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DNA computing for gastric cancer analysis and functional classification

Early identification of key biomarkers of malignant cancer is vital for patients’ prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing...

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Detalles Bibliográficos
Autores principales: Chen, Congzhou, Chen, Xin, Li, Xin, Shi, Xiaolong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729876/
https://www.ncbi.nlm.nih.gov/pubmed/36506309
http://dx.doi.org/10.3389/fgene.2022.1064715
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author Chen, Congzhou
Chen, Xin
Li, Xin
Shi, Xiaolong
author_facet Chen, Congzhou
Chen, Xin
Li, Xin
Shi, Xiaolong
author_sort Chen, Congzhou
collection PubMed
description Early identification of key biomarkers of malignant cancer is vital for patients’ prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing system for cancer analysis. First, gene chips were obtained through bioinformatical training. These microRNA data and clinical traits were obtained from the Cancer Genome Atlas (TCGA) dataset. Second, we analyzed the expression data by using a weighted gene co-expression network (WGCNA) and found four biomarkers for two clinic features, respectively. Last, we constructed a DSD-based DNA computing system for cancer analysis. The inputs of the system are these identified biomarkers; the outputs are the fluorescent signals that represent their corresponding traits. The experiment and simulation results demonstrated the reliability of the DNA computing system. This DSD simulation system is lab-free but clinically meaningful. We expect this innovative method to be useful for rapid and accurate cancer diagnosis.
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spelling pubmed-97298762022-12-09 DNA computing for gastric cancer analysis and functional classification Chen, Congzhou Chen, Xin Li, Xin Shi, Xiaolong Front Genet Genetics Early identification of key biomarkers of malignant cancer is vital for patients’ prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing system for cancer analysis. First, gene chips were obtained through bioinformatical training. These microRNA data and clinical traits were obtained from the Cancer Genome Atlas (TCGA) dataset. Second, we analyzed the expression data by using a weighted gene co-expression network (WGCNA) and found four biomarkers for two clinic features, respectively. Last, we constructed a DSD-based DNA computing system for cancer analysis. The inputs of the system are these identified biomarkers; the outputs are the fluorescent signals that represent their corresponding traits. The experiment and simulation results demonstrated the reliability of the DNA computing system. This DSD simulation system is lab-free but clinically meaningful. We expect this innovative method to be useful for rapid and accurate cancer diagnosis. Frontiers Media S.A. 2022-11-24 /pmc/articles/PMC9729876/ /pubmed/36506309 http://dx.doi.org/10.3389/fgene.2022.1064715 Text en Copyright © 2022 Chen, Chen, Li and Shi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Chen, Congzhou
Chen, Xin
Li, Xin
Shi, Xiaolong
DNA computing for gastric cancer analysis and functional classification
title DNA computing for gastric cancer analysis and functional classification
title_full DNA computing for gastric cancer analysis and functional classification
title_fullStr DNA computing for gastric cancer analysis and functional classification
title_full_unstemmed DNA computing for gastric cancer analysis and functional classification
title_short DNA computing for gastric cancer analysis and functional classification
title_sort dna computing for gastric cancer analysis and functional classification
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729876/
https://www.ncbi.nlm.nih.gov/pubmed/36506309
http://dx.doi.org/10.3389/fgene.2022.1064715
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