J Am Med Inform Assoc - Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.

Tópicos

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Resumo

JECTIVE: Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes.METHODS: Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes.RESULTS: Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively.CONCLUSIONS: Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies.

Resumo Limpo

jectiv cancer can involv gene dysregul via multipl mechan singl level genom data fulli elucid tumor behavior due presenc numer genom variat within level biolog system previous propos graphbas integr approach combin multiom data includ copi number alter methyl mirna gene express data predict clinic outcom cancer howev genom featur like interact genom featur complex signal regulatori network sinc cancer caus alter pathway complet processesmethod propos new graphbas framework integr multiom data genom knowledg improv power predict clinic outcom elucid interplay differ level highlight valid propos framework use ovarian cancer dataset cancer genom atlas predict stage grade surviv outcomesresult integr multiom data genom knowledg construct predefin featur result higher perform clinic outcom predict higher stabil grade outcom model gene express data produc area receiv oper characterist curv auc howev model integr pathway gene ontolog chromosom gene set motif gene set consist outperform model genom data attain auc respectivelyconclus integr multiom data genom knowledg improv understand molecular pathogenesi under biolog cancer improv diagnost prognost indic effect therapi

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