A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients
Abstract Rheumatoid arthritis (RA) is an autoimmune disease that exhibits a high degree of heterogeneity, marked by unpredictable disease flares and significant variations in the response to available treatments. The lack of optimal stratification for RA patients may be a contributing factor to the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-023-04426-7 |
| _version_ | 1827708678330908672 |
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| author | Qi Wang Qi-Chao Gao Qi-Chuan Wang Li Wu Qi Yu Pei-Feng He |
| author_facet | Qi Wang Qi-Chao Gao Qi-Chuan Wang Li Wu Qi Yu Pei-Feng He |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | Abstract Rheumatoid arthritis (RA) is an autoimmune disease that exhibits a high degree of heterogeneity, marked by unpredictable disease flares and significant variations in the response to available treatments. The lack of optimal stratification for RA patients may be a contributing factor to the poor efficacy of current treatment options. The objective of this study is to elucidate the molecular characteristics of RA through the utilization of mitochondrial genes and subsequently construct and authenticate a diagnostic framework for RA. Mitochondrial proteins were obtained from the MitoCarta database, and the R package limma was employed to filter for differentially expressed mitochondrial genes (MDEGs). Metascape was utilized to perform enrichment analysis, followed by an unsupervised clustering algorithm using the ConsensuClusterPlus package to identify distinct subtypes based on MDEGs. The immune microenvironment, biological pathways, and drug response were further explored in these subtypes. Finally, a multi-biomarker-based diagnostic model was constructed using machine learning algorithms. Utilizing 88 MDEGs present in transcript profiles, it was possible to classify RA patients into three distinct subtypes, each characterized by unique molecular and cellular signatures. Subtype A exhibited a marked activation of inflammatory cells and pathways, while subtype C was characterized by the presence of specific innate lymphocytes. Inflammatory and immune cells in subtype B displayed a more modest level of activation (Wilcoxon test P < 0.05). Notably, subtype C demonstrated a stronger correlation with a superior response to biologics such as infliximab, anti-TNF, rituximab, and methotrexate/abatacept (P = 0.001) using the fisher test. Furthermore, the mitochondrial diagnosis SVM model demonstrated a high degree of discriminatory ability in distinguishing RA in both training (AUC = 100%) and validation sets (AUC = 80.1%). This study presents a pioneering analysis of mitochondrial modifications in RA, offering a novel framework for patient stratification and potentially enhancing therapeutic decision-making. |
| first_indexed | 2024-03-10T17:07:57Z |
| format | Article |
| id | doaj.art-81f35c082b2246eda28765eae655184b |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-03-10T17:07:57Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-81f35c082b2246eda28765eae655184b2023-11-20T10:45:11ZengBMCJournal of Translational Medicine1479-58762023-08-0121111410.1186/s12967-023-04426-7A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patientsQi Wang0Qi-Chao Gao1Qi-Chuan Wang2Li Wu3Qi Yu4Pei-Feng He5School of Basic Medical Sciences, Shanxi Medical UniversitySchool of Basic Medical Sciences, Shanxi Medical UniversitySchool of Basic Medical Sciences, Shanxi Medical UniversitySchool of Basic Medical Sciences, Shanxi Medical UniversityShanxi Key Laboratory of Big Data for Clinical Decision ResearchShanxi Key Laboratory of Big Data for Clinical Decision ResearchAbstract Rheumatoid arthritis (RA) is an autoimmune disease that exhibits a high degree of heterogeneity, marked by unpredictable disease flares and significant variations in the response to available treatments. The lack of optimal stratification for RA patients may be a contributing factor to the poor efficacy of current treatment options. The objective of this study is to elucidate the molecular characteristics of RA through the utilization of mitochondrial genes and subsequently construct and authenticate a diagnostic framework for RA. Mitochondrial proteins were obtained from the MitoCarta database, and the R package limma was employed to filter for differentially expressed mitochondrial genes (MDEGs). Metascape was utilized to perform enrichment analysis, followed by an unsupervised clustering algorithm using the ConsensuClusterPlus package to identify distinct subtypes based on MDEGs. The immune microenvironment, biological pathways, and drug response were further explored in these subtypes. Finally, a multi-biomarker-based diagnostic model was constructed using machine learning algorithms. Utilizing 88 MDEGs present in transcript profiles, it was possible to classify RA patients into three distinct subtypes, each characterized by unique molecular and cellular signatures. Subtype A exhibited a marked activation of inflammatory cells and pathways, while subtype C was characterized by the presence of specific innate lymphocytes. Inflammatory and immune cells in subtype B displayed a more modest level of activation (Wilcoxon test P < 0.05). Notably, subtype C demonstrated a stronger correlation with a superior response to biologics such as infliximab, anti-TNF, rituximab, and methotrexate/abatacept (P = 0.001) using the fisher test. Furthermore, the mitochondrial diagnosis SVM model demonstrated a high degree of discriminatory ability in distinguishing RA in both training (AUC = 100%) and validation sets (AUC = 80.1%). This study presents a pioneering analysis of mitochondrial modifications in RA, offering a novel framework for patient stratification and potentially enhancing therapeutic decision-making.https://doi.org/10.1186/s12967-023-04426-7Rheumatoid arthritisMitochondrial proteinsImmune microenvironmentUnsupervised machine learningStratification |
| spellingShingle | Qi Wang Qi-Chao Gao Qi-Chuan Wang Li Wu Qi Yu Pei-Feng He A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients Journal of Translational Medicine Rheumatoid arthritis Mitochondrial proteins Immune microenvironment Unsupervised machine learning Stratification |
| title | A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients |
| title_full | A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients |
| title_fullStr | A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients |
| title_full_unstemmed | A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients |
| title_short | A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients |
| title_sort | compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients |
| topic | Rheumatoid arthritis Mitochondrial proteins Immune microenvironment Unsupervised machine learning Stratification |
| url | https://doi.org/10.1186/s12967-023-04426-7 |
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