Prognostic value of microRNAs in hepatocellular carcinoma: a meta-analysis
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Yue Zhang1, Chao Wei1, Cong-Cong Guo1, Rong-Xiu Bi2, Jin Xie2, Dong-Hui Guan2, Chuan-Hua Yang3 and Yue-Hua Jiang4
1First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong, People’s Republic of China
2Department of Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, People’s Republic of China
3Department of Cardiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, People’s Republic of China
4Central Laboratory, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, People’s Republic of China
Yue-Hua Jiang, email: [email protected]
Keywords: microRNA, hepatocellular carcinoma, prognosis, meta-analysis
Received: May 14, 2017 Accepted: August 29, 2017 Published: September 14, 2017
Background: Numerous articles reported that dysregulated expression levels of microRNAs (miRNAs) correlated with survival time of hepatocellular carcinoma (HCC) patients. However, there has not been a comprehensive meta-analysis to evaluate the accurate prognostic value of miRNAs in HCC.
Materials and Methods: Studies, published in English, estimating expression levels of miRNAs with any survival curves in HCC were identified up until 15 April, 2017 by performing online searches in PubMed, EMBASE, Web of Science and Cochrane Database of Systematic Reviews by two independent authors. The pooled hazard ratios (HR) with 95% confidence intervals (CI) were used to estimate the correlation between miRNA expression and overall survival (OS).
Results: 54 relevant articles about 16 miRNAs, with 6464 patients, were ultimately included. HCC patients with high expression of tissue miR-9 (HR = 2.35, 95% CI = 1.46–3.76), miR-21 (HR = 1.76, 95% CI = 1.29–2.41), miR-34c (HR = 1.64, 95% CI = 1.05–2.57), miR-155 (HR = 2.84, 95% CI = 1.46–5.51), miR-221 (HR = 1.76, 95% CI = 1.02–3.04) or low expression of tissue miR-22 (HR = 2.29, 95% CI = 1.63–3.21), miR-29c (HR = 1.35, 95% CI = 1.10–1.65), miR-34a (HR = 1.84, 95% CI = 1.30–2.59), miR-199a (HR = 2.78, 95% CI = 1.89–4.08), miR-200a (HR = 2.64, 95% CI = 1.86–3.77), miR-203 (HR = 2.20, 95% CI = 1.61–3.00) have significantly poor OS (P < 0.05). Likewise, HCC patients with high expression of blood miR-21 (HR = 1.73, 95% CI = 1.07–2.80), miR-192 (HR = 2.42, 95% CI = 1.15–5.10), miR-224 (HR = 1.56, 95% CI = 1.14–2.12) or low expression of blood miR-148a (HR = 2.26, 95% CI = 1.11–4.59) have significantly short OS (P < 0.05).
Conclusions: In conclusion, tissue miR-9, miR-21, miR-22, miR-29c, miR-34a, miR-34c, miR-155, miR-199a, miR-200a, miR-203, miR-221 and blood miR-21, miR-148a, miR-192, miR-224 demonstrate significantly prognostic value. Among them, tissue miR-9, miR-22, miR-155, miR-199a, miR-200a, miR-203 and blood miR-148a, miR-192 are potential prognostic candidates for predicting OS in HCC.
Numerous studies reported expression levels of tissue [1–194] or blood [195–221] miRNAs were related with prognosis of HCC patients. HCC is one of the most common tumors, over 700,000 new cases are reported yearly, and HCC is considered as the third primary etiology of tumor-associated mortality rate globally [222–224]. In spite of enormous process in diagnosis and comprehensive therapy over the last few decades, HCC patients still have poor prognosis, primarily due to its high rate of recurrence  and metastasis .
miRNAs, a cluster of endogenous short non-coding single strand RNAs, serve as significant post-transcriptional regulatory factor of genetic expression via interacting with the 3′-UTR of the targeted mRNAs . Conspicuously, due to widespread RNAase in the blood environment, circulating miRNAs displayed predominant stability. As a noninvasive detection method, circulating miRNA (blood) demonstrated more potential value as diagnostic and prognostic biomarkers than tissue miRNAs. Studies [228, 229] conducted in preclinical models and cancer patients proved that malignant tumor influences expression levels of miRNAs in the blood and that certain serum miRNAs are correlated with particular cancers. Though the way requires more validation, the finding possibly discloses the avenue to a creative method of detecting cancers via measurement of serum or plasma miRNAs.
Thus far, substantial investigations have discovered that miRNAs are involved and play a crucial role in the carcinogenesis of HCC [230, 231] while some miRNAs are up-regulated and others down-regulated in HCC. For example, Wong et al.  gained contrasting results that identifiable difference in miRNA expression pattern could not be discovered between primary HCC and venous metastases. However, comparing venous metastases to primary HCC, a prominent universal decrease of miRNA expression levels was assayed. Their results indicated that miRNA abnormality relatively early occured in liver carcinogenesis and the later universally decreased miRNA aggravated the preexisting miRNA abnormity to further accelerate HCC metastasis.
Nevertheless, there has not been a synthetic meta-analysis to assess precise prognostic value of miRNAs in HCC. As a consequence, it is of vital significance to develop a meta-analysis with an aim to evaluate it.
Figure 1 showed a flow chart with details about the study selection process.
Figure 1: Flow diagram of literature search and selection.
Tables 1 (tissue) and 2 (blood) showed the frequency of researches evaluating prognostic value of miRNAs, including miRNA name, number of investigations assessing prognostic value, and reference.
Table 1: Frequency of studies estimating prognostic value of tissue miRNA expression in hepatocellular carcinoma
Highlighted studies were included in the present meta-analysis; N: Number of studies estimating prognostic value; R: References.
Table 2: Frequency of studies estimating prognostic value of blood miRNA expression in hepatocellular carcinoma
Highlighted studies were included in the present meta-analysis; N: Number of studies estimating prognostic value; R: References.
Supplementary Table 1 comprehensively presented the characteristics and details (names of miRNAs, information about the included articles, detected samples, sample size, stage, cut-off value, detection methods, follow-up, survival outcome with HR and 95% CI) of studies with Kaplan-Meier survival curves (K-M curves) in HCC. If the survival outcome was not furnished directly and merely as K-M curves, we used the software Engauge Digitizer version 4.1  to extract the data from K-M curves. Additionally, if both the univariate and multivariate outcomes were covered, we just chose the latter in that the confounding factors were corrected.
Table 3 presented a summary of the HR estimated from pooled analysis for the included miRNAs. A total of 16 miRNAs were screened by our present study.
Table 3: Summary of the HR for miRNA expression in hepatocellular carcinoma
HR: hazard ratios; CI: confidence intervals; OS: overall survival; DFS: disease-free survival; RFS: recurrence-free survival; PFS: progression-free survival; MFS: metastasis-free survival; mMultivariate analysis.
Significantly prognostic value of high tissue miR-21 expression in OS
Six studies [19, 20, 22–25] focused on the correlation between high tissue miR-21 level and OS, suggesting that HCC patients with high tissue miR-21 level demonstrated a significantly worse OS than those with low tissue miR-21 level (HR = 1.76, 95% CI = 1.29–2.41, P < 0.01, Figure 2A).
Figure 2: (A) Forest plot of the analyses about high expression of tissue miR-21 and OS, DFS or OS (multivariate analysis); (B) Publication bias of the analysis about high expression of tissue miR-21 and OS and (C) Sensitivity analysis of the study about high expression of tissue miR-21 and OS.
For the purpose of evaluating publication bias on OS of HCC patients with high tissue miR-21 level, we employed the Begg’s funnel plot (Figure 2B). Accordingly, the P value was 0.21, suggesting nonexistent publication bias.
The sensitivity analysis did not manifest variances among the outcomes in terms of the exclusion of any single research (Figure 2C) in the estimation on OS of HCC patients with high tissue miR-21 level, indicating that no individual investigation significantly affected the merged HR with 95% CI.
No significantly prognostic value of high blood miR-122 expression in OS
Six researches [1, 4, 11–14] concentrated on the relationship between high blood miR-122 level and OS, manifesting that there was no significant correlation between high blood miR-122 level and OS (HR = 0.89, 95% CI = 0.49–1.60, P = 0.69, Figure 3A).
Figure 3: (A) Forest plot of the analyses about high expression of blood miR-122 and OS or DFS; (B) Publication bias of the analysis about high expression of blood miR-122 and OS and (C) Sensitivity analysis of the study about high expression of blood miR-122 and OS.
For the sake of estimating publication bias on OS of HCC patients with high blood miR-122 level, we employed the Begg’s funnel plot (Figure 3B). Consequently, the P value was 0.56, suggesting nonexistent publication bias.
The sensitivity analysis did not manifest variances among the outcomes in terms of the exclusion of any single research (Figure 3C) in the estimation on OS of HCC patients with high blood miR-122 level, indicating that no individual investigation significantly affected the merged HR with 95% CI.
We employed the meta-regression to seek source of heterogeneity (I2 = 84.8%) on OS of HCC patients with high blood miR-122 level. The details were shown in Table 4, and source of heterogeneity was significantly caused by maximum months of follow-up (P = 0.01).
Table 4: Results of meta-regression on OS of blood miR-122 expression in hepatocellular carcinoma
Germany, China, South Korea
295, 136, 120, 122, 161, 62
26, 48, 96, 40, 79, 125
< 48, ≥ 48
Tissue miR-9, miR-22, miR-29c, miR-34a, miR-34c, miR-155, miR-199a, miR-200a, miR-203, miR-221 and blood miR-21, miR-148a, miR-192, miR-224 have significantly prognostic values in OS
Table 3 and Figures 4–7 showed the details.
Tissue miR-29a, miR-148a and blood miR-29a do not have significantly prognostic values in OS
Table 3 and Figures 4, 5 and 7 showed the details.
Figure 4: Forest plot of the analyses about high expression of tissue miR-9 or low expression of tissue miR-22, 29a, 29c and OS, DFS/RFS or RFS.
Figure 5: Forest plot of the analyses about high expression of tissue miR-34c, 155 or low expression of tissue miR-34a, 148a and OS, PFS/RFS/DFS, DFS, RFS or RFS/DFS.
Figure 6: Forest plot of the analyses about high expression of tissue miR-221 or low expression of tissue miR-199a, 200a, 203 and OS, RFS or RFS/MFS/DFS.
Figure 7: Forest plot of the analyses about high expression of blood miR-21, 29a, 192, 224 or low expression of blood miR-148a and RFS/DFS, OS or PFS/DFS.
Numerous articles reported that dysregulated expression levels of miRNAs correlated with survival time of HCC patients [1–221]. Nevertheless, there has not been a comprehensive meta-analysis to assess the accurate prognostic value of miRNAs in HCC. Therefore, it was conducted to estimate the relationship between dysregulated miRNA level and survival time of HCC patients.
For HCC patients, tissue miR-9, miR-21, miR-22, miR-29c, miR-34a, miR-34c, miR-155, miR-199a, miR-200a, miR-203, miR-221 and blood miR-21, miR-148a, miR-192, miR-224 demonstrate significantly prognostic value (P < 0.05). In the light of our reference standard, tissue miR-9, miR-22, miR-155, miR-199a, miR-200a, miR-203 and blood miR-148a, miR-192 potential prognostic candidates for predicting the OS of HCC patients (HR ≥ 2).
Molecular mechanisms for included miRNAs
For included miRNAs in the current study, a summary of miRNAs with changed levels, their possible targets and pathways enrolled in the present study has been presented in Table 5. From the data of the table, these potential targets and pathways may be involved with survival outcome of HCC patients.
Table 5: Summary of miRNAs with altered expression, their potential targets and pathways entered this study
2, 19–25, 197–200
4, 26, 27
Cell invasion, migration, proliferation, tumourigenicity and YWHAZ/AKT1/foxo3a signaling
9, 37, 38, 200, 202
38, 40, 41
FOXM1, MYC, BCL2, AXL
Cell apoptosis, chemoresistance, proliferation, viability and FOXM1/MYC signaling
Cell cycle, growth, invasion and proliferation
195, 198, 205–208
87-89, 208, 211
Cell invasion, migration and proliferation
Cell apoptosis, cycle, invasion, proliferation and tumorigenesis
11, 113, 114
HIF1A, VEGFA, IGF1, IGF2
Cell growth, invasion, proliferation and Warburg effect
MACC1, CDK6, ZEB2
Cell cycle, growth, metastasis and proliferation
Cell apoptosis, invasion and proliferation
Cell apoptosis and growth
GALNT4: polypeptide N-acetylgalacosaminyltransferase 4; YWHAZ: tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta; HDAC4: histone deacetylase 4; AKT1: AKT serine/threonine kinase 1; foxo3a: forkhead box O3A; SIRT1: sirtuin 1; FOXM1: forkhead box M1; MYC: MYC proto-oncogene, bHLH transcription factor; BCL2: BCL2, apoptosis regulator; AXL: AXL receptor tyrosine kinase; NCKAP1: NCK associated protein 1; USP4: ubiquitin specific peptidase 4; SIP1: Sip1p; ARID2: AT-rich interaction domain 2; FBXW7: F-box and WD repeat domain containing 7; HIF1A: hypoxia inducible factor 1 alpha subunit; VEGFA: vascular endothelial growth factor A; IGF1: insulin like growth factor 1; IGF2: insulin like growth factor 2; MACC1: MACC1, MET transcriptional regulator; CDK6: cyclin dependent kinase 6; ZEB2: zinc finger E-box binding homeobox 2; ADAM9: ADAM metallopeptidase domain 9; HULC: hepatocellular carcinoma up-regulated long non-coding RNA; BMF: Bcl2 modifying factor.
Strengths of the meta-analysis
There are a few strengths in this study, which are as follows: (1) nearly all articles estimating associations between miRNA level and survival result of HCC patients are shown in the current meta-analysis; (2) the number about HCC patients of all researches included in this study are more than or equal to 30, which makes the meta-analyses more convincing; (3) the Begg’s funnel plot and sensitivity analysis were used for miR-21 and miR-122, which excluded publication bias and excessive influence of individual study; (4) we employed meta-regression to seek source of heterogeneity, which indicated that months of follow-up were significantly associated with it; (5) studies merely proposing HR or 95% CI without K-M curves were excluded.
Simultaneously, there are also limitations for the current work: (1) only English articles were included by us, which possibly excluded some studies written in other languages; (2) not all the articles assessing associations between miRNA level and survival time were included in the present study, which might neglect some potential miRNAs; (3) a few variables emerged among the included investigations, including different kinds of samples from HCC patients at different stages, cut-off values and detection methods, and only random-effects models were employed for all meta-analyses; (4) although overall studies included 54 relevant articles and 6464 patients in the present study, the number of articles and patients may be not enough for 16 miRNAs focused on.
Implications for future clinical and scientific research
With expression profiles shown in Tables 1 and 2, we can conveniently find relevant article about a single miRNA. Thus, the present study tendency for miRNAs in HCC can be easily obtained by basic researchers. Meanwhile, combined detection of multi-miRNAs can greatly increase the predict level for HCC patients. Besides, for clinical doctors, combined use of tissue and blood from HCC patients can bring about synergistic effect to estimation of prognosis.
MATERIALS AND METHODS
Search strategy, inclusion criteria and exclusion criteria
The details were presented in Table 6. Two authors (Yue Zhang and Chao Wei) independently performed this comprehensive online search.
Table 6: Information of search methods and criteria of inclusion and exclusion
4 search engines, including PubMed, EMBASE, Web of Science and Cochrane Database of Systematic Reviews
15 April, 2017
mir and hepatocellular carcinoma
(1) Patients with hepatocellular carcinoma;
(2) Expression of miRNAs and survival outcome in tissue, plasma or serum were measured;
(3) At least, one of survival curves about overall survival (OS), cause-specific survival (CSS), disease-free survival (DFS), recurrence-free survival (RFS), progression-free survival (PFS) and metastasis-free survival (MFS) was measured, with or without the HR or 95% CI;
(4) Full text articles published in English
(1) Reviews, letters or laboratory studies without original data and retracted articles;
(2) Frequency of studies estimating prognostic value of miRNAs ≤ 2 in tissue;
(3) Studies which cannot be merged;
(4) If more than one article had been published on the identical study cohort, only the most comprehensive study was selected for the present meta-analysis
Yue Zhang and Chao Wei confirmed all eligible investigations that analyzed the prognostic value of miRNAs in HCC, and Yue-Hua Jiang reassessed uncertain data.
All analyses were conducted using Stata version 13.0 (StataCorp, College Station, Texas, USA). The relative effect sizes for HR were characterized as moderate (protective [0.51–0.75] or contributory [1.35–1.99]) and large (≤ 0.50 or ≥ 2). The HR was considered significant at the P < 0.05 level if the 95% CI did not include the value 1. If the P values from OS and other survival results about corresponding miRNAs were inconsistent, the HR from OS was considered to the main reference standard. Because different types of samples (tissue, plasma and serum) from HCC patients at different disease stages, cut-off values and miRNA methods were used in individual studies, random-effects models (DerSimonian-Laird method) were more appropriate than fixed-models (Mantel-Haenszel method) for most of the analyses. Consequently, the random-effects models were used in the current meta-analysis. Source of heterogeneity was explored through meta-regression. Publication bias was estimated using the Begg’s funnel plot. A two-tailed P value < 0.05 was considered significant. Sensitivity analysis (influence analysis) was carried out to test how powerful the combined effect size was to removal of individual investigation. If the point assessment was out of the 95% CI of the pooled effect size after it was removed from the analysis, an individual study was doubted to have excessive influence.
In conclusion, tissue miR-9, miR-21, miR-22, miR-29c, miR-34a, miR-34c, miR-155, miR-199a, miR-200a, miR-203, miR-221 and blood miR-21, miR-148a, miR-192, miR-224 demonstrate significantly prognostic value. Among them, tissue miR-9, miR-22, miR-155, miR-199a, miR-200a, miR-203 and blood miR-148a, miR-192 are potential prognostic candidates for predicting OS in HCC.
Study concept and design: Yue Zhang (e-mail: [email protected]) and Yue-Hua Jiang.
Acquisition of data: Yue Zhang and Chao Wei.
Analysis and interpretation of data: Yue Zhang, Chao Wei and Cong-Cong Guo.
Drafting of the manuscript: Yue Zhang.
Revision of manuscript: Yue Zhang, Chao Wei, Cong-Cong Guo, Rong-Xiu Bi, Jin Xie, Dong-Hui Guan, Chuan-Hua Yang and Yue-Hua Jiang.
Supervision of work: Rong-Xiu Bi, Jin Xie, Dong-Hui Guan, Chuan-Hua Yang and Yue-Hua Jiang.
All authors read and approved the final manuscript.
CONFLICTS OF INTEREST
The authors declare that they have no conflicts of interest.
This work was supported by the National Natural Science Foundation of China (No. 81673807).
Role of funding source: The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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