Identification of mitochondrial function‐associated lncRNAs in septic mice myocardium

The present study aimed to analyze long noncoding RNA (lncRNA) and messenger RNA (mRNA) expression profiles in septic mice heart and to identify potential lncRNAs and mRNAs that be responsible for cardiac mitochondrial dysfunction during sepsis. Mice were treated with 10 mg/kg of lipopolysaccharides to induce sepsis. LncRNAs and mRNAs expression were evaluated by using lncRNA and mRNA microarray or real‐time polymerase chain reaction technique. LncRNA‐mRNA coexpression network assay, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. The results showed that 1275 lncRNAs were differentially expressed in septic myocardium compared with those in the control group. A total of 2769 mRNAs were dysregulated in septic mice heart, most of which are mainly related to the process of inflammation, mitochondrial metabolism, oxidative stress, and apoptosis. Coexpression network analysis showed that 14 lncRNAs were highly correlated with 11 mitochondria‐related differentially expressed mRNA. Among all lncRNAs and their cis‐acting mRNAs, 41 lncRNAs‐mRNA pairs (such as NONMMUG004378 and Apaf1 gene) were enriched in GO terms and KEGG pathways. In summary, we gained some specific lncRNAs and their potential target mRNAs that might be involved in mitochondrial dysfunction in septic myocardium. These findings provide a panoramic view of lncRNA and might allow developing new treatment strategies for sepsis.     

A comprehensive approach to the molecular determinants of lifespan using a Boolean model of geroconversion

Altered molecular responses to insulin and growth factors (GF) are responsible for late‐life shortening diseases such as type‐2 diabetes mellitus (T2DM) and cancers. We have built a network of the signaling pathways that control S‐phase entry and a specific type of senescence called geroconversion. We have translated this network into a Boolean model to study possible cell phenotype outcomes under diverse molecular signaling conditions. In the context of insulin resistance, the model was able to reproduce the variations of the senescence level observed in tissues related to T2DM's main morbidity and mortality. Furthermore, by calibrating the pharmacodynamics of mTOR inhibitors, we have been able to reproduce the dose‐dependent effect of rapamycin on liver degeneration and lifespan expansion in wild‐type and HER2–neu mice. Using the model, we have finally performed an in silico prospective screen of the risk–benefit ratio of rapamycin dosage for healthy lifespan expansion strategies. We present here a comprehensive prognostic and predictive systems biology tool for human aging.     

The variant at TGFBRAP1 is significantly associated with type 2 diabetes mellitus and affects diabetes‐related miRNA expression

While the transforming growth factor‐β1 (TGF‐β1) regulates the growth and proliferation of pancreatic β‐cells, its receptors trigger the activation of Smad network and subsequently induce the insulin resistance. A case‐control was conducted to evaluate the associations of the polymorphisms of TGF‐β1 receptor‐associated protein 1 (TGFBRAP1) and TGF‐β1 receptor 2 (TGFBR2) with type 2 diabetes mellitus (T2DM), and its genetic effects on diabetes‐related miRNA expression. miRNA microarray chip was used to screen T2DM‐related miRNA and 15 differential expressed miRNAs were further validated in 75 T2DM and 75 normal glucose tolerance (NGT). The variation of rs2241797 (T/C) at TGFBRAP1 showed significant association with T2DM in case‐control study, and the OR (95% CI) of dominant model for cumulative effects was 1.204 (1.060‐1.370), Bonferroni corrected P < 0.05. Significant differences in the fast glucose and HOMA‐β indices were observed amongst the genotypes of rs2241797. The expression of has‐miR‐30b‐5p and has‐miR‐93‐5p was linearly increased across TT, TC, and CC genotypes of rs2241797 in NGT, P_trend values were 0.024 and 0.016, respectively. Our findings suggest that genetic polymorphisms of TGFBRAP1 may contribute to the genetic susceptibility of T2DM by mediating diabetes‐related miRNA expression.     

Identification and development of long non‐coding RNA‐associated regulatory network in colorectal cancer

Colorectal cancer (CRC) is one of the leading causes of cancer‐associated death globally. Long non‐coding RNAs (lncRNAs) have been identified as micro RNA (miRNA) sponges in a competing endogenous RNA (ceRNA) network and are involved in the regulation of mRNA expression. This study aims to construct a lncRNA‐associated ceRNA network and investigate the prognostic biomarkers in CRC. A total of 38 differentially expressed (DE) lncRNAs, 23 DEmiRNAs and 27 DEmRNAs were identified by analysing the expression profiles of CRC obtained from The Cancer Genome Atlas (TCGA). These RNAs were chosen to develop a ceRNA regulatory network of CRC, which comprised 125 edges. Survival analysis showed that four lncRNAs, six miRNAs and five mRNAs were significantly associated with overall survival. A potential regulatory axis of ADAMTS9‐AS2/miR‐32/PHLPP2 was identified from the network. Experimental validation was performed using clinical samples by quantitative real‐time PCR (qRT‐PCR), which showed that expression of the genes in the axis was associated with clinicopathological features and the correlation among them perfectly conformed to the ‘ceRNA theory’. Overexpression of ADAMTS9‐AS2 in colon cancer cell lines significantly inhibited the miR‐32 expression and promoted PHLPP2 expression, while ADAMTS9‐AS2 knockdown had the opposite effects. The constructed novel ceRNA network may provide a comprehensive understanding of the mechanisms of CRC carcinogenesis. The ADAMTS9‐AS2/miR‐32/PHLPP2 regulatory axis may serve as a potential therapeutic target for CRC.     

To construct a ceRNA regulatory network as prognostic biomarkers for bladder cancer

Emerging evidence demonstrates that competing endogenous RNA (ceRNA) hypothesis has played a role in molecular biological mechanisms of cancer occurrence and development. But the effect of ceRNA network in bladder cancer (BC), especially lncRNA‐miRNA‐mRNA regulatory network of BC, was not completely expounded. By means of The Cancer Genome Atlas (TCGA) database, we compared the expression of RNA sequencing (RNA‐Seq) data between 19 normal bladder tissue and 414 primary bladder tumours. Then, weighted gene co‐expression network analysis (WGCNA) was conducted to analyse the correlation between two sets of genes with traits. Interactions between miRNAs, lncRNAs and target mRNAs were predicted by MiRcode, miRDB, starBase, miRTarBase and TargetScan. Next, by univariate Cox regression and LASSO regression analysis, the 86 mRNAs obtained by prediction were used to construct a prognostic model which contained 4 mRNAs (ACTC1 + FAM129A + OSBPL10 + EPHA2). Then, by the 4 mRNAs in the prognostic model, a ceRNA regulatory network with 48 lncRNAs, 14 miRNAs and 4 mRNAs was constructed. To sum up, the ceRNA network can further explore gene regulation and predict the prognosis of BC patients.     

Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.     

Combined analysis of whole‐exon sequencing and lncRNA sequencing in type 2 diabetes mellitus patients with obesity

This study sought to find more exon mutation sites and lncRNA candidates associated with type 2 diabetes mellitus (T2DM) patients with obesity (O‐T2DM). We used O‐T2DM patients and healthy individuals to detect mutations in their peripheral blood by whole‐exon sequencing. And changes in lncRNA expression caused by mutation sites were studied at the RNA level. Then, we performed GO analysis and KEGG pathway analysis. We found a total of 277 377 mutation sites between O‐T2DM and healthy individuals. Then, we performed a DNA‐RNA joint analysis. Based on the screening of harmful sites, 30 mutant genes shared in O‐T2DM patients were screened. At the RNA level, mutations of 106 differentially expressed genes were displayed. Finally, a consensus mutation site and differential expression consensus gene screening were performed. In the current study, the results revealed significant differences in exon sites in peripheral blood between O‐T2DM and healthy individuals, which may play an important role in the pathogenesis of O‐T2DM by affecting the expression of the corresponding lncRNA. This study provides clues to the molecular mechanisms of metabolic disorders in O‐T2DM patients at the DNA and RNA levels, as well as biomarkers of the risk of these disorders.     

急性髓系白血病mRNA与非编码RNA差异表达谱及CeRNA调控网络分析

利用GEO数据库(gene expression omnibus database)通过生物信息学分析方法探讨急性髓系白血病(acute myelogenous leukemia,AML)的发病机制。检索GEO数据库中AML相关芯片数据集GSE142698、GSE142699和GSE96535。利用GEO2R分析得到差异mRNAs、miRNAs以及差异lncRNAs。利用在线生物信息学分析工具DAVID对差异mRNAs进行GO富集分析和KEGG通路分析。利用miRWalk数据库预测AML相关miRNAs的靶向mRNAs,利用Spongescan数据库预测AML相关miRNAs的靶向lncRNAs,构建lncRNA-miRNA-mRNA竞争性内源RNA （competing endogenous RNA,ceRNA）调控网络。共筛选出29个显著差异mRNAs、70个显著差异miRNAs和20 005个显著差异lncRNAs。GO富集分析和KEGG通路分析显示,差异表达基因主要涉及蛋白磷酸化、细胞分裂、细胞增殖的负调控、基因表达的正向调节、周期蛋白依赖的丝氨酸/苏氨酸激酶活性的调节等生物过程以及细胞周期、细胞衰老、癌症通路、PI3K-Akt通路等信号通路。将miRWalk数据库预测的靶向mRNAs与差异mRNAs取交集,Spongescan数据库预测的靶向lncRNAs与差异lncRNAs取交集,分别确定了25个mRNAs、6个lncRNAs参与AML相关ceRNA调控网络的构建。结果表明,lncRNAs可能作为关键的ceRNA,通过调控miRNA和相关靶基因参与AML的发生与发展,研究结果为AML诊断和治疗的分子生物学研究提供了新的依据。     

Identification of messenger and long noncoding RNAs associated with gallbladder cancer via gene expression profile analysis

The present study aimed to investigate the long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) involved in the progression of gallbladder cancer and explore the potential physiopathologic mechanisms of gallbladder cancer in terms of competing endogenous RNAs (ceRNAs). The original lncRNA and mRNA expression profile data (nine gallbladder cancer tissues samples and nine normal gallbladder samples) in GSE76633 was downloaded from the Gene Expression Omnibus database. Differentially expressed mRNAs and lncRNAs between gallbladder cancer tissue and normal control were selected and the pathways in which they are involved were analyzed using bioinformatics analyses. MicroRNAs (miRNAs) were also predicted based on the differentially expressed mRNAs. Finally, the co-expression relation between lncRNA and mRNA was analyzed and the ceRNA network was constructed by combining the lncRNA-miRNA, miRNA-mRNA, and lncRNA-mRNA pairs. Overall, 373 significantly differentially expressed mRNAs and 47 lncRNAs were identified between cancer and normal tissue samples. The upregulated genes were significantly enriched in the extracellular matrix (ECM)-receptor interaction pathway, while the downregulated genes were involved in the complement and coagulation cascades. Altogether, 128 co-expression relations between lncRNA and mRNA were obtained. In addition, 196 miRNA-mRNA regulatory relations and 145 miRNA-lncRNA relation pairs were predicted. Finally, the lncRNA-miRNA-gene ceRNA network was constructed by combining the three types of relation pairs, such as XLOC_011309-miR-548c-3p-SPOCK1 and XLOC_012588-miR-765-CEACAM6. mRNAs and lncRNAs may be involved in gallbladder cancer progression via ECM-receptor interaction pathways and the complement and coagulation cascades. Moreover, ceRNAs such as XLOC_011309-miR-548c-3p-SPOCK1 and XLOC_012588-miR-765-CEACAM6 can also be implicated in the pathogenesis of gallbladder cancer.     

Different expression profile of mRNA and long noncoding RNA in autoimmune thyroid diseases patients

Increasing evidence has found that long noncoding RNAs (lncRNAs) and message RNAs (mRNAs) play an important role in the progress of autoimmune thyroid diseases (AITD). So, in this study, the different expressed of lncRNA and mRNA was screened by microarray analysis and quantitative real-time polymerase chain reaction (PCR). To further investigate the relationship among the differentially expressed genes, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene ontology (GO) were used for biofunctions and signaling pathways analysis, respectively. Finally, the interaction relationship between lncRNA and mRNAs was analysis with lncRNA-mRNA coexpression network. The result found that the abnormal expression of lncRNAs and mRNAs were 1615 and 1913, respectively. The altered genes included CD40LG, IFNG, CTLA4, FAS, STAT1, STAT3, and STAT4. These were enriched in presentation and antigen processing, Th1 and Th2 cell differentiation, natural killer cell-mediated cytotoxicity, B cell receptor signaling pathway, Th17 cell differentiation, and cell adhesion molecules (CAMs), all of which had been suggested to be associated with immunopathogenic mechanisms and AITD-induced pathophysiologic changes. A coexpression network profile was contained with 126 network nodes and 477 connections which were based on seven mRNAs and 119 interacted lncRNAs. The outcomes of differentially expressed lncRNAs and their coreralated mRNAs in our study revealed that lncRNAs involved in immunopathogenic mechanisms may play a crital role in the pathogenesis of AITD.     

An atlas and analysis of bovine skeletal muscle long noncoding RNAs

Long noncoding RNAs (lncRNAs) have various biological functions and have been extensively studied in recent years. However, the identification and characterization of bovine lncRNAs in skeletal muscle has been very limited compared with that of lncRNAs in other model organisms. In this study, 7188 bovine skeletal muscle lncRNAs were identified by RNA‐Seq and a stringent screening procedure in four different muscle tissues. These lncRNAs shared many characteristics with other mammalian lncRNAs, such as a shorter open reading frame and lower expression level than for mRNAs. Furthermore, the chromosomal locations and global expression patterns for these lncRNAs are also described in detail. More importantly, we detected the important interaction relationships of lncRNAs–miRNAs–mRNAs related to muscle development among 36 lncRNAs, 62 miRNAs and 12 mRNAs. Our results provide a global expression pattern of lncRNAs specific to bovine skeletal muscle and provide important targets for revealing the function of bovine muscle development by thoroughly studying the interaction relationships of lncRNAs–miRNAs–mRNAs.     

Genome-wide identification of a competing endogenous RNA network in cholangiocarcinoma

Cholangiocarcinoma (CCA) is the second widespread liver tumor with relatively poor survival. Increasing evidence in recent studies showed long noncoding RNAs (lncRNAs) exert a crucial impact on the development and progression of CCA based on the mechanism of competing endogenous RNAs (ceRNAs). However, functional roles and regulatory mechanisms of lncRNA-regulated ceRNA in CCA, are only partially understood. The expression profile of messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) downloaded from The Cancer Genome Atlas were comprehensively investigated. Differential expression of these three types of RNA between CCA and corresponding precancerous tissues were screened out for further analysis. On the basis of interactive information generated from miRDB, miRTarBase, TargetScan, and miRcode public databases, we then constructed an mRNA-miRNA-lncRNA regulatory network. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were conducted to identify the biological function of the ceRNA network involved in CCA. As a result, 2883 mRNAs, 136 miRNAs, and 993 lncRNAs were screened out as differentially expressed RNAs in CCA. In addition, a ceRNA network in CCA was constructed, composing of 50 up and 27 downregulated lncRNAs, 14 up and 7 downregulated miRNAs, 29 up and 25 downregulated mRNAs. Finally, gene set enrichment and pathway analysis indicated our CCA-specific ceRNA network was related with cancer-related pathway and molecular function. In conclusion, our research identified a novel lncRNA-related ceRNA network in CCA, which might act as a potential therapeutic target for patients with CCA.