Literature DB >> 29291025

Prognostic value of microRNAs in hepatocellular carcinoma: a meta-analysis.

Yue Zhang1, Chao Wei1, Cong-Cong Guo1, Rong-Xiu Bi2, Jin Xie2, Dong-Hui Guan2, Chuan-Hua Yang3, Yue-Hua Jiang4.   

Abstract

BACKGROUND: Numerous articles reported that dysregulated expression levels of miRNAs correlated with survival time of HCC patients. However, there has not been a comprehensive meta-analysis to evaluate the accurate prognostic value of miRNAs in HCC.
DESIGN: Meta-analysis.
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.

Entities:  

Keywords:  hepatocellular carcinoma; meta-analysis; microRNA; prognosis

Year:  2017        PMID: 29291025      PMCID: PMC5739810          DOI: 10.18632/oncotarget.20883

Source DB:  PubMed          Journal:  Oncotarget        ISSN: 1949-2553


INTRODUCTION

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 [225] and metastasis [226]. 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 [227]. 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. [232] 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.

RESULTS

Study selection

Figure 1 showed a flow chart with details about the study selection process.
Figure 1

Flow diagram of literature search and selection

Study frequency

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

miRNRmiRNRmiRNRmiRNRmiRNRmiRNR
11129b238, 39125a1701922107, 1083301143520g1167
71229c338, 40, 41125b271, 72193a129331-3p11445221168
9–123, 430a-5p142126173193b129338-3p1145542-5p1169
9–223, 430a143128-3p1741941109339-5p11465451170
935–730b-5p144129-5p1751952110, 1113651147589-5p1171
10b1830b19129–2176197111237011485921172
15a1930c19130a177199a-5p211, 1133722149, 1506081173
15b1930d13130b178199a*111437511516101174
17-5p11031145135a218, 79199a1114381196221175
18a11133a-3p146137280, 81199b-5p111538311526251176
18b11234a-5p147139-5p182200a3116–11842111416301177
19a11334a348–501391832033119–1214242141, 1536341178
19b11434b250, 51140-5p1842041107425-3p11546382179, 180
20a215, 1634c-3p152145185205112242911557441181
20b11734c250, 51146a1862102123, 12443219876-5p19
21a11892a153148a387–8921111254511156885-5p1182
2182, 19–2593154148b290, 912122126, 1274521157892a1183
2234, 26, 2798155149292, 932142114, 12845411589402184, 185
23a228, 2999a256, 57150195216b112945511599441186
23b12999b1581511962181130486-3p1911801187
24130100259, 60152197219-5p1131486-5p116012461188
25231, 32101261, 62155-3p198221520, 132–13548911611268a1189
26a1331031215539, 99, 1002221136494116212691190
26b-5p134105–116318211012241137497116313231191
27b135106b264, 6518311022961138503116431271192
28-3p136107121851103302d1139511–113367723, 141
28-5p136122266, 67187-3p11043251140511–21344581193
29a-5p137124–1168188-5p110532623, 14151111414782-3p1194
29a29, 3812416919111063291142519a2165, 166

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

miRNRmiRNRmiRNR
11195139-5p12092181216
10b-3p1196148a2208, 2112211217
214197–200148b1211224-5p1198
24-3p120115012122241218
26a12001521211311-3p1214
29a-3p1202181a-5p12133351219
29a12001821214422a1209
961203192-5p12024241220
10112041921208486-5p1209
1226195, 198, 205–208200a119812461208
125b1209210121512901208
128-21210215120844631221

Highlighted studies were included in the present meta-analysis; N: Number of studies estimating prognostic value; R: References.

Highlighted studies were included in the present meta-analysis; N: Number of studies estimating prognostic value; R: References. Highlighted studies were included in the present meta-analysis; N: Number of studies estimating prognostic value; R: References.

Study characteristics

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 [233] 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.

Meta-analysis

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

miRNASurvivalanalysisNumberof articlesIncludedstudiesHR95% CIFigureP valueHeterogeneity(Higgins I2 statistic)Totalpatients
High miR-9OS25, 72.351.46–3.764< 0.01I2 = 35.3%, P = 0.21320
High miR-9DFS/RFS26,72.491.57–3.974< 0.01I2 = 0.0%, P = 0.80180
High miR-21OS619, 20, 22–251.761.29–2.412A< 0.01I2 = 17.1%, P = 0.30461
High miR-21DFS32, 21, 223.481.89–6.442A< 0.01I2 = 40.4%, P = 0.19274
High miR-21OSm222, 232.721.49–4.952A< 0.01I2 = 0.0%, P = 0.58231
High miR-21RFS/DFS2197, 2001.110.62–1.9670.73I2 = 72.7%, P = 0.06246
High miR-21OS2198, 1991.731.07–2.8070.03I2 = 59.0%, P = 0.12233
Low miR-22OS24, 272.291.63–3.214< 0.01I2 = 0.0%, P = 0.85564
Low miR-29aOS39, 37, 381.290.91–1.8140.15I2 = 47.8%, P = 0.15657
Low miR-29aRFS29, 370.820.38–1.7740.61I2 = 83.0%, P = 0.02434
High miR-29aPFS/DFS2200, 2021.120.32–3.9470.86I2 = 89.1%, P < 0.01194
Low miR-29cOS338, 40, 411.351.10–1.654< 0.01I2 = 0.0%, P = 0.47467
Low miR-34aOS447–501.841.30–2.595< 0.01I2 = 48.7%, P = 0.12339
Low miR-34aPFS/RFS/DFS347, 49, 501.431.17–1.745< 0.01I2 = 20.3%, P = 0.29309
High miR-34cOS250, 521.641.05–2.5750.03I2 = 0.0%, P = 0.41156
High miR-34cDFS350–521.150.72–1.8550.56I2 = 72.5%, P = 0.03236
High miR-122OS6195, 198, 205–2080.890.49–1.603A0.69I2 = 84.8%, P < 0.01896
High miR-122DFS2205, 2081.620.40–6.413A0.50I2 = 50.6%, P = 0.16182
Low miR-148aOS287, 881.830.80–4.2050.15I2 = 67.2%, P < 0.05356
Low miR-148aRFS387–891.370.99–1.9150.06I2 = 0.0%, P = 0.55445
Low miR-148aOS2208, 2112.261.11–4.5970.03I2 = 0.0%, P = 0.99138
High miR-155OS398–1002.841.46–5.515< 0.01I2 = 57.8%, P = 0.09269
High miR-155RFS/DFS39, 99, 1002.091.56–2.795< 0.01I2 = 0.0%, P = 0.66440
High miR-192OS2202, 2082.421.15–5.1070.02I2 = 6.7%, P = 0.30136
High miR-192PFS/DFS2202, 2081.970.96–4.0370.06I2 = 2.5%, P = 0.31136
Low miR-199aOS2113, 1142.781.89–4.086< 0.01I2 = 0.0%, P = 0.38239
Low miR-200aOS3116–1182.641.86–3.776< 0.01I2 = 0.0%, P = 0.91336
Low miR-203OS2119, 1212.201.61–3.006< 0.01I2 = 0.0%, P = 0.45204
Low miR-203RFS2119, 1202.120.40–11.1660.37I2 = 76.1%, P = 0.04161
High miR-221OS320, 132, 1351.761.02–3.0460.04I2 = 67.6%, P < 0.05240
High miR-221RFS/MFS/DFS4132–1352.261.53–3.356< 0.01I2 = 50.4%, P = 0.09334
High miR-224OS2198, 2181.561.14–2.127< 0.01I2 = 24.2%, P = 0.25318

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.

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.

(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.

Publication bias

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.

Sensitivity analysis

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.

(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.

Meta-regression

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

VariablesDetailstau2I2 (%)Adj R2 (%)P value
Year2013–20160.7387.82–29.290.46
CountryGermany, China, South Korea0.5086.3411.270.34
DesignProspective, Retrospective0.6188.00–21.490.51
SampleSerum, Plasma0.4587.4320.850.16
Number295, 136, 120, 122, 161, 620.6986.03–21.900.47
StageNone, I–IV0.5287.598.140.39
MethodqRT-PCR, RT-qPCR0.5187.0110.360.22
Follow-up26, 48, 96, 40, 79, 1250.2884.2850.820.08
Follow-up< 48, ≥ 480.009.67100.000.01

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.
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 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

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 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

DISCUSSION

Status quo

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.

Main discoveries

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

miRNAReferenceExpressionPotential targetPathway
95–7UpGALNT4None
212, 19–25, 197–200UpNoneNone
224, 26, 27DownYWHAZ, HDAC4Cell invasion, migration, proliferation, tumourigenicity and YWHAZ/AKT1/foxo3a signaling
29a9, 37, 38, 200, 202UpNoneNone
29c38, 40, 41DownSIRT1None
34a47–50DownFOXM1, MYC, BCL2, AXLCell apoptosis, chemoresistance, proliferation, viability and FOXM1/MYC signaling
34c50–52DownNCKAP1Cell cycle, growth, invasion and proliferation
122195, 198, 205–208NoneNoneNone
148a87-89, 208, 211DownUSP4, SIP1Cell invasion, migration and proliferation
1559, 98–100UpARID2, FBXW7Cell apoptosis, cycle, invasion, proliferation and tumorigenesis
192202, 208UpNoneNone
199a11, 113, 114DownHIF1A, VEGFA, IGF1, IGF2Cell growth, invasion, proliferation and Warburg effect
200a116-118DownMACC1, CDK6, ZEB2Cell cycle, growth, metastasis and proliferation
203119–121DownADAM9, HULCCell apoptosis, invasion and proliferation
22120, 132–135UpBmfCell apoptosis and growth
224198, 218NoneNoneNone

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.

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.

Limitations

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

MethodsInformation
Search strategy4 search engines, including PubMed, EMBASE, Web ofScience and Cochrane Database of Systematic Reviews
Search deadline15 April, 2017
Search termsmir and hepatocellular carcinoma
Inclusion criteria(1) Patients with hepatocellular carcinoma;(2) Expression of miRNAs and survival outcome intissue, 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-freesurvival (PFS) and metastasis-free survival (MFS)was measured, with or without the HR or 95% CI;(4) Full text articles published in English
Exclusion criteria(1) Reviews, letters or laboratory studies withoutoriginal data and retracted articles;(2) Frequency of studies estimating prognostic valueof miRNAs ≤ 2 in tissue;(3) Studies which cannot be merged;(4) If more than one article had been published on theidentical study cohort, only the most comprehensivestudy was selected for the present meta-analysis

Quality assessment

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.

Statistical analysis

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.

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.
  228 in total

1.  The microRNA miR-139 suppresses metastasis and progression of hepatocellular carcinoma by down-regulating Rho-kinase 2.

Authors:  Carmen Chak-Lui Wong; Chun-Ming Wong; Edmund Kwok-Kwun Tung; Sandy Leung-Kuen Au; Joyce Man-Fong Lee; Ronnie Tung-Ping Poon; Kwan Man; Irene Oi-Lin Ng
Journal:  Gastroenterology       Date:  2010-10-15       Impact factor: 22.682

2.  Circulating microRNAs as stable blood-based markers for cancer detection.

Authors:  Patrick S Mitchell; Rachael K Parkin; Evan M Kroh; Brian R Fritz; Stacia K Wyman; Era L Pogosova-Agadjanyan; Amelia Peterson; Jennifer Noteboom; Kathy C O'Briant; April Allen; Daniel W Lin; Nicole Urban; Charles W Drescher; Beatrice S Knudsen; Derek L Stirewalt; Robert Gentleman; Robert L Vessella; Peter S Nelson; Daniel B Martin; Muneesh Tewari
Journal:  Proc Natl Acad Sci U S A       Date:  2008-07-28       Impact factor: 11.205

3.  microRNA-22, downregulated in hepatocellular carcinoma and correlated with prognosis, suppresses cell proliferation and tumourigenicity.

Authors:  J Zhang; Y Yang; T Yang; Y Liu; A Li; S Fu; M Wu; Z Pan; W Zhou
Journal:  Br J Cancer       Date:  2010-09-14       Impact factor: 7.640

4.  MicroRNA-21 induces resistance to the anti-tumour effect of interferon-α/5-fluorouracil in hepatocellular carcinoma cells.

Authors:  Y Tomimaru; H Eguchi; H Nagano; H Wada; A Tomokuni; S Kobayashi; S Marubashi; Y Takeda; M Tanemura; K Umeshita; Y Doki; M Mori
Journal:  Br J Cancer       Date:  2010-10-26       Impact factor: 7.640

5.  Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases.

Authors:  Xi Chen; Yi Ba; Lijia Ma; Xing Cai; Yuan Yin; Kehui Wang; Jigang Guo; Yujing Zhang; Jiangning Chen; Xing Guo; Qibin Li; Xiaoying Li; Wenjing Wang; Yan Zhang; Jin Wang; Xueyuan Jiang; Yang Xiang; Chen Xu; Pingping Zheng; Juanbin Zhang; Ruiqiang Li; Hongjie Zhang; Xiaobin Shang; Ting Gong; Guang Ning; Jun Wang; Ke Zen; Junfeng Zhang; Chen-Yu Zhang
Journal:  Cell Res       Date:  2008-10       Impact factor: 25.617

6.  MicroRNA-221 targets Bmf in hepatocellular carcinoma and correlates with tumor multifocality.

Authors:  Laura Gramantieri; Francesca Fornari; Manuela Ferracin; Angelo Veronese; Silvia Sabbioni; George Adrian Calin; Gian Luca Grazi; Carlo Maria Croce; Luigi Bolondi; Massimo Negrini
Journal:  Clin Cancer Res       Date:  2009-08-11       Impact factor: 12.531

7.  Effects of microRNA-29 on apoptosis, tumorigenicity, and prognosis of hepatocellular carcinoma.

Authors:  Yujuan Xiong; Jian-Hong Fang; Jing-Ping Yun; Jine Yang; Ying Zhang; Wei-Hua Jia; Shi-Mei Zhuang
Journal:  Hepatology       Date:  2010-03       Impact factor: 17.425

8.  Diagnostic and prognostic implications of microRNAs in human hepatocellular carcinoma.

Authors:  Wenxi Li; Lu Xie; Xianghuo He; Jinjun Li; Kang Tu; Lin Wei; Jun Wu; Yong Guo; Xi Ma; Pingping Zhang; Zhimei Pan; Xin Hu; Yingjun Zhao; Haiyang Xie; Guoping Jiang; Taoyang Chen; Jianneng Wang; Shusen Zheng; Jing Cheng; Dafang Wan; Shengli Yang; Yixue Li; Jianren Gu
Journal:  Int J Cancer       Date:  2008-10-01       Impact factor: 7.396

9.  MiR-222 overexpression confers cell migratory advantages in hepatocellular carcinoma through enhancing AKT signaling.

Authors:  Queenie W-L Wong; Arthur K-K Ching; Anthony W-H Chan; Kwong-Wai Choy; Ka-Fai To; Paul B-S Lai; Nathalie Wong
Journal:  Clin Cancer Res       Date:  2010-01-26       Impact factor: 12.531

10.  Practical methods for incorporating summary time-to-event data into meta-analysis.

Authors:  Jayne F Tierney; Lesley A Stewart; Davina Ghersi; Sarah Burdett; Matthew R Sydes
Journal:  Trials       Date:  2007-06-07       Impact factor: 2.279

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  12 in total

Review 1.  MicroRNA regulation of liver cancer stem cells.

Authors:  Weiyang Lou; Jingxing Liu; Yanjia Gao; Guansheng Zhong; Bisha Ding; Liang Xu; Weimin Fan
Journal:  Am J Cancer Res       Date:  2018-07-01       Impact factor: 6.166

2.  Serum miRNAs are differentially altered by ethanol and caffeine consumption in rats.

Authors:  M Martinez; I M U Rossetto; R M S Arantes; F S N Lizarte; L F Tirapelli; D P C Tirapelli; L G A Chuffa; F E Martinez
Journal:  Toxicol Res (Camb)       Date:  2019-07-17       Impact factor: 3.524

3.  miR-224 Regulates the Aggressiveness of Hepatoma Cells Through the IL-6/STAT3/SMAD4 Pathway.

Authors:  Fangmei An; Xiongbo Wu; Yunan Zhang; Dayang Chen; Yexin Lin; Fang Wu; Junli Ding; Min Xia; Qiang Zhan
Journal:  Turk J Gastroenterol       Date:  2021-06       Impact factor: 1.852

Review 4.  Dairy consumption and hepatocellular carcinoma risk.

Authors:  Bodo C Melnik
Journal:  Ann Transl Med       Date:  2021-04

5.  XIST Induced by JPX Suppresses Hepatocellular Carcinoma by Sponging miR-155-5p.

Authors:  Xiu Qing Lin; Zhi Ming Huang; Xin Chen; Fang Wu; Wei Wu
Journal:  Yonsei Med J       Date:  2018-09       Impact factor: 2.759

6.  lncRNA KCNQ1OT1 enhances the chemoresistance of oxaliplatin in colon cancer by targeting the miR-34a/ATG4B pathway.

Authors:  Yongchao Li; Changfeng Li; Dandan Li; Lei Yang; Jingpeng Jin; Bin Zhang
Journal:  Onco Targets Ther       Date:  2019-04-09       Impact factor: 4.147

7.  The circular RNA PVT1/miR-203/HOXD3 pathway promotes the progression of human hepatocellular carcinoma.

Authors:  Yiqing Zhu; Yan Liu; Bang Xiao; Hui Cai; Meng Liu; Liye Ma; Huirong Yin; Fang Wang
Journal:  Biol Open       Date:  2019-09-24       Impact factor: 2.422

8.  High Blood miR-802 Is Associated With Poor Prognosis in HCC Patients by Regulating DNA Damage Response 1 (REDD1)-Mediated Function of T Cells.

Authors:  Chao Jiang; Xueyan Liu; Meng Wang; Guoyue Lv; Guangyi Wang
Journal:  Oncol Res       Date:  2019-04-12       Impact factor: 5.574

Review 9.  Prospects of Noncoding RNAs in Hepatocellular Carcinoma.

Authors:  Huaixiang Zhou; Qiuran Xu; Chao Ni; Song Ye; Xiaowu Xu; Xiaoge Hu; Jiahong Jiang; Yeting Hong; Dongsheng Huang; Liu Yang
Journal:  Biomed Res Int       Date:  2018-07-26       Impact factor: 3.411

10.  Inverse relationship between the level of miRNA 148a-3p and both TGF-β1 and FIB-4 in hepatocellular carcinoma.

Authors:  Ashraf A Dawood; Amany A Saleh; Osama Elbahr; Suzy Fawzy Gohar; Mona S Habieb
Journal:  Biochem Biophys Rep       Date:  2021-07-20
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