Your browser doesn't support javascript.
loading
Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model.
Zhang, Enchong; Shiori, Fujisawa; Zhang, Mo; Wang, Peng; He, Jieqian; Ge, Yuntian; Song, Yongsheng; Shan, Liping.
  • Zhang E; Department of Urology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Shiori F; Department of Breast Endocrine Surgery, Tohoku University Hospital, Sendai, Japan.
  • Zhang M; Department of Urology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Wang P; Department of Urology, Shengjing Hospital of China Medical University, Shenyang, China.
  • He J; Department of Urology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Ge Y; Department of Urology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Song Y; Department of Urology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Shan L; Department of Urology, Shengjing Hospital of China Medical University, Shenyang, China.
Front Mol Biosci ; 8: 676138, 2021.
Article en En | MEDLINE | ID: mdl-34124157
ABSTRACT
Prostate cancer (PCa) is the most common malignancy among men worldwide. However, its complex heterogeneity makes treatment challenging. In this study, we aimed to identify PCa subtypes and a gene signature associated with PCa prognosis. In particular, nine PCa-related pathways were evaluated in patients with PCa by a single-sample gene set enrichment analysis (ssGSEA) and an unsupervised clustering analysis (i.e., consensus clustering). We identified three subtypes with differences in prognosis (Risk_H, Risk_M, and Risk_L). Differences in the proliferation status, frequencies of known subtypes, tumor purity, immune cell composition, and genomic and transcriptomic profiles among the three subtypes were explored based on The Cancer Genome Atlas database. Our results clearly revealed that the Risk_H subtype was associated with the worst prognosis. By a weighted correlation network analysis of genes related to the Risk_H subtype and least absolute shrinkage and selection operator, we developed a 12-gene risk-predicting model. We further validated its accuracy using three public datasets. Effective drugs for high-risk patients identified using the model were predicted. The novel PCa subtypes and prognostic model developed in this study may improve clinical decision-making.
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Año: 2021 Tipo del documento: Article