Emerging epigenetic mechanisms of long non-coding RNAs

Non-coding RNAs in BRAF-mutant melanoma: targets, indicators, and therapeutic potential

Melanoma, a highly aggressive skin cancer, is often driven by BRAF mutations, such as the V600E mutation, which promotes cancer growth through the MAPK pathway and contributes to treatment resistance. Understanding the role of non-coding RNAs (ncRNAs) in these processes is crucial for developing new therapeutic strategies. This review aims to elucidate the relationship between ncRNAs and BRAF mutations in melanoma, focusing on their regulatory roles and impact on treatment resistance. We comprehensively reviewed current literature to synthesize evidence on ncRNA-mediated regulation of BRAF-mutant melanoma and their influence on therapeutic responses. Key ncRNAs, including microRNAs and long ncRNAs, were identified as significant regulators of melanoma development and therapy resistance. MicroRNAs such as miR-15/16 and miR-200 families modulate critical pathways like Wnt signaling and melanogenesis. Long ncRNAs like ANRIL and SAMMSON play roles in cell growth, invasion, and drug susceptibility. Specific ncRNAs, such as BANCR and RMEL3, intersect with the MAPK pathway, highlighting their potential as therapeutic targets or biomarkers in BRAF-mutant melanoma. Additionally, ncRNAs involved in drug resistance, such as miR-579-3p and miR-1246, target processes like autophagy and immune checkpoint regulation. This review highlights the pivotal roles of ncRNAs in regulating BRAF-mutant melanoma and their contribution to drug resistance. These findings underscore the potential of ncRNAs as biomarkers and therapeutic targets, paving the way for innovative treatments to improve outcomes for melanoma patients.

Exploring ncRNAs in epilepsy: From oxidative stress regulation to therapy

Epilepsy is a prevalent neurological illness which is linked with high worldwide burdens. Oxidative stress (OS) is recognized to be among the contributors that trigger the advancement of epilepsy, affecting neuronal excitability and synaptic transmission. Various types of non-coding RNAs (ncRNAs) are known to serve vital functions in many disease mechanisms, including epilepsy. The current review sought to understand better the mechanisms through which these ncRNAs regulate epilepsy's OS-related pathways. We investigated the functions of microRNAs in controlling gene expression at the post-translatory stage and their involvement in OS and neuroinflammation. We also looked at the different regulatory roles of long ncRNAs, including molecular scaffolding, enhancer, and transcriptional activator, during OS. Circular RNAs and their capability to act as miRNA decoys and their consequential impact on epilepsy development were also explored. Our review aimed to improve the current understanding of novel therapies for epilepsy based on the role of ncRNAs in OS pathways. We also demonstrated the roles of ncRNAs in epilepsy treatment and diagnosis, explaining that these molecules play vital roles that could be used in therapy as biomarkers.

Fusion of multi-source relationships and topology to infer lncRNA-protein interactions

Long non-coding RNAs (lncRNAs) are important factors involved in biological regulatory networks. Accurately predicting lncRNA-protein interactions (LPIs) is vital for clarifying lncRNA's functions and pathogenic mechanisms. Existing deep learning models have yet to yield satisfactory results in LPI prediction. Recently, graph autoencoders (GAEs) have seen rapid development, excelling in tasks like link prediction and node classification. We employed GAE technology for LPI prediction, devising the FMSRT-LPI model based on path masking and degree regression strategies and thereby achieving satisfactory outcomes. This represents the first known integration of path masking and degree regression strategies into the GAE framework for potential LPI inference. The effectiveness of our FMSRT-LPI model primarily relies on four key aspects. First, within the GAE framework, our model integrates multi-source relationships of lncRNAs and proteins with LPN's topological data. Second, the implemented masking strategy efficiently identifies LPN's key paths, reconstructs the network, and reduces the impact of redundant or incorrect data. Third, the integrated degree decoder balances degree and structural information, enhancing node representation. Fourth, the PolyLoss function we introduced is more appropriate for LPI prediction tasks. The results on multiple public datasets further demonstrate our model's potential in LPI prediction.

Transcriptomic Profiles of Long Noncoding RNAs and Their Target Protein-Coding Genes Reveals Speciation Adaptation on the Qinghai-Xizang (Tibet) Plateau in Orinus

Long noncoding RNAs (lncRNAs) are RNA molecules longer than 200 nt, which lack the ability to encode proteins and are involved in multifarious growth, development, and regulatory processes in plants and mammals. However, the environmental-regulated expression profiles of lncRNAs in Orinus that may associated with their adaptation on the Qinghai-Xizang (Tibet) Plateau (QTP) have never been characterized. Here, we utilized transcriptomic sequencing data of two Orinus species (O. thoroldii and O. kokonoricus) to identify 1624 lncRNAs, including 1119 intergenic lncRNAs, 200 antisense lncRNAs, five intronic lncRNAs, and 300 sense lncRNAs. In addition, the evolutionary relationships of Orinus lncRNAs showed limited sequence conservation among 39 species, which implied that Orinus-specific lncRNAs contribute to speciation adaptation evolution. Furthermore, considering the cis-regulation mechanism, from 286 differentially expressed lncRNAs (DElncRNAs) and their nearby protein coding genes (PCGs) between O. thoroldii and O. kokonoricus, 128 lncRNA-PCG pairs were obtained in O. thoroldii, whereas 92 lncRNA-PCG pairs were obtained in O. kokonoricus. In addition, a total of 19 lncRNA-PCG pairs in O. thoroldii and 14 lncRNA-PCG pairs in O. kokonoricus were found to participate in different biological processes, indicating that the different expression profiles of DElncRNAs between O. thoroldii and O. kokonoricus were associated with their adaptation at different elevations on the QTP. We also found several pairs of DElncRNA nearby transcription factors (TFs), indicating that these DElncRNAs regulate the expression of TFs to aid O. thoroldii in adapting to the environment. Therefore, this work systematically identified a series of lncRNAs in Orinus, laying the groundwork for further exploration into the biological function of Orinus in environmental adaptation.

ML-NPI: Predicting Interactions between Noncoding RNA and Protein Based on Meta-Learning in a Large-Scale Dynamic Graph

Deep learning methods can accurately study noncoding RNA protein interactions (NPI), which is of great significance in gene regulation, human disease, and other fields. However, the computational method for predicting NPI in large-scale dynamic ncRNA protein bipartite graphs is rarely discussed, which is an online modeling and prediction problem. In addition, the results published by researchers on the Web site cannot meet real-time needs due to the large amount of basic data and long update cycles. Therefore, we propose a real-time method based on the dynamic ncRNA-protein bipartite graph learning framework, termed ML-GNN, which can model and predict the NPIs in real time. Our proposed method has the following advantages: first, the meta-learning strategy can alleviate the problem of large prediction errors in sparse neighborhood samples; second, dynamic modeling of newly added data can reduce computational pressure and predict NPIs in real-time. In the experiment, we built a dynamic bipartite graph based on 300000 NPIs from the NPInterv4.0 database. The experimental results indicate that our model achieved excellent performance in multiple experiments. The code for the model is available at https://github.com/taowang11/ML-NPI, and the data can be downloaded freely at http://bigdata.ibp.ac.cn/npinter4.

Epigenetic regulators controlling osteogenic lineage commitment and bone formation

Bone formation and homeostasis are controlled by environmental factors and endocrine regulatory cues that initiate intracellular signaling pathways capable of modulating gene expression in the nucleus. Bone-related gene expression is controlled by nucleosome-based chromatin architecture that limits the accessibility of lineage-specific gene regulatory DNA sequences and sequence-specific transcription factors. From a developmental perspective, bone-specific gene expression must be suppressed during the early stages of embryogenesis to prevent the premature mineralization of skeletal elements during fetal growth in utero. Hence, bone formation is initially inhibited by gene suppressive epigenetic regulators, while other epigenetic regulators actively support osteoblast differentiation. Prominent epigenetic regulators that stimulate or attenuate osteogenesis include lysine methyl transferases (e.g., EZH2, SMYD2, SUV420H2), lysine deacetylases (e.g., HDAC1, HDAC3, HDAC4, HDAC7, SIRT1, SIRT3), arginine methyl transferases (e.g., PRMT1, PRMT4/CARM1, PRMT5), dioxygenases (e.g., TET2), bromodomain proteins (e.g., BRD2, BRD4) and chromodomain proteins (e.g., CBX1, CBX2, CBX5). This narrative review provides a broad overview of the covalent modifications of DNA and histone proteins that involve hundreds of enzymes that add, read, or delete these epigenetic modifications that are relevant for self-renewal and differentiation of mesenchymal stem cells, skeletal stem cells and osteoblasts during osteogenesis.

Enhancer-promoter specificity in gene transcription: molecular mechanisms and disease associations

Although often located at a distance from their target gene promoters, enhancers are the primary genomic determinants of temporal and spatial transcriptional specificity in metazoans. Since the discovery of the first enhancer element in simian virus 40, there has been substantial interest in unraveling the mechanism(s) by which enhancers communicate with their partner promoters to ensure proper gene expression. These research efforts have benefited considerably from the application of increasingly sophisticated sequencing- and imaging-based approaches in conjunction with innovative (epi)genome-editing technologies; however, despite various proposed models, the principles of enhancer-promoter interaction have still not been fully elucidated. In this review, we provide an overview of recent progress in the eukaryotic gene transcription field pertaining to enhancer-promoter specificity. A better understanding of the mechanistic basis of lineage- and context-dependent enhancer-promoter engagement, along with the continued identification of functional enhancers, will provide key insights into the spatiotemporal control of gene expression that can reveal therapeutic opportunities for a range of enhancer-related diseases.

Prediction of LncRNA-Protein Interactions Based on Kernel Combinations and Graph Convolutional Networks

The complexes of long non-coding RNAs bound to proteins can be involved in regulating life activities at various stages of organisms. However, in the face of the growing number of lncRNAs and proteins, verifying LncRNA-Protein Interactions (LPI) based on traditional biological experiments is time-consuming and laborious. Therefore, with the improvement of computing power, predicting LPI has met new development opportunity. In virtue of the state-of-the-art works, a framework called LncRNA-Protein Interactions based on Kernel Combinations and Graph Convolutional Networks (LPI-KCGCN) has been proposed in this article. We first construct kernel matrices by taking advantage of extracting both the lncRNAs and protein concerning the sequence features, sequence similarity features, expression features, and gene ontology. Then reconstruct the existent kernel matrices as the input of the next step. Combined with known LPI interactions, the reconstructed similarity matrices, which can be used as features of the topology map of the LPI network, are exploited in extracting potential representations in the lncRNA and protein space using a two-layer Graph Convolutional Network. The predicted matrix can be finally obtained by training the network to produce scoring matrices w.r.t. lncRNAs and proteins. Different LPI-KCGCN variants are ensemble to derive the final prediction results and testify on balanced and unbalanced datasets. The 5-fold cross-validation shows that the optimal feature information combination on a dataset with 15.5% positive samples has an AUC value of 0.9714 and an AUPR value of 0.9216. On another highly unbalanced dataset with only 5% positive samples, LPI-KCGCN also has outperformed the state-of-the-art works, which achieved an AUC value of 0.9907 and an AUPR value of 0.9267.

GATLGEMF: A graph attention model with line graph embedding multi-complex features for ncRNA-protein interactions prediction

Non-coding RNA (ncRNA) plays an important role in many fundamental biological processes, and it may be closely associated with many complex human diseases. NcRNAs exert their functions by interacting with proteins. Therefore, identifying novel ncRNA-protein interactions (NPIs) is important for understanding the mechanism of ncRNAs role. The computational approach has the advantage of low cost and high efficiency. Machine learning and deep learning have achieved great success by making full use of sequence information and structure information. Graph neural network (GNN) is a deep learning algorithm for complex network link prediction, which can extract and discover features in graph topology data. In this study, we propose a new computational model called GATLGEMF. We used a line graph transformation strategy to obtain the most valuable feature information and input this feature information into the attention network to predict NPIs. The results on four benchmark datasets show that our method achieves superior performance. We further compare GATLGEMF with the state-of-the-art existing methods to evaluate the model performance. GATLGEMF shows the best performance with the area under curve (AUC) of 92.41% and 98.93% on RPI2241 and NPInter v2.0 datasets, respectively. In addition, a case study shows that GATLGEMF has the ability to predict new interactions based on known interactions. The source code is available at https://github.com/JianjunTan-Beijing/GATLGEMF.

Expression, Functional Polymorphism, and Diagnostic Values of MIAT rs2331291 and H19 rs217727 Long Non-Coding RNAs in Cerebral Ischemic Stroke Egyptian Patients

Cerebral ischemic stroke (CIS) is a severe cerebral vascular event. This research aimed to evaluate the role of single-nucleotide polymorphisms (SNPs) of the lncRNAs MIAT rs2331291 and H19 rs217727 and epigenetic methylation in the expression patterns of serum lncRNA H19 in CIS Egyptian patients. It included 80 CIS cases and 40 healthy subjects. Serum MIAT expression levels decreased, whereas serum H19 expression levels increased among CIS compared to controls. For MIAT rs2331291, there were significant differences in the genotypic and allelic frequencies between the CIS and healthy subjects at p = 0.02 and p = 0.0001, respectively. Our findings illustrated a significantly increased MIAT T/T genotype frequency in hypertensive CIS compared to non-hypertensive CIS at p = 0.004. However, H19 rs217727 gene frequency C/C was not significantly higher in non-hypertensive CIS than in hypertensive CIS. The methylation of the H19 gene promoter was significantly higher in CIS patients compared to healthy subjects. The level of MIAT was positively correlated with serum H19 in CIS. Receiver operating characteristics (ROC) analysis revealed that serum MIAT and H19 have a high diagnostic potential for distinguishing CIS subjects from healthy ones. In conclusion, the MIAT-rs2331291 polymorphism might serve as a novel potential indicator of CIS.

LncRNAs and CircRNAs as Strategies against Pathological Conditions Caused by a Hypoxic/Anoxic State

Brain damage can be induced by oxygen deprivation. It is known that hypoxic or anoxic conditions can lead to changes in the expression levels of non-coding RNAs (ncRNAs), which, in turn, can be related to Central Nervous System (CNS) injuries. Therefore, it could be useful to investigate the involvement of non-coding RNAs (ncRNAs), as well as the underlying mechanisms which are able to modulate them in brain damage induced by hypoxic or anoxic conditions. In this review, we focused on recent research that associates these conditions with long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). The results of this review demonstrate that the expression of both lncRNAs and circRNAs can be influenced by oxygen deprivation conditions and so they can contribute to inducing damage or providing neuroprotection by affecting specific molecular pathways. Furthermore, several experimental studies have shown that ncRNA activity can be regulated by compounds, thus also modifying their transcriptomic profile and their effects on CNS damages induced by hypoxic/anoxic events.

LncRNA CYP4A22-AS1 promotes the progression of lung adenocarcinoma through the miR-205-5p/EREG and miR-34c-5p/BCL-2 axes

OBJECTIVES

Lung adenocarcinoma (LUAD) exhibits a higher fatality rate among all cancer types worldwide, yet the precise mechanisms underlying its initiation and progression remain unknown. Mounting evidence suggests that long non-coding RNAs (lncRNAs) exert significant regulatory roles in cancer development and progression. Nevertheless, the precise involvement of lncRNA CYP4A22-AS1 in LUAD remains incompletely comprehended.

METHODS

Bioinformatics analyses evaluated the expression level of CYP4A22-AS1 in lung adenocarcinoma and paracancer. The LUAD cell line with a high expression of CYP4A22-AS1 was constructed to evaluate the role of CYP4A22-AS1 in the proliferation and metastasis of LUAD by CCK8, scratch healing, transwell assays, and animal experiments. We applied transcriptome and microRNA sequencing to examine the mechanism of CYP4A22-AS1 enhancing the proliferation and metastasis of LUAD. Luciferase reporter gene analyses, west-blotting, and qRT-PCR were carried out to reveal the interaction between CYP4A22-AS1, miR-205-5p/EREG, and miR-34c-5p/BCL-2 axes.

RESULTS

CYP4A22-AS1 expression was significantly higher in LUAD tissues than in the adjacent tissues. Furthermore, we constructed a LUAD cell line with a high expression of CYP4A22-AS1 and noted that the high expression of CYP4A22-AS1 significantly enhanced the proliferation and metastasis of LUAD. We applied transcriptome and microRNA sequencing to examine the mechanism of CYP4A22-AS1 enhancing the proliferation and metastasis of LUAD. CYP4A22-AS1 increased the expression of EREG and BCL-2 by reducing the expression of miR-205-5p and miR-34-5p and activating the downstream signaling pathway of EGFR and the anti-apoptotic signaling pathway of BCL-2, thereby triggering the proliferation and metastasis of LUAD. The transfection of miR-205-5p and miR-34-5p mimics inhibited the role of CYP4A22-AS1 in enhancing tumor progression.

CONCLUSION

This study elucidates the molecular mechanism whereby CYP4A22-AS1 overexpression promotes LUAD progression through the miR-205-5p/EREG and miR-34c-5p/BCL-2 axes.

Identification of two novel lipid metabolism-related long non-coding RNAs (SNHG17 and LINC00837) as potential signatures for osteosarcoma prognosis and precise treatment

OBJECTIVE

Dysregulated lipid metabolism enhances the development and advancement of many cancers, including osteosarcoma (OS); however, the underlying mechanisms are still largely unknown. Therefore, this investigation aimed to elucidate novel potential lipid metabolism-related long non-coding RNAs (lncRNAs) that regulate OS development and provide novel signatures for its prognosis and precise treatment.

MATERIALS AND METHODS

The GEO datasets (GSE12865 and GSE16091) were downloaded and analyzed using R software packages. Immunohistochemistry (IHC) was used to evaluate protein levels in OS tissues while real-time qPCR was used to measure lncRNA levels, and MTT assays were used to assess OS cell viability.

RESULTS

Two lipid metabolism-associated lncRNAs (LM-lncRNAs), small nucleolar RNA host gene 17 (SNHG17) and LINC00837, were identified as efficient and independent prognostic indicators for OS. In addition, further experiments confirmed that SNHG17 and LINC00837 were significantly elevated in OS tissues and cells than para-cancerous counterparts. Knockdown of SNHG17 and LINC00837 synergistically suppressed the viability of OS cells, whereas overexpression of the two lncRNAs promoted OS cell proliferation. Moreover, bioinformatics analysis was conducted to construct six novel SNHG17-microRNA-mRNA competing endogenous RNA (ceRNA) networks, and three lipid metabolism-associated genes (MIF, VDAC2, and CSNK2A2) were found to be abnormally upregulated in OS tissues, suggesting that they were potential effector genes of SNHG17.

CONCLUSION

In summary, SNHG17 and LINC00837 were found to promote OS cell malignancy, suggesting their use as ideal biomarkers for OS prognosis and treatment.

Predicting lncRNA-protein interactions with bipartite graph embedding and deep graph neural networks

Background: Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes. Investigation of the lncRNA-protein interaction contributes to discovering the undetected molecular functions of lncRNAs. In recent years, increasingly computational approaches have substituted the traditional time-consuming experiments utilized to crack the possible unknown associations. However, significant explorations of the heterogeneity in association prediction between lncRNA and protein are inadequate. It remains challenging to integrate the heterogeneity of lncRNA-protein interactions with graph neural network algorithms. Methods: In this paper, we constructed a deep architecture based on GNN called BiHo-GNN, which is the first to integrate the properties of homogeneous with heterogeneous networks through bipartite graph embedding. Different from previous research, BiHo-GNN can capture the mechanism of molecular association by the data encoder of heterogeneous networks. Meanwhile, we design the process of mutual optimization between homogeneous and heterogeneous networks, which can promote the robustness of BiHo-GNN. Results: We collected four datasets for predicting lncRNA-protein interaction and compared the performance of current prediction models on benchmarking dataset. In comparison with the performance of other models, BiHo-GNN outperforms existing bipartite graph-based methods. Conclusion: Our BiHo-GNN integrates the bipartite graph with homogeneous graph networks. Based on this model structure, the lncRNA-protein interactions and potential associations can be predicted and discovered accurately.

Long non-coding RNA MYU promotes ovarian cancer cell proliferation by sponging miR-6827-5p and upregulating HMGA1

Background: Long non-coding RNAs (lncRNAs) have been confirmed to play vital roles in tumorigenesis. LncRNA MYU has recently been reported as an oncogene in several kinds of tumors. However, MYU's expression status and potential involvement in ovarian cancer (OC) remain unclear. In this study, we explored the underlying role of MYU in OC. Methods and results: The expression of MYU was upregulated in OC tissues, and MYU's overexpression was significantly correlated with the FIGO stage and lymphatic metastasis. Knockdown of MYU inhibited cell proliferation in SKOV3 and A2780 cells. Mechanistically, MYU directly interacted with miR-6827-5p in OC cells; HMGA1 is a downstream target gene of miR-6827-5p. Furthermore, MYU knockdown increased the expression of miR-6827-5p and decreased the expression of HMGA1. Restoration of HMGA1 expression reversed the influence on cell proliferation caused by MYU knockdown. Conclusion: MYU functions as a ceRNA that positively regulates HMGA1 expression by sponging miR-6827-5p in OC cells, which may provide a potential target and biomarker for the diagnosis or prognosis of OC.

Predicting ncRNA-protein interactions based on dual graph convolutional network and pairwise learning

Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA-proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological traits, diseases, etc. Traditional experimental methods can accomplish this work but are often labor-intensive and expensive. Machine learning and deep learning methods have achieved great success by exploiting sufficient sequence or structure information. Graph Neural Network (GNN)-based methods consider the topology in ncRNA-protein graphs and perform well on tasks like NPI prediction. Based on GNN, some pairwise constraint methods have been developed to apply on homogeneous networks, but not used for NPI prediction on heterogeneous networks. In this paper, we construct a pairwise constrained NPI predictor based on dual Graph Convolutional Network (GCN) called NPI-DGCN. To our knowledge, our method is the first to train a heterogeneous graph-based model using a pairwise learning strategy. Instead of binary classification, we use a rank layer to calculate the score of an ncRNA-protein pair. Moreover, our model is the first to predict NPIs on the ncRNA-protein bipartite graph rather than the homogeneous graph. We transform the original ncRNA-protein bipartite graph into two homogenous graphs on which to explore second-order implicit relationships. At the same time, we model direct interactions between two homogenous graphs to explore explicit relationships. Experimental results on the four standard datasets indicate that our method achieves competitive performance with other state-of-the-art methods. And the model is available at https://github.com/zhuoninnin1992/NPIPredict.

The Novel lncRNA ENST00000530525 Affects ANO1, Contributing to Blood-Brain Barrier Injury in Cultured hCMEC/D3 Cells Under OGD/R Conditions

Ischemic stroke (IS) is a major neurological disease with high fatality and residual disability burdens. Long noncoding RNAs (lncRNAs) have been found to play an important role in IS. However, the roles and significance of most lncRNAs in IS are still unknown. This study was performed to identify differentially expressed (DE) lncRNAs using a lncRNA microarray in whole blood samples of patients suffering from acute cerebral ischemia. Bioinformatics analyses, including GO, KEGG pathway enrichment analysis, and proximity to putative stroke risk location analysis were performed. The novel lncRNA, ENST00000530525, significantly decreased after IS. Furthermore, we evaluated lncRNA ENST00000530525 expression in cultured hCMEC/D3 cells under oxygen-glucose deprivation/reoxygenation (OGD/R) conditions using fluorescent in situ hybridization (FISH) and quantitative real-time polymerase chain reaction (RT-qPCR) analysis. To investigate the function of lncRNA ENST00000530525, its over-expression (OE) and negative control (NC) plasmids were transfected into hCMEC/D3 cells, and cell viability was detected by a cell counting kit-8 (CCK-8) assay after OGD/R. LncRNA ENST00000530525 and ANO1 expression were investigated using RT-qPCR and immunofluorescence. For blood-brain barrier (BBB) permeability, FITC-dextran transendothelial permeability assay and tight junction (TJ) protein immunofluorescence assays were performed. There were 3352 DE lncRNAs in the blood samples of acute IS patients. The validation results were consistent with the gene chip data. The GO and KEGG results showed that these lncRNAs were mainly related to oxygen and glucose metabolism, leukocyte transendothelial migration, mitophagy and cellular senescence. Among these, lncRNA ENST00000530525 was the most highly downregulated lncRNA and it was mapped within the IS-associated gene anoctamin-1 (ANO1). We further found that lncRNA ENST00000530525 was downregulated in hCMEC/D3 cells under 4 h OGD and 20 h reoxygenation (OGD4/R20) conditions. Upregulating lncRNA ENST00000530525 by plasmid transfection decreased cell viability while increasing ANO1 expression and it contributed to BBB injury in hCMEC/D3 cells after OGD4/R20. The lncRNA ENST00000530525 might play deleterious roles in post-stroke pathogenesis. These results show that some DE lncRNAs in humans participate through characteristic roles in post-stroke pathogenesis; thus, the roles and significance of some novel lncRNAs in IS warrant further study.

Effects of ischemic postconditioning and long non-coding RNAs in ischemic stroke

Stroke is a main cause of disability and death among adults in China, and acute ischemic stroke accounts for 80% of cases. The key to ischemic stroke treatment is to recanalize the blocked blood vessels. However, more than 90% of patients cannot receive effective treatment within an appropriate time, and delayed recanalization of blood vessels causes reperfusion injury. Recent research has revealed that ischemic postconditioning has a neuroprotective effect on the brain, but the mechanism has not been fully clarified. Long non-coding RNAs (lncRNAs) have previously been associated with ischemic reperfusion injury in ischemic stroke. LncRNAs regulate important cellular and molecular events through a variety of mechanisms, but a comprehensive analysis of potential lncRNAs involved in the brain protection produced by ischemic postconditioning has not been conducted. In this review, we summarize the common mechanisms of cerebral injury in ischemic stroke and the effect of ischemic postconditioning, and we describe the potential mechanisms of some lncRNAs associated with ischemic stroke.

dbEssLnc: A manually curated database of human and mouse essential lncRNA genes

Long non-coding RNAs (lncRNAs) play important roles in many biological processes. Knocking out or knocking down some lncRNAs will lead to lethality or infertility. These lncRNAs are called essential lncRNAs. Knowledges of essential lncRNAs are important in establishing minimal genomes of living cells, developing drug therapies and early diagnostic approaches for complex diseases. However, existing databases focus on collecting essential coding genes. Essential non-coding gene records are rare in existing databases. A comprehensive collection of essential non-coding genes, particularly essential lncRNA genes, is demanded. We manually curated 207 essential lncRNAs from literatures for establishing a database on essential lncRNAs, which is named as dbEssLnc (Database of essential lncRNAs). The dbEssLnc database has a web-based user-friendly interface for the users to browse, to search, to visualize and to blast search records in the database. The dbEssLnc database is freely accessible at https://esslnc.pufengdu.org. All data and source codes for mirroring the dbEssLnc database have been deposited in GitHub (https://github.com/yyZhang14/dbEssLnc).

LncRNA SNHG11 enhances bevacizumab resistance in colorectal cancer by mediating miR-1207-5p/ABCC1 axis

Long noncoding RNAs (lncRNAs) have been reported to serve as vital regulators in the chemoresistance of human cancers, including colorectal cancer (CRC). In this study, we aimed to explore the functions of lncRNA small nucleolar RNA host gene 11 (SNHG11) in the resistance of CRC to bevacizumab. Quantitative real-time PCR, western blot assay or immunohistochemistry assay were performed to examine the expression of SNHG11, microRNA-1207-5p (miR-1207-5p), ATP binding cassette subfamily C member 1 (ABCC1) and Ki67. Cell Counting Kit-8 assay was conducted to evaluate bevacizumab resistance and cell viability. 5'-ethynyl-2'-deoxyuridine analysis, flow cytometry analysis and wound-healing assay were conducted for cell proliferation, apoptosis and migration, respectively. Dual-luciferase reporter assay and RNA immunoprecipitation assay were employed to analyze the relations among SNHG11, miR-1207-5p and ABCC1. Murine xenograft model assay was employed to analyze bevacizumab resistance in vivo. The exosomes were observed under transmission electron microscopy. SNHG11 was overexpressed in bevacizumab-resistant CRC tissues and cells. Knockdown of SNHG11 restrained bevacizumab resistance, repressed cell proliferation and migration, and promoted apoptosis in bevacizumab-resistant CRC cells. MiR-1207-5p served as the target of SNHG11 and SNHG11 regulated bevacizumab resistance by targeting miR-1207-5p. ABCC1 was the target gene of miR-1207-5p. Overexpression of miR-1207-5p inhibited bevacizumab resistance and cell progression in bevacizumab-resistant CRC cells, with ABCC1 elevation abrogated the impacts. SNHG11 silencing repressed bevacizumab resistance in vivo. In addition, exosomal SNHG11 was upregulated in bevacizumab-resistant CRC cells. SNHG11 contributes to bevacizumab resistance in CRC depending on the modulation of miR-1207-5p and ABCC1.

Comprehensive Analysis of the Prognosis and Drug Sensitivity of Differentiation-Related lncRNAs in Papillary Thyroid Cancer

Dedifferentiation is the main concern associated with radioactive iodine (RAI) refractoriness in patients with papillary thyroid cancer (PTC), and the underlying mechanisms of PTC dedifferentiation remain unclear. The present work aimed to identify a useful signature to indicate dedifferentiation and further explore its role in prognosis and susceptibility to chemotherapy drugs. A total of five prognostic-related DR-lncRNAs were selected to establish a prognostic-predicting model, and corresponding risk scores were closely associated with the infiltration of immune cells and immune checkpoint blockade. Moreover, we built an integrated nomogram based on DR-lncRNAs and age that showed a strong ability to predict the 3- and 5-year overall survival. Interestingly, drug sensitivity analysis revealed that the low-risk group was more sensitive to Bendamustine and TAS-6417 than the high-risk group. In addition, knockdown of DR-lncRNAs (DPH6-DT) strongly promoted cell proliferation, invasion, and migration via PI3K-AKT signal pathway in vitro. Furthermore, DPH6-DT downregulation also increased the expression of vimentin and N-cadherin during epithelial-mesenchymal transition. This study firstly confirms that DR-lncRNAs play a vital role in the prognosis and immune cells infiltration in patients with PTC, as well as a predictor of the drugs' chemosensitivity. Based on our results, DR-lncRNAs can serve as a promising prognostic biomarkers and treatment targets.

LncRNA MAGI2-As3 Suppresses the Proliferation and Invasion of Cervical Cancer by Sponging MiR-15b

BACKGROUND

Cervical cancer is the leading cause of cancer deaths among women, and more than 85% of cervical cancer deaths occur in low and middle-income countries. The purpose of this study is to investigate the functions of MAGI2-AS3 and miR-15b in cervical cancer.

MATERIALS AND METHODS

The mRNA levels of MAGI2-AS3, miR-15b, and CCNE1 were evaluated using RT-qPCR assay. Dual-luciferase reporter gene assay was used to confirm whether miR-15b binds to CCNE1.

RESULTS

LncRNA MAGI2-AS3 was downregulated, while miR-15b was upregulated in cervical cancer. Cervical cancer patients with low expression of MAGI2-AS3 have a poor prognosis. Upregulation of MAGI2-AS3 inhibited proliferative and invasive abilities of HeLa cells via regulating the expression of miRNA-15b. MiR-15b inhibitor suppressed cell proliferation and invasion. CCNE1 was a direct target gene of miR-15b, which binds to the 3'-UTR of its mRNA. MiR-15b partially reversed the inhibitory effect of overexpression of MAGI2-AS3 on the proliferation and invasion of HeLa cells. MAGI2-AS3 mediated the expression of CCNE1 in HeLa cells.

CONCLUSION

LncRNA MAGI2-AS3 inhibits the proliferation and invasion of cervical cancer cells via the miRNA-15/CCNE1 axis. Our results illustrates that MAGI2-AS3 can be used as a useful clinical predictor for early diagnosis and prognosis assessment of cervical cancer.

Positive feedback loop of lncRNA FAM201A/miR‑146a‑5p/POU2F1 regulates IL‑1β‑induced chondrocyte injury in vitro

Numerous studies have previously demonstrated that long non-coding RNAs (lncRNAs) serve an important regulatory role in osteoarthritis (OA). In particular, the lncRNA family with sequence similarity 201 member A (FAM201A) was previously found to be downregulated in necrotic femoral head samples. However, the role of FAM201A in IL-1β-induced chondrocyte injury remains unclear. It was hypothesized that FAM201A may exert a protective effect on IL-1β-induced chondrocyte injury in OA by sponging microRNAs (miRNAs/miRs). The purpose of the present study was to explore the role and molecular mechanism of FAM201A in IL-1β-induced chondrocyte injury. A model of OA was established by stimulation C-28/I2 cell with IL-1β in vitro. The expression levels of FAM201A following IL-1β-induced chondrocyte injury were detected via reverse transcription-quantitative PCR. Luciferase reporter assay was used to assess the possible associations among FAM201A, miR-146a-5p and POU class 2 homeobox 1 (POU2F1). Chromatin immunoprecipitation assay was performed to analyze the interaction between POU2F1 and miR-146a-5p. ELISA, TUNEL and western blotting were performed to measure the level of inflammation, lactate dehydrogenase release, apoptosis and the expression of apoptosis-related proteins (Bcl-2, Bax, cleaved caspase 3 and cleaved caspase 9), respectively. The expression levels of FAM201A were found to be downregulated following IL-1β-induced chondrocyte injury. Overexpression of FAM201A exerted a protective effect against IL-1β-induced chondrocyte injury. In addition, FAM201A could upregulate the expression levels of POU2F1 by sponging miR-146a-5p. Further experiments revealed that POU2F1 could bind to the promoter region of FAM201A and subsequently regulate the expression levels of POU2F1, indicating a role for the FAM201A/miR-146a-5p/POU2F1 positive feedback loop in IL-1β-induced chondrocyte injury. The present study revealed the protective effects of the FAM201A/miR-146a-5p/POU2F1 positive feedback loop on IL-1β-induced chondrocyte injury and provided a potential therapeutic target for OA.

Neuroprotective effects of long noncoding RNAs involved in ischemic postconditioning after ischemic stroke

During acute reperfusion, the expression profiles of long noncoding RNAs in adult rats with focal cerebral ischemia undergo broad changes. However, whether long noncoding RNAs are involved in neuroprotective effects following focal ischemic stroke in rats remains unclear. In this study, RNA isolation and library preparation was performed for long noncoding RNA sequencing, followed by determining the coding potential of identified long noncoding RNAs and target gene prediction. Differential expression analysis, long noncoding RNA functional enrichment analysis, and co-expression network analysis were performed comparing ischemic rats with and without ischemic postconditioning rats. Rats were subjected to ischemic postconditioning via the brief and repeated occlusion of the middle cerebral artery or femoral artery. Quantitative real-time reverse transcription-polymerase chain reaction was used to detect the expression levels of differentially expressed long noncoding RNAs after ischemic postconditioning in a rat model of ischemic stroke. The results showed that ischemic postconditioning greatly affected the expression profile of long noncoding RNAs and mRNAs in the brains of rats that underwent ischemic stroke. The predicted target genes of some of the identified long noncoding RNAs (cis targets) were related to the cellular response to ischemia and stress, cytokine signal transduction, inflammation, and apoptosis signal transduction pathways. In addition, 15 significantly differentially expressed long noncoding RNAs were identified in the brains of rats subjected to ischemic postconditioning. Nine candidate long noncoding RNAs that may be related to ischemic postconditioning were identified by a long noncoding RNA expression profile and long noncoding RNA-mRNA co-expression network analysis. Expression levels were verified by quantitative real-time reverse transcription-polymerase chain reaction. These results suggested that the identified long noncoding RNAs may be involved in the neuroprotective effects associated with ischemic postconditioning following ischemic stroke. The experimental animal procedures were approved by the Animal Experiment Ethics Committee of Kunming Medical University (approval No. KMMU2018018) in January 2018.

Expression of oncogenic long noncoding RNA PSMG3-antisense 1 in lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is one of the most common subtypes of lung cancer that accounts for ~50% of all lung cancer cases. Long noncoding RNA (lncRNA) PSMG3-antisense (AS) 1 has been suggested to play an important role in various types of cancer. Therefore, the aim of the present study was to investigate the role of PSMG3-AS1 using clinical specimens and data from 130 patients with LUSC. The expression levels of PSMG3-AS1 and miR-143-3p were detected in LUSC specimens, and the correlation between lncRNA PSMG3-AS1 expression and patient clinical characteristics was analyzed. Cell Counting Kit-8, Transwell migration and invasion assays were used to investigate the functional role of PSMG3-AS1 in LUSC. The mechanism of PSMG3-AS1 on LUSC cells was also investigated using a luciferase activity assay with wild-type or mutated PSMG3-AS1. PSMG3-AS1 was found to be upregulated in LUSC, and high expression was associated with positive lymph node metastasis and a higher TNM stage. The results of multivariate Cox regression analysis revealed that PSMG3-AS1 may serve as an independent prognostic indicator in LUSC. Furthermore, inhibiting PSMG3-AS1 expression reduced tumor cell proliferative, migratory and invasive abilities. Moreover, PSMG3-AS1 was found to be closely associated with miR-143-3p in LUSC, and thus may become a potential prognostic marker and therapeutic target for the treatment of LUSC in the future.

A putative role for lncRNAs in epigenetic regulation of memory

The central dogma of molecular genetics is defined as encoded genetic information within DNA, transcribed into messenger RNA, which contain the instructions for protein synthesis, thus imparting cellular functionality and ultimately life. This molecular genetic theory has given birth to the field of neuroepigenetics, and it is now well established that epigenetic regulation of gene transcription is critical to the learning and memory process. In this review, we address a potential role for a relatively new player in the field of epigenetic crosstalk - long non-coding RNAs (lncRNAs). First, we briefly summarize epigenetic mechanisms in memory formation and examine what little is known about the emerging role of lncRNAs during this process. We then focus discussions on how lncRNAs interact with epigenetic mechanisms to control transcriptional programs under various conditions in the brain, and how this may be applied to regulation of gene expression necessary for memory formation. Next, we explore how epigenetic crosstalk in turn serves to regulate expression of various individual lncRNAs themselves. To highlight the importance of further exploring the role of lncRNA in epigenetic regulation of gene expression, we consider the significant relationship between lncRNA dysregulation and declining memory reserve with aging, Alzheimer's disease, and epilepsy, as well as the promise of novel therapeutic interventions. Finally, we conclude with a discussion of the critical questions that remain to be answered regarding a role for lncRNA in memory.

Hypoxia related long non-coding RNAs in ischemic stroke

With high rates of mortality and disability, stroke has caused huge social burden, and 85% of which is ischemic stroke. In recent years, it is a progressive discovery of long non-coding RNA (lncRNA) playing an important regulatory role throughout ischemic stroke. Hypoxia, generated from reduction or interruption of cerebral blood flow, leads to changes in lncRNA expression, which then influence disease progression. Therefore, we reviewed studies on expression of hypoxia-related lncRNAs and relevant molecular mechanism in ischemic stroke. Considering that hypoxia-inducible factor (HIF) is a crucial regulator in hypoxic progress, we mainly focus on the HIF-related lncRNA which regulates the expression of HIF or is regulated by HIF, further reveal their pathogenesis and adaption after brain ischemia and hypoxia, so as to find effective biomarker and therapeutic targets.

NPI-RGCNAE: Fast predicting ncRNA-protein interactions using the Relational Graph Convolutional Network Auto-Encoder

- ncRNAs play important roles in a variety of biological processes by interacting with RNA-binding proteins. Therefore, identifying ncRNA-protein interactions is important to understanding the biological functions of ncRNAs. Since experimental methods to determine ncRNA-protein interactions are always costly and time-consuming, computational methods have been proposed as alternative approaches. We developed a novel method NPI-RGCNAE (predicting ncRNA-Protein Interactions by the Relational Graph Convolutional Network Auto-Encoder). With a reliable negative sample selection strategy, we applied the Relational Graph Convolutional Network encoder and the DistMult decoder to predict ncRNA-protein interactions in an accurate and efficient way. By using the 5-fold cross-validation, we found that our method achieved a comparable performance to all state-of-the-art methods. Our method requires less than 10% training time of all state-of-the-art methods. It is a more efficient choice with large datasets in practice. All datasets and source codes of NPI-RGCNAE have been deposited in a public Github repository (https://github.com/Angelia0hh/NPI-RGCNAE).

Knockdown of LINC01224 Suppresses Colon Cancer Progression by Sponging miR-485-5p to Downregulate MCL1

Background

Colon cancer (CC) is the most commonly occurring malignant tumor in the world. The current cancer treatment options have been less effective especially in the advanced stages of CC and patients have poor overall survival. Hence, there is an urgent need to explore novel molecular therapeutic targets for CC treatment.

Methods

qRT-PCR was performed to detect the levels of lncRNA LINC01224 (LINC01224), microRNA-485-5p (miR-485-5p), MCL1 in CC tumor tissues or cell lines. Two si-RNAs against LINC01224 were used to silence the level of LINC01224, and CCK-8 assay, colony formation assay, and transwell assay were performed to explore the role of LINC01224 on the proliferation, migration, and invasion of CC cell lines. Kaplan–Meier method was applied for evaluating the association between LINC01224 level and the overall survival of CC patients. Through bioinformatics analysis, we found that LINC01224 sponged miR-485-5p and consequently targeted MCL1. Dual-luciferase reporter assay, RNA pull-down assay, qRT-PCR, and Western blot assay were conducted for verification of the interactions among LINC01224, miR-485-5p, and MCL1. Furthermore, the role of LINC01224/miR-485-5p/MCL1 axis in CC progression was investigated by CCK-8 assay, colony formation assay, and transwell assay.

Results

LINC01224 was highly expressed in CC tumor tissues and CC cell lines, and its expression was associated with the overall survival of CC patients. The LINC01224-siRNAs (si-LINC01224) markedly suppressed the level of LINC01224 in CC cell lines (HT29 and SW480 cells) and consequently significantly suppressed the proliferation, migration, and invasion of the HT29 and SW480 cells. LINC01224 was verified to sponge miR-485-5p and consequently targeted MCL1. MiR-485-5p inhibitor or MCL1 overexpression (MCL1 OE) markedly restored the repressive effect of the si-LINC01224 pool on MCL1 expression level, as well as proliferation, migration, and invasion of HT29 and SW480 cells.

Conclusion

This study identified LINC01224/miR-485-5p/MCL1 axis as a novel molecular therapeutic target involved in CC progression.

Strategies to Improve the Efficiency of Transplantation with Mesenchymal Stem Cells for the Treatment of Ischemic Stroke: A Review of Recent Progress

Cerebral ischemia is a common global disease that is characterized by a loss of neurological function and a poor prognosis in many patients. However, only a limited number of treatments are available for this condition at present. Given that the efficacies of these treatments tend to be poor, cerebral ischemia can create a significant burden on patients, families, and society. Mesenchymal stem cell (MSC) transplantation treatment has shown significant potential in animal models of ischemic stroke; however, the specific mechanisms underlying this effect have yet to be elucidated. Furthermore, clinical trials have yet to yield promising results. Consequently, there is an urgent need to identify new methods to improve the efficiency of MSC transplantation as an optimal treatment for ischemic stroke. In this review, we provide an overview of recent scientific reports concerning novel strategies that promote MSC transplantation as an effective therapeutic approach, including physical approaches, chemical agents, traditional Chinese medicines and extracts, and genetic modification. Our analyses showed that two key factors need to be considered if we are to improve the efficacy of MSC transplantation treatments: survival ability and homing ability. We also highlight the importance of other significant mechanisms, including the enhanced activation of MSCs to promote neurogenesis and angiogenesis, and the regulation of permeability in the blood-brain barrier. Further in-depth investigations of the specific mechanisms underlying MSC transplantation treatment will help us to identify effective methods that improve the efficiency of MSC transplantation for ischemic stroke. The development of safer and more effective methods will facilitate the application of MSC transplantation as a promising adjuvant therapy for the treatment of poststroke brain damage.

HOTTIP downregulation reduces neuronal damage and microglial activation in Parkinson's disease cell and mouse models

HOXA transcript at the distal tip (HOTTIP), a newly identified long noncoding RNA, has been shown to exhibit anti-inflammatory effects and inhibit oxygen-glucose deprivation-induced neuronal apoptosis. However, its role in Parkinson’s disease (PD) remains unclear. 1-Methyl-4-phenylpyridium (MPP⁺) and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) were used to establish PD models in SH-SY5Y and BV2 cells and in C57BL/6 male mice, respectively. In vitro, after HOTTIP knockdown by sh-HOTTIP transfection, HOTTIP and FOXO1 overexpression promoted SH-SY5Y apoptosis, BV2 microglial activation, proinflammatory cytokine expression, and nuclear factor kappa-B and NACHT, LRR and PYD domains-containing protein 3 inflammasome activation. Overexpression of miR-615-3p inhibited MPP⁺-induced neuronal apoptosis and microglial inflammation and ameliorated HOTTIP- and FOXO1-mediated nerve injury and inflammation. In vivo, HOTTIP knockdown alleviated motor dysfunction in PD mice and reduced neuronal apoptosis and microglial activation in the substantia nigra. These findings suggest that inhibition of HOTTIP mitigates neuronal apoptosis and microglial activation in PD models by modulating miR-615-3p/FOXO1. This study was approved by the Ethics Review Committee of the Affiliated Hospital of Qingdao University, China (approval No. UDX-2018-042) in June 2018.

Nano-CuO causes cell damage through activation of dose-dependent autophagy and mitochondrial lncCyt b-AS/ND5-AS/ND6-AS in SH-SY5Y cells

Metal copper oxide nanoparticles (nano-CuO) are under mass production and have been widely utilized in many fields including catalysis, gas sensors, semiconductor materials, etc. The broad applications of nano-CuO have increased the possibility of risk to incidental exposure to the environment, and therefore, an in-depth investigation of their effects on live cells is required. This study investigated the impact of the nano-CuO on SH-SY5Y cells, and findings showed that the ratio of LC3-II/LC3-I was significantly increased in SH-SY5Y cells when the cells were treated with nano-CuO. However, if the autophagy inhibitor Bafilomycin A1 (Baf A1) was co-treated, the ratio of LC3-II/LC3-I was further improved. These outcomes might indicate that autophagy flux was permanently elevated by adding nano-CuO. Further results found highly activated levels of long noncoding RNAs (lncRNAs) under nano-CuO treatment. The data illustrate a mechanism that nano-CuO can promote autophagy and activate lncCyt b-AS/ND5-AS/ND6-AS in SH-SY5Y cells and have critical implications for nanoparticle biomedical applications.

Recent advances in predicting protein-lncRNA interactions using machine learning methods

Long non-coding RNAs (LncRNAs) are a type of RNA with little or no protein-coding ability. Their length is more than 200 nucleotides. A large number of studies have indicated that lncRNAs play a significant role in various biological processes, including chromatin organizations, epigenetic programmings, transcriptional regulations, post-transcriptional processing, and circadian mechanism at the cellular level. Since lncRNAs perform vast functions through their interactions with proteins, identifying lncRNA-protein interaction is crucial to the understandings of the lncRNA molecular functions. However, due to the high cost and time-consuming disadvantage of experimental methods, a variety of computational methods have emerged. Recently, many effective and novel machine learning methods have been developed. In general, these methods fall into two categories: semi-supervised learning methods and supervised learning methods. The latter category can be further classified into the deep learning-based method, the ensemble learning-based method, and the hybrid method. In this paper, we focused on supervised learning methods. We summarized the state-of-the-art methods in predicting lncRNA-protein interactions. Furthermore, the performance and the characteristics of different methods have also been compared in this work. Considering the limits of the existing models, we analyzed the problems and discussed future research potentials.

LncRNA RUSC1-AS1 promotes osteosarcoma progression through regulating the miR-340-5p and PI3K/AKT pathway

Dysregulation of long noncoding RNA (lncRNA) is frequently involved in the progression and development of osteosarcoma. LncRNA RUSC1-AS1 is reported to be upregulated and acts as an oncogene in hepatocellular carcinoma, cervical cancer and breast cancer. However, its role in osteosarcoma has not been studied yet. In the present study, we investigated the role of RUSC1-AS1 in osteosarcoma both in vitro and in vivo. The results showed that the expression of RUSC1-AS1 was significantly upregulated in osteosarcoma cell line U2OS and HOS compared to that in human osteoblast cell line hFOB1.19. Similar results were found in human samples. Silencing RUSC1-AS1 by siRNA significantly inhibited U2OS and HOS cell proliferation and invasion, measured by CCK-8 and transwell assay. Besides, knockdown of RUSC1-AS1 increased cell apoptosis in osteosarcoma cell lines. In addition, RUSC1-AS1 promoted the epithelial-mesenchymal transition (EMT) process of osteosarcoma cells. In vivo experiments confirmed that RUSC1-AS1 knockdown had an inhibitory effect on osteosarcoma tumor growth. Mechanically, we showed that RUSC1-AS1 directly binds to and inhibits miR-340-5p and activates the PI3K/AKT signaling pathway. In conclusion, our study demonstrated that RUSC1-AS1 promoted osteosarcoma development both in vitro and in vivo through sponging to miR-340-5p and activating the PI3K/AKT signaling pathway. Therefore, RUSC1-AS1 becomes a potential therapeutic target for osteosarcoma.

Long Noncoding RNAs MALAT1 and ANRIL Gene Variants and the Risk of Cerebral Ischemic Stroke: An Association Study

Cerebral ischemic stroke (CIS) is one of the primary causes of death worldwide and a major cause of long-term disability. Long noncoding RNAs (lncRNAs) have emerged as crucial mediators in the pathology of CIS; however, their potential importance is yet to be discovered. Herein, we examined the association of four single-nucleotide polymorphisms (SNPs) with the risk of CIS, their correlation with the lncRNAs, MALAT1 and ANRIL, expression, and the potential of serum MALAT1 and ANRIL as biomarkers for CIS. A total of 100 CIS patients and 100 healthy controls were recruited in the study. Genotyping and expression analysis of MALAT1 and ANRIL SNPs were carried out by qPCR. The present results showed that serum MALAT1 was downregulated, while serum ANRIL was overexpressed in CIS patients, relative to controls. MALAT1 downregulation discriminated CIS patients from controls by receiver-operating-characteristic analysis. Moreover, serum ANRIL denoted good diagnostic accuracy. MALAT1 rs619586 AA and rs3200401 CT, TT were associated with increased CIS risk, whereas ANRIL rs10965215 GG was found to be protective. The studied ANRIL rs10738605 polymorphism was not associated with CIS susceptibility. Notably, the G variant of MALAT1 rs619586 demonstrated a higher serum MALAT1 expression level. Multivariate logistic regression analysis revealed serum MALAT1 as well as MALAT1 rs3200401 CT + TT as independent predictors of CIS. Additionally, a negative association was found between the serum MALAT1 level and the National Institutes of Health Stroke Scale score. In conclusion, MALAT1 rs619586 and rs3200401 and ANRIL rs10965215 are novel prospective noninvasive diagnostic biomarkers for CIS predisposition.

NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks

Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative ways. In this work, we collected five benchmarking datasets for predicting NPIs. Based on these datasets, we evaluated and compared the prediction performances of existing machine-learning based methods. Graph neural network (GNN) is a recently developed deep learning algorithm for link predictions on complex networks, which has never been applied in predicting NPIs. We constructed a GNN-based method, which is called Noncoding RNA-Protein Interaction prediction using Graph Neural Networks (NPI-GNN), to predict NPIs. The NPI-GNN method achieved comparable performance with state-of-the-art methods in a 5-fold cross-validation. In addition, it is capable of predicting novel interactions based on network information and sequence information. We also found that insufficient sequence information does not affect the NPI-GNN prediction performance much, which makes NPI-GNN more robust than other methods. As far as we can tell, NPI-GNN is the first end-to-end GNN predictor for predicting NPIs. All benchmarking datasets in this work and all source codes of the NPI-GNN method have been deposited with documents in a GitHub repo (https://github.com/AshuiRUA/NPI-GNN).

Development of a multiplex methylation-sensitive restriction enzyme-based SNP typing system for deconvolution of semen-containing mixtures

The identification of mixed stains has always been a difficult problem in personal identification in the forensic field. In recent years, tissue-specific methylation sites have proven to be very stable biomarkers for distinguishing tissue origin. However, it is still challenging to perform tissue source identification and individual identification simultaneously. In this study, we developed a method that uses tissue-specific methylation markers combined with single-nucleotide polymorphism (SNP) markers to detect semen from mixed biofluids and to identify individuals simultaneously. Semen-specific CpG markers were chosen from the literature and further validated utilizing methylation-sensitive restriction endonuclease (MSRE) combined with PCR technology. The neighboring SNP markers were searched in the flanking sequence of the target CpG within 400 bp, and SNP typing was then carried out through a single-base extension reaction followed by capillary electrophoresis. Eventually, a method of MSRE combined with SNaPshot that could detect 12 compound CpG-SNP markers was developed. Using this system, 10 ng of total DNA and DNA mixture with semen content up to 25% could be typed successfully. Moreover, the cumulative discrimination power of the system in the northern Chinese Han population is 0.9998. This study provides a valuable strategy for forensic practice to perform tissue origin and individual identification from mixed stains simultaneously.

Exploring the secrets of brain transcriptional regulation: developing methodologies, recent significant findings, and perspectives

Exploring and revealing the secret of the function of the human brain has been the dream of mankind and science. Delineating brain transcriptional regulation has been extremely challenging, but recent technological advances have facilitated a deeper investigation of molecular processes in the brain. Tracing the molecular regulatory mechanisms of different gene expression profiles in the brain is divergent and has made it possible to connect spatial and temporal variations in gene expression to distributed properties of brain structure and function. Here, we review the molecular diversity of the brain among rodents, non-human primates and humans. We also discuss the molecular mechanism of non-coding DNA/RNA at the transcriptional/post-transcriptional level based on recent technical advances to highlight an improved understanding of the complex transcriptional network in the brain. Spatiotemporal and single-cell transcriptomics have attempted to gain novel insight into the development and evolution of the brain as well as the progression of human diseases. Although it is clear that the field is developing and challenges remain to be resolved, the impressive recent progress provides a solid foundation to better understand the brain and evidence-based recommendations for the diagnosis and treatment of brain diseases.

[Differential mRNA expression in C57BL/6 mice with bleomycin-induced pulmonary fibrosis and its association with LncRNA co-expression network]

OBJECTIVE

To study the changes in mRNA and long non-coding RNA (lncRNA) expression profiles in a mouse model of bleomycin-induced lung fibrosis and identify lung fibrosis-related mRNA for coding-noncoding coexpression (CNC) bioinformatics analysis of the differential lncRNAs.

METHODS

Lung fibrosis was induced by intratracheal injection of bleomycin in 10 C57BL/6 mice and another 10 mice with intratracheal injection of saline served as the control group. Lung tissues were harvested from the mice at 14 days after the injections and lung fibrosis was assessed using Masson and HE staining. LncRNA chip technology was used to screen the differentially expressed mRNAs and lncRNAs in mice with lung fibrosis, and GO and KEGG pathway analyses of the differential mRNAs were performed using NCBI database and UCSC database to identify possible fibrosis-related mRNAs, which were validated by qRT-PCR to construct a coding and non-coding co- expression network with the differential lncRNAs.

RESULTS

Compared with the control mice, the mice with intratracheal injection of bleomycin showed obvious lung fibrosis. The results of gene chip analysis showed that 127 mRNAs were upregulated and 184 mRNAs were down-regulated in the model group as compared with the control group. GO and pathway analysis suggested that the differentially expressed genes participated mainly in immune response, cell differentiation, and cytoskeletons; the involved signal pathways were associated mainly with cytokine and cytokine receptor interaction and chemokine signal transduction. Bioinformatics analysis identified a significant coexpression network between the fibrosisrelated mRNA and the differentially expressed lncRNA.

CONCLUSIONS

In mice with lung fibrosis, the differential expressions of fibrosis-related mRNAs in the lung tissues are closely correlated with the co- expressions of a large number of differential lncRNAs, which points to a new direction for investigation of the pathogenesis of pulmonary fibrosis.

Autophagy-Associated lncRNAs: Promising Targets for Neurological Disease Diagnosis and Therapy

Neurological diseases are a major threat to global public health and prosperity. The number of patients with neurological diseases is increasing due to the population aging and increasing life expectancy. Autophagy is one of the crucial mechanisms to maintain nerve cellular homeostasis. Numerous studies have demonstrated that autophagy plays a dual role in neurological diseases. Long noncoding RNAs (lncRNAs) are a vital class of noncoding RNAs with a length of more than 200 nucleotides and cannot encode proteins themselves but are expressed in most neurological diseases. An early phase, emerging knowledge has revealed that long noncoding RNAs (lncRNAs) are crucial in autophagy regulation. Furthermore, autophagy-associated lncRNAs can promote the development of neurological diseases or slow their progression. In this review, we introduce a general overview of lncRNA functional mechanisms and summarizes the recent progress of lncRNAs on autophagy regulation in neurological diseases to reveal possible novel therapeutic targets or useful biomarkers.

LncRNA RP11-395G23.3 suppresses the endometrial cancer progression via regulating microRNA-205-5p/PTEN axis

The focal point of this research was the functional role of RP11-395G23.3 in endometrial cancer (EC). The expression of RP11-395G23.3, microRNA (miRNA)-205-5p, and their target proteins were detected by quantitative real-time polymerase chain reaction and western-blot analyses. Flow cytometry and proliferation, Transwell, and wound healing assays were used to detect the effects of RP11-395G23.3 and miRNA-205-5p on tumor cell migration and proliferation in vitro. RP11-395G23.3 expression was negatively related to miRNA-205-5p, but positively related to phosphatase and tensin homolog (PTEN) expression in human EC tissues. We discovered that low RP11-395G23.3 expression was significantly related to advanced histological grade and lymphovascular space invasion in EC patients. In addition, overexpression of RP11-395G23.3 significantly inhibited the proliferation, invasion, migration, and induced apoptosis of Ishikawa and HEC-1A cells in vitro. Our results also showed that RP11-395G23.3 could directly bind to miRNA-205-5p through its miRNA response elements and eliminate the inhibitory effect of targeting gene PTEN, thus leading to the signaling pathway of phosphatidylinositol-3-kinase/AKT inactivation. We demonstrated for the first time that RP11-395G23.3 may inhibit the development and pathogenesis of EC by acting as a sponge for miRNA-205-5p and increasing PTEN expression. RP11-395G23.3 may be a target for the diagnosis and treatment of EC.

LINC01224 Exhibits Cancer-Promoting Activity in Epithelial Ovarian Cancer Through microRNA-485-5p-Mediated PAK4 Upregulation

Purpose

Long intergenic non-protein coding RNA 1224 (LINC01224) plays vital roles in the tumorigenesis and progression of hepatocellular carcinoma. Here, we determined LINC01224 expression in epithelial ovarian cancer (EOC) tissues and cells. We also assessed the effects of LINC01224 knockdown on the malignant phenotype of EOC cells both in vitro and in vivo. Furthermore, the detailed molecular mechanisms underlying the oncogenic actions of LINC01224 in EOC cells were elucidated.

Methods

Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect LINC01224 expression in EOC tissues and cells. EOC cells were transfected with small interfering RNAs, and cell proliferation, apoptosis, migration, and invasion were assessed using Cell Counting Kit-8 assay, flow cytometry, cell migration assays, and cell invasion assays, respectively. Using tumor xenografts, the effects of LINC01224 silencing on EOC tumor growth were analyzed in vivo. The mechanism underlying LINC01224 regulation of malignant processes in EOC cells was explored using bioinformatics, RNA immunoprecipitation assay, qRT-PCR, Western blotting, and rescue experiments.

Results

LINC01224 expression was upregulated in EOC tissues and cells. LINC01224 upregulation was correlated to tumor size, the International Federation of Gynecology and Obstetrics stage, and lymph node metastasis. LINC01224 depletion in EOC cells suppressed cell proliferation, migration, and invasion and facilitated cell apoptosis in vitro. LINC01224 downregulation also hindered EOC tumor growth in vivo. Mechanistically, LINC01224 served as a competing endogenous RNA for microRNA-485-5p (miR-485-5p) and consequently increased p21-activated kinase 4 (PAK4) expression in EOC cells. Furthermore, miR-485-5p inhibition or PAK4 upregulation significantly abrogated the effects of LINC01224 depletion in EOC cells.

Conclusion

LINC01224/miR-485-5p/PAK4 formed a competing endogenous RNA network regulating the aggressive behavior of EOC. Therefore, targeting this pathway may be an attractive therapeutic strategy for EOC.

Long Noncoding RNA Serve as a Potential Predictive Biomarker for Breast Cancer: A Meta-Analysis

Purpose

The detection of long noncoding RNA (lncRNA) is a novel method for breast cancer diagnosis. The purpose of this meta-analysis was to evaluate the clinical significance of lncRNAs in identification of human breast cancer.

Methods

Electronic databases, including PubMed (176), EMBASE (167), Cochrane Library (4), Web of Science (273), CNKI (41), VIP (18), and wanfang (21), were searched for relevant original articles. Diagnostic capacity of lncRNAs was assessed by pooled sensitivity and specificity, area under the summary receiver operating characteristic curve (AUC), diagnostic odds ratio (DOR), and subgroup and meta-regression analysis. Stata and Meta-Disc software were used to conduct the meta-analysis.

Results

33 articles including 4500 cases were identified in our meta-analysis. lncRNAs sustained a high diagnostic efficacy; the pooled sensitivity, specificity, AUC, and DOR of lncRNAs in differentiating BC from controls were 0.74 (95% CI: 0.69-0.78), 0.78 (95% CI: 0.72-0.83), 0.82 (95% CI: 0.79-0.85), and 10.01 (95% CI: 7.13-14.06), respectively. The subgroup analysis showed that the diagnostic efficacy of lncRNAs in Asian populations was higher than that in Caucasians; lncRNAs in BC were lower than those in TNBC and were higher in plasma and serum specimens than in tissues. In addition, heterogeneity was clearly apparent but was not caused by the threshold effect.

Conclusion

This meta-analysis suggested that lncRNAs might be promising biomarkers for identifying breast cancer, and its clinical application warrants further investigation.

Long noncoding RNA PAHAL modulates locust behavioural plasticity through the feedback regulation of dopamine biosynthesis

Some long noncoding RNAs (lncRNAs) are specifically expressed in brain cells, implying their neural and behavioural functions. However, how lncRNAs contribute to neural regulatory networks governing the precise behaviour of animals is less explored. Here, we report the regulatory mechanism of the nuclear-enriched lncRNA PAHAL for dopamine biosynthesis and behavioural adjustment in migratory locusts (Locusta migratoria), a species with extreme behavioral plasticity. PAHAL is transcribed from the sense (coding) strand of the gene encoding phenylalanine hydroxylase (PAH), which is responsible for the synthesis of dopamine from phenylalanine. PAHAL positively regulates PAH expression resulting in dopamine production in the brain. In addition, PAHAL modulates locust behavioral aggregation in a population density-dependent manner. Mechanistically, PAHAL mediates PAH transcriptional activation by recruiting serine/arginine-rich splicing factor 2 (SRSF2), a transcription/splicing factor, to the PAH proximal promoter. The co-activation effect of PAHAL requires the interaction of the PAHAL/SRSF2 complex with the promoter-associated nascent RNA of PAH. Thus, the data support a model of feedback modulation of animal behavioural plasticity by an lncRNA. In this model, the lncRNA mediates neurotransmitter metabolism through orchestrating a local transcriptional loop.

The neurotransmitter dopamine is crucial for the neuronal and behavioral response in animals. Phenylalanine hydroxylase (PAH) is involved in dopamine biosynthesis and behavioral regulation in the migratory locust. However, the molecular mechanism for the fine tuning of PAH expression in behavioral response remains ambiguous. Here we discovered a nuclear-enriched lncRNA PAHAL that is transcribed from the coding strand of the PAH gene in the locust (i.e., sense lncRNA). PAHAL positively regulated PAH expression and dopamine production in the brain. In addition, PAHAL modulated behavioral aggregation of the locust. Mechanistically, PAHAL mediated the transcriptional activation of PAH by recruiting SRSF2, a transcription/splicing factor, to the promoter-associated nascent RNA of PAH. These data support a model of feedback modulation of dopamine biosynthesis and behavioral plasticity via a sense lncRNA in the catecholamine metabolic pathway.

SNP rs12982687 affects binding capacity of lncRNA UCA1 with miR-873-5p: involvement in smoking-triggered colorectal cancer progression

BACKGROUND

This investigation was arranged to elucidate whether single nucleotide polymorphisms (SNPs) of lncRNA UCA1 was implicated in elevating colorectal cancer (CRC) risk by interacting with environmental exposures.

METHODS

LncRNASNP database was firstly adopted to predict SNPs that possibly affected binding of UCA1 with miRNAs and then the interactive effect of SNPs and environmental exposure on CRC risk was evaluated by recurring to type 2 gene-environment interactions (GEI) model. Besides, MTT assay, colony formation assay, transwell assay and wound healing assay were performed to assess the activity of CRC cell lines which carried distinct genotypes of specific SNPs. The impact of nicotine on activity of CRC cells was also appraised.

RESULTS

SNP rs12982687 of UCA1 intervened in the binding capacity of UCA1 with several miRNAs, especially miR-873-5p. MiRNAs regulated by UCA1, as predicted by mirPath software, shared genes that were enriched in HIF1 signaling pathway. Moreover, homozygote TT of rs12982687 reduced CRC risk among smokers, and CRC cells that carried rs12982687 (CC) displayed strong migration and invasion. By contrast, miR-873-5p mimic, which reduced UCA1 expression, delayed metastasis of CRC cells (all P < 0.05). Additionally, nicotine not merely elevated UCA1 and HIF-1α expressions in CRC cells, but also facilitated proliferation and metastasis of CRC cells (P < 0.05).

CONCLUSIONS

SNP rs12982687 was involved in smoking-triggered CRC progression, given its influence on UCA1's binding with miR-873-5p and HIF-1 signaling.

Senescence-associated genes and non-coding RNAs function in pancreatic cancer progression

Pancreatic cancer is a major cause of mortality with a poor diagnosis and prognosis that most often occurs in elderly patients. Few studies, however, focus on the interplay of age and pancreatic cancer at the transcriptional level. Here we evaluated the possible roles of age-dependent, differentially expressed genes (DEGs) in pancreatic cancer. These DEGs were used to construct a correlation network and clustered in six gene modules, among which two modules were highly correlated with patients' survival time. Integrating different datasets, including ATAC-Seq and ChIP-Seq, we performed multi-parallel analyses and identified eight age-dependent protein coding genes and two non-coding RNAs as potential candidates. These candidates, together with KLF5, a potent functional transcription factor in pancreatic cancer, are likely to be key elements linking cellular senescence and pancreatic cancer, providing insights on the balance between them, as well as on diagnosis and subsequent prognosis of pancreatic cancer.

Long Noncoding RNA SNHG7, a Molecular Sponge for microRNA-485, Promotes the Aggressive Behavior of Cervical Cancer by Regulating PAK4

Purpose

A long noncoding RNA called small nucleolar RNA host gene 7 (SNHG7) is known to be a key regulator of biological processes in multiple human cancer types. In this study, our aims were to determine the expression status of SNHG7 in cervical cancer, to figure out the detailed roles of SNHG7 in cervical cancer cells, and to identify the mechanism underlying the activity of SNHG7 in cervical cancer.

Methods

Reverse-transcription quantitative PCR was performed to measure SNHG7 expression in cervical cancer. A Cell Counting Kit-8 assay, flow-cytometric analysis, cell migration and invasion assays, and a tumor xenograft experiment were conducted to respectively determine the effects of SNHG7 on cervical cancer cell proliferation, apoptosis, migration, and invasion in vitro and tumor growth in vivo.

Results

SNHG7 was found to be markedly upregulated in cervical cancer tissues and cell lines. Higher SNHG7 expression significantly correlated with FIGO stage, lymph node metastasis, the depth of cervical invasion, and shorter overall survival in patients with cervical cancer. Functional experiments indicated that a SNHG7 knockdown attenuated proliferation, migration, and invasiveness and promoted apoptosis of cervical cancer cells in vitro. The SNHG7 knockdown also slowed tumor growth in vivo. Further investigation showed that SNHG7 acts as a competing endogenous RNA for microRNA-485 (miR-485) in cervical cancer cells, and the inhibitory actions of the SNHG7 knockdown on the malignant phenotype were reversed by miR-485 inhibition. P21-activated kinase 4 (PAK4) was identified as a direct target gene of miR-485 in cervical cancer, and PAK4 expression was promoted by SNHG7.

Conclusion

SNHG7 functions as an oncogenic RNA in cervical cancer, competitively binds to miR-485, and thereby upregulates PAK4. This SNHG7–miR-485–PAK4 regulatory network may provide insights into the pathogenesis of cervical cancer, and can help in the identification of novel diagnostic and therapeutic approaches for cervical cancer.

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).

The role of non-coding RNAs in neuroprotection and angiogenesis following ischemic stroke

Stroke is the leading cause of death and physical disability worldwide. Non-coding RNAs (ncRNAs) are endogenous molecules that play key roles in the pathophysiology and retrieval processes following ischemic stroke. The potential of ncRNAs, especially microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in neuroprotection and angiogenesis highlights their potential as targets for therapeutic intervention. In this review, we document the miRNAs and lncRNAs that have been reported to exert regulatory actions in neuroprotective and angiogenic processes through different mechanisms involving their interaction with target coding genes. We believe that exploration of the expression profiles and the possible functions of ncRNAs during the recovery processes will help comprehension of the molecular mechanisms responsible for neuroprotection and angiogenesis, and may also contribute to find biomarkers and targets for future stroke intervention.

lncRNA NR_120420 promotes SH-SY5Y cells apoptosis by regulating NF-κB after oxygen and glucose deprivation

Stroke has serious implications on patients and a huge impact on society. The current treatment regimens with drug for acute cerebral infarction are unsatisfactory. Here, we explore whether the two long non-coding RNA (lncRNA) candidates from preliminary research regulate apoptosis after cerebral infarction, and evaluate the underlying mechanism of action. Bioinformatics analysis of the lncRNA microarray in the preliminary research of our group was performed. Changes in the expression of candidate lncRNAs in SH-SY5Y cells were detected by quantitative polymerase chain reaction (qPCR) after treatment with seven different oxygen and glucose deprivation (OGD) methods. The changes were detected after transfection of cells with six small-interfering RNAs (siRNAs). Cell models were established by OGD after transfection with siRNAs. Cell viability was evaluated with the cell counting kit 8 (CCK8) assay, while TUNEL staining and flow cytometry analysis were performed to determine apoptosis. Changes in the expression and phosphorylation of three proteins were detected by western blotting after the knockdown of NR_120420. Changes in the expression and phosphorylation of P65 protein were detected by western blotting after this cell model was treated with PDTC. Cells were transfected with siNR_120420 and treated with and without PDTC, followed by analysis of cell viability and apoptosis. Bioinformatics analysis revealed that the differentially expressed lncRNAs after acute cerebral infarction were mainly involved in nuclear factor kappa B (NF-κB) and apoptosis. Expression of the two lncRNA candidates in SH-SY5Y cells was the maximum after incubation under the OGD condition for 8 h. The knockdown efficiency was more than 60% for four of the six siRNAs, and knockdown of NR_120420 increased the cell viability and decreased the percentage of TUNEL-positive cells and apoptotic cells. Knockdown of lnc-GCH1-2:3 resulted in none of these effects. Phosphorylation of NF-κB (P65) decreased significantly after the knockdown of NR_120420. Expression and phosphorylation of P65 was significantly reduced after it was treated with PDTC. The inhibitor of NF-κB (PDTC) could abolish the effect of NR_120420 on the regulation of apoptosis in this cell model. Both NR_120420 and lnc-GCH1-2:3 had significant changes in this cell model. Knockdown of NR_120420 inhibited the apoptosis of cells, while NR_120420 knockdown inhibited apoptosis after cerebral infarction by downregulating the phosphorylation of a subunit of NF-κB (P65). This study may provide new idea for improving drug treatment of acute cerebral infarction.