ceRNA Regulation Research Articles

Identification of a functional circRNA-miRNA-mRNA regulatory network in infantile hemangioma by bioinformatics analysis.

Several circRNA have been reported to serve critical roles in various biological processes of human body. The present study aimed to build a circRNA-based competing endogenous RNA (ceRNA) network and explore the regulatory mechanisms of circRNA in infantile hemangiomas (IH). Differentially expressed circRNA, miRNA, and mRNA were downloaded from the gene expression synthesis (GEO) microarray database (GSE98795, GSE69136, and GSE127487). Cancer-specific circRNA database (CSCD), miRDB and Targetscan were employed to predict the targets of RNA. A total of 855 DEcircRNAs, 69 differentially expressed miRNAs (DEmiRNAs), and 3233 differentially expressed mRNAs (DEmRNAs) appeared as genes that were aberrantly expressed in IH. The circRNA-miRNA-mRNA network was constructed based on 108 circRNAs, 7 miRNAs, 274 mRNAs in IH. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis indicated hypoxia-inducible factors (HIF)-1 signaling pathway and Notch signaling pathway were significantly enriched in IH with being constructed a ceRNA regulatory network. Furthermore, protein-protein interaction (PPI) network and Cytoscape showed the top 10 hub genes that regulate angiogenesis, namely FBXW7, CBLB, HECW2, FBXO32, FBXL7, KLHL5, EP300, MAPK1, MEF2C, and PLCG1. Our findings provide a deeper understanding the circRNA-related ceRNA regulatory mechanism in IH. This study further perfected the circRNA-miRNA-mRNA regulatory network related to IH and explored the potential function of mRNA in this network. It provides more understanding for the circRNA-related ceRNA regulation mechanism in the pathogenesis of IH.

Identification of a hippocampal lncRNA-regulating network in a natural aging rat model

BackgroundDysregulation of long noncoding RNA (lncRNA) expression is related to aging and age-associated neurodegenerative diseases, and the lncRNA expression profile in the aging hippocampus is not well characterized. In the present investigation, the changed mRNAs and lncRNAs were confirmed via deep RNA sequencing. GO and KEGG pathway analyses were conducted to investigate the principal roles of the clearly dysregulated mRNAs and lncRNAs. Subsequently, through the prediction of miRNAs via which mRNAs and lncRNAs bind together, a competitive endogenous RNA network was constructed.ResultsA total of 447 lncRNAs and 182 mRNAs were upregulated, and 385 lncRNAs and 144 mRNAs were downregulated. Real-time reverse transcription-polymerase chain reaction validated the reliability of mRNA and lncRNA sequencing. KEGG pathway and GO analyses revealed that differentially expressed (DE) mRNAs were associated with cell adhesion molecules (CAMs), the p53 signaling pathway (SP), phagosomes, PPAR SP and ECM—receptor interactions. KEGG pathway and GO analyses showed that the target genes of the DE lncRNAs were related to cellular senescence, the p53 signaling pathway, leukocyte transendothelial migration and tyrosine metabolism. Coexpression analyses showed that 561 DE lncRNAs were associated with DE mRNAs. A total of 58 lncRNA–miRNA–mRNA target pairs were confirmed in this lncRNA‒miRNA‒mRNA network, comprising 10 mRNAs, 13 miRNAs and 38 lncRNAs.ConclusionsWe found specific lncRNAs and mRNAs in the hippocampus of natural aging model rats, as well as abnormal regulatory ceRNA networks. Our outcomes help explain the pathogenesis of brain aging and provide direction for further research.

Analysis of circRNA expression in chicken HD11 cells in response to avian pathogenic E.coli.

Avian pathogenic E. coli (APEC), one of the widespread zoonotic-pathogen, can cause a series of diseases collectively known as colibacillosis. This disease can cause thousands of million dollars economic loss each year in poultry industry and threaten to human health via meat or egg contamination. However, the detailed molecular mechanism underlying APEC infection is still not fully understood. Circular RNAs, a new type of endogenous noncoding RNA, have been demonstrated to involve in various biological processes. However, it is still not clear whether the circRNAs participate in host response against APEC infection. Herein, we utilized the high-throughput sequence technology to identify the circRNA expression profiles in APEC infected HD11 cells. A total of 49 differentially expressed (DE) circRNAs were detected in the comparison of APEC infected HD11 cells vs. wild type HD11 cells, which were involved in MAPK signaling pathway, Endocytosis, Focal adhesion, mTOR signaling pathway, and VEGF signaling pathway. Specifically, the source genes (BRAF, PPP3CB, BCL2L13, RAB11A, and TSC2) and their corresponding DE circRNAs may play a significant role in APEC infection. Moreover, based on ceRNA regulation, we constructed the circRNA-miRNA network and identified a couple of important regulatory relationship pairs related to APEC infection, including circRAB11A-gga-miR-125b-3p, circRAB11A-gga-miR-1696, and circTSC2-gga-miR-1649-5p. Results indicate that the aforementioned specific circRNAs and circRNA-miRNA network might have important role in regulating host immune response against APEC infection. This study is the first time to investigate the circRNAs expression profile and the biological function of the source genes of the identified DE circRNAs after APEC infection of chicken HD11 cells. These results would contribute to a better understanding of the molecular mechanisms in host response against APEC infection.

CeRNAR: An R package for identification and analysis of ceRNA-miRNA triplets.

Competitive endogenous RNA (ceRNA) represents a novel mechanism of gene regulation that controls several biological and pathological processes. Recently, an increasing number of in silico methods have been developed to accelerate the identification of such regulatory events. However, there is still a need for a tool supporting the hypothesis that ceRNA regulatory events only occur at specific miRNA expression levels. To this end, we present an R package, ceRNAR, which allows identification and analysis of ceRNA-miRNA triplets via integration of miRNA and RNA expression data. The ceRNAR package integrates three main steps: (i) identification of ceRNA pairs based on a rank-based correlation between pairs that considers the impact of miRNA and a running sum correlation statistic, (ii) sample clustering based on gene-gene correlation by circular binary segmentation, and (iii) peak merging to identify the most relevant sample patterns. In addition, ceRNAR also provides downstream analyses of identified ceRNA-miRNA triplets, including network analysis, functional annotation, survival analysis, external validation, and integration of different tools. The performance of our proposed approach was validated through simulation studies of different scenarios. Compared with several published tools, ceRNAR was able to identify true ceRNA triplets with high sensitivity, low false-positive rates, and acceptable running time. In real data applications, the ceRNAs common to two lung cancer datasets were identified in both datasets. The bridging miRNA for one of these, the ceRNA for MAP4K3, was identified by ceRNAR as hsa-let-7c-5p. Since similar cancer subtypes do share some biological patterns, these results demonstrated that our proposed algorithm was able to identify potential ceRNA targets in real patients. In summary, ceRNAR offers a novel algorithm and a comprehensive pipeline to identify and analyze ceRNA regulation. The package is implemented in R and is available on GitHub (https://github.com/ywhsiao/ceRNAR).

Whole-transcriptome RNA sequencing reveals the global molecular responses and circRNA/lncRNA-miRNA-mRNA ceRNA regulatory network in chicken fat deposition

Fat deposition is a vital factor affecting the economics of poultry production. Numerous studies on fat deposition have been done. However, the molecular regulatory mechanism is still unclear. In the present study, the whole-transcriptome RNA sequencing in abdominal fat, back skin, and liver both high- and low-abdominal fat groups was used to uncover the competitive endogenous RNA (ceRNA) regulation network related to chicken fat deposition. The results showed that differentially expressed (DE) genes in abdominal fat, back skin, liver were 1207(784 mRNAs, 330 lncRNAs, 41 circRNAs, 52 miRNAs), 860 (607 mRNAs, 166 lncRNAs, 26 circRNAs, 61 miRNAs), and 923 (501 mRNAs, 262 lncRNAs, 15 circRNAs, 145 miRNAs), respectively. The ceRNA regulatory network analysis indicated that the fatty acid metabolic process, monocarboxylic acid metabolic process, carboxylic acid metabolic process, glycerolipid metabolism, fatty acid metabolism, and peroxisome proliferator-activated receptor (PPAR) signaling pathway took part in chicken fat deposition. Meanwhile, we scan the important genes, FADS2, HSD17B12, ELOVL5, AKR1E2, DGKQ, GPAM, PLIN2, which were regulated by gga-miR-460b-5p, gga-miR-199-5p, gga-miR-7470-3p, gga-miR-6595-5p, gga-miR-101-2-5p. While these miRNAs were competitive combined by lncRNAs including MSTRG.18043, MSTRG.7738, MSTRG.21310, MSTRG.19577, and circRNAs including novel_circ_PTPN2, novel_circ_CTNNA1, novel_circ_PTPRD. This finding provides new insights into the regulatory mechanism of mRNA, miRNA, lncRNA, and circRNA in chicken fat deposition.

A 70‑RNA model based on SVR and RFE for predicting the pancreatic cancer clinical prognosis.

Researches on the prognosis of pancreatic cancer is of great significance to improve the patient treatment effect and survival. Current researches mainly focus on the prediction of the survival status and the determination of prognostic markers. Each patient has its own characteristics, there is no report about the prediction of survival time. However, accurate prediction of survival time is critical for personalized medicine. In this paper, a hybrid algorithm of Support Vector Regression (SVR) and Recursive Feature Elimination (RFE) was used to construct a quantitative prediction model of Overall Survival (OS) for pancreatic cancer patients, 70 RNAs related to OS were determined, including 33 mRNAs, 28 lncRNAs, and 9 miRNAs. The results of 10-fold cross-validation (R2 is 0.9693) and the generalization ability (R2 is 0.9666) showed that the model has reliable predictive performance and these 70 RNAs are important factors influencing the OS of pancreatic cancer patients. To further study the relationship between RNA-RNA interaction and the survival, competitive endogenous RNA (ceRNA) regulation network was constructed. Degree centrality, betweenness centrality and closeness centrality of nodes in the ceRNA network showed that hsa-mir-570, hsa-mir-944, hsa-mir-6506, hsa-mir-3136, MMP16, PLGLB2, HPGD, FUT1, MFSD2A, SULT1E1, SLC13A5, ZNF488, F2RL2, TNFRSF8, TNFSF11, FHDC1, ISLR2 and THSD7B are hub nodes, which are key RNAs closely determining the OS of pancreatic cancer patients.

Analysis of characteristic genes and ceRNA regulation mechanism of endometriosis based on full transcriptional sequencing.

Background: Endometriosis is a common gynecological disorder that usually causes infertility, pelvic pain, and ovarian masses. This study aimed to mine the characteristic genes of endometriosis, and explore the regulatory mechanism and potential therapeutic drugs based on whole transcriptome sequencing data and resources from public databases, providing a theoretical basis for the diagnosis and treatment of endometriosis. Methods: The transcriptome data of the five eutopic (EU) and ectopic (EC) endometrium samples were obtained from Beijing Obstetrics and Gynecology Hospital, Beijing, China, and dinified as the own data set. The expression and clinical data of EC and EU samples in GSE25628 and GSE7305 datasets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds). Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify the endometriosis-related differentially expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted by the “clusterProfiler” R package. Then, characteristic genes for endometriosis were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithm. The expression of characteristic genes was verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western-blot. The receiver operating characteristic (ROC) curve was used to evaluate the discriminatory ability of characteristic genes. We assessed the abundance of infiltrating immune cells in each sample using MCP-counter and ImmuCellAI algorithms. The competitive endogenous RNA (ceRNA) regulatory network of characteristic genes was created by Cytoscape and potential targeting drugs were obtained in the CTD database. Results: 44 endometriosis-related differentially expressed genes were obtained from GSE25628 and the own dataset. Subsequently, LASSO and SVM-RFE algorithms identified four characteristic genes, namely ACLY, PTGFR, ADH1B, and MYOM1. The results of RT-PCR and western-blot were consistent with those of sequencing. The result of ROC curves indicated that the characteristic genes had powerful abilities in distinguishing EC samples from EU samples. Infiltrating immune cells analysis suggested that there was a certain difference in immune microenvironment between EC and EU samples. The characteristic genes were significantly correlated with specific differential immune cells between EC and EU samples. Then, a ceRNA regulatory network of characteristic genes was constructed and showed a total of 7, 11, 11, and 1 miRNA associated with ACLY, ADH1B, PTGFR, and MYOM1, respectively. Finally, we constructed a gene-compound network and mined 30 drugs targeting ACLY, 33 drugs targeting ADH1B, 13 drugs targeting MYOM1, and 12 drugs targeting PTGFR. Conclusion: Comprehensive bioinformatic analysis was used to identify characteristic genes, and explore ceRNA regulatory network and potential therapeutic agents for endometriosis. Altogether, these findings provide new insights into the diagnosis and treatment of endometriosis.

Integrated Analysis of LncRNA-Mediated ceRNA Network in Calcific Aortic Valve Disease.

Background: The high morbidity and mortality of calcific aortic valve disease (CAVD) represents an unmet clinical need to investigate the molecular mechanisms involved. Evidence suggests that long non-coding RNAs (lncRNAs) can act as competitive endogenous RNAs (ceRNAs) by binding to microRNAs and regulating target genes in cardiovascular diseases. Nevertheless, the role of lncRNAs related ceRNA regulation in CAVD remains unclear. Methods: RNAseq data of human diseased aortic valves were downloaded from GEO data sets (GSE153555, GSE199718), and differentially expressed lncRNAs (DElncRNAs), mRNAs (DEmRNAs) between CAVD and non-calcific aortic valve tissues with limma R package. Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Set Enrichment analysis (GSEA) were performed with clusterProfiler and gesaplot2 R package. The pivotal microRNAs were predicted by three databases intersection including TargetScan, MiRwalk, miRDB according to the genes related to the crucial pathways. ENCORI was used to predict targeted lncRNAs of hub microRNAs. We constructed lncRNA-miRNA-mRNA ceRNA network with Cytoscape software. The lncRNAs in ceRNA network were verified by RT-qPCR in human 30 calcific and 20 noncalcified aortic valve tissues. Results: In total, 1739 DEmRNAs and 266 DElncRNAs were identified in CAVD. GO, KEGG pathway, GSEA annotations suggested that most of these genes are enriched in extracellular matrix (ECM)-reporter interaction pathways. The ceRNA networks associated with ECM-reporter interaction are constructed and related lncRNAs including H19, SNHG3 and ZNF436-AS1 were significant upregulated in human calcific aortic valve tissues, which might be potential therapeutic targets for CAVD. Conclusions: In this study, we proposed a novel lncRNA-miRNA-mRNA ceRNA network related to ECM-reporter interaction pathways, which potentially regulates CAVD progression.

Identification of the KCNQ1OT1/ miR-378a-3p/ RBMS1 Axis as a Novel Prognostic Biomarker Associated With Immune Cell Infiltration in Gastric Cancer.

Background: Gastric cancer (GC) is the second leading cause of cancer-related mortality and the fifth most common cancer worldwide. However, the underlying mechanisms of competitive endogenous RNAs (ceRNAs) in GC are unclear. This study aimed to construct a ceRNA regulation network in correlation with prognosis and explore a prognostic model associated with GC. Methods: In this study, 1,040 cases of GC were obtained from TCGA and GEO datasets. To identify potential prognostic signature associated with GC, Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression were employed. The prognostic value of the signature was validated in the GEO84437 training set, GEO84437 test set, GEO15459 set, and TCGA-STAD. Based on the public databases, TargetScan and starBase, an mRNA-miRNA-lncRNA regulatory network was constructed, and hub genes were identified using the CytoHubba plugin. Furthermore, the clinical outcomes, immune cell infiltration, genetic variants, methylation, and somatic copy number alteration (sCNA) associated with the ceRNA network were derived using bioinformatics methods. Results: A total of 234 prognostic genes were identified. GO and GSEA revealed that the biological pathways and modules related to immune response and fibroblasts were considerably enriched in GC. A nomogram was generated to provide accurate prognostic outcomes and individualized risk estimates, which were validated in the training, test dataset, and two independent validation datasets. Thereafter, an mRNA-miRNA-lncRNA regulatory network containing 4 mRNAs, 22 miRNAs, 201 lncRNAs was constructed. The KCNQ1OT1/hsa-miR-378a-3p/RBMS1 ceRNA network associated with the prognosis was obtained by hub gene analysis and correlation analysis. Importantly, we found that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may play a vital role in the diagnosis and prognosis of GC patients based on Cox regression analyses. Furthermore, our findings demonstrated that mutations and sCNA of the KCNQ1OT1/miR-378a-3p/RBMS1 axis were associated with increased immune infiltration, while the abnormal upregulation of the axis was primarily a result of hypomethylation. Conclusion: Our findings suggest that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may be a potential prognostic biomarker and therapeutic target for GC. Moreover, such findings provide insights into the molecular mechanisms of GC pathogenesis.

Systematic Investigation of Immune-Related lncRNA Landscape Reveals a Potential Long Non-Coding RNA Signature for Predicting Prognosis in Renal Cell Carcinoma.

Background: Renal cell carcinoma (RCC) is the predominant type of malignant tumor in kidney cancer. Finding effective biomarkers, particularly those based on the tumor immune microenvironments (TIME), is critical for the prognosis and diagnosis of RCC. Increasing evidence has revealed that long non-coding RNAs (lncRNAs) play a crucial role in cancer immunity. However, the comprehensive landscape of immune infiltration-associated lncRNAs and their potential roles in the prognosis and diagnosis of RCC remain largely unexplored. Methods: Based on transcriptomic data of 261 RCC samples, novel lncRNAs were identified using a custom pipeline. RCC patients were classified into different immune groups using unsupervised clustering algorithms. Immune-related lncRNAs were obtained according to the immune status of RCC. Competing endogenous RNAs (ceRNA) regulation network was constructed to reveal their functions. Expression patterns and several tools such as miRanda, RNAhybrid, miRWalk were used to define lncRNAs-miRNAs-mRNAs interactions. Univariate Cox, LASSO, and multivariate Cox regression analyses were performed on the training set to construct a tumorigenesis-immune-infiltration-related (TIR)-lncRNA signature for predicting the prognosis of RCC. Independent datasets involving 531 RCC samples were used to validate the TIR-lncRNA signature. Results: Tens of thousands of novel lncRNAs were identified in RCC samples. Comparing tumors with controls, 1,400 tumorigenesis-related (TR)-lncRNAs, 1269 TR-mRNAs, and 192 TR-miRNAs were obtained. Based on the infiltration of immune cells, RCC patients were classified into three immune clusters. By comparing immune-high with immune-low groups, 241 TIR-lncRNAs were identified, many of which were detected in urinary samples. Based on lncRNA-miRNA-mRNA interactions, we constructed a ceRNA network, which included 25 TR-miRNAs, 28 TIR-lncRNAs, and 66 TIR-mRNAs. Three TIR lncRNAs were identified as a prognostic signature for RCC. RCC patients in the high-risk group exhibited worse OS than those in the low-risk group in the training and testing sets (p < 0.01). The AUC was 0.9 in the training set. Univariate and multivariate Cox analyses confirmed that the TIR-lncRNA signature was an independent prognostic factor in the training and testing sets. Conclusion: Based on the constructed immune-related lncRNA landscape, 241 TIR-lncRNAs were functionally characterized, three of which were identified as a novel TIR-lncRNA signature for predicting the prognosis of RCC.

Bioinformatics-based analysis reveals IDR-1018-mediated ceRNA regulation network for protective effect on hypoxia-ischemic brain injury in neonatal mice

Activation of an innate immune response serves as a key, contributing factor in perinatal brain injury. The current study sought to evaluate the clinical significance of innate defense regulatory peptide 1018 (IDR-1018)-derived peptide mediating ceRNA regulation network as a biomarker in neonatal mice with hypoxic-ischemic brain damage (HIBD). Firstly, bioinformatics analyses were performed to screen the HIBD-related candidate genes, miRNAs, and lncRNAs. The StarBase, miRDB, and LncBase databases were retrieved to obtain the lncRNA-miRNA-mRNA network, which revealed the ceRNA regulatory network mediated by IDR-1018. Subsequently, RT-qPCR was adopted to determine the expression patterns of MIAT, miR-7a-5p, and Plp2 in neonatal mice with HIBD after treatment with IDR-1018. Moreover, the relationship among mRNA, miRNA, and lncRNA in primary hippocampal neurons was verified by means of dual-luciferase reporter assay and RIP assay. Initial findings demonstrated that Plp2, mmu-miR-7a-5p, and three lncRNAs (MIAT, XIST, and 1700020I14RIK) were related to HIBD. Moreover, IDR-1018 could relieve HIBD in neonatal mice. Plp2 and MIAT were down-regulated, while mmu-miR-7a-5p was up-regulated in the striatum, hippocampus, and cortical tissues of the neonatal mice with HIBD, whereas treatment with the IDR-1018 could revere these trends. Additionally, MIAT acted as a ceRNA of miR-7a-5p to elevate Plp2 expression. In conclusion, our findings highlighted that IDR-1018 relieved HIBD in neonatal mice via the MIAT/miR-7a-5p/Plp2 axis.

CeRNAshiny: An Interactive R/Shiny App for Identification and Analysis of ceRNA Regulation.

The competing endogenous RNA (ceRNA) network is a newly discovered post-transcriptional regulation that controls both physiological and pathological progresses. Increasing research studies have been pivoted on this theory to explore the function of novel non-coding RNAs, pseudogenes, circular RNAs, and messenger RNAs. Although there are several R packages or computational tools to analyze ceRNA networks, an urgent need for easy-to-use computational tools still remains to identify ceRNA regulation. Besides, the conventional tools were mainly devoted to investigating ceRNAs in malignancies instead of those in neurodegenerative diseases. To fill this gap, we developed ceRNAshiny, an interactive R/Shiny application, which integrates widely used computational methods and databases to provide and visualize the construction and analysis of the ceRNA network, including differential gene analysis and functional annotation. In addition, demo data in ceRNAshiny could provide ceRNA network analyses about neurodegenerative diseases such as Parkinson’s disease. Overall, ceRNAshiny is a user-friendly application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying ceRNA networks.

Genome-Wide Identification and Characterization of Long Noncoding RNAs in Populus × canescens Roots Treated With Different Nitrogen Fertilizers.

Nitrate (NO3−) and ammonium (NH4+) are the primary forms of inorganic nitrogen acquired by plant roots. LncRNAs, as key regulators of gene expression, are a class of non-coding RNAs larger than 200 bp. However, knowledge about the regulatory role of lncRNAs in response to different nitrogen forms remains limited, particularly in woody plants. Here, we performed strand-specific RNA-sequencing of P. × canescens roots under three different nitrogen fertilization treatments. In total, 324 lncRNAs and 6,112 mRNAs were identified as showing significantly differential expression between the NO3− and NH4NO3 treatments. Moreover, 333 lncRNAs and 6,007 mRNAs showed significantly differential expression between the NH4+ and NH4NO3 treatments. Further analysis suggested that these lncRNAs and mRNAs have different response mechanisms for different nitrogen forms. In addition, functional annotation of cis and trans target mRNAs of differentially expressed lncRNAs indicated that 60 lncRNAs corresponding to 49 differentially expressed cis and trans target mRNAs were involved in plant nitrogen metabolism and amino acid biosynthesis and metabolism. Furthermore, 42 lncRNAs were identified as putative precursors of 63 miRNAs, and 28 differentially expressed lncRNAs were potential endogenous target mimics targeted by 96 miRNAs. Moreover, ceRNA regulation networks were constructed. MSTRG.6097.1, MSTRG.13550.1, MSTRG.2693.1, and MSTRG.12899.1, as hub lncRNAs in the ceRNA networks, are potential candidate lncRNAs for studying the regulatory mechanism in poplar roots under different nitrogen fertilization treatments. The results provide a basis for obtaining insight into the molecular mechanisms of lncRNA responses to different nitrogen forms in woody plants.

Integrative analysis of circRNA, miRNA, and mRNA profiles to reveal ceRNA regulation in chicken muscle development from the embryonic to post-hatching periods

BackgroundThe growth and development of skeletal muscle are regulated by protein-coding genes and non-coding RNA. Circular RNA (circRNA) is a type of non-coding RNA involved in a variety of biological processes, especially in post-transcriptional regulation. To better understand the regulatory mechanism of circRNAs during the development of muscle in chicken, we performed RNA-seq with linear RNA depletion for chicken breast muscle in 12 (E 12) and17 (E 17) day embryos, and 1 (D 1), 14 (D 14), 56 (D 56), and 98 (D 98) days post-hatch.ResultsWe identified 5755 differentially expressed (DE)-circRNAs during muscle development. We profiled the expression of DE-circRNAs and mRNAs (identified in our previous study) at up to six time points during chicken muscle development and uncovered a significant profile (profile 16) for circRNA upregulation during aging in muscle tissues. To investigate competing endogenous RNA (ceRNA) regulation in muscle and identify muscle-related circRNAs, we constructed a circRNA-miRNA-mRNA regulatory network using the circRNAs and mRNAs from profile 16 and miRNAs identified in our previous study, which included 361 miRNAs, 68 circRNAs, 599 mRNAs, and 31,063 interacting pairs. Functional annotation showed that upregulated circRNAs might contribute to glycolysis/gluconeogenesis, biosynthesis of amino acids, pyruvate metabolism, carbon metabolism, glycogen and sucrose metabolism through the ceRNA network, and thus affected postnatal muscle development by regulating muscle protein deposition. Of them, circRNA225 and circRNA226 from the same host gene might be key circRNAs that could regulate muscle development by interacting with seven common miRNAs and 207 mRNAs. Our experiments also demonstrated that there were interactions among circRNA225, gga-miR-1306-5p, and heat shock protein alpha 8 (HSPA8).ConclusionsOur results suggest that adequate supply of nutrients such as energy and protein after hatching may be a key factor in ensuring chicken yield, and provide several candidate circRNAs for future studies concerning ceRNA regulation during chicken muscle development.

Construction of a lncRNA-associated competing endogenous RNA regulatory network after traumatic brain injury in mouse

Traumatic brain injury (TBI) is a major public health problem worldwide which causes high mortality and disability. Functioning as microRNA (miRNA) sponges, long non-coding RNA (lncRNA) regulates the expression of protein-coding genes in a competing endogenous RNA (ceRNA) network. However, the lncRNA-associated ceRNA in TBI remains unclear. In this study, we processed the raw SRR files of mice cortex samples of sham injury (n = 3) and TBI groups (n = 3) to count files. Then, the expression profiles of lncRNAs and mRNAs were identified, and 86 differentially expressed (DE) lncRNAs and 1201 DEmRNAs between sham and TBI groups were identified. The DEmRNAs were used to perform enrichment analyses. Next, a lncRNA-miRNA-mRNA regulatory ceRNA network was constructed. The network consisted of 23 mRNAs, 5 miRNAs and 2 lncRNAs. The expression alternations of the 5 miRNAs were validated via qRT-PCR. The subnetwork of hub lncRNA Neat1 was extracted. We identified a potential inflammatory associated regulatory axis: Neat1/miR-31-5p/Myd88 axis. The PPI network based on DEmRNA involved in ceRNA network was constructed PPI networks to identify the hub genes. Finally, DElncRNAs and DEmRNAs were selected randomly and validated by qRT-PCR. In conclusion, with the lncRNA-miRNA-mRNA ceRNA network provided above, we can improve our understanding of the regulatory mechanisms and interaction among lncRNAs, miRNAs and mRNAs in TBI process.

Construction of PARPi Resistance-related Competing Endogenous RNA Network.

Objective: Ovarian cancer is a kind of common gynecological malignancy in women. PARP inhibitors (PARPi) have been approved for ovarian cancer treatment. However, the primary and acquired resistance have limited the application of PARPi. The mechanisms remain to be elucidated. Methods: In this study, we characterized the expression profiles of mRNA and nonconding RNAs (ncRNAs) and constructed the regulatory networks based on RNA sequencing in PARPi Olaparib-induced ovarian cancer cells. Results: We found that the functions of the differentially expressed genes were enriched in "PI3K/AKT signaling pathway," "MAPK signaling pathway" and "metabolic process". The functions of DELs (cis) were enriched in "Human papillomavirus infection""tight junction" "MAPK signaling pathway". As the central regulator of ceRNAs, the differentially expressed miRNAs were enriched in "Human papillomavirus infection" "MAPK signaling pathway" "Ras signaling pathway". According to the degree of interaction, we identified 3 lncRNAs, 2 circRNAs, 7 miRNAs, and 12 mRNA as the key regulatory ceRNA axis, in which miR-320b was the important mediator. Conclusion: Here, we revealed the key regulatory lncRNA (circRNA)-miRNA-mRNA axis and their involved pathways in the PARPi resistant ovarian cancer cells. These findings provide new insights into exploring the ceRNA regulatory networks and developing new targets for PARPi resistance.

Construction of lncRNA-Mediated Competing Endogenous RNA Networks Correlated With T2 Asthma.

Background: Precise classification has been reported as a central challenge in the clinical research on diagnosis and prediction of treatment efficacy in asthma. In this study, the aim was to investigate the underlying competing endogenous RNA network mechanism of asthma, especially T2 asthma, as well as to find more diagnostic biomarkers and effective therapeutic targets. Methods: Multiple sets of T2 asthma airway biopsy transcription profiles were collected, which involved long non-coding RNA (lncRNA), mRNA, and microRNA (miRNA). DIANA-LncBase, targetscan, mirwalk, and miRDB databases were employed to predict interactions between lncRNAs, miRNAs and target mRNAs. To identify mRNAs correlated with T2 asthma, differential expression and network analyses were conducted through weighted gene co-expression network analysis (WGCNA). Subsequently, the expressions of potential biomarkers were examined through qRT-PCR analysis in the T2 asthma coreinteracting cellular factor (IL-13/IL-33) induced experimental model. Lastly, the ceRNA network was confirmed by plasmid transfection and RNAi experiments in a 16HBE cell line. Results: 30 lncRNAs, 22 miRNAs and 202 mRNAs were differentially expressed in airway biopsies from T2 asthma patients. As indicated by the ROC analysis, the lncRNA, PCAT19, had high diagnostic accuracy (AUC >0.9) in distinguishing T2 asthma patients from non-T2 asthma patients and healthy controls. Furthermore, a competing ceRNA network was established, consisting of 13 lncRNAs, 12 miRNAs, as well as eight mRNAs. The reliability of this network was verified by testing several representative interactions in the network. Conclusion: To the best of our knowledge, this study has been the first to establish an lncRNA-mediated ceRNA regulatory network for studying T2 asthma. The findings of this study may elucidate the pathogenesis and help find potential therapeutic targets for T2 asthma. In T2 asthma, PCAT19-dominated ceRNA regulation networks may play a critical role, and PCAT19 may serve as a potential immune-related biomarker for asthma and other respiratory diseases correlated with eosinophilic inflammation.

LncRNA ADAMTS9-AS1/circFN1 Competitively Binds to miR-206 to Elevate the Expression of ACTB, Thus Inducing Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease and can result in substantial disability. The current study explored the potentials of long noncoding RNA- (lncRNA-) circular RNA- (circRNA-) microRNA- (miRNA-) messenger RNA (mRNA) networks in HCM. Firstly, HCM-related microarray data were procured from the GEO database, with differentially expressed genes (DEGs) obtained. HCM-related target genes were retrieved in combination with GeneCards and CTD databases, and candidate target genes were subsequently obtained by intersection screening. Further, an interaction network diagram of candidate target genes was constructed using the STRING database, and the hub genes in the network were determined according to the core degree. The “ClusterProfiler” package of the R software was adopted for GO and KEGG analyses of candidate target genes, to analyze the potential molecular pathways in HCM. Next, upstream miRNA, lncRNA, and circRNA of ACTB were predicted with RNAInter, mirDIP, TargetScan, DIANA-LncBase, and StarBase databases, followed by construction of lncRNA/circRNA-miRNA-mRNA coexpression networks. ACTB, miR-206, circFN1, and ADAMTS9-AS1 expression in peripheral blood samples from HCM patients and normal healthy controls were detected using RT-qPCR. Moreover, rat cardiomyocyte cell lines H9c2 and HEK293 cells were selected for in vitro verification of competitive endogenous RNA (ceRNA) regulation mechanism. A total of 15 candidate target genes related to HCM were screened using the online databases. Further protein-protein interaction analysis identified ACTB as the hub gene for HCM. The targeted binding relationship between miR-206, miR-145-5p, miR-1-3p, and ACTB was found. Furthermore, ADAMTS9-AS1 and circFN1 were discovered as the upstream genes of miR-206. Moreover, ADAMTS9-AS1, circFN1, and ACTB were found to be poorly expressed, and miR-206 was highly expressed in HCM. In vitro experimentation further confirmed that ADAMTS9-AS1 and circFN1 could competitively bind to miR-206, thereby augmenting ACTB expression. Taken all, ADAMTS9-AS1/circFN1-miR-206-ACTB regulatory network may involve in HCM occurrence, providing a novel theoretical basis for in-depth understanding of mechanism of HCM.

Identification of Potential Molecular Mechanism Related to Infertile Endometriosis.

ObjectivesIn this research, we aim to explore the bioinformatic mechanism of infertile endometriosis in order to identify new treatment targets and molecular mechanism.MethodsThe Gene Expression Omnibus (GEO) database was used to download MRNA sequencing data from infertile endometriosis patients. The “limma” package in R software was used to find differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to classify genes into modules, further obtained the correlation coefficient between the modules and infertility endometriosis. The intersection genes of the most disease-related modular genes and DEGs are called gene set 1. To clarify the molecular mechanisms and potential therapeutic targets for infertile endometriosis, we used Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia (KEGG) enrichment, Protein-Protein Interaction (PPI) networks, and Gene Set Enrichment Analysis (GSEA) on these intersecting genes. We identified lncRNAs and miRNAs linked with infertility and created competing endogenous RNAs (ceRNA) regulation networks using the Human MicroRNA Disease Database (HMDD), mirTarBase database, and LncRNA Disease database.ResultsFirstly, WGCNA enrichment analysis was used to examine the infertile endometriosis dataset GSE120103, and we discovered that the Meorangered1 module was the most significantly related with infertile endometriosis. The intersection genes were mostly enriched in the metabolism of different amino acids, the cGMP-PKG signaling pathway, and the cAMP signaling pathway according to KEGG enrichment analysis. The Meorangered1 module genes and DEGs were then subjected to bioinformatic analysis. The hub genes in the PPI network were performed KEGG enrichment analysis, and the results were consistent with the intersection gene analysis. Finally, we used the database to identify 13 miRNAs and two lncRNAs linked to infertility in order to create the ceRNA regulatory network linked to infertile endometriosis.ConclusionIn this study, we used a bioinformatics approach for the first time to identify amino acid metabolism as a possible major cause of infertility in patients with endometriosis and to provide potential targets for the diagnosis and treatment of these patients.

Elevation of ADAM12 facilitates tumor progression by enhancing metastasis and immune infiltration in gastric cancer.

A disintegrin and metalloprotease 12 (ADAM12), an essential transmembrane protein with metalloprotease, cell binding and intracellular signal-regulating capabilities, has been reported to play a crucial role in various types of cancers. However, the biological function of ADAM12 in gastric cancer (GC) remains unclear. Bioinformatic and experimental analyses were used to determine the expression level and prognostic value of ADAM12 in GC. The level of DNA methylation and the competing endogenous RNA (ceRNA) network was identified using MethSurv, Starbase3.0, miRNet2.0 and experimental analyses. Then, the co-expression profiles of ADAM12 were determined and subjected to enrichment analysis using the LinkedOmics database. The protein-protein interaction network and the docking model of ADAM12 were constructed using the GeneMANIA, STRING, and HDOCK webservers. The role of ADAM12 in tumor metastasis and immune infiltration was investigated using in vitro assays and TIMER database exploration. It was found that ADAM12 was overexpressed and was correlated with a poor prognosis of GC patients. In addition, the aberrant DNA methylation status and ceRNA regulation may contribute to the upregulation of ADAM12 in GC. Moreover, the enrichment analysis revealed that ADAM12 is involved in multiple vital biological functions and pathways, such as 'macrophage activation', 'extracellular matrix binding' and 'ECM-receptor interaction'. Subsequently, the protein-protein interaction network and molecular docking model demonstrated that follistatin like 3 (FSTL3) is a potential binding partner of ADAM12. Finally, it was demonstrated that ADAM12 promotes tumor metastasis, immune infiltration and M2 macrophage polarization in GC. In summary, these results highlight the potential of ADAM12 to be used as a therapeutic target for GC.