Identifying miRNA/mRNA negative regulation pairs in colorectal cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhou X
PROVIDER: S-EPMC4534763 | biostudies-other | 2015
REPOSITORIES: biostudies-other
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Project description:BACKGROUND Nasopharyngeal carcinoma (NPC) is a common malignancy in South-East Asia. NPC is characterized by distant metastasis and poor prognosis. The pathophysiological mechanism of nasopharyngeal carcinoma is unknown. This study aimed to identify the crucial miRNAs in nasopharyngeal carcinoma and their target genes, and to discover the potential mechanism of nasopharyngeal carcinoma development. MATERIAL AND METHODS Microarray expression profiling of miRNA and mRNA from the Gene Expression Omnibus database was downloaded, and we performed a significance analysis of differential expression. An interaction network of miRNAs and target genes was constructed. The underlying function of differentially expressed genes was predicted through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. To validate the microarray analysis data, significantly different expression levels of miRNAs and target genes were validated by quantitative real-time polymerase chain reaction. RESULTS We identified 27 differentially expressed miRNAs and 982 differentially expressed mRNAs between NPC and normal control tissues. 12 miRNAs and 547 mRNAs were up-regulated and 15 miRNAs and 435 mRNAs were down-regulated in NPC samples. We found a total of 1185 negative correlation pairs between miRNA and mRNA. Differentially expressed target genes were significantly enriched in pathways in cancer, cell cycle, and cytokine-cytokine receptor interaction signaling pathways. Significantly differentially expressed miRNAs and genes, such as hsa-miR-205, hsa-miR-18b, hsa-miR-632, hsa-miR-130a, hsa-miR-34b, PIGR, SMPD3, CD22, DTX4, and CDC6, may play essential roles in the development of nasopharyngeal carcinoma. CONCLUSIONS hsa-miR-205, hsa-miR-18b, hsa-miR-632, hsa-miR-130a, and hsa-miR-34b may be related to the development of nasopharyngeal carcinoma by regulating the genes involved in pathways in cancer and cell cycle signaling pathways.
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Project description:Existing methods often fail to recognize the conversions for the biological roles of the pairs of genes and microRNAs (miRNAs) between the tumor and normal samples. We have developed a novel cluster scoring method to identify messenger RNA (mRNA) and miRNA interaction pairs and clusters while considering tumor and normal samples jointly. Our method has identified 54 significant clusters for 15 cancer types selected from The Cancer Genome Atlas project. We also determined the shared clusters across tumor types and/or subtypes. In addition, we compared gene and miRNA overlap between lists identified in our liver hepatocellular carcinoma (LIHC) study and regulatory relationships reported from human and rat nonalcoholic fatty liver disease studies (NAFLD). Finally, we analyzed biological functions for the single significant cluster in LIHC and uncovered a significantly enriched pathway (phospholipase D signaling pathway) with six genes represented in the cluster, symbols: DGKQ, LPAR2, PDGFRB, PIK3R3, PTGFR and RAPGEF3.
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Project description:Increasing evidence shows microRNAs (miRNAs) are engaged in hepatocelluar carcinoma (HCC). To identify novel feasible miRNA/mRNA pairs involved in hepatocarcinogenesis, an in-depth analysis of miRNomes in human non-tumor liver and HCC samples was carried out in this study. Firstly, differentially expressed miRNAs were obtained from deep sequencing of 15 liver samples, and verified in an independent data from The Cancer Genome Atlas (TCGA) database. Then, differentially expressed mRNA targets were selected from TCGA, and the differential miRNA/mRNA pairs with negative correlations were screened out. Finally, functional enrichment analysis was used to predict the functions of miRNA/mRNA pairs in HCC. In our study, 81 miRNA/mRNA pairs and 7 novel miRNAs were found. We constructed a hub interaction model with 9 miRNA/mRNA pairs to further investigate molecular mechanism of HCC. Survival analysis identified nine genes (hsa-miR-137, hsa-miR-490, BIRC5, TOP2A, CDC25C, IGF2BP1, IQGAP3, NCAPG and VIPR1) with significant influence on prognosis of HCC patients. In conclusion, the miRNA/mRNA pairs identified in our study may have some potential values to be further studied in progression, diagnosis and prognosis of HCC.
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Project description:BackgroundOvarian cancer (OC) is a gynecological malignancy with the highest mortality rate. Cisplatin (DDP) based chemotherapy is a standard strategy for ovarian cancer. Despite good response rates for initial chemotherapy, almost 80% of the patients treated with DDP based chemotherapy will experience recurrence due to drug-resistant, which will ultimately result in fatality. The aim of the present study was to examine the pathogenesis and potential molecular markers of cisplatin-resistant OC by studying the differential expression of mRNAs and miRNAs between cisplatin resistant OC cell lines and normal cell lines.MethodsTwo mRNA datasets (GSE58470 and GSE45553) and two miRNA sequence datasets (GSE58469 and GSE148251) were downloaded from the Gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were screened by the NetworkAnalyst. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. The protein-protein interaction network was constructed using STRING and Cytoscape software to identify the molecular mechanisms of key signaling pathways and cellular activities. FunRich and MiRNATip databases were used to identify the target genes of the DEMs.ResultsA total of 380 DEGs, and 5 DEMs were identified. Protein-protein interaction (PPI) network of DEGs containing 379 nodes and 1049 edges was constructed, and 4 key modules and 24 hub genes related to cisplatin-resistant OC were screened. Two hundred ninety-nine target genes of the 5 DEMs were found out. Subsequently, one of these 299 target genes (UBB) belonging to the hub genes of GSE58470 and GSE45553 was identified by MCODE and CytoHubba,which was regulated by one miRNA (mir-454).ConclusionsOne miRNA-mRNA regulatory pairs (mir-454-UBB) was established. Taken together, our study provided evidence concerning the alteration genes involved in cisplatin-resistant OC, which will help to unravel the mechanisms underlying drug resistant.
| S-EPMC8063430 | biostudies-literature
Project description:Nowadays, the current bioinformatic methods have been increasingly applied in the field of oncological research. In this study, we expect a better understanding of the molecular mechanism of gastric cancer from the bioinformatic methods. By systematically addressing the differential expression of microRNAs (miRNAs) and mRNAs between gastric cancer specimens and normal gastric specimens with the application of bioinformatics tools, A total of 206 DEGs and 38 DEMs were identified. The Gene Ontology (GO) analysis of Annotation, Visualization and Integrated Discovery (DAVID) database revealed that the differentially expressed genes (DEGs) were significantly enriched in biological process, molecular function and cellular component, while Kyoto Encyclopedia of Genes and Genomes (KEGG) database showed DEGs were significantly enriched in 8 signal pathways. The miRNA-gene regulatory network was constructed based on 385 miRNA-gene (DEM-DEG) pairs, consisting of 35 miRNAs and 107 target genes. In the regulatory network, the top 5 up-regulated genes were Transmembrane Protease, Serine 11B (TMPRSS11B), regulator of G protein signaling 1 (RGS1), cysteine rich angiogenic inducer 61 (CYR61), inhibin subunit beta A (INHBA), syntrophin gamma 1 (SNTG1), and the top 5 down-regulated genes were tumor necrosis factor receptor superfamily, member 19 (TNFRSF19), pleckstrin homology domain containing B2 (PLEKHB2), Tax1 binding protein 3 (TAX1BP3), presenilin enhancer, gamma-secretase subunit (PSENEN), NME/NM23 nucleoside diphosphate kinase 3 (NME3). Based on the gastric cancer patient database from Kaplan-Meier Plotter tools, we found that 8 of 10 genes with most significant changes in the miRNA-gene regulatory network possessed a prognostic value for survival time of gastric cancer patients. Patients with higher level of RGS1, PLEKHB2, TAX1BP3 and PSENEN in gastric cancer had a longer survival time compared with the patients with lower level of these genes. On the contrary, patients with higher level of INHBA, SNTG1, TNFRSF19 and NME3 were found associated with a shorter survival time. In conclusion, our findings provided several potential targets regarding gastric cancer, which may result in a new strategy to treat gastric cancer from a system rather than a single-gene perspective.
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Project description:BackgroundThe aim of this study was to explore the underlying molecular mechanism and potential molecular biomarkers of chronic pancreatitis (CP) and construct a miRNA-mRNA regulation network.MethodsTo explore the involvement of miRNAs in CP, we downloaded the miRNA and mRNA expression profiles of CP patients and healthy controls and identified the differentially expressed miRNAs and genes. Functional analysis was conducted and significant pathways were utilized. Finally, the miRNA-mRNA regulation network of CP was constructed.ResultsA total of 44 miRNA risk gene pathway relationships were identified, and a complex regulation network was constructed with 3 genes (ABL1, MYC, and ANAPC13) having the highest degree in affecting the network of CP. Importantly, 4 risk genes (NOTCH3, COX5A, THBS1, and KARS) and 1 risk miRNA (hsa-miR-324-5p) were identified with high prediction accuracy.ConclusionsIn conclusion, we analyzed miRNAs and mRNAs expression profiles in CP, 1 risk miRNA, and 4 risk genes were identified with high prediction accuracy as biomarkers of CP. Although further evaluation in clinical study is needed, our findings provide new insights into the pathogenesis of CP and may improve the diagnosis and therapy by identifying novel targets.
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Project description:BackgroundIdentifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.ResultsIn this paper, we propose a method, weighted similarity network fusion (WSNF), to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs), transcription factors (TFs) and messenger RNAs (mRNAs) and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients) and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some miRNA-TF-mRNA sub-networks vary across different identified subtypes. In addition, pathway enrichment analyses show that the top pathways involving the most differentially expressed genes in each of the identified subtypes are different. The results would provide valuable information for understanding the mechanisms characterising different cancer subtypes and assist the design of treatment therapies. All datasets and the R scripts to reproduce the results are available online at the website: http://nugget.unisa.edu.au/Thuc/cancersubtypes/.
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Project description:BackgroundmicroRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments.ResultsIn this research, we aim to find miRNA targets which are consistent across different breast cancer subtypes. Thus, first of all, we apply the Pam50 method to categorize BRCA samples into different "environment" groups based on different cancer subtypes. Then we use the invariant causal prediction method to find miRNA-mRNA regulatory relationships across subtypes. We validate the results with the miRNA-transfected experimental data and the results show that our method outperforms the state-of-the-art methods. In addition, we also integrate this new method with the Pearson correlation analysis method and Lasso in an ensemble method to take the advantages of these methods. We then validate the results of the ensemble method with the experimentally confirmed data and the ensemble method shows the best performance, even comparing to the proposed causal method.ConclusionsThis research found miRNA targets which are consistent across different breast cancer subtypes. Further functional enrichment analysis shows that miRNAs involved in the regulatory relationships predicated by the proposed methods tend to synergistically regulate target genes, indicating the usefulness of these methods, and the identified miRNA targets could be used in the design of wet-lab experiments to discover the causes of breast cancer.
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Project description:Testicular cancer is the most common cancer in men aged between 15 and 35 and more than 90% of testicular neoplasms are originated at germ cells. Recent research has shown the impact of microRNAs (miRNAs) in different types of cancer, including testicular germ cell tumor (TGCT). MicroRNAs are small non-coding RNAs which affect the development and progression of cancer cells by binding to mRNAs and regulating their expressions. The identification of functional miRNA-mRNA interactions in cancers, i.e. those that alter the expression of genes in cancer cells, can help delineate post-regulatory mechanisms and may lead to new treatments to control the progression of cancer. A number of sequence-based methods have been developed to predict miRNA-mRNA interactions based on the complementarity of sequences. While necessary, sequence complementarity is, however, not sufficient for presence of functional interactions. Alternative methods have thus been developed to refine the sequence-based interactions using concurrent expression profiles of miRNAs and mRNAs. This study aims to find functional cancer-specific miRNA-mRNA interactions in TGCT. To this end, the sequence-based predicted interactions are first refined using an ensemble learning method, based on two well-known methods of learning miRNA-mRNA interactions, namely, TaLasso and GenMiR++. Additional functional analyses were then used to identify a subset of interactions to be most likely functional and specific to TGCT. The final list of 13 miRNA-mRNA interactions can be potential targets for identifying TGCT-specific interactions and future laboratory experiments to develop new therapies.
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