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Identification of key biomarkers for STAD using filter feature selection approaches

Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer death worldwide. Discovery of diagnostic biomarkers prompts the early detection of GC. In this study, we used limma method combined with joint mutual information (JMI), a machine learning algorithm, to identify...

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Autores principales: Wang, Yangyang, Wang, Jihan, Hu, Ya, Shangguan, Jingbo, Song, Qiying, Xu, Jing, Wang, Hanping, Xue, Mengju, Wang, Liping, Zhang, Yuanyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674689/
https://www.ncbi.nlm.nih.gov/pubmed/36400805
http://dx.doi.org/10.1038/s41598-022-21760-w
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author Wang, Yangyang
Wang, Jihan
Hu, Ya
Shangguan, Jingbo
Song, Qiying
Xu, Jing
Wang, Hanping
Xue, Mengju
Wang, Liping
Zhang, Yuanyuan
author_facet Wang, Yangyang
Wang, Jihan
Hu, Ya
Shangguan, Jingbo
Song, Qiying
Xu, Jing
Wang, Hanping
Xue, Mengju
Wang, Liping
Zhang, Yuanyuan
author_sort Wang, Yangyang
collection PubMed
description Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer death worldwide. Discovery of diagnostic biomarkers prompts the early detection of GC. In this study, we used limma method combined with joint mutual information (JMI), a machine learning algorithm, to identify a signature of 11 genes that performed well in distinguishing tumor and normal samples in a stomach adenocarcinoma cohort. Other two GC datasets were used to validate the classifying performances. Several of the candidate genes were correlated with GC tumor progression and survival. Overall, we highlight the application of feature selection approaches in the analysis of high-dimensional biological data, which will improve study accuracies and reduce workloads for the researchers when identifying potential tumor biomarkers.
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spelling pubmed-96746892022-11-20 Identification of key biomarkers for STAD using filter feature selection approaches Wang, Yangyang Wang, Jihan Hu, Ya Shangguan, Jingbo Song, Qiying Xu, Jing Wang, Hanping Xue, Mengju Wang, Liping Zhang, Yuanyuan Sci Rep Article Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer death worldwide. Discovery of diagnostic biomarkers prompts the early detection of GC. In this study, we used limma method combined with joint mutual information (JMI), a machine learning algorithm, to identify a signature of 11 genes that performed well in distinguishing tumor and normal samples in a stomach adenocarcinoma cohort. Other two GC datasets were used to validate the classifying performances. Several of the candidate genes were correlated with GC tumor progression and survival. Overall, we highlight the application of feature selection approaches in the analysis of high-dimensional biological data, which will improve study accuracies and reduce workloads for the researchers when identifying potential tumor biomarkers. Nature Publishing Group UK 2022-11-18 /pmc/articles/PMC9674689/ /pubmed/36400805 http://dx.doi.org/10.1038/s41598-022-21760-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Article
Wang, Yangyang
Wang, Jihan
Hu, Ya
Shangguan, Jingbo
Song, Qiying
Xu, Jing
Wang, Hanping
Xue, Mengju
Wang, Liping
Zhang, Yuanyuan
Identification of key biomarkers for STAD using filter feature selection approaches
title Identification of key biomarkers for STAD using filter feature selection approaches
title_full Identification of key biomarkers for STAD using filter feature selection approaches
title_fullStr Identification of key biomarkers for STAD using filter feature selection approaches
title_full_unstemmed Identification of key biomarkers for STAD using filter feature selection approaches
title_short Identification of key biomarkers for STAD using filter feature selection approaches
title_sort identification of key biomarkers for stad using filter feature selection approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674689/
https://www.ncbi.nlm.nih.gov/pubmed/36400805
http://dx.doi.org/10.1038/s41598-022-21760-w
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