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Identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning
BACKGROUND: Osteoarthritis is a very common clinical disease in middle-aged and elderly individuals, and with the advent of ageing, the incidence of this disease is gradually increasing. There are few studies on the role of basement membrane (BM)-related genes in OA. METHOD: We used bioinformatics a...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464276/ https://www.ncbi.nlm.nih.gov/pubmed/37612746 http://dx.doi.org/10.1186/s12920-023-01601-z |
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author | Huang, Xiaojing Meng, Hongming Shou, Zeyu Yu, Jiahuan Hu, Kai Chen, Liangyan Zhou, Han Bai, Zhibiao Chen, Chun |
author_facet | Huang, Xiaojing Meng, Hongming Shou, Zeyu Yu, Jiahuan Hu, Kai Chen, Liangyan Zhou, Han Bai, Zhibiao Chen, Chun |
author_sort | Huang, Xiaojing |
collection | PubMed |
description | BACKGROUND: Osteoarthritis is a very common clinical disease in middle-aged and elderly individuals, and with the advent of ageing, the incidence of this disease is gradually increasing. There are few studies on the role of basement membrane (BM)-related genes in OA. METHOD: We used bioinformatics and machine learning methods to identify important genes related to BMs in OA patients and performed immune infiltration analysis, lncRNA‒miRNA-mRNA network prediction, ROC analysis, and qRT‒PCR. RESULT: Based on the results of machine learning, we determined that LAMA2 and NID2 were the key diagnostic genes of OA, which were confirmed by ROC and qRT‒PCR analyses. Immune analysis showed that LAMA2 and NID2 were closely related to resting memory CD4 T cells, mast cells and plasma cells. Two lncRNAs, XIST and TTTY15, were simultaneously identified, and lncRNA‒miRNA‒mRNA network prediction was performed. CONCLUSION: LAMA2 and NID2 are important potential targets for the diagnosis and treatment of OA. |
format | Online Article Text |
id | pubmed-10464276 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104642762023-08-30 Identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning Huang, Xiaojing Meng, Hongming Shou, Zeyu Yu, Jiahuan Hu, Kai Chen, Liangyan Zhou, Han Bai, Zhibiao Chen, Chun BMC Med Genomics Research BACKGROUND: Osteoarthritis is a very common clinical disease in middle-aged and elderly individuals, and with the advent of ageing, the incidence of this disease is gradually increasing. There are few studies on the role of basement membrane (BM)-related genes in OA. METHOD: We used bioinformatics and machine learning methods to identify important genes related to BMs in OA patients and performed immune infiltration analysis, lncRNA‒miRNA-mRNA network prediction, ROC analysis, and qRT‒PCR. RESULT: Based on the results of machine learning, we determined that LAMA2 and NID2 were the key diagnostic genes of OA, which were confirmed by ROC and qRT‒PCR analyses. Immune analysis showed that LAMA2 and NID2 were closely related to resting memory CD4 T cells, mast cells and plasma cells. Two lncRNAs, XIST and TTTY15, were simultaneously identified, and lncRNA‒miRNA‒mRNA network prediction was performed. CONCLUSION: LAMA2 and NID2 are important potential targets for the diagnosis and treatment of OA. BioMed Central 2023-08-23 /pmc/articles/PMC10464276/ /pubmed/37612746 http://dx.doi.org/10.1186/s12920-023-01601-z Text en © The Author(s) 2023 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Huang, Xiaojing Meng, Hongming Shou, Zeyu Yu, Jiahuan Hu, Kai Chen, Liangyan Zhou, Han Bai, Zhibiao Chen, Chun Identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning |
title | Identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning |
title_full | Identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning |
title_fullStr | Identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning |
title_full_unstemmed | Identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning |
title_short | Identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning |
title_sort | identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464276/ https://www.ncbi.nlm.nih.gov/pubmed/37612746 http://dx.doi.org/10.1186/s12920-023-01601-z |
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