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Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics.
Logotheti, Souzana; Papadaki, Eugenia; Zolota, Vasiliki; Logothetis, Christopher; Vrahatis, Aristidis G; Soundararajan, Rama; Tzelepi, Vasiliki.
Afiliação
  • Logotheti S; Department of Pathology, University of Patras, 26504 Patras, Greece.
  • Papadaki E; Department of Pathology, University of Patras, 26504 Patras, Greece.
  • Zolota V; Department of Informatics, Ionian University, 49100 Corfu, Greece.
  • Logothetis C; Department of Pathology, University of Patras, 26504 Patras, Greece.
  • Vrahatis AG; Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Soundararajan R; Department of Informatics, Ionian University, 49100 Corfu, Greece.
  • Tzelepi V; Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Cancers (Basel) ; 15(17)2023 Sep 01.
Article em En | MEDLINE | ID: mdl-37686633
Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.
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Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: Cancers (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Grécia

Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: Cancers (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Grécia