RNA-seq experiment to identify differentially expressed genes due to the transduction of LIN28A
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE98429 | GEO | 2019/06/27
REPOSITORIES: GEO
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Project description:The aim of the present study was to screen the differentially expressed genes (DEGs) associated with glaucoma and investigate the changing patterns of the expression of these genes. The GSE2378 gene microarray data of glaucoma was downloaded from the Gene Expression Omnibus database, which included seven normal samples and eight glaucoma astrocyte samples. Taking into account the corresponding associations between the probe ID and gene symbols, the DEGs were identified prior to and subsequent to the summation of probe level values using the Limma package in R language, followed by Gene Ontology (GO) and pathway enrichment analyses. Interaction networks of the DEGs were constructed using the Biomolecular Interaction Network Database, and cluster analysis of the genes in the networks was performed using ClusterONE. Subsequent to the summation of probe value, a total of 223 genes were identified as DEGs between the normal and glaucoma samples, including 74 downregulated and 149 upregulated genes. In addition, the DEGs were found to be associated with several functions, including response to wounding, extracellular region part and calcium ion binding. The most significantly enriched pathways were complement and coagulation cascades, arrhythmogenic right ventricular cardiomyopathy and extracellular matrix (ECM)?receptor interaction. Furthermore, interaction networks were constructed of the DEGs prior to and subsequent to the summation of probe values, and HNF4A and CEBPD were identified as hub genes. Additionally, 37 and 31 GO terms were identified to be enriched in the two DEGs of the networks prior to and subsequent to summation, respectively. The results indicated the identified genes associated with ECM as important, and the CEBPD gene was considered to be a critical gene in glaucoma. The findings of the present study offer a potential reference value in further investigations of glaucoma at the gene level.
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Project description:BackgroundIdentifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.ResultsHere we present a new geometrical multivariate approach to identify DEG called the Characteristic Direction. We demonstrate that the Characteristic Direction method is significantly more sensitive than existing methods for identifying DEG in the context of transcription factor (TF) and drug perturbation responses over a large number of microarray experiments. We also benchmarked the Characteristic Direction method using synthetic data, as well as RNA-Seq data. A large collection of microarray expression data from TF perturbations (73 experiments) and drug perturbations (130 experiments) extracted from the Gene Expression Omnibus (GEO), as well as an RNA-Seq study that profiled genome-wide gene expression and STAT3 DNA binding in two subtypes of diffuse large B-cell Lymphoma, were used for benchmarking the method using real data. ChIP-Seq data identifying DNA binding sites of the perturbed TFs, as well as known drug targets of the perturbing drugs, were used as prior knowledge silver-standard for validation. In all cases the Characteristic Direction DEG calling method outperformed other methods. We find that when drugs are applied to cells in various contexts, the proteins that interact with the drug-targets are differentially expressed and more of the corresponding genes are discovered by the Characteristic Direction method. In addition, we show that the Characteristic Direction conceptualization can be used to perform improved gene set enrichment analyses when compared with the gene-set enrichment analysis (GSEA) and the hypergeometric test.ConclusionsThe application of the Characteristic Direction method may shed new light on relevant biological mechanisms that would have remained undiscovered by the current state-of-the-art DEG methods. The method is freely accessible via various open source code implementations using four popular programming languages: R, Python, MATLAB and Mathematica, all available at: http://www.maayanlab.net/CD.
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Project description:Background:Osteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis. Methods:The raw data of GSE89408 were downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by R software. The DAVID database was used for pathway and gene ontology analysis, and p<0.05 and gene count >2 were set as the cut-off point. Moreover, protein-protein interaction (PPI) network construction was applied for exploring the hub genes in osteoarthritis. The expression levels of the top ten hub genes in knee osteoarthritis synovial membranes and controls were detected by quantitative real-time PCR system. Results:A total of 229 DEGs were identified in osteoarthritis synovial membranes compared with normal synovial membranes, including 145 upregulated and 84 downregulated differentially expressed genes. The KEGG pathway analysis results showed that up-DEGs were enriched in proteoglycans in cytokine-cytokine receptor interaction, chemokine signaling pathway, rheumatoid arthritis, and TNF signaling pathway, whereas down-DEGs were enriched in the PPAR signaling pathway and AMPK signaling pathway. The qRT-PCR results showed that the expression levels of ADIPOQ, IL6, and CXCR1 in the synovium of osteoarthritis were significantly increased (p <0.05).
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