153 resultados para Androgen receptor, Steroid hormones, Co-regulators, Prostate cancer, Genomic, Steroidogenesis


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Advanced-stage prostate cancer (PCa) patients are often diagnosed with bone metastases. Bone metastases remain incurable and therapies are palliative. PCa cells prevalently cause osteoblastic lesions, characterized by an excess of bone formation. The prevailing concept indicates that PCa cancer cell secrete an excess of paracrine factors stimulating osteoblasts directly or indirectly, thereby leading to an excess of bone formation. The exact mechanisms by which bone formation stimulates PCa cell growth are mostly elusive. In this review, the mechanisms of PCa cancer cell osteotropism, the cancer cell-induced response within the bone marrow/bone stroma, and therapeutic stromal targets will be summarized.

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Localized prostate cancer (PCa) is a clinically heterogeneous disease, which presents with variability in patient outcomes within the same risk stratification (low, intermediate or high) and even within the same Gleason scores. Genomic tools have been developed with the purpose of stratifying patients affected by this disease to help physicians personalize therapies and follow-up schemes. This review focuses on these tissue-based tools. At present, four genomic tools are commercially available: Decipher™, Oncotype DX®, Prolaris® and ProMark®. Decipher™ is a tool based on 22 genes and evaluates the risk of adverse outcomes (metastasis) after radical prostatectomy (RP). Oncotype DX® is based on 17 genes and focuses on the ability to predict outcomes (adverse pathology) in very low-low and low-intermediate PCa patients, while Prolaris® is built on a panel of 46 genes and is validated to evaluate outcomes for patients at low risk as well as patients who are affected by high risk PCa and post-RP. Finally, ProMark® is based on a multiplexed proteomics assay and predicts PCa aggressiveness in patients found with similar features to Oncotype DX®. These biomarkers can be helpful for post-biopsy decision-making in low risk patients and post-radical prostatectomy in selected risk groups. Further studies are needed to investigate the clinical benefit of these new technologies, the financial ramifications and how they should be utilized in clinics.

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INTRODUCTION The aim of the study was to identify the appropriate level of Charlson comorbidity index (CCI) in older patients (>70 years) with high-risk prostate cancer (PCa) to achieve survival benefit following radical prostatectomy (RP). METHODS We retrospectively analyzed 1008 older patients (>70 years) who underwent RP with pelvic lymph node dissection for high-risk prostate cancer (preoperative prostate-specific antigen >20 ng/mL or clinical stage ≥T2c or Gleason ≥8) from 14 tertiary institutions between 1988 and 2014. The study population was further grouped into CCI < 2 and ≥2 for analysis. Survival rate for each group was estimated with Kaplan-Meier method and competitive risk Fine-Gray regression to estimate the best explanatory multivariable model. Area under the curve (AUC) and Akaike information criterion were used to identify ideal 'Cut off' for CCI. RESULTS The clinical and cancer characteristics were similar between the two groups. Comparison of the survival analysis using the Kaplan-Meier curve between two groups for non-cancer death and survival estimations for 5 and 10 years shows significant worst outcomes for patients with CCI ≥ 2. In multivariate model to decide the appropriate CCI cut-off point, we found CCI 2 has better AUC and p value in log rank test. CONCLUSION Older patients with fewer comorbidities harboring high-risk PCa appears to benefit from RP. Sicker patients are more likely to die due to non-prostate cancer-related causes and are less likely to benefit from RP.