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Systematic assessment of prognostic molecular features across cancers.
Santhanam, Balaji; Oikonomou, Panos; Tavazoie, Saeed.
Afiliação
  • Santhanam B; Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
  • Oikonomou P; Department of Systems Biology, Columbia University, New York, NY 10032, USA.
  • Tavazoie S; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.
Cell Genom ; 3(3): 100262, 2023 Mar 08.
Article em En | MEDLINE | ID: mdl-36950380
Precision oncology promises accurate prediction of disease trajectories by utilizing molecular features of tumors. We present a systematic analysis of the prognostic potential of diverse molecular features across large cancer cohorts. We find that the mRNA expression of biologically coherent sets of genes (modules) is substantially more predictive of patient survival than single-locus genomic and transcriptomic aberrations. Extending our analysis beyond existing curated gene modules, we find a large novel class of highly prognostic DNA/RNA cis-regulatory modules associated with dynamic gene expression within cancers. Remarkably, in more than 82% of cancers, modules substantially improve survival stratification compared with conventional clinical factors and prominent genomic aberrations. The prognostic potential of cancer modules generalizes to external cohorts better than conventionally used single-gene features. Finally, a machine-learning framework demonstrates the combined predictive power of multiple modules, yielding prognostic models that perform substantially better than existing histopathological and clinical factors in common use.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article