MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

Identification of several lncRNA-mRNA pairs associated with marbling trait between Nanyang and Angus cattle

BACKGROUND

The marbling trait of cattle muscles, being a key indicator, played an important role in evaluating beef quality. Two breeds of cattle, namely a high-marbling (Angus) and a low-marbling (Nanyang) one, with their cattle muscles selected as our samples for transcriptome sequencing, were aimed to identify differentially expressed long non-coding RNAs (lncRNAs) and their targets associated with the marbling trait.

RESULTS

Transcriptome sequencing identified 487 and 283 differentially expressed mRNAs and lncRNAs respectively between the high-marbling (Angus) and low-marbling (Nanyang) cattle muscles. Twenty-seven pairs of differentially expressed lncRNAs-mRNAs, including eighteen lncRNAs and eleven target genes, were found to be involved in fat deposition and lipid metabolism. We established a positive correlation between fourteen up-regulated (NONBTAT000849.2, MSTRG.9591.1, NONBTAT031089.1, MSTRG.3720.1, NONBTAT029718.1, NONBTAT004228.2, NONBTAT007494.2, NONBTAT011094.2, NONBTAT015080.2, NONBTAT030943.1, NONBTAT021005.2, NONBTAT021004.2, NONBTAT025985.2, and NONBTAT023845.2) and four down-regulated (NONBTAT000850.2, MSTRG.22188.3, MSTRG.22188.4, and MSTRG.22188.5) lncRNAs and eleven genes related to adiponectin family protein (ADIPOQ), cytochrome P450 family (CYP4V2), 3-hydroxyacyl-CoA dehydratase family (HACD4), kinesin family (KIF5C), lipin family (LPIN2), perilipin family (PLIN1), prostaglandin family (PTGIS), solute carrier family (SLC16A7, SLC2213, and SLCO4C1), and containing a transmembrane domain protein family (VSTM1).

CONCLUSIONS

These candidate genes and lncRNAs can be regarded as being responsible for regulating the marbling trait of cattle. lncRNAs along with the variations in intramuscular fat marbling established a foundation for elucidating the genetic basis of high marbling in cattle.

Long Noncoding RNAs CAT2064 and CAT2042 may Function as Diagnostic Biomarkers for Prostate Cancer by Affecting Target MicrorRNAs

Prostate cancer (PCa) is the second most common cancer in men throughout the world, and the main cause of cancer death. Long noncoding RNAs (lncRNAs) act as crucial regulators in many human cancers. In this research, we measured the expression level of novel lncRNAs and their associated micro-RNAs (miRNAs) in PCa. In the present research, three lncRNAs were selected using the Mitranscriptome projec (CAT2064, CAT2042, and CAT2164.2). Samples of prostate tissue (20 PCa, and 20 BPH) and blood (14 PCa, and 14 BPH) were collected and the Real-time Quantitative Polymerase Chain Reaction (RT-qPCR) was used to measure the expression levels of the lncRNAs and their associated miRNAs. Based on our results, CAT2064 was significantly increased and CAT2042 was significantly decreased in human PCa tissue in comparison with BPH tissue. To discriminate PCa from BPH, CAT2064 (P < 0.05; 0.8750 AUC-ROC) showed a better potential as a diagnostic molecular biomarker compared to CAT2042 (P < 0.05; 0.8454 AUC-ROC). Furthermore, RT-qPCR results measured in blood samples from PCa patients showed a higher expression level of CAT2064 (P < 0.0001; AUC-ROC value of 0.8914) in comparison to CAT2042. CAT2064 and CAT2042 showed a positive correlation with the expression of miR-5095 and miR-1273a (r = 0.02885, 0.3202; P = 0.9413, 0.2266, respectively). CAT2064 and CAT2042 also had a negative correlation with miR-1304-3p and miR-1285-5p (r = - 0.3877, - 0.09330; P = 0.15, 0.7311, respectively). Collectively, CAT2064 and CAT2042 and their miRNA targets may constitute a regulatory network in PCa, and could serve as novel biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12291-021-00999-6.

Genome-Wide Identification and Characterization of miRNAs and Natural Antisense Transcripts Show the Complexity of Gene Regulatory Networks for Secondary Metabolism in Aristolochia contorta

Aristolochia contorta Bunge is an academically and medicinally important plant species. It belongs to the magnoliids, with an uncertain phylogenetic position, and is one of the few plant species lacking a whole-genome duplication (WGD) event after the angiosperm-wide WGD. A. contorta has been an important traditional Chinese medicine material. Since it contains aristolochic acids (AAs), chemical compounds with nephrotoxity and carcinogenicity, the utilization of this plant has attracted widespread attention. Great efforts are being made to increase its bioactive compounds and reduce or completely remove toxic compounds. MicroRNAs (miRNAs) and natural antisense transcripts (NATs) are two classes of regulators potentially involved in metabolism regulation. Here, we report the identification and characterization of 223 miRNAs and 363 miRNA targets. The identified miRNAs include 51 known miRNAs belonging to 20 families and 172 novel miRNAs belonging to 107 families. A negative correlation between the expression of miRNAs and their targets was observed. In addition, we identified 441 A. contorta NATs and 560 NAT-sense transcript (ST) pairs, of which 12 NATs were targets of 13 miRNAs, forming 18 miRNA-NAT-ST modules. Various miRNAs and NATs potentially regulated secondary metabolism through the modes of miRNA-target gene-enzyme genes, NAT-STs, and NAT-miRNA-target gene-enzyme genes, suggesting the complexity of gene regulatory networks in A. contorta. The results lay a solid foundation for further manipulating the production of its bioactive and toxic compounds.

Multifaced roles of the long non-coding RNA DRAIC in cancer progression

Long non-coding RNAs (lncRNA) are functional RNAs, with over 200 nucleotides in length and lacking protein-coding potential. Studies have indicated that lncRNAs are important gene regulators under physiological conditions. Aberrant lncRNA expression is associated with the initiation and progression of various diseases, including cancers. High-throughput transcriptome analyses have revealed thousands of lncRNAs as putative tumor suppressors or promoters in various cancers, but the detailed molecular mechanisms of each lncRNA remain unclear. Downregulated RNA In Cancer, inhibitor of cell invasion and migration (DRAIC) (also known as LOC145837 and RP11-279F6.1) is a lncRNA that inhibits or promotes cancer progression with several modes of action. DRAIC was originally identified as a tumor-suppressive lncRNA in prostate adenocarcinoma. Subsequent studies also revealed that it has an anti-tumor role in glioblastoma, triple-negative breast cancer, and stomach adenocarcinoma. However, DRAIC exhibits oncogenic functions in other malignancies, such as lung adenocarcinoma and esophageal carcinoma, indicating its highly context-dependent effects on cancer progression and clinical outcomes. DRAIC and its associated pathways regulate various biological processes, including proliferation, invasion, metastasis, autophagy, and neuroendocrine function. This review introduces the multifaceted roles of DRAIC, particularly in cancer progression, and discusses its biological significance and clinical implications.

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.

LncRTPred: Predicting RNA-RNA mode of interaction mediated by lncRNA

Long non-coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA-mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real-time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML-based lncRNA-mRNA target prediction model- 'LncRTPred'. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non-interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease-specific lncRNA-mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA-mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred.

The Long Non-Coding RNA SNHG1 Predicts Severity of Acute Pancreatitis and Stimulates Pancreatic Cell Apoptosis and Inflammatory Response

Acute pancreatitis (AP) is a common digestive emergency, needs early prediction and recognition. The study examined the clinical value of long non-coding RNA SNHG1 in AP, and explored its related mechanism for AP. A total of 288 AP cases and 150 healthy persons were recruited, the AP patients were grouped based on AP severity. AR42J cells were treated with 100nM caerulein to stimulate AP in vitro. qRT-PCR was performed for mRNA detection. Receiver operating characteristic (ROC) curve was drawn for diagnostic significance evaluation. The relationship of SNHG1 and miR-140-3p was verified via luciferase reporter and RNA immunoprecipitation (RIP) assay. AP cases had high expression of SNHG1, and it can differentiate AP cases from healthy people with the area under the curve (AUC) of 0.899. Severe AP cases had high values of SNHG1, which was independently related to AP severity. SNHG1 knockdown relieved caerulein-induced AR42J cell apoptosis and inflammatory response. miR-140-3p interacted with SNHG1, and reversed the role of SNHG1 in caerulein-induced AR42J cell injury. RAB21 was a candidate target of miR-140-3p, and was at high expression in AP cell models. SNHG1 may be a promising biomarker for the detection of AP, and serves as a potential biological marker for further risk stratification in the management of AP. SNHG1 knockdown can relieve inflammatory responses and pancreatic cell apoptosis by absorbing miR-140-3p.

METTL3/YTHDC1-mediated upregulation of LINC00294 promotes hepatocellular carcinoma progression

Hepatocellular carcinoma (HCC) is a highly prevalent malignancy and the third highest contributor to cancer-associated deaths globally. Research has increasingly demonstrated a strong correlation between long noncoding RNAs (lncRNAs) and the incidence and progression of HCC. Nonetheless, the exact mechanism whereby the function of lncRNAs in HCC has not been elucidated. This study explored the pathological role of LINC00294 in HCC, as well as the modulatory mechanism involved. Based on the "The Cancer Genome Atlas (TCGA)" database and validation in HCC cell lines and tissues, the expression of LINC00294 was discovered to be upregulated in HCC tissues and correlated with tumor grade and the prognosis of patients with HCC. Functionally, LINC00294 stimulated the proliferation of HCC cells as well as the Warburg effect (aerobic glycolysis) to enhance progression of tumor in vivo. Mechanistically, METTL3/YTHDC1-mediated N6-methyladenosine (m6A) modification underwent a significant enrichment within LINC00294 and was shown to enhance its RNA stability. Moreover, LINC00294 promoted the interaction between YTHDC1 and HK2 and GLUT1 mRNA. Overall, our study illustrates the m6A modification-mediated epigenetic mechanism of LINC00294 expression and regulatory role in HK2and GLUT1 mRNA expression and indicate LINC00294 as a potential biomarker panel for prognostic prediction and treatment in HCC.

Noncoding RNA as an influential epigenetic modulator with promising roles in cancer therapeutics

The epigenetic landscape has an important role in cellular homeostasis and its deregulation leads to cancer. Noncoding (nc)RNA networks function as major regulators of cellular epigenetic hallmarks via regulation of vital processes, such as histone modification and DNA methylation. They are integral intracellular components affecting multiple oncogenic pathways. Thus, it is important to elucidate the effects of ncRNA networks on epigenetic programming that lead to the initiation and progression of cancer. In this review, we summarize the effects of epigenetic modification influenced by ncRNA networks and crosstalk between diverse classes of ncRNA, which could aid the development of patient-specific cancer therapeutics targeting ncRNAs, thereby altering cellular epigenetics.

Long noncoding RNA POU6F2-AS2 contributes to the aggressiveness of nonsmall-cell lung cancer via microRNA-125b-5p-mediated E2F3 upregulation

The role of the majority of long noncoding RNAs (lncRNAs) in the progression of nonsmall-cell lung cancer (NSCLC) remains elusive, despite their potential value, thus warranting in-depth studies. For example, detailed functions of the lncRNA POU6F2 antisense RNA 2 (POU6F2-AS2) in NSCLC are unknown. Herein, we investigated the expression status of POU6F2-AS2 in NSCLC. Furthermore, we systematically delineated the biological roles of POU6F2-AS2 in NSCLC alongside its downstream molecular events. We measured the expression levels of POU6F2-AS2 using quantitative real-time polymerase chain reaction and performed a series of functional experiments to address its regulatory effects in NSCLC cells. Using bioinformatic platforms, RNA immunoprecipitation, luciferase reporter assays, and rescue experiments, we investigated the potential mechanisms of POU6F2-AS2 in NSCLC. Subsequently, we confirmed the remarkable overexpression of POU6F2-AS2 in NSCLC using The Cancer Genome Atlas database and our own cohort. Functionally, inhibiting POU6F2-AS2 decreased NSCLC cell proliferation, colony formation, and motility, whereas POU6F2-AS2 overexpression exhibited contrasting effects. Mechanistically, POU6F2-AS2 acts as an endogenous decoy for microRNA-125b-5p (miR-125b-5p) in NSCLC that causes the overexpression of the E2F transcription factor 3 (E2F3). Moreover, suppressing miR-125b-5p or increasing E2F3 expression levels sufficiently recovered the anticarcinostatic activities in NSCLC induced by POU6F2-AS2 silencing. Thus, POU6F2-AS2 aggravates the oncogenicity of NSCLC by targeting the miR-125b-5p/E2F3 axis. Our findings suggest that POU6F2-AS2 is a novel therapeutic target for NSCLC.

Constructing discriminative feature space for LncRNA-protein interaction based on deep autoencoder and marginal fisher analysis

Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of lncRNA with proteins. Recently, some machine learning methods were proposed to detect lncRNA-protein interactions according to the distribution of known interactions. The performances of these methods were largely dependent upon: (1) how exactly the distribution of known interactions was characterized by feature space; (2) how discriminative the feature space was for distinguishing lncRNA-protein interactions. Because the known interactions may be multiple and complex model, it remains a challenge to construct discriminative feature space for lncRNA-protein interactions. To resolve this problem, a novel method named DFRPI was developed based on deep autoencoder and marginal fisher analysis in this paper. Firstly, some initial features of lncRNA-protein interactions were extracted from the primary sequences and secondary structures of lncRNA and protein. Secondly, a deep autoencoder was exploited to learn encode parameters of the initial features to describe the known interactions precisely. Next, the marginal fisher analysis was employed to optimize the encode parameters of features to characterize a discriminative feature space of the lncRNA-protein interactions. Finally, a random forest-based predictor was trained on the discriminative feature space to detect lncRNA-protein interactions. Verified by a series of experiments, the results showed that our predictor achieved the precision of 0.920, recall of 0.916, accuracy of 0.918, MCC of 0.836, specificity of 0.920, sensitivity of 0.916 and AUC of 0.906 respectively, which outperforms the concerned methods for predicting lncRNA-protein interaction. It may be suggested that the proposed method can generate a reasonable and effective feature space for distinguishing lncRNA-protein interactions accurately. The code and data are available on https://github.com/D0ub1e-D/DFRPI.

Lnc-HZ05 regulates BPDE-inhibited human trophoblast cell proliferation and affects the occurrence of miscarriage by directly binding with miR-hz05

Approximately 15-25% pregnant women end with miscarriage in the world. Environmental BaP (benzo(a)pyrene) and its terminal metabolite BPDE (benzo(a)pyrene-7,8-dihydrodiol-9,10-epoxide) may result in the dysfunctions of trophoblast cells, which might further lead to RM (recurrent miscarriage). However, potential mechanisms remain unelucidated. In this work, we identified a novel lnc-HZ05 highly expressed and a novel miR-hz05 lowly expressed in both trophoblast cells exposed to BPDE and human RM tissues. MiR-hz05 reduces FOXO3a mRNA level by weakening its mRNA stability. Lnc-HZ05 increases the expression of FOXO3a by acting as a ceRNA for miR-hz05, and then increases P21 level and reduces CDK2 level. Thus, cell cycle is arrested at G0/G1 phase and trophoblast proliferation is inhibited. Lnc-HZ05 harboring wild-type binding site for miR-hz05, but not its mutant site, could upregulate FOXO3a expression. In normal trophoblast cells, relatively less lnc-HZ05 and more miR-hz05 activate FOXO3a/P21/CDK2 pathway and promote trophoblast proliferation, giving normal pregnancy. In RM tissues and BPDE-treated human trophoblast cells, lnc-HZ05 is increased and miR-hz05 is reduced, both of which suppress this pathway and inhibit cell proliferation, and finally lead to miscarriage. Thus, lnc-HZ05 and miR-hz05 simultaneously regulate cell cycle and proliferation of BPDE-exposed trophoblast cells and miscarriage, providing new perspectives and clinical understandings in the occurrence of unexplained miscarriage.

GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA-PCG associations

Long non-coding RNAs (lncRNAs) can disrupt the biological functions of protein-coding genes (PCGs) to cause cancer. However, the relationship between lncRNAs and PCGs remains unclear and difficult to predict. Machine learning has achieved a satisfactory performance in association prediction, but to our knowledge, it is currently less used in lncRNA-PCG association prediction. Therefore, we introduce GAE-LGA, a powerful deep learning model with graph autoencoders as components, to recognize potential lncRNA-PCG associations. GAE-LGA jointly explored lncRNA-PCG learning and cross-omics correlation learning for effective lncRNA-PCG association identification. The functional similarity and multi-omics similarity of lncRNAs and PCGs were accumulated and encoded by graph autoencoders to extract feature representations of lncRNAs and PCGs, which were subsequently used for decoding to obtain candidate lncRNA-PCG pairs. Comprehensive evaluation demonstrated that GAE-LGA can successfully capture lncRNA-PCG associations with strong robustness and outperformed other machine learning-based identification methods. Furthermore, multi-omics features were shown to improve the performance of lncRNA-PCG association identification. In conclusion, GAE-LGA can act as an efficient application for lncRNA-PCG association prediction with the following advantages: It fuses multi-omics information into the similarity network, making the feature representation more accurate; it can predict lncRNA-PCG associations for new lncRNAs and identify potential lncRNA-PCG associations with high accuracy.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration

As the traditional treatment for glioma, the most common central nervous system malignancy with poor prognosis, the efficacy of high-intensity surgery combined with radiotherapy and chemotherapy is not satisfactory. The development of individualized scientific treatment strategy urgently requires the guidance of signature with clinical predictive value. In this study, five prognosis-related differentially expressed immune-related genes (PR-DE-IRGs) (CCNA2, HMGB2, CASP3, APOBEC3C, and BMP2) highly associated with glioma were identified for a prognostic model through weighted gene co-expression network analysis, univariate Cox and lasso regression. Kaplan-Meier survival curves, receiver operating characteristic curves and other methods have shown that the model has good performance in predicting the glioma patients’ prognosis. Further combined nomogram provided better predictive performance. The signature’s guiding value in clinical treatment has also been verified by multiple analysis results. We also constructed a comprehensive competing endogenous RNA (ceRNA) regulatory network based on the protective factor BMP2 to further explore its potential role in glioma progression. Numerous immune-related biological functions and pathways were enriched in a high-risk population. Further multi-omics integrative analysis revealed a strong correlation between tumor immunosuppressive environment/IDH1 mutation and signature, suggesting that their cooperation plays an important role in glioma progression.

LPInsider: a webserver for lncRNA–protein interaction extraction from the literature

Background

Long non-coding RNA (LncRNA) plays important roles in physiological and pathological processes. Identifying LncRNA–protein interactions (LPIs) is essential to understand the molecular mechanism and infer the functions of lncRNAs. With the overwhelming size of the biomedical literature, extracting LPIs directly from the biomedical literature is essential, promising and challenging. However, there is no webserver of LPIs relationship extraction from literature.

Results

LPInsider is developed as the first webserver for extracting LPIs from biomedical literature texts based on multiple text features (semantic word vectors, syntactic structure vectors, distance vectors, and part of speech vectors) and logistic regression. LPInsider allows researchers to extract LPIs by uploading PMID, PMCID, PMID List, or biomedical text. A manually filtered and highly reliable LPI corpus is integrated in LPInsider. The performance of LPInsider is optimal by comprehensive experiment on different combinations of different feature and machine learning models.

Conclusions

LPInsider is an efficient analytical tool for LPIs that helps researchers to enhance their comprehension of lncRNAs from text mining, and also saving their time. In addition, LPInsider is freely accessible from http://www.csbg-jlu.info/LPInsider/ with no login requirement. The source code and LPIs corpus can be downloaded from https://github.com/qiufengdiewu/LPInsider.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04665-3.

Long RNA-Mediated Chromatin Regulation in Fission Yeast and Mammals

As part of a complex network of genome control, long regulatory RNAs exert significant influences on chromatin dynamics. Understanding how this occurs could illuminate new avenues for disease treatment and lead to new hypotheses that would advance gene regulatory research. Recent studies using the model fission yeast Schizosaccharomyces pombe (S. pombe) and powerful parallel sequencing technologies have provided many insights in this area. This review will give an overview of key findings in S. pombe that relate long RNAs to multiple levels of chromatin regulation: histone modifications, gene neighborhood regulation in cis and higher-order chromosomal ordering. Moreover, we discuss parallels recently found in mammals to help bridge the knowledge gap between the study systems.

Long non-coding RNAs and their potential impact on diagnosis, prognosis, and therapy in prostate cancer: racial, ethnic, and geographical considerations

INTRODUCTION

Advances in high-throughput sequencing have greatly advanced our understanding of long non-coding RNAs (lncRNAs) in a relatively short period of time. This has expanded our knowledge of cancer, particularly how lncRNAs drive many important cancer phenotypes via their regulation of gene expression.

AREAS COVERED

Men of African descent are disproportionately affected by PC in terms of incidence, morbidity, and mortality. LncRNAs could serve as biomarkers to differentiate low-risk from high-risk diseases. Additionally, they may represent therapeutic targets for advanced and castrate-resistant cancer. We review current research surrounding lncRNAs and their association with PC. We discuss how lncRNAs can provide new insights and diagnostic biomarkers for African American men. Finally, we review advances in computational approaches that predict the regulatory effects of lncRNAs in cancer.

EXPERT OPINION

PC diagnostic biomarkers that offer high specificity and sensitivity are urgently needed. PC specific lncRNAs are compelling as diagnostic biomarkers owing to their high tissue and tumor specificity and presence in bodily fluids. Recent studies indicate that PCA3 clinical utility might be restricted to men of European descent. Further work is required to develop lncRNA biomarkers tailored for men of African descent.

Long noncoding RNA SNHG25 promotes the malignancy of endometrial cancer by sponging microRNA-497-5p and increasing FASN expression

BACKGROUND

Small nucleolar RNA host gene 25 (SNHG25), a long noncoding RNA, has been well-studied in epithelial ovarian cancer. However, the specific functions of SNHG25 in endometrial cancer (EC) have not been studied yet. In this study, we aimed to elucidate the clinical significance of SNHG25 in EC and determine the regulatory activity of SNHG25 on the tumor-associated EC phenotype. We also thoroughly explored the molecular mechanisms underlying SNHG25 function in EC.

METHODS

Gene expression was measured using quantitative real-time polymerase chain reaction. The detailed functions of SNHG25 in EC were examined by performing loss-of-function experiments. Moreover, the regulatory mechanisms involving SNHG25, microRNA-497-5p, and fatty acid synthase (FASN) were unveiled using the luciferase reporter assay and RNA immunoprecipitation.

RESULTS

We observed a high level of SNHG25 in EC using the TCGA dataset and our study cohort. Patients with a high SNHG25 level had shorter overall survival than those with a low SNHG25 level. SNHG25 deficiency resulted in tumor-repressing activities in EC cells by decreasing cell proliferation, migration, and invasion and promoting cell apoptosis. Furthermore, the function of SNHG25 depletion in impairing tumor growth in vivo was confirmed. SNHG25 sequestered miR-497-5p as a competing endogenous RNA in EC and consequently positively regulated FASN expression. Thus, the decrease in miR-497-5p or increase in FASN could neutralize the modulatory actions of SNHG25 knockdown in EC cells.

CONCLUSIONS

The depletion of SNHG25 impedes the oncogenicity of EC by targeting the miR-497-5p/FASN axis. The newly elucidated SNHG25/miR-497-5p/FASN pathway may be a promising target for the molecular-targeted management of EC.

lncRNA LINC00355 Acts as a Novel Biomarker and Promotes Glioma Biological Activities via the Regulation of miR-1225/FNDC3B

Background

Accumulating evidence has implicated long noncoding RNAs (lncRNAs) in glioma progression. Here, we aimed to explore the potential roles of a novel lncRNA, LINC00355, in glioma and to clarify the underlying mechanisms.

Methods

RT-PCR was used to examine the relative expressions of LINC00355 in glioma cell lines and specimen samples. The clinicopathological and prognostic significances of LINC00355 in glioma patients were statistically analyzed. To determine cell activities, CCK-8, clonogenic assays, flow cytometry, migration, and invasion assays were performed. Moreover, the potential mechanisms of LINC00355 were investigated by bioinformatics assays and luciferase reporter assays.

Results

LINC00355 expression was increased in glioma cell lines and specimens, and higher LINC00355 expression predicted advanced clinical progress and reduced overall survival and disease-free survival in glioma patients. Functionally, LINC00355 depletion promoted cell proliferation, invasion, and migration in glioma cells and induced apoptosis of glioma cells, whereas LINC00355 upregulation resulted in the opposite effects in vitro. Mechanistic assays revealed that LINC00355 as a sponge for miR-1225 repressed fibronectin type III domain-containing 3B (FNDC3B) expressions.

Conclusion

Our findings revealed the tumor-promotive roles of LINC00355 in the progression of glioma, indicating that LINC00355 exhibited ceRNA functions via modulating miR-1225/FNDC3B axis.

PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions

BACKGROUND

Plant long non-coding RNAs (lncRNAs) play vital roles in many biological processes mainly through interactions with RNA-binding protein (RBP). To understand the function of lncRNAs, a fundamental method is to identify which types of proteins interact with the lncRNAs. However, the models or rules of interactions are a major challenge when calculating and estimating the types of RBP.

RESULTS

In this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence and structural information, named PRPI-SC. PRPI-SC predicts interactions between lncRNAs and proteins based on the k-mer features of RNAs and proteins. Experiments proved good results on Arabidopsis thaliana and Zea mays datasets (ATH948 and ZEA22133). The accuracy rates of ATH948 and ZEA22133 datasets were 88.9% and 82.6%, respectively. PRPI-SC also performed well on some public RNA protein interaction datasets.

CONCLUSIONS

PRPI-SC accurately predicts the interaction between plant lncRNA and protein, which plays a guiding role in studying the function and expression of plant lncRNA. At the same time, PRPI-SC has a strong generalization ability and good prediction effect for non-plant data.

Novel lncRNA-HZ04 promotes BPDE-induced human trophoblast cell apoptosis and miscarriage by upregulating IP3 R1 /CaMKII/SGCB pathway by competitively binding with miR-hz04

Normal pregnancy is essential for human reproduction. However, BaP (benzo(a)pyrene) and its metabolite BPDE (benzo(a)pyrene-7,8-dihydrodiol-9,10-epoxide) could cause dysfunctions of human trophoblast cells and might further induce miscarriage. Yet, the underlying mechanisms remain largely unknown. Herein, we identified a novel upregulated lnc-HZ04 and a novel downregulated miR-hz04 in villous tissues of unexplained recurrent miscarriage (RM) relative to those in healthy control tissues and also in BPDE-treated human trophoblast cells. Lnc-HZ04 directly and specifically bound with miR-hz04, diminished the reduction effects of miR-hz04 on IP₃ R₁ mRNA expression level and on IP₃ R₁ mRNA stability, and then activated the Ca²⁺ -mediated IP₃ R₁ /p-CaMKII/SGCB pathway, which further promoted trophoblast cell apoptosis. The miR-hz04 target site on lnc-HZ04 played crucial roles in these regulations. In normal trophoblast, relatively less lnc-HZ04 and more miR-hz04 suppressed this apoptosis pathway and gave normal pregnancy. After exposure to BPDE or in RM tissues, p53 was upregulated, which might promote p53-mediated lnc-HZ04 transcription. Relatively more lnc-HZ04 and less miR-hz04 activated this apoptosis pathway and might further induce miscarriage. BaP could also induce mice miscarriage by upregulating its corresponding murine apoptosis pathway. Therefore, BPDE-induced apoptosis of human trophoblast cells was associated with the occurrence of miscarriage. This work discovered the regulation roles of lnc-HZ04 and miR-hz04 and provided scientific and clinical understanding of the occurrence of unexplained miscarriage.

FGGA-lnc: automatic gene ontology annotation of lncRNA sequences based on secondary structures

The study of long non-coding RNAs (lncRNAs), greater than 200 nucleotides, is central to understanding the development and progression of many complex diseases. Unlike proteins, the functionality of lncRNAs is only subtly encoded in their primary sequence. Current in-silico lncRNA annotation methods mostly rely on annotations inferred from interaction networks. But extensive experimental studies are required to build these networks. In this work, we present a graph-based machine learning method called FGGA-lnc for the automatic gene ontology (GO) annotation of lncRNAs across the three GO subdomains. We build upon FGGA (factor graph GO annotation), a computational method originally developed to annotate protein sequences from non-model organisms. In the FGGA-lnc version, a coding-based approach is introduced to fuse primary sequence and secondary structure information of lncRNA molecules. As a result, lncRNA sequences become sequences of a higher-order alphabet allowing supervised learning methods to assess individual GO-term annotations. Raw GO annotations obtained in this way are unaware of the GO structure and therefore likely to be inconsistent with it. The message-passing algorithm embodied by factor graph models overcomes this problem. Evaluations of the FGGA-lnc method on lncRNA data, from model and non-model organisms, showed promising results suggesting it as a candidate to satisfy the huge demand for functional annotations arising from high-throughput sequencing technologies.

RNA-seq of human T cells after hematopoietic stem cell transplantation identifies Linc00402 as a regulator of T cell alloimmunity

Mechanisms governing allogeneic T cell responses after solid organ and allogeneic hematopoietic stem cell transplantation (HSCT) are incompletely understood. To identify lncRNAs that regulate human donor T cells after clinical HSCT, we performed RNA sequencing on T cells from healthy individuals and donor T cells from three different groups of HSCT recipients that differed in their degree of major histocompatibility complex (MHC) mismatch. We found that lncRNA differential expression was greatest in T cells after MHC-mismatched HSCT relative to T cells after either MHC-matched or autologous HSCT. Differential expression was validated in an independent patient cohort and in mixed lymphocyte reactions using ex vivo healthy human T cells. We identified Linc00402, an uncharacterized lncRNA, among the lncRNAs differentially expressed between the mismatched unrelated and matched unrelated donor T cells. We found that Linc00402 was conserved and exhibited an 88-fold increase in human T cells relative to all other samples in the FANTOM5 database. Linc00402 was also increased in donor T cells from patients who underwent allogeneic cardiac transplantation and in murine T cells. Linc00402 was reduced in patients who subsequently developed acute graft-versus-host disease. Linc00402 enhanced the activity of ERK1 and ERK2, increased FOS nuclear accumulation, and augmented expression of interleukin-2 and Egr-1 after T cell receptor engagement. Functionally, Linc00402 augmented the T cell proliferative response to an allogeneic stimulus but not to a nominal ovalbumin peptide antigen or polyclonal anti-CD3/CD28 stimulus. Thus, our studies identified Linc00402 as a regulator of allogeneic T cell function.

Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer

It is becoming increasingly evident that the non-coding genome and transcriptome exert great influence over their coding counterparts through complex molecular interactions. Among non-coding RNAs (ncRNA), long non-coding RNAs (lncRNAs) in particular present increased potential to participate in dysregulation of post-transcriptional processes through both RNA and protein interactions. Since such processes can play key roles in contributing to cancer progression, it is desirable to continue expanding the search for lncRNAs impacting cancer through post-transcriptional mechanisms. The sheer diversity of mechanisms requires diverse resources and methods that have been developed and refined over the past decade. We provide an overview of computational resources as well as proven low-to-high throughput techniques to enable identification and characterisation of lncRNAs in their complex interactive contexts. As more cancer research strategies evolve to explore the non-coding genome and transcriptome, we anticipate this will provide a valuable primer and perspective of how these technologies have matured and will continue to evolve to assist researchers in elucidating post-transcriptional roles of lncRNAs in cancer.

Downregulation of LncRNA DARS-AS1 Inhibits the Tumorigenesis of Cervical Cancer via Inhibition of IGF2BP3

Background

Evidence has been shown that long noncoding RNAs (lncRNAs) play an important role in the development of cervical cancer. Recently, lncRNA DARS-AS1 was reported to be dysregulated in several cancer types; however, the role of DARS-AS1 in cervical cancer remains unclear.

Methods

Flow cytometry and transwell invasion assays were performed to determine the apoptosis and invasion in cervical cancer cells. In addition, RNA pull-down and fluorescence in situ hybridization (FISH) assays were conducted to assess the interaction between DARS-AS1 and IGF2BP3 in cervical cancer cells.

Results

Downregulation of DARS-AS1 significantly induced apoptosis and cell cycle arrest in cervical cancer cells. Meanwhile, the invasion ability of cervical cancer cells was inhibited by DARS-AS1 knockdown as well. RNA pull-down and FISH results showed that DARS-AS1 interacted with IGF2BP3. Mechanistically, DARS-AS1 positively regulated IGF2BP3 expression via stabilization of IGF2BP3 mRNA. Rescue assays confirmed that DARS-AS1 regulated the progression of cervical cancer through interacting with IGF2BP3 in vitro. In addition, in vivo experiments revealed that downregulation of DARS-AS1 inhibited tumor growth in SiHa xenograft model.

Conclusion

In this study, we found that downregulation of DARS-AS1 could inhibit the growth of cervical cancer cells via inhibition of IGF2BP3, suggesting DARS-AS1 might serve as a potential target for the treatment of cervical cancer.

Alternative splicing and cancer: a systematic review

The abnormal regulation of alternative splicing is usually accompanied by the occurrence and development of tumors, which would produce multiple different isoforms and diversify protein expression. The aim of the present study was to conduct a systematic review in order to describe the regulatory mechanisms of alternative splicing, as well as its functions in tumor cells, from proliferation and apoptosis to invasion and metastasis, and from angiogenesis to metabolism. The abnormal splicing events contributed to tumor progression as oncogenic drivers and/or bystander factors. The alterations in splicing factors detected in tumors and other mis-splicing events (i.e., long non-coding and circular RNAs) in tumorigenesis were also included. The findings of recent therapeutic approaches targeting splicing catalysis and splicing regulatory proteins to modulate pathogenically spliced events (including tumor-specific neo-antigens for cancer immunotherapy) were introduced. The emerging RNA-based strategies for the treatment of cancer with abnormally alternative splicing isoforms were also discussed. However, further studies are still required to address the association between alternative splicing and cancer in more detail.

DeepLGP: a novel deep learning method for prioritizing lncRNA target genes

MOTIVATION

Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurrence and development of complex diseases. Recent wet-lab experiments have shown that lncRNAs function by regulating the expression of protein-coding genes (PCGs), which could also be the mechanism responsible for causing diseases. Currently, lncRNA-related biological data are increasing rapidly. Whereas, no computational methods have been designed for predicting the novel target genes of lncRNA.

RESULTS

In this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing target PCGs of lncRNA. First, gene and lncRNA features were selected, these included their location in the genome, expression in 13 tissues and miRNA-mediated lncRNA-gene pairs. Next, GCN was applied to convolve a gene interaction network for encoding the features of genes and lncRNAs. Then, these features were used by the convolutional neural network for prioritizing target genes of lncRNAs. In 10-cross validations on two independent datasets, DeepLGP obtained high area under curves (0.90-0.98) and area under precision-recall curves (0.91-0.98). We found that lncRNA pairs with high similarity had more overlapped target genes. Further experiments showed that genes targeted by the same lncRNA sets had a strong likelihood of causing the same diseases, which could help in identifying disease-causing PCGs.

AVAILABILITY AND IMPLEMENTATION

https://github.com/zty2009/LncRNA-target-gene.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Novel lnc-HZ03 and miR-hz03 promote BPDE-induced human trophoblastic cell apoptosis and induce miscarriage by upregulating p53/SAT1 pathway

Normal pregnancy is essential for human reproduction. However, environmental BaP (benzo(a)pyrene) and its metabolite BPDE (benzo(a)pyrene-7,8-dihydrodiol-9,10-epoxide) induce dysfunctions of human trophoblastic cells, which could further result in miscarriage. Yet, the molecular mechanisms remain poorly understood. In this work, a novel lnc-HZ03 and a novel miR-hz03 were identified. Both lnc-HZ03 and miR-hz03 were highly expressed in human recurrent miscarriage villous tissues and in BPDE-exposed trophoblastic cells. Lnc-HZ03 and miR-hz03 upregulated each other, forming a positive feedback loop. MiR-hz03 could also upregulate p53 level by enhancing its mRNA stability. Both lnc-HZ03 and p53 mRNA contained the target site for miR-hz03 and could directly interact with miR-hz03. It was this target site instead of its mutant on lnc-HZ03 that regulated p53 expression. Subsequently, the upregulated p53 facilitated SAT1 transcription and enhanced SAT1-catalyzed spermine metabolism, which further resulted in trophoblastic cell apoptosis and induced miscarriage. All together, the p53/SAT1 pathway upregulated by lnc-HZ03 and miR-hz03 could promote BPDE-induced human trophoblastic cell apoptosis and the occurrence of miscarriage, shedding novel light on the causes of miscarriage. Graphical abstract Lnc-HZ03 and miR-hz03 regulate the occurrence of recurrent miscarriage (RM). In human trophoblastic cells, lnc-HZ03 upregulates miR-hz03 level. MiR-hz03 increases the RNA stability of lnc-HZ03 and p53 mRNA. P53 promotes SAT1 transcription and reduces its cellular spermine content, resulting in cell apoptosis. Under normal conditions, lnc-HZ03/miR-hz03 and p53/SAT1 pathways are downregulated, maintaining normal pregnancy. After exposure to BPDE, lnc-HZ03/miR-hz03 and p53/SAT1 pathways are upregulated and finally induce miscarriage.

DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms

BACKGROUND

Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to identify these interactions are expensive and time-consuming, many computational methods have been proposed. Although these computational methods have achieved promising prediction performance, they neglect the fact that a gene may encode multiple protein isoforms and different isoforms of the same gene may interact differently with the same lncRNA.

RESULTS

In this study, we propose a novel method, DeepLPI, for predicting the interactions between lncRNAs and protein isoforms. Our method uses sequence and structure data to extract intrinsic features and expression data to extract topological features. To combine these different data, we adopt a hybrid framework by integrating a multimodal deep learning neural network and a conditional random field. To overcome the lack of known interactions between lncRNAs and protein isoforms, we apply a multiple instance learning (MIL) approach. In our experiment concerning the human lncRNA-protein interactions in the NPInter v3.0 database, DeepLPI improved the prediction performance by 4.7% in term of AUC and 5.9% in term of AUPRC over the state-of-the-art methods. Our further correlation analyses between interactive lncRNAs and protein isoforms also illustrated that their co-expression information helped predict the interactions. Finally, we give some examples where DeepLPI was able to outperform the other methods in predicting mouse lncRNA-protein interactions and novel human lncRNA-protein interactions.

CONCLUSION

Our results demonstrated that the use of isoforms and MIL contributed significantly to the improvement of performance in predicting lncRNA and protein interactions. We believe that such an approach would find more applications in predicting other functional roles of RNAs and proteins.

Long non-coding RNA (lncRNA) and epithelial-mesenchymal transition (EMT) in colorectal cancer: a systematic review

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related death. Epithelial-mesenchymal transition (EMT) is a major process in tumor metastasis development. This systematic review aims to describe the role of long non-coding RNA (lncRNA) in EMT in CRC.

METHODS

The electronic databases, PubMed, Cochrane, and EMBASE, were searched from January1990 to June 2019 to identify studies examining lncRNA and their role in mediating EMT in CRC. Studies examining clinical specimens and/or in vitro experiments were included.

RESULTS

In 61 identified studies, 54 lncRNAs were increased in CRC compared to normal colorectal epithelium. Increased lncRNA expression was frequently associated with worse survival. Many lncRNAs mediate their effect through competitive endogenous RNA or transcription factor regulation. The ZEB1, 2/E-cadherin, Wnt/β-catenin signaling, and chromatin remodeling pathways are discussed in particular.

CONCLUSIONS

lncRNAs are major regulators of EMT and predictor adverse outcome in CRC patients. Future research must focus on delineating lncRNA function prior to potential clinical use.

Functional impacts of non-coding RNA processing on enhancer activity and target gene expression

Tight regulation of gene expression is orchestrated by enhancers. Through recent research advancements, it is becoming clear that enhancers are not solely distal regulatory elements harboring transcription factor binding sites and decorated with specific histone marks, but they rather display signatures of active transcription, showing distinct degrees of transcription unit organization. Thereby, a substantial fraction of enhancers give rise to different species of non-coding RNA transcripts with an unprecedented range of potential functions. In this review, we bring together data from recent studies indicating that non-coding RNA transcription from active enhancers, as well as enhancer-produced long non-coding RNA transcripts, may modulate or define the functional regulatory potential of the cognate enhancer. In addition, we summarize supporting evidence that RNA processing of the enhancer-associated long non-coding RNA transcripts may constitute an additional layer of regulation of enhancer activity, which contributes to the control and final outcome of enhancer-targeted gene expression.

Challenges and Strategies in Ascribing Functions to Long Noncoding RNAs

Long noncoding RNAs (lncRNAs) are involved in many physiological and pathological processes, such as development, aging, immunity, and cancer. Mechanistically, lncRNAs exert their functions through interaction with proteins, genomic DNA, and other RNA, leading to transcriptional and post-transcriptional regulation of gene expression, either in cis or in trans; it is often difficult to distinguish between these two regulatory mechanisms. A variety of approaches, including RNA interference, antisense oligonucleotides, CRISPR-based methods, and genetically engineered mouse models, have yielded abundant information about lncRNA functions and underlying mechanisms, albeit with many discrepancies. In this review, we elaborate on the challenges in ascribing functions to lncRNAs based on the features of lncRNAs, including the genomic location, copy number, domain structure, subcellular localization, stability, evolution, and expression pattern. We also describe a framework for the investigation of lncRNA functions and mechanisms of action. Rigorous characterization of cancer-implicated lncRNAs is critical for the identification of bona fide anticancer targets.

A novel positive feedback loop of linc02042 and c-Myc mediated by YBX1 promotes tumorigenesis and metastasis in esophageal squamous cell carcinoma

BACKGROUND

Long non-coding RNA (lncRNA) is a class of endogenous RNA with a length of more than 200 nucleotides, which is emerging as a pivotal player in cancer development and progression. However, the functional roles of many members in this class remain largely uncharacterized. In the present study, we explored the biological relevance of linc02042 in esophageal squamous cell carcinoma (ESCC).

METHODS

qRT-PCR was used to detect the levels of linc02042 and c-Myc. Western blot was used to assess protein expression level. CCK-8 and Transwell assays were employed to test ESCC cell proliferation and invasion, respectively. The mice study including xenograft tumor and lung metastasis models was used to determine the role of linc02042 in vivo. RNA pull-down, ChIP and luciferase reporter assays were employed to test the relationship between linc02042, YBX1 and c-Myc.

RESULTS

Linc02042 was found to be markedly upregulated in ESCC cell lines, tissues and plasma, and was closely correlated with malignant clinical features. Knockdown of linc02042 significantly inhibited ESCC cell viability and invasion in vitro as well as tumor growth and lung metastasis in vivo, whereas overexpression of linc02042 resulted in the opposite results. Mechanistically, linc02042 acted as a scaffold for YBX-1 binding to the 3'-UTR of c-Myc mRNA, leading to enhanced c-Myc mRNA stability, thereby facilitating ESCC growth and metastasis. Moreover, in turn, c-Myc was able to transcriptionally elevate linc02042 by directly binding to the E-box motif proximal to the transcription start site (TSS) of linc02042 promoter. Clinically, linc02042 was identified as an effective diagnostic and prognostic biomarker for ESCC patients, and its expression was strongly positively correlated with c-Myc expression in ESCC tissues.

CONCLUSION

Our data suggest that linc02042 plays an important tumor-promoting role in ESCC, which lays a foundation for considering it as a potential target for ESCC patients.

LINC00662 promotes hepatocellular carcinoma progression via altering genomic methylation profiles

The identification of viability-associated long noncoding RNAs (lncRNAs) is a means of uncovering therapeutic approaches for hepatocellular carcinoma (HCC). In addition, aberrant genome-wide hypomethylation has been implicated in HCC initiation and progression. However, the relationship between lncRNA dysregulation and genome-wide hypomethylation in hepatocarcinogenesis has not been fully elucidated. A novel lncRNA named LINC00662 was previously demonstrated to play a role in gastrointestinal cancer. In this study, we demonstrated that this lncRNA was correlated with survival and exhibited oncogenic properties, both in vitro and in vivo. Moreover, we determined that LINC00662 could lead to genome-wide hypomethylation and alter the genomic methylation profile by synchronously reducing the S-adenosylmethionine (SAM) level and enhancing the S-adenosylhomocysteine (SAH) level. Mechanistically, LINC00662 was determined to regulate the key enzymes influencing SAM and SAH levels, namely, methionine adenosyltransferase 1A (MAT1A) and S-adenosylhomocysteine hydrolase (AHCY), by RNA-RNA and RNA-protein interactions. In addition, we demonstrated that some SAM-dependent HCC-promoting genes could be regulated by LINC00662 by altering the methylation status of their promoters via the LINC00662-coupled axes of MAT1A/SAM and AHCY/SAH. Taken together, the results of this this study indicate that LINC00662 could be a potential biomarker for HCC therapy. More importantly, we proposed a new role of lncRNA in regulating genomic methylation to promote oncogene activation.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

Genome-Wide Identification of Long Non-Coding RNAs and Their Regulatory Networks Involved in Apis mellifera ligustica Response to Nosema ceranae Infection

Long non-coding RNAs (lncRNAs) are a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins, and lncRNAs have been proven to play pivotal roles in a wide range of biological processes in animals and plants. However, knowledge of expression patterns and potential roles of honeybee lncRNA response to Nosema ceranae infection is completely unknown. Here, we performed whole transcriptome strand-specific RNA sequencing of normal midguts of Apis mellifera ligustica workers (Am7CK, Am10CK) and N. ceranae-inoculated midguts (Am7T, Am10T), followed by comprehensive analyses using bioinformatic and molecular approaches. A total of 6353 A. m. ligustica lncRNAs were identified, including 4749 conserved lncRNAs and 1604 novel lncRNAs. These lncRNAs had minimal sequence similarities with other known lncRNAs in other species; however, their structural features were similar to counterparts in mammals and plants, including shorter exon and intron length, lower exon number, and lower expression level, compared with protein-coding transcripts. Further, 111 and 146 N. ceranae-responsive lncRNAs were identified from midguts at 7-days post-inoculation (dpi) and 10 dpi compared with control midguts. Twelve differentially expressed lncRNAs (DElncRNAs) were shared by Am7CK vs. Am7T and Am10CK vs. Am10T comparison groups, while the numbers of unique DElncRNAs were 99 and 134, respectively. Functional annotation and pathway analysis showed that the DElncRNAs may regulate the expression of neighboring genes by acting in cis and trans fashion. Moreover, we discovered 27 lncRNAs harboring eight known miRNA precursors and 513 lncRNAs harboring 2257 novel miRNA precursors. Additionally, hundreds of DElncRNAs and their target miRNAs were found to form complex competitive endogenous RNA (ceRNA) networks, suggesting that these DElncRNAs may act as miRNA sponges. Furthermore, DElncRNA-miRNA-mRNA networks were constructed and investigated, the results demonstrated that a portion of the DElncRNAs were likely to participate in regulating the host material and energy metabolism as well as cellular and humoral immune host responses to N. ceranae invasion. Our findings revealed here offer not only a rich genetic resource for further investigation of the functional roles of lncRNAs involved in the A. m. ligustica response to N. ceranae infection, but also a novel insight into understanding the host-pathogen interaction during honeybee microsporidiosis.

Crosstalk Between Long Non-coding RNAs, Micro-RNAs and mRNAs: Deciphering Molecular Mechanisms of Master Regulators in Cancer

Cancer is a complex disease, and its study requires deep understanding of several biological processes and their regulation. It is an accepted fact that non-coding RNAs are vital components of the regulation and cross-talk among cancer-related signaling pathways that favor tumor aggressiveness and metastasis, such as neovascularization, angiogenesis, and vasculogenic mimicry. Both long non-coding RNAs (lncRNAs) and micro-RNAs (miRNAs) have been described as master regulators of cancer on their own; yet there is accumulating evidence that, besides regulating mRNA expression through independent mechanisms, these classes of non-coding RNAs interact with each other directly, fine-tuning the effects of their regulation. While still relatively scant, research on the lncRNA-miRNA-mRNA axis regulation is growing at a fast rate, it is only in the last 5 years, that lncRNA-miRNA interactions have been identified in tumor-related vascular processes. In this review, we summarize the current progress of research on the cross-talk between lncRNAs and miRNAs in the regulation of neovascularization, angiogenesis and vasculogenic mimicry.

BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information

The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.

PCAT-1: A pivotal oncogenic long non-coding RNA in human cancers

Prostate cancer-associated transcript 1 (PCAT-1) is a newly identified long non-coding RNA comprising two exons, located in the Chr8q24 gene desert approximately 725 kb upstream of the MYC oncogene. PCAT-1 is dysregulated and acts as an oncogene in different types of cancers and has been implicated in several processes correlated with carcinogenesis, such as cell proliferation, invasion, metastasis, apoptosis, cell cycle, chemoresistance, and homologous recombination. The mechanisms underlying the effects of PCAT-1 are complex and involve multiple factors and signaling pathways. In this paper, we systematically review the multiple pathological functions of PCAT-1 in diverse malignancies to elucidate its potential molecular mechanisms and to provide new directions for future research.

Analysis of the androgen receptor-regulated lncRNA landscape identifies a role for ARLNC1 in prostate cancer progression

The androgen receptor (AR) plays a critical role in the development of the normal prostate as well as prostate cancer. Using an integrative transcriptomic analysis of prostate cancer cell lines and tissues, we identified ARLNC1 (AR-regulated long non-coding RNA 1) as an important long non-coding RNA that is strongly associated with AR signaling in prostate cancer progression. Not only was ARLNC1 induced by AR protein, ARLNC1 stabilized the AR transcript via RNA-RNA interaction. ARLNC1 knockdown suppressed AR expression, global AR signaling, and prostate cancer growth in vitro and in vivo. Taken together, these data support a role for ARLNC1 in maintaining a positive feedback loop that potentiates AR signaling during prostate cancer progression, and identifies ARLNC1 as a novel therapeutic target.