Circulating LncRNAs Microarray Profiling in Hidradenitis suppurativa
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Federica Farinella
PROVIDER: E-MTAB-13699 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:LncRNA expression profiling for liver tissues of mice fed for NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
2022-11-24 | E-MTAB-9167 | biostudies-arrayexpress
Project description:Dermal fibroblasts from human individuals - unstimulated.
2019-11-17 | E-MTAB-7167 | biostudies-arrayexpress
Project description:We studied an Italian family with three NB patients, two siblings and one of their cousins carrying the R1192P mutation in the ALK gene (that has been found mutated in a fraction of familial NBs). However, because some individuals harboring mutations in this gene do not develop this tumor, additional genetic alterations appear to be required for NB pathogenesis. In this family, a comparison between somatic and germline DNA copy number changes in the two affected siblings and their relatives by an high resolution array-based Comparative Genomic Hybridization (CGH) technique was performed.
2013-08-02 | E-MEXP-3948 | biostudies-arrayexpress
Project description:Long noncoding RNAs (lncRNAs) play a key role in regulating immunological functions. Their impact on the chronic inflammatory disease multiple sclerosis (MS), however, remains unknown. We investigated the expression of lncRNAs in peripheral blood mononuclear cells (PBMCs) of patients with MS and try to explain their possible role in the process of MS. we recruited 26 MS patients according to the revised McDonald Criteria. Then we chosen 6 patients for microarray analysis randomly. Microarray assays identified outstanding differences in lncRNA expression, which were verified through real-time PCR. LncRNA functions were annotated for target genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and regulatory relationships between lncRNAs and target genes were analyzed using the “cis” and “trans” model.
2017-12-08 | E-MTAB-6193 | biostudies-arrayexpress
Project description:Inflammatory bowel disease (IBD) is a complex multi-factorial inflammatory disease with Crohn’s disease (CD) and ulcerative colitis (UC) being the two most common forms. A number of transcriptional profiling studies have provided compelling evidence that describe the role of protein-coding genes and microRNAs in modulating the immune responses in IBD. In the present study, we performed a genome-wide transcriptome profiling of lncRNAs and protein-coding genes in inflamed and non-inflamed colon pinch biopsies from the IBD patients using expression microarrays platform. In this study, we identified widespread dysregulation of lncRNAs and protein-coding genes in both inflamed and non-inflamed CD and UC compared to the healthy controls. In case of inflamed CD and UC (iCD and iUC), we identified 438 and 745 differentially expressed lncRNAs, respectively, while in case of the non-inflamed CD and UC (niCD and niUC), we identified 12 and 19 differentially expressed lncRNAs, respectively. We also observed significant enrichment (p-value < 0.001, Pearson’s Chi-squared test) for 96 differentially expressed lncRNAs and 154 protein-coding genes within the IBD susceptibility loci. Furthermore, we found strong positive expression correlations for the intersecting and cis-neighboring differentially expressed IBD loci-associated lncRNA-protein-coding gene pairs. The functional annotation analysis of differentially expressed genes revealed that they are involved in immune response, pro-inflammatory cytokine activity and MHC protein complex. The lncRNA expression profiling in both inflamed and non-inflamed CD and UC, successfully stratified IBD patients from the healthy controls. Taken together, the identified lncRNA transcriptional signature along with clinically relevant parameters suggests their potential as biomarkers in IBD. A total of 96 biopsy samples (including 6 samples used as technical replicates) extracted from different colonic locations from 45 patients (CD=13, UC=20, Controls=12) were profiled using Agilent Custom 8x60K format lncRNA expression microarray. In Gencode v15 lncRNA microarray design, each lncRNA transcript is targeted by two probes covering 22,001 lncRNA transcripts corresponding to 12,963 lncRNA genes. In addition, each array contains 17,535 randomly-selected protein-coding targets, of which 15,182 (unique 12,787) correspond to protein-coding genes.
2015-04-10 | E-GEOD-67106 | biostudies-arrayexpress
Project description:The mRNA microarray analysises was performed to explore the expression profiles of miRNAs using the same liver tissues of NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: miRCURY™ LNA expression array information. Data Analysis for miRNAs: 1. Low intensity filtering and data normalization: After low intensity miRNAs filtering, raw signal intensities are normalized in Median method. (miRNAs that intensities>=30 in all samples are chosen for calculating normalization factor) 2. Quality assessment of miRNA data after filtering: Contains box plot, Correlation Matrix and scatter plot for miRNAs after normalization. 3. Differentially expressed miRNAs screening: Contains significant differentially expressed miRNAs that pass Volcano Plot filtering. (Fold Change>=1.5, P-value<=0.05) 4. Heat map and hierarchical clustering: Hierarchical clustering on the significant differentially expressed miRNAs that passed Volcano Plots filtering. Sample RNA Quality Control: Sample quality control data file from Nanodrop 1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction for microarray, experiment, and data analysis. FAQ: Frequently asked question Additional miRNA Array Analysis (charge an extra fee): Prediction Analysis for Microarrays (PAM analysis) miRNA Target Gene Prediction and Functional Analysis Additional files provided: Graphs (*.jpg) Raw Intensity File(*.xls, raw miRNAs signal intensity) Layout File (*.gal, the files contain information on the positioning of the capture probes on the array and microRNA annotations for your species of interest) Raw data files produced by GenePix Pro 6.0
2023-06-01 | E-MTAB-9169 | biostudies-arrayexpress
Project description:Periodontitis can impair the osteogenic differentiation of human periodontal mesenchymal stem cells, but the underlying molecular mechanisms are still poorly understood. Long noncoding RNAs (lncRNAs) have been demonstrated to play significant roles under both physiologic and pathological conditions. We performed comprehensive lncRNAs profiling by lncRNA microarray to identify differentially expressed long noncoding RNA expression between Periodontal ligament stem cells from healthy Periodontal tissue and periodontal ligament stem cells from inflammatory periodontal tissue. Our analysis identified 233 lncRNAs and 423 mRNAs that were differently expressed (fold change >2.0, p-value < 0.05) between the two groups of cells. The GO analysis revealed that the significantly down-regulated biological processes included multicellular organismal process, developmental process and multicellular organismal development and the significantly up-regulated biological processes included cellular process, biological regulation and response to stimulus in periodontal ligament stem cells from inflammatory periodontal tissue. The Pathway analysis revealed that the differentially expressed mRNAs may involved in Focal adhesion, ECM-receptor interaction, Bacterial invasion of epithelial cells, Long-term depression, Circadian entrainment and HIF-1 signaling pathway. Two-condition experiment, periodontal ligament stem cells from healthy periodontal tissue (hPDLSCs) vs. periodontal ligament stem cells from inflammatory periodontal tissue (pPDLSCs), Biological replicates: 3 control replicates (hPDLSCs), 3 testing replicates (pPDLSCs).
2016-02-19 | E-GEOD-78074 | biostudies-arrayexpress
Project description:The underlying pathogenetic factors generating the innate immune signal necessary for T cell activation, initiation and chronification of Hidradenitis suppurativa (HS, also known as Acne inversa) are still poorly understood. Emerging evidence suggests that defective keratinocyte function critically contributes to HS disease development and progression. To elucidate the role of keratinocytes in HS lesion formation, we compared the transcriptomes of isolated lesional and perilesional HS epidermis by RNA sequencing.
| EGAS00001005544 | EGA
Project description:The pathogenesis of acne has been linked to multiple factors such as increased sebum production, inflammation, follicular hyperkeratinization, and the action of Propionibacterium acnes within the follicle. In an attempt to understand the specific genes involved in inflammatory acne, we performed gene expression profiling in acne patients. Skin biopsies were obtained from an inflammatory papule and from normal skin in six patients with acne. Biopsies were also taken from normal skin of six subjects without acne. Gene array expression profiling was conducted using Affymetrix HG-U133A 2.0 arrays comparing lesional to nonlesional skin in acne patients and comparing nonlesional skin from acne patients to skin from normal subjects. Within the acne patients, 211 genes are upregulated in lesional skin compared to nonlesional skin. A significant proportion of these genes are involved in pathways that regulate inflammation and extracellular matrix remodeling, and they include matrix metalloproteinases 1 and 3, IL-8, human beta-defensin 4, and granzyme B. These data indicate a prominent role of matrix metalloproteinases, inflammatory cytokines, and antimicrobial peptides in acne lesions. These studies are the first describing the comprehensive changes in gene expression in inflammatory acne lesions and are valuable in identifying potential therapeutic targets in inflammatory acne. Experiment Overall Design: total 18 chips. 6 for acne lesion samples, 6 for normal skin samples, 6 for non-acne patient normal skin samples
2008-06-14 | E-GEOD-6475 | biostudies-arrayexpress
Project description:HIV-infected long-term non-progressors (LTNPs) are a special group of people who can naturally control HIV and maintain good host immunity. Their mechanism is related to viruses, host genetic characteristics, immune response and other factors, but the relationship between the activation level of innate immunity and long-term non-progression is not clear. Therefore,we hypothesize that LTNPs may have some founction to control the HIV replication in vivo.In this study, we used RNA-seq to investigate the transcription（lncRNA, miRNA and mRNA) profiles of LTNPs and typical progressors (TPs).This study is showing expression spectrum of mRNA and non-coding RNA between HIV-infected long-term non-progressors and typical progressors, and analyze the role of mRNA,IncRNA and miRNA in the HIV infected long-term non-progression based on whole transcriptome level.This study reveals the mechanism of different disease progression between LTNPs and TPs through functional enrichment analysis and differentially expressed genes(DEGs) analysis.
2021-02-10 | E-MTAB-9997 | biostudies-arrayexpress