A bioinformatics-to-clinic sequential approach to analysis of prostate cancer biomarkers using TCGA datasets and clinical samples: a new method for precision oncology?

ABSTRACT: Biomarker-driven cancer therapy has met with significant clinical success. Identification of a biomarker implicated in a malignant phenotype and linked to poor clinical outcome is required if we are to develop these types of therapies. A subset of prostate adenocarcinoma (PACa) cases are treatment-resistant, making them an attractive target for such an approach. To identify target molecules implicated in shorter survival of patients with PACa, we established a bioinformatics-to-clinic sequential analysis approach, beginning with 2-step in silico analysis of a TCGA dataset for localized PACa. The effect of candidate genes identified by in silico analysis on survival was then assessed using biopsy specimens taken at the time of initial diagnosis of localized and metastatic PACa

SUBMITTER: Yoshie H 

PROVIDER: S-EPMC5725118 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature