Investigation of the miRNA and mRNA Coexpression Network and Their Prognostic Value in Hepatocellular Carcinoma
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Abstract:
To identify pivotal differentially expressed miRNAs and genes and construct their regulatory network in hepatocellular carcinoma.mRNA (GSE101728) and microRNA (GSE108724) microarray datasets were obtained from the NCBI Gene Expression Omnibus (GEO) database. Then, we identified the differentially expressed miRNAs and mRNAs. Sequentially, transcription factor enrichment and gene ontology (GO) enrichment analysis for miRNA were performed. Target genes of these differential miRNAs were obtained using packages in R language (R package multiMiR). After that, downregulated miRNAs were matched with target mRNAs which were upregulated, while upregulated miRNAs were paired with downregulated target mRNA using scripts written in Perl. An miRNA-mRNA network was constructed and visualized in Cytoscape software. For miRNAs in the network, survival analysis was performed. And for genes in the network, we did gene ontology (GO) and KEGG pathway enrichment analysis.A total of 35 miRNAs and 295 mRNAs were involved in the network. These differential genes were enriched in positive regulation of cell-cell adhesion, positive regulation of leukocyte cell-cell adhesion, and so on. Eight differentially expressed miRNAs were found to be associated with the OS of patients with HCC. Among which, miR-425 and miR-324 were upregulated while the other six, including miR-99a, miR-100, miR-125b, miR-145, miR-150, and miR-338, were downregulated.In conclusion, these results can provide a potential research direction for further studies about the mechanisms of how miRNA affects malignant behavior in hepatocellular carcinoma.Keywords:
KEGG
Gene regulatory network
Fold change
PURPOSE: The purpose of this study was to examine the infancy changes of metabolic gene expression level in mouse gastrocnemius muscle after after 1 hour acute treadmill exercise.
METHODS: C57BL/6 mouse were randomly divided into control (n=5) and 1 hour acute treadmill exercise intervention group (n=5). After the intervention, we extracted total mRNA from the mouse gastrocnemius following the whole set of genes were analyzed by microarray using Affymetrix GeneChip Clariom_S_Mouse Array. The significantly meaningful differentially expressed genes (DEGs) were extracted and compared with the bioinformatic tools. Further analysis of DEGs were conducted using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology database. Significant cut-off by Fold change and LPE test were used as statis tical test.
RESULTS: Fifty six upregulated and 65 downregulated DEGs were identified after the 1 hour of treadmill exercise. Nr4a3, Nr4a2, Btg2, Otud1, Sik1, Thbs1, Irs2 were included in the top 10 upregulated genes and Ube2l6, Scd3 were one of the most downregulated genes in the DEGs. In gene ontology analysis, metabolic process (>70 counts), organic substance metabolic process (>70) and cellular metabolic process (>60) were in the top 10 terms in the category of biological process. In the molecular function category, binding and protein binding term had more than 80s and 60s count genes each and they were statistically significant (p≤.001) with located on first and second place. In KEGG pathway enrichment analysis, many DEGs were related to MAPK signaling pathway, AMPK signaling pathway, Metabolic pathway, Protein processing in endoplasmic reticulum and Glycolysis/gluconeogenesis pathway. Also, several DEGs (HK1, HK2, Adh1 etc.) related to metabolism were statistically significant.
CONCLUSIONS: One hour acute treadmill exercise could sufficiently change some energy metabolism and adaptation related genes in mouse gastrocnemius muscle. In this microarray analysis, Nr4a3, Nr4a2, Btg2, Otud1, Sik1, Thbs1, Irs2, Ube2l6, and Scd3 were newly categorized as DEGs in respect of energy metabolism.
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Metabolic pathway
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PURPOSE The purpose of this study was to examine the infancy changes of metabolic gene expression level in mouse gastrocnemius muscle after after 1 hour acute treadmill exercise. METHODS C57BL/6 mouse were randomly divided into control (n=5) and 1 hour acute treadmill exercise intervention group (n=5). After the intervention, we extracted total mRNA from the mouse gastrocnemius following the whole set of genes were analyzed by microarray using Affymetrix GeneChip Clariom_S_Mouse Array. The significantly meaningful differentially expressed genes (DEGs) were extracted and compared with the bioinformatic tools. Further analysis of DEGs were conducted using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology database. Significant cut-off by Fold change and LPE test were used as statistical test. RESULTS Fifty six upregulated and 65 downregulated DEGs were identified after the 1 hour of treadmill exercise. Nr4a3, Nr4a2, Btg2, Otud1, Sik1, Thbs1, Irs2 were included in the top 10 upregulated genes and Ube2l6, Scd3 were one of the most downregulated genes in the DEGs. In gene ontology analysis, metabolic process (>70 counts), organic substance metabolic process (>70) and cellular metabolic process (>60) were in the top 10 terms in the category of biological process. In the molecular function category, binding and protein binding term had more than 80s and 60s count genes each and they were statistically significant (pâ¤.001) with located on first and second place. In KEGG pathway enrichment analysis, many DEGs were related to MAPK signaling pathway, AMPK signaling pathway, Metabolic pathway, Protein processing in endoplasmic reticulum and Glycolysis/gluconeogenesis pathway. Also, several DEGs (HK1, HK2, Adh1 etc.) related to metabolism were statistically significant. CONCLUSIONS One hour acute treadmill exercise could sufficiently change some energy metabolism and adaptation related genes in mouse gastrocnemius muscle. In this microarray analysis, Nr4a3, Nr4a2, Btg2, Otud1, Sik1, Thbs1, Irs2, Ube2l6, and Scd3 were newly categorized as DEGs in respect of energy metabolism. ìì¸ì´: 물ì§ëì¬ ì ì ì, KEGG pathway, ì ì ì ì¨í¨ë¡ì§, ë¹ë³µê·¼, ë§ì´í¬ë¡ì´ë ì´, ì¼íì± ì ì°ì ì´ë Keywords: Metabolic gene, KEGG pathway, Gene ontology, Gastrocnemius, Microarray, Acute aerobic exercise
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Background and Aims: mRNA expression analysis by microarrays can contribute to an enhanced diagnosis and to find altered biological pathways for explanation of the pathomechanism of the CRC. Our aim was to evaluate and interpret the chip analysis results according to the role of the differently expressed genes in genetic and biochemical pathways.
Pathway Analysis
Biological pathway
Genetic Analysis
Gene chip analysis
Expression (computer science)
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Microarray databases
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Breast cancer is one of the most common malignant tumors in the world. Long-term maintenance treatment is important for breast cancer. However, effective maintenance treatment is lacking for triple-negative breast cancer (TNBC). Traditional Chinese medicine (TCM) has shown its potential anticancer roles as an effective maintenance treatment for TNBC. However its mechanisms remained unclear. In this study, we detected the differentially expressed genes (DEGs) after treatment with Huaier aqueous extract by using microarray profiling in MDA-MB-231 cells. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and gene-gene interaction network were conducted to confirm the altered biological functions induced by Huaier extract. Screening of DEGs gave 387 genes (226 upregulated and 161 downregulated) in MDA-MB-231 cells which were regulated significantly by Huaier extract. GO and KEGG pathway analysis suggested that a number of functions were affected by Huaier, including proliferation, apoptosis, migration, and angiogenesis. Gene-gene interaction network showed the detailed molecular signal-net. Based on microarray data, we studied several functions of Huaier extract and in return verified the results of microarray profiling. This study had important guidance roles and indicated new research directions.
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Triple-negative breast cancer
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Gene expression experiments are common in molecular biology, for example in order to identify genes which play a certain role in a specified biological framework. For that purpose expression levels of several thousand genes are measured simultaneously using DNA microarrays. Comparing two distinct groups of tissue samples to detect those genes which are differentially expressed one statistical test per gene is performed, and resulting p-values are adjusted to control the false discovery rate. In addition, the expression change of each gene is quantified by some effect measure, typically the log fold change. In certain cases, however, a gene with a significant p-value can have a rather small fold change while in other cases a non-significant gene can have a rather large fold change. The biological relevance of the change of gene expression can be more intuitively judged by a fold change then merely by a p-value. Therefore, confidence intervals for the log fold change which accompany the adjusted p-values are desirable.In a new approach, we employ an existing algorithm for adjusting confidence intervals in the case of high-dimensional data and apply it to a widely used linear model for microarray data. Furthermore, we adopt a concept of different relevance categories for effects in clinical trials to assess biological relevance of genes in microarray experiments. In a brief simulation study the properties of the adjusting algorithm are maintained when being combined with the linear model for microarray data. In two cancer data sets the adjusted confidence intervals can indicate significance of large fold changes and distinguish them from other large but non-significant fold changes. Adjusting of confidence intervals also corrects the assessment of biological relevance.Our new combination approach and the categorization of fold changes facilitates the selection of genes in microarray experiments and helps to interpret their biological relevance.
Fold change
False Discovery Rate
Multiple comparisons problem
Relevance
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Gene chip analysis
Microarray databases
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Gene chip analysis
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Microarray databases
Gene chip analysis
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