Endogenous RNA Regulatory Network Research Articles

Integrating multiple microarray dataset analysis and machine learning methods to reveal the key genes and regulatory mechanisms underlying human intervertebral disc degeneration.

Intervertebral disc degeneration (IDD), a major cause of lower back pain, has multiple contributing factors including genetics, environment, age, and loading history. Bioinformatics analysis has been extensively used to identify diagnostic biomarkers and therapeutic targets for IDD diagnosis and treatment. However, multiple microarray dataset analysis and machine learning methods have not been integrated. In this study, we downloaded the mRNA, microRNA (miRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA) expression profiles (GSE34095, GSE15227, GSE63492 GSE116726, GSE56081 and GSE67566) associated with IDD from the GEO database. Using differential expression analysis and recursive feature elimination, we extracted four optimal feature genes. We then used the support vector machine (SVM) to make a classification model with the four optimal feature genes. The ROC curve was used to evaluate the model’s performance, and the expression profiles (GSE63492, GSE116726, GSE56081, and GSE67566) were used to construct a competitive endogenous RNA (ceRNA) regulatory network and explore the underlying mechanisms of the feature genes. We found that three miRNAs (hsa-miR-4728-5p, hsa-miR-5196-5p, and hsa-miR-185-5p) and three circRNAs (hsa_circRNA_100723, hsa_circRNA_104471, and hsa_circRNA_100750) were important regulators with more interactions than the other RNAs across the whole network. The expression level analysis of the three datasets revealed that BCAS4 and SCRG1 were key genes involved in IDD development. Ultimately, our study proposes a novel approach to determining reliable and effective targets in IDD diagnosis and treatment.

MiR-608 rs4919510 Polymorphism May Affect Susceptibility to Colorectal Cancer by Upregulating MRPL43 Expression.

There are many studies on the association between miR-608 rs4919510 polymorphism and susceptibility to colorectal cancer (CRC). However, the role of rs4919510 in CRC development and its underlying mechanism remain unclear. We first evaluated the gene that may be regulated by the variation of rs4919510 through a two-stage expression quantitative trait loci analysis and then compared the expression of that identified gene in CRC tissues and adjacent nontumor tissues. Next, methyl thiazolyl tetrazolium (MTT) assay, transwell assay, and flow cytometry analyses were performed to investigate the in vitro capacity of cell proliferation, migration, invasion, apoptosis, and cell cycle of CRC cells, respectively. Finally, through bioinformatics prediction, we contrasted the regulatory network and identified microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) that could regulate the obtained gene. We found that the variant G allele of rs4919510 located in miR-608 was associated with a potentially increased expression of MRPL43 in colon tissues (p = 0.065). Moreover, the results of functional experiments suggested that knockdown of the MRPL43 gene could inhibit the growth of the CRC HCT-116 cell line and promote apoptosis. Additionally, the cell cycle of CRC HCT-116 cell line was significantly arrested at the G2 phase. Next, we obtained a competing endogenous RNA regulatory network of MRPL43 with 17 pairs of miRNAs-lncRNAs by bioinformatics prediction, out of which, survival analysis indicated that different expression levels of miR-193b-3p (p = 0.0269) and miR-194-3p (p = 0.0113) were associated with overall survival in CRC patients. The rs4919510 variant G allele in miR-608 may increase the proliferation, invasion, and migration ability and decrease the apoptosis of CRC HCT-116 cell line by upregulating the expression of MRPL43, ultimately may affect the risk of CRC. Moreover, miR-193b-3p and miR-194-3p that target MRPL43 may serve as potential predictive biomarkers of CRC survival.

Exploring biomarkers and therapeutic targets for pressure overload induced heart failure based on microarray data.

Heart failure (HF) is an end stage heart condition with poor prognosis which brings about tremendous social medical cost. Along decades, mechanism and treatments of HF have been under restless research. In the present study, we first established pressure overload induced HF model using transaortic arch constriction (TAC) method in mice. The global expression profiles of long noncoding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) were obtained by microarray probes, which were further confirmed by quantitative PCR (qPCR). Bioinformatics analysis was performed using multiple methods including volcano plotting, heatmapping and hierarchical clustering, Gene Ontology (GO) and pathway enrichment analysis, and competing endogenous RNA (ceRNA) regulatory network construction. Totally, 1,139 differentially expressed mRNAs (DEmRNAs), 3,830 lncRNAs (DElncRNAs) and 13 miRNAs (DEmiRNAs) were identified in HF group compared to control group, which could distinctly differentiate HF from normal control and were potential candidate biomarkers for HF. GO and pathway enrichment analysis revealed that multiple significant biological processes and pathways were involved in HF pathogenesis, such as extracellular matrix structural constituent, proteinaceous extracellular matrix, positive regulation of apoptotic process and integrin signaling pathway. Nine DElncRNAs, 3 DEmiRNAs and 25 DEmRNAs were filtrated out to construct a ceRNA network, which visually displayed their regulatory roles with therapeutic target potential. The present study identified differentially expressed RNAs that might be involved in the pathogenesis and progression of HF. The outcomes shed lights into the underlying mechanisms for HF and provided candidate biomarkers and intervention targets for further research.

A prognostic long non-coding RNA-associated competing endogenous RNA network in head and neck squamous cell carcinoma.

BackgroundThis study aimed to develop multi-RNA-based models using a competing endogenous RNA (ceRNA) regulatory network to provide survival risk prediction in head and neck squamous cell carcinoma (HNSCC).MethodsAll long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA expression data and clinicopathological features related to HNSCC were derived from The Cancer Genome Atlas. Differentially expressed RNAs were calculated using R. Prognostic factors were identified using univariate Cox regression analysis. Functional analysis was performed using GO, KEGG pathways, and PPI network. Based on the results, we derived a risk signature and compared high- and low-risk subgroups using LASSO regression analysis. Survival analysis and the relationship between risk signature and clinicopathological features were performed using log-rank tests and Cox regression analysis. A ceRNA regulatory network was constructed, and prognostic lncRNAs and miRNA expression levels were validated in vitro and in vivo.ResultsA list of 207 lncRNAs, 18 miRNAs and 362 mRNAs related to overall survival was established. Five lncRNAs (HOTTIP, LINC00460, RMST, SFTA1P, and TM4SF19-AS1), one miRNA (hsa-miR-206), and one mRNA (STC2) were used to construct the ceRNA network. Three prognostic models contained 13 lncRNAs, eight miRNAs, and 17 mRNAs, which correlated with the patient status, disease-free survival (DFS), stage, grade, T stage, N stage, TP53 mutation status, angiolymphatic invasion, HPV status, and extracapsular spread. KEGG pathway analysis revealed significant enrichment of “Transcriptional misregulation in cancer” and “Neuroactive ligand-receptor interaction.” In addition, HOTTIP, LINC00460, miR-206 and STC2 were validated in GTEx data, GEO microarrays and six HNSCC cell lines.ConclusionsOur findings clarify the interaction of ceRNA regulatory networks and crucial clinicopathological features. These results show that prognostic biomarkers can be identified by constructing multi-RNA-based prognostic models, which can be used for survival risk prediction in patients with HNSCC.

Identification of Biomarkers to Construct a Competing Endogenous RNA Network and Establishment of a Genomic-Clinicopathologic Nomogram to Predict Survival for Children with Rhabdoid Tumors of the Kidney.

Rhabdoid tumor of the kidney (RTK) is a rare and severely malignant tumor occurring in infancy and early childhood, with the overall outcomes remain poor. Neither gene regulatory networks nor biomarkers to predict the prognostic outcomes have been elucidated in RTK. In this study, RNA sequencing data were obtained to identify differentially expressed messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) between RTK samples and normal samples. A total of 4217 mRNAs, 284 lncRNAs, and 286 miRNAs were screened out. Of those, 103 mRNAs, 80 lncRNAs, and 45 miRNAs were identified for a competing endogenous RNA (ceRNA) regulatory network, in which three significant modules were identified. A protein-protein interaction (PPI) network was constructed, and the hub-gene cluster consisted of four core genes (EXOSC2, PAK1IP1, WDR43, and POLR1D) was selected. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed to analyze the functional characteristics of differentially expressed mRNAs. Subsequently, among 211 mRNAs, 8 lncRNAs, and 12 miRNAs associated with overall survival (OS) obtained by univariate Cox analysis, 5 mRNAs, 7 lncRNAs, and 7 miRNAs were identified and the risk score formulas were constructed correspondingly using the least absolute shrinkage and selection operator (LASSO) Cox regression model analysis. The log-rank tests and Kaplan-Meier analyses were performed to confirm the predictive value of the risk scores for OS in RTK patients. A genomic-clinicopathologic nomogram integrating the stage and risk scores based on RNAs was established and demonstrated high predictive accuracy and clinical value, which was validated through calibration curves, time-dependent receiver operating characteristic (ROC) curve analyses, and decision curve analysis (DCA). In conclusion, this study not only provided potential insights into the mechanisms underlying RTK, but also presented a practicable tool for predicting the prognosis in children with RTK.

Role of long noncoding RNA-mediated competing endogenous RNA regulatory network in hepatocellular carcinoma.

Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) are noncoding RNAs (ncRNAs) that occupy over 90% of the human genome, and their main function is to directly or indirectly regulate messenger RNA (mRNA) expression and participate in the tumorigenesis and progression of malignances. In particular, some lncRNAs can interact with miRNAs as competing endogenous RNAs (ceRNAs) to modulate mRNA expression. Accordingly, these RNA molecules are interrelated and coordinate to form a dynamic lncRNA-mediated ceRNA regulatory network. Mounting evidence has revealed that lncRNAs that act as ceRNAs are closely related to tumorigenesis. To date, numerous studies have established many different regulatory networks in hepatocellular carcinoma (HCC), and perturbations in these ceRNA interactions may result in the initiation and progression of HCC. Herein, we emphasize recent advances concerning the biological function of lncRNAs as ceRNAs in HCC, with the aim of elucidating the molecular mechanism underlying these HCC-related RNA molecules and providing novel insights into the diagnosis and treatment of HCC.

Construction of a circRNA-Related ceRNA Prognostic Regulatory Network in Breast Cancer.

PurposeAccumulating evidence has indicated that circRNAs are closely involved in tumorigenesis and progression of human cancers. However, the molecular mechanism underlying function of circRNAs in breast cancer has not been thoroughly elucidated. Currently, we aimed to characterize the circRNA-related competing endogenous RNA (ceRNA) regulatory network in breast cancer and to construct prognostic model.Materials and MethodsFirst, we constructed circRNA expression profiles for paired breast cancer in a Chinese population using a human circRNA microarray. Expression profiles of circRNAs, miRNAs, and mRNAs were retrieved from our circRNA dataset, the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We applied the limma and edgeR packages to identify differentially expressed RNAs. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules of mRNAs. Next, a ceRNA network was established based on circRNA–miRNA and miRNA–mRNA intersections. Both Cox regression analysis and ROC curve analysis were performed to generate prognostic model. Additionally, we performed Gene Set Enrichment Analysis (GSEA) on prognostic signatures.ResultsTotal of 59 circRNAs, 98 miRNAs and 3966 mRNAs were identified as differentially expressed RNAs. We first identified 38 miRNA-mRNA pairs and 38 circRNA-miRNA pairs to construct the circRNA–miRNA-mRNA regulatory network and then generated a prognostic model based on 7 signatures (MMD, SLC29A4, CREB5, FOS, ANKRD29, MYOCD, and PIGR), and patients with high-risk scores presented poor prognosis. Several cancer-related pathways were enriched, including the TGF-β pathway, the focal adhesion pathway, and the JAK-STAT signaling pathway, and 20 prognostic ceRNA regulatory networks were subsequently identified.ConclusionIn all, we screened a series of dysregulated circRNAs, miRNAs, and mRNAs, and constructed circRNA-related ceRNA network in breast cancer. Our findings may help to deepen the understanding of circRNA-related regulatory mechanisms. Moreover, we generated a prognostic model that provided new insight into postoperative management for breast cancer.

Knockdown of lncRNA ANRIL inhibits the development of cisplatin resistance by upregulating miR‑98 in lung cancer cells.

Emerging evidence has demonstrated that abnormally expressed long non‑coding (lnc) RNAs contribute to drug resistance in various types of malignancy. LncRNA antisense non‑coding RNA in the inhibitor of cyclin‑dependent kinase 4 locus (ANRIL) exerts oncogenic activity and acts as a key player in a variety of carcinomas, including non‑small cell lung cancer. The present study aimed to investigate the functional roles of ANRIL in cisplatin (DDP) resistance of lung cancer and the underlying mechanism involved in the competing endogenous RNA regulatory network. The expression levels of ANRIL and microRNA (miR)‑98 in lung cancer tissues and DDP‑resistant lung cancer cells were assessed by reverse transcription‑quantitative (RT‑q) PCR. The Pearson's correlation coefficient was used to determine the correlation between the expression levels of ANRIL and miR‑98 in lung cancer tissues. Cell proliferation and apoptosis were detected by MTT assay and flow cytometry analysis, respectively. The expression levels of proliferation‑ and apoptosis‑associated proteins were detected by western blot analysis. The potential interaction between ANRIL and miR‑98 was confirmed by RNA immunoprecipitation (RIP), dual luciferase reporter assay and RT‑qPCR. The results of the present study demonstrated that ANRIL was upregulated and miR‑98 was downregulated in lung cancer tissues and A549/DDP cells. In addition, the Pearson's correlation analysis revealed that the expression of ANRIL was significantly negatively correlated with that of miR‑98 in lung cancer tissues. ANRIL overexpression reversed the DDP‑induced cell proliferation suppression and apoptosis in lung cancer cells, whereas ANRIL knockdown exhibited the opposite effects. RIP, dual luciferase reporter assay and RT‑qPCR analysis results demonstrated that ANRIL directly interacted with miR‑98 and suppressed miR‑98 expression. Furthermore, transfection with the miR‑98 mimics promoted DDP‑induced cell proliferation inhibition and apoptosis in lung cancer cells, and these effects were partially reversed by ANRIL overexpression. In conclusion, ANRIL knockdown inhibited the development of DDP resistance by upregulating miR‑98, providing novel insights into the molecular mechanism of ANRIL involvement in DDP resistance of lung cancer cells.

Differentially expressed lncRNAs, miRNAs and mRNAs with associated ceRNA networks in a mouse model of myocardial ischemia/reperfusion injury.

Non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs/miRs), have significant regulatory effects on a number of biological processes in myocardial ischemia/reperfusion (I/R) injury, including cell differentiation, proliferation and apoptosis. In the present study, the expression levels of lncRNAs, miRNAs and mRNAs were evaluated in a mouse model of myocardial I/R injury. The potential functions of these differentially expressed genes were then analyzed via Gene Ontology and pathway analyses. Additionally, the interactions between lncRNA-miRNA-mRNA were predicted by constructing a competing endogenous RNA regulatory network. It was found that 14,366 lncRNAs, 151 miRNAs and 9,377 mRNAs were differentially expressed in mice hearts after I/R compared with the Sham group (fold change >2; P<0.05). The results indicated that these differentially expressed genes were involved in multiple molecular functions, including ‘guanosine diphosphate binding’, ‘RNA polymerase II carboxy-terminal domain kinase activity’, ‘TATA-binding protein-class protein binding’, ‘nicotinamide adenine dinucleotide binding’ and ‘protein phosphatase type 2A regulator activity’. The interactions between lncRNA-miRNA-mRNA, including five lncRNAs, 38 miRNAs and 196 mRNAs, were predicted, specifically Gm12040-mmu-miR-125a-5p-decapping mRNA 1B, Rpl7l1-ps1-mmu-miR-124-3p-G protein-coupled receptor 146, Gm11407-mmu-miR-190a-5p-homeobox and leucine zipper encoding (HOMEZ), 1600029O15Rik-mmu-miR-132-3p-HOMEZ and AK155692-mmu-miR-1224-3p-activating transcription factor 6β. Collectively, these findings provided novel insights for future research on lncRNAs, miRNAs and mRNAs in myocardial I/R injury.

Integrative analysis of competitive endogenous RNA network reveals the regulatory role of non-coding RNAs in high-glucose-induced human retinal endothelial cells.

BackgroundIncreasing evidence has suggested that non-coding RNAs (ncRNAs) play critical roles in the pathogenesis of diabetic retinopathy (DR), but their underlying mechanisms remain unclear. The purpose of this study was to determine latent key genes and to structure a competing endogenous RNA (ceRNA) regulatory network to discover the potential molecular mechanisms governing the effects of high glucose on human retinal endothelial cells (HRECs).MethodsWe obtained microarray data for long non-coding RNA (lncRNA) and mRNA of high-glucose-induced HREC samples from NCBI GEO datasets. The ceRNA network was screened using intersecting prediction results from miRcode, TargetScan, miRTarBase and miRDB. The protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes and hub genes were obtained using the cytoHubba app. The ClusterProfiler package was applied for performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The expression of key RNAs was verified using the qRT-PCR method. A key ceRNA subnetwork was constructed based on the criticality of the genes and its binding sites were verified by luciferase reporter assay. The viability and apoptosis of HRECs were tested using the transfection of the miR-449c inhibitor.ResultsA total of 3,328 lncRNAs and 2,017 mRNAs were screened for differentially expressed (DE) profiles. The newly constructed ceRNA network was composed of 410 lncRNAs, 35 miRNAs and 122 mRNAs. The 10 hub genes were identified through the PPI network. GO and KEGG analysis revealed that DE mRNAs were mainly related to the positive regulation of the mRNA catabolic process, cell polarity, and the G1/S transition of mitotic and cell cycle signaling pathways. QRT-PCR was used to verify RNAs and the most important genes were screened out. A key ceRNA subnetwork OIP5-AS1/miR-449c/MYC was established. The binding site was verified by luciferase reporter assay. The expression levels of OIP5-AS1 and MYC increased after miR-449c inhibitor transfection, miR-449c decreased, HRECs activity increased, and apoptosis decreased, compared with the control group.ConclusionWe successfully built the key ceRNA subnetwork, OIP5-AS1/miR-449c/MYC, by applying the GEO database for data analysis and mining. The results from the ceRNA network allow us to better understand the effect of ncRNAs on HRECs under hyperglycemic conditions and the pathogenesis of DR.

Identification of circRNA and mRNA expression profiles and functional networks of vascular tissue in lipopolysaccharide-induced sepsis.

Sepsis is the most common cause of death in intensive care units. This study investigated the circular RNA (circRNA) and mRNA expression profiles and functional networks of the aortic tissue in sepsis. We established a lipopolysaccharide (LPS)‐induced rat sepsis model. High‐throughput sequencing was performed on the aorta tissue to identify differentially expressed (DE) circRNAs and mRNAs, which were validated by real‐time quantitative polymerase chain reaction (RT‐qPCR). Bioinformatic analysis was carried out and coding and non‐coding co‐expression (CNC) and competing endogenous RNA (ceRNA) regulatory networks were constructed to investigate the mechanisms. In total, 373 up‐regulated and 428 down‐regulated circRNAs and 2063 up‐regulated and 2903 down‐regulated mRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs showed that the down‐regulated genes were mainly enriched in the process of energy generation. CNC and ceRNA regulatory networks were constructed with seven DE circRNAs. The results of functional enrichment analysis of CNC target genes revealed the important role of circRNAs in inflammatory response. The ceRNA network also highlighted the significant enrichment in calcium signalling pathway. Significant alterations in circRNAs and mRNAs were observed in the aortic tissue of septic rats. In addition, CNC and ceRNA networks were established.

High-Throughput Sequencing and Exploration of the lncRNA-circRNA-miRNA-mRNA Network in Type 2 Diabetes Mellitus.

Objective Long noncoding RNA (lncRNA) and circular RNA (circRNA) are receiving increasing attention in diabetes research. However, there are still many unknown lncRNAs and circRNAs that need further study. The aim of this study is to identify new lncRNAs and circRNAs and their potential biological functions in type 2 diabetes mellitus (T2DM). Methods RNA sequencing and differential expression analysis were used to identify the noncoding RNAs (ncRNAs) and mRNAs that were expressed abnormally between the T2DM and control groups. The competitive endogenous RNA (ceRNA) regulatory network revealed the mechanism of lncRNA and circRNA coregulating gene expression. The biological functions of lncRNA and circRNA were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The candidate hub mRNAs were selected by the protein-protein interaction (PPI) network and validated by using the Gene Expression Omnibus (GEO) database. Results Differential expression analysis results showed that 441 lncRNAs (366 upregulated and 75 downregulated), 683 circRNAs (354 upregulated and 329 downregulated), 93 miRNAs (63 upregulated and 30 downregulated), and 2923 mRNAs (1156 upregulated and 1779 downregulated) were identified as remarkably differentially expressed in the T2DM group. The ceRNA regulatory network showed that a single lncRNA and circRNA can be associated with multiple miRNAs, and then, they coregulate more mRNAs. Functional analysis showed that differentially expressed lncRNA (DElncRNA) and differentially expressed circRNA (DEcircRNA) may play important roles in the mTOR signaling pathway, lysosomal pathway, apoptosis pathway, and tuberculosis pathway. In addition, PIK3R5, AKT2, and CLTA were hub mRNAs screened out that were enriched in an important pathway by establishing the PPI network. Conclusions This study is the first study to explore the molecular mechanisms of lncRNA and circRNA in T2DM through the ceRNA network cofounded by lncRNA and circRNA. Our study provides a novel insight into the T2DM from the ceRNA regulatory network.

Identification of candidate lncRNAs and circRNAs regulating WNT3/β-catenin signaling in essential hypertension.

Mounting evidence suggests that noncoding RNAs (ncRNAs) contribute to the pathogenesis of cardiovascular diseases. However, their role in essential hypertension (EH) is still unclear. We therefore identified differentially expressed long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) in EH patients from a high-risk population group and constructed a competing endogenous RNA regulatory network that predicts interactions of potential diagnostic and therapeutic relevance between specific lncRNA/circRNA-microRNA-mRNA triplets. Our analysis identified two lncRNAs, transmembrane protein 183A pseudogene (LOC646616) and leucine aminopeptidase 3 pseudogene 2 (LAP3P2), and two circRNAs, hsa_circ_0039388 and hsa_circ_0038648, that are highly co-expressed with both wingless-type MMTV integration site family member 3 (WNT3) and calcium/calmodulin-dependent protein kinase II inhibitor 2 (CAMK2N2) mRNAs and also share common microRNA binding sites with these two transcripts. We also confirmed that a mutually regulated network composed of LOC646616/microRNA-637/WNT3 controls WNT3 expression and influences viability and invasive properties in human arterial smooth muscle cells in vitro. These findings highlight a novel ncRNA-based regulatory mechanism potentially driving WNT/β-catenin activation in EH, and suggest that the identified ncRNAs may represent useful biomarkers and therapeutic targets for this condition.

The whole-transcriptome landscape of muscle and adipose tissues reveals the ceRNA regulation network related to intramuscular fat deposition in yak

BackgroundThe Intramuscular fat (IMF) content in meat products, which is positively correlated with meat quality, is an important trait considered by consumers. The regulation of IMF deposition is species specific. However, the IMF-deposition-related mRNA and non-coding RNA and their regulatory network in yak (Bos grunniens) remain unknown. High-throughput sequencing technology provides a powerful approach for analyzing the association between transcriptome-related differences and specific traits in animals. Thus, the whole transcriptomes of yak muscle and adipose tissues were screened and analyzed to elucidate the IMF deposition-related genes. The muscle tissues were used for IMF content measurements.ResultsSignificant differences were observed between the 0.5- and 2.5-year-old yaks. Several mRNAs, miRNAs, lncRNAs and circRNAs were generally expressed in both muscle and adipose tissues. Between the 0.5- and 2.5-year-old yaks, 149 mRNAs, 62 miRNAs, 4 lncRNAs, and 223 circRNAs were differentially expressed in muscle tissue, and 72 mRNAs, 15 miRNAs, 9 lncRNAs, and 211 circRNAs were differentially expressed in adipose tissue. KEGG annotation revelved that these differentially expressed genes were related to pathways that maintain normal biological functions of muscle and adipose tissues. Moreover, 16 mRNAs, 5 miRNAs, 3 lncRNAs, and 5 circRNAs were co-differentially expressed in both types of tissue. We suspected that these co-differentially expressed genes were involved in IMF-deposition in the yak. Additionally, LPL, ACADL, SCD, and FASN, which were previously shown to be associated with the IMF content, were identified in the competing endogenous RNA (ceRNA) regulatory network that was constructed on the basis of the IMF deposition-related genes. Three ceRNA subnetworks also revealed that TCONS-00016416 and its target SIRT1 “talk” to each other through the same miR-381-y and miR-208 response elements, whereas TCONS-00061798 and its target PRKCA, and TCONS-00084092 and its target LPL “talk” to each other through miR-122-x and miR-499-y response elements, respectively.ConclusionTaken together, our results reveal the potential mRNA and noncoding RNAs involved in IMF deposition in the yak, providing a useful resource for further research on IMF deposition in this animal species.

Identification of METTL3 as an Adverse Prognostic Biomarker in Hepatocellular Carcinoma.

N6-methyladenosine (m6A), the most prominent mRNA modification, plays a critical role in many physiological and pathological processes. However, the roles of m6A RNA modification in hepatocellular carcinoma (HCC) remain largely unknown. We investigated the mRNA expression and clinical significance of m6A-related genes using data from The Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma cohort. Mutation, copy number variation (CNV), methylation, differential expression, and gene ontology analyses, gene set enrichment analysis and the construction of a competing endogenous RNA (ceRNA) regulatory network were performed to investigate the underlying mechanisms of the aberrant expression of m6A-related genes. m6A-related genes were frequently dysregulated in cancers but with a cancer-specific pattern. METTL3, YTHDF2, and ZC3H13 were found to be independent prognostic factors of overall survival (OS); however, only METTL3 was found to be an independent prognostic factor of recurrence-free survival (RFS). Joint effects analysis showed the predictive capacity of combining METTL3, YTHDF2, and ZC3H13 for HCC OS. Then the potential mechanisms of METTL3 were further explored due to its prognostic role in both OS and RFS. CNV and DNA methylation, but not somatic mutations, might contribute to the abnormal upregulation of METTL3 in HCC. Significantly altered genes, microRNAs, and lncRNAs were identified, and a ceRNA regulatory network was constructed to explain the upregulation of METTL3 in HCC. Our study identified several m6A-related genes, especially METTL3, that could be potential prognostic biomarkers in HCC.

A Key mRNA-miRNA-lncRNA Competing Endogenous RNA Triple Sub-network Linked to Diagnosis and Prognosis of Hepatocellular Carcinoma.

Growing evidence has illustrated critical roles of competing endogenous RNA (ceRNA) regulatory network in human cancers including hepatocellular carcinoma. In this study, we aimed to find promising diagnostic and prognostic biomarkers for patients with hepatocellular carcinoma. Three novel unfavorable prognosis-associated genes (CELSR3, GPSM2, and CHEK1) was first identified. We also demonstrated that these genes were significantly upregulated in hepatocellular carcinoma cell lines and tissues. Next, 154 potential miRNAs of CELSR3, GPSM2, and CHEK1 were predicted. CHEK1-hsa-mir-195-5p/hsa-mir-497-5p and GPSM2-hsa-mir-122-5p axes were defined as two key pathways in carcinogenesis of hepatocellular carcinoma by combination of in silico analysis and experimental validation. Subsequently, lncRNAs binding to hsa-mir-195-5p, hsa-mir-497-5p, and hsa-mir-122-5p were predicted via starBase and miRNet databases. After performing expression analysis and survival analysis for these predicted lncRNAs, we showed that nine lncRNAs (SNHG1, SNHG12, LINC00511, HCG18, FGD5-AS1, CERS6-AS1, NUTM2A-AS1, SNHG16, and ASB16-AS1) were markedly increased in hepatocellular carcinoma and their upregulation indicated poor prognosis. Moreover, a similar mRNA-miRNA-lncRNA analysis for six “known” genes (CLEC3B, DNASE1L3, PTTG1, KIF2C, XPO5, and UBE2S) was performed. Subsequently, a comprehensive mRNA-miRNA-lncRNA triple ceRNA network linked to prognosis of patients with hepatocellular carcinoma was established. Moreover, all RNAs in this network exhibited significantly diagnostic values for patients with hepatocellular carcinoma. In summary, the current study constructed a mRNA-miRNA-lncRNA ceRNA network associated with diagnosis and prognosis of hepatocellular carcinoma.

Construction and analysis of competitive endogenous RNA regulatory network related to gastric cancer

Objective: To construct the competitive endogenous RNA (ceRNA) network related to gastric cancer and explore the molecular mechanism. Methods: The expression profiles of lncRNA, miRNA and mRNA in gastric cancer and paracancer tissues were analyzed by biochip technology, edgeR package in R software was used to filtrate differential expression genes (multiple change of >1.5 times, P<0.05) and volcano map was drawn. Based on the online miRNA-lncRNA prediction tool lncBase database and the miRNA Target gene prediction database (miRTarBase, target-scan, miRDB, starBase), the relationship between miRNA, lncRNA and mRNA was predicted. Cytoscape software was used to construct lncRNA-miRNA-mRNA ceRNA network and key genes (hub genes) were identified based on cytohubba calculation of degree score of each node. Then Hub genes related to the prognosis of gastric cancer were verified in the TCGA database. The GO and KEGG enrichment analysis of differentially expressed mRNA was performed using the online biological information annotation database DAVID, P<0.05 and false discovery rate (FDR)<0.05 were used as cut-off criteria. R software was used to download the RNA sequencing data and mirna-seq data of gastric cancer and adjacent tissues in TCGA database, edgeR package was used to screen out differentially expressed mRNA, miRNA and lncRNA, and some differentially expressed genes in our data were verified. In OncoLnc database, STAD project of TCGA data was selected and hub gene was input. Patients were divided into two groups based on the median value for hub genes and Kaplan-meier analysis was performed. Results: The differentially expressed 766 mRNA, 110 lncRNA and 10 miRNA were screened out, among them 90 mRNA, 4 lncRNA and 6 miRNA were used to construct the ceRNA network, and 2 of the 20 hub genes were related to the prognosis of patients. MLK7-AS1, SPP1, SULF1, hsa-miR-1307-3p were upregulated in gastric cancer tissues from our biochip, while MT2A, MT1X were downregulated, which were consistent with the results of TCGA gastric cancer database. The differentially expressed mRNAs were significantly enriched in the biological process (BP) and the mineral absorption pathway. CHST1 was negatively correlated while miR-183-5p was positively corelated with the survival of patients. Conclusion: The establishment of ceRNA network for gastric cancer is conducive to further understanding of the molecular biological mechanism. CHST1 and miR-183-5p can be used as prognostic factors of gastric cancer.

Comprehensive analysis of dysregulated exosomal long non-coding RNA networks associated with arteriovenous malformations.

Arteriovenous malformations (AVMs) are congenital vascular lesions with a high tendency for aggravation and recurrence after treatment, and their genesis remains enigmatic. In this study, we investigated exosomal long non-coding RNA (lncRNA) and mRNA expression and constructed a competitive endogenous RNA regulatory network in AVMs. Ethics approval was provided, and informed written consent was given prior to the inclusion of all participants. Blood samples were obtained from patients with AVMs and healthy controls at Shanghai Ninth People's Hospital, China, from May to November 2018, and total exosomes were isolated and validated. Differentially expressed exosomal lncRNAs and mRNAs were detected by RNA-seq, analysed by bioinformatic methods and validated by qRT-PCR. A competitive endogenous RNA regulatory network was constructed. The characteristics of the captured extracellular vesicles conformed to the features of exosomes. A total of 117 dysregulated exosomal lncRNAs and 1159 dysregulated exosomal mRNAs were identified in AVMs. qRT-PCR demonstrated that the exosomal lncRNAs MIR4435-1HG, LINC00657, LOC101927854 and SEPT5-GP1BB were upregulated in AVM exosomes. The Gene Ontology (GO) terms haemopoiesis and negative regulation of neuron projection development were significantly enriched in relation to dysregulated exosomal cis lncRNAs. A total of 199 GO terms and 80 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched for the dysregulated exosomal mRNAs. In the exosomal lncRNA-miRNA-mRNA-related ceRNA regulatory network, the top 3 significant modules involved 31 dysregulated exosomal lncRNAs and 114 dysregulated exosomal mRNAs, which were enriched in the Rap 1, Ras, MAPK signalling pathways and platelet activation KEGG pathway. This study comprehensively identified dysregulated exosomal lncRNAs and mRNAs in AVMs, demonstrated the involvement of dysregulated lncRNA and mRNA patterns in AVMs and constructed an exosomal competitive endogenous RNA regulatory network.

A 6 lncRNA-Based Risk Score System for Predicting the Recurrence of Colon Adenocarcinoma Patients

Colon adenocarcinoma (COAD) is a common type of colon cancer, and post-operative recurrence and metastasis may occur in COAD patients. This study is designed to build a risk score system for COAD patients. The Cancer Genome Atlas (TCGA) dataset of COAD (the training set) was downloaded, and GSE17538 and GSE39582 (the validation sets) from Gene Expression Omnibus database were obtained. The differentially expressed RNAs (DERs) were analyzed by limma package. Using survival package, the independent prognosis-associated long non-coding RNAs (lncRNAs) were selected for constructing risk score system. After the independent clinical prognostic factors were screened out using survival package, a nomogram survival model was constructed using rms package. Furthermore, competitive endogenous RNA (ceRNA) regulatory network and enrichment analyses separately were performed using Cytoscape software and DAVID tool. Totally 404 DERs between recurrence and non-recurrence groups were identified. Based on the six independent prognosis-associated lncRNAs (including H19, KCNJ2-AS1, LINC00899, LINC01503, PRKAG2-AS1, and SRRM2-AS1), the risk score system was constructed. After the independent clinical prognostic factors (Pathologic M, pathologic T, and RS model status) were identified, the nomogram survival model was built. In the ceRNA regulatory network, there were three lncRNAs, four miRNAs, and 77 mRNAs. Additionally, PPAR signaling pathway and hedgehog signaling pathway were enriched for the mRNAs in the ceRNA regulatory network. The risk score system and the nomogram survival model might be used for predicting COAD recurrence. Besides, PPAR signaling pathway and hedgehog signaling pathway might affect the recurrence of COAD patients.

MicroRNAs: pivotal regulators in acute myeloid leukemia.

MicroRNAs are a class of small non-coding RNAs that are 19-22 nucleotides in length and regulate a variety of biological processes at the post-transcriptional level. MicroRNA dysregulation disrupts normal biological processes, resulting in tumorigenesis. Acute myeloid leukemia is an invasive hematological malignancy characterized by the abnormal proliferation and differentiation of immature myeloid cells. Due to the low 5-year survival rate, there is an urgent need to discover novel diagnostic markers and therapeutic targets. In recent years, microRNAs have been shown to play important roles in hematological malignancies by acting as tumor suppressors and oncogenes. MicroRNAs have the potential to be a breakthrough in the diagnosis and treatment of acute myeloid leukemia. In this review, we summarize the biology of microRNAs and discuss the relationships between microRNA dysregulation and acute myeloid leukemia in the following aspects: signaling pathways, the abnormal biological behavior of acute myeloid leukemia cells, the clinical application of microRNAs and competing endogenous RNA regulatory networks.