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A recently discovered dimension of post-transcriptional gene regulation involves co-regulatory crosstalk between RNA transcripts, which compete for common pools of microRNA (miRNA) molecules. These competing endogenous RNAs (ceRNAs), or natural miRNA sponges, have an active role in regulating miRNA availability within the cell and form intertwined regulatory networks. Recent reports have implicated diverse RNA species including protein-coding messenger RNAs and non-coding RNAs as ceRNAs in human development and diseases including human cancer. In this review, we discuss the most recent discoveries that implicate natural miRNA decoys in human cancer biology, as well as exciting advances in the study of ceRNA networks and dynamics. The structure and topology of intricate genome-scale ceRNA networks can be predicted computationally, and their dynamic response to fluctuations in ceRNA and miRNA levels can be studied via mathematical modeling. Additionally, the development of new methods to quantitatively determine absolute expression levels of miRNA and ceRNA molecules have expanded the capacity to accurately study the efficiency of ceRNA crosstalk in diverse biological models. These major milestones are of critical importance to identify key components of ceRNA regulatory networks that could aid the development of new approaches to cancer diagnostics and oligonucleotide-based therapeutics.

Accumulating evidence has highlighted the important roles of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in tumor biology. However, the roles of cancer specific lncRNAs in lncRNA-related ceRNA network of lung adenocarcinoma (LUAD) are still unclear. In the present study, the 465 RNA sequencing profiles in LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database, which provides large sample RNA sequencing data free of charge, and 41 cancer specific lncRNAs, 25 miRNAs and 1053 mRNAs (fold change >2, P<0.05) were identified. Then, the lncRNA-miRNA-mRNA ceRNA network of LUAD was constructed with 29 key lncRNAs, 24 miRNAs and 72 mRNAs. Subsequently, we selected these 29 key lncRNAs to analyze their correlation with clinical features, and 21 of them were aberrantly expressed with tumor pathological stage, TNM staging system, lymph node metastasis and patient outcome assessment, respectively. Furthermore, there were 5 lncRNAs (BCRP3, LINC00472, CHIAP2, BMS1P20 and UNQ6494) positively correlated with overall survival (OS, log-rank P<0.05). Finally, 7 cancer specific lncRNAs were randomly selected to verify the expression in 53 newly diagnosed LUAD patients using qRT-PCR. The expression results between TCGA and qRT-PCR were 100% in agreement. The correlation between AFAP1-AS1 and LINC00472 and clinical features were also confirmed. Thus, our results showed the lncRNA expression profiles and we constructed an lncRNA-miRNA-mRNA ceRNA network in LUAD. The present study provides novel insight for better understanding of lncRNA-related ceRNA network in LUAD and facilitates the identification of potential biomarkers for diagnosis and prognosis.

Cross-talk between competing endogenous RNAs (ceRNAs) play key roles in tumor development. In this study, we performed exon-level expression profiling on 26 glioblastomas (GBMs) and 6 controls to identify long non-coding RNAs (lncRNAs) of GBM initiation and progression using lncRNA-mediated ceRNA network (LMCN). The mRNA and lncRNA expression data, as well as miRNA-target interactions were firstly collected. Then, we used hypergeometric test to detect the lncRNA-mRNA interactions, followed by the construction of LMCN based on Pearson correlation coefficient. With the goal of investigation of the network organization, degree distribution of LMCN was performed. Next, the synergistic, competing lncRNA modules were identified using jActiveModule plug-in of Cytoscape. Moreover, we implemented the pathway analysis for its mRNAs in the module to explore the functions of significant lncRNAs. Using the criteria of degrees >50, 8 hub genes were identified, including EPB41L4A-AS1, ZRANB2-AS2, XIST, HOTAIR, TRAF3IP2-AS1, TPT1-AS1, PVT1 and DLG1-AS1. Furthermore, 1 synergistic, competitive module was identified. In this module, lncRNAs XIST and PVT1 were also the hubs in the synergistic, competing lncRNA module. Functional annotation demonstrated that 5 pathways were identified, including cytokine-cytokine receptor interaction, neuroactive ligand-receptor interaction, and mTOR signaling pathway. We have successfully identified several hubs (such as XIST and PVT1) and significant pathways (for instance, cytokine-cytokine receptor interaction, and neuroactive ligand-receptor interactions) for GBM via establishing the LMCN. These findings might offer potential biomarkers to early diagnose, and predict GBM prognosis in the future.

Autophagy plays an important role in the occurrence and development of hepatocellular carcinoma (HCC). We aimed to develop an autophagy-related genes signature predicting the prognosis of HCC and to depict a competing endogenous RNA (ceRNA) network. Differentially expressed autophagy-related genes (DE-ATGs), miRNAs and lncRNAs and clinical data of HCC patients were extracted from TCGA. The GO and KEGG analysis were performed to investigate the gene function. Univariate and multivariate Cox regression analysis were used to identify a prognostic signature with the DE-ATGs. And a nomogram, adapted to the clinical characteristics, was established. Then, we established a ceRNA network related to autophagy genes. We screened out 27 differentially expressed genes which were enriched in GO and KEGG pathways related to autophagy and cancers. In univariate and multivariate Cox regression analysis, , , and were screened out to establish a prognostic risk score model (AUC=0.749, <0.01). Kaplan-Meier survival analysis showed that the overall survival of high-risk patients was significantly worse. Furthermore, the signature was validated in the other two independent databases. The nomogram, including the autophagy-related risk signature, gender, stage and TNM, was constructed and validated (C-index=0.736). Finally, the ceRNA network was established based on DE-ATGs, differentially expressed miRNAs and lncRNAs. We constructed a reliable prognostic model of HCC with autophagy-related genes and depicted a ceRNA network of DE-ATGs in HCC which provides a basis for the study of post-transcriptional modification and regulation of autophagy-related genes in HCC.

Long noncoding RNA (lncRNA) is emerging as an essential regulator in the development and progression of cancer, including cervical cancer (CC). In this study, we found a CC-related lncRNA, KCNMB2-AS1, which was significantly overexpressed in CC and linked to poor outcomes. Depletion of KCNMB2-AS1 remarkably inhibited CC cell proliferation and induced apoptosis. In vivo xenograft models revealed that knockdown of KCNMB2-AS1 evidently delayed tumor growth. Mechanistically, KCNMB2-AS1 was predominantly located in the cytoplasm and served as a competing endogenous RNA to abundantly sponge miR-130b-5p and miR-4294, resulting in the upregulation of IGF2BP3, a well-documented oncogene in CC. Moreover, IGF2BP3 was able to bind KCNMB2-AS1 by three N 6 -methyladenosine (m 6 A) modification sites on KCNMB2-AS1, in which IGF2BP3 acted as an m 6 A “reader” and stabilized KCNMB2-AS1. Thus, KCNMB2-AS1 and IGF2BP3 formed a positive regulatory circuit that enlarged the tumorigenic effect of KCNMB2-AS1 in CC. Together, our data clearly suggest that KCNMB2-AS1 is a novel oncogenic m 6 A-modified lncRNA in CC, targeting KCNMB2-AS1 and its related molecules implicate the therapeutic possibility for CC patients.

Non-coding RNA (ncRNA) has been shown to regulate diverse cellular processes and functions through controlling gene expression. Long non-coding RNAs (lncRNAs) act as a competing endogenous RNAs (ceRNAs) where microRNAs (miRNAs) and lncRNAs regulate each other through their biding sites. Interactions of miRNAs and lncRNAs have been reported to trigger decay of the targeted lncRNAs and have important roles in target gene regulation. These interactions form complicated and intertwined networks. Certain lncRNAs encode miRNAs and small nucleolar RNAs (snoRNAs), and may regulate expression of these small RNAs as precursors. SnoRNAs have also been reported to be precursors for PIWI-interacting RNAs (piRNAs) and thus may regulate the piRNAs as a precursor. These miRNAs and piRNAs target messenger RNAs (mRNAs) and regulate gene expression. In this review, we will present and discuss these interactions, cross-talk, and co-regulation of ncRNAs and gene regulation due to these interactions.

The three major members of non-coding RNAs (ncRNAs), named microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), play an important role in hepatocellular carcinoma (HCC) development. Recently, the competing endogenous RNA (ceRNA) regulation model described lncRNA/circRNA as a sponge for miRNAs to indirectly regulate miRNA downstream target genes. Accumulating evidence has indicated that ceRNA regulatory networks are associated with biological processes in HCC, including cancer cell growth, epithelial to mesenchymal transition (EMT), metastasis, and chemoresistance. In this review, we summarize recent discoveries, which are specific ceRNA regulatory networks (lncRNA/circRNA-miRNA-mRNA) in HCC and discuss their clinical significance.

The etiology of epilepsy is ascribed to the synchronized aberrant neuronal activity within the brain. Circular RNAs (circRNAs), a class of non‐coding RNAs characterized by their circular structures and covalent linkage, exert a substantial influence on this phenomenon. CircRNAs possess stereotyped replication, transience, repetitiveness, and paroxysm. Additionally, MicroRNA (miRNA) plays a crucial role in the regulation of diverse pathological processes, including epilepsy. CircRNA is of particular significance due to its ability to function as a competing endogenous RNA, thereby sequestering or inhibiting miRNA activity through binding to target mRNA. Our review primarily concentrates on elucidating the pathological and functional roles, as well as the underlying mechanisms, of circRNA–miRNA–mRNA networks in epilepsy. Additionally, it explores the potential utility of these networks for early detection and therapeutic intervention. CircRNA–miRNA–mRNA networks in epilepsy. CirRNAs can bind to microRNA response elements through sponging and regulate their expression downstream according to the competitive endogenous RNA hypothesis (ceRNA). The circRNA–miRNA–mRNA axes hold potential as valuable tools for comprehending the pathogenesis, diagnosis, and treatment of epilepsy. We summarize the possible regulatory networks of circRNA–miRNA–mRNA in epilepsy.

Background Increasing evidence has underscored the role of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in the development and progression of tumors. Nevertheless, lncRNA biomarkers in lncRNA-related ceRNA network that can predict the prognosis of breast cancer (BC) are still lacking. The aim of our study was to identify potential lncRNA signatures capable of predicting overall survival (OS) of BC patients. Methods The RNA sequencing data and clinical characteristics of BC patients were obtained from the Cancer Genome Atlas database, and differentially expressed lncRNA (DElncRNAs), DEmRNAs, and DEmiRNAs were then identified between BC and normal breast tissue samples. Subsequently, the lncRNA–miRNA–mRNA ceRNA network of BC was established, and the gene oncology enrichment analyses for the DEmRNAs interacting with lncRNAs in the ceRNA network was implemented. Using univariate and multivariate Cox regression analyses, a four-lncRNA signature was developed and used for predicting the survival in BC patients. We applied receiver operating characteristic analysis to assess the performance of our model. Results A total of 1061 DElncRNAs, 2150 DEmRNAs, and 82 DEmiRNAs were identified between BC and normal breast tissue samples. A lncRNA–miRNA–mRNA ceRNA network of BC was established, which comprised of 8 DEmiRNAs, 48 DElncRNAs, and 10 DEmRNAs. Further gene oncology enrichment analyses revealed that the DEmRNAs interacting with lncRNAs in the ceRNA network participated in cell leading edge, protease binding, alpha-catenin binding, gamma-catenin binding, and adenylate cyclase binding. A univariate regression analysis of the DElncRNAs revealed 7 lncRNAs (ADAMTS9-AS1, AC061992.1, LINC00536, HOTAIR, AL391421.1, TLR8-AS1 and LINC00491) that were associated with OS of BC patients. A multivariate Cox regression analysis demonstrated that 4 of those lncRNAs (ADAMTS9-AS1, LINC00536, AL391421.1 and LINC00491) had significant prognostic value, and their cumulative risk score indicated that this 4-lncRNA signature independently predicted OS in BC patients. Furthermore, the area under the curve of the 4-lncRNA signature associated with 3-year survival was 0.696. Conclusions The current study provides novel insights into the lncRNA-related ceRNA network in BC and the 4 lncRNA biomarkers may be independent prognostic signatures in predicting the survival of BC patients.

Background The limited knowledge of miRNA–lncRNA interactions is considered as an obstruction of revealing the regulatory mechanism. Accumulating evidence on Human diseases indicates that the modulation of gene expression has a great relationship with the interactions between miRNAs and lncRNAs. However, such interaction validation via crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) experiments that inevitably costs too much money and time but with unsatisfactory results. Therefore, more and more computational prediction tools have been developed to offer many reliable candidates for a better design of further bio-experiments. Methods In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA–lncRNA interactions (GKLOMLI). Given an observed miRNA–lncRNA interaction network, the Gaussian kernel-based method was employed to output two similarity matrixes of miRNAs and lncRNAs. Based on the integrated matrix combined with similarity matrixes and the observed interaction network, a linear optimization-based link prediction model was trained for inferring miRNA–lncRNA interactions. Results To evaluate the performance of our proposed method, k -fold cross-validation (CV) and leave-one-out CV were implemented, in which each CV experiment was carried out 100 times on a training set generated randomly. The high area under the curves (AUCs) at 0.8623 ± 0.0027 (2-fold CV), 0.9053 ± 0.0017 (5-fold CV), 0.9151 ± 0.0013 (10-fold CV), and 0.9236 (LOO-CV), illustrated the precision and reliability of our proposed method. Conclusion GKLOMLI with high performance is anticipated to be used to reveal underlying interactions between miRNA and their target lncRNAs, and deciphers the potential mechanisms of the complex diseases.