DNA computing for gastric cancer analysis and functional classification

Congzhou Chen; Xin Chen; Xin Li; Xiaolong Shi

doi:10.3389/fgene.2022.1064715

DNA computing for gastric cancer analysis and functional classification

Front Genet. 2022 Nov 24:13:1064715. doi: 10.3389/fgene.2022.1064715. eCollection 2022.

Authors

Congzhou Chen¹, Xin Chen², Xin Li³, Xiaolong Shi²

Affiliations

¹ School of Computer Science, Peking University, Beijing, China.
² Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.
³ Department Genecology 2, Renmin Hospital of Wuhan University, Wuhan, China.

Abstract

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.

Keywords: DNA computing; DNA strand displacement (DSD); cancer diagnosis; co-expression network; miRNA biomarkers.