Brief. Bioinformatics - Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA.

Tópicos

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Resumo

With accumulating research on the interconnections among different types of genomic regulations, researchers have found that multidimensional genomic studies outperform one-dimensional studies in multiple aspects. Among many sources of multidimensional genomic data, The Cancer Genome Atlas (TCGA) provides the public with comprehensive profiling data on >30 cancer types, making it an ideal test bed for conducting and comparing different analyses. In this article, the analysis goal is to apply several existing methods and associate multidimensional genomic measurements with cancer outcomes in particular prognosis, with special focus on the predictive power of genomic signatures. We exploit clinical data and four types of genomic measurement including mRNA gene expression, DNA methylation, microRNA and copy number alterations for breast invasive carcinoma, glioblastoma multiforme, acute myeloid leukemia and lung squamous cell carcinoma collected by TCGA. To accommodate the high dimensionality, we extract important features using Principal Component Analysis, Partial Least Squares and Least Absolute Shrinkage and Selection Operator (Lasso), which are representative of dimension reduction and variable selection techniques and have been extensively adopted, and fit Cox survival models with combined important features. We calibrate the predictive power of each type of genomic measurement for the prognosis of four cancer types and find that the results vary across cancers. Our analysis also suggests that for most of the cancers in our study and the adopted methods, there is no substantial improvement in prediction when adding other genomic measurement after gene expression and clinical covariates have been included in the model. This is consistent with the findings that molecular features measured at the transcription level affect clinical outcomes more directly than those measured at the DNA/epigenetic level.

Resumo Limpo

accumul research interconnect among differ type genom regul research found multidimension genom studi outperform onedimension studi multipl aspect among mani sourc multidimension genom data cancer genom atlas tcga provid public comprehens profil data cancer type make ideal test bed conduct compar differ analys articl analysi goal appli sever exist method associ multidimension genom measur cancer outcom particular prognosi special focus predict power genom signatur exploit clinic data four type genom measur includ mrna gene express dna methyl microrna copi number alter breast invas carcinoma glioblastoma multiform acut myeloid leukemia lung squamous cell carcinoma collect tcga accommod high dimension extract import featur use princip compon analysi partial least squar least absolut shrinkag select oper lasso repres dimens reduct variabl select techniqu extens adopt fit cox surviv model combin import featur calibr predict power type genom measur prognosi four cancer type find result vari across cancer analysi also suggest cancer studi adopt method substanti improv predict ad genom measur gene express clinic covari includ model consist find molecular featur measur transcript level affect clinic outcom direct measur dnaepigenet level

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