Identification of potential prognostic microRNA biomarkers for predicting survival in patients with hepatocellular carcinoma

Abstract

Background: The aim of the present study was to identify potential prognostic microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) prognosis prediction based on a dataset from The Cancer Genome Atlas (TCGA).

Materials and methods: A miRNA sequencing dataset and corresponding clinical parameters of HCC were obtained from TCGA. Genome-wide univariate Cox regression analysis was used to screen prognostic differentially expressed miRNAs (DEMs), and multivariable Cox regression analysis was used for prognostic signature construction. Comprehensive survival analysis was performed to evaluate the prognostic value of the prognostic signature.

Results: Five miRNAs were regarded as prognostic DEMs and used for prognostic signature construction. The five-DEM prognostic signature performed well in prognosis prediction (adjusted P < 0.0001, adjusted hazard ratio = 2.249, 95% confidence interval =1.491-3.394), and time-dependent receiver-operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.765, 0.745, 0.725, and 0.687 for 1-, 2-, 3-, and 5-year HCC overall survival (OS) prediction, respectively. Comprehensive survival analysis of the prognostic signature suggests that the risk score model could serve as an independent factor of HCC and perform better in prognosis prediction than other traditional clinical indicators. Functional assessment of the target genes of hsa-mir-139 and hsa-mir-5003 indicates that they were significantly enriched in multiple biological processes and pathways, including cell proliferation and cell migration regulation, pathways in cancer, and the cyclic adenosine monophosphate (cAMP) signaling pathway.

Conclusion: Our study indicates that the novel miRNA expression signature may be a potential prognostic biomarker for HCC patients.

Keywords: TCGA; biomarker; hepatocellular carcinoma; miRNA; prognosis.

Figure 1

Volcano plot of DEMs in HCC. Abbreviations: DEMs, differentially expressed microRNAs; FDR, false discovery rate; HCC, hepatocellular carcinoma.

Figure 2

Heat map of 320 DEMs in HCC. Abbreviations: DEMs, differentially expressed microRNAs; HCC, hepatocellular carcinoma.

Figure 3

The Kaplan–Meier curves of five prognostic DEMs in HCC. Notes: The order of Kaplan–Meier curves of five prognostic DEMs were as follows: hsa-mir-105-2 (A), hsa-mir-5003 (B), hsa-mir-101-2 (C), hsa-mir-139 (D), and hsa-mir-9-3 (E). Abbreviations: DEMs, differentially expressed microRNAs; HCC, hepatocellular carcinoma.

Figure 4

ROC curves of five prognostic DEMs to distinguish HCC tissue from adjacent normal liver tissue. Notes: The order of ROC curves of five prognostic DEMs were as follows: hsa-mir-105-2 (A), hsa-mir-5003 (B), hsa-mir-101-2 (C), hsa-mir-139 (D), and hsa-mir-9-3 (E). Abbreviations: AUC, area under the curve; CI, confidence interval; DEMs, differentially expressed microRNAs; HCC, hepatocellular carcinoma; ROC, receiver–operating characteristic.

Figure 5

Prognostic risk score model analysis of five prognostic DEMs in HCC patients. Notes: (A) From top to bottom are the risk score, patients’ survival status distribution, and five prognostic DEMs’ expression heat map for low- and high-risk groups. (B) Kaplan–Meier curves for low- and high-risk groups. (C) ROC curve for predicting survival in HCC patients by the risk score. Abbreviations: DEMs, differentially expressed microRNAs; HCC, hepatocellular carcinoma; ROC, receiver–operating characteristic.

Figure 6

Expression level of five prognostic DEMs in tumor tissue and adjacent normal liver tissue, low- and high-risk groups, respectively. Notes: (A) Scatter plot of five prognostic DEMs’ expression level between HCC tumor tissue and adjacent normal liver tissue. (B) Scatter plot of five prognostic DEMs’ expression level between low- and high-risk groups; *P<0.05; ****P<0.0001. Abbreviations: DEMs, differentially expressed microRNAs; HCC, hepatocellular carcinoma.

Figure 7

The predictive value of the risk score for the HCC clinical parameters. Notes: ROC curve of risk score for clinical parameters: tumor stage: cutoff by stage I+II and stage III+IV (A); histologic grade: cutoff by G1+G2 and G3+G4 (B); serum AFP: cutoff by 400 ng/mL (C); MVI: cutoff by with and without MVI (D); and Child–Pugh score: cutoff by Child A and B+C (E); Ishak fibrosis score: cutoff by 0 and 1+2+3+4+5+6 (F); radical resection: cutoff by R0 and R1+R2+RX (G). Abbreviations: AFP, α-fetoprotein; AUC, area under the curve; CI, confidence interval; HCC, hepatocellular carcinoma; ROC, receiver-operating characteristic; MVI, microvascular invasion.

Figure 8

The relationship between risk score and clinical information. Notes: (A) Stratified analysis of association between risk score and OS in HCC. (B) Nomogram for predicting the 1-, 3-, and 5-year event (death) with risk score and clinical information. Abbreviations: AFP, α-fetoprotein; CI, confidence interval; HCC, hepatocellular carcinoma; MVI, microvascular invasion; OS, overall survival.

Figure 9

Joint effects analysis of OS stratified by risk score and HCC clinical parameters. Notes: Joint effects analysis stratified by risk score and following clinical parameters: tumor stage (A), histologic grade (B), serum AFP (C), microvascular invasion (D), Child–Pugh score (E), Ishak fibrosis score (F), and radical resection (G). Abbreviations: AFP, α-fetoprotein; HCC, hepatocellular carcinoma; OS, overall survival.

Figure 10

The prognostic miRNAs-target genes interactions network and their enrichment analysis results. Notes: (A) The prognostic miRNAs-target genes interactions network. (B) GO term enrichment results. (C) KEGG enrichment results. Abbreviations: GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.