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- Peng, Z., et al.
(författare)
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An expression signature at diagnosis to estimate prostate cancer patients' overall survival
- 2014
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Ingår i: Prostate Cancer and Prostatic Diseases. - : Springer Science and Business Media LLC. - 1365-7852 .- 1476-5608. ; 17:1, s. 81-90
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: This study aimed to identify biomarkers for estimating the overall and prostate cancer (PCa)-specific survival in PCa patients at diagnosis. METHODS: To explore the importance of embryonic stem cell (ESC) gene signatures, we identified 641 ESC gene predictors (ESCGPs) using published microarray data sets. ESCGPs were selected in a stepwise manner, and were combined with reported genes. Selected genes were analyzed by multiplex quantitative polymerase chain reaction using prostate fine-needle aspiration samples taken at diagnosis from a Swedish cohort of 189 PCa patients diagnosed between 1986 and 2001. Of these patients, there was overall and PCa-specific survival data available for 97.9%, and 77.9% were primarily treated by hormone therapy only. Univariate and multivariate Cox proportional hazard ratios and Kaplan-Meier plots were used for the survival analysis, and a k-nearest neighbor (kNN) algorithm for estimating overall survival. RESULTS: An expression signature of VGLL3, IGFBP3 and F3 was shown sufficient to categorize the patients into high-, intermediate- and low-risk subtypes. The median overall survival times of the subtypes were 3.23, 4.00 and 9.85 years, respectively. The difference corresponded to hazard ratios of 5.86 (95% confidence interval (CI): 2.91-11.78, P<0.001) for the high-risk subtype and 3.45 (95% CI: 1.79-6.66, P<0.001) for the intermediate-risk compared with the low-risk subtype. The kNN models that included the gene expression signature outperformed the one designed on clinical parameters alone. CONCLUSIONS: The expression signature can potentially be used to estimate overall survival time. When validated in future studies, it could be integrated in the routine clinical diagnostic and prognostic procedure of PCa for an optimal treatment decision based on the estimated survival benefit.
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| 3. |
- Li, CD, et al.
(författare)
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A gene expression signature to predicit overall, prostate cancer, and non-prostate cancer survival
- 2013
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Ingår i: JOURNAL OF CLINICAL ONCOLOGY. - : American Society of Clinical Oncology (ASCO). - 0732-183X .- 1527-7755. ; 31:6
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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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