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  • Li, CD, et al. (författare)
  • A gene expression signature to predicit overall, prostate cancer, and non-prostate cancer survival
  • 2013
  • Ingår i: JOURNAL OF CLINICAL ONCOLOGY. - : American Society of Clinical Oncology (ASCO). - 0732-183X .- 1527-7755. ; 31:6
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • 51 Background: For prostate cancer patients, prostate cancer specific and non-prostate cancer specific survival have the same importance. This study aimed at identifying expression biomarkers that can predict prostate cancer specific, non-prostate cancer specific and overall survival at diagnosis. Methods: Selected ESCGPs (embryonic stem cell gene predictors) and control genes were analyzed by multiplex quantitative PCR using prostate fine-needle aspiration samples taken at diagnosis from a Swedish cohort of 189 prostate cancer patients diagnosed between 1986 and 2000. Of all patients, 97.9% had overall and cancer-specific survival data and 77.9% were primarily treated only by hormone therapy. The cohort was divided into one discovery and two validation subsets. Univariate and multivariate Cox proportional hazard ratios and Kaplan-Meier plots were used for the survival analysis. A published dataset was used for external validation. Results: An expression signature of F3, VGLL3 and IGFBP3, was sufficient to categorize the patients into high-risk, intermediate-risk and low-risk subtypes. The median overall survival of the subtypes was 3.23, 4.00 and 9.85 years respectively. The difference corresponded to HRs of 5.86 (95% CI 2.91-11.78, P<0.001) for the high-risk and 3.45 (95% CI 1.79-6.66, P<0.001) for the intermediate-risk compared to the low-risk subtype. This signature is significant in correlation to overall, cancer-specific and non-cancer specific survival in both univariate and multivariate analyses including common clinical parameters. Conclusions: These results suggest that these novel expression biomarkers and the expression signature could be used to improve the accuracy of the currently available clinical tools for predicting overall, cancer-specific and non-cancer specific survival and selecting patients with potential survival benefit from hormone treatment.
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