Biomarker prediction in autism spectrum disorder using a network-based approach

BMC Medical Genomics

Research

Fatemeh Zare-Mirakabad¹  Maryam Rastegari¹  Najmeh Salehi² 

[1] Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran, Polytechnic), P.O. Box: 15875-4413, 424, Hafez Ave, Tehran, Iran;School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran;National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran;

关键词: miRNA;    Co-expression network;    Gene expression;    Set cover;   

DOI  :  10.1186/s12920-023-01439-5

 received in 2022-08-31, accepted in 2023-01-12,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAutism is a neurodevelopmental disorder that is usually diagnosed in early childhood. Timely diagnosis and early initiation of treatments such as behavioral therapy are important in autistic people. Discovering critical genes and regulators in this disorder can lead to early diagnosis. Since the contribution of miRNAs along their targets can lead us to a better understanding of autism, we propose a framework containing two steps for gene and miRNA discovery.MethodsThe first step, called the FA_gene algorithm, finds a small set of genes involved in autism. This algorithm uses the WGCNA package to construct a co-expression network for control samples and seek modules of genes that are not reproducible in the corresponding co-expression network for autistic samples. Then, the protein–protein interaction network is constructed for genes in the non-reproducible modules and a small set of genes that may have potential roles in autism is selected based on this network. The second step, named the DMN_miRNA algorithm, detects the minimum number of miRNAs related to autism. To do this, DMN_miRNA defines an extended Set Cover algorithm over the mRNA–miRNA network, consisting of the selected genes and corresponding miRNA regulators.ResultsIn the first step of the framework, the FA_gene algorithm finds a set of important genes; TP53, TNF, MAPK3, ACTB, TLR7, LCK, RAC2, EEF2, CAT, ZAP70, CD19, RPLP0, CDKN1A, CCL2, CDK4, CCL5, CTSD, CD4, RACK1, CD74; using co-expression and protein–protein interaction networks. In the second step, the DMN_miRNA algorithm extracts critical miRNAs, hsa-mir-155-5p, hsa-mir-17-5p, hsa-mir-181a-5p, hsa-mir-18a-5p, and hsa-mir-92a-1-5p, as signature regulators for autism using important genes and mRNA–miRNA network. The importance of these key genes and miRNAs is confirmed by previous studies and enrichment analysis.ConclusionThis study suggests FA_gene and DMN_miRNA algorithms for biomarker discovery, which lead us to a list of important players in ASD with potential roles in the nervous system or neurological disorders that can be experimentally investigated as candidates for ASD diagnostic tests.

【 授权许可】

CC BY   
© The Author(s) 2023

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