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

Xiuhong HUANG; Xiaoli XIE; Huiqing JIANG

doi:10.3969/j.issn.1001-5256.2021.05.026

Volume 37 Issue 5

May 2021

Turn off MathJax

Article Contents

Abstract

References

Supplements

Article Navigation > Journal of Clinical Hepatology > 2021 > 37(5): 1110-1115

PDF( 4272 KB)

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

DOI: 10.3969/j.issn.1001-5256.2021.05.026

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

Received Date: 2020-10-13
Accepted Date: 2020-11-16
Published Date: 2021-05-20

Abstract

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

FullText(HTML)

References(21)

References

[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.