Dataset Information

Prognostic Value of Prostaglandin-endoperoxide Synthase 2 Polymorphisms in Prostate Cancer Recurrence after Radical Prostatectomy.

ABSTRACT:

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Backgroud: Increasing evidence suggests the involvement of chronic inflammation in the progression of prostate cancer, and prostaglandin-endoperoxide synthase 2 (PTGS2), also known as cyclooxygenase-2, catalyzes the rate-limiting steps of the pathway. We hypothesized that genetic variants of PTGS2 can influence the outcome of prostate cancer patients.

Methods

We genotyped five haplotype-tagging single-nucleotide polymorphisms (SNPs) to detect common genetic variations across the PTGS2 region in 458 prostate cancer patients treated with radical prostatectomy.

Results

One SNP, rs4648302, was associated with disease recurrence. Five-year recurrence-free survival rate increased according to the number of variant alleles inherited (55.6%, 70.7%, and 100.0% for patients with different genotypes; P = 0.037), and the effect was maintained in multivariable analysis. Public dataset analyses also suggested that PTGS2 expression was correlated with prostate cancer prognosis.

Conclusion

Our results indicated that PTGS2 could be a potential prognostic marker to improve the prediction of disease recurrence in prostate cancer patients.

SUBMITTER: Lee CH

PROVIDER: S-EPMC5027188 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Prognostic Value of Prostaglandin-endoperoxide Synthase 2 Polymorphisms in Prostate Cancer Recurrence after Radical Prostatectomy.

Lee Cheng-Hsueh CH Pao Jiunn-Bey JB Lu Te-Ling TL Lee Hong-Zin HZ Lee Yung-Chin YC Liu Chia-Chu CC Huang Chao-Yuan CY Lin Victor C VC Yu Chia-Cheng CC Yin Hsin-Ling HL Huang Shu-Pin SP Bao Bo-Ying BY

International journal of medical sciences 20160811 9

<h4>Unlabelled</h4>Backgroud: Increasing evidence suggests the involvement of chronic inflammation in the progression of prostate cancer, and prostaglandin-endoperoxide synthase 2 (PTGS2), also known as cyclooxygenase-2, catalyzes the rate-limiting steps of the pathway. We hypothesized that genetic variants of PTGS2 can influence the outcome of prostate cancer patients.<h4>Methods</h4>We genotyped five haplotype-tagging single-nucleotide polymorphisms (SNPs) to detect common genetic variations a ...[more]

PMID: 27647999

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Project description:Accurate estimation of recurrence risk is needed for optimal treatment of clinically localized prostate cancer (PCa) patients. We combined classical clinicopathological predictors of biochemical recurrence (BCR) and molecular markers into a new risk score for predicting BCR after radical prostatectomy (RP). A retrospective study which included 122 PCa patients who attended our department between 2000 and 2007 was conducted. Total RNA was isolated from formalin-fixed paraffin embedded PCa tissue. Gene expression patterns were analyzed in 21 selected samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in an independent retrospective series of 101 patients with ≥ 10 years follow-up who underwent RP for clinically localized PCa. Univariate and multivariate logistic regression analysis were used to identify individual predictors of BCR. A risk score (RS) for predicting BCR including clinicopathological and gene expression data was developed and Kaplan-Meier curves were generated. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin. In a median follow-up of 120 months (range 3-190), 37 patients developed BCR (36.6%). Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR<0.1). A set of 41 genes were validated by quantitative PCR. Regression analysis showed that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion and positive margins were independent prognostic factors of BCR. A RS generated using these gene expression and clinicopathological variables was able to discriminate between two groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, p<0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the two genes of the model were not found. In conclusion, identification of new gene expression patterns associated with a high probability of BCR in clinically localized PCa patients treated with RP, and their combination with clinicopathological variables in a robust, easy-to-use and reliable classifier may contribute to improve PCa risk stratification and, consequently, to tailor treatment and surveillance strategies in these patients.

Project description:Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa. Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan-Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis. Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701-0.827). Finally, we constructed a nomogram to predict BCR of PCa. Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making. Subjects: Bioinformatics, oncology, urology.

Dataset Information

Prognostic Value of Prostaglandin-endoperoxide Synthase 2 Polymorphisms in Prostate Cancer Recurrence after Radical Prostatectomy.

Unlabelled

Methods

Results

Conclusion

Publications

Prognostic Value of Prostaglandin-endoperoxide Synthase 2 Polymorphisms in Prostate Cancer Recurrence after Radical Prostatectomy.

Similar Datasets

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