LncReg: a reference resource for lncRNA-associated regulatory networks.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhou Z
PROVIDER: S-EPMC4565966 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Gastric cancer is a tumor type characterized by lymph node metastasis and the invasion of local tissues. There is thus a critical need to clarify the molecular mechanisms governing gastric cancer onset and progression to guide the treatment of this disease. Long non-coding RNAs and mRNA expression profiles associated with early and local advanced gastric cancer were examined through microarray analyses, with GO and KEGG analyses being employed as a means of exploring the functional roles of those long non-coding RNAs and mRNAs that were differentially expressed in gastric cancer. In total, 1005 and 1831 lncRNAs and mRNAs, respectively, were found to be differentially expressed between early and local advanced gastric cancer. GO and KEGG analyses revealed several pathways and processes that were dysregulated, including the RNA transport, ECM-receptor interaction, and mRNA splicing pathways. In co-expression networks, E2F1, E2F4, and STAT2 were identified as key transcriptional regulators of these processes. Moreover, thrombospondin-2 was confirmed as being expressed at high levels in more advanced gastric cancer by both the GEO and TCGA databases. RNA-sequencing analyses of SGC-790 cells transfected to express thrombospondin-2 further revealed this gene to enhance NF-kB and TNF pathway signaling activity. These results offer insight into gastric cancer-related regulatory networks and suggest thrombospondin-2 to be an important oncogene that drives the progression of this deadly cancer type.
| S-EPMC10031762 | biostudies-literature
Project description:Background: Ovarian cancer (OC) is the most lethal malignancy in the female reproductive system. Growing evidences demonstrates that competing endogenous RNA (ceRNA) network play crucial roles in the occurrence and progression of tumors. Therefore, we aimed to explore and identify novel mRNA-miRNA-lncRNA ceRNA networks associated with prognosis of OC. Methods: The differentially expressed gene (DEGs) of four expression profiles datasets (GSE5438, GSE40595, GSE38666 and GSE26712) were collected from Gene Expression Omnibus (GEO) database and analyzed with NetworkAnalyst. Intersection of DEGs were further employed for Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis. Protein-protein interaction (PPI) network and hub genes of DEGs were also identified. The expression levels and survival analysis of the hub genes in OC and their upstream miRNAs and lncRNAs were performed by various bioinformatics databases. More importantly, ceRNA networks were constructed based on mRNA-miRNA-lncRNA in OC. Results: A total of 178 DEGs including 38 upregulated and 140 downregulated genes from intersected DEGs of four expression profiles were identified in OC. Functional enrichment analysis suggested that the commonly DEGs were enriched in regulating enzyme inhibitor activity, glycosaminoglycan and G protein-coupled receptor binding, cell morphogenesis, and involved in pathways including metabolic process, proteoglycans in cancer. Top 10 hub genes with higher connectivity degree were selected for subsequent expression and prognosis analysis. After take expression levels and prognostic roles of hub genes and their upstream miRNAs and lncRNAs in OC into consideration, 2 mRNAs (TACC3 and CXCR4), 2 miRNAs (hsa-miR-425-5p and hsa-miR-146a-5p) and 3 lncRNAs (FUT8-AS1, LINC00665 and LINC01535) were significantly associated with the poor prognosis of OC. The mRNA-miRNA-lncRNA networks (TACC3-hsa-miR-425-5p-FUT8-AS1 and CXCR4-hsa-miR-146a-5p-LINC00665/LINC01535) were eventually constructed in OC based on ceRNA mechanism. Conclusion: We successfully constructed novel ceRNA network associated with the prognosis of ovarian cancer, which may provide a new strategy for early diagnosis and therapeutic intervention of OC.
| S-EPMC7592000 | biostudies-literature
Project description:Long noncoding (lnc)RNAs are a group of RNAs with a length greater than 200 nt that do not encode a protein but play an essential role in regulating the expression of target genes in normal biological contexts as well as pathologic processes including tumorigenesis. The lncRNA metastasis-associated lung adenocarcinoma transcript (MALAT)-1 has been widely studied in cancer. In this review, we describe the known functions of MALAT-1; its mechanisms of action; and associated signaling pathways and their clinical significance in different cancers. In most malignancies, including lung, colorectal, thyroid, and other cancers, MALAT-1 functions as an oncogene and is upregulated in tumors and tumor cell lines. MALAT-1 has a distinct mechanism of action in each cancer type and is thus at the center of large gene regulatory networks. Dysregulation of MALAT-1 affects cellular processes such as alternative splicing, epithelial-mesenchymal transition, apoptosis, and autophagy, which ultimately results in the abnormal cell proliferation, invasion, and migration that characterize cancers. In other malignancies, such as glioma and endometrial carcinoma, MALAT-1 functions as a tumor suppressor and thus forms additional regulatory networks. The current evidence indicates that MALAT-1 and its associated signaling pathways can serve as diagnostic or prognostic biomarker or therapeutic target in the treatment of many cancers.
| S-EPMC7575067 | biostudies-literature
Project description:The stage of decompensation is termed end-stage liver cirrhosis. Patients with decompensated cirrhosis (DCC) often have a variety of comorbidities that contribute to exacerbation of the disease and its high mortality rate. By comparing differential gene expression, transcriptomic analysis is useful for exploring relevant functional changes during disease progression. The purpose of this study was to identify differentially expressed long noncoding RNAs (lncRNAs) and mRNAs in patients with decompensated cirrhosis and to further explore the functions as well as interactions between lncRNAs and mRNAs. Four patients with decompensated cirrhosis and four controls with liver cirrhosis were recruited in this study. RNA was isolated from peripheral blood mononuclear cells, and RNA-seq was used for transcriptome analysis. The functions of differentially expressed mRNAs were revealed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a regulatory network was also constructed. A total of 1046 differentially expressed mRNAs and 1175 lncRNAs were identified between the decompensated cirrhosis patients and cirrhosis controls. Functional enrichment analyses indicated enrichment of genes involved in pathways related to inflammation and cellular metabolic activities. In addition, the findings suggested that the phagosome/endosome/autophagy-lysosome pathway might play an important role in cirrhotic decompensation. In summary, this study identified differentially expressed mRNAs (DE-mRNAs) and DE-lncRNAs and predicted the biological processes and signaling pathways involved in cirrhotic decompensation, which might provide new ideas for further revealing the molecular mechanism of DCC pathogenesis.
| S-EPMC9691389 | biostudies-literature
Project description:BackgroundDetailed information on DNA-binding transcription factors (the key players in the regulation of gene expression) and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum.ResultsNow we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user) can be analyzed in the context of known transcriptional regulatory networks to predict putative contradictions or further gene regulatory interactions. Furthermore, it integrates protein clusters by means of heuristically solving the weighted graph cluster editing problem. In addition, it provides Web Service based access to up to date gene annotation data from GenDB.ConclusionThe release 4.0 of CoryneRegNet is a comprehensive system for the integrated analysis of procaryotic gene regulatory networks. It is a versatile systems biology platform to support the efficient and large-scale analysis of transcriptional regulation of gene expression in microorganisms. It is publicly available at http://www.CoryneRegNet.DE.
| S-EPMC2194740 | biostudies-literature
Project description:BACKGROUND:Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. DESCRIPTION:We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org. CONCLUSIONS:With SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses.
| S-EPMC3599410 | biostudies-literature
Project description:Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) are involved in the mechanism underlying cerebral dysfunction after deep hypothermic circulatory arrest (DHCA), although the exact details have not been elucidated. To explore the expression profiles of lncRNAs and miRNAs in DHCA cerebral injury, we determined the lncRNA, miRNA and mRNA expression profiles in the cerebral cortex of DHCA and sham rats. First, a rat model of DHCA was established, and high-throughput sequencing was performed to analyze the differentially expressed RNAs (DERNAs). Then, the principal functions of the significantly deregulated genes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Expression networks (lncRNAs-miRNAs-mRNAs and transcription factors (TFs)-miRNAs-mRNAs) were also established. Finally, the expression of DERNAs was confirmed by quantitative real-time PCR (RT-qPCR). We identified 89 lncRNAs, 45 miRNAs and 59 mRNAs between the DHCA and sham groups and constructed a comprehensive competitive endogenous RNAs (ceRNAs) network. A TF-miRNA-mRNA regulatory network was also established. Finally, we predicted that Lcorl-miR-200a-3p-Ttr, BRD4-Ccl2 and Ep300-miR-200b-3p-Tmem72 may participate in the pathogenesis of DHCA cerebral injury.
| S-EPMC8719624 | biostudies-literature
Project description:BackgroundTranscription regulatory networks are composed of interactions between transcription factors and their target genes. Whereas unicellular networks have been studied extensively, metazoan transcription regulatory networks remain largely unexplored. Caenorhabditis elegans provides a powerful model to study such metazoan networks because its genome is completely sequenced and many functional genomic tools are available. While C. elegans gene predictions have undergone continuous refinement, this is not true for the annotation of functional transcription factors. The comprehensive identification of transcription factors is essential for the systematic mapping of transcription regulatory networks because it enables the creation of physical transcription factor resources that can be used in assays to map interactions between transcription factors and their target genes.ResultsBy computational searches and extensive manual curation, we have identified a compendium of 934 transcription factor genes (referred to as wTF2.0). We find that manual curation drastically reduces the number of both false positive and false negative transcription factor predictions. We discuss how transcription factor splice variants and dimer formation may affect the total number of functional transcription factors. In contrast to mouse transcription factor genes, we find that C. elegans transcription factor genes do not undergo significantly more splicing than other genes. This difference may contribute to differences in organism complexity. We identify candidate redundant worm transcription factor genes and orthologous worm and human transcription factor pairs. Finally, we discuss how wTF2.0 can be used together with physical transcription factor clone resources to facilitate the systematic mapping of C. elegans transcription regulatory networks.ConclusionwTF2.0 provides a starting point to decipher the transcription regulatory networks that control metazoan development and function.
| S-EPMC1414109 | biostudies-literature
Project description:Lung cancer is the second most commonly diagnosed carcinoma and is the leading cause of cancer death. Although significant progress has been made towards its understanding and treatment, unraveling the complexities of lung cancer is still hampered by a lack of comprehensive knowledge on the mechanisms underlying the disease. High-throughput and multidimensional genomic data have shed new light on cancer biology. In this study, we developed a network-based approach integrating somatic mutations, the transcriptome, DNA methylation, and protein-DNA interactions to reveal the key regulators in lung adenocarcinoma (LUAD). By combining Bayesian network analysis with tissue-specific transcription factor (TF) and targeted gene interactions, we inferred 15 disease-related core regulatory networks in co-expression gene modules associated with LUAD. Through target gene set enrichment analysis, we identified a set of key TFs, including known cancer genes that potentially regulate the disease networks. These TFs were significantly enriched in multiple cancer-related pathways. Specifically, our results suggest that hepatitis viruses may contribute to lung carcinogenesis, highlighting the need for further investigations into the roles that viruses play in treating lung cancer. Additionally, 13 putative regulatory long non-coding RNAs (lncRNAs), including three that are known to be associated with lung cancer, and nine novel lncRNAs were revealed by our study. These lncRNAs and their target genes exhibited high interaction potentials and demonstrated significant expression correlations between normal lung and LUAD tissues. We further extended our study to include 16 solid-tissue tumor types and determined that the majority of these lncRNAs have putative regulatory roles in multiple cancers, with a few showing lung-cancer specific regulations. Our study provides a comprehensive investigation of transcription factor and lncRNA regulation in the context of LUAD regulatory networks and yields new insights into the regulatory mechanisms underlying LUAD. The novel key regulatory elements discovered by our research offer new targets for rational drug design and accompanying therapeutic strategies.
| S-EPMC5793165 | biostudies-literature
Project description:PURPOSE:Prostate cancer (PCa) causes a common male urinary system malignant tumour, and the molecular mechanisms of PCa are related to the abnormal regulation of various signalling pathways. An increasing number of studies have suggested that mRNAs, miRNAs, lncRNAs, and TFs could play important roles in various biological processes that are associated with cancer pathogenesis. This study aims to reveal functional genes and investigate the underlying molecular mechanisms of PCa with bioinformatics. METHODS:Original gene expression profiles were obtained from the GSE64318 and GSE46602 datasets in the Gene Expression Omnibus (GEO). We conducted differential screens of the expression of genes (DEGs) between two groups using the online tool GEO2R based on the R software limma package. Interactions between differentially expressed miRNAs, mRNAs and lncRNAs were predicted and merged with the target genes. Co-expression of miRNAs, lncRNAs and mRNAs was selected to construct mRNA-miRNA-lncRNA interaction networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the DEGs. Protein-protein interaction (PPI) networks were constructed, and transcription factors were annotated. Expression of hub genes in the TCGA datasets was verified to improve the reliability of our analysis. RESULTS:The results demonstrate that 60 miRNAs, 1578 mRNAs and 61 lncRNAs were differentially expressed in PCa. The mRNA-miRNA-lncRNA networks were composed of 5 miRNA nodes, 13 lncRNA nodes, and 45 mRNA nodes. The DEGs were mainly enriched in the nuclei and cytoplasm and were involved in the regulation of transcription, related to sequence-specific DNA binding, and participated in the regulation of the PI3K-Akt signalling pathway. These pathways are related to cancer and focal adhesion signalling pathways. Furthermore, we found that 5 miRNAs, 6 lncRNAs, 6 mRNAs and 2 TFs play important regulatory roles in the interaction network. The expression levels of EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT were significantly different between PCa and normal prostate tissue. CONCLUSION:Based on the current study, large-scale effects of interrelated mRNAs, miRNAs, lncRNAs, and TFs established a new prostate cancer network. In addition, we conducted functional module analysis within the network. In conclusion, this study provides new insight for exploration of the molecular mechanisms of PCa and valuable clues for further research into the process of tumourigenesis and its development in PCa.
| S-EPMC6107126 | biostudies-literature