您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2020, Vol. 1 ›› Issue (7): 47-52.doi: 10.6040/j.issn.1671-7554.0.2020.0663

• • 上一篇    下一篇

胃癌miRNAs预后风险评分模型的构建与应用

史爽1,2,李娟1,2,米琦1,2,王允山1,2,杜鲁涛1,2,王传新1,2   

  1. 1.山东大学第二医院检验医学中心, 山东 济南 250033;2. 山东省肿瘤标志物检测工程实验室, 山东 济南 250033
  • 出版日期:2020-07-20 发布日期:2020-07-10
  • 通讯作者: 王传新. E-mail:cxwang@sdu.edu.cn
  • 基金资助:
    山东省重大科技创新工程项目(2018YFJH0505);山东大学基本科研业务费专项资金资助(2018JC002)

Construction and application of a miRNAs prognostic risk assessment model of gastric cancer

SHI Shuang1,2, LI Juan1,2, MI Qi1,2, WANG Yunshan1,2, DU Lutao1,2, WANG Chuanxin1,2   

  1. 1. Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, Shandong, China;
    2. Tumor Marker Detection Engineering Laboratory of Shandong Province, Jinan 250033, Shandong, China
  • Online:2020-07-20 Published:2020-07-10

PDF (PC)

475

摘要： 目的 筛选与胃癌预后存在关联性的微小RNAs(miRNAs)生物标志物,构建风险评分模型用于患者预后评估。 方法 基于人类癌症和肿瘤基因图谱(TCGA)数据库下载胃癌miRNAs表达谱数据及样本相关临床信息,通过“DESeq2”软件包对miRNAs表达谱进行差异分析。采用单因素Cox回归分析和Kaplan-Meier生存分析筛选与预后存在关联性的miRNAs,并将预后miRNAs纳入多因素Cox回归分析用于预后风险评分模型的构建。通过“timeROC”软件包绘制受试者工作特征曲线(ROC),对模型效能进行评价。最后通过在线数据库对miRNAs可能结合的信使RNAs(mRNAs)进行预测,并通过基因本体(GO)、京都基因与基因组百科全书(KEGG)预测其功能。 结果 以log2 | Fold Change |>1,P<0.05为标准,筛选得到248个胃癌组织中差异表达的miRNAs。通过单因素Cox回归分析及Kaplan-Meier生存分析筛选到6个与患者总体生存率有关联性的差异表达的miRNAs,随后使用多因素Cox回归分析成功构建胃癌miRNAs预后风险评分模型,风险评分=0.048 35×miR-181b-1 +0.112 06×miR-548d-1+0.068 00×miR-675+0.075 87×miR-708+1.175 21×miR-4640+0.089 89×miR-4709。Kaplan-Meier生存曲线结果显示,风险评分高的患者预后较差(P<0.001);模型5年总体生存率ROC曲线下面积(AUC)为0.776,证明该模型能够有效预测胃癌患者预后风险。GO和KEGG功能分析结果显示,模型miRNAs分子参与多个肿瘤相关代谢通路。 结论 成功构建了miRNAs预后风险评分模型,且该模型对胃癌患者生存状态具有良好的预测效能。

关键词: 胃癌, miRNAs, 预后, TCGA数据库, Cox风险评分模型

Abstract: Objective To identify microRNAs(miRNAs)biomarkers related to the prognosis of gastric cancer patients, and construct a miRNA risk assessment model for survival prediction. Methods The miRNA expression profile of gastric cancer patients and relevant clinical data were obtained from the Cancer Genome Atlas(TCGA)database. Differentially expressed miRNAs were identified with “DESeq2” package. The miRNAs related to prognosis were screened with univariate Cox regression and Kaplan-Meier analysis, which were analyzed with multivariate Cox regression to construct a prognostic risk assessment model. A receiver operating characteristic(ROC)curve was drawn with “time ROC” package to evaluate the effectiveness of the model. Finally, the messenger RNAs(mRNAs)that miRNAs might bind to were predicted with online database, and the possible functions were predicted with gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG). Results With Log2|Fold Change|>1 and P<0.05 as the standards, 248 differentially expressed miRNAs in gastric cancer tissues were identified. Univariate Cox regression and Kaplan-Meier analysis screened out 6 differentially expressed miRNAs which had significant correlation with prognosis to construct a prognostic risk assessment model. The risk score=0.048 35×miR-181b-1 +0.112 06×miR-548d-1+0.068 00×miR-675+0.075 87×miR-708+1.175 21×miR-4640+0.089 89×miR-4709. Kaplan-Meier analysis showed patients with high risks had a poor prognosis(P<0.001). The area under the ROC curve(AUC)of the 5-year overall survival rate was 0.776, indicating the model was able to predict the prognostic risk. GO and KEGG analysis showed miRNAs were involved in a few signaling pathways of gastric cancer. Conclusion A miRNAs prognostic risk assessment model was successfully constructed based on bioinformatics analysis, which was proved by Kaplan-Meier analysis and ROC curve to have good prediction effects on the survival of gastric cancer patients.

Key words: Gastric cancer, miRNAs, Prognosis, TCGA database, Cox hazards regression model

中图分类号: 

  • R574
[1] Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2018, 68(6): 394-424.
[2] Lu Y, Tang L, Zhang Q, et al. microRNA-613 inhibits the progression of gastric cancer by targeting CDK9 [J]. Artif Cells Nanomed Biotechnol, 2018, 46(5): 980-984.
[3] Taniyama D, Taniyama K, Kuraoka K, et al. Long-term follow-up study of gastric adenoma; tumor-associated macrophages are associated to carcinoma development in gastric adenoma [J]. Gastric Cancer, 2017, 20(6): 929-939.
[4] Bernards N, Creemers GJ, Nieuwenhuijzen GA, et al. No improvement in median survival for patients with metastatic gastric cancer despite increased use of chemotherapy [J]. Ann Oncol, 2013, 24(12): 3056-3060.
[5] Hunt RH, Camilleri M, Crowe SE, et al. The stomach in health and disease [J]. Gut, 2015, 64(10): 1650-1668.
[6] Bria E, De Manzoni G, Beghelli S, et al. A clinical-biological risk stratification model for resected gastric cancer: Prognostic impact of Her2, Fhit, and APC expression status [J]. Ann Oncol, 2013, 24(3): 693-701.
[7] Allemani C, Matsuda T, Di Carlo V, et al. Global surveillance of trends in cancer survival 2000-14(concord-3): Analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries [J]. Lancet, 2018, 391(10125): 1023-1075.
[8] Bartel DP. MicroRNAs: Genomics, biogenesis, mechanism, and function [J]. Cell, 2004, 116(2): 281-297.
[9] Iwakawa HO, Tomari Y. The functions of microRNAs: mRNA decay and translational repression [J]. Trends Cell Biol, 2015, 25(11): 651-665.
[10] Zhang J, Tian ZL, Zhang WT, et al. Clinicopathological and prognostic significance of miR-4317 expression in gastric cancer patients [J]. Eur Rev Med Pharmacol Sci, 2019, 23(7): 2803-2808.
[11] Yang X, Zhang Z, Zhang L, et al. microRNA hsa-miR-3923 serves as a diagnostic and prognostic biomarker for gastric carcinoma [J]. Sci Rep, 2020, 10(1): 4672.
[12] 贾晓晨, 贾勇圣, 孟文静, 等. 基于TCGA数据库建立的八基因预后模型在乳腺癌中的应用[J]. 天津医药, 2018, 46(8): 856-861. JIA Xiaochen, JIA Yongsheng, MENG Wenjing, et al. Identification of prognostic eight-gene signature model in breast cancer using integrated TCGA database [J]. Tianjin Yi Yao, 2018, 46(8): 856-861.
[13] 吴洁. 139例胃癌内镜诊断与病理诊断对比及分析[J]. 临床检验杂志(电子版), 2020, 9(1): 93.
[14] 肖恒华. 胃癌预后miRNA生物标志物的筛选及其生物学功能分析[D]. 衡阳:南华大学, 2018.
[15] Wang Z, Li Y, Cao J, et al. microRNA profile identifies miR-6165 could suppress gastric cancer migration and invasion by targeting STRN4 [J]. Onco Targets Ther, 2020, 13: 1859-1869. doi: 10.2147/OTT.S208024.
[16] Khan AQ, Ahmed EI, Elareer NR, et al. Role of miRNA-regulated cancer stem cells in the pathogenesis of human malignancies [J]. Cells, 2019, 8(8): 840. doi: 10.3390/cells8080840.
[17] Hill M, Tran N. MicroRNAs regulating microRNAs in cancer [J]. Trends Cancer, 2018, 4(7): 465-468.
[18] Rupaimoole R, Slack FJ. microRNA therapeutics: Towards a new era for the management of cancer and other diseases [J]. Nat Rev Drug Discov, 2017, 16(3): 203-222.
[19] Hu X, Zhang M, Miao J, et al. miRNA-4317 suppresses human gastric cancer cell proliferation by targeting ZNF322 [J]. Cell Biol Int, 2018, 42(8): 923-930.
[20] Cai H, Lin H, Cao W, et al. Downregulation of miR-519a predicts poor prognosis and contributes to tumor progression in gastric cancer [J]. Oncol Res Treat, 2020, 43(1-2): 19-26.
[21] Shao Q, Xu J, Guan X, et al. In vitro and in vivo effects of miRNA-19b/20a/92a on gastric cancer stem cells and the related mechanism [J]. Int J Med Sci, 2018, 15(1): 86-94.
[22] Ishikawa D, Yoshikawa K, Takasu C, et al. Expression level of microRNA-449a predicts the prognosis of patients with gastric cancer [J]. Anticancer Res, 2020, 40(1): 239-244.
[23] Liu G, Xiang T, Wu QF, et al. Long noncoding RNA h19-derived miR-675 enhances proliferation and invasion via RUNX1 in gastric cancer cells [J]. Oncol Res, 2016, 23(3): 99-107.
[24] Li X, Zhong X, Pan X, et al. Tumor suppressive microRNA-708 targets Notch1 to suppress cell proliferation and invasion in gastric cancer [J]. Oncol Res, 2018. doi: 10.3727/096504018X15179680859017.
[25] Yu M, Yu HL, Li QH, et al. miR-4709 overexpression facilitates cancer proliferation and invasion via downregulating NR3C2 and is an unfavorable prognosis factor in colon adenocarcinoma [J]. J Biochem Mol Toxicol, 2019, 33(12): e22411. doi: 10.1002/jbt.22411.
[26] Hu C, Yu M, Ren Y, et al. PP2A inhibition from LB100 therapy enhances daunorubicin cytotoxicity in secondary acute myeloid leukemia via miR-181b-1 upregulation [J]. Sci Rep, 2017, 7(1): 2894. doi: 10.1038/s41598-017-03058-4.
[27] Lv SQ, Kim YH, Giulio F, et al. Genetic alterations in microRNAs in medulloblastomas [J]. Brain Pathol, 2012, 22(2): 230-239.
[28] Lin S, Song S, Sun R, et al. Oncogenic circular RNA Hsa-circ-000684 interacts with microRNA-186 to upregulate ZEB1 in gastric cancer [J]. FASEB J, 2020. doi: 10.1096/fj.201903246R.
[29] He H, Liu J, Li W, et al. miR-210-3p inhibits proliferation and migration of C6 cells by targeting Iscu [J]. Neurochem Res, 2020. doi: 10.1007/s11064-020-03043-w.
[1] 郑苏,陈述花,李华,邓劼,陈春红,王晓慧,冯卫星,韩萧迪,张雨佳,李娜,李莫,方方. 脑电变化和BASED评分与54例婴儿痉挛症促肾上腺皮质激素疗效的相关性[J]. 山东大学学报 (医学版), 2022, 60(9): 91-96.
[2] 王丽慧,高敏,孔北华. 子宫血管肉瘤2例报告并文献复习[J]. 山东大学学报 (医学版), 2022, 60(9): 108-112.
[3] 付佳,王路路,胡帅,陈哲平,刘东义,李青松,卢国栋,张贺,赵鑫,冯昌. 不同入路腰方肌阻滞对胃癌根治患者术后恢复的影响[J]. 山东大学学报 (医学版), 2022, 60(8): 50-57.
[4] 高中霞,张铭,樊明德,谭晨阳,王梦迪,王超,樊跃飞,丁守銮,王成伟. 伽玛刀治疗81例肺癌脑转移瘤的疗效及预后因素[J]. 山东大学学报 (医学版), 2022, 60(8): 44-49.
[5] 贺士卿,李皖皖,董书晴,牟婧怡,刘宇莹,魏思雨,刘钊,张家新. 基于数据库构建乳腺癌焦亡相关基因的预后风险模型[J]. 山东大学学报 (医学版), 2022, 60(8): 34-43.
[6] 张玉凤,徐敏,邢秀丽,逄曙光,户克庆. 689例非ST段抬高型心肌梗死患者的临床流行病学特征[J]. 山东大学学报 (医学版), 2022, 60(7): 118-122.
[7] 包舒晴,杨明月,刘端瑞,汪运山,郏雁飞. NOX4在幽门螺旋杆菌诱导胃癌细胞ROS中的作用[J]. 山东大学学报 (医学版), 2022, 60(6): 19-25.
[8] 王景,谢艳,李培龙,杜鲁涛,王传新. GZMB基因cg16212145位点的异常甲基化芯片测定对胃癌早筛的价值[J]. 山东大学学报 (医学版), 2022, 60(6): 26-34.
[9] 高惠茹,杜甜甜,王允山,杜鲁涛,王传新. 基于单细胞转录组测序数据分析胃癌调节性T细胞特征[J]. 山东大学学报 (医学版), 2022, 60(5): 43-49.
[10] 李琳琳,王凯. 基于生物信息学预测肝细胞癌预后基因[J]. 山东大学学报 (医学版), 2022, 60(5): 50-58.
[11] 郑昊天,王光辉,赵小刚,王亚东,曾榆凯,杜贾军. 基于数据库LKB1突变肺腺癌DNA异常甲基化位点构建的预后风险模型[J]. 山东大学学报 (医学版), 2022, 60(3): 51-58.
[12] 陈立晓,英信江,陈歆维,王菲,孙臻峰,董频. 下咽鳞癌蛋白质谱鉴定及预后靶分子筛选[J]. 山东大学学报 (医学版), 2021, 59(9): 140-147.
[13] 褚晏,刘端瑞,朱文帅,樊荣,马晓丽,汪运山,郏雁飞. DNA甲基化转移酶在胃癌中的表达及其临床意义[J]. 山东大学学报 (医学版), 2021, 59(7): 1-9.
[14] 陈丽宇,肖娟,吕仙忠,段宝敏,洪凡真. 影响孕产妇下肢深静脉血栓预后的危险因素分析[J]. 山东大学学报 (医学版), 2021, 59(7): 38-42.
[15] 田瑶天,王宝,李叶琴,王滕,田力文,韩波,王翠艳. 基于可解释性心脏磁共振参数的机器学习模型预测儿童心肌炎的预后[J]. 山东大学学报 (医学版), 2021, 59(7): 43-49.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 王焕亮,孙宝柱,杜洪玫,周长青,张丽. 不同麻醉监测指标调控异丙酚麻醉的比较[J]. 山东大学学报(医学版), 2006, 44(5): 471 -474 .
[2] 黄庆,田辉,李林,梁飞,刘贤锡 . 老年人肺癌组织中鸟氨酸脱羧酶基因表达及其临床意义[J]. 山东大学学报(医学版), 2006, 44(6): 556 -559 .
[3] 朱梅佳,韩巨,王新怡,鹿伟,王爱华,关心华,曹霞,曹秉振. 伴有皮层下梗死和白质脑病的常染色体显性遗传性脑动脉病临床病理研究[J]. 山东大学学报(医学版), 2006, 44(8): 834 -839 .
[4] 于慧1,2 ，陈少华1 ，赵家军2 ，高聆3
. 乙醇对人肝L02细胞糖原和GSK3β、PAMPK的影响[J]. 山东大学学报(医学版), 2009, 47(04): 75 -78 .
[5] 曾季平,王丽娜,王立祥,任晓辉,张孟业,夏文,崔行. 氯化锰致PC12细胞损伤的研究[J]. 山东大学学报(医学版), 2006, 44(5): 467 -470 .
[6] 舒雅,齐峰. 下颌角托在肥胖患者全麻诱导中的应用[J]. 山东大学学报(医学版), 2007, 45(10): 1072 -1074 .
[7] 宋海岩,武玉玲,张艳萍. 牡蛎提取物对高温致神经管畸形中凋亡细胞的保护作用[J]. 山东大学学报(医学版), 2007, 45(2): 113 -116 .
[8] 宋永红,马春红,吕红娟,朱传福,聂向民,王玫,刘艳,张萍 . 中国北方汉族人群HLA基因多态性研究[J]. 山东大学学报(医学版), 2007, 45(6): 546 -553 .
[9] 王志刚,丁　璇,孙　鹏/sup>,王成伟,郝晓光,潘　顺 . 术前脑血管造影在血管内支架成形术治疗缺血性脑血管病中的应用[J]. 山东大学学报(医学版), 2007, 45(2): 146 -148 .
[10] 侯晓阳,卜培莉,张运,冯进波,刘春喜,李传保,郝明秀. 过氧化酶体增殖物激活受体α抑制血管紧张素Ⅱ促心肌纤维化作用的试验研究[J]. 山东大学学报(医学版), 2007, 45(7): 665 -668 .
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发