis than other molecular subtypes. Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) serve a crucial role in a wide variety of biological processes by interacting with microRNAs (miRNAs) as competing endogenous RNAs (ceRNAs) and, thus, affect the expression of target genes in multiple types of cancer. Seven datasets from the Gene Expression Omnibus (GEO) database, including 444 tumor and 88 healthy tissue samples, were utilized to investigate the underlying mechanisms of TNBC and identify prognostic biomarkers. Differentially expressed genes (DEGs) were further validated in The Cancer Genome Atlas database and the associations between their expression levels and clinical information were analyzed to identify prognostic values. A potential lncRNA-miRNA-mRNA ceRNA network was also constructed. Finally, 69 mRNAs from the integrated Gene Expression Omnibus datasets were identified as DEGs using the robust rank aggregation method with |log2FC|>1 and adjusted P<0.01 set as the significance cut-off levels. In addition, 29 lncRNAs, 21 miRNAs and 27 mRNAs were included in the construction of the ceRNA network. The present study elucidated the mechanisms underlying the progression of TNBC and identified novel prognostic biomarkers for TNBC.

Computational models for lncRNA function prediction and functional similarity calculation

From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function-related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.

Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs

Long noncoding RNA (lncRNA) transcripts have emerging impacts in cancer studies, which suggests their potential as novel therapeutic agents. However, the molecular mechanism behind their treatment effects is still unclear. Here, we designed a computational model to Associate LncRNAs with Anti-Cancer Drugs (ALACD) based on a bilevel optimization model, which optimized the gene signature overlap in the upper level and imputed the missing lncRNA-gene association in the lower level. ALACD predicts genes coexpressed with lncRNAs mean while matching drug’s gene signatures. This model allows us to borrow the target gene information of small molecules to understand the mechanisms of action of lncRNAs and their roles in cancer. The ALACD model was systematically applied to the 10 cancer types in The Cancer Genome Atlas (TCGA) that had matched lncRNA and mRNA expression data. Cancer type-specific lncRNAs and associated drugs were identified. These lncRNAs show significantly different expression levels in cancer patients. Follow-up functional and molecular pathway analysis suggest the gene signatures bridging drugs and lncRNAs are closely related to cancer development. Importantly, patient survival information and evidence from the literature suggest that the lncRNAs and drug-lncRNA associations identified by the ALACD model can provide an alternative choice for cancer targeting treatment and potential cancer pognostic biomarkers. The ALACD model is freely available at https://github.com/wangyc82/ALACD-v1.

LncRNAs are RNA transcripts that are longer than 200 bp and do not encode proteins. Recent experimental studies have indicated the crucial role of lncRNAs in cancer. We proposed a computational model, ALACD, to understand a lncRNA’s molecular mechanism by associating it with a drug through the drug’s target genes. ALACD reveals lncRNAs, the associated anti-cancer drug, and the induced gene signatures that are involved in the regulation of cancer. Furthermore, these cancer-related lncRNAs are differentially expressed in cancer patients and closely associated with patient survival.

lncRNA DGCR5 Up-Regulates TGF-β1, Increases Cancer Cell Stemness and Predicts Survival of Prostate Cancer Patients

Background

Long non-coding RNA (lncRNA) DiGeorge syndrome critical region gene 5 (DGCR5) plays different roles in different types of human cancer, but its role in prostate cancer (PC) has not been reported.

Methods

DGCR5 and TGF-β1 expression in paired tumor and adjacent healthy tissues from 64 PC patients was analyzed by performing RT-qPCR. A 5-year follow-up study was performed to analyze the prognostic value of DGCR5 for PC. The interaction between DGCR5 and TGF-β1 was analyzed by overexpression experiments. Cell stemness was analyzed by cell stemness assay.

Results

In our study, we found that DGCR5 was down-regulated in tumor tissues than in adjacent healthy tissues of PC patients, but TGF-β1 was up-regulated in the tumor tissues. DGCR5 expression was not affected by clinical stages, but low DGCR5 level in the tumor was correlated with poor survival. DGCR5 and TGF-β1 were inversely correlated in tumor tissues but not in adjacent healthy tissues. DGCR5 over-expression resulted in down-regulation of TGF-β1, while TGF-β1 treatment did not significantly affect DGCR5 expression. DGCR5 over-expression led to decreased stemness of PC cells, but TGF-β1 treatment played a reverse role and attenuated the effects of DGCR5 over-expression. DGCR5 may decrease the stemness of PC cells by down-regulating TGF-β1.

DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations

MicroRNAs (miRNAs) play a critical role in human diseases. Determining the association between miRNAs and disease contributes to elucidating the pathogenesis of liver diseases and seeking the effective treatment method. Despite great recent advances in the field of the associations between miRNAs and diseases, implementing association verification and recognition efficiently at scale presents serious challenges to biological experimental approaches. Thus, computational methods for predicting miRNA-disease association have become a research hotspot. In this paper, we present a new computational method, named distance-based sequence similarity for miRNA-disease association prediction (DBMDA), that directly learns a mapping from miRNA sequence to a Euclidean space. The notable feature of our approach consists of inferring global similarity from region distances that can be figured by chaos game representation algorithm based on the miRNA sequences. In the 5-fold cross-validation experiment, the area under the curve (AUC) obtained by DBMDA in predicting potential miRNA-disease associations reached 0.9129. To assess the effectiveness of DBMDA more effectively, we compared it with different classifiers and former prediction models. Besides, we constructed two case studies for prostate neoplasms and colon neoplasms. Results show that 39 and 39 out of the top 40 predicted miRNAs were confirmed by other databases, respectively. BDMDA has made new attempts in sequence similarity and achieved excellent results, while at the same time providing a new perspective for predicting the relationship between diseases and miRNAs. The source code and datasets explored in this work are available online from the University of Chinese Academy of Sciences (http://220.171.34.3:81/).

LncRNA PTCSC3 Inhibits Tumor Growth and Cancer Cell Stemness in Gastric Cancer by Interacting with lncRNA Linc-pint

Background

The tumor suppressor role of lncRNA PTCSC3 has been reported in papillary thyroid carcinoma, our study aimed to investigate its involvement in gastric cancer.

Methods

Tumor tissues and adjacent healthy tissues were collected from gastric cancer patients. Expression of PTCSC3 and lncRNA Linc-pint in these tissues was analyzed by RT-qPCR. The interaction between PTCSC3 and Linc-pint was analyzed by overexpression experiments. Cell proliferation and stemness were analyzed by CCK-8 assay and cell stemness assay, respectively.

Results

PTCSC3 and lncRNA Linc-pint were both downregulated in tumor tissues than in adjacent healthy tissues of gastric cancer patients. Low levels of PTCSC3 and Linc-pint were closely correlated with poor survival. PTCSC3 and Linc-pint overexpression inhibited tumor growth and cancer cell stemness, while Linc-pint knockdown played an opposite role an attenuated the effects of PTCSC3 overexpression. Expression levels of PTCSC3 and Linc-pint were significantly correlated in tumor tissues but not in adjacent healthy tissues. Overexpression of PTCSC3 and Linc-pint upregulated the expression of each other.

Conclusion

PTCSC3 inhibits tumor growth and cancer cell stemness in gastric cancer by interacting with lncRNA Linc-pint.

IDLDA: An Improved Diffusion Model for Predicting LncRNA-Disease Associations

It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA–disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and vice versa, we developed an improved diffusion model to predict potential lncRNA–disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.

Reconstruction and Analysis of the lncRNA-miRNA-mRNA Network Based on Competitive Endogenous RNA Reveal Functional lncRNAs in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is an important cause of sudden death and heart failure with an unknown etiology. Recent studies have suggested that long non-coding RNA (lncRNA) can interact with microRNA (miRNA) and indirectly interact with mRNA through competitive endogenous RNA (ceRNA) activities. However, the mechanism of ceRNA in DCM remains unclear. In this study, a miRNA array was first performed using heart samples from DCM patients and healthy controls. For further validation, we conducted real-time quantitative reverse transcription (RT)-PCR using samples from DCM patients and a doxorubicin-induced rodent model of cardiomyopathy, revealing that miR-144-3p and miR-451a were down-regulated, and miR-21-5p was up-regulated. Based on the ceRNA theory, we constructed a global triple network using data from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) and our miRNA array. The lncRNA-miRNA-mRNA network comprised 22 lncRNA nodes, 32 mRNA nodes, and 11 miRNA nodes. Hub nodes and the number of relationship pairs were then analyzed, and the results showed that two lncRNAs (NONHSAT001691 and NONHSAT006358) targeting miR-144/451 were highly related to DCM. Then, cluster module and random walk with restart for the ceRNA network were analyzed and identified four lncRNAs (NONHSAT026953/NONHSAT006250/NONHSAT133928/NONHSAT041662) targeting miR-21 that were significantly related to DCM. This study provides a new strategy for research on DCM or other diseases. Furthermore, lncRNA-miRNA pairs may be regarded as candidate diagnostic biomarkers or potential therapeutic targets of DCM.

Long non-coding RNA linc00152 acting as a promising oncogene in cancer progression

The incidence and mortality rate of cancer continues to gradually increase, although considerable research effort has been directed at elucidating the molecular mechanisms underlying biomarkers responsible for tumorigenesis. Accumulated evidence indicates that the long non-coding RNAs (lncRNAs), which are transcribed but not translated into functional proteins, contribute to cancer development. Recently, linc00152 (an lncRNA) was identified as a potent oncogene in various cancer types, and shown to be involved in cancer cell proliferation, invasiveness, and motility by sponging tumor-suppressive microRNAs acting as a competing endogenous RNA, binding to gene promoters acting as a transcriptional regulator, and binding to functional proteins. In this review, we focus on the oncogenic role of linc00152 in tumorigenesis and provided an overview of recent clinical studies on the effects of linc00152 expression in human cancers.

WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network

BACKGROUND

An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Inferring potential related microbes for diseases can not only promote disease prevention, diagnosis and treatment, but also provide valuable information for drug development. Considering that experimental methods are expensive and time-consuming, developing computational methods is an alternative choice. However, most of existing methods are biased towards well-characterized diseases and microbes. Furthermore, existing computational methods are limited in predicting potential microbes for new diseases.

RESULTS

Here, we developed a novel computational model to predict potential human microbe-disease associations (MDAs) based on Weighted Meta-Graph (WMGHMDA). We first constructed a heterogeneous information network (HIN) by combining the integrated microbe similarity network, the integrated disease similarity network and the known microbe-disease bipartite network. And then, we implemented iteratively pre-designed Weighted Meta-Graph search algorithm on the HIN to uncover possible microbe-disease pairs by cumulating the contribution values of weighted meta-graphs to the pairs as their probability scores. Depending on contribution potential, we described the contribution degree of different types of meta-graphs to a microbe-disease pair with bias rating. Meta-graph with higher bias rating will be assigned greater weight value when calculating probability scores.

CONCLUSIONS

The experimental results showed that WMGHMDA outperformed some state-of-the-art methods with average AUCs of 0.9288, 0.9068 ±0.0031 in global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. In the case studies, 9, 19, 37 and 10, 20, 45 out of top-10, 20, 50 candidate microbes were manually verified by previous reports for asthma and inflammatory bowel disease (IBD), respectively. Furthermore, three common human diseases (Crohn's disease, Liver cirrhosis, Type 1 diabetes) were adopted to demonstrate that WMGHMDA could be efficiently applied to make predictions for new diseases. In summary, WMGHMDA has a high potential in predicting microbe-disease associations.

Identification of key protein-coding genes and lncRNAs in spontaneous neutrophil apoptosis

Polymorphonuclear leukocytes (PMNs) are the most abundant cells of the innate immune system in humans, and spontaneous PMN apoptosis plays crucial roles in maintaining neutrophil homeostasis and resolving inflammation. However, the detailed mechanisms of spontaneous PMN apoptosis remain to be elucidated. By analysis of the public microarray dataset GSE37416, we identified a total of 3050 mRNAs and 220 long non-coding RNAs (lncRNAs) specifically expressed during PMN apoptosis in a time-dependent manner. By short time-series expression miner (STEM) analysis, Gene Ontology analysis, and lncRNA-mRNA co-expression network analyses, we identified some key molecules specifically related to PMN apoptosis. STEM analysis identified 12 gene profiles with statistically significance, including 2 associated with apoptosis. Protein-protein interaction (PPI) network analysis of the genes from 2 profiles and lncRNA-mRNA co-expression network analysis identified a 12-gene hub (including NFκB1 and BIRC3) associated with apoptosis, as well as 2 highly correlated lncRNAs (THAP9-AS1, and AL021707.6). We experimentally examined the expression profiles of two mRNA (NFκB1 and BIRC3) and two lncRNAs (THAP9-AS1 andAL021707.6) by quantitative real-time polymerase chain reaction to confirm their time-dependent expressions. These data altogether demonstrated that these genes are involved in the regulation of spontaneous neutrophil apoptosis and the corresponding gene products could also serve as potential key regulatory molecules for PMN apoptosis and/or therapeutic targets for over-reactive inflammatory response caused by the abnormality in PMN apoptosis.

LncRNA LINC01116 Promotes Cancer Cell Proliferation, Migration And Invasion In Gastric Cancer By Positively Interacting With lncRNA CASC11

Purpose

The oncogenic roles of lncRNA LINC01116 have been reported in several types of cancer, while its involvement in gastric cancer is unknown. This study aimed to investigate the involvement of LINC01116 in gastric cancer.

Methods

Gene expression was detected by qPCR. Correlations were analyzed by linear regression. Overexpression and siRNA silencing techniques were used to analyze gene functions. Cell invasion and migration were analyzed by Transwell assays.

Results

LINC01116 and lncRNA CASC11 were both upregulated in cancer tissues compared to cancer-adjacent tissues. Expression levels of LINC01116 and CASC11 were increased with the increase in clinical stages. Expression levels of LINC01116 and CASC11 were positively correlated. Overexpression of LINC01116 mediated the upregulated CASC11 in gastric cancer cells, and CASC11 overexpression also led to overexpressed LINC01116. Overexpression of LINC01116 and CASC11 led to promoted invasion and migration of gastric cancer cells. Rescue experiments showed that CASC11 knockdown attenuated the effects of LINC01116 overexpression. Overexpression of LINC01116 failed to significantly affect cancer cell proliferation.

Conclusion

LINC01116 promoted cancer cell invasion and migration in gastric cancer by positively interacting with CASC11.

Computational Methods for Identifying Similar Diseases

Although our knowledge of human diseases has increased dramatically, the molecular basis, phenotypic traits, and therapeutic targets of most diseases still remain unclear. An increasing number of studies have observed that similar diseases often are caused by similar molecules, can be diagnosed by similar markers or phenotypes, or can be cured by similar drugs. Thus, the identification of diseases similar to known ones has attracted considerable attention worldwide. To this end, the associations between diseases at the molecular, phenotypic, and taxonomic levels were used to measure the pairwise similarity in diseases. The corresponding performance assessment strategies for these methods involving the terms “category-based,” “simulated-patient-based,” and “benchmark-data-based” were thus further emphasized. Then, frequently used methods were evaluated using a benchmark-data-based strategy. To facilitate the assessment of disease similarity scores, researchers have designed dozens of tools that implement these methods for calculating disease similarity. Currently, disease similarity has been advantageous in predicting noncoding RNA (ncRNA) function and therapeutic drugs for diseases. In this article, we review disease similarity methods, evaluation strategies, tools, and their applications in the biomedical community. We further evaluate the performance of these methods and discuss the current limitations and future trends for calculating disease similarity.

Deep Learning in the Study of Protein-Related Interactions

Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein- drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

Functions and Regulatory Mechanisms of lncRNAs in Skeletal Myogenesis, Muscle Disease and Meat Production

Myogenesis is a complex biological process, and understanding the regulatory network of skeletal myogenesis will contribute to the treatment of human muscle related diseases and improvement of agricultural animal meat production. Long noncoding RNAs (lncRNAs) serve as regulators in gene expression networks, and participate in various biological processes. Recent studies have identified functional lncRNAs involved in skeletal muscle development and disease. These lncRNAs regulate the proliferation, differentiation, and fusion of myoblasts through multiple mechanisms, such as chromatin modification, transcription regulation, and microRNA sponge activity. In this review, we presented the latest advances regarding the functions and regulatory activities of lncRNAs involved in muscle development, muscle disease, and meat production. Moreover, challenges and future perspectives related to the identification of functional lncRNAs were also discussed.

Portrait of Tissue-Specific Coexpression Networks of Noncoding RNAs (miRNA and lncRNA) and mRNAs in Normal Tissues

Genes that encode proteins playing a role in more than one biological process are frequently dependent on their tissue context, and human diseases result from the altered interplay of tissue- and cell-specific processes. In this work, we performed a computational approach that identifies tissue-specific co-expression networks by integrating miRNAs, long-non-coding RNAs, and mRNAs in more than eight thousands of human samples from thirty normal tissue types. Our analysis (1) shows that long-non coding RNAs and miRNAs have a high specificity, (2) confirms several known tissue-specific RNAs, and (3) identifies new tissue-specific co-expressed RNAs that are currently still not described in the literature. Some of these RNAs interact with known tissue-specific RNAs or are crucial in key cancer functions, suggesting that they are implicated in tissue specification or cell differentiation.

Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

The Identification and Analysis of mRNA-lncRNA-miRNA Cliques From the Integrative Network of Ovarian Cancer

Ovarian cancer is one of the leading causes of cancer mortality in women. Since little clinical symptoms were shown in the early period of ovarian cancer, most patients were found in phases III-IV or with abdominal metastasis when diagnosed. The lack of effective early diagnosis biomarkers makes ovarian cancer difficult to screen. However, in essence, the fundamental problem is we know very little about the regulatory mechanisms during tumorigenesis of ovarian cancer. There are emerging regulatory factors, such as long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), which have played important roles in cancers. Therefore, we analyzed the RNA-seq profiles of 407 ovarian cancer patients. An integrative network of 20,424 coding RNAs (mRNAs), 10,412 lncRNAs, and 742 miRNAs were construed with variance inflation factor (VIF) regression method. The mRNA-lncRNA-miRNA cliques were identified from the network and analyzed. Such promising cliques showed significant correlations with survival and stage of ovarian cancer and characterized the complex sponge regulatory mechanism, suggesting their contributions to tumorigenicity. Our results provided novel insights of the regulatory mechanisms among mRNAs, lncRNAs, and miRNAs and highlighted several promising regulators for ovarian cancer detection and treatment.

Link clustering explains non-central and contextually essential genes in protein interaction networks

Recent studies have shown that many essential genes (EGs) change their essentiality across various contexts. Finding contextual EGs in pathogenic conditions may facilitate the identification of therapeutic targets. We propose link clustering as an indicator of contextual EGs that are non-central in protein-protein interaction (PPI) networks. In various human and yeast PPI networks, we found that 29–47% of EGs were better characterized by link clustering than by centrality. Importantly, non-central EGs were prone to change their essentiality across different human cell lines and between species. Compared with central EGs and non-EGs, non-central EGs had intermediate levels of expression and evolutionary conservation. In addition, non-central EGs exhibited a significant impact on communities at lower hierarchical levels, suggesting that link clustering is associated with contextual essentiality, as it depicts locally important nodes in network structures.

SKF-LDA: Similarity Kernel Fusion for Predicting lncRNA-Disease Association

Recently, prediction of lncRNA-disease associations has attracted more and more attentions. Various computational models have been proposed; however, there is still room to improve the prediction accuracy. In this paper, we propose a kernel fusion method with different types of similarities for the lncRNAs and diseases. The expression similarity and cosine similarity are used for lncRNAs, and the semantic similarity and cosine similarity are used for the diseases. To eliminate the noise effect, a neighbor constraint is enforced to refine all the similarity matrices before fusion. Experimental results show that the proposed similarity kernel fusion (SKF)-LDA method has the superiority performance in terms of AUC values and other measurements. In the schemes of LOOCV and 5-fold CV, AUC values of SKF-LDA achieve 0.9049 and 0.8743±0.0050 respectively. In addition, the conducted case studies of three diseases (hepatocellular carcinoma, lung cancer, and prostate cancer) show that SKF-LDA can predict related lncRNAs accurately.

Bioinformatics analysis identifies hub genes and pathways in nasopharyngeal carcinoma

The aim of the present study was to identify genes associated with and the underlying mechanisms in nasopharyngeal carcinoma (NPC) using microarray data. GSE12452 and GSE34573 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was utilized to obtain differentially expressed genes (DEGs). In addition, the Database for Annotation, Visualization and Integrated Discovery was used to perform pathway enrichment analyses for DEGs using the Gene Ontology (GO) annotation along with the Kyoto Encyclopedia of Genes and Genomes (KEGG). Furthermore, Cytoscape was used to perform module analysis of the protein-protein interaction (PPI) network and pathways of the hub genes were studied. A total of 298 genes were ascertained as DEGs in the two datasets. To functionally categorize these DEGs, we obtained 82 supplemented GO terms along with 7 KEGG pathways. Subsequently, a PPI network consisting of 10 hub genes with high degrees of interaction was constructed. These hub genes included cyclin-dependent kinase (CDK) 1, structural maintenance of chromosomes (SMC) 4, kinetochore-associated (KNTC) 1, kinesin family member (KIF) 23, aurora kinase A (AURKA), ATAD (ATPase family AAA domain containing) 2, NDC80 kinetochore complex component, enhancer of zeste 2 polycomb repressive complex 2 subunit, BUB1 mitotic checkpoint serine/threonine kinase and protein regulator of cytokinesis 1. CDK1, SMC4, KNTC1, KIF23, AURKA and ATAD2 presented with high areas under the curve in receiver operator curves, suggesting that these genes may be diagnostic markers for nasopharyngeal carcinoma. In conclusion, it was proposed that CDK1, SMC4, KNTC1, KIF23, AURKA and ATAD2 may be involved in the tumorigenesis of NPC. Furthermore, they may be utilized as molecular biomarkers in early diagnosis of NPC.

Survival analysis and functional annotation of long non-coding RNAs in lung adenocarcinoma

Long non-coding RNAs (lncRNAs) are a subclass of non-protein coding transcripts that are involved in several regulatory processes and are considered as potential biomarkers for almost all cancer types. This study aims to investigate the prognostic value of lncRNAs for lung adenocarcinoma (LUAD), the most prevalent subtype of lung cancer. To this end, the processed data of The Cancer Genome Atlas LUAD were retrieved from GEPIA and circlncRNAnet databases, matched with each other and integrated with the analysis results of a non-small cell lung cancer plasma RNA-Seq study. Then, the data were filtered in order to separate the differentially expressed lncRNAs that have a prognostic value for LUAD. Finally, the selected lncRNAs were functionally annotated using a bioinformatic and systems biology approach. Accordingly, we identified 19 lncRNAs as the novel LUAD prognostic lncRNAs. Also, based on our results, all 19 lncRNAs might be involved in lung cancer-related biological processes. Overall, we suggested several novel biomarkers and drug targets which could help early diagnosis, prognosis and treatment of LUAD patients.

Ensemble of decision tree reveals potential miRNA-disease associations

In recent years, increasing associations between microRNAs (miRNAs) and human diseases have been identified. Based on accumulating biological data, many computational models for potential miRNA-disease associations inference have been developed, which saves time and expenditure on experimental studies, making great contributions to researching molecular mechanism of human diseases and developing new drugs for disease treatment. In this paper, we proposed a novel computational method named Ensemble of Decision Tree based MiRNA-Disease Association prediction (EDTMDA), which innovatively built a computational framework integrating ensemble learning and dimensionality reduction. For each miRNA-disease pair, the feature vector was extracted by calculating the statistical measures, graph theoretical measures, and matrix factorization results for the miRNA and disease, respectively. Then multiple base learnings were built to yield many decision trees (DTs) based on random selection of negative samples and miRNA/disease features. Particularly, Principal Components Analysis was applied to each base learning to reduce feature dimensionality and hence remove the noise or redundancy. Average strategy was adopted for these DTs to get final association scores between miRNAs and diseases. In model performance evaluation, EDTMDA showed AUC of 0.9309 in global leave-one-out cross validation (LOOCV) and AUC of 0.8524 in local LOOCV. Additionally, AUC of 0.9192+/-0.0009 in 5-fold cross validation proved the model's reliability and stability. Furthermore, three types of case studies for four human diseases were implemented. As a result, 94% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 96% (Breast Neoplasms) and 88% (Carcinoma Hepatocellular) of top 50 predicted miRNAs were confirmed by experimental evidences in literature.

A novel collaborative filtering model for LncRNA-disease association prediction based on the Naïve Bayesian classifier

BACKGROUND

Since the number of known lncRNA-disease associations verified by biological experiments is quite limited, it has been a challenging task to uncover human disease-related lncRNAs in recent years. Moreover, considering the fact that biological experiments are very expensive and time-consuming, it is important to develop efficient computational models to discover potential lncRNA-disease associations.

RESULTS

In this manuscript, a novel Collaborative Filtering model called CFNBC for inferring potential lncRNA-disease associations is proposed based on Naïve Bayesian Classifier. In CFNBC, an original lncRNA-miRNA-disease tripartite network is constructed first by integrating known miRNA-lncRNA associations, miRNA-disease associations and lncRNA-disease associations, and then, an updated lncRNA-miRNA-disease tripartite network is further constructed through applying the item-based collaborative filtering algorithm on the original tripartite network. Finally, based on the updated tripartite network, a novel approach based on the Naïve Bayesian Classifier is proposed to predict potential associations between lncRNAs and diseases. The novelty of CFNBC lies in the construction of the updated lncRNA-miRNA-disease tripartite network and the introduction of the item-based collaborative filtering algorithm and Naïve Bayesian Classifier, which guarantee that CFNBC can be applied to predict potential lncRNA-disease associations efficiently without entirely relying on known miRNA-disease associations. Simulation results show that CFNBC can achieve a reliable AUC of 0.8576 in the Leave-One-Out Cross Validation (LOOCV), which is considerably better than previous state-of-the-art results. Moreover, case studies of glioma, colorectal cancer and gastric cancer demonstrate the excellent prediction performance of CFNBC as well.

CONCLUSIONS

According to simulation results, due to the satisfactory prediction performance, CFNBC may be an excellent addition to biomedical researches in the future.

Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION

Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.

Identification and Functional Inference for Tumor-Associated Long Non-Coding RNA

Gastric cancer is one of the top leading causes of cancer mortality worldwide especially in China. In recent years, some lncRNAs are discovered to be dysregulated in many cancers. The study on long non-coding RNAs (lncRNAs) relationship with cancers has attracted increasing attention. The molecular mechanism of gastric cancer remains largely unclear factors, especially for lncRNAs. Experiments are feasible to obtain related information, however, experimental identification of cancer-related lncRNAs usually possesses high time complexity and high cost. In this paper, a computational method is proposed to determine the relationship between lncRNA and gastric cancer by reusing the exon-based array of gastric cancer. One specific lncRNAs LINC00365 and its target differentially expressed genes whose products are predicted as blood, urine, or salvia-excretory are identified to be candidates for a combined biomarker for gastric cancer. Further biological function and molecular mechanism of the gastric cancer related lncRNAs and coding gene biomarkers are inferred in terms of multi-source biological knowledge.

Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction

Researchers have realized that microRNAs (miRNAs) play significant roles in the pathogenesis of various diseases. Although many computational models have been proposed to predict the associations between miRNAs and diseases, prediction performance could still be improved. In this paper, we propose a novel self-weighted, multi-kernel, multi-label learning (SwMKML) method to predict disease-related miRNAs. SwMKML adaptively learns two optimal kernel matrices for both miRNAs and diseases from multiple kernels constructed from known miRNA-disease associations. Moreover, the miRNA-disease associations predicted from both spaces are updated simultaneously based on a multi-label framework. Compared with four state-of-the-art computational models, SwMKML achieved best results of 95.5%, 93.1%, and 84.1% in global leave-one-out cross-validation, 5-fold cross-validation, and overall prediction accuracy, respectively. A case study conducted on head and neck neoplasms further identified two potential prognostic biomarkers, hsa-mir-125b-1 and hsa-mir-125b-2, for the disease. SwMKML is freely available at Github, and we anticipate that it may become an effective tool for potential miRNA-disease association prediction.

NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations

BACKGROUND

Predicting meaningful miRNA-disease associations (MDAs) is costly. Therefore, an increasing number of researchers are beginning to focus on methods to predict potential MDAs. Thus, prediction methods with improved accuracy are under development. An efficient computational method is proposed to be crucial for predicting novel MDAs. For improved experimental productivity, large biological datasets are used by researchers. Although there are many effective and feasible methods to predict potential MDAs, the possibility remains that these methods are flawed.

RESULTS

A simple and effective method, known as Nearest Profile-based Collaborative Matrix Factorization (NPCMF), is proposed to identify novel MDAs. The nearest profile is introduced to our method to achieve the highest AUC value compared with other advanced methods. For some miRNAs and diseases without any association, we use the nearest neighbour information to complete the prediction.

CONCLUSIONS

To evaluate the performance of our method, five-fold cross-validation is used to calculate the AUC value. At the same time, three disease cases, gastric neoplasms, rectal neoplasms and colonic neoplasms, are used to predict novel MDAs on a gold-standard dataset. We predict the vast majority of known MDAs and some novel MDAs. Finally, the prediction accuracy of our method is determined to be better than that of other existing methods. Thus, the proposed prediction model can obtain reliable experimental results.

Amplification of the CD24 Gene Is an Independent Predictor for Poor Prognosis of Breast Cancer

CD24 is a glycosyl-phosphatidyl-inositol linked glycoprotein expressed in a broad range of cell types including cancer cells. Although it is overexpressed in nearly 70% of human cancers, copy number variation of the CD24 locus has not been reported for any cancer. Here, we analyzed the genomics, transcriptomics, and clinical data of 1082 breast cancer (BRCA) samples and other cancer samples from the clinically annotated genomic database, The Cancer Genome Atlas (TCGA). The GISTIC2 method was applied to stratify the CD24 copy number, and Cox regression was performed to compare hazard ratio (HR) of CD24 overexpression, amplification and other traditional prognosis features for overall survival (OS). Our data demonstrated that CD24 amplification strongly correlated with its mRNA overexpression as well as TP53 mutant, cancer proliferation and metastasis features. In particular, CD24 amplification was enriched in basal-like subtype samples and associated with poor clinical outcome. Surprisingly, based on the univariate Cox regression analysis, CD24 overexpression (HR = 1.62, P = 0.010) and copy number amplification (HR = 1.79, P = 0.022) was more relevant to OS than TP53 mutant, mutation counts, diagnosis age, and BRCA subtypes. And based on multivariate survival analysis, CD24 amplification remained the most significant and independent predictor for worse OS (HR = 1.88, P = 0.015).

Integrating Bipartite Network Projection and KATZ Measure to Identify Novel CircRNA-Disease Associations

Accumulating biological experiments have shown that circRNAs are closely related to the occurrence and development of many complex human diseases. During recent years, the associations of circRNA with disease have caused more and more researchers to pay attention and to analyze their correlation mechanisms. However, experimental methods for determining the associations of circRNA with a particular disease are still expensive, difficult, and time consuming. Moreover, the available databases related to circRNA-disease correlations have only recently been updated, and only a few computational methods are constructed to predict potential circRNA-disease correlations. Taking into account the limitations of experimental studies, we develop a novel computational method, named IBNPKATZ, for predicting potential circRNA-disease associations, which integrates the bipartite network projection algorithm and KATZ measure. This model is based on the known circRNA-disease associations, combining circRNA similarity and disease similarity. Specifically, the circRNA similarity is derived from the average of the semantic similarity and the Gaussian interaction profile (GIP) kernel similarity of circRNA. Similarly, disease similarity is the mean of the semantic similarity and the GIP kernel similarity of disease. Furthermore, it is semi-supervised and does not require negative samples. Finally, IBNPKATZ achieves reliable AUC of 0.9352 in the leave-one-out cross validation, and case studies show that the circRNA-disease correlations predicted by our method can be successfully demonstrated by relevant experiments. The IBNPKATZ is expected to be a useful biomedical research tool for predicting potential circRNA-disease associations.

LncRNA PACER is down-regulated in osteoarthritis and regulates chondrocyte apoptosis and lncRNA HOTAIR expression

LncRNA PACER is a chondrocyte inflammation-associated long non-coding RNA (lncRNA), and chondrocyte inflammation is involved in osteoarthritis (OA). We observed that plasma PACER was down-regulated, while plasma HOTAIR was up-regulated in OA patients. Altered plasma levels of PACER and HOTAIR distinguished OA patients from healthy controls. PACER and HOTAIR were inversely correlated in both OA patients and healthy controls. PACER overexpression mediated the down-regulation of HOTAIR, while HOTAIR overexpression did not significantly affect PACER. PACER overexpression led to inhibited, while HOTAIR overexpression led to promoted apoptosis of chondrocyte. HOTAIR overexpression attenuated the effects of PACER overexpression. Therefore, lncRNA PACER is down-regulated in OA and regulates chondrocyte apoptosis by down-regulating lncRNA HOTAIR.

Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization

Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the “omic” diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines.

Screening key genes and signaling pathways in colorectal cancer by integrated bioinformatics analysis

The aim of the present study was to identify potential key genes associated with the progression and prognosis of colorectal cancer (CRC). Differentially expressed genes (DEGs) between CRC and normal samples were screened by integrated analysis of gene expression profile datasets, including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to identify the biological role of DEGs. In addition, a protein‑protein interaction network and survival analysis were used to identify the key genes. The profiles of GSE9348, GSE22598 and GSE113513 were downloaded from the GEO database. A total of 405 common DEGs were identified, including 236 down‑ and 169 upregulated. GO analysis revealed that the downregulated DEGs were mainly enriched in 'detoxification of copper ion' [biological process, (BP)], 'oxidoreductase activity, acting on CH‑OH group of donors, NAD or NADP as acceptor' [molecular function, (MF)] and 'brush border' [cellular component, (CC)]. Upregulated DEGs were mainly involved in 'nuclear division' (BP), 'snoRNA binding' (MF) and 'nucleolar part' (CC). KEGG pathway analysis revealed that DEGs were mainly involved in 'mineral absorption', 'nitrogen metabolism', 'cell cycle' and 'caffeine metabolism'. A PPI network was constructed with 268 nodes and 1,027 edges. The top one module was selected, and it was revealed that module‑related genes were mainly enriched in the GO terms 'sister chromatid segregation' (BP), 'chemokine activity' (MF), and 'condensed chromosome (CC)'. The KEGG pathway was mainly enriched in 'cell cycle', 'progesterone‑mediated oocyte maturation', 'chemokine signaling pathway', 'IL‑17 signaling pathway', 'legionellosis', and 'rheumatoid arthritis'. DNA topoisomerase II‑α (TOP2A), mitotic arrest deficient 2 like 1 (MAD2L1), cyclin B1 (CCNB1), checkpoint kinase 1 (CHEK1), cell division cycle 6 (CDC6) and ubiquitin conjugating enzyme E2 C (UBE2C) were indicated as hub genes. Furthermore, survival analysis revealed that TOP2A, MAD2L1, CDC6 and CHEK1 may serve as prognostic biomarkers in CRC. The present study provided insights into the molecular mechanism of CRC that may be useful in further investigations.

LncRNAs Regulatory Networks in Cellular Senescence

Long noncoding RNAs (lncRNAs) are a class of transcripts longer than 200 nucleotides with no open reading frame. They play a key role in the regulation of cellular processes such as genome integrity, chromatin organization, gene expression, translation regulation, and signal transduction. Recent studies indicated that lncRNAs are not only dysregulated in different types of diseases but also function as direct effectors or mediators for many pathological symptoms. This review focuses on the current findings of the lncRNAs and their dysregulated signaling pathways in senescence. Different functional mechanisms of lncRNAs and their downstream signaling pathways are integrated to provide a bird’s-eye view of lncRNA networks in senescence. This review not only highlights the role of lncRNAs in cell fate decision but also discusses how several feedback loops are interconnected to execute persistent senescence response. Finally, the significance of lncRNAs in senescence-associated diseases and their therapeutic and diagnostic potentials are highlighted.

LncRNA-Disease Association Prediction Using Two-Side Sparse Self-Representation

Evidences increasingly indicate the involvement of long non-coding RNAs (lncRNAs) in various biological processes. As the mutations and abnormalities of lncRNAs are closely related to the progression of complex diseases, the identification of lncRNA-disease associations has become an important step toward the understanding and treatment of diseases. Since only a limited number of lncRNA-disease associations have been validated, an increasing number of computational approaches have been developed for predicting potential lncRNA-disease associations. However, how to predict potential associations precisely through computational approaches remains challenging. In this study, we propose a novel two-side sparse self-representation (TSSR) algorithm for lncRNA-disease association prediction. By learning the self-representations of lncRNAs and diseases from known lncRNA-disease associations adaptively, and leveraging the information provided by known lncRNA-disease associations and the intra-associations among lncRNAs and diseases derived from other existing databases, our model could effectively utilize the estimated representations of lncRNAs and diseases to predict potential lncRNA-disease associations. The experiment results on three real data sets demonstrate that our TSSR outperforms other competing methods significantly. Moreover, to further evaluate the effectiveness of TSSR in predicting potential lncRNAs-disease associations, case studies of Melanoma, Glioblastoma, and Glioma are carried out in this paper. The results demonstrate that TSSR can effectively identify some candidate lncRNAs associated with these three diseases.

Upregulation of the long noncoding RNA lncPolE contributes to intervertebral disc degeneration by negatively regulating DNA polymerase epsilon

Long noncoding RNAs (lncRNAs) are critical regulators of gene transcription. Our previous results have demonstrated that iron deficiency accelerates intervertebral disc degeneration (IDD) by affecting the stability of the DNA polymerase epsilon (Polε) complex. Here, we discovered that the novel lncRNA lncPolE functions as a negative regulator of Polε. The expression of lncPolE in IDD tissues was upregulated compared to its expression in healthy control tissues, and this was in contrast to the PolE1 expression levels. The increased lncPolE level was significantly correlated with the severity of IDD. Ectopic expression of lncPolE in human nucleus pulposus cells (hNPCs) was able to decrease PolE1 levels and cause apoptosis, while the specific knockdown of lncPolE in primary NP cells (pNPCs) from IDD patients can restore PolE1 levels. Interestingly, iron depletion or supplementation can affect the expression of lncPolE. Further analyses indicated that the downregulation of DNA methylation in the promoter region of lncPolE caused its overexpression. Collectively, our results suggest that the aberrant expression of lncPolE contributes to the pathogenesis of IDD by negatively regulating PolE1 in iron deficient conditions, and this may provide a new avenue to alleviate IDD progression in clinical treatment.

Computational analysis of non-coding RNAs in Alzheimer's disease

Latest studies have shown that Long Noncoding RNAs corresponds to a crucial factor in neurodegenerative diseases and next-generation therapeutic targets. A wide range of advanced computational methods for the analysis of Noncoding RNAs mainly includes the prediction of RNA and miRNA structures. The problems that concern representations of specific biological structures such as secondary structures are either characterized as NP-complete or with high complexity. Numerous algorithms and techniques related to the enumeration of sequential terms of biological structures and mainly with exponential complexity have been constructed until now. While BACE1-AS, NATRad18, 17A, and hnRNP Q lnRNAs have been found to be associated with Alzheimer's disease, in this research study the significance of the most known β-turn-forming residues between these proteins is computationally identified and discussed, as a potentially crucial factor on the regulation of folding, aggregation and other intermolecular interactions.

Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes

A lot of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related to human diseases. Identifying disease-related lncRNAs (disease lncRNAs) is critical for understanding the pathogenesis and etiology of diseases. Most of the previous methods focus on prioritizing the potential disease lncRNAs based on shallow learning methods. The methods fail to extract the deep and complex feature representations of lncRNA-disease associations. Furthermore, nearly all the methods ignore the discriminative contributions of the similarity, association, and interaction relationships among lncRNAs, disease, and miRNAs for the association prediction. A dual convolutional neural networks with attention mechanisms based method is presented for predicting the candidate disease lncRNAs, and it is referred to as CNNLDA. CNNLDA deeply integrates the multiple source data like the lncRNA similarities, the disease similarities, the lncRNA-disease associations, the lncRNA-miRNA interactions, and the miRNA-disease associations. The diverse biological premises about lncRNAs, miRNAs, and diseases are combined to construct the feature matrix from the biological perspectives. A novel framework based on the dual convolutional neural networks is developed to learn the global and attention representations of the lncRNA-disease associations. The left part of the framework exploits the various information contained by the feature matrix to learn the global representation of lncRNA-disease associations. The different connection relationships among the lncRNA, miRNA, and disease nodes and the different features of these nodes have the discriminative contributions for the association prediction. Hence we present the attention mechanisms from the relationship level and the feature level respectively, and the right part of the framework learns the attention representation of associations. The experimental results based on the cross validation indicate that CNNLDA yields superior performance than several state-of-the-art methods. Case studies on stomach cancer, lung cancer, and colon cancer further demonstrate CNNLDA's ability to discover the potential disease lncRNAs.

A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration

A lot of research studies have shown that many complex human diseases are associated not only with microRNAs (miRNAs) but also with long noncoding RNAs (lncRNAs). However, most of the current existing studies focus on the prediction of disease-related miRNAs or lncRNAs, and to our knowledge, until now, there are few literature studies reported to pay attention to the study of impact of miRNA-lncRNA pairs on diseases, although more and more studies have shown that both lncRNAs and miRNAs play important roles in cell proliferation and differentiation during the recent years. The identification of disease-related genes provides great insight into the underlying pathogenesis of diseases at a system level. In this study, a novel model called PADLMHOOI was proposed to predict potential associations between diseases and lncRNA-miRNA pairs based on the higher-order orthogonal iteration, and in order to evaluate its prediction performance, the global and local LOOCV were implemented, respectively, and simulation results demonstrated that PADLMHOOI could achieve reliable AUCs of 0.9545 and 0.8874 in global and local LOOCV separately. Moreover, case studies further demonstrated the effectiveness of PADLMHOOI to infer unknown disease-related lncRNA-miRNA pairs.

LncRNA DILC participates in rheumatoid arthritis by inducing apoptosis of fibroblast-like synoviocytes and down-regulating IL-6

IL-6 produced by human fibroblast-like synoviocytes (HFLS) promotes rheumatoid arthritis (RA), while lncRNA DILC regulates liver cancer stem cells by inhibiting IL-6. Therefore, lncRNA DILC may participate in RA. In the present study, we found that plasma lncRNA DILC was down-regulated, while IL-6 was up-regulated in RA patients than in healthy controls. Plasma levels of lncRNA DILC and IL-6 were significantly and inversely correlated only in RA patients. Overexpression of lncRNA DILC resulted in promoted apoptosis of HFLS isolated from RA patients, while lncRNA DILC siRNA silencing played an opposite role. In addition, overexpression of lncRNA DILC also resulted in inhibited IL-6 expression in HFLS isolated from RA patients. Therefore, lncRNA DILC may participate in RA by inducing apoptosis of HFLS and down-regulating IL-6.

Dual Laplacian regularized matrix completion for microRNA-disease associations prediction

Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNA-disease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNA-disease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNA-disease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.