Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning
ObjectiveExosomes as important carriers of intercellular communication have frequently appeared in recent studies related to osteoarthritis (OA), while the specific mechanism of exosome action in osteoarthritis remains unclear. The aim of this study was to identify potential exosome-related biomarke...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1596912/full |
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| author | Tianyang Li Jinpeng Wei Hua Wu Chen Chen |
| author_facet | Tianyang Li Jinpeng Wei Hua Wu Chen Chen |
| author_sort | Tianyang Li |
| collection | DOAJ |
| description | ObjectiveExosomes as important carriers of intercellular communication have frequently appeared in recent studies related to osteoarthritis (OA), while the specific mechanism of exosome action in osteoarthritis remains unclear. The aim of this study was to identify potential exosome-related biomarkers in osteoarthritis, to explore the role and mechanism of exosome-related genes in articular cartilage.MethodsThe data on exosome related genes and normal and OA cartilage genes were obtained through online databases. The potential mechanisms of these genes were revealed by multiple gene enrichment analysis algorithms. Machine learning methods were utilized to identify exosome-related differential genes (ERDEGs) with highly correlated OA features (Hub OA-ERDEGs). In addition, we created a nomogram to assess the ability of Hub OA-ERDEGs to diagnose OA. Single-sample gene set enrichment analysis (ssGSEA) was used to observe the infiltration characteristics of immune cells in OA and their relationship with Hub OA-ERDEGs.ResultsThe results of screening Hub OA-ERDEGs using machine learning algorithms show that: TOLLIP, ALB, HP, RHOBTB3, GSTM2, S100A8 and AKR1B1 were significantly up-regulated or down-regulated in OA samples and verified by qRT- PCR for validation. Using the ssGSEA algorithm, we discovered that 8 types of immune cell infiltration and 5 types of immune cell activation. |
| format | Article |
| id | doaj-art-1cae73694a3743a88dc09106065815be |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-1cae73694a3743a88dc09106065815be2025-08-20T04:14:17ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-08-011610.3389/fimmu.2025.15969121596912Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learningTianyang LiJinpeng WeiHua WuChen ChenObjectiveExosomes as important carriers of intercellular communication have frequently appeared in recent studies related to osteoarthritis (OA), while the specific mechanism of exosome action in osteoarthritis remains unclear. The aim of this study was to identify potential exosome-related biomarkers in osteoarthritis, to explore the role and mechanism of exosome-related genes in articular cartilage.MethodsThe data on exosome related genes and normal and OA cartilage genes were obtained through online databases. The potential mechanisms of these genes were revealed by multiple gene enrichment analysis algorithms. Machine learning methods were utilized to identify exosome-related differential genes (ERDEGs) with highly correlated OA features (Hub OA-ERDEGs). In addition, we created a nomogram to assess the ability of Hub OA-ERDEGs to diagnose OA. Single-sample gene set enrichment analysis (ssGSEA) was used to observe the infiltration characteristics of immune cells in OA and their relationship with Hub OA-ERDEGs.ResultsThe results of screening Hub OA-ERDEGs using machine learning algorithms show that: TOLLIP, ALB, HP, RHOBTB3, GSTM2, S100A8 and AKR1B1 were significantly up-regulated or down-regulated in OA samples and verified by qRT- PCR for validation. Using the ssGSEA algorithm, we discovered that 8 types of immune cell infiltration and 5 types of immune cell activation.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1596912/fullosteoarthritisexosomemachine learningimmune infiltrationbiomarkers |
| spellingShingle | Tianyang Li Jinpeng Wei Hua Wu Chen Chen Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning Frontiers in Immunology osteoarthritis exosome machine learning immune infiltration biomarkers |
| title | Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning |
| title_full | Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning |
| title_fullStr | Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning |
| title_full_unstemmed | Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning |
| title_short | Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning |
| title_sort | analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning |
| topic | osteoarthritis exosome machine learning immune infiltration biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1596912/full |
| work_keys_str_mv | AT tianyangli analysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinosteoarthritisbyintegratingbioinformaticsandmachinelearning AT jinpengwei analysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinosteoarthritisbyintegratingbioinformaticsandmachinelearning AT huawu analysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinosteoarthritisbyintegratingbioinformaticsandmachinelearning AT chenchen analysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinosteoarthritisbyintegratingbioinformaticsandmachinelearning |