ArticleBMC neurology2025
A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells.
Article in BMC neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe 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).
methodsThe 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.
resultTwo 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.
conclusionBLK, P2RX5, GP1BA, and PF4 emerge as potential predictors of future disease activity in individuals with MS.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.