A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells
Abstract Aim The purpose of this research study was to develop and validate a gene signature based on peripheral blood mononuclear cells (PBMCs) for predicting the time to the next relapse in multiple sclerosis (MS). Methods The GSE15245 dataset (N = 94) was divided into a training set (N = 65) and...
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BMC
2025-07-01
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| Series: | BMC Neurology |
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| Online Access: | https://doi.org/10.1186/s12883-025-04231-3 |
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| author | Huimin Zhang Jiahui Yang Xiaobo Zhang Chaoyi Wu Zhen Zhao Ming Yang Zhaoping Wu |
| author_facet | Huimin Zhang Jiahui Yang Xiaobo Zhang Chaoyi Wu Zhen Zhao Ming Yang Zhaoping Wu |
| author_sort | Huimin Zhang |
| collection | DOAJ |
| description | Abstract Aim The purpose of this research study was to develop and validate a gene signature based on peripheral blood mononuclear cells (PBMCs) for predicting the time to the next relapse in multiple sclerosis (MS). Methods The GSE15245 dataset (N = 94) was divided into a training set (N = 65) and a testing set (N = 29). First, the training set was analyzed using weighted gene co-expression network analysis (WGCNA) to identify key modules that were highly correlated with the timing of the next acute relapse. Subsequently, the hub genes within these key modules were subjected to univariate Cox regression analysis, and genes related to the recurrence time of MS were identified. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to refine the extraction further. Then, the gene signatures were constructed using multivariate Cox regression. The efficacy of the model that was based on the training set database was evaluated using receiver operating characteristic (ROC) curves and validated using an independent testing set. Additionally, gene signatures were also validated for differential expression using an external independent dataset, GSE21942 (N = 29), along with experimental verification. Result Two key modules were identified with WGCNA. Univariate Cox regression analysis yielded 30 genes related to the relapse time of MS from these two modules, and then LASSO regression analysis further refined the selection to four genes, namely, BLK, P2RX5, GP1BA, and PF4. These four genes were used within the training dataset to build a Cox regression model, and this showed high prediction performance in the training as well as the testing datasets. Both external dataset analysis and experimental validation corroborated the differential expression of BLK and P2RX5 in patients with MS. Conclusion BLK, P2RX5, GP1BA, and PF4 emerge as potential predictors of future disease activity in individuals with MS. |
| format | Article |
| id | doaj-art-c8a342723a9849b3a2de5b0892ba4a56 |
| institution | Kabale University |
| issn | 1471-2377 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | BMC Neurology |
| spelling | doaj-art-c8a342723a9849b3a2de5b0892ba4a562025-08-20T03:38:14ZengBMCBMC Neurology1471-23772025-07-0125111310.1186/s12883-025-04231-3A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cellsHuimin Zhang0Jiahui Yang1Xiaobo Zhang2Chaoyi Wu3Zhen Zhao4Ming Yang5Zhaoping Wu6Department of Neurology, The First People’s Hospital of Lin’an DistrictDepartment of Neurology, The Affiliated Zhuzhou Hospital Xiangya Medical College CSUDepartment of Neurology, First People’s Hospital of Changde CityDepartment of Neurology, The First People’s Hospital of Lin’an DistrictDepartment of Neurology, The Affiliated Zhuzhou Hospital Xiangya Medical College CSUDepartment of Neurology, The Quzhou Affiliated Hospital of Wenzhou Medical UniversityDepartment of Neurology, The Quzhou Affiliated Hospital of Wenzhou Medical UniversityAbstract Aim The purpose of this research study was to develop and validate a gene signature based on peripheral blood mononuclear cells (PBMCs) for predicting the time to the next relapse in multiple sclerosis (MS). Methods The GSE15245 dataset (N = 94) was divided into a training set (N = 65) and a testing set (N = 29). First, the training set was analyzed using weighted gene co-expression network analysis (WGCNA) to identify key modules that were highly correlated with the timing of the next acute relapse. Subsequently, the hub genes within these key modules were subjected to univariate Cox regression analysis, and genes related to the recurrence time of MS were identified. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to refine the extraction further. Then, the gene signatures were constructed using multivariate Cox regression. The efficacy of the model that was based on the training set database was evaluated using receiver operating characteristic (ROC) curves and validated using an independent testing set. Additionally, gene signatures were also validated for differential expression using an external independent dataset, GSE21942 (N = 29), along with experimental verification. Result Two key modules were identified with WGCNA. Univariate Cox regression analysis yielded 30 genes related to the relapse time of MS from these two modules, and then LASSO regression analysis further refined the selection to four genes, namely, BLK, P2RX5, GP1BA, and PF4. These four genes were used within the training dataset to build a Cox regression model, and this showed high prediction performance in the training as well as the testing datasets. Both external dataset analysis and experimental validation corroborated the differential expression of BLK and P2RX5 in patients with MS. Conclusion BLK, P2RX5, GP1BA, and PF4 emerge as potential predictors of future disease activity in individuals with MS.https://doi.org/10.1186/s12883-025-04231-3Cox regressionGene signatureMultiple sclerosisPeripheral blood mononuclear cellsRelapse prediction |
| spellingShingle | Huimin Zhang Jiahui Yang Xiaobo Zhang Chaoyi Wu Zhen Zhao Ming Yang Zhaoping Wu A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells BMC Neurology Cox regression Gene signature Multiple sclerosis Peripheral blood mononuclear cells Relapse prediction |
| title | A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells |
| title_full | A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells |
| title_fullStr | A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells |
| title_full_unstemmed | A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells |
| title_short | A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells |
| title_sort | novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells |
| topic | Cox regression Gene signature Multiple sclerosis Peripheral blood mononuclear cells Relapse prediction |
| url | https://doi.org/10.1186/s12883-025-04231-3 |
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