Dataset Information

Study of gene expression of peripheral blood T lymphocytes from age-series of monozygotic twin couples

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE74937 | GEO | 2016/06/30

SECONDARY ACCESSION(S): PRJNA301967

REPOSITORIES: GEO

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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.

Dataset Information

Study of gene expression of peripheral blood T lymphocytes from age-series of monozygotic twin couples

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