Microarray based prediction of circular RNA expression in multiple sclerosis
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ABSTRACT:
PROVIDER: PRJNA721442 | ENA |
REPOSITORIES: ENA
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Project description:The aim was to investigate the total circular RNA profile in patients with relapsing–remitting multiple sclerosis and healthy control. We analyzed close to 14,000 individual circRNA per sample.
2021-05-31 | GSE171950 | GEO
Project description:To investigate the differential expression profiling of circular RNAs (circRNAs) between acquired middle ear cholesteatoma and normal skin, and to identify potential circRNAs contributing to the etiopathogenesis of middle ear cholesteatoma, circRNA microarray analysis and functional prediction were performed.
2021-12-23 | GSE171006 | GEO
Project description:<p>Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work towards a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n=15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC=0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.</p><p><br></p><p><strong>Cohort 1 assays</strong> are reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS1464' rel='noopener noreferrer' target='_blank'><strong>MTBLS1464</strong></a>.</p><p><strong>Cohort 2 assays</strong> are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS558' rel='noopener noreferrer' target='_blank'><strong>MTBLS558</strong></a>.</p>
2023-05-31 | MTBLS1464 | MetaboLights
Project description:Microarray analysis and functional prediction of differentially-expressed circular RNAs in acquired middle ear cholesteatoma
| PRJNA718088 | ENA
Project description:Identification of circular RNAs in amyotrophic lateral sclerosis
| PRJNA890244 | ENA
Project description:Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature Keywords: Disease prediction Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days. If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing.
2009-07-15 | E-GEOD-15245 | biostudies-arrayexpress
Project description:Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature Keywords: Disease prediction
2009-07-15 | GSE15245 | GEO
Project description:Identification of circular RNAs in amyotrophic lateral sclerosis I
| PRJNA889852 | ENA
Project description:Identification of circular RNAs in amyotrophic lateral sclerosis II
| PRJNA889853 | ENA
Project description:Progressive multiple sclerosis
| PRJNA966951 | ENA