ArticleInternational journal of clinical and experimental pathology2015
Screening key genes associated with congenital heart defects in Down syndrome based on differential expression network.
Article in International journal of clinical and experimental pathology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Mature miR-99a Upregulation in the Amniotic Fluid Samples from Female Fetus Down Syndrome Pregnancies: A Pilot Study.Medicina (Kaunas, Lithuania) · 2019Article
- Combination of Genetics and Nanotechnology for Down Syndrome Modification: A Potential Hypothesis and Review of the Literature.Iranian journal of public health · 2019Article
- Regulatory mechanisms underlying sepsis progression in patients with tumor necrosis factor-α genetic variations.Experimental and therapeutic medicine · 2016Article
- Missing Links Between Genetically Inherited Molecules in Split Cord Malformation and Other Anomaly: A Bench to Bedside Approach.Journal of pediatric neurosciencesArticle
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDown syndrome (DS) is the most common viable chromosomal disorder with intellectual impairment and several other developmental abnormalities. Forty to fifty percent of newborns with DS have some form of congenital heart defects (CHD). The genome of CHD in DS has already been obtained, but the underlying genomic or gene expression variation that contributes to the manifestation of a CHD in DS is still unknown.
objectiveThis study was aimed to analyze key genes of patients with CHD in DS.
methodsDifferential expression network (DEN) approach was employed to analyze the dyeregulated genes and pathways in this study. First, the differentially expressed genes (DEGs) between CHD in DS and normal subjects were screened based on the microarray expression data. Next, the differential interactions were identified using spearman correlation coefficients of edges in different conditions. The DEN was then constructed combining both DEGs and differential interactions, and HUB genes were gained by degree centrality analysis of DEN. Meanwhile, disease genes included in the DEN were also ascertained.
resultsWhen analyzing gene expression values in different conditions, no DEGs were identified. While, a total of 984 gene pairs with significant differential expression were identified. Finally, the DEN was constructed only using differential edges in our study. In this network, eight HUB genes were identified, and thereinto four genes (UBC, APP, HUWE1 and SRC) were both HUB genes and disease genes.
conclusionsDEN approach should be taken as a useful complement to traditional differential genes methods. We provide several potential underlying biomarkers for CHD in DS.
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26339408PMC4555736W1224124939What OpenQuestion holds
Registered trials
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