Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning
Abstract Background Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease characterized by clinical and pathological diversity. Mitochondrial dysfunction has been identified as a critical pathogenetic factor in SLE. However, the specific molecular aspects and regulatory roles of th...
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | BMC Rheumatology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41927-023-00369-0 |
| _version_ | 1827590289937661952 |
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| author | Haoguang Li Lu Zhou Wei Zhou Xiuling Zhang Jingjing Shang Xueqin Feng Le Yu Jie Fan Jie Ren Rongwei Zhang Xinwang Duan |
| author_facet | Haoguang Li Lu Zhou Wei Zhou Xiuling Zhang Jingjing Shang Xueqin Feng Le Yu Jie Fan Jie Ren Rongwei Zhang Xinwang Duan |
| author_sort | Haoguang Li |
| collection | DOAJ |
| description | Abstract Background Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease characterized by clinical and pathological diversity. Mitochondrial dysfunction has been identified as a critical pathogenetic factor in SLE. However, the specific molecular aspects and regulatory roles of this dysfunction in SLE are not fully understood. Our study aims to explore the molecular characteristics of mitochondria-related genes (MRGs) in SLE, with a focus on identifying reliable biomarkers for classification and therapeutic purposes. Methods We sourced six SLE-related microarray datasets (GSE61635, GSE50772, GSE30153, GSE99967, GSE81622, and GSE49454) from the Gene Expression Omnibus (GEO) database. Three of these datasets (GSE61635, GSE50772, GSE30153) were integrated into a training set for differential analysis. The intersection of differentially expressed genes with MRGs yielded a set of differentially expressed MRGs (DE-MRGs). We employed machine learning algorithms—random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) logistic regression—to select key hub genes. These genes’ classifying potential was validated in the training set and three other validation sets (GSE99967, GSE81622, and GSE49454). Further analyses included differential expression, co-expression, protein-protein interaction (PPI), gene set enrichment analysis (GSEA), and immune infiltration, centered on these hub genes. We also constructed TF-mRNA, miRNA-mRNA, and drug-target networks based on these hub genes using the ChEA3, miRcode, and PubChem databases. Results Our investigation identified 761 differentially expressed genes (DEGs), mainly related to viral infection, inflammatory, and immune-related signaling pathways. The interaction between these DEGs and MRGs led to the identification of 27 distinct DE-MRGs. Key among these were FAM210B, MSRB2, LYRM7, IFI27, and SCO2, designated as hub genes through machine learning analysis. Their significant role in SLE classification was confirmed in both the training and validation sets. Additional analyses included differential expression, co-expression, PPI, GSEA, immune infiltration, and the construction of TF-mRNA, miRNA-mRNA, and drug-target networks. Conclusions This research represents a novel exploration into the MRGs of SLE, identifying FAM210B, MSRB2, LYRM7, IFI27, and SCO2 as significant candidates for classifying and therapeutic targeting. |
| first_indexed | 2024-03-09T01:14:48Z |
| format | Article |
| id | doaj.art-9123b2f42e944dddba762e53538723a2 |
| institution | Directory Open Access Journal |
| issn | 2520-1026 |
| language | English |
| last_indexed | 2024-03-09T01:14:48Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Rheumatology |
| spelling | doaj.art-9123b2f42e944dddba762e53538723a22023-12-10T12:35:34ZengBMCBMC Rheumatology2520-10262023-12-017111510.1186/s41927-023-00369-0Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learningHaoguang Li0Lu Zhou1Wei Zhou2Xiuling Zhang3Jingjing Shang4Xueqin Feng5Le Yu6Jie Fan7Jie Ren8Rongwei Zhang9Xinwang Duan10Department of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Rheumatology and Immunology, the Second Affiliated Hospital of Nanchang UniversityAbstract Background Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease characterized by clinical and pathological diversity. Mitochondrial dysfunction has been identified as a critical pathogenetic factor in SLE. However, the specific molecular aspects and regulatory roles of this dysfunction in SLE are not fully understood. Our study aims to explore the molecular characteristics of mitochondria-related genes (MRGs) in SLE, with a focus on identifying reliable biomarkers for classification and therapeutic purposes. Methods We sourced six SLE-related microarray datasets (GSE61635, GSE50772, GSE30153, GSE99967, GSE81622, and GSE49454) from the Gene Expression Omnibus (GEO) database. Three of these datasets (GSE61635, GSE50772, GSE30153) were integrated into a training set for differential analysis. The intersection of differentially expressed genes with MRGs yielded a set of differentially expressed MRGs (DE-MRGs). We employed machine learning algorithms—random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) logistic regression—to select key hub genes. These genes’ classifying potential was validated in the training set and three other validation sets (GSE99967, GSE81622, and GSE49454). Further analyses included differential expression, co-expression, protein-protein interaction (PPI), gene set enrichment analysis (GSEA), and immune infiltration, centered on these hub genes. We also constructed TF-mRNA, miRNA-mRNA, and drug-target networks based on these hub genes using the ChEA3, miRcode, and PubChem databases. Results Our investigation identified 761 differentially expressed genes (DEGs), mainly related to viral infection, inflammatory, and immune-related signaling pathways. The interaction between these DEGs and MRGs led to the identification of 27 distinct DE-MRGs. Key among these were FAM210B, MSRB2, LYRM7, IFI27, and SCO2, designated as hub genes through machine learning analysis. Their significant role in SLE classification was confirmed in both the training and validation sets. Additional analyses included differential expression, co-expression, PPI, GSEA, immune infiltration, and the construction of TF-mRNA, miRNA-mRNA, and drug-target networks. Conclusions This research represents a novel exploration into the MRGs of SLE, identifying FAM210B, MSRB2, LYRM7, IFI27, and SCO2 as significant candidates for classifying and therapeutic targeting.https://doi.org/10.1186/s41927-023-00369-0Systemic Lupus Erythematosus (SLE)Mitochondria-related genes (MRGs)BiomarkersBioinformaticsMachine learning |
| spellingShingle | Haoguang Li Lu Zhou Wei Zhou Xiuling Zhang Jingjing Shang Xueqin Feng Le Yu Jie Fan Jie Ren Rongwei Zhang Xinwang Duan Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning BMC Rheumatology Systemic Lupus Erythematosus (SLE) Mitochondria-related genes (MRGs) Biomarkers Bioinformatics Machine learning |
| title | Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning |
| title_full | Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning |
| title_fullStr | Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning |
| title_full_unstemmed | Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning |
| title_short | Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning |
| title_sort | decoding the mitochondrial connection development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning |
| topic | Systemic Lupus Erythematosus (SLE) Mitochondria-related genes (MRGs) Biomarkers Bioinformatics Machine learning |
| url | https://doi.org/10.1186/s41927-023-00369-0 |
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