Real time PCR array for cancer inflamation and immunity and tumor metastasis on mice with liver metastasis treated with miR-122 delivery vs miRNC delivery
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE158368 | GEO | 2020/09/23
REPOSITORIES: GEO
Similar Datasets
Project description:This experiment was done to compare cancer inflamation and immunity profile between liver tissues of mice with liver metastasis which treated with miR-122 delivery compared with those treated with miRNC delivery as control.
2020-09-23 | GSE158365 | GEO
Project description:This experiment was done to compare mouse metastasis gene profile between liver tissues of mice with liver metastasis which treated with miR-122 delivery compared with those treated with miRNC delivery as control.
2020-09-23 | GSE158366 | GEO
Project description:Real time PCR array for cancer inflamation and immunity on mice with liver metastasis treated with miR-122 delivery vs miRNC delivery
| PRJNA665028 | ENA
Project description:Real time PCR array for cancer inflamation and immunity and tumor metastasis on mice with liver metastasis treated with miR-122 delivery vs miRNC delivery
| PRJNA665023 | ENA
Project description:Real time PCR array for mouse tumor metastasis on mice with liver metastasis treated with miR-122 delivery vs miRNC delivery
| PRJNA665026 | ENA
Project description:Liver metastasis is a leading cause of cancer morbidity and mortality. Thus, there has been strong interest in the development of therapeutics that can effectively prevent liver metastasis. One potential strategy is to utilize molecules that have broad effects on the liver microenvironment, such as miR-122, a liver-specific miRNA that is a key regulator of diverse hepatic functions. Here we report the development of a nanoformulation miR-122 as a therapeutic agent for preventing liver metastasis. We engineered a galactose-targeted lipid calcium phosphate (Gal-LCP) nanoformulation of miR-122. This nanotherapeutic elicited no significant toxicity and delivered miR-122 into hepatocytes with specificity and high efficiency. Across multiple colorectal cancer liver metastasis models, treatment with Gal-LCP miR-122 treatment effectively prevented colorectal cancer liver metastasis and prolonged survival. Mechanistic studies revealed that delivery of miR-122 was associated with downregulation of key genes involved in metastatic and cancer inflammation pathways, including several proinflammatory factors, matrix metalloproteinases, and other extracellular matrix degradation enzymes. Moreover, Gal-LCP miR-122 treatment was associated with an increased CD8+/CD4+ T-cell ratio and decreased immunosuppressive cell infiltration, which makes the liver more conducive to antitumor immune response. Collectively, this work presents a strategy to improve cancer prevention and treatment with nanomedicine-based delivery of miRNA. SIGNIFICANCE: Highly specific and efficient delivery of miRNA to hepatocytes using nanomedicine has therapeutic potential for the prevention and treatment of colorectal cancer liver metastasis.
| S-EPMC8732321 | biostudies-literature
Project description:To isolate exosomes from colon tissue, colon tissue were removed and gently disaggregated with tweezers in dissociation buffer, followed by incubation at 37°C for 1 h. The disaggregated samples were centrifuged at 1,000 g for 10 min, 2,000 g for 20 min, 4,000 g for 30 min and 10,000 g for 1 h with the supernatant being retained each time. The tissue exosomes were collected by centrifuging the samples at 100,000 g for 1.5 h at 4°C, and the pellet was suspended in ice-cold PBS. Total RNA was isolated from cells, exosomes and tissue using a miRNeasy mini kit (Qiagen). miRNA expression profiling including exosomes and their donor cells or tissues was performed using the Qiagen miScript miRNA PCR Array Mouse miRBase Profiler. To isolate exosomes from liver tissue, a 20-G catheter was inserted into the portal vein of anaesthetized mice. The perfused liver tissue, as well as colon tissue were removed and gently disaggregated with tweezers in dissociation buffer, followed by incubation at 37°C for 1 h. The disaggregated samples were centrifuged at 1,000 g for 10 min, 2,000 g for 20 min, 4,000 g for 30 min and 10,000 g for 1 h with the supernatant being retained each time. The tissue exosomes were collected by centrifuging the samples at 100,000 g for 1.5 h at 4°C, and the pellet was suspended in ice-cold PBS. Total RNA was isolated from cells, exosomes and tissue using a miRNeasy mini kit (Qiagen). miRNA expression profiling including exosomes and their donor cells or tissues was performed using the Qiagen miScript miRNA PCR Array Mouse miRBase Profiler.
2017-01-04 | GSE93092 | GEO
Project description:A robust, rapid and flexible real-time PCR assay for hierarchical genetic typing of clinical and environmental isolates of Francisella is presented. Typing markers were found by multiple genome and gene comparisons, from which 23 canonical single nucleotide polymorphisms (canSNPs) and 11 canonical insertion-deletion mutations (canINDELs) were selected to provide phylogenetic guidelines for classification from genus to isolate level. The specificity of the developed assay, which uses 68 wells of a 96-well real-time PCR format with a detection limit of 100 pg DNA, was assessed using 62 Francisella isolates of diverse genetic and geographical origins. It was then successfully used for typing 14 F. tularensis subsp. holarctica isolates obtained from tularemia patients in Sweden in 2008 and five more genetically diverse Francisella isolates of global origins. When applied to human ulcer specimens for direct pathogen detection the results were incomplete due to scarcity of DNA, but sufficient markers were identified to detect fine-resolution differences among F. tularensis subsp. holarctica isolates causing infection in the patients. In contrast to other real-time PCR assays for Francisella, which are typically designed for specific detection of a species, subspecies, or strain, this type of assay can be easily tailored to provide appropriate phylogenetic and/or geographical resolution to meet the objectives of the analysis.
| S-EPMC2793073 | biostudies-literature
Project description:BackgroundThere are currently many different methods for processing and summarizing probe-level data from Affymetrix oligonucleotide arrays. It is of great interest to validate these methods and identify those that are most effective. There is no single best way to do this validation, and a variety of approaches is needed. Moreover, gene expression data are collected to answer a variety of scientific questions, and the same method may not be best for all questions. Only a handful of validation studies have been done so far, most of which rely on spike-in datasets and focus on the question of detecting differential expression. Here we seek methods that excel at estimating relative expression. We evaluate methods by identifying those that give the strongest linear association between expression measurements by array and the "gold-standard" assay. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) is generally considered the "gold-standard" assay for measuring gene expression by biologists and is often used to confirm findings from microarray data. Here we use qRT-PCR measurements to validate methods for the components of processing oligo array data: background adjustment, normalization, mismatch adjustment, and probeset summary. An advantage of our approach over spike-in studies is that methods are validated on a real dataset that was collected to address a scientific question.ResultsWe initially identify three of six popular methods that consistently produced the best agreement between oligo array and RT-PCR data for medium- and high-intensity genes. The three methods are generally known as MAS5, gcRMA, and the dChip mismatch mode. For medium- and high-intensity genes, we identified use of data from mismatch probes (as in MAS5 and dChip mismatch) and a sequence-based method of background adjustment (as in gcRMA) as the most important factors in methods' performances. However, we found poor reliability for methods using mismatch probes for low-intensity genes, which is in agreement with previous studies.ConclusionWe advocate use of sequence-based background adjustment in lieu of mismatch adjustment to achieve the best results across the intensity spectrum. No method of normalization or probeset summary showed any consistent advantages.
| S-EPMC1360686 | biostudies-literature
Project description:BackgroundA number of gene-profiling methodologies have been applied to microRNA research. The diversity of the platforms and analytical methods makes the comparison and integration of cross-platform microRNA profiling data challenging. In this study, we systematically analyze three representative microRNA profiling platforms: Locked Nucleic Acid (LNA) microarray, beads array, and TaqMan quantitative real-time PCR Low Density Array (TLDA).Methodology/principal findingsThe microRNA profiles of 40 human osteosarcoma xenograft samples were generated by LNA array, beads array, and TLDA. Results show that each of the three platforms perform similarly regarding intra-platform reproducibility or reproducibility of data within one platform while LNA array and TLDA had the best inter-platform reproducibility or reproducibility of data across platforms. The endogenous controls/probes contained in each platform have been observed for their stability under different treatments/environments; those included in TLDA have the best performance with minimal coefficients of variation. Importantly, we identify that the proper selection of normalization methods is critical for improving the inter-platform reproducibility, which is evidenced by the application of two non-linear normalization methods (loess and quantile) that substantially elevated the sensitivity and specificity of the statistical data assessment.ConclusionsEach platform is relatively stable in terms of its own microRNA profiling intra-reproducibility; however, the inter-platform reproducibility among different platforms is low. More microRNA specific normalization methods are in demand for cross-platform microRNA microarray data integration and comparison, which will improve the reproducibility and consistency between platforms.
| S-EPMC3037970 | biostudies-literature