Diagnostic Accuracy of Circulating microRNAs for Hepatitis C Virus-Associated Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

DOI: https://doi.org/10.21203/rs.3.rs-251826/v1

Abstract

Aims: The purpose of this study was to perform an assessment of circulating microRNAs as promising biomarker for Hepatitis C virus (HCV)-associated hepatocellular carcinoma (HCV-HCC) through a meta-analysis.

Methods: A comprehensive literatures search extended up to March 1, 2020 in PubMed, Cochrane library, Embase, Web of Science, Scopus and Ovid databases. The collected data were analyzed by random-effects model because of high heterogeneity, the pooled sensitivity (SEN), specificity (SPE), positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were used to explore the diagnostic performance of circulating miRNAs. Subgroup and threshold effect analysis were further carried out to explore the heterogeneity.

Results: Overall, 16 articles that included a total of 3606 HCV-HCC patients and 3387 HCV patients without HCC were collected. The pooled estimates indicated miRNAs could distinguish HCC patients from chronic hepatitis C (CHC) and HCV-associated liver cirrhosis (HCV-LC), with a SEN of 0.83 (95% CI, 0.79-0.87), a SPE of 0.77 (95% CI, 0.71-0.82), a DOR of 17 (95% CI, 12-28), and an AUC of 0.87 (95% CI, 0.84-0.90). The combination of miRNAs and AFP showed a better diagnostic accuracy than each alone. Subgroup analysis demonstrated that diagnostic accuracy of miRNAs was better for plasma types, up-regulated miRNAs, and miRNA clusters. There was no evidence of publication bias in Deeks’ funnel plot.

Conclusions: The summarized results suggested that circulating miRNAs, especially for miRNA clusters, have a relatively high diagnostic value for HCV-HCC, which could be served as non-invasive screening tool. 

1. Background

Hepatitis C virus (HCV) infection is the main risk factor in hepatocellular carcinoma (HCC) development. Approximately 399,000 people are estimated to die annually from HCV-associated liver cirrhosis (HCV-LC) and HCC[1]. The rate of progression from chronic hepatitis C (CHC) to HCC is variable and the cancerogenesis mechanism of HCV has yet completely known[2]. In addition, the only strategy to implement for HCV-HCC is still prevention despite advances in an era of all-oral direct-acting antivirals (DAAs) regimens[3]. However, detection of early stage HCC remains difficult due to technical challenge in non-invasive methods[4]. Therefore, new biomarkers with higher diagnostic accuracy is mandatory for early HCV-associated HCC (HCV-HCC).

MicroRNAs (miRNAs) could regulate gene expression and control cellular processes[5]. Numerous studies indicate that dysregulation of miRNAs expression lead to pathological processes of several types of cancer[6]. Recently, it has been increasingly recognized the meaningful properties of circulating miRNAs as the potential biomarkers for HCC[7]. Several HCV-HCC related miRNAs, such as miR-16, miR-122, miR-150, miR-182, miR-199a, miR-211, and miR-224, have been confirmed[811]. However, no consensus on diagnosis accuracy of circulating miRNAs for HCV-HCC has yet emerged. In the present study, a systematic review and meta-analysis was performed to evaluate the expression levels of circulating miRNAs of patients with HCV infections, in order to clarify the diagnostic accuracy of HCC from CHC and HCV-LC.

2. Methods

2.1 Search strategy and literatures selection

According to the guidelines of diagnostic meta-analysis, a systematic search of the literatures was performed by two investigators (WY and JPT) using the sources of Pubmed,Cochrane library༌Embase༌Web of Science, Scopus and Ovid from inception through the end of March 1, 2020. The retrieval terms included: "Liver Neoplasms" or "Hepatic Neoplasms" or “Liver Cancers” or "Carcinoma, Hepatocellular" or "Liver Cell Carcinoma" and "Hepatitis C" and "microRNAs" or "miRNA".

Literatures included according to the following information: (1) both HCC groups and control groups ware HCV-related; (2) the detection of the circulating miRNAs was related to HCV-HCC; (3) true positive (TP), true negative (TN), false positive (FP), and false negative (FN) of the miRNAs were reported or could be calculated. On the other hand, the exclusion criteria were as follows: (1) meta-analysis, case reports, reviews or letters; (2) repetitive research; (3) the obtained miRNAs were not from blood; (4) insufficient data was not available for the diagnosis value.

2.2 Data collection and quality assessment

The final set of the included studies was assessed by two investigators (JJZ and JPT). The final judgment originated from any disagreements were made by a third investigator (YCH). The data of included studies were extracted including the name of first author, publication year, ethnicity, the type and alteration of circulating miRNAs, sample source, normalization controls, alpha-fetoprotein (AFP), numbers of HCC, CHC and HCV-LC, and numbers of TP, TN, FP, FN observations.

The quality of included studies was assessed using Quality Diagnostic Accuracy Studies-2 (QUADAS-2) criteria by two independent authors (WY and JJZ) [12]. The disagreement was settled by a third reviewer (LZ).

2.3 Data synthesis and analysis

All the statistical analysis was conducted by STATA version 14 (STATA Corp, College Station, TX, USA). The pooled sensitivity (SEN), specificity (SPE), positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were calculated for circulating miRNAs using bivariate random-effects regression model. In addition, potential sources of heterogeneity were explored using threshold effect analysis and regression analysis. Then subgroup analysis was further analyzed based on varied factors. Moreover, differences between the overall accuracy (OA) of miRNAs, AFP or the combination of miRNAs and AFP in discriminating HCV-HCC patients from controls were analyzed using SPSS Statistics 20 (IBM, China). Publication bias were assessed by Deeks’ funnel plot. P-value less than 0.05 was considered statistically significant.

3. Results

3.1 Included studies

The process of studies selection is shown graphically on Fig. 1. A total of 2994 articles were identified from initial literatures search, including 332 in Pubmed, 495 in Embase, 1269 in Web of Science, and 617 in Scopus and 281 in Ovid. After preliminary selection, 1858 articles were removed due to duplicate records and unfit literary forms. Finally, 16 articles were included according to inclusion and exclusion criteria [911, 1325].

Of the 16 articles, we extracted 39 studies including 3607 HCV-HCC patients and 3387 HCV infected patients as control population. The characteristics and information of the included studies are shown in Table 1. Quantitative real-time reverse transcription PCR (qRT-PCR) was used to measure the expression of miRNAs from 34 serum specimens and 5 plasma specimens. Among the 39 studies, 4 studies assessed multiple miRNAs for HCV-HCC diagnosis, and the other 35 studies were focused on a single miRNA. The conduct of patient selection introduced unclear risk in 8 articles [10, 1317, 19, 24] (Supplementary Fig. 1).

Table 1

Characteristics of studies included in this meta-analysis.

First author

Year

Region

MicroRNAs

Regulation mode

Specimen

Internal reference types in qRT-PCR

Sample size

Diagnostic power

Ref.

Case

Number

Control

Number

SEN (%)

SPE (%)

AUC

El-Garem H

2014

Egypt

miR-211

Downregulated

Serum

SNORD68

HCC

30

LC + HC

60

87

40

0.655

[9]

El-Abd NE

2015

Egypt

miR-16

Downregulated

Serum

RNU48

HCC

40

CHC

40

57.5

70

0.638

[10]

Motawi TK

2015

Egypt

miR-19a

Downregulated

Serum

SNORD68

HCC

112

LC

50

85.7

75

0.816

[13]

Motawi TK

2015

Egypt

miR-296

Upregulated

Serum

SNORD68

HCC

112

LC

50

76.9

56.5

0.666

[13]

Motawi TK

2015

Egypt

miR-195

Downregulated

Serum

SNORD68

HCC

112

LC

50

66.7

70.8

0.665

[13]

Motawi TK

2015

Egypt

miR-192

Upregulated

Serum

SNORD68

HCC

112

LC

50

78.6

62.5

0.730

[13]

Motawi TK

2015

Egypt

miR-34a

Upregulated

Serum

SNORD68

HCC

112

LC

50

51.9

82.6

0.663

[13]

Motawi TK

2015

Egypt

miR-146a

Upregulated

Serum

SNORD68

HCC

112

LC

50

96.4

73.9

0.880

[13]

Amr KS

2017

Egypt

miR-122

Downregulated

Plasma

RUN6B

HCC

40

CHC

40

87.5

97.5

NA

[11]

Amr KS

2017

Egypt

miR-244

Upregulated

Plasma

RUN6B

HCC

40

CHC

40

87.5

97

NA

[11]

Shaker O

2017

Egypt

miR-101-1

Downregulated

Serum

SNORD68

HCC

37

LC + HC

78

73

71

0.763

[14]

Shaker O

2017

Egypt

miR-221

Upregulated

Serum

SNORD68

HCC

37

LC + HC

78

56.8

73.9

0.673

[14]

Elemeery MN

2017

Egypt

miR-214-5P

Downregulated

Serum

SNORD68

HCC

224

CHC

250

92.2

75.5

0.842

[15]

Elemeery MN

2017

Egypt

miR-494

Upregulated

Serum

SNORD68

HCC

224

CHC

250

77

56

0.631

[15]

Elemeery MN

2017

Egypt

miR-138b

Downregulated

Serum

SNORD68

HCC

224

CHC

250

68.2

58.2

0.642

[15]

Elemeery MN

2017

Egypt

miR-125b

Downregulated

Serum

SNORD68

HCC

224

CHC

250

92.6

55.4

0.769

[15]

Elemeery MN

2017

Egypt

miR-1269

Upregulated

Serum

SNORD68

HCC

224

CHC

250

78.6

59.8

0.691

[15]

Elemeery MN

2017

Egypt

miR-145

Downregulated

Serum

SNORD68

HCC

224

CHC

250

81.5

51.5

0.624

[15]

Elemeery MN

2017

Egypt

miR-375

Downregulated

Serum

SNORD68

HCC

224

CHC

250

96.4

69.3

0.811

[15]

Elemeery MN

2017

Egypt

miRNA clusters(4)

NA

Serum

SNORD68

HCC

224

Fibrosis

150

96,7

94.3

0.945

[15]

Elemeery MN

2017

Egypt

miRNA clusters(4)

NA

Serum

SNORD68

HCC

224

Fibrosis

100

98.7

98.3

0.990

[15]

Shaheen NMH

2018

Egypt

miR-182

Downregulated

Serum

Cel-miR-39

HCC

40

CHC

20

72.5

65

0.675

[16]

Shaheen NMH

2018

Egypt

miR-150

Downregulated

Serum

Cel-miR-39

HCC

40

CHC

20

67.5

70

0.704

[16]

Rashad NM

2018

Egypt

miR-27a

Upregulated

Serum

SNORD68

HCC

51

LC

39

96.7

71.7

0.897

[17]

Rashad NM

2018

Egypt

miR-18b

Upregulated

Serum

SNORD68

HCC

51

LC

39

75.6

46.7

0.732

[17]

Rashad NM

2018

Egypt

miRNA clusters(2)

Upregulated

Serum

SNORD68

HCC

51

LC

39

91.1

71.7

0.821

[17]

El-Hamouly MS

2019

Egypt

miR-301

Upregulated

Plasma

U6

HCC

42

CHC

48

78.6

89.6

0.890

[18]

Ali LH

2019

Egypt

miR-215

Upregulated

Serum

SNORD68

HCC

60

LC

60

97.1

91

0.997

[19]

Shehab-Eldeen S

2019

Egypt

miR-122

Downregulated

Plasma

U6

HCC

20

CHC

20

95

81

0.930

[20]

Shehab-Eldeen S

2019

Egypt

miR-224

Upregulated

Plasma

U6

HCC

20

CHC

20

85

79

0.770

[20]

Sun Q

2019

China

miR-331-3p

Upregulated

Serum

Cel-miR-39

HCC

40

CHC

106

62.5

74.5

0.748

[21]

Sun Q

2019

China

miR-23b-3p

Downregulated

Serum

Cel-miR-39

HCC

40

CHC

106

85.8

65

0.806

[21]

Sun Q

2019

China

miR-331-3p

Upregulated

Serum

Cel-miR-39

HCC

40

LC

47

75

85.1

0.832

[21]

Sun Q

2019

China

miR-23b-3p

Downregulated

Serum

Cel-miR-39

HCC

40

LC

47

85.1

65

0.796

[21]

Oura K

2019

Japan

miR-125a-5p

Upregulated

Serum

Cel-miR-39

HCC

20

LC + CHC

20

80

100

0.980

[22]

Weis A

2019

Australia

miRNA clusters(3)

NA

Serum

SNORD68

HCC

20

CHC

20

80

95

0.940

[23]

Fatma A

2019

Egypt

miR-19a

Upregulated

Serum

SNORD68

HCC

40

LC + CHC

40

60

67.5

0.616

[24]

Fatma A

2019

Egypt

miR-223

Downregulated

Serum

SNORD68

HCC

40

LC + CHC

40

60

95

0.816

[24]

Aly DM

2020

Egypt

miR-let-7a-1

Downregulated

Serum

SNORD68

HCC

40

LC

20

70

82.5

0.740

[25]

qRT-PCR, quantitative real-time reverse transcription PCR; HCC, hepatocellular carcinoma; LC, liver cirrhosis; CHC, chronic hepatitis C; SEN: sensitivity; SPE, Specificity; AUC: area under the curve; Ref., references.

3.2 Accurate diagnosis of miRNAs compared with AFP in HCV-HCC patients

The threshold effect was evaluated before data combination. The correlation coefficient was 0.33 (P = 0.11), indicated no significant threshold effect in our study.

Significant heterogeneity were observed in 39 studies (I-squared = 91.83 % for SEN, I-squared = 89.91 % for SPE, I-squared = 88.8 % for DOR, respectively), therefore, random-effects model was selected to the overall analysis. Forest plots of SEN, SPE and DOR results are shown in Fig. 2a-c. The overall pooled results were summarized as follows: SEN 0.83 (95% CI, 0.79–0.87), SPE 0.77 (95% CI, 0.71–0.82), PLR 3.6 (95% CI, 2.8–4.7), NLR 0.21 (95% CI, 0.16–0.29), and DOR 17 (95% CI, 12–28) (Supplementary Table 1). The AUC value was 0.87 (95% CI, 0.84–0.90) in the overall SROC curve (Fig. 2d). The above results manifested that the diagnostic accuracy of circulating miRNAs for HCC is relatively high.

Among all these articles, 13 studies determined the accuracy of AFP diagnosis, and 16 studies determined A combination of miRNAs and AFP for HCV-HCC patients affecting the accuracy of diagnosis. miRNAs combined with AFP showed a higher accuracy than AFP alone with SEN of 0.88 versus 0.65, SPE of 0.88 versus 0.95, PLR of 7.1 versus 12.0, NLR of 0.14 versus 0.37, DOR of 51 versus 33, and AUC of 0.93 versus 0.85, respectively (Fig. 3a-c and Supplementary Table 1). The OA value analysis indicated that the combination of miRNAs and AFP had a significantly higher accuracy for HCV-HCC than AFP or miRNAs alone (P < 0.000). Although the DOR of AFP is higher than miRNAs alone (33 versus 17), there was no significant difference existed in the diagnostic accuracy of the OA value between the two methods (Fig. 3d-f).

3.3 Meta-regression analysis to exploring Sources of Heterogeneity:

Meta-regression analysis was used to explore sources of heterogeneity. Region, specimen types, regulation mode, internal reference types, miRNAs profiling, sample size, control groups were internal considered as parameters (Table 2). It can be seen from the results that the specimen types (P = 0.03), regulation mode (P = 0.01), miRNAs profiling (P < 0.01) had statistical significance. However, the parameter region (P = 0.07), internal reference types (P = 0.09), sample size (P = 0.12) and control groups (P = 0.14) were not statistically significant.

Table 2

The meta-regression analysis of variable parameters

Parameters

Sensitivity

Specificity

Joint Model

Estimate (95% CI)

Coef.

Z

P>|z|

Estimate (95% CI)

Coef.

Z

P>|z|

I-squared (95% CI)

LRTChi

P value

Region

0.75 (0.45–0.92)

1.09

-0.77

0.44

0.92 (0.74–0.98)

2.43

1.66

0.10

61.57 (13.37–100.00)

5.2

0.07

Specimen types

0.88 (0.71–0.96)

2.00

0.77

0.51

0.93 (0.82–0.97)

2.57

2.47

0.01

72.39 (38.81–100.00)

7.24

0.03

Regulation mode

0.88 (0.81–0.93)

2.02

1.17

0.24

0.87 (0.79–0.92)

1.90

2.21

0.03

77.68 (51.46–100.00)

8.96

0.01

Internal reference types

0.78 (0.63–0.89)

1.28

-0.77

0.44

0.85 (0.74–0.92)

1.77

1.35

0.18

59.09 (7.72–100.00)

4.89

0.09

miRNAs profiling

0.96 (0.89–0.98)

3.11

2.70

0.01

0.94 (0.84–0.98)

2.70

2.73

0.01

85.14 (69.07–100.00)

13.46

< 0.01

Sample size

0.88 (0.78–0.93)

1.95

0.90

0.37

0.69 (0.53–0.81)

0.80

-1.33

0.26

52.19 (0.00-100.00)

4.18

0.12

Control groups

0.86 (0.79–0.91)

1.78

0.54

0.59

0.82 (0.75–0.88)

1.54

1.25

0.21

49.72 (0.00-100.00)

3.98

0.14

miRNAs, MicroRNAs; 95% CI, 95 % confidence intervals; Coef., coefficient.

3.4 Subgroup analyses

Subgroup analyses were performed based on region, specimen types, regulation mode, internal reference types, miRNAs profiling, sample size, source of control. Majority of the research populations were Egypt (33 studies contained 3407 HCV-HCC patients and 3041 controls) with the pooled SEN of 0.84 (95% CI 0.79–0.89), SPE of 0.76 (95% CI 0.69–0.82), PLR of 3.5 (95% CI 2.6–4.6), NLR of 0.21 (95% CI 0.15–0.30), DOR 17 (95% CI 9–30) of and AUC of 0.87 (95% CI 0.84–0.90). Additionally, the difference among subgroup analysis based on internal reference types, miRNAs profiling, and sample size was minimal as well (Table 3).

Table 3

Summary diagnostic power based on subgroup analyses

Subgroup

Number of studies

Number of HCC

Number of control

SEN (95% CI)

SPE (95% CI)

PLR (95% CI)

NLR (95% CI)

DOR (95% CI)

AUC (95% CI)

Region

Africa

33

3407

3041

0.84 (0.79–0.89)

0.76 (0.69–0.82)

3.5 (2.6–4.6)

0.21 (0.15–0.30)

17 (9–30)

0.87 (0.84–0.90)

Asia

5

180

326

0.77 (0.68–0.85)

0.78 (0.63–0.88)

3.5 (2.0-5.9)

0.29 (0.20–0.42)

12 (6–26)

0.84 (0.80–0.87)

Specimen types

Serum

34

3445

3219

0.83 (0.77–0.87)

0.74 (0.68–0.79)

3.2 (2.5–4.1)

0.23 (0.17–0.32)

14 (8–23)

0.85 (0.82–0.88)

Plasm

5

162

168

0.86 (0.79–0.90)

0.91 (0.83–0.96)

10.0 (4.7–21.2)

0.16 (0.11–0.23)

64 (25–164)

0.87 (0.84–0.90)

Regulation mode

Upregulated

18

1388

1276

0.81 (0.74–0.87)

0.81 (0.74–0.87)

3.5 (2.5–4.9)

0.25 (0.17–0.36)

14 (7–27)

0.86 (0.82–0.89)

Downregulated

18

1751

1841

0.82 (0.75–0.87)

0.71 (0.63–0.78)

2.8 (2.2–3.6)

0.25 (0.18,0.35)

11 (7–18)

0.83 (0.80–0.86)

Internal reference types

SNORD 68

26

3145

2813

0.85 (0.79–0.90)

0.74 (0.66–0.80)

3.2 (2.4–4.4)

0.20 (0.13–0.31)

16 (8–31)

0.86 (0.83–0.89)

non-SNORD 68

13

462

574

0.79 (0.72–0.84)

0.83 (0.74–0.90)

4.6 (2.8–7.6)

0.26 (0.19–0.35)

18 (8–39)

0.86 (0.83–0.89)

miRNAs profiling

Single miRNA

35

3088

3078

0.81 (0.76–0.85)

0.74 (0.68–0.79)

3.1 (2.5–3.9)

0.26 (0.20–0.33)

12 (8–19)

0.84 (0.81–0.87)

miRNA clusters

4

519

309

0.95 (0.89–0.98)

0.92 (0.79–0.97)

11.8 (4.1–34.4)

0.05 (0.02–0.13)

237 (33-1687)

0.98 (0.97–0.99)

Sample size

< 100

19

705

659

0.80 (0.74–0.85)

0.82 (0.72–0.88)

4.4 (2.8–6.8)

0.25 (0.19–0.33)

18 (9–33)

0.87 (0.83–0.89)

> 100

20

2902

2728

0.86 (0.78–0.91)

0.73 (0.65–0.80)

3.2 (2.3–4.4)

0.19 (0.12–0.32)

16 (7–36)

0.86 (0.82–0.89)

Control groups

CHC

18

1950

2230

0.83 (0.77–0.88)

0.74 (0.66–0.82)

3.3 (2.3–4.5)

0.23 (0.16–0.32)

14 (8–26)

0.86 (0.83–0.89)

LC

13

1005

591

0.84 (0.75–0.90)

0.73 (0.66–0.79)

3.1 (2.4–4.2)

0.22 (0.14–0.36)

14 (7–28)

0.84 (0.80–0.87)

miRNAs, MicroRNAs; 95% CI, 95 % confidence intervals; SEN, sensitivity; SPE, specificity; PLR, positive likelihood ratios; NLR, negative likelihood ratios; DOR, diagnostic odds ratio; AUC, area under the curve; HCC, hepatocellular carcinoma; LC, liver cirrhosis; CHC, chronic hepatitis C.

The types of specimen could influence the diagnostic accuracy. miRNAs from plasma showed higher quality of detection than that from serum and the corresponding pooled SEN, SPE, PLR, NLR, DOR, and AUC were 0.86 versus 0.83, 0.91 versus 0.74, 10.0 versus 3.2, 0.16 versus 0.23, 64 versus 14, 0.87 versus 0.85, respectively. It is of note that the pooled SEN and DOR were significantly higher among multiple miRNAs subgroup compared with single miRNA (SEN 0.95 versus 0.81, DOR 237 versus 12), indicating a significantly higher diagnostic accuracy of miRNA clusters for HCV-HCC.

There are 18 studies (2230 patients) of CHC and 13 studies of HCC-LC (591 patients) as controls. As shown in Table 3 and Supplementary Fig. 2–3, the analysis based on source of control demonstrated the no significant difference of diagnostic accuracy between CHC and HCV-LC. However, among CHC group, miRNAs combined with AFP displayed a better diagnostic accuracy than miRNAs alone. The pooled results were displayed as follows: SEN 0.89 (95% CI 0.83–0.93), SPE 0.88 (95% CI 0.76–0.95), PLR 7.7 (95% CI 3.5–17), NLR 0.12 (95% CI 0.07–0.20), DOR 63 (95% CI 20–203) and AUC 0.94 (95% CI 0.92–0.96).

3.5 Publication bias and clinical utility of index

Deeks’ funnel plot asymmetry test was conducted to investigate the publication bias of included studies. The P-value of 0.43 for overall circulating miRNAs indicated little possibility of publication bias in our meta-analysis. In addition, P-value of publication bias for AFP, miRNAs combined with AFP, CHC and HCV-LC were 0.13, 0.91, 0.31, and 0.80, respectively (Fig. 4a-e).

The post-test probabilities were assessed by Fagan’s Nomogram. When prior probability was 20%, post-test positive probability was 48% with a PLR of 4 and negative probability was 5% with NLR of 0.21 (Fig. 4f).

4. Discussion

DAA shows effective against HCV, however, direct evidence on the effects of antiviral therapy on HCV-HCC remains limited. Furthermore, the development of non-invasive markers for screening of HCC presents a challenge during the last decades. Fortunately and interestingly, accumulating evidence shows that aberrant miRNAs expression profiles have been associated with the development of HCC[6]. Previous study showed that miRNAs were correlated in hepatocarcinogenic effect of HCV[26]. However, different reports have the discrepancies due to samples, technical variations and analysis methods. Therefore, we conducted this meta-analysis to evaluate the clinical value of circulating miRNAs in diagnosis of HCV-HCC.

According to our results, circulating miRNAs showed high diagnostic accuracy for HCV-HCC detection, with SEN of 0.83 (95% CI, 0.79–0.87), SPE of 0.77 (95% CI, 0.71–0.82), PLR of 3.6 (95% CI, 2.8–4.7), NLR of 0.21 (95% CI, 0.16–0.29), DOR of 17 (95% CI, 12–28) and AUC of 0.87 (95% CI, 0.84–0.90). A significant improvement in the sensitivity was observed when circulating miRNAs combined with AFP than using alone (P < 0.000). Moreover, we have characterized the role of miRNA clusters as diagnostic and prognostic markers for distinction of HCV-HCC from CHC and HCV-LC subgroup.

Currently, available diagnostic or prognostic biomarkers have limited accuracy for HCC[27]. AFP is the most widely used for HCC, however, serum AFP levels are related to both HCC and benign liver diseases, such as hepatitis and cirrhosis [2829]. Precious studies have demonstrated that miRNAs could be served as high-precision detection of HCC biomarker[30]. In this present study, although the DOR of AFP is higher than miRNAs alone (33 versus 17), no statistical difference of OA value was observed. Similarly, He et al. found SEN and AUC-SROC of AFP for HCC were significantly less than miRNAs, while the DOR of AFP was higher than miRNAs[31]. The possible reasons for this are associated with the cut-off value of AFP, stage of HCC, and tumor size. Growing evidence indicated that miRNAs had a better performance compared with AFP in detection of early-stage HCV-HCC from CHC and LC, such as miR-331-3p, miR-23b-3p, miR-19a, miR-223, miR-122, miR-199a, miR-16, miR-101-1 and miR-221[10, 14, 21, 24]. In addition, the OA value of miRNAs combined with AFP had a significantly higher accuracy for HCV-HCC than AFP or miRNAs alone (P < 0.000). These findings together with previous results indicated that circulating miRNAs could be used as putative diagnostic and prognostic biomarkers for HCV-HCC.

In the subgroup analyses, our findings revealed that miRNAs from plasma had higher precision detection for HCV-HCC than that from serum. The DOR of plasm and serum miRNAs was 64 (95% CI 25–164) versus 14 (95% CI 8–23), and AUC was 0.87 (95% CI 0.84–0.90) versus 0.85 (95% CI 0.82–0.88), respectively. Previous studies reported that miRNAs concentration in plasma is higher than serum due to more proteins in plasma [3233]. However, the opposite results were also found in serum [31]. Therefore, further studies are needed to confirm application of specimen types in clinical practice. Interestingly, our study revealed differences in DOR (237 versus 12) when selecting miRNA clusters for HCV-HCC diagnosis. However, the miRNAs panel has not been definitely decided yet due to differentially expressed circulating miRNAs in HCV-HCC[13, 23]. All the above researches suggested that multiple miRNAs panel may be a promising prospect for application as a non-invasive method for HCV-HCC.

Although the results are promising, several limitations need to be addressed. First, some related studies, such as letters, editorials, case reports and conference proceedings, were not included. Second, most studies included in this meta-analysis were from Egypt, having an adverse effect on population selection bias. Third, different cut-off values were not extracted due to limited data, which may result in a latent problem when interpreting the results. Therefore, the results of this study need more higher quality studies for confirmation in the future.

5. Conclusions

In conclusion, miRNAs could distinguish HCV-HCC from CHC and LC with high sensitivity and specificity. Combined application of miRNAs and AFP was more effective. In addition, the diagnostic accuracy of miRNA clusters was significantly high in HCV-HCC patients. Therefore, the results of our study strongly suggested that there is a real possibility of using circulating miRNAs as potential non-invasive biomarker of HCV-HCC.

Abbreviations

miRNAs, circulating microRNAs;

HCV, Hepatitis C virus;

HCV-HCC, HCV-associated hepatocellular carcinoma;

qRT-PCR, quantitative real-time reverse transcription PCR;

HCC, hepatocellular carcinoma;

LC, liver cirrhosis;

CHC, chronic hepatitis C;

SEN: sensitivity;

SPE, Specificity;

AUC: area under the curve;

PLR, positive likelihood ratios;

NLR, negative likelihood ratios;

SROC, summary receiver operating characteristic;

95% CI, 95 % confidence intervals;

Coef., coefficient;

Ref., references;

DOR, diagnostic odds ratio.

Declarations

Ethics approval and consent to participate. Not applicable.

Consent to publish. All authors have seen and approved the content and fulfil the journal’s requirements for authorship.

Availability of data and materials. All data generated or analysed during this study are included in this published article and its supplementary information files.

Competing interests. Not applicable.

Funding. This work was supported by the Department of Health of Zhejiang province [2020KY409].

Authors' Contributions. The work presented here was carried out in collaboration between all authors. WY and YQQ developed the concept and designed the study. JJZ, JPT and YCH carried out the literatures research and studies selection. WY, JPT and XMS co-worked on associated data collection. The qualities of included studies were carried out by WY, ZJJ and LZ. Data synthesis and analysis were carried out by WY, JPT and JJZ. The manuscript was written by JJZ and corrected by WY and YQQ. All authors discussed the results and implications and commented on the manuscript at all stages.

Acknowledgements. Not applicable.

References

  1. Rasche A, Sander AL, Corman VM, Drexler JF. Evolutionary biology of human hepatitis viruses. J Hepatol. 2019;70(3):501–20. doi:10.1016/j.jhep.2018.11.010.
  2. Petruzziello A. Epidemiology of Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) Related Hepatocellular Carcinoma. Open Virol J. 2018;12:26–32. doi:10.2174/1874357901812010026.
  3. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61(2):69–90. doi:10.3322/caac.20107.
  4. Yang JD. Detect or not to detect very early stage hepatocellular carcinoma? The western perspective. Clin Mol Hepatol. 2019;25(4):335–43. doi:10.3350/cmh.2019.0010.
  5. Armand-Labit V, Pradines A. Circulating cell-free microRNAs as clinical cancer biomarkers. Biomol Concepts. 2017;8(2):61–81. doi:10.1515/bmc-2017-0002.
  6. Ali Syeda Z, Langden SSS, Munkhzul C, Lee M, Song SJ. Regulatory Mechanism of MicroRNA Expression in Cancer. Int J Mol Sci 2020;21(5). pii: E1723. doi: 10.3390/ijms21051723.
  7. Pezzuto F, Buonaguro L, Buonaguro FM, Tornesello ML. The Role of Circulating Free DNA and MicroRNA in Non-Invasive Diagnosis of HBV- and HCV-Related Hepatocellular Carcinoma. Int J Mol Sci 2018;19(4). pii: E1007. doi:10.3390/ijms19041007.
  8. Piluso A, Gragnani L, Fognani E, et al. Deregulation of microRNA expression in peripheral blood mononuclear cells from patients with HCV-related malignancies. Hepatol Int. 2015;9(4):586–93. doi:10.1007/s12072-015-9658-5.
  9. El-Garem H, Ammer A, Shehab H, et al. Circulating microRNA, miR-122 and miR-221 signature in Egyptian patients with chronic hepatitis C related hepatocellular carcinoma. World J Hepatol 2014;6(11):818 – 24. doi: 10.4254/wjh.v6.i11.818.
  10. El-Abd NE, Fawzy NA, El-Sheikh SM, Soliman ME. Circulating miRNA-122, miRNA-199a, and miRNA-16 as Biomarkers for Early Detection of Hepatocellular Carcinoma in Egyptian Patients with Chronic Hepatitis C Virus Infection. Mol Diagn Ther. 2015;19(4):213–20. doi:10.1007/s40291-015-0148-1.
  11. Amr KS, Elmawgoud Atia HA, Elazeem Elbnhawy RA, Ezzat WM. Early diagnostic evaluation of miR-122 and miR-224 as biomarkers for hepatocellular carcinoma. Genes Dis. 2017;4(4):215–21. doi:10.1016/j.gendis.2017.10.003.
  12. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36. doi:10.7326/0003-4819-155-8-201110180-00009.
  13. Motawi TK, Shaker OG, El-Maraghy SA, Senousy MA. Serum MicroRNAs as Potential Biomarkers for Early Diagnosis of Hepatitis C Virus-Related Hepatocellular Carcinoma in Egyptian Patients. PLoS One. 2015;10(9):e0137706. doi:10.1371/journal.pone.0137706.
  14. Shaker O, Alhelf M, Morcos G, Elsharkawy A. miRNA-101-1 and miRNA-221 expressions and their polymorphisms as biomarkers for early diagnosis of hepatocellular carcinoma. Infect Genet Evol. 2017;51:173–81. doi:10.1016/j.meegid.2017.03.030.
  15. Elemeery MN, Badr AN, Mohamed MA, Ghareeb DA. Validation of a serum microRNA panel as biomarkers for early diagnosis of hepatocellular carcinoma post-hepatitis C infection in Egyptian patients. World J Gastroenterol. 2017;23(21):3864–75. doi:10.3748/wjg.v23.i21.3864.
  16. Shaheen NMH, Zayed N, Riad NM, et al. Role of circulating miR-182 and miR-150 as biomarkers for cirrhosis and hepatocellular carcinoma post HCV infection in Egyptian patients. Virus Res. 2018;255:77–84. doi:10.1016/j.virusres.2018.07.004.
  17. Rashad NM, El-Shal AS, Shalaby SM, Mohamed SY. Serum miRNA-27a and miRNA-18b as potential predictive biomarkers of hepatitis C virus-associated hepatocellular carcinoma. Mol Cell Biochem. 2018;447(1–2):125–36. doi:10.1007/s11010-018-3298-8.
  18. El-Hamouly MS, Azzam AA, Ghanem SE, El-Bassal FI, Shebl N, Shehata AMF. Circulating microRNA-301 as a promising diagnostic biomarker of hepatitis C virus-related hepatocellular carcinoma. Mol Biol Rep. 2019;46(6):5759–65. doi:10.1007/s11033-019-05009-w.
  19. Ali LH, Higazi AM, Moness HM, et al. Clinical significances and diagnostic utilities of both miR-215 and squamous cell carcinoma antigen-IgM versus alpha-fetoprotein in Egyptian patients with hepatitis C virus-induced hepatocellular carcinoma. Clin Exp Gastroenterol. 2019;12:51–66. doi:10.2147/CEG.S179832.
  20. Shehab-Eldeen S, Nada A, Abou-Elela D, El-Naidany S, Arafat E, Omar T. Diagnostic Performance of microRNA-122 and microRNA-224 in Hepatitis C Virus-Induced Hepatocellular Carcinoma (HCC). Asian Pac J Cancer Prev. 2019;20(8):2515–22. doi:10.31557/APJCP.2019.20.8.2515.
  21. Sun Q, Li J, Jin B, Wang T, Gu J. Evaluation of miR-331-3p and miR-23b-3p as serum biomarkers for hepatitis c virus-related hepatocellular carcinoma at early stage. Clin Res Hepatol Gastroenterol. 2020;44(1):21–8. doi:10.1016/j.clinre.2019.03.011.
  22. Oura K, Fujita K, Morishita A, et al. Serum microRNA-125a-5p as a potential biomarker of HCV-associated hepatocellular carcinoma. Oncol Lett. 2019;18(1):882–90. doi:10.3892/ol.2019.10385.
  23. Weis A, Marquart L, Calvopina DA, Genz B, Ramm GA, Skoien R. Serum MicroRNAs as Biomarkers in Hepatitis C: Preliminary Evidence of a MicroRNA Panel for the Diagnosis of Hepatocellular Carcinoma. Int J Mol Sci 2019;20(4). pii: E864. doi:10.3390/ijms20040864.
  24. Fatma A, Reham A, Mona M, et al. Study of serum microRNA19a and microRNA223 as potential biomarkers for early diagnosis of hepatitis C virus-related hepatocellular carcinoma. Gene Reports 2019;15.
  25. Aly DM, Gohar NA, Abd El-Hady AA, Khairy M, Abdullatif MM. Serum microRNA let-7a-1/let-7d/let-7f and miRNA 143/145 Gene Expression Profiles as Potential Biomarkers in HCV Induced Hepatocellular Carcinoma. Asian Pac J Cancer Prev. 2020;21(2):555–62. doi:10.31557/APJCP.2020.21.2.555.
  26. Motawi TK, Shaker OG, El-Maraghy SA, Senousy MA. Serum interferon-related microRNAs as biomarkers to predict the response to interferon therapy in chronic hepatitis C genotype 4. PLoS One. 2015;10(3):e0120794. doi:10.1371/journal.pone.0120794.
  27. Ahn JC, Teng PC, Chen PJ, et al. Detection of circulating tumor cells and their implications as a novel biomarker for diagnosis, prognostication, and therapeutic monitoring in hepatocellular carcinoma. Hepatology. 2020. doi:10.1002/hep.31165.
  28. Collier J, Sherman M. Screening for hepatocellular carcinoma. Hepatology. 1998;27(1):273–8. doi:10.1002/hep.510270140.
  29. Oka H, Tamori A, Kuroki T, Kobayashi K, Yamamoto S. Prospective study of alpha-fetoprotein in cirrhotic patients monitored for development of hepatocellular carcinoma. Hepatology. 1994;19(1):61–6.
  30. Mao D, Chen H, Tang Y, Li J, Cao Y, Zhao J. Application of Isothermal Nucleic Acid Signal Amplification in the Detection of Hepatocellular Carcinoma-Associated MicroRNA. Chempluschem. 2019;84(1):8–17. doi:10.1002/cplu.201800382.
  31. He S, Hu XW, Wang D, Han LF, Zhang DC, Wei C. Accuracy of microRNAs for the diagnosis of hepatocellular carcinoma: A systematic review and meta-analysis. Clin Res Hepatol Gastroenterol. 2016;40(4):405–17. doi:10.1016/j.clinre.2016.02.001.
  32. Arroyo JD, Chevillet JR, Kroh EM, et al. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci U S A. 2011;108(12):5003–8. doi:10.1073/pnas.1019055108.
  33. McDonald JS, Milosevic D, Reddi HV, Grebe SK, Algeciras-Schimnich A. Analysis of circulating microRNA: preanalytical and analytical challenges. Clin Chem. 2011;57(6):833–40. doi:10.1373/clinchem.2010.157198.