Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability
BackgroundAs Systemic Sclerosis (SSc) is a connective tissue ailment that impacts various bodily systems. The study aims to clarify the molecular subtypes of SSc, with the ultimate objective of establishing a diagnostic model that can inform clinical treatment decisions.MethodsFive microarray datase...
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1257802/full |
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| author | Qi Wang Qi Wang Chen-Long Li Chen-Long Li Li Wu Li Wu Jing-Yi Hu Qi Yu Qi Yu Sheng-Xiao Zhang Pei-Feng He Pei-Feng He |
| author_facet | Qi Wang Qi Wang Chen-Long Li Chen-Long Li Li Wu Li Wu Jing-Yi Hu Qi Yu Qi Yu Sheng-Xiao Zhang Pei-Feng He Pei-Feng He |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | BackgroundAs Systemic Sclerosis (SSc) is a connective tissue ailment that impacts various bodily systems. The study aims to clarify the molecular subtypes of SSc, with the ultimate objective of establishing a diagnostic model that can inform clinical treatment decisions.MethodsFive microarray datasets of SSc were retrieved from the GEO database. To eliminate batch effects, the combat algorithm was applied. Immune cell infiltration was evaluated using the xCell algorithm. The ConsensusClusterPlus algorithm was utilized to identify SSc subtypes. Limma was used to determine differential expression genes (DEGs). GSEA was used to determine pathway enrichment. A support vector machine (SVM), Random Forest(RF), Boruta and LASSO algorithm have been used to select the feature gene. Diagnostic models were developed using SVM, RF, and Logistic Regression (LR). A ROC curve was used to evaluate the performance of the model. The compound-gene relationship was obtained from the Comparative Toxicogenomics Database (CTD).ResultsThe identification of three immune subtypes in SSc samples was based on the expression profiles of immune cells. The utilization of 19 key intersectional DEGs among subtypes facilitated the classification of SSc patients into three robust subtypes (gene_ClusterA-C). Gene_ClusterA exhibited significant enrichment of B cells, while gene_ClusterC showed significant enrichment of monocytes. Moderate activation of various immune cells was observed in gene_ClusterB. We identified 8 feature genes. The SVM model demonstrating superior diagnostic performance. Furthermore, correlation analysis revealed a robust association between the feature genes and immune cells. Eight pertinent compounds, namely methotrexate, resveratrol, paclitaxel, trichloroethylene, formaldehyde, silicon dioxide, benzene, and tetrachloroethylene, were identified from the CTD.ConclusionThe present study has effectively devised an innovative molecular subtyping methodology for patients with SSc and a diagnostic model based on machine learning to aid in clinical treatment. The study has identified potential molecular targets for therapy, thereby offering novel perspectives for the treatment and investigation of SSc. |
| first_indexed | 2024-03-11T20:25:04Z |
| format | Article |
| id | doaj.art-35cfbaa322d84738b28dd131a0aa004b |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-11T20:25:04Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj.art-35cfbaa322d84738b28dd131a0aa004b2023-10-02T17:09:04ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-10-011410.3389/fimmu.2023.12578021257802Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capabilityQi Wang0Qi Wang1Chen-Long Li2Chen-Long Li3Li Wu4Li Wu5Jing-Yi Hu6Qi Yu7Qi Yu8Sheng-Xiao Zhang9Pei-Feng He10Pei-Feng He11School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Big Data for Clinical Decision Research, Taiyuan, ChinaSchool of Basic Medical Sciences, Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Big Data for Clinical Decision Research, Taiyuan, ChinaSchool of Basic Medical Sciences, Shanxi Medical University, Taiyuan, ChinaDepartment of Anesthesiology , Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, ChinaSchool of Management, Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Big Data for Clinical Decision Research, Taiyuan, ChinaSchool of Management, Shanxi Medical University, Taiyuan, ChinaDepartment of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Big Data for Clinical Decision Research, Taiyuan, ChinaSchool of Management, Shanxi Medical University, Taiyuan, ChinaBackgroundAs Systemic Sclerosis (SSc) is a connective tissue ailment that impacts various bodily systems. The study aims to clarify the molecular subtypes of SSc, with the ultimate objective of establishing a diagnostic model that can inform clinical treatment decisions.MethodsFive microarray datasets of SSc were retrieved from the GEO database. To eliminate batch effects, the combat algorithm was applied. Immune cell infiltration was evaluated using the xCell algorithm. The ConsensusClusterPlus algorithm was utilized to identify SSc subtypes. Limma was used to determine differential expression genes (DEGs). GSEA was used to determine pathway enrichment. A support vector machine (SVM), Random Forest(RF), Boruta and LASSO algorithm have been used to select the feature gene. Diagnostic models were developed using SVM, RF, and Logistic Regression (LR). A ROC curve was used to evaluate the performance of the model. The compound-gene relationship was obtained from the Comparative Toxicogenomics Database (CTD).ResultsThe identification of three immune subtypes in SSc samples was based on the expression profiles of immune cells. The utilization of 19 key intersectional DEGs among subtypes facilitated the classification of SSc patients into three robust subtypes (gene_ClusterA-C). Gene_ClusterA exhibited significant enrichment of B cells, while gene_ClusterC showed significant enrichment of monocytes. Moderate activation of various immune cells was observed in gene_ClusterB. We identified 8 feature genes. The SVM model demonstrating superior diagnostic performance. Furthermore, correlation analysis revealed a robust association between the feature genes and immune cells. Eight pertinent compounds, namely methotrexate, resveratrol, paclitaxel, trichloroethylene, formaldehyde, silicon dioxide, benzene, and tetrachloroethylene, were identified from the CTD.ConclusionThe present study has effectively devised an innovative molecular subtyping methodology for patients with SSc and a diagnostic model based on machine learning to aid in clinical treatment. The study has identified potential molecular targets for therapy, thereby offering novel perspectives for the treatment and investigation of SSc.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1257802/fullsystemic sclerosisunsupervised machine learningmolecular subtypesimmune microenvironmentdiagnostic |
| spellingShingle | Qi Wang Qi Wang Chen-Long Li Chen-Long Li Li Wu Li Wu Jing-Yi Hu Qi Yu Qi Yu Sheng-Xiao Zhang Pei-Feng He Pei-Feng He Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability Frontiers in Immunology systemic sclerosis unsupervised machine learning molecular subtypes immune microenvironment diagnostic |
| title | Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability |
| title_full | Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability |
| title_fullStr | Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability |
| title_full_unstemmed | Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability |
| title_short | Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability |
| title_sort | distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability |
| topic | systemic sclerosis unsupervised machine learning molecular subtypes immune microenvironment diagnostic |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1257802/full |
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