Construction of a lncRNA-PCG bipartite network and identification of cancer-related lncRNAs: a case study in prostate cancer

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

The Expression Patterns and Implications of MALAT1, MANCR, PSMA3-AS1 and miR-101 in Esophageal Adenocarcinoma

Esophageal adenocarcinoma (EAC) is a malignant tumor with poorly understood molecular mechanisms. This study endeavors to elucidate how the long non-coding RNAs (lncRNAs) MALAT1, MANCR and PSMA3-AS1, as well as the microRNA miR-101, exhibit specific expression patterns in the pathogenesis and prognosis of EAC. A total of 50 EAC tissue samples (tumors and lymph nodes) and a control group comprising 26 healthy individuals were recruited. The samples underwent quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analyses. The relative expression levels of MALAT1, MANCR, PSMA3-AS1, and miR-101 were ascertained and correlated with various clinicopathological parameters including TNM staging, tumor characteristics (size and grade of the tumor) lymphatic invasion, disease-free (DFS) and overall survival (OS) of EAC patients. Quantitative analyses revealed that MALAT1 and MANCR were significantly upregulated in EAC tumors and positive lymph nodes when compared to control tissues (p < 0.05). Such dysregulations correlated positively with advanced lymphatic metastases and a higher N stage. DFS in the subgroup of patients with negative lymph nodes was higher in the setting of low-MANCR-expression patients compared to patients with high MANCR expression (p = 0.02). Conversely, miR-101 displayed a significant downregulation in EAC tumors and positive lymph nodes (p < 0.05), and correlated negatively with advanced tumor stage, lymphatic invasion and the grade of the tumor (p = 0.006). Also, patients with low miR-101 expression showed a tendency towards inferior overall survival. PSMA3-AS1 did not demonstrate statistically significant alterations (p > 0.05). This study reveals MALAT1, MANCR, and miR-101 as putative molecular markers for prognostic evaluation in EAC and suggests their involvement in EAC progression.

Long non-coding RNA DNMBP-AS1 promotes prostate cancer development by regulating LCLAT1

Prostate cancer (PCa) is as a serious threat to male's health around the world. Recent studies have indicated that long non-coding RNAs (lncRNAs) occupy an important position in various human cancers. However, the function and mechanism of lncRNA DNMBP antisense RNA 1 (DNMBP-AS1) in PCa is rarely investigated. RT-qPCR analysis was used to test gene expression. CCK-8, colony formation, EdU staining and transwell assays were conducted to assess the function of DNMBP-AS1 on PCa cell behaviors. RNA pull down, RIP and luciferase reporter assays were implemented to verify the mechanism of DNMBP-AS1. DNMBP-AS1 was obviously up-regulated in PCa cell lines. Functionally, DNMBP-AS1 knockdown weakened cell proliferation, migration and invasion of PCa. Mechanistically, DNMBP-AS1 sponged microRNA-6766-3p (miR-6766-3p) to regulate lysocardiolipin acyltransferase 1 (LCLAT1) expression. Furthermore, DNMBP-AS1 could stabilize LCLAT1 expression by recruiting ELAV like RNA binding protein 1 (ELAVL1). Consequently, rescue assays demonstrated that DNMBP-AS1 regulated PCa cell proliferation, migration and invasion through enhancing LCLAT1 expression. Collectively, we elucidated the function and regulatory mechanism of DNMBP-AS1 and provided the first evidence of DNMBP-AS1 as a driver for PCa.

Construction of lncRNA and mRNA co-expression network associated with nasopharyngeal carcinoma progression

Nasopharyngeal carcinoma is a type of head and neck cancer with a high incidence in men. In the past decades, the survival rate of NPC has remained around 70%, but it often leads to treatment failure due to its distant metastasis or recurrence. The lncRNA-mRNA regulatory network has not been fully elucidated. We downloaded the NPC-related gene expression datasets GSE53819 and GSE12452 from the Gene Expression Omnibus database; GSE53819 included 18 NPC tissues and 18 normal tissues, and GSE12452 included 31 NPC tissues and 10 normal tissues. Weighted gene co-expression network analysis was performed on mRNA and lncRNA to screen out modules that were highly correlated with tumor progression. The two datasets were subjected to differential analysis after removing batch effects, and then Venn diagrams were used to screen for overlapping genes in the module genes and differential genes. The lncRNA-mRNA co-expression network was then constructed, and key mRNAs were identified by MCODE analysis and expression analysis. GSEA analysis and qRT-PCR were performed on key mRNAs. Through a series of analyses, we speculated that BTK, CD72, PTPN6, and VAV1 may be independent predictors of the prognosis of NPC patients.Taken together, our study provides potential candidate biomarkers for NPC diagnosis, prognosis, or precise treatment.

Construction of a Co-Expression Network for lncRNAs and mRNAs Related to Urothelial Carcinoma of the Bladder Progression

Carcinoma of urinary bladder is the most familiar cancer of the urinary tract, with the highest incidence in men. However, its prognosis and treatment have not improved significantly in the last 30 years. The main reason for this may be related to the alteration and regulation of genes. These alterations in genes that play a crucial role in cell cycle regulation may result in high-grade tumors and may alter drug sensitivity. Notably, the role of lncRNA in bladder cancer, especially the lncRNA-mRNA regulatory network, has not been fully elucidated. In this manuscript, we compared RNA sequencing (RNA-seq) data from 19 normal bladder tissues and 411 primary bladder tumor tissues using The Cancer Genome Atlas (TCGA) data bank, subjected differentially expressed mRNAs and lncRNAs to weighted gene co-expression network analysis, and screened out modules highly correlated with tumor progression. Subsequently, a lncRNA-mRNA co-expression network was built, and two key mRNAs were identified via COX regression analysis. Kaplan-Meier curve analysis revealed that the overall survival of sick people in the high-risk section was significantly shorter than those in the low-risk section. Therefore, this lncRNA-mRNA-based co-expression pattern may be used clinically to predict the prognosis of carcinoma of urinary bladder people. Our study not only provides a genetic target for carcinoma of urinary bladder therapy but also provides new ideas for people in the medical profession to discover the treatment of various tumors.

Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview

Long non-coding RNAs (lncRNAs) are usually located in the nucleus and cytoplasm of cells. The transcripts of lncRNAs are >200 nucleotides in length and do not encode proteins. Compared with small RNAs, lncRNAs have longer sequences, more complex spatial structures, and more diverse and complex mechanisms involved in the regulation of gene expression. LncRNAs are widely involved in the biological processes of cells, and in the occurrence and development of many human diseases. Many studies have shown that lncRNAs can induce the occurrence of diseases, and some lncRNAs undergo specific changes in tumor cells. Research into the roles of lncRNAs has covered the diagnosis of, for example, cardiovascular, cerebrovascular, and central nervous system diseases. The bioinformatics of lncRNAs has gradually become a research hotspot and has led to the discovery of a large number of lncRNAs and associated biological functions, and lncRNA databases and recognition models have been developed. In this review, the research progress of lncRNAs is discussed, and lncRNA-related databases and the mechanisms and modes of action of lncRNAs are described. In addition, disease-related lncRNA methods and the relationships between lncRNAs and human lung adenocarcinoma, rectal cancer, colon cancer, heart disease, and diabetes are discussed. Finally, the significance and existing problems of lncRNA research are considered.

Detecting lncRNA-Cancer Associations by Combining miRNAs, Genes, and Prognosis With Matrix Factorization

Motivation: Long non-coding RNAs (lncRNAs) play important roles in cancer development. Prediction of lncRNA–cancer association is necessary for efficiently discovering biomarkers and designing treatment for cancers. Currently, several methods have been developed to predict lncRNA–cancer associations. However, most of them do not consider the relationships between lncRNA with other molecules and with cancer prognosis, which has limited the accuracy of the prediction.

Method: Here, we constructed relationship matrices between 1,679 lncRNAs, 2,759 miRNAs, and 16,410 genes and cancer prognosis on three types of cancers (breast, lung, and colorectal cancers) to predict lncRNA–cancer associations. The matrices were iteratively reconstructed by matrix factorization to optimize low-rank size. This method is called detecting lncRNA cancer association (DRACA).

Results: Application of this method in the prediction of lncRNAs–breast cancer, lncRNA–lung cancer, and lncRNA–colorectal cancer associations achieved an area under curve (AUC) of 0.810, 0.796, and 0.795, respectively, by 10-fold cross-validations. The performances of DRACA in predicting associations between lncRNAs with three kinds of cancers were at least 6.6, 7.2, and 6.9% better than other methods, respectively. To our knowledge, this is the first method employing cancer prognosis in the prediction of lncRNA–cancer associations. When removing the relationships between cancer prognosis and genes, the AUCs were decreased 7.2, 0.6, and 5% for breast, lung, and colorectal cancers, respectively. Moreover, the predicted lncRNAs were found with greater numbers of somatic mutations than the lncRNAs not predicted as cancer-associated for three types of cancers. DRACA predicted many novel lncRNAs, whose expressions were found to be related to survival rates of patients. The method is available at https://github.com/Yanh35/DRACA.

Using lncRNA Sequencing to Reveal a Putative lncRNA-mRNA Correlation Network and the Potential Role of PCBP1-AS1 in the Pathogenesis of Cervical Cancer

Background/Aims

Long non-coding RNAs (lncRNAs) play important roles in many diseases and participate in posttranscriptional regulatory networks in tumors. However, the functions of major lncRNAs in cervical cancer are unclear. Therefore, the aim of this study was to construct a lncRNA-mRNA coexpression functional network and analyze lncRNAs that might contribute to the pathogenesis of cervical cancer.

Methods

Differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between three pairs of cervical cancer tissues and adjacent mucosa were identified by lncRNA microarray analysis. LncRNA-mRNA correlation analysis and functional enrichment were performed on the DEGs. From the correlation network, PCBP1-AS1 was selected as a candidate for further analysis. PCBP1-AS1 expression was examined by qPCR, and Kaplan-Meier survival, clinicopathology, GSEA, and immune infiltration analysis of PCBP1-AS1 were performed. The immune responses of PCBP1-AS1 expression in cervical cancer were analyzed using TIMER and western blot. PCBP1-AS1 was knocked down and overexpressed to evaluate its role in cell proliferation, migration, and invasion.

Results

A total of 130 lncRNAs were significantly differentially expressed in cervical cancer patient samples compared with control samples. Differentially expressed mRNAs in the lncRNA-mRNA interaction network were involved in the EMT process. Combined with the Kaplan-Meier survival analyses, the coexpression network revealed that PCBP1-AS1 was significantly associated with OS and clinicopathological parameters in cervical cancer patients. Moreover, PCBP1-AS1 expression was not only significantly increased in cervical cancer specimens but also associated with tumor stage, TNM, and invasion. GSEA revealed that PCBP1-AS1 is closely correlated with cell biological function via the p53 and notch signaling pathways. TIMER analysis revealed that the numbers of NK cells and M2 macrophages decreased when PCBP1-AS1 expression was high, which was consistent with the western blot results in clinical samples. Furthermore, in vitro experiments showed that high expression of PCBP1-AS1 promoted cell proliferation, migration, and invasion.

Conclusions

Transcriptomic and lncRNA-mRNA correlation analyses revealed that PCBP1-AS1 plays a key role as an independent prognostic factor in patients with cervical cancer. The identification of PCBP1-AS1 as a new biomarker for cervical cancer could help explain how changes in the immune environment promote cervical cancer development.

Identification of a Multi-Long Noncoding RNA Signature for the Diagnosis of Type 1 Diabetes Mellitus

Due to the increasing prevalence of type 1 diabetes mellitus (T1DM) and its complications, there is an urgent need to identify novel methods for predicting the occurrence and understanding the pathogenetic mechanisms of the disease. Accumulated data have demonstrated the potential of long noncoding RNAs (lncRNAs), as biomarkers in establishing diagnosis and predicting prognosis of numerous diseases. Yet, little is known about the expression patterns and regulatory roles of lncRNAs in the pathogenesis of T1DM and whether they can be used as diagnostic biomarkers for the disease. To further explore these questions, in the present study, we conducted a comparative analysis of the expression patterns of lncRNAs between 20 T1DM patients and 42 health controls by retrospectively analyzing a published microarray data set. Our results indicate that, compared with healthy controls, diabetic patients had altered levels of lncRNAs. Then, we used three time cross-validation strategy and support vector machine to propose a specific 26-lncRNA signature (termed 26LncSigT1DM). This 26LncSigT1DM signature can be used to effectively distinguish between healthy and diabetic individuals (area under the curve = 0.825) of a validation cohort. After the 26LncSigT1DM was prospectively validated, we used Pearson correlation to identify 915 mRNAs, whose expression levels were positively correlated with those of the 26 lncRNAs. According to their Gene Ontology annotations, these mRNAs participate in processes including cellular response to stimulus, cell communication, multicellular organismal process, and cell motility. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that the genes encoding the 915 mRNAs may be associated with the NOD-like receptor signaling pathway, transforming growth factor β signaling pathway, and mineral absorption, suggesting that the deregulation of these lncRNAs may mediate inflammatory abnormalities and immune dysfunctions, which jointly promote the pathogenesis of T1DM. Thus, our study identifies a novel diagnostic tool and may shed more light on the molecular mechanisms underlying the pathogenesis of T1DM.

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.

lncRNA SNHG7 affects malignant tumor behaviors through downregulation of EZH2 in uveal melanoma cell lines

Previous studies have demonstrated that the long non-coding RNA, small nucleolar RNA host gene 7 (SNHG7) plays an important role in several types of cancer; however, its role in the development of uveal melanoma (UM) remains unclear. The present study investigated the effect of SNHG7 on the prognosis of UM, as well as on cell proliferation, cell cycle and apoptosis of UM cell lines. Furthermore, the present study aimed to determine the molecular mechanisms underlying these effects. The association between SNHG7 and prognosis of UM was analyzed using detailed SNHG7 mRNA expression data and clinical information from The Cancer Genome Atlas database. Reverse transcription-quantitative PCR was used in order to detect the differential expression of SNHG7 in UM tissues and cell lines. Cell proliferation was detected using Cell Counting Kit-8 assays, following overexpression of SNHG7. A cell cycle assay was performed using propidium iodide/RNase staining. An apoptosis assay was performed using the Annexin-V-Fluorescein isothiocyanate apoptosis detection kit. The expression of enhancer of zeste homolog 2 (EZH2) was measured via western blotting. The results of the present study indicated that low expression of SNHG7 was associated with poor prognosis. Furthermore, increasing the expression of SNHG7 inhibited the proliferation of UM cells, suppressed cell cycle progression and promoted apoptosis. Western blot analysis results revealed that overexpression of SNHG7 downregulated EZH2 protein expression levels in UM cell lines. The results of the present study demonstrated that SNHG7 inhibited malignant transformation of UM cells by regulating EZH2 expression.

Exosomal lncRNAs and cancer: connecting the missing links

Motivation

Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and clinically valuable tools for early diagnosis, prognosis and potentially the targeted treatment of cancer. The content of EVs is closely related to the type and status of the EV-secreting cell. Circulating exosomes are a source of stable RNAs including mRNAs, microRNAs and long non-coding RNAs (lncRNAs).

Results

This review outlines the links between EVs, lncRNAs and cancer. We highlight communication networks involving the tumor microenvironment, the immune system and metastasis. We show examples supporting the value of exosomal lncRNAs as cancer biomarkers and therapeutic targets. We demonstrate how a system biology approach can be used to model cell-cell communication via exosomal lncRNAs and to simulate effects of therapeutic interventions. In addition, we introduce algorithms and bioinformatics resources for the discovery of tumor-specific lncRNAs and tools that are applied to determine exosome content and lncRNA function. Finally, this review provides a comprehensive collection and guide to databases for exosomal lncRNAs.

Supplementary information

Supplementary data are available at Bioinformatics online.

Biosensors for epigenetic biomarkers detection: A review

Epigenetic inheritance is a heritable change in gene function independent of alterations in nucleotide sequence. It regulates the normal cellular activities of the organisms by affecting gene expression and transcription, and its abnormal expression may lead to the developmental disorder, senile dementia, and carcinogenesis progression. Thus, epigenetic inheritance is recognized as an important biomarker, and the accurate quantification of epigenetic inheritance is crucial to clinical diagnosis, drug development and cancer treatment. Noncoding RNA, DNA methylation and histone modification are the most common epigenetic biomarkers. The conventional biosensors (e.g., northern blotting, radiometric, mass spectrometry and immunosorbent biosensors) for epigenetic biomarkers assay usually suffer from hazardous radiation, complicated manipulation, and time-consuming procedures. To facilitate the practical applications, some new biosensors including colorimetric, luminescent, Raman scattering spectroscopy, electrochemical and fluorescent biosensors have been developed for the detection of epigenetic biomarkers with simplicity, rapidity, high throughput and high sensitivity. In this review, we summarize the recent advances in epigenetic biomarkers assay. We classify the biosensors into the direct amplification-free and the nucleotide amplification-assisted ones, and describe the principles of various biosensors, and further compare their performance for epigenetic biomarkers detection. Moreover, we discuss the emerging trends and challenges in the future development of epigenetic biomarkers biosensors.

A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and aid in finding biomarkers for disease diagnosis, treatment, and prevention. In this paper, we constructed a bipartite network based on known lncRNA-disease associations; based on this work, we proposed a novel model for inferring potential lncRNA-disease associations. Specifically, we analyzed the properties of the bipartite network and found that it closely followed a power-law distribution. Moreover, to evaluate the performance of our model, a leave-one-out cross-validation (LOOCV) framework was implemented, and the simulation results showed that our computational model significantly outperformed previous state-of-the-art models, with AUCs of 0.8825, 0.9004, and 0.9292 for known lncRNA-disease associations obtained from the LncRNADisease database, Lnc2Cancer database, and MNDR database, respectively. Thus, our approach may be an excellent addition to the biomedical research field in the future.

Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods

With the development and improvement of next-generation sequencing technology, a great number of noncoding RNAs have been discovered. Long noncoding RNAs (lncRNAs) are the biggest kind of noncoding RNAs with more than 200 nt nucleotides in length. There are increasing evidences showing that lncRNAs play key roles in many biological processes. Therefore, the mutation and dysregulation of lncRNAs have close association with a number of complex human diseases. Identifying the most likely interaction between lncRNAs and diseases becomes a fundamental challenge in human health. A common view is that lncRNAs with similar function tend to be related to phenotypic similar diseases. In this chapter, we firstly introduce the concept of lncRNA, their biological features, and available data resources. Further, the recent computational approaches are explored to identify interactions between long noncoding RNAs and diseases, including their advantages and disadvantages. The key issues and potential future works of predicting interactions between long noncoding RNAs and diseases are also discussed.

Model based on GA and DNN for prediction of mRNA-Smad7 expression regulated by miRNAs in breast cancer

Background

The Smad7 protein is negative regulator of the TGF-β signaling pathway, which is upregulated in patients with breast cancer. miRNAs regulate proteins expressions by arresting or degrading the mRNAs. The purpose of this work is to identify a miRNAs profile that regulates the expression of the mRNA coding for Smad7 in breast cancer using the data from patients with breast cancer obtained from the Cancer Genome Atlas Project.

Methods

We develop an automatic search method based on genetic algorithms to find a predictive model based on deep neural networks (DNN) which fit the set of biological data and apply the Olden algorithm to identify the relative importance of each miRNAs.

Results

A computational model of non-linear regression is shown, based on deep neural networks that predict the regulation given by the miRNA target transcripts mRNA coding for Smad7 protein in patients with breast cancer, with R² of 0.99 is shown and MSE of 0.00001. In addition, the model is validated with the results in vivo and in vitro experiments reported in the literature. The set of miRNAs hsa-mir-146a, hsa-mir-93, hsa-mir-375, hsa-mir-205, hsa-mir-15a, hsa-mir-21, hsa-mir-20a, hsa-mir-503, hsa-mir-29c, hsa-mir-497, hsa-mir-107, hsa-mir-125a, hsa-mir-200c, hsa-mir-212, hsa-mir-429, hsa-mir-34a, hsa-let-7c, hsa-mir-92b, hsa-mir-33a, hsa-mir-15b, hsa-mir-224, hsa-mir-185 and hsa-mir-10b integrate a profile that critically regulates the expression of the mRNA coding for Smad7 in breast cancer.

Conclusions

We developed a genetic algorithm to select best features as DNN inputs (miRNAs). The genetic algorithm also builds the best DNN architecture by optimizing the parameters. Although the confirmation of the results by laboratory experiments has not occurred, the results allow suggesting that miRNAs profile could be used as biomarkers or targets in targeted therapies.

Electronic supplementary material

The online version of this article (10.1186/s12976-018-0095-8) contains supplementary material, which is available to authorized users.

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence.

Identification of long non-coding RNAs biomarkers for early diagnosis of myocardial infarction from the dysregulated coding-non-coding co-expression network

Long non-coding RNAs (lncRNAs) have recently been shown as novel promising diagnostic or prognostic biomarkers for various cancers. However, lncRNA expression patterns and their predictive value in early diagnosis of myocardial infarction (MI) have not been systematically investigated. In our study, we performed a comprehensive analysis of lncRNA expression profiles in MI and found altered lncRNA expression pattern in MI compared to healthy samples. We then constructed a lncRNA-mRNA dysregulation network (DLMCEN) by integrating aberrant lncRNAs, mRNAs and their co-dysregulation relationships, and found that some of mRNAs were previously reported to be involved in cardiovascular disease, suggesting the functional roles of dysregulated lncRNAs in the pathogenesis of MI. Therefore, using support vector machine (SVM) and leave one out cross-validation (LOOCV), we developed a 9-lncRNA signature (termed 9LncSigAMI) from the discovery cohort which could distinguish MI patients from healthy samples with accuracy of 95.96%, sensitivity of 93.88% and specificity of 98%, and validated its predictive power in early diagnosis of MI in another completely independent cohort. Functional analysis demonstrated that these nine lncRNA biomarkers in the 9LncSigAMI may be involved in myocardial innate immune and inflammatory response, and their deregulation may lead to the dysfunction of the inflammatory and immune system contributing to MI recurrence. With prospective validation, the 9LncSigAMI identified by our work will provide additional diagnostic information beyond other known clinical parameters, and increase the understanding of the molecular mechanism underlying the pathogenesis of MI.

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

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

ILNCSIM: improved lncRNA functional similarity calculation model

Increasing observations have indicated that lncRNAs play a significant role in various critical biological processes and the development and progression of various human diseases. Constructing lncRNA functional similarity networks could benefit the development of computational models for inferring lncRNA functions and identifying lncRNA-disease associations. However, little effort has been devoted to quantifying lncRNA functional similarity. In this study, we developed an Improved LNCRNA functional SIMilarity calculation model (ILNCSIM) based on the assumption that lncRNAs with similar biological functions tend to be involved in similar diseases. The main improvement comes from the combination of the concept of information content and the hierarchical structure of disease directed acyclic graphs for disease similarity calculation. ILNCSIM was combined with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association to further evaluate its performance. As a result, new model obtained reliable performance in the leave-one-out cross validation (AUCs of 0.9316 and 0.9074 based on MNDR and Lnc2cancer databases, respectively), and 5-fold cross validation (AUCs of 0.9221 and 0.9033 for MNDR and Lnc2cancer databases), which significantly improved the prediction performance of previous models. It is anticipated that ILNCSIM could serve as an effective lncRNA function prediction model for future biomedical researches.

Integrative analyses of transcriptome sequencing identify novel functional lncRNAs in esophageal squamous cell carcinoma

Long non-coding RNAs (lncRNAs) have a critical role in cancer initiation and progression, and thus may mediate oncogenic or tumor suppressing effects, as well as be a new class of cancer therapeutic targets. We performed high-throughput sequencing of RNA (RNA-seq) to investigate the expression level of lncRNAs and protein-coding genes in 30 esophageal samples, comprised of 15 esophageal squamous cell carcinoma (ESCC) samples and their 15 paired non-tumor tissues. We further developed an integrative bioinformatics method, denoted URW-LPE, to identify key functional lncRNAs that regulate expression of downstream protein-coding genes in ESCC. A number of known onco-lncRNA and many putative novel ones were effectively identified by URW-LPE. Importantly, we identified lncRNA625 as a novel regulator of ESCC cell proliferation, invasion and migration. ESCC patients with high lncRNA625 expression had significantly shorter survival time than those with low expression. LncRNA625 also showed specific prognostic value for patients with metastatic ESCC. Finally, we identified E1A-binding protein p300 (EP300) as a downstream executor of lncRNA625-induced transcriptional responses. These findings establish a catalog of novel cancer-associated functional lncRNAs, which will promote our understanding of lncRNA-mediated regulation in this malignancy.

A comparative analysis reveals the dosage sensitivity and regulatory patterns of lncRNA in prostate cancer

Although the key roles of long non-coding RNAs (lncRNAs) in multiple diseases are well documented, the relationship between the lncRNA copy number and expression is unknown. Here, we present a comprehensive study that demonstrates the impact of miRNA-TF co-regulatory motifs on the dosage effects of protein-coding genes (PCGs) and lncRNAs in prostate cancer. By integrating copy number profiles, expression profiles and regulatory relationships with miRNAs and transcription factors, we reveal that lncRNAs and PCGs correlate with distinct dosage sensitivity and regulatory pattern characteristics. We also show that lncRNAs from different genomic regions have different features. Using a custom-built framework, we identified 24 candidate prostate cancer-related lncRNAs based on the known properties of established prostate-related lncRNAs. Our method will enable the identification of cancer-related lncRNAs, which will provide new insights into cancer lncRNA biology.

LncRNAs and cancer

Long non-coding RNAs (lncRNAs) are a group of non-coding RNAs composed of >200 nucleotides. Recent studies have revealed that lncRNAs exert an important role in the development and progression of cancer. In this review, the involvement of the most extensively investigated lncRNAs in cancers of the digestive, respiratory, reproductive, urinary and central nervous systems are discussed. LncRNAs function via molecular and biochemical mechanisms that include cis- and trans-regulation of gene expression, epigenetic modulation in the nucleus and post-transcriptional control in the cytoplasm. Although the detailed biological functions and molecular mechanisms of the majority of lncRNAs remain to be elucidated, this review aims to provide a novel insight into the diagnosis and treatment of cancer using lncRNAs.