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Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches
BACKGROUND: Osteoarthritis (OA) is a multifaceted chronic joint disease characterized by complex mechanisms. It has a detrimental impact on the quality of life for individuals in the middle-aged and elderly population while also imposing a significant socioeconomic burden. At present, there remains...
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/PMC10559406/ https://www.ncbi.nlm.nih.gov/pubmed/37805587 http://dx.doi.org/10.1186/s12920-023-01672-y |
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author | Da, Zheng Guo, Rui Sun, Jianjian Wang, Ai |
author_facet | Da, Zheng Guo, Rui Sun, Jianjian Wang, Ai |
author_sort | Da, Zheng |
collection | PubMed |
description | BACKGROUND: Osteoarthritis (OA) is a multifaceted chronic joint disease characterized by complex mechanisms. It has a detrimental impact on the quality of life for individuals in the middle-aged and elderly population while also imposing a significant socioeconomic burden. At present, there remains a lack of comprehensive understanding regarding the pathophysiology of OA. The objective of this study was to examine the genes, functional pathways, and immune infiltration characteristics associated with the development and advancement of OA. METHODS: The Gene Expression Omnibus (GEO) database was utilized to acquire gene expression profiles. The R software was employed to conduct the screening of differentially expressed genes (DEGs) and perform enrichment analysis on these genes. The OA-characteristic genes were identified using the Weighted Gene Co-expression Network Analysis (WGCNA) and the Lasso algorithm. In addition, the infiltration levels of immune cells in cartilage were assessed using single-sample gene set enrichment analysis (ssGSEA). Subsequently, a correlation analysis was conducted to examine the relationship between immune cells and the OA-characteristic genes. RESULTS: A total of 80 DEGs were identified. As determined by functional enrichment, these DEGs were associated with chondrocyte metabolism, apoptosis, and inflammation. Three OA-characteristic genes were identified using WGCNA and the lasso algorithm, and their expression levels were then validated using the verification set. Finally, the analysis of immune cell infiltration revealed that T cells and B cells were primarily associated with OA. In addition, Tspan2, HtrA1 demonstrated a correlation with some of the infiltrating immune cells. CONCLUSIONS: The findings of an extensive bioinformatics analysis revealed that OA is correlated with a variety of distinct genes, functional pathways, and processes involving immune cell infiltration. The present study has successfully identified characteristic genes and functional pathways that hold potential as biomarkers for guiding drug treatment and facilitating molecular-level research on OA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01672-y. |
format | Online Article Text |
id | pubmed-10559406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105594062023-10-08 Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches Da, Zheng Guo, Rui Sun, Jianjian Wang, Ai BMC Med Genomics Research BACKGROUND: Osteoarthritis (OA) is a multifaceted chronic joint disease characterized by complex mechanisms. It has a detrimental impact on the quality of life for individuals in the middle-aged and elderly population while also imposing a significant socioeconomic burden. At present, there remains a lack of comprehensive understanding regarding the pathophysiology of OA. The objective of this study was to examine the genes, functional pathways, and immune infiltration characteristics associated with the development and advancement of OA. METHODS: The Gene Expression Omnibus (GEO) database was utilized to acquire gene expression profiles. The R software was employed to conduct the screening of differentially expressed genes (DEGs) and perform enrichment analysis on these genes. The OA-characteristic genes were identified using the Weighted Gene Co-expression Network Analysis (WGCNA) and the Lasso algorithm. In addition, the infiltration levels of immune cells in cartilage were assessed using single-sample gene set enrichment analysis (ssGSEA). Subsequently, a correlation analysis was conducted to examine the relationship between immune cells and the OA-characteristic genes. RESULTS: A total of 80 DEGs were identified. As determined by functional enrichment, these DEGs were associated with chondrocyte metabolism, apoptosis, and inflammation. Three OA-characteristic genes were identified using WGCNA and the lasso algorithm, and their expression levels were then validated using the verification set. Finally, the analysis of immune cell infiltration revealed that T cells and B cells were primarily associated with OA. In addition, Tspan2, HtrA1 demonstrated a correlation with some of the infiltrating immune cells. CONCLUSIONS: The findings of an extensive bioinformatics analysis revealed that OA is correlated with a variety of distinct genes, functional pathways, and processes involving immune cell infiltration. The present study has successfully identified characteristic genes and functional pathways that hold potential as biomarkers for guiding drug treatment and facilitating molecular-level research on OA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01672-y. BioMed Central 2023-10-07 /pmc/articles/PMC10559406/ /pubmed/37805587 http://dx.doi.org/10.1186/s12920-023-01672-y 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 Da, Zheng Guo, Rui Sun, Jianjian Wang, Ai Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches |
title | Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches |
title_full | Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches |
title_fullStr | Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches |
title_full_unstemmed | Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches |
title_short | Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches |
title_sort | identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559406/ https://www.ncbi.nlm.nih.gov/pubmed/37805587 http://dx.doi.org/10.1186/s12920-023-01672-y |
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