Screening key genes associated with congenital heart defects in Down syndrome based on differential expression network

• Published in: International journal of clinical and experimental pathology Vol. 8; no. 7; pp. 8385 - 8393
• Main Authors: Yu, Shan, Yi, Huani, Wang, Zhimin, Dong, Juan
• Format: Journal Article
• Language: English
• Published: United States e-Century Publishing Corporation 01.01.2015
• Subjects: Amyloid beta-Protein Precursor - genetics; Case-Control Studies; Down Syndrome - diagnosis; Down Syndrome - genetics; Gene Expression Profiling - methods; Gene Expression Regulation; Gene Regulatory Networks; Genes, src; Genetic Association Studies; Genetic Markers; Genetic Predisposition to Disease; Heart Defects, Congenital - diagnosis; Heart Defects, Congenital - genetics; Humans; Oligonucleotide Array Sequence Analysis; Original; Phenotype; Risk Factors; Ubiquitin-Protein Ligases - genetics; differential expression network; Down syndrome; centrality analysis; Congenital heart defects
• ISSN: 1936-2625; 1936-2625

Down 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. This study was aimed to analyze key genes of patients with CHD in DS. Differential 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. When 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. DEN approach should be taken as a useful complement to traditional differential genes methods. We provide several potential underlying biomarkers for CHD in DS.