Autophagy regulated by lncRNA HOTAIR contributes to the cisplatin-induced resistance in endometrial cancer cells

Objectives

To identify whether lncRNAs (long non-coding RNA) participate in the regulation of cisplatin-resistant induced autophagy in endometrial cancer cells.

Results

Autophagy activity was significantly boosted in cisplatin-resistant Ishikawa cells, a human endometrial cancer cell line, compared with that in parental Ishikawa cells. After analyzing the overall long noncoding RNA (lncRNA) profiling, a meaningful lncRNA, HOTAIR, was identified. It was down-regulated simultaneously in cisplatin-resistant Ishikawa cells and parental Ishikawa cells treated with cisplatin. RNA interference of HOTAIR reduced the proliferation of cisplatin-resistant Ishikawa cells and enhanced the autophagy activity of cisplatin-resistant Ishikawa cells with or without cisplatin treatment, in addition, beclin-1, multidrug resistance (MDR), and P-glycoprotein (P-gp) were mediated by lncRNA HOTAIR.

Conclusions

It is clear that lncRNAs, specifically HOTAIR, can regulate the cisplatin-resistance ability of human endometrial cancer cells through the regulation of autophagy by influencing Beclin-1, MDR, and P-gp expression.

     

Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph

Background

Current knowledge and data on miRNA-lncRNA interactions is still limited and little effort has been made to predict target lncRNAs of miRNAs. Accumulating evidences suggest that the interaction patterns between lncRNAs and miRNAs are closely related to relative expression level, forming a titration mechanism. It could provide an effective approach for characteristic feature extraction. In addition, using the coding non-coding co-expression network and sequence data could also help to measure the similarities among miRNAs and lncRNAs. By mathematically analyzing these types of similarities, we come up with two findings that (i) lncRNAs/miRNAs tend to collaboratively interact with miRNAs/lncRNAs of similar expression profiles, and vice versa, and (ii) those miRNAs interacting with a cluster of common target genes tend to jointly target at the common lncRNAs.

Methods

In this work, we developed a novel group preference Bayesian collaborative filtering model called GBCF for picking up a top-k probability ranking list for an individual miRNA or lncRNA based on the known miRNA-lncRNA interaction network.

Results

To evaluate the effectiveness of GBCF, leave-one-out and k-fold cross validations as well as a series of comparison experiments were carried out. GBCF achieved the values of area under ROC curve of 0.9193, 0.8354+/??0.0079, 0.8615+/??0.0078, and 0.8928+/??0.0082 based on leave-one-out, 2-fold, 5-fold, and 10-fold cross validations respectively, demonstrating its reliability and robustness.

Conclusions

GBCF could be used to select potential lncRNA targets of specific miRNAs and offer great insights for further researches on ceRNA regulation network.

     

Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk

Background

With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell differentiation regulation and other aspects. However, the majority of the lncRNAs have not been functionally characterized. Explore the function of lncRNAs and the regulatory network has become a hot research topic currently.

Methods

In the work, a network-based model named BiRWLGO is developed. The ultimate goal is to predict the probable functions for lncRNAs at large scale. The new model starts with building a global network composed of three networks: lncRNA similarity network, lncRNA-protein association network and protein-protein interaction (PPI) network. After that, it utilizes bi-random walk algorithm to explore the similarities between lncRNAs and proteins. Finally, we can annotate an lncRNA with the Gene Ontology (GO) terms according to its neighboring proteins.

Results

We compare the performance of BiRWLGO with the state-of-the-art models on a manually annotated lncRNA benchmark with known GO terms. The experimental results assert that BiRWLGO outperforms other methods in terms of both maximum F-measure (F_max) and coverage.

Conclusions

BiRWLGO is a relatively efficient method to predict the functions of lncRNA. When protein interaction data is integrated, the predictive performance of BiRWLGO gains a great improvement.

     

Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations

Background

Evidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification.

Results

In this study, we develop a novel method for the prediction of lncRNA-disease associations using bi-random walks on a network merging the similarities of lncRNAs and diseases. Particularly, this method applies a Laplacian technique to normalize the lncRNA similarity matrix and the disease similarity matrix before the construction of the lncRNA similarity network and disease similarity network. The two networks are then connected via existing lncRNA-disease associations. After that, bi-random walks are applied on the heterogeneous network to predict the potential associations between the lncRNAs and the diseases. Experimental results demonstrate that the performance of our method is highly comparable to or better than the state-of-the-art methods for predicting lncRNA-disease associations. Our analyses on three cancer data sets (breast cancer, lung cancer, and liver cancer) also indicate the usefulness of our method in practical applications.

Conclusions

Our proposed method, including the construction of the lncRNA similarity network and disease similarity network and the bi-random walks algorithm on the heterogeneous network, could be used for prediction of potential associations between the lncRNAs and the diseases.

     

CRlncRNA: a manually curated database of cancer-related long non-coding RNAs with experimental proof of functions on clinicopathological and molecular features

Background

Recent studies demonstrated that long non-coding RNAs (lncRNAs) could be intricately implicated in cancer-related molecular networks, and related to cancer occurrence, development and prognosis. However, clinicopathological and molecular features for these cancer-related lncRNAs, which are very important in bridging lncRNA basic research with clinical research, fail to well settle to integration.

Results

After manually reviewing more than 2500 published literature, we collected the cancer-related lncRNAs with the experimental proof of functions. By integrating from literature and public databases, we constructed CRlncRNA, a database of cancer-related lncRNAs. The current version of CRlncRNA embodied 355 entries of cancer-related lncRNAs, covering 1072 cancer-lncRNA associations regarding to 76 types of cancer, and 1238 interactions with different RNAs and proteins. We further annotated clinicopathological features of these lncRNAs, such as the clinical stages and the cancer hallmarks. We also provided tools for data browsing, searching and download, as well as online BLAST, genome browser and gene network visualization service.

Conclusions

CRlncRNA is a manually curated database for retrieving clinicopathological and molecular features of cancer-related lncRNAs supported by highly reliable evidences. CRlncRNA aims to provide a bridge from lncRNA basic research to clinical research. The lncRNA dataset collected by CRlncRNA can be used as a golden standard dataset for the prospective experimental and in-silico studies of cancer-related lncRNAs. CRlncRNA is freely available for all users at http://crlnc.xtbg.ac.cn.

     

CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features

Background

Long noncoding RNAs (lncRNAs) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction accuracy and efficiency. In addition, the quick-update data of cancer, as well as the discovery of new mechanism, also underlay the possibility of improvement of cancer-related lncRNA prediction algorithm. In this study, we introduced CRlncRC, a novel Cancer-Related lncRNA Classifier by integrating manifold features with five machine-learning techniques.

Results

CRlncRC was built on the integration of genomic, expression, epigenetic and network, totally in four categories of features. Five learning techniques were exploited to develop the effective classification model including Random Forest (RF), Naïve bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbors (KNN). Using ten-fold cross-validation, we showed that RF is the best model for classifying cancer-related lncRNAs (AUC?=?0.82). The feature importance analysis indicated that epigenetic and network features play key roles in the classification. In addition, compared with other existing classifiers, CRlncRC exhibited a better performance both in sensitivity and specificity. We further applied CRlncRC to lncRNAs from the TANRIC (The Atlas of non-coding RNA in Cancer) dataset, and identified 121 cancer-related lncRNA candidates. These potential cancer-related lncRNAs showed a certain kind of cancer-related indications, and many of them could find convincing literature supports.

Conclusions

Our results indicate that CRlncRC is a powerful method for identifying cancer-related lncRNAs. Machine-learning-based integration of multiple features, especially epigenetic and network features, had a great contribution to the cancer-related lncRNA prediction. RF outperforms other learning techniques on measurement of model sensitivity and specificity. In addition, using CRlncRC method, we predicted a set of cancer-related lncRNAs, all of which displayed a strong relevance to cancer as a valuable conception for the further cancer-related lncRNA function studies.

     

Hepatitis B virus x protein induces epithelial-mesenchymal transition of hepatocellular carcinoma cells by regulating long non-coding RNA

Background

It has been widely accepted that hepatitis B virus X protein (HBx) plays an important role in hepatocellular carcinoma (HCC). This study aimed to explore the function of long non-coding RNAs (lncRNAs) in the epithelial-mesenchymal transition (EMT) induced by HBx.

Methods

The association between HBx and EMT markers was detected using immunohistochemistry in HCC tissues. The effect of HBx on HCC EMT was assessed through morphological analysis, transwell assay, metastatic in vivo study and detection of EMT markers. LncRNA microarray was used to screen the differently expressed lncRNAs. Small interfering RNA and Western blot were used to analyse the function and mechanism of the locked lncRNA.

Results

HBx was negatively correlated with the epithelial marker E-cadherin but positively correlated with the mesenchymal marker vimentin in HCC tissues. HBx induced the mesenchymal phenotype and improved the metastatic ability of HCC cells. Meanwhile, HBx down-regulated E-cadherin, whereas it up-regulated vimentin. In HCC cells, HBx altered the expression of 2002 lncRNAs by more than 2-fold. One of them was ZEB2-AS1. Inhibition of ZEB2-AS1 can compensate for the EMT phenotype and reverse the expression of EMT markers regulated by HBx. Additionally, HBx affected the Wnt signalling pathway.

Conclusions

HBx promotes HCC cell metastasis by inducing EMT, which is at least partly mediated by lncRNAs.

     

Identifying sequence features that drive ribosomal association for lncRNA

Background

With the increasing number of annotated long noncoding RNAs (lncRNAs) from the genome, researchers are continually updating their understanding of lncRNAs. Recently, thousands of lncRNAs have been reported to be associated with ribosomes in mammals. However, their biological functions or mechanisms are still unclear.

Results

In this study, we tried to investigate the sequence features involved in the ribosomal association of lncRNA. We have extracted ninety-nine sequence features corresponding to different biological mechanisms (i.e., RNA splicing, putative ORF, k-mer frequency, RNA modification, RNA secondary structure, and repeat element). An \(\mathcal {L}1\)-regularized logistic regression model was applied to screen these features. Finally, we obtained fifteen and nine important features for the ribosomal association of human and mouse lncRNAs, respectively.

Conclusion

To our knowledge, this is the first study to characterize ribosome-associated lncRNAs and ribosome-free lncRNAs from the perspective of sequence features. These sequence features that were identified in this study may shed light on the biological mechanism of the ribosomal association and provide important clues for functional analysis of lncRNAs.

     

Measures of co-expression for improved function prediction of long non-coding RNAs

Background

Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression.

Results

We have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes.

Conclusion

This work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data. The software used for this investigation is freely available.

     

Circular RNA expression profiles during the differentiation of mouse neural stem cells

Background

Circular RNAs (circRNAs) have recently been found to be expressed in human brain tissue, and many lines ofevidence indicate that circRNAs play regulatory roles in neurodevelopment. Proliferation and differentiation of neural stem cells (NSCs) are critical parts during development of central nervous system (CNS).To date, there have been no reports ofcircRNA expression profiles during the differentiation of mouse NSCs. We hypothesizethat circRNAs mayregulate gene expression in the proliferation anddifferentiation of NSCs.

Results

In this study, we obtained NSCs from the wild-type C57BL/6 J mouse fetal cerebral cortex. We extracted total RNA from NSCs in different differentiation stagesand then performed RNA-seq. By analyzing the RNA-Seq data, we found 37circRNAs and 4182 mRNAs differentially expressedduringthe NSC differentiation. Gene Ontology (GO) enrichment analysis of thecognate linear genes of these circRNAsrevealed that some enriched GO terms were related to neural activity. Furthermore, we performed a co-expression network analysis of these differentially expressed circRNAs and mRNAs. The result suggested a stronger GO enrichmentin neural features for both the cognate linear genes of circRNAs and differentially expressed mRNAs.

Conclusion

We performed the first circRNA investigation during the differentiation of mouse NSCs. Wefound that12 circRNAs might have regulatory roles duringthe NSC differentiation, indicating that circRNAs might be modulated during NSC differentiation.Our network analysis suggested the possible complex circRNA-mRNA mechanisms during differentiation, and future experimental workis need to validate these possible mechanisms.

     

Anti-aggregant tau mutant promotes neurogenesis

Background

The microtubule-associated protein Tau plays a role in neurodegeneration as well as neurogenesis. Previous work has shown that the expression of the pro-aggregant mutant Tau repeat domain causes strong aggregation and pronounced neuronal loss in the hippocampus whereas the anti-aggregant form has no deleterious effects. These two proteins differ mainly in their propensity to form ß structure and hence to aggregate.

Methods

To elucidate the basis of these contrasting effects, we analyzed organotypic hippocampal slice cultures (OHSCs) from transgenic mice expressing the repeat domain (RD) of Tau with the anti-aggregant mutation (Tau^RDΔKPP) and compared them with slices containing pro-aggregant Tau^RDΔK. Transgene expression in the hippocampus was monitored via a sensitive bioluminescence reporter gene assay (luciferase).

Results

The expression of the anti-aggregant Tau^RDΔKPP leads to a larger volume of the hippocampus at a young age due to enhanced neurogenesis, resulting in an increase in neuronal number. There were no signs of activation of microglia and astrocytes, indicating the absence of an inflammatory reaction. Investigation of signaling pathways showed that Wnt-5a was strongly decreased whereas Wnt3 was increased. A pronounced increase in hippocampal stem cell proliferation (seen by BrdU) was observed as early as P8, in the CA regions where neurogenesis is normally not observed. The increase in neurons persisted up to 16 months of age.

Conclusion

The data suggest that the expression of anti-aggregant Tau^RDΔKPP enhances hippocampal neurogenesis mediated by the canonical Wnt signaling pathway, without an inflammatory reaction. This study points to a role of tau in brain development and neurogenesis, in contrast to its detrimental role in neurodegeneration at later age.

     

miR-124 promotes proliferation and differentiation of neuronal stem cells through inactivating Notch pathway

Background

Neural stem cells (NSCs) are able to differentiate into neurons and astroglia. miRNAs have been demonstrated to be involved in NSC self-renewal, proliferation and differentiation. However, the exact role of miR-124 in the development of NSCs and its underlying mechanism remain to be explored.

Methods

Primary NSCs were isolated from embryos of Wistar rats. Immunocytochemistry was used to stain purified NSCs. miR-124, Delta-like 4 (DLL4), ki-67, Nestin, β-tubulin III, glial fibrillary acidic protein (GFAP), HES1, HEY2, and cyclin D1 (CCND1) expressions were detected by qRT-PCR and western blot. The interaction between miR-124 and DLL4 was confirmed by luciferase reporter assay. Cell proliferation was assessed by MTT assay.

Results

NSCs could self-proliferate and differentiate into neurons and astrocyte. miR-124 was up-regulated and DLL4 was down-regulated during NSC differentiation. DLL4 was identified as a target of miR-124 in NSCs. Ectopic expression of miR-124 or knockdown of DLL4 promoted the proliferation and the formation of NSCs to neurospheres. Moreover, miR-124 overexpression or DLL4 down-regulation improved β-tubulin III expression but decreased GFAP expression in NSCs. Furthermore, enforced expression of DLL4 partially reversed the effects of miR-124 on NSCs proliferation and differentiation. Elevated expression of miR-124 suppressed the expressions of HES1, HEY2, and CCND1 in NSCs, while these effects were attenuated following the enhancement of DLL4 expression.

Conclusion

miR-124 promoted proliferation and differentiation of NSCs through inactivating Notch pathway.

     

Long non-coding RNA H19 regulates E2F1 expression by competitively sponging endogenous miR-29a-3p in clear cell renal cell carcinoma

Background

Numerous recent studies indicate that the long non-coding RNAs (lncRNAs) are frequently abnormal expressed and take critical roles in many cancers. Renal cell carcinoma is the secondary malignant tumors in the urinary system and has high mortality and morbidity. Around 80% of RCCs is clear cell renal cell carcinoma (ccRCC) and is characterized by high metastasis and relapse rate. However, the clinical significances of lncRNAs in ccRCC are still unknown.

Methods

The human cancer lncRNA PCR array (Yingbio) was performed to detect the differentially expressed lncRNAs in human ccRCC samples. Real-time PCR (RT-PCR), dual-luciferase assay, RNA binding protein immunoprecipitation (RIP) assay, transwell assay, CCK-8 assay, and western blot were performed to explore the molecular mechanism of lncRNAs in ccRCC cell migration and invasion.

Results

In this study, lncRNA-H19 was high expressed and negatively correlated with miR-29a-3p in ccRCC. By bioinformatics software, dual-luciferase reporter and RIP assays, we verified that miR-29a-3p was identified as a direct target of lncRNA-H19. RT-PCR and western blot demonstrated that down-regulated lncRNA-H19 could affect the expression of miR-29a-3p targeting E2F1 with competitively binding miR-29a-3p. Furthermore, transwell assays indicated that lncRNA-H19 knockdown inhibited cells migration and invasion, but this effect was attenuated by co-transfection of lncRNA-H19 siRNA and miR-29a-3p inhibitor. Over expression of E2F1 could rescue lncRNA-H19 siRNA induced suppression on cell migration and invasion in ccRCC cells.

Conclusions

These results show a possible competing endogenous RNAs regulatory network involving lncRNA-H19 regulates E2F1 expression by competitively sponging endogenous miR-29a-3p in ccRCC. This mechanism may contribute to a better understanding of ccRCC pathogenesis, and lncRNA-H19 may be further considered as a potential therapeutic target for ccRCC intervention.

     

The lncRNA MEG3 downregulation leads to osteoarthritis progression via miR-16/SMAD7 axis

Background

Osteoarthritis (OA) is a chronic joint disease and there is no a definitive cure at present. Long non-coding RNAs (lncRNAs) have been confirmed to play important roles in the development of OA. However, the underlying mechanism of lncRNA maternally expressed gene 3 (MEG3) in OA has not been well elucidated.

Methods

The rat OA model and interleukin-1β (IL-1β)-induced rat chondrocytes were constructed. The expression pattern of lncRNA MEG3 and miR-16 was detected by RT-qPCR assay in cartilage tissues of rat OA model. The effect of MEG3 and miR-16 on IL-1β-induced chondrocytes was evaluated on the basis of cell viability and apoptosis. Then, the interaction among MEG3, miR-16 SMAD7 was explored by dual-luciferase reporter assay and RIP assay.

Results

It is found that lncRNA MEG3 was down-regulated and miR-16 was up-regulated in rat OA cartilage tissues. MEG3 knockdown promoted proliferation and inhibited apoptosis, while miR-16 knockdown suppressed proliferation and promoted apoptosis in IL-1β-induced rat chondrocytes. Moreover, MEG3 was involved in miR-16 pathway and MEG3 suppressed miR-16 expression. Additionally, SMAD7 was a target gene of miR-16 and miR-16 suppressed SMAD7 expression in IL-1β-induced chondrocytes. Moreover, the expression of SMAD7 induced by MEG3 or si-MEG3 was markedly reversed by the introduction of miR-16 or anti-miR-16. Furthermore, MEG3 exerted its anti-proliferation and pro-apoptosis by regulating miR-16 and SMAD7.

Conclusion

MEG3 was down-regulated and miR-16 was up-regulated in cartilage tissues of rat OA model. MEG3 knockdown might lead to the progression of OA through miR-16/SMAD7 axis.