Identification of multi-target effects of Huaier aqueous extract via microarray profiling in triple-negative breast cancer cells
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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.Keywords:
KEGG
Triple-negative breast cancer
To identify the differentially expressed gene profiles in myocardium of patients with heart failure using human whole genomic oligonucleotide microarray-assisted pathway analysis.Phalanx whole genomic oligonucleotide microarrays were used to detect the gene expression profiles of myocardium in four patients died of heart failure and 4 brain died patients without heart diseases. The microarray findings were confirmed by real-time quantitative reverse transcriptase-polymerase chain reaction. The genes with a threshold of 1.2 times fold-change were selected and BioCarta Pathway and KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway databases were used to identify functionally related gene pathways.A total of 2806 genes with differentially expression were detected between the failing and non-failing heart samples, expression changes of 399 genes were more than 2-folds. Eleven pathways were identified by BioCarta pathway database and sixteen pathways were identified by KEGG PATHWAY Database.Genomic microarray-assisted pathway analysis could help to identify gene expression profiles in failing heart.
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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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PRV,PPV and JEV detection microarray have been prepared in this test.The microarray has been fabricated by spotting target genes on animated slide with the best concentration 200 mg/L of each target gene firstly,then the slide dried,hydrated,UV cross-linked and washed.Probes were labelled by PCR with CY3-dCTP to evaluate the qualification of the microarray.The best concentration of the probe was 3 000 μg/L,with the sensitivity of the microarray detection system of 3 μg/L.The results showed that PRV,PPV and JEV can be detected simultaneously by the microarray with high sensitivity and specificity.The microarray can be reutilized for at least 10 times and conserved for at least 4 months.
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This study aimed to use gene chips to investigate differential gene expression profiles in the occurrence and development of acute myocardial infarction (AMI). The study included 12 AMI patients and 12 healthy individuals. Total mRNA of peripheral bloodwas extracted and reversed-transcribed to cDNA for microarray analysis. After establishing two pools with three subjects each (3 AMI patients and 3 healthy individuals), the remaining samples were used for RT-qPCR to confirm the microarray data. From the microarray results, seven genes were randomly selected for RT-qPCR. RT-qPCR results were analyzed by the 2-ΔΔCt method. Microarray analysis showed that 228 genes were up- regulated and 271 were down-regulated (p ≤ 0.05, |logFC| > 1). Gene ontology showed that these genes belong to 128 cellular components, 521 biological processes, and 151 molecular functions. KEGG pathway analysis showed that these genes are involved in 107 gene pathways. RT-qPCR results for the seven genes showed expression levels consistent with those obtained by microarray. Thus, microarray data could be used to select the pathogenic genes for AMI. Investigating the abnormal expression of these differentially expressed genes might suggest efficient strategies for the prevention, diagnosis, and treatment of AMI.
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A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system. This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression (fold change) but they are weakly expressed in experimental studies (based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies; such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016 (up- and downregulated) genes were determined to be related to Schwann cells. However, no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes. Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.
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Murine transplantation models are used extensively to research immunological rejection and tolerance. Here we studied both murine heart and liver allograft models using microarray technology. We had difficulty in identifying genes related to acute rejections expressed in both heart and liver transplantation models using two standard methodologies: Student's t test and linear models for microarray data (Limma). Here we describe a new method, standardized fold change (SFC), for differential analysis of microarray data. We estimated the performance of SFC, the t test and Limma by generating simulated microarray data 100 times. SFC performed better than the t test and showed a higher sensitivity than Limma where there is a larger value for fold change of expression. SFC gave better reproducibility than Limma and the t test with real experimental data from the MicroArray Quality Control platform and expression data from a mouse cardiac allograft. Eventually, a group of significant overlapping genes was detected by SFC in the expression data of mouse cardiac and hepatic allografts and further validated with the quantitative RT-PCR assay. The group included genes for important reactions of transplantation rejection and revealed functional changes of the immune system in both heart and liver of the mouse model. We suggest that SFC can be utilized to stably and effectively detect differential gene expression and to explore microarray data in further studies.
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