Affiliation:
1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China
2. College of Applied Science, Beijing University of Technology, Beijing 10024, China
Abstract
Identifying the key genes of autism is of great significance for understanding its pathogenesis and improving the clinical level of medicine. In this paper, we use the structural parameters (average degree) of gene correlation networks to identify genes related to autism and study its pathogenesis. Based on the gene expression profiles of 82 autistic patients (the experimental group, E) and 64 healthy persons (the control group, C) in NCBI database, spearman correlation networks are established, and their average degrees under different thresholds are analyzed. It is found that average degrees of C and E are basically separable at the full thresholds. This indicates that there is a clear difference between the network structures of C and E, and it also suggests that this difference is related to the mechanism of disease. By annotating and enrichment analysis of the first 20 genes (MD-Gs) with significant difference in the average degree, we find that they are significantly related to gland development, cardiovascular development, and embryogenesis of nervous system, which support the results in Alter et al.’s original research. In addition, FIGF and CSF3 may play an important role in the mechanism of autism.
Funder
National Natural Science Foundation of China
Subject
Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine
Cited by
5 articles.
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