Comprehensive literature data-mining analysis reveals a broad genetic network functionally associated with autism spectrum disorder

Xu,Cheng; Cao,Hongbao; Zhang,Fuquan; Cheadle,Chris

doi:10.3892/ijmm.2018.3845

Comprehensive literature data-mining analysis reveals a broad genetic network functionally associated with autism spectrum disorder

Authors:
- Cheng Xu
- Hongbao Cao
- Fuquan Zhang
- Chris Cheadle
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Affiliations: Department of Magnetic Resonance Imaging, Shanxi Province People's Hospital, Taiyuan, Shanxi 030001, P.R China, Department of Genomics Research, Elsevier R&D Solutions, Elsevier Inc., Rockville, MD 20852, USA, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, Jiangsu 214151, P.R China, Department of Genomics Research, Elsevier R&D Solutions, Elsevier Inc., Rockville, MD 20852, USA
Published online on: August 28, 2018 https://doi.org/10.3892/ijmm.2018.3845
Pages: 2353-2362
Copyright: © Xu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Previous studies have indicated that genetic factors are the predominate cause of Autism spectrum disorder (ASD). Nevertheless, to the best of our knowledge, to date no systematic study has summarized these data and provided an objective, complete list of genes with demonstrated associations with ASD. The present study included a literature data mining analysis of >2,064 articles including publications from January 2000 to April 2016, which identified 488 ASD target genes. Gene set enrichment analysis (GSEA), sub‑network enrichment analysis (SNEA) and network connectivity analysis (NCA) were conducted to assess the functional profile and pathogenic significance of these genes. A total of 2 literature metrics were proposed to prioritize the curated ASD genes with specific significance. This approach resulted in the development of an ASD genetic database. Subsequent analysis indicated that 391 of the 488 genes were enriched in 97 biological pathways (P<1x10‑8), demonstrating significant functional associations with each other. The majority of these curated ASD genes also serve significant roles in the pathogenesis of other neuropsychiatric disorders. These results suggest that the genetic causes of ASD are within a large network composed of functionally‑associated genes. The genetic database, together with the metric scores developed in the present study, provides a basis for future biological/genetic modeling in the field.

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