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A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset
As one type of complex disease, gastric cancer has high mortality rate, and there are few effective treatments for patients in advanced stage. With the development of biological technology, a large amount of multiple-omics data of gastric cancer are generated, which enables computational method to d...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044773/ https://www.ncbi.nlm.nih.gov/pubmed/33868380 http://dx.doi.org/10.3389/fgene.2021.644378 |
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author | Zhang, Ge Xue, Zijing Yan, Chaokun Wang, Jianlin Luo, Huimin |
author_facet | Zhang, Ge Xue, Zijing Yan, Chaokun Wang, Jianlin Luo, Huimin |
author_sort | Zhang, Ge |
collection | PubMed |
description | As one type of complex disease, gastric cancer has high mortality rate, and there are few effective treatments for patients in advanced stage. With the development of biological technology, a large amount of multiple-omics data of gastric cancer are generated, which enables computational method to discover potential biomarkers of gastric cancer. That will be very important to detect gastric cancer at earlier stages and thus assist in providing timely treatment. However, most of biological data have the characteristics of high dimension and low sample size. It is hard to process directly without feature selection. Besides, only using some omic data, such as gene expression data, provides limited evidence to investigate gastric cancer associated biomarkers. In this research, gene expression data and DNA methylation data are integrated to analyze gastric cancer, and a feature selection approach is proposed to identify the possible biomarkers of gastric cancer. After the original data are pre-processed, the mutual information (MI) is applied to select some top genes. Then, fold change (FC) and T-test are adopted to identify differentially expressed genes (DEG). In particular, false discover rate (FDR) is introduced to revise p_value to further screen genes. For chosen genes, a deep neural network (DNN) model is utilized as the classifier to measure the quality of classification. The experimental results show that the approach can achieve superior performance in terms of accuracy and other metrics. Biological analysis for chosen genes further validates the effectiveness of the approach. |
format | Online Article Text |
id | pubmed-8044773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80447732021-04-15 A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset Zhang, Ge Xue, Zijing Yan, Chaokun Wang, Jianlin Luo, Huimin Front Genet Genetics As one type of complex disease, gastric cancer has high mortality rate, and there are few effective treatments for patients in advanced stage. With the development of biological technology, a large amount of multiple-omics data of gastric cancer are generated, which enables computational method to discover potential biomarkers of gastric cancer. That will be very important to detect gastric cancer at earlier stages and thus assist in providing timely treatment. However, most of biological data have the characteristics of high dimension and low sample size. It is hard to process directly without feature selection. Besides, only using some omic data, such as gene expression data, provides limited evidence to investigate gastric cancer associated biomarkers. In this research, gene expression data and DNA methylation data are integrated to analyze gastric cancer, and a feature selection approach is proposed to identify the possible biomarkers of gastric cancer. After the original data are pre-processed, the mutual information (MI) is applied to select some top genes. Then, fold change (FC) and T-test are adopted to identify differentially expressed genes (DEG). In particular, false discover rate (FDR) is introduced to revise p_value to further screen genes. For chosen genes, a deep neural network (DNN) model is utilized as the classifier to measure the quality of classification. The experimental results show that the approach can achieve superior performance in terms of accuracy and other metrics. Biological analysis for chosen genes further validates the effectiveness of the approach. Frontiers Media S.A. 2021-03-25 /pmc/articles/PMC8044773/ /pubmed/33868380 http://dx.doi.org/10.3389/fgene.2021.644378 Text en Copyright © 2021 Zhang, Xue, Yan, Wang and Luo. 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 Zhang, Ge Xue, Zijing Yan, Chaokun Wang, Jianlin Luo, Huimin A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset |
title | A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset |
title_full | A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset |
title_fullStr | A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset |
title_full_unstemmed | A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset |
title_short | A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset |
title_sort | novel biomarker identification approach for gastric cancer using gene expression and dna methylation dataset |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044773/ https://www.ncbi.nlm.nih.gov/pubmed/33868380 http://dx.doi.org/10.3389/fgene.2021.644378 |
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