Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks

Background More and more evidence showed that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human sophisticated diseases. Therefore, predicting human lncRNA-disease associations is a challenging and urgently task in bioinformatics to research of human sophisticated diseases. Results In the work, a global network-based computational framework called as LRWRHLDA were proposed which is a universal network-based method. Firstly, four isomorphic networks include lncRNA similarity network, disease similarity network, gene similarity network and miRNA similarity network were constructed. And then, six heterogeneous networks include known lncRNA-disease, lncRNA-gene, lncRNA-miRNA, disease-gene, disease-miRNA, and gene-miRNA associations network were applied to design a multi-layer network. Finally, the Laplace normalized random walk with restart algorithm in this global network is suggested to predict the relationship between lncRNAs and diseases. Conclusions The ten-fold cross validation is used to evaluate the performance of LRWRHLDA. As a result, LRWRHLDA achieves an AUC of 0.98402, which is higher than other compared methods. Furthermore, LRWRHLDA can predict isolated disease-related lnRNA (isolated lnRNA related disease). The results for colorectal cancer, lung adenocarcinoma, stomach cancer and breast cancer have been verified by other researches. The case studies indicated that our method is effective.

Cell-free DNA 5-hydroxymethylcytosine profiles of long non-coding RNA genes enable early detection and progression monitoring of human cancers

Background 5-Hydroxymethylcytosine (5hmC) is a significant DNA epigenetic modification. However, the 5hmC modification alterations in genomic regions encoding long non-coding RNA (lncRNA) and their clinical significance remain poorly characterized. Results A three-phase discovery–modeling–validation study was conducted to explore the potential of the plasma-derived 5hmC modification level in genomic regions encoding lncRNAs as a superior alternative biomarker for cancer diagnosis and surveillance. Genome-wide 5hmC profiles in the plasma circulating cell-free DNA of 1632 cancer and 1379 non-cancerous control samples from different cancer types and multiple centers were repurposed and characterized. A large number of altered 5hmC modifications were distributed at genomic regions encoding lncRNAs in cancerous compared with healthy subjects. Furthermore, most 5hmC-modified lncRNA genes were cancer-specific, with only a relatively small number of 5hmC-modified lncRNA genes shared by various cancer types. A 5hmC-LncRNA diagnostic score (5hLD-score) comprising 39 tissue-shared 5hmC-modified lncRNA gene markers was developed using elastic net regularization. The 5hLD-score was able to accurately distinguish tumors from healthy controls with an area under the curve (AUC) of 0.963 [95% confidence interval (CI) 0.940–0.985] and 0.912 (95% CI 0.837–0.987) in the training and internal validation cohorts, respectively. Results from three independent validations confirmed the robustness and stability of the 5hLD-score with an AUC of 0.851 (95% CI 0.786–0.916) in Zhang’s non-small cell lung cancer cohort, AUC of 0.887 (95% CI 0.852–0.922) in Tian’s esophageal cancer cohort, and AUC of 0.768 (95% CI 0.746–0.790) in Cai’s hepatocellular carcinoma cohort. In addition, a significant association was identified between the 5hLD-score and the progression from hepatitis to liver cancer. Finally, lncRNA genes modified by tissue-specific 5hmC alteration were again found to be capable of identifying the origin and location of tumors. Conclusion The present study will contribute to the ongoing effort to understand the transcriptional programs of lncRNA genes, as well as facilitate the development of novel invasive genomic tools for early cancer detection and surveillance.

Insights Into mRNA and Long Non-coding RNA Profiling RNA Sequencing in Uterus of Chickens With Pink and Blue Eggshell Colors

The blue egg is both of biological interest and economic importance for consumers, egg retailers, and scientists. To date, the genetic mechanisms underlying pigment have mainly focused on protein-coding genes. However, the underpinning mechanism of non-coding RNAs on the pigment deposition among different eggshell colors remains unknown. In this study, RNA sequencing was employed to profile the uterine gland transcriptome (lncRNA and mRNA) of 15 Changshun blue eggshell layers, to better understand the genetic mechanisms of deposition of blue eggshell color. Results showed that differentially expressed mRNAs, GO terms, and KEGG pathways among pink-eggshell and blue-eggshell chickens were mainly targeting immune- and transporter-related terms with the SLC family, IgJ, CD family, and MTMR genes. Furthermore, the progesterone-mediated oocyte maturation and cortisol synthesis and secretion pathway with targeted gene PGR and Pbx1 were significantly enriched between blue- and pink-eggshell chickens. Integrating analysis of lncRNA and mRNA profiles predicted 4 and 25 lncRNA–gene pairs by antisense and cis analysis. They were relative to immune, nerve, and lipids and amino acid metabolisms, porphyrin, and chlorophyll metabolism with targeted gene FECH and oxidative phosphorylation and cardiac muscle contraction pathways with targeted gene COX6A1. Within blue-eggshell chickens, the GO terms hindbrain tangential cell migration and phosphatidylinositol monophosphate phosphatase activity with targeted gene Plxna2 and MTRM1 were identified. Integrating analysis of lncRNA and mRNA profiles predicted 8 and 22 lncRNA–gene pairs. Most pathways were mainly enriched on lipid-related metabolisms as found in mRNA sequencing. The lncRNAs did exert similar functions in color formation by modulating pigment disposition and immune- and lipid-related metabolisms. Our results provide a catalog of chicken uterine lncRNAs and genes worthy of further studies to understand their roles in the selection for blue eggshell color layers.

Characterization of testis-specific LINC01016 gene reveals isoform-specific roles in controlling biological processes

Long noncoding RNAs (lncRNAs) have emerged as critical regulators of biological processes. However, the aberrant expression of an isoform from the same lncRNA gene could lead to RNA with altered functions due to changes in their conformations, leading to diseases. Here, we describe a detailed characterization of the gene which encodes long intergenic non-protein coding RNA 01016 (LINC01016, a.k.a., LncRNA1195) with a focus on its structure, exon usage, and expression in human and macaque tissues. In this study, we show that it is among the highly expressed lncRNAs in the testis, exclusively conserved among non-human primates, suggesting its recent evolution and is expressed and processed into 12 distinct RNAs in testis, cervix, and uterus tissues. Further, we integrate de novo annotation of expressed LINC01016 transcripts and isoform-dependent gene expression analyses to show that human LINC01016 is a multi-exon gene, processed through differential exon usage with isoform-specific roles. Furthermore, in cervical, testicular, and uterine cancers, LINC01016 isoforms are differentially expressed, and their expression is predictive of survival in these cancers. The study has revealed an essential aspect of lncRNA biology, which is rarely associated with coding RNAs that lncRNA genes are precisely processed to generate isoforms with distinct biological roles in specific tissues.

A pair of long intergenic non-coding RNA LINC00887 variants act antagonistically to control Carbonic Anhydrase IX transcription upon hypoxia in tongue squamous carcinoma progression

Background Long noncoding RNAs (lncRNAs) are important regulators in tumor progression. However, their biological functions and underlying mechanisms in hypoxia adaptation remain largely unclear. Results Here, we established a correlation between a Chr3q29-derived lncRNA gene and tongue squamous carcinoma (TSCC) by genome-wide analyses. Using RACE, we determined that two novel variants of this lncRNA gene are generated in TSCC, namely LINC00887_TSCC_short (887S) and LINC00887_TSCC_long (887L). RNA-sequencing in 887S or 887L loss-of-function cells identified their common downstream target as Carbonic Anhydrase IX (CA9), a gene known to be upregulated by hypoxia during tumor progression. Mechanistically, our results showed that the hypoxia-augmented 887S and constitutively expressed 887L functioned in opposite directions on tumor progression through the common target CA9. Upon normoxia, 887S and 887L interacted. Upon hypoxia, the two variants were separated. Each RNA recognized and bound to their responsive DNA cis-acting elements on CA9 promoter: 887L activated CA9’s transcription through recruiting HIF1α, while 887S suppressed CA9 through DNMT1-mediated DNA methylation. Conclusions We provided hypoxia-permitted functions of two antagonistic lncRNA variants to fine control the hypoxia adaptation through CA9.

The association between expression of lncRNAs in patients with GDM

Objective: Gestational diabetes mellitus (GDM) is common worldwide and seriously threatens maternal and infant health. The expression of non-coding RNA is tissue-specific and highly stable in eukaryotic cells and the circulatory system, which can act as an early molecular marker of GDM. Methods: The differential expression of lncRNA and mRNA in the peripheral blood of patients with GDM (experimental group) and healthy pregnant women (control group) was analysed via lncRNA gene chip. Employing biological function clustering and KEGG signal pathway analysis, we selected the mRNAs and lncRNAs closely related to the insulin signal pathway of GDM to analys the possible regulatory mechanism in the pathogenesis of GDM. The sequencing results were further verified via quantitative polymerase chain reaction (Q-PCR). Results: lncRNA microarray analysis revealed 7498 genes (3592 upregulated, 3906 downregulated) differentially expressed in the GDM group and healthy pregnant women control group, including 1098 differentially expressed lncRNAs (609 upregulated, 489 downregulated). According to the regulatory pathway of lncRNA mRNA network,six lncRNAs and four mRNAs were found to play a significant role in insulin resistance. Conclusions: The lncRNAs ERMP1,TSPAN32 and MRPL38 form a co-expression network with TPH1, which is mainly involved in the tryptophan metabolism pathway and in the development of GDM, Moreover, lncRNA RPL13P5 forms a co-expression network with the TSC2 gene via the pi3k-akt and insulin signalling pathways, which are involved in the process of insulin resistance in GDM.

A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs

Background LncRNAs (Long non-coding RNAs) are a type of non-coding RNA molecule with transcript length longer than 200 nucleotides. LncRNA has been novel candidate biomarkers in cancer diagnosis and prognosis. However, it is difficult to discover the true association mechanism between lncRNAs and complex diseases. The unprecedented enrichment of multi-omics data and the rapid development of machine learning technology provide us with the opportunity to design a machine learning framework to study the relationship between lncRNAs and complex diseases. Results In this article, we proposed a new machine learning approach, namely LGDLDA (LncRNA-Gene-Disease association networks based LncRNA-Disease Association prediction), for disease-related lncRNAs association prediction based multi-omics data, machine learning methods and neural network neighborhood information aggregation. Firstly, LGDLDA calculates the similarity matrix of lncRNA, gene and disease respectively, and it calculates the similarity between lncRNAs through the lncRNA expression profile matrix, lncRNA-miRNA interaction matrix and lncRNA-protein interaction matrix. We obtain gene similarity matrix by calculating the lncRNA-gene association matrix and the gene-disease association matrix, and we obtain disease similarity matrix by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates the neighborhood information in similarity matrices by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs. Conclusions Compared with lncRNA-disease prediction methods, our proposed method takes into account more critical information and obtains the performance improvement cancer-related lncRNA predictions. Randomly split data experiment results show that the stability of LGDLDA is better than IDHI-MIRW, NCPLDA, LncDisAP and NCPHLDA. The results on different simulation data sets show that LGDLDA can accurately and effectively predict the disease-related lncRNAs. Furthermore, we applied the method to three real cancer data including gastric cancer, colorectal cancer and breast cancer to predict potential cancer-related lncRNAs.

Data integration and evolutionary analysis of long non-coding RNAs in 25 flowering plants

Background Long non-coding RNAs (lncRNAs) play vital roles in many important biological processes in plants. Currently, a large fraction of plant lncRNA studies center at lncRNA identification and functional analysis. Only a few plant lncRNA studies focus on understanding their evolutionary history, which is crucial for an in-depth understanding of lncRNAs. Therefore, the integration of large volumes of plant lncRNA data is required to deeply investigate the evolution of lncRNAs. Results We present a large-scale evolutionary analysis of lncRNAs in 25 flowering plants. In total, we identified 199,796 high-confidence lncRNAs through data integration analysis, and grouped them into 5497 lncRNA orthologous families. Then, we divided the lncRNAs into groups based on the degree of sequence conservation, and quantified the various characteristics of 756 conserved Arabidopsis thaliana lncRNAs. We found that compared with non-conserved lncRNAs, conserved lncRNAs might have more exons, longer sequence length, higher expression levels, and lower tissue specificities. Functional annotation based on the A. thaliana coding-lncRNA gene co-expression network suggested potential functions of conserved lncRNAs including autophagy, locomotion, and cell cycle. Enrichment analysis revealed that the functions of conserved lncRNAs were closely related to the growth and development of the tissues in which they were specifically expressed. Conclusions Comprehensive integration of large-scale lncRNA data and construction of a phylogenetic tree with orthologous lncRNA families from 25 flowering plants was used to provide an oversight of the evolutionary history of plant lncRNAs including origin, conservation, and orthologous relationships. Further analysis revealed a differential characteristic profile for conserved lncRNAs in A. thaliana when compared with non-conserved lncRNAs. We also examined tissue specific expression and the potential functional roles of conserved lncRNAs. The results presented here will further our understanding of plant lncRNA evolution, and provide the basis for further in-depth studies of their functions.

Long Noncoding RNA KCNMB2-AS1 Promotes SMAD5 by Targeting miR-3194-3p to Induce Bladder Cancer Progression

PurposeBladder cancer is a common malignant tumor of the urinary system, with the fourth-highest incidence of male malignant tumors in Europe and the United States. So far, the mechanism of bladder cancer progression and metastasis has not been clarified. The aim of our study was to validate the way of long noncoding RNA (lncRNA) KCNMB2-AS1 on the metabolism and growth of bladder cancer cells by miR-3194-3p/SMAD5.Patients and MethodsThe Gene Expression was analyzed by qRT-PCR in bladder cancer tissues and cell lines, with the highly expressed KCNMB2-AS1 screened out. Cell proliferation was detected by Edu staining and clone formation assay, cell migration, and invasion by wound healing and transwell assays. Cell stemness was determined by assessing sphere-forming ability and stemness marker. Correlation between miRNA and lncRNA/gene was verified by dual‐luciferase assay and RIP, and the effect of KCNMB2-AS1 on bladder cancer growth by nude mice tumor formation experiment.ResultsHere, we revealed the increased level of KCNMB2-AS1 in bladder cancer for the first time. Knockdown of KCNMB2-AS1 in vitro prevented the ability of proliferation, metastasis, and stemness of cancer cells. In vivo, the silencing of KCNMB2-AS1 also prevented tumor growth in vivo. Next, we revealed that KCNMB2-AS1 could interact with miR-3194-3p and uncovered that SAMD5 was a downstream target of miR-3194-3p.ConclusionIn conclusion, KCNMB2-AS1 mediated the bladder cancer cells progress by regulating the miR-3194-3p/SAMD5 signal pathway, which would provide a new target for bladder cancer research.

Long Non-Coding RNA Expression in Laser Micro-Dissected Luminal A and Triple Negative Breast Cancer Tissue Samples—A Pilot Study

Background and Objectives: Breast cancer (BC) remains one of the major causes of cancer death in women worldwide. The difficulties in assessing the deep molecular mechanisms involved in this pathology arise from its high complexity and diverse tissue subtypes. Long non-coding RNAs (lncRNAs) were shown to have great tissue specificity, being differentially expressed within the BC tissue subtypes. Materials and Methods: Herein, we performed lncRNA profiling by PCR array in triple negative breast cancer (TNBC) and luminal A tissue samples from 18 BC patients (nine TNBC and nine luminal A), followed by individual validation in BC tissue and cell lines. Tissue samples were previously archived in formalin-fixed paraffin-embedded (FFPE) samples, and the areas of interest were dissected using laser capture microdissection (LCM) technology. Results: Two lncRNAs (OTX2-AS1 and SOX2OT) were differentially expressed in the profiling analysis (fold change of 205.22 and 0.02, respectively, p < 0.05 in both cases); however, they did not reach statistical significance in the individual validation measurement (p > 0.05) when analyzed with specific individual assays. In addition, GAS5 and NEAT1 lncRNAs were individually assessed as they were previously described in the literature as being associated with BC. GAS5 was significantly downregulated in both TNBC tissues and cell lines compared to luminal A samples, while NEAT1 was significantly downregulated only in TNBC cells vs. luminal A. Conclusions: Therefore, we identified GAS5 lncRNA as having a differential expression in TNBC tissues and cells compared to luminal A, with possible implications in the molecular mechanisms of the TNBC subtype. This proof of principle study also suggests that LCM could be a useful technique for limiting the sample heterogeneity for lncRNA gene expression analysis in BC FFPE tissues. Future studies of larger cohort sizes are needed in order to assess the biomarker potential of lncRNA GAS5 in BC.