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. 2013 Jan;4(1-2):54-60.
doi: 10.1177/1947601913481354.

Distinct cancer-specific survival in metastatic prostate cancer patients classified by a panel of single nucleotide polymorphisms of cancer-associated genes

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Distinct cancer-specific survival in metastatic prostate cancer patients classified by a panel of single nucleotide polymorphisms of cancer-associated genes

Norihiko Tsuchiya et al. Genes Cancer. 2013 Jan.

Abstract

Individual genetic variations may have a significant influence on the survival of metastatic prostate cancer (PCa) patients. We aimed to identify target genes and their variations involved in the survival of PCa patients using a single nucleotide polymorphism (SNP) panel. A total of 185 PCa patients with bone metastasis at the initial diagnosis were analyzed. Germline DNA in each patient was genotyped using a cancer SNP panel that contained 1,421 SNPs in 408 cancer-related genes. SNPs associated with survival were screened by a log-rank test. Fourteen SNPs in 6 genes, XRCC4, PMS1, GATA3, IL13, CASP8, and IGF1, were identified to have a statistically significant association with cancer-specific survival. The cancer-specific survival times of patients grouped according to the number of risk genotypes of 6 SNPs selected from the 14 SNPs differed significantly (0-1 v. 2-3 v. 4-6 risk genotypes; P = 7.20 × 10(-8)). The high-risk group was independently associated with survival in a multivariate analysis that included conventional clinicopathological variables (P = 0.0060). We identified 14 candidate SNPs in 6 cancer-related genes, which were associated with poor survival in patients with metastatic PCa. A panel of SNPs may help predict the survival of those patients.

Keywords: bone metastasis; prostate cancer; single nucleotide polymorphism; survival.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Cancer-specific survival of patients according to risk categorization using candidate SNPs identified by array analysis. A prognostic scoring index using the 14 SNPs selecz the screening was developed by incorporating the difference in their effect sizes to classify high-risk and low-risk groups. Its predictive accuracy was assessed by the LOOCV analysis for the whole model building process. The median cancer-specific survival time of the high-risk group was significantly shorter than that in low-risk patients (P = 0.0050).
Figure 2.
Figure 2.
Cancer-specific survival of patients according to the number of risk genotypes of 6 representative SNPs selected from 6 candidate genes. Each patient was assigned to 1 of 3 groups according to the number of risk genotypes in 6 representative SNPs selected from each candidate gene: 0-1, 2-3, and 4-6. The cancer-specific survival times differed significantly among the 3 risk groups (P = 7.20 × 10−8).
Figure 3.
Figure 3.
Cancer-specific survival based on the number of risk genotypes for 6 representative SNPs in the following groups: (A) ALP <350 IU/L, (B) ALP ≥350 IU/L, (C) Gleason score <9, and (D) Gleason score ≥9. There were significant differences in cancer-specific survival between patients stratified by the number of risk genotypes (0-3 v. 4-6) in the subgroups shown: (A) P = 1.8 × 10−7, (C) P = 0.0004, and (D) P = 0.0004.

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