lncRNA Protein Research Articles

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.

Predicting lncRNA-Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model.

Long non-coding RNAs (lncRNAs) play important roles in various biological processes, where lncRNA–protein interactions are usually involved. Therefore, identifying lncRNA–protein interactions is of great significance to understand the molecular functions of lncRNAs. Since the experiments to identify lncRNA–protein interactions are always costly and time consuming, computational methods are developed as alternative approaches. However, existing lncRNA–protein interaction predictors usually require prior knowledge of lncRNA–protein interactions with experimental evidences. Their performances are limited due to the number of known lncRNA–protein interactions. In this paper, we explored a novel way to predict lncRNA–protein interactions without direct prior knowledge. MiRNAs were picked up as mediators to estimate potential interactions between lncRNAs and proteins. By validating our results based on known lncRNA–protein interactions, our method achieved an AUROC (Area Under Receiver Operating Curve) of 0.821, which is comparable to the state-of-the-art methods. Moreover, our method achieved an improved AUROC of 0.852 by further expanding the training dataset. We believe that our method can be a useful supplement to the existing methods, as it provides an alternative way to estimate lncRNA–protein interactions in a heterogeneous network without direct prior knowledge. All data and codes of this work can be downloaded from GitHub (https://github.com/zyk2118216069/LncRNA-protein-interactions-prediction).

LncRNA LINK-A regulates ROCK1 expression in early-stage pancreatic adenocarcinoma.

Long intergenic non-coding RNA for kinase activation (LINK-A) has been characterized as an oncogenic long non-coding (lnc)RNA in triple-negative breast cancer and ovarian carcinoma, but its involvement in other malignancies remains elusive. In the present study, it was determined that the plasma levels of LINK-A lncRNA and Rho-associated protein kinase 1 (ROCK1) were significantly increased in patients with pancreatic adenocarcinoma compared with those in healthy controls. The plasma levels of LINK-A lncRNA were positively correlated with the plasma levels of ROCK1 in pancreatic adenocarcinoma patients, but not in healthy controls. Silencing of LINK-A led to inhibition of pancreatic adenocarcinoma cell proliferation, migration and invasion. Simultaneous overexpression of ROCK1 attenuated the inhibitory effect of LINK-A silencing on cancer cell proliferation, migration and invasion. Overexpression of LINK-A lncRNA led to upregulation of ROCK1 expression, while overexpression of ROCK1 had no significant effect on LINK-A lncRNA expression. It may therefore be concluded that LINK-A lncRNA may have a role in pancreatic adenocarcinoma, at least in part, by promoting ROCK1 expression.

The potential influence of long non-coding RNA PRKG1-AS1 on oral squamous cell carcinoma: A comprehensive study based on bioinformatics and in vitro validation.

Oral squamous cell carcinoma (OSCC) is one of the most frequent malignancies in oral cancer. Herein, we aimed to investigate the influence of lncRNA protein kinase cGMP-dependent type I-Antisense RNA 1 (PRKG1-AS1) in OSCC progression. Basing on the data acquired from TCGA database, the expression and prognostic value of PRKG1-AS1 in OSCC patients were assessed. The expression of PRKG1-AS1 in OSCC cells was determined by quantitative real-time polymerase chain reaction (qRT-PCR). Cell growth was evaluated by Cell Counting Kit-8 (CCK8) and colony-forming assays. Transwell assay was employed to test cell invasion and migration. The protein expression of epithelial-mesenchymal transition (EMT)-related markers was detected by Western blotting. The consequences displayed that PRKG1-AS1 was highly expressed in OSCC tissues and high expression of PRKG1-AS1 predicted poor outcomes. The expression of PRKG1-AS1 was higher in CAL27, SCC-9, and SCC-4 than that in normal human oral keratinocytes (NHOK). The results of biological experiments showed that deficiency of PRKG1-AS1 suppressed cell growth, invasion, and migration in CAL27 cells, and over-expression of PRKG1-AS1 accelerated cell growth, invasion, and migration in SCC-4 cells. Finally, silencing of PRKG1-AS1 obviously facilitated the protein expression levels of E-cadherin and reduced levels of N-cadherin, Vimentin, and Snail in CAL27 cells whereas over-expression of PRKG1-AS1 led to opposite results in SCC-4 cells. These outcomes indicated that PRKG1-AS1 functioned as a facilitator in OSCC cell growth, migration, and invasion, which all might be achieved by regulating EMT.

Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction.

Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction.

LPI-BLS: Predicting lncRNA–protein interactions with a broad learning system-based stacked ensemble classifier

Many experiment results show that long non-coding RNAs (lncRNAs) play crucial roles in many biological processes, implementing their functions through interaction with RNA-binding proteins (RBPs). Considering that the experimental identification of lncRNA–protein interactions is expensive and time-consuming, many computational methods are proposed to uncover the potential lncRNA–protein interactions. In this study, we develop a novel computational method (namely LPI-BLS) to predict the lncRNA–protein interactions by using the broad learning system and building a stacked ensemble classifier with a logistical regression model. LPI-BLS first adopts the broad learning system to predict the lncRNA–protein interactions. Broad learning system is an alternative way of learning in deep structure and a flat network with few parameters. Then, the results of multiple individual broad learning systems are fed into the stacked ensemble classifier built with a logistical regression to further improve the predictive performance. Compared with other state-of-the-art methods in 5-fold cross-validation test, LPI-BLS has the best performance with the accuracy of 0.902 on RPI488 dataset, the average accuracy of 0.927 on RPI7317 dataset. The results in the independent test also show that our LPI-BLS can effectively predict the lncRNA–protein interactions. The source code can be freely downloaded from https://github.com/NWPU-903PR/LPI_BLS.

LncRNA PDIA3P regulates cell proliferation and invasion in non‑small cell lung cancer

Increasing evidence has shown that long non-coding RNAs (lncRNAs) serve a critical role in tumor progression. The lncRNA protein disulfide isomerase family A member 3 pseudogene 1 (PDIA3P) is associated with the progression of oral squamous cell carcinoma and multiple myeloma. However, the roles of lncRNA PDIA3P in non-small cell lung cancer (NSCLC) remain unknown. In the present study, it was demonstrated that lncRNA PDIAP3 expression was significantly increased in patients with NSCLC, particularly in patients with an advanced Tumor-Node-Metastasis stages and lymph-node metastasis. The Gene Expression Profiling Interactive Analysis database revealed that patients with NSCLC with high lncRNA PDIA3P expression had a poorer disease-free survival rate. Furthermore, functional assays suggested that PDIA3P inhibition significantly suppressed the proliferation and invasion of NSCLC cells in vitro and reduced tumor growth in vivo. In addition, lncRNA PDIA3P enhanced the activity of the Wnt/β-catenin pathway in progression of NSCLC. The present study showed that lncRNA PDIA3P promoted NSCLC progression by regulating Wnt/β-catenin signaling, highlighting a potential therapeutic target for treating patients with NSCLC treatment.

Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods.

Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/.

Retracted Article: Long noncoding RNA PTPRG-AS1 regulates growth of glioma cells by sponging miR-185-5p.

Previous studies have found that long noncoding RNA (lncRNA) protein tyrosine phosphatase, receptor type, G, antisense (PTPRG-AS1) was upregulated in glioma cells. Our study aimed to explore the detailed molecular mechanisms of PTPRG-AS1 involved in glioma progression. qRT-PCR assay was performed to measure the expressions of PTPRG-AS1 and microRNA-185-5p (miR-185-5p). Cell viability, migration, invasion, and apoptosis were determined by CCK-8 assay, colony formation assay, transwell assay, and flow cytometry assay. Autophagy was evaluated using GFP-LC3 puncta analysis and western blot. Luciferase reporter and RIP assays were employed to explore the association between PTPRG-AS1 and miR-185-5p. Our data showed PTPRG-AS1 was upregulated in glioma cells and tissues. Besides, high expression of PTPRG-AS1 was positively associated with a low survival rate. Upregulation of PTPRG-AS1 promoted proliferation, migration, invasion, colony formations, and autophagy, and inhibited cell apoptosis in U373-MG cells. By contrast, PTPRG-AS1 downregulation had the inverse effect in SHG44 cells. PTPRG-AS1 negatively regulated the expression of miR-185-5p in U373-MG and SHG44 cells and the expression of miR-185-5p was decreased in glioma tissues and cells. In addition, miR-185-5p overexpression suppressed proliferation, metastasis, colony formations, and autophagy, while inducing cell apoptosis in SHG44 cells. As expected, miR-185-5p depletion exhibited the inverse effect in U373-MG cells. Enhanced expression of miR-185-5p attenuated the effect of PTPRG-AS1 upregulation on U373-MG cells, while silencing of miR-185-5p undermined the effect of downregulation of PTPRG-AS1 on SHG44 cells. Our data disclosed that LncRNA PTPRG-AS1 was upregulated in glioma cells and tissues. PTPRG-AS1 regulated glioma proliferation, invasion, migration, apoptosis and autophagy by sponging miR-185-5p in vitro. A new signaling pathway PTPRG-AS1/miR-185-5p was first observed in glioma.

SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don’t have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

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

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

LPGNMF: Predicting Long Non-Coding RNA and Protein Interaction Using Graph Regularized Nonnegative Matrix Factorization.

Long non-coding RNAs (lncRNA) play crucial roles in a variety of biological processes and complex diseases. Massive studies have indicated that lncRNAs interact with related proteins to exert regulation of cellular biological processes. Because it is time-consuming and expensive to determine lncRNA-protein interaction by experiment, more accurate predictions of interaction by computational methods are imperative. We propose a novel computational approach, predicting lncRNA-protein interaction using graph regularized nonnegative matrix factorization (LPGNMF), to discover unobserved lncRNA-protein association. First, we calculate lncRNA similarity and protein similarity by integrating the lncRNA expression information and gene ontology information. Subsequently, we utilize graph regularized nonnegative matrix factorization framework to predict potential interactions for all lncRNA simultaneously. In the cross validation test, LPGNMF achieves an AUC of 85.2 percent, higher than those of other compared methods. In addition, novel lncRNA-protein interactions detected by LPGNMF are validated by literatures or database. The results indicate that our method is effective to discover potential lncRNA-protein interaction.

HyPR-MS for Multiplexed Discovery of MALAT1, NEAT1, and NORAD lncRNA Protein Interactomes.

RNA-protein interactions are integral to the regulation of gene expression. RNAs have diverse functions and the protein interactomes of individual RNAs vary temporally, spatially, and with physiological context. These factors make the global acquisition of individual RNA-protein interactomes an essential endeavor. Although techniques have been reported for discovery of the protein interactomes of specific RNAs they are largely laborious, costly, and accomplished singly in individual experiments. We developed HyPR-MS for the discovery and analysis of the protein interactomes of multiple RNAs in a single experiment while also reducing design time and improving efficiencies. Presented here is the application of HyPR-MS to simultaneously and selectively isolate the interactomes of lncRNAs MALAT1, NEAT1, and NORAD. Our analysis features the proteins that potentially contribute to both known and previously undiscovered roles of each lncRNA. This platform provides a powerful new multiplexing tool for the efficient and cost-effective elucidation of specific RNA-protein interactomes.

Probing the functions of long non-coding RNAs by exploiting the topology of global association and interaction network

Long non-coding RNAs (lncRNAs) are involved in many biological processes, such as immune response, development, differentiation and gene imprinting and are associated with diseases and cancers. But the functions of the vast majority of lncRNAs are still unknown. Predicting the biological functions of lncRNAs is one of the key challenges in the post-genomic era. In our work, We first build a global network including a lncRNA similarity network, a lncRNA–protein association network and a protein–protein interaction network according to the expressions and interactions, then extract the topological feature vectors of the global network. Using these features, we present an SVM-based machine learning approach, PLNRGO, to annotate human lncRNAs. In PLNRGO, we construct a training data set according to the proteins with GO annotations and train a binary classifier for each GO term. We assess the performance of PLNRGO on our manually annotated lncRNA benchmark and a protein-coding gene benchmark with known functional annotations. As a result, the performance of our method is significantly better than that of other state-of-the-art methods in terms of maximum F-measure and coverage.

LncRNA PDIA3P interacts with c-Myc to regulate cell proliferation via induction of pentose phosphate pathway in multiple myeloma

Multiple myeloma (MM), the second most common hematologic malignancy, is an incurable disease characterized by the accumulation of malignant plasma cells within the bone marrow. Though great progresses have been made in understanding the mechanisms of MM, metabolic plasticity and drug resistance remain largely unknown. In this study, we found lncRNA Protein disulfide isomerase family A member 3 pseudogene 1 (PDIA3P) is highly expressed in MM and is associated with the survival rate of MM patients. PDIA3P regulates MM growth and drug resistance through Glucose 6-phosphate dehydrogenase (G6PD) and the pentose phosphate pathway (PPP). Mechanistically, we revealed that PDIA3P interacts with c-Myc to enhance its transactivation activity and binding to G6PD promoter, stimulating G6PD expression and PPP flux. Our study identified PDIA3P as a novel c-Myc interacting lncRNA and elucidated crucial roles for PDIA3P in metabolic regulation of MM, providing a potential therapeutic target for MM patients.

Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs.

The human transcriptome contains thousands of long non-coding RNAs (lncRNAs). Characterizing their function is a current challenge. An emerging concept is that lncRNAs serve as protein scaffolds, forming ribonucleoproteins and bringing proteins in proximity. However, only few scaffolding lncRNAs have been characterized and the prevalence of this function is unknown. Here, we propose the first computational approach aimed at predicting scaffolding lncRNAs at large scale. We predicted the largest human lncRNA–protein interaction network to date using the catRAPID omics algorithm. In combination with tissue expression and statistical approaches, we identified 847 lncRNAs (∼5% of the long non-coding transcriptome) predicted to scaffold half of the known protein complexes and network modules. Lastly, we show that the association of certain lncRNAs to disease may involve their scaffolding ability. Overall, our results suggest for the first time that RNA-mediated scaffolding of protein complexes and modules may be a common mechanism in human cells.

The lncRNA HULC functions as an oncogene by targeting ATG7 and ITGB1 in epithelial ovarian carcinoma

Highly upregulated in liver cancer (HULC) is a long noncoding RNA (lncRNA), which has recently been identified as a key regulator in the progression of hepatocellular carcinoma, gliomas and gastric cancer. However, its role in epithelial ovarian carcinoma (EOC) remains unknown. In this study, HULC expression was examined in EOC, borderline and benign ovarian tumors, and normal ovarian tissues by RT-PCR. Ovarian cancer cell phenotypes, as well as autophagy-associated proteins were examined after HULC overexpression or downregulation by plasmid or small interfering RNA (siRNA) transfection, respectively. LncRNA–protein interactions were examined by ribonucleoprotein immunoprecipitation (RIP) assays. We found that HULC expression levels were higher in EOC tissues than normal samples. HULC overexpression induced cell proliferation, migration, invasion, whereas reduced cell apoptosis in vitro and induced tumor growth in vivo. In contrast, downregulation of HULC by siRNA transfection reduced cell proliferation, migration and invasion, and induced cell apoptosis and autophagy. Our results showed that HULC overexpression reduced ATG7, LC3-II and LAMP1 expression, while inducing SQSTM1 (P62) and ITGB1 expression. HULC downregulation had the opposite effects. Furthermore, RIP indicated that ATG7 interacted with HULC; ATG7 downregulation also induced cell proliferation, reduced apoptosis and inhibited autophagy in vitro by reducing LC3-II and LAMP1 expression, while inducing SQSTM1 expression. Furthermore, ATG7 co-transfection with HULC reversed the oncogenic effects of HULC both in vitro and in vivo; however, downregulating ATG7 did not affect cell migration and invasive ability. We found that ITGB1 siRNA co-transfection with HULC reversed the function of HULC in inducing ovarian cancer cell migration and invasive ability. Taken together, our results show that HULC may promote ovarian carcinoma tumorigenesis by inhibiting ATG7 and inducing progression by regulating ITGB1.

The lncRNA PDIA3P Interacts with miR-185-5p to Modulate Oral Squamous Cell Carcinoma Progression by Targeting Cyclin D2.

Long noncoding RNAs (lncRNAs) are emerging as important regulators during tumorigenesis by serving as competing endogenous RNAs (ceRNAs). In this study, the qRT-PCR results indicated that the lncRNA protein disulfide isomerase family A member 3 pseudogene 1 (PDIA3P) was overexpressed in oral squamous cell carcinoma (OSCC) and decreased the survival rate of OSCC patients. CCK-8 and clonal colony formation assays were used to detect the effects of PDIA3P on proliferation. Results revealed that silencing PDIA3P by small interfering RNA (siRNA) inhibited OSCC cell proliferation and repressed tumor growth and reduced the expression of proliferation antigen Ki-67 in vivo. Furthermore, the interaction between PDIA3P and miRNAs was then analyzed by qRT-PCR and luciferase reporter gene assay. We found that PDIA3P negatively regulated miR-185-5p in OSCC cells. Simultaneously, we found that silencing PDIA3P by siRNA suppressed proliferation via miR-185-5p in OSCC cells. Moreover, silencing PDIA3P by siRNA inhibited CCND2 protein (no influence on mRNA levels) expression via miR-185-5p in OSCC cells, and CCND2 facilitated cell proliferation of SCC4 and SCC15 cells induced by sh-PDIA3P#1. Therefore, our study demonstrated that PDIA3P may be a therapeutic target for the treatment of OSCC.

The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions

Long non-coding RNAs (lncRNAs) have gained wide attentions because of their essential functions in a variety of biological processes. Though precise functions and mechanisms of most lncRNAs remain unknown, studies show that lncRNAs generally exert functions through interactions with the corresponding RNA-binding proteins. The experimental detection of lncRNA–protein interactions is costly and time-consuming. In this paper, we propose a linear neighborhood propagation method (LPLNP), to predict lncRNA–protein interactions. LPLNP calculates the linear neighborhood similarity in the feature space, and transfers it into the interaction space, and predict unobserved interactions between the lncRNAs and proteins by a label propagation process. Our study shows that the LPLNP model based on the known lncRNA–protein interactions can produce high-accuracy performances, achieving an AUPR score of 0.42. Furthermore, we incorporate biological information of lncRNAs and proteins into the LPLNP model, and can further increase the performances, achieving an AUPR score of 0.4584. The case study demonstrates that many lncRNA–protein interactions predicted by our method can be validated, indicating that our method is a useful tool for lncRNA–protein interaction prediction. The source code and the dataset used in the paper are available at: https://github.com/BioMedicalBigDataMiningLabWhu/lncRNA-protein-interaction-prediction.

Expression of H19 imprinted gene in patients with repeated implantation failure during the window of implantation.

To detect the expression level of imprinted gene H19 in the endometrium of patients with repeated implantation failure (RIF) who were undergoing in vitro fertilization and embryo transfer (IVF-ET) or frozen embryo transfer (FET). Patients who underwent IVF/FET in Reproductive Medicine Center of Xiangya Hospital during April 1, 2015 to October 31, 2015 were chosen. The endometrium was collected from patients with repeated implantation failure during mid-luteal phase. Real-time quantitative reverse transcription PCR (qRT-PCR) and western blot were used to determine the expression of H19 lncRNA and integrin β3 protein. Data analysis was performed using SPSS 19.0. When compared with the control group, decreased expression of H19 was detected in the RIF group (P=0.04). The expression of integrin β3 protein was also decreased in the RIF group compared to that in the control group (P=0.04). The expression of H19 lncRNA was positively correlated with the expression of integrin β3 protein (R=0.477, P=0.018). The expression of H19 lncRNA and integrin β3 protein were down-regulated in the RIF patients.