Dataset Information

Microarray?based screening of differentially expressed genes in glucocorticoid?induced avascular necrosis.

ABSTRACT: The underlying mechanisms of glucocorticoid (GC)?induced avascular necrosis of the femoral head (ANFH) have yet to be fully understood, in particular the mechanisms associated with the change of gene expression pattern. The present study aimed to identify key genes with a differential expression pattern in GC?induced ANFH. E?MEXP?2751 microarray data were downloaded from the ArrayExpress database. Differentially expressed genes (DEGs) were identified in 5 femoral head samples of steroid?induced ANFH rats compared with 5 placebo?treated rat samples. Gene Ontology (GO) and pathway enrichment analyses were performed upon these DEGs. A total 93 DEGs (46 upregulated and 47 downregulated genes) were identified in GC?induced ANFH samples. These DEGs were enriched in different GO terms and pathways, including chondrocyte differentiation and detection of chemical stimuli. The enrichment map revealed that skeletal system development was interconnected with several other GO terms by gene overlap. The literature mined network analysis revealed that 5 upregulated genes were associated with femoral necrosis, including parathyroid hormone receptor 1 (PTHR1), vitamin D (1,25?Dihydroxyvitamin D3) receptor (VDR), collagen, type II, ?1, proprotein convertase subtilisin/kexin type 6 and zinc finger protein 354C (ZFP354C). In addition, ZFP354C and VDR were identified to transcription factors. Furthermore, PTHR1 was revealed to interact with VDR, and ??2?macroglobulin (A2M) interacted with fibronectin 1 (FN1) in the PPI network. PTHR1 may be involved in GC?induced ANFH via interacting with VDR. A2M may also be involved in the development of GC?induced ANFH through interacting with FN1. An improved understanding of the molecular mechanisms underlying GC?induced ANFH may provide novel targets for diagnostics and therapeutic treatment.

SUBMITTER: Huang G

PROVIDER: S-EPMC5436162 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Microarray‑based screening of differentially expressed genes in glucocorticoid‑induced avascular necrosis.

Huang Gangyong G Wei Yibing Y Zhao Guanglei G Xia Jun J Wang Siqun S Wu Jianguo J Chen Feiyan F Chen Jie J Shi Jingshen J

Molecular medicine reports 20170405 6

The underlying mechanisms of glucocorticoid (GC)‑induced avascular necrosis of the femoral head (ANFH) have yet to be fully understood, in particular the mechanisms associated with the change of gene expression pattern. The present study aimed to identify key genes with a differential expression pattern in GC‑induced ANFH. E‑MEXP‑2751 microarray data were downloaded from the ArrayExpress database. Differentially expressed genes (DEGs) were identified in 5 femoral head samples of steroid‑induced ...[more]

PMID: 28393228

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Project description:BackgroundThe small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework.ResultsWe present a novel Bayesian moderated-T, which we show to perform favorably in simulations, with two real, dual-channel microarray experiments and in two controlled single-channel experiments. In simulations, the new method achieved greater power while correctly estimating the true proportion of false positives, and in the analysis of two publicly-available "spike-in" experiments, the new method performed favorably compared to all tested alternatives. We also applied our method to two experimental datasets and discuss the additional biological insights as revealed by our method in contrast to the others. The R-source code for implementing our algorithm is freely available at http://eh3.uc.edu/ibmt.ConclusionWe use a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic (IBMT). The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e. t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.

Dataset Information

Microarray?based screening of differentially expressed genes in glucocorticoid?induced avascular necrosis.

Publications

Microarray‑based screening of differentially expressed genes in glucocorticoid‑induced avascular necrosis.

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