Crafting a Personalized Prognostic Model for Malignant Prostate Cancer Patients Using Risk Gene Signatures Discovered through TCGA-PRAD Mining, Machine Learning, and Single-Cell RNA-Sequencing

Background: Prostate cancer is a significant clinical issue, particularly for high Gleason score (GS) malignancy patients. Our study aimed to engineer and validate a risk model based on the profiles of high-GS PCa patients for early identification and the prediction of prognosis. Methods: We conduct...

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Detalles Bibliográficos
Autores principales: Lyu, Feng, Gao, Xianshu, Ma, Mingwei, Xie, Mu, Shang, Shiyu, Ren, Xueying, Liu, Mingzhu, Chen, Jiayan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297172/
https://www.ncbi.nlm.nih.gov/pubmed/37370891
http://dx.doi.org/10.3390/diagnostics13121997

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297172/
https://www.ncbi.nlm.nih.gov/pubmed/37370891
http://dx.doi.org/10.3390/diagnostics13121997

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