Network Structure Analysis Identifying Key Genes of Autism and Its Mechanism

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.

Identifying Genes Related to Acute Myocardial Infarction Based on Network Control Capability

“…RNA samples were isolated from subjects’ CD146+ cells and processed by Affymetrix human U133 Plus 2.0 array (Affymetrix, Santa Clara, CA, USA), from which two groups of Discovery and Validation were formed [ 36 ]: The Discovery group consisted of a dataset of 22 healthy subjects (control group) and 21 acute myocardial infarction patients (AMI group), and the Validation group consisted of a dataset of 28 healthy subjects and 28 AMI patients. In [ 36 ], Discovery was taken as the research object and 126 differential expression genes (DEGs) were screened out according to the hypothesis test of gene expression profiles [ 37 ] ( p < 0.0001). This paper still uses these 126 DEGs as the research object and does not carry out any processing of excluded data.…”

Clustering Analysis Supports the Detection of Biological Processes Related to Autism Spectrum Disorder

“…Besides the shared genetic networks, our enrichment analysis also signaled some processes that differentiate the three clusters, and that could possibly cause phenotypic dissimilarities. Actually, the analysis brought to light the presence of cluster-specific processes, which were not shared with the other clusters; for example, in the case of the biological process “kidney development” in Cluster A, or “cardiac septum development” in Cluster C. Although this observation should be considered with caution, as the relative FDR p -values, though statistically significant, are close to the threshold of <0.005, it is noteworthy that recently ASD-related processes regarding the heart and kidney have become features of scientific and clinical interest [ 63 , 64 ]. Both cluster-specific and common atypical biological processes might interact to shape the specific phenotype of each individual.…”

Integrative Functional Genomic Analysis in Multiplex Autism Families from Kazakhstan

“…Despite a considerable genetic heterogeneity underlying ASD, there is compelling evidence that a large number of risk genes can be integrated into a much smaller number of proteinprotein interaction (PPI) networks [26]. Previous studies have shown that ASD genes functionally converge in synapse development, axon alignment, neuron motility, synaptic transmission, chromatin remodeling, transcription and translation regulation, ion transport, and cell adhesion [27][28][29][30][31][32]. As far as we know, these studies were mainly focused on investigating genes affected in children with ASD, but not in relatives with subclinical phenotypes of BAP.…”