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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9729876 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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