微RNA风险评分模型预测肝细胞癌预后的价值分析

黄秀红; 谢肖立; 姜慧卿

doi:10.3969/j.issn.1001-5256.2021.05.026

微RNA风险评分模型预测肝细胞癌预后的价值分析

DOI: 10.3969/j.issn.1001-5256.2021.05.026

河北医科大学第二医院消化内科，石家庄 050000

基金项目:

国家自然科学基金青年基金项目 (81770601);

河北省重点研发计划健康医疗与生物医药专项项目 (182777117D)

利益冲突声明：本研究不存在研究者、伦理委员会成员、受试者监护人以及与公开研究成果有关的利益冲突。

作者贡献声明: 黄秀红负责酝酿和设计实验，下载、分析数据，统计分析，论文撰写；谢肖立负责协助论文修改；姜慧卿负责研究指导、论文修改。

详细信息

作者简介:
黄秀红(1995—)，女，主要从事肝硬化及内镜治疗

通信作者:
姜慧卿，jianghq@aliyun.com

中图分类号: R735.7
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- 文章访问数: 431
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- 被引次数: 0
出版历程
- 收稿日期: 2020-10-13
- 录用日期: 2020-11-16
- 出版日期: 2021-05-20

Value of a microRNA risk score model in predicting the prognosis of hepatocellular carcinoma

Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China

摘要

摘要: 目的下载癌症基因组图谱(TCGA)数据库中肝细胞癌(HCC)miRNA转录组数据进行数据挖掘，筛选与HCC预后相关的miRNA，构建miRNA风险评分模型，并评估其对HCC预后的预测价值。方法从TCGA数据库下载HCC样本miRNA表达量及临床数据，应用R语言筛选出HCC与癌旁组织的差异表达miRNA，将其与临床数据整合后，随机分为训练集和测试集，对训练集依次进行单因素Cox及LASSO-Cox回归分析，筛选与HCC预后相关的miRNA并构建风险评分模型，并使用Kaplan-Meier分析评估模型的稳健性及是否可以预测同一临床分期的患者预后，最后通过受试者工作特征曲线(ROC)计算曲线下面积(AUC)，在训练集、测试集及二者的合集中比较该模型和传统TNM分期的预测准确性。结果共筛选出300个差异基因，LASSO-Cox回归分析显示hsa-miR-139-5p、hsa-miR-1180-3p、hsa-miR-1269b、hsa-miR-3680-3p、hsa-miR-509-3-5p、hsa-miR-31-5p与HCC预后相关，根据构建的miRNA风险评分模型，计算每个样本的风险得分，并根据中位风险得分值将样本划分为高风险组和低风险组。Kaplan-Meier曲线显示，训练集与测试集的高风险组患者的生存率显著低于低风险组患者(P＜0.05)。ROC曲线结果显示，训练集、测试集及合集样本中，miRNA模型及TNM分期的AUC分别为0.817、0.667，0.808、0.665与0.814、0.663。独立预后分析结果显示，该miRNA评分模型可作为HCC的独立预后因子(P＜0.05)。结论 hsa-miR-139-5p、hsa-miR-1180-3p、hsa-miR-1269b、hsa-miR-3680-3p、hsa-miR-509-3-5p、hsa-miR-31-5p与HCC预后相关，在训练集、测试集及合集样本中miRNA风险评分模型的预测准确性均优于TNM分期。分层分析表明，该模型还可预测同一TNM分期患者的预后，在临床工作中具有一定参考价值，可作为独立预测HCC患者预后的模型。
- 癌, 肝细胞 /
- 微RNAs /
- 预后
Abstract: Objective To screen out the microRNAs (miRNAs) associated with the prognosis of hepatocellular carcinoma (HCC) through data mining of miRNA transcriptome data of HCC downloaded from The Cancer Genome Atlas (TCGA) database, to establish a miRNA risk score model, and to investigate its value in predicting the prognosis of HCC. Methods The miRNA expression data and clinical data of HCC samples were downloaded from TCGA database and R language was used to screen out differentially expressed miRNAs between HCC tissue and adjacent tissue, which were randomly divided into training set and testing set after being integrated into clinical data. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed for the training set to screen out the miRNAs associated with the prognosis of HCC, and then a miRNA risk score model was established. The Kaplan-Meier method was used to evaluate the robustness of the model and whether it could predict the prognosis of patients in the same clinical stage. Finally, the receiver operating characteristic (ROC) curve was plotted and the area under the ROC curve (AUC) was calculated to compare the predictive accuracy of the model versus TNM staging in the training set, the testing set, and the entire set. Results A total of 300 differentially expressed miRNAs were screened out and the LASSO Cox regression analysis revealed that hsa-miR-139-5p, hsa-miR-1180-3p, hsa-miR-1269b, hsa-miR-3680-3p, hsa-miR-509-3-5p, and hsa-miR-31-5p were associated with the prognosis of HCC. The risk score was calculated for each sample according to the established miRNA risk score model, and the samples were divided into high-risk group and low-risk group according to the median risk score. The Kaplan-Meier curve showed that in both training and testing sets, the high-risk group had a significantly lower survival rate than the low-risk group (P < 0.05). The ROC curve was used to evaluate the prediction efficiency of this model, and the results showed that in the training set, the testing set, and the entire set, the miRNA model had an AUC of 0.817, 0.808, and 0.814, respectively, while TNM staging had an AUC of 0.667, 0.665, and 0.663, respectively. The results of independent prognostic analysis also showed that this miRNA score model could be used as an independent prognostic factor for HCC (P < 0.05). Conclusion Hsa-miR-139-5p, hsa-miR-1180-3p, hsa-miR-1269b, hsa-miR-3680-3p, hsa-miR-509-3-5p, and hsa-miR-31-5p are associated with the prognosis of HCC, and the miRNA risk score model has a better prediction accuracy than TNM staging in the training set, the testing set, and the entire set. The stratified analysis also shows that the model can predict the prognosis of patients within the same TNM stage, and therefore, it has a certain reference value in clinical practice and can be used as an independent model for predicting the prognosis of HCC patients.
- Hepatocellular Carcinoma /
- MicroRNAs /
- Prognosis

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图 1 LASSO回归构建miRNA模型

注：a，LASSO筛选与生存相关的miRNAs；b，与HCC患者预后相关的miRNAs的LASSO系数。

下载: 全尺寸图片幻灯片

图 2 6个miRNAs危险比(HR)的森林图

注：**, P＜0.01；***, P＜0.001。

下载: 全尺寸图片幻灯片

图 3 高风险组和低风险组的Kaplan-Meier生存分析

注：a，训练集生存曲线；b，测试集生存曲线。

下载: 全尺寸图片幻灯片

图 4 训练集风险评分分析(扫描二维码查看原图)

注：a，训练集风险图；b，训练集生存状态图；c，训练集风险热图

下载: 全尺寸图片幻灯片

图 5 测试集风险评分分析(扫描二维码查看原图)

注：a，测试集风险图；b，测试集生存状态图；c，测试集风险热图。

下载: 全尺寸图片幻灯片

图 6 不同亚组的Kaplan-Meier生存分析

注：a，低TNM组(Ⅰ和Ⅱ期)生存曲线；b，高TNM组(Ⅲ和Ⅳ期)生存曲线。

下载: 全尺寸图片幻灯片

图 7 miRNA模型和TNM分期的ROC曲线

注：a，miRNA模型和TNM分期在训练集的ROC曲线；b，miRNA模型和TNM分期在测试集的ROC曲线；c，miRNA模型和TNM分期在合集的ROC曲线。

下载: 全尺寸图片幻灯片

图 8 临床特征与风险得分的独立预后分析

注：a，单因素分析；b，多因素分析。

下载: 全尺寸图片幻灯片

表 1 TCGA数据库HCC患者的临床数据

项目	数值[例(%)]
生存状态
存活	234(66.5)
死亡	118(33.5)
性别
女性	112(31.8)
男性	240(68.2)
年龄
＜60岁	166(47.2)
≥60	186(52.8)
肿瘤分级
G1	46(13.1)
G2	171(48.6)
G3	120(34.1)
G4	13(3.7)
未知	2(0.5)
TNM分期
Ⅰ	175(49.7)
Ⅱ	86(24.4)
Ⅲ	86(24.4)
Ⅳ	5(1.5)
T分期
T1	176(50.0)
T2	88(25.0)
T3	77(21.9)
T4	10(2.8)
未知	1(0.3)
M分期
M0	269(76.4)
M1	4(1.2)
未知	79(22.4)
N分期
N0	257(73.0)
N1	4(1.2)
未知	91(25.8)

下载: 导出CSV

参考文献(21)

[1]	FORNER A, REIG M, BRUIX J. Hepatocellular carcinoma[J]. Lancet, 2018, 391(10127): 1301-1314. DOI: 10.1016/S0140-6736(18)30010-2.
[2]	VILLANUEVA A. Hepatocellular carcinoma[J]. New Engl J Med, 2019, 380(15): 1450-1462. DOI: 10.1056/NEJMra1713263.
[3]	KULIK L, EL-SERAG HB. Epidemiology and management of hepatocellular carcinoma[J]. Gastroenterology, 2019, 156(2): 477-491. e1. DOI: 10.1053/j.gastro.2018.08.065.
[4]	YANG JD, HAINAUT P, GORES GJ, et al. A global view of hepatocellular carcinoma: Trends, risk, prevention and management[J]. Nat Rev Gastroenterol Hepatol, 2019, 16(10): 589-604. DOI: 10.1038/s41575-019-0186-y.
[5]	LIN S, GREGORY RI. MicroRNA biogenesis pathways in cancer[J]. Nat Rev Cancer, 2015, 15(6): 321-333. DOI: 10.1038/nrc3932.
[6]	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. DOI: 10.1038/nrd.2016.246.
[7]	YERUKALA SATHIPATI S, HO SY. Identifying a miRNA signature for predicting the stage of breast cancer[J]. Sci Rep, 2018, 8(1): 16138. DOI: 10.1038/s41598-018-34604-3.
[8]	KANWAL R, PLAGA AR, LIU X, et al. MicroRNAs in prostate cancer: Functional role as biomarkers[J]. Cancer Lett, 2017, 407: 9-20. DOI: 10.1016/j.canlet.2017.08.011.
[9]	DEB B, UDDIN A, CHAKRABORTY S. miRNAs and ovarian cancer: An overview[J]. J Cell Physiol, 2018, 233(5): 3846-3854. DOI: 10.1002/jcp.26095.
[10]	Bureau of Medical Administration National Health Commission of the People's Republic of China. Guidelines for diagnosis and treatment of primary liver cancer in China (2019 edition)[J]. J Clin Hepatol, 2020, 36(2): 277-292. DOI: 10.3969/j.issn.1001-5256.2020.02.007. 中华人民共和国国家卫生健康委员会医政医管局. 原发性肝癌诊疗规范(2019年版)[J]. 临床肝胆病杂志, 2020, 36(2): 277-292. DOI: 10.3969/j.issn.1001-5256.2020.02.007.
[11]	TOMCZAK K, CZERWIŃSKA P, WIZNEROWICZ M. The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge[J]. Contemp Oncol (Pozn), 2015, 19(1A): a68-a77. DOI: 10.5114/wo.2014.47136.
[12]	LONG J, ZHANG L, WAN X, et al. A four-gene-based prognostic model predicts overall survival in patients with hepatocellular carcinoma[J]. J Cell Mol Med, 2018, 22(12): 5928-5938. DOI: 10.1111/jcmm.13863.
[13]	WANG X, GAO J, ZHOU B, et al. Identification of prognostic markers for hepatocellular carcinoma based on miRNA expression profiles[J]. Life Sci, 2019, 232: 116596. DOI: 10.1016/j.lfs.2019.116596.
[14]	JIA D, LI S, LI D, et al. Mining TCGA database for genes of prognostic value in glioblastoma microenvironment[J]. Aging (Albany NY), 2018, 10(4): 592-605. DOI: 10.18632/aging.101415.
[15]	LI Y, GU J, XU F, et al. Transcriptomic and functional network features of lung squamous cell carcinoma through integrative analysis of GEO and TCGA data[J]. Sci Rep, 2018, 8(1): 15834. DOI: 10.1038/s41598-018-34160-w.
[16]	NAGY A, LANCZKY A, MENYHART O, et al. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets[J]. Sci Rep, 2018, 8(1): 9227. DOI: 10.1038/s41598-018-27521-y.
[17]	TIBSHIRANI R. The lasso method for variable selection in the Cox model[J]. Stat Med, 1997, 16(4): 385-395. DOI: 10.1002/(sici)1097-0258(19970228)16:4<385::aid-sim380>3.0.co;2-3.
[18]	WEI L, WANG X, LV L, et al. The emerging role of microRNAs and long noncoding RNAs in drug resistance of hepatocellular carcinoma[J]. Mol Cancer, 2019, 18(1): 147. DOI: 10.1186/s12943-019-1086-z.
[19]	SADRI NAHAND J, BOKHARAEI-SALIM F, SALMANINEJAD A, et al. microRNAs: Key players in virus-associated hepatocellular carcinoma[J]. J Cell Physiol, 2019, 234(8): 12188-12225. DOI: 10.1002/jcp.27956.
[20]	WONG CM, TSANG FH, NG IO. Non-coding RNAs in hepatocellular carcinoma: Molecular functions and pathological implications[J]. Nat Rev Gastroenterol Hepatol, 2018, 15(3): 137-151. DOI: 10.1038/nrgastro.2017.169.
[21]	HUA S, LEI L, DENG L, et al. miR-139-5p inhibits aerobic glycolysis, cell proliferation, migration, and invasion in hepatocellular carcinoma via a reciprocal regulatory interaction with ETS1[J]. Oncogene, 2018, 37(12): 1624-1636. DOI: 10.1038/s41388-017-0057-3.