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

BACKGROUND: Autism 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. S...

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Autores principales: Rastegari, Maryam, Salehi, Najmeh, Zare-Mirakabad, Fatemeh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869547/
https://www.ncbi.nlm.nih.gov/pubmed/36691005
http://dx.doi.org/10.1186/s12920-023-01439-5
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author Rastegari, Maryam
Salehi, Najmeh
Zare-Mirakabad, Fatemeh
author_facet Rastegari, Maryam
Salehi, Najmeh
Zare-Mirakabad, Fatemeh
author_sort Rastegari, Maryam
collection PubMed
description BACKGROUND: Autism 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. METHODS: The 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. RESULTS: In 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. CONCLUSION: This 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01439-5.
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spelling pubmed-98695472023-01-24 Biomarker prediction in autism spectrum disorder using a network-based approach Rastegari, Maryam Salehi, Najmeh Zare-Mirakabad, Fatemeh BMC Med Genomics Research BACKGROUND: Autism 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. METHODS: The 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. RESULTS: In 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. CONCLUSION: This 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01439-5. BioMed Central 2023-01-23 /pmc/articles/PMC9869547/ /pubmed/36691005 http://dx.doi.org/10.1186/s12920-023-01439-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Rastegari, Maryam
Salehi, Najmeh
Zare-Mirakabad, Fatemeh
Biomarker prediction in autism spectrum disorder using a network-based approach
title Biomarker prediction in autism spectrum disorder using a network-based approach
title_full Biomarker prediction in autism spectrum disorder using a network-based approach
title_fullStr Biomarker prediction in autism spectrum disorder using a network-based approach
title_full_unstemmed Biomarker prediction in autism spectrum disorder using a network-based approach
title_short Biomarker prediction in autism spectrum disorder using a network-based approach
title_sort biomarker prediction in autism spectrum disorder using a network-based approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869547/
https://www.ncbi.nlm.nih.gov/pubmed/36691005
http://dx.doi.org/10.1186/s12920-023-01439-5
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