Tsuchiya Norihiko, Matsui Shigeyuki, Narita Shintaro, Kamba Tomomi, Mitsuzuka Koji, Hatakeyama Shingo, Horikawa Yohei, Inoue Takamitsu, Saito Seiichi, Ohyama Chikara, Arai Yoich, Ogawa Osamu, Habuchi Tomonori
Department of Urology, Akita University Graduate School of Medicine, Akita, Japan.
Genes Cancer. 2013 Jan;4(1-2):54-60. doi: 10.1177/1947601913481354.
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.
个体基因变异可能对转移性前列腺癌(PCa)患者的生存有重大影响。我们旨在使用单核苷酸多态性(SNP)面板鉴定参与PCa患者生存的靶基因及其变异。对初诊时共有185例发生骨转移的PCa患者进行了分析。使用包含408个癌症相关基因中1421个SNP的癌症SNP面板对每位患者的种系DNA进行基因分型。通过对数秩检验筛选与生存相关的SNP。在6个基因XRCC4、PMS1、GATA3、IL13、CASP8和IGF1中发现14个SNP与癌症特异性生存有统计学显著关联。根据从14个SNP中选出的6个SNP的风险基因型数量分组的患者的癌症特异性生存时间有显著差异(0 - 1个风险基因型对2 - 3个风险基因型对4 - 6个风险基因型;P = 7.20×10⁻⁸)。在纳入传统临床病理变量的多变量分析中,高危组与生存独立相关(P = 0.0060)。我们在6个癌症相关基因中鉴定出14个候选SNP,它们与转移性PCa患者的不良生存相关。一组SNP可能有助于预测这些患者的生存。