天津医药

天津医药 ›› 2017, Vol. 45 ›› Issue (3): 258-262.doi: 10.11958/20170046

• 临床研究 • 上一篇    下一篇

低表达 miR-1301 与卵巢癌患者不良预后的相关分析

高红叶， 李莲， 郑红△   

  1. 天津医科大学肿瘤医院， 肿瘤流行病与生物统计研究室， 国家肿瘤临床医学研究中心， 天津市肿瘤防治重点实验室， 天津市恶性肿瘤临床医学研究中心 （邮编 300060）
  • 收稿日期:2017-01-10 修回日期:2017-01-17 出版日期:2017-03-15 发布日期:2017-03-21
  • 通讯作者: 郑红 E-mail:zhengh64@aliyun.com
  • 基金资助:
    国家自然科学基金资助项目 （81470153）

Correlation between low expression of miR-1301 and poor prognosis in patients with ovarian cancer

GAO Hong-ye, LI Lian, ZHENG Hong△   

  1. Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital,National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy,Tianjin Clinical Research Center for Cancer, Tianjin 300060, China
  • Received:2017-01-10 Revised:2017-01-17 Published:2017-03-15 Online:2017-03-21
  • Contact: ZHENG Hong E-mail:zhengh64@aliyun.com

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摘要： 摘要： 目的 利用 TCGA 数据库分析卵巢癌组织中 miR-1301 的表达及临床意义， 并预测与卵巢癌预后相关的 miR-1301 的靶基因。方法 对 TCGA 数据库中卵巢癌数据集进行整理， 按照 miR-1301 表达的中位数分为低表达 组和高表达组。按照年龄分为＜60 岁和≥60 岁； 肿瘤位置分为单侧和双侧； 临床分期分为早期（Ⅰ+Ⅱ期）和晚期（Ⅲ+Ⅳ期）； 肿瘤最大径分为＜2 cm 和≥2 cm； 肿瘤分级分为 G1+G2 和 G3+G4； 有无残余瘤分为无残余和有残余。卡方检验分析不同临床特征卵巢癌患者 miR-1301 表达差异是否有统计学意义。采用 Kaplan-Meier（K-M）分析不同临床特征患者生存率差异， 多因素 Cox 比例风险回归模型分析卵巢癌患者预后影响因素。利用生物信息学方法预测 miR-1301 的靶基因并应用 K-M 法进行生存分析， 采用 Pearson 和 Spearman 分析靶基因表达与 miR-1301 表达的相关性。结果 不同临床特征卵巢癌患者 miR-1301 表达差异无统计学意义。K-M 生存分析和 Cox 回归分析显示， miR-1301 是影响卵巢癌预后的独立危险因素。miR-1301 低表达的卵巢癌患者预后不良（P＜0.05）。生物信息学分析显示， miR-1301 的靶基因 PAQR5、 ATP2B1、 KPNA3、 TSC22D3、 PTRF、 HS6ST3 的高表达与卵巢癌患者不良预后相关，其中 PTRF、 HS6ST3 与 miR-1301 的表达呈负相关（P＜0.05）， HS6ST3 和 PTRF 高表达者预后差。结论miR-1301 低表达的卵巢癌患者预后不良， miR-1301 可能成为预测卵巢癌患者预后的新靶标。HS6ST3 和 PTRF 为与卵巢癌预后相关的 miR-1301 的靶基因。

关键词: 微 RNAs, 卵巢肿瘤, 预后, 计算生物学, miR-1301, TCGA数据库

Abstract: Abstract: Objective To analyze the expression of miR-1301 and clinical significance in patients of ovarian cancer using public database, and predict the possible target genes of miR- 1301 related to the prognosis of ovarian cancer.Methods Data of ovarian cancer in the TCGA database were collated. Ovarian cancer patients were divided into two groups(low expression group and high expression group) according to the median of miR- 1301 expression. Patients were divided into two groups according to age (＜60 years vs. ≥60 years), tumor location (unilateral vs. bilateral), clinical stage (Ⅰ+Ⅱ vs.Ⅲ+Ⅳ), longest dimension of tumor (＜2 cm vs. ≥2 cm), tumor grade (G1+G2 vs. G3+G4) and residual tumor (no residue vs.residual). Chi-square test was used to analyze the difference of miR-1301 expression in different patients of ovarian cancer.Kaplan- Meier (K- M) was used to analyze the survival difference of different factors, and multivariate Cox proportional hazards regression model was used to analyze the prognostic factors of patients with ovarian cancer. The target genes of miR-1301 were predicted by bioinformatics method, and survival analysis was performed by K-M. The correlation between the expression of target gene and miR-1301 expression was analyzed by Pearson and Spearman correlation methods. Results Chi-square test showed that there was no significant difference in the expression of miR-1301 in different clinical features of ovarian cancer. K-M survival analysis and Cox regression analysis showed that miR-1301 was an independent risk factor for the prognosis of ovarian cancer. Low expression of miR-1301 was associated with poor prognosis in patients with ovarian cancer (P＜0.05). The results of bioinformatics analysis displayed that high expressions of miR-1301 target genes including PAQR5, ATP2B1, KPNA3, TSC22D3, PTRF and HS6ST3 were associated with poor prognosis. Among them, PTRF and HS6ST3 expressions were negatively correlated with expression of miR- 1301 (P＜0.05). Conclusion Low expression of miR-1301 is significantly associated with poor prognosis of patients with ovarian cancer, which may provide a new target for the prediction of prognosis of ovarian cancer.

Key words: microRNAs, ovarian neoplasms, prognosis, computational biology, miR-1301, TCGA database