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Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods
Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The m...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6125376/ https://www.ncbi.nlm.nih.gov/pubmed/30214455 http://dx.doi.org/10.3389/fgene.2018.00246 |
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author | Li, Jing Lan, Chun-Na Kong, Ying Feng, Song-Shan Huang, Tao |
author_facet | Li, Jing Lan, Chun-Na Kong, Ying Feng, Song-Shan Huang, Tao |
author_sort | Li, Jing |
collection | PubMed |
description | Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression profiles of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular organism growth played important roles in OA. Our results shed light on OA diagnosis through liquid biopsy. |
format | Online Article Text |
id | pubmed-6125376 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61253762018-09-13 Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods Li, Jing Lan, Chun-Na Kong, Ying Feng, Song-Shan Huang, Tao Front Genet Genetics Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression profiles of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular organism growth played important roles in OA. Our results shed light on OA diagnosis through liquid biopsy. Frontiers Media S.A. 2018-08-30 /pmc/articles/PMC6125376/ /pubmed/30214455 http://dx.doi.org/10.3389/fgene.2018.00246 Text en Copyright © 2018 Li, Lan, Kong, Feng and Huang. http://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 Li, Jing Lan, Chun-Na Kong, Ying Feng, Song-Shan Huang, Tao Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods |
title | Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods |
title_full | Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods |
title_fullStr | Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods |
title_full_unstemmed | Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods |
title_short | Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods |
title_sort | identification and analysis of blood gene expression signature for osteoarthritis with advanced feature selection methods |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6125376/ https://www.ncbi.nlm.nih.gov/pubmed/30214455 http://dx.doi.org/10.3389/fgene.2018.00246 |
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