Your browser doesn't support javascript.
loading
Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering.
Crippa, Valentina; Malighetti, Federica; Villa, Matteo; Graudenzi, Alex; Piazza, Rocco; Mologni, Luca; Ramazzotti, Daniele.
Afiliação
  • Crippa V; Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy. Electronic address: v.crippa15@campus.unimib.it.
  • Malighetti F; Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy. Electronic address: f.malighetti@campus.unimib.it.
  • Villa M; Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy. Electronic address: m.villa96@campus.unimib.it.
  • Graudenzi A; Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy.
  • Piazza R; Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy.
  • Mologni L; Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy. Electronic address: luca.mologni@unimib.it.
  • Ramazzotti D; Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy. Electronic address: daniele.ramazzotti@unimib.it.
Comput Biol Med ; 162: 107064, 2023 08.
Article em En | MEDLINE | ID: mdl-37267828
Cancer patients show heterogeneous phenotypes and very different outcomes and responses even to common treatments, such as standard chemotherapy. This state-of-affairs has motivated the need for the comprehensive characterization of cancer phenotypes and fueled the generation of large omics datasets, comprising multiple omics data reported for the same patients, which might now allow us to start deciphering cancer heterogeneity and implement personalized therapeutic strategies. In this work, we performed the analysis of four cancer types obtained from the latest efforts by The Cancer Genome Atlas, for which seven distinct omics data were available for each patient, in addition to curated clinical outcomes. We performed a uniform pipeline for raw data preprocessing and adopted the Cancer Integration via MultIkernel LeaRning (CIMLR) integrative clustering method to extract cancer subtypes. We then systematically review the discovered clusters for the considered cancer types, highlighting novel associations between the different omics and prognosis.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Genômica / Neoplasias Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Genômica / Neoplasias Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article