Study of gene expression of peripheral blood T lymphocytes from age-series of monozygotic twin couples
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Daniel Remondini
PROVIDER: E-GEOD-74937 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:In this study we analyse a gene expression dataset, obtained by microarray hybridization, consisting of T lymphocytes from 27 healthy monozygotic twin couples with ages ranging over whole adult lifespan (22 to 98 years). This peculiar experimental design, combined with a ridge regression approach, allowed us to define an optimal low-dimensional gene expression signature from which chronological age can be estimated. Our work identified for the first time a group of 125 genes whose expression can be considered a signature of physiologic T cell aging. This signature has been further validated in another dataset of samples of the same cell type, but hybridized over a different platform, showing a significantly good performance that confirmed the power of the chosen analysis design. Moreover, the same signature was applied on a publicly available dataset with an analogous time series design, but hybridized on a different platform and consisting of a different cell type (human muscle cells). The lower regression performance obtained in this case indicates that the signature is cell-type specific.
2016-06-30 | GSE74937 | GEO
Project description:Background. In a previous publication we introduced a novel approach to identify genes that hold predictive information about treatment outcome. A linear regression model was fitted by using the least angle regression algorithm (LARS) with the expression profiles of a construction set of 18 glioma progenitor cells enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed predicting therapy-induced impairment of proliferation in vitro. Prediction performance was validated in leave one out cross validation. Methods. In this study, we used an additional validation set of 18 serum-free short-term treated in vitro cell cultures to test the predictive properties of the signature in an independent cohort. We assessed proliferation rates together with transcriptome-wide expression profiles after Sunitinib treatment of each individual cell culture, following the methods of the previous publication. Results. We confirmed treatment-induced expression changes in our validation set, but our signature failed to predict proliferation inhibition. Conclusion. Although the gene signature published from our construction set exhibited good prediction accuracy in cross validation, we were not able to validate the signature in an independent validation data set. Reasons could be regression to the mean, the moderate numbers of samples, or too low differences in the response to proliferation inhibition. At this stage and based on the presented results, we conclude that the signature does not warrant further developmental steps towards clinical application. 40 samples; 18 brain tumor initiating cells (BTICs); 2 treatment conditions: 1 µM Sunitinib or 0.00025% DMSO with supplementation of VEGF and PDGF-AB for 6 hours
2016-02-21 | E-GEOD-76990 | biostudies-arrayexpress
Project description:Background: Recently a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N-) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. Materials and Methods: Gene expression profiling of frozen samples from 198 N- systemically untreated patients was performed at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were performed with the genomic risk and adjusted for the clinical risk, defined by Adjuvant!Online. Results: The actual 5- and 10-year time to distant metastasis (TDM) were 98% (88%-100%) and 94% (83%-98%) respectively for the good profile group and 76% (68%- 82%) and 73% (65%-79%) for the poor profile group. The actual 5- and 10-year overall survival (OS) were 98% (88%-100%) and 87% (73%-94%) respectively for the good profile group and 84% (77%-89%) and 72% (63%-78%) for the poor profile group. We observed a strong time-dependency of this signature, leading to an adjusted HR of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years, and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for TDM and OS respectively. Conclusion: This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases. Experiment Overall Design: dataset of microarray experiments from primary breast tumors used to validate the 76-gene signature (VERIDEX). No replicate, no reference sample.
2008-06-16 | E-GEOD-7390 | biostudies-arrayexpress
Project description:A definition of RNA expression changes that correlate with liver response programs and an understanding of the similarities and differences in responses to different classes of chemicals would aid in new chemical or drug characterization and add to our understanding of liver biology. We have used a supervised classification approach to systematically mine a large microarray database derived from livers of compound-treated rats. Thirty-four distinct signatures (classifiers) for pharmacological and toxicological endpoints resolvable by gene expression can be identified. The contribution of genes to signatures is not correlated to average expression or amplitude of regulation, and pre-selection of genes can significantly reduce signature performance. Just 200 genes are sufficient to classify all endpoints and can form the basis of a small diagnostic array useful in toxicogenomics. Signature genes were enriched in xenobiotic and acute phase response genes as well as un-annotated genes, suggesting that not all key genes in liver xenobiotic responses have been identified. Individual signatures can be re-derived up to 25 times from a gene set cyclically .stripped. of the signature genes. The union of these non-overlapping sets was used to describe the biological mechanisms of liver fibrosis. Guidelines for commercial use: http://www.iconixbiosciences.com/guidelineCommUse.pdf Keywords: dose response, time course, compound treatment Treatment of male Sprague-Dawley rats with 344 compounds at various doses and durations, in biological triplicate, along with vehicle-matched control animals. Liver samples were assayed for gene expression. A total of 1695 samples were hybridized to single-channel CodeLink RU1 arrays. Biological triplicates were combined with matched control samples to calculate log ratios. Classifiers were generated and evaluated for 2112 biological questions, resulting in 34 distinct, high-performance "signatures."
2008-03-28 | E-GEOD-8858 | biostudies-arrayexpress
Project description:Purpose: Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake. Methods: A balanced training set (n=71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison. Results: The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial Least Squares using 3 components (PLS-3) was the best performing model in the training dataset cross-validation (Root Mean Square Error, RMSE=0.443) and was validated further in an independent validation dataset (n=13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE=0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35), and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35), among others. Conclusions: PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments.
2018-01-22 | GSE107754 | GEO
Project description:Melioidosis, caused by Gram negative bacteria Burkholderia pseudomallei, is a major type of community-acquired septicemia in Southeast Asia and Northern Australia with high mortality and morbidity rate. More accurate and rapid diagnosis is needed for improving the management of septicemic melioidosis. We previously identified 37-gene candidate signature to distinguish septicemic melioidosis from sepsis due to other pathogens. The aims of this current study were to independently validate our previous biomarker and consolidate gene selection from each of our microarray data set for establishing a targeted assay for the differential diagnosis of melioidosis. Blood samples were collected from patients who presented with severe inflammatory response syndromes from 3 provincial hospitals in Northeast of Thailand during September 2009 and November 2011. Only culture-confirmed sepsis were included in the study (n=166). We generated a new microarray dataset comprising of 29 patients with septicemic melioidosis and 54 patients with sepsis due to other pathogens. Validation of the 37-gene signature using this new dataset demonstrated the prediction accuracy of approximately 80% for detecting type of sepsis. In order to develop a nanoliter-scale high throughput PCR technology, we further identified additional gene signature from this new microarray dataset and by revisiting our published data. Altogether 85 genes including 6 housekeeping genes were selected. Using multi-steps iteration approach we could reduce the number of biomarkers to 12 genes while the performance is comparable to that of the full panel. The high performance (accuracy >70%) of this 12-gene signature could be validated in a second independent set of samples. The 12-gene panel identified by our study provides high performance for the differential diagnosis of septicemic melioidosis. This finding will be useful for improving the management of septicemic melioidosis in term of diagnosis, treatment and follow up. Total RNA from whole blood obtained from patients with sepsis caused by B.pseudomallei (n=29) or other pathogens (n=54) and uninfected controls (28 healthy and 27 subjects with type 2 diabetes mellitus) were collected. In order to validate the published signature, microarray data were generated from these samples. This dataset was also used for an independent selection of signature for septicemic melioidosis. The same RNA samples were used for validation by a high throughput real-time PCR technique, Fluidigm.
2015-06-04 | E-GEOD-69528 | biostudies-arrayexpress
Project description:The objective of this study was to develop and validate predictive models to assess the risk of death in patients with acute diquat (DQ) poisoning using innovative machine learning techniques. The study analyzed data from 201 consecutive patients admitted for deliberate oral intake of DQ between February 2018 and August 2023 at the First Hospital and Shengjing Hospital of China Medical University. Initial clinical data were collected, and four machine learning methods—logistic regression, random forest, support vector machine (SVM), and gradient boosting—were applied to build the prediction models. The dataset was split into a training set and a test set in an 8:2 ratio. The performance of these models was evaluated in terms of discrimination, calibration, and clinical decision curve analysis (DCA). Additionally, the SHapley Additive ExPlanations (SHAP) interpretation tool was used to provide an intuitive explanation of the risk of death in patients with DQ poisoning by calculating the contribution and impact of each feature on the final prediction. The areas under the receiver operating characteristic curves (AUCs) for the models were 0.91 for logistic regression, 0.98 for random forest, 0.96 for SVM, and 0.94 for gradient boosting. The random forest model demonstrated the best predictive performance with the highest AUC of 0.98, highest F1-score (0.90), highest Matthews correlation coefficient (0.79), highest accuracy (0.90), and the lowest Brier score (0.07). The study concludes that these machine learning models, combined with SHAP, provide reliable and interpretable tools for predicting the death risk in patients with acute DQ poisoning, with the random forest model being identified as the best performing model.
2024-07-18 | MODEL2407180001 | BioModels
Project description:This experiment is a test of a reagent that can provide a basis for performance assessments on Affymetrix RAE230A, CodeLink UniSet Rat I, and Agilent G4130A rat oligonucleotide arrays. The reagent is a set of two mixed tissue RNA samples formulated to contain different ratios of RNA for each of four rat tissues (brain, liver, kidney, and testes). The analytes in this assay bind to a subset of gene probes that were matched across platforms to the same UniGene cluster or GenBank accession number, were determined to be tissue-selective on each of the 3 platforms using average baseline expression levels in single tissue samples from control animals, and have signal intensities that span the dynamic range of each platform. This reagent forms the basis of a ratiometric assay in a complex biological background that can be used to objectively assess the effect of different array processing methods and conditions on microarray data comparability within and across platforms, and could be a paradigm for similar performance standards for mouse and human arrays.
2010-03-30 | E-TABM-16 | biostudies-arrayexpress
Project description:Melioidosis, caused by Gram negative bacteria Burkholderia pseudomallei, is a major type of community-acquired septicemia in Southeast Asia and Northern Australia with high mortality and morbidity rate. More accurate and rapid diagnosis is needed for improving the management of septicemic melioidosis. We previously identified 37-gene candidate signature to distinguish septicemic melioidosis from sepsis due to other pathogens. The aims of this current study were to independently validate our previous biomarker and consolidate gene selection from each of our microarray data set for establishing a targeted assay for the differential diagnosis of melioidosis. Blood samples were collected from patients who presented with severe inflammatory response syndromes from 3 provincial hospitals in Northeast of Thailand during September 2009 and November 2011. Only culture-confirmed sepsis were included in the study (n=166). We generated a new microarray dataset comprising of 29 patients with septicemic melioidosis and 54 patients with sepsis due to other pathogens. Validation of the 37-gene signature using this new dataset demonstrated the prediction accuracy of approximately 80% for detecting type of sepsis. In order to develop a nanoliter-scale high throughput PCR technology, we further identified additional gene signature from this new microarray dataset and by revisiting our published data. Altogether 85 genes including 6 housekeeping genes were selected. Using multi-steps iteration approach we could reduce the number of biomarkers to 12 genes while the performance is comparable to that of the full panel. The high performance (accuracy >70%) of this 12-gene signature could be validated in a second independent set of samples. The 12-gene panel identified by our study provides high performance for the differential diagnosis of septicemic melioidosis. This finding will be useful for improving the management of septicemic melioidosis in term of diagnosis, treatment and follow up.
2015-06-04 | GSE69528 | GEO