
==== Front
Noncoding RNA Res
Noncoding RNA Res
Non-coding RNA Research
2468-0540
KeAi Publishing

S2468-0540(24)00129-X
10.1016/j.ncrna.2024.08.004
Original Research Article
Plasma microRNA-15a/16-1 serves as a non-invasive indicator of liver fibrosis severity in individuals with chronic hepatitis B
Wei Huan a1
Bi Yanhua ab1
Liao Chunhong a
Huang Yuehua huangyh53@mail.sysu.edu.cn
ac⁎
Lian Yifan lianyf6@mail.sysu.edu.cn
a⁎⁎
a Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
b School of Public Health, Sun Yat-sen University, Guangzhou, China
c Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
⁎ Corresponding author. Guangdong Provincial Key Laboratory of Liver Disease Research and Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, China. huangyh53@mail.sysu.edu.cn
⁎⁎ Corresponding author. Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. lianyf6@mail.sysu.edu.cn
1 Tow authors contributed equally.

15 8 2024
12 2024
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© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

The lack of effective non-invasive diagnostic methods for liver fibrosis hinders timely treatment for chronic hepatitis B (CHB) patients, leading to the progression of advanced liver disease. Circulating microRNAs offer a non-invasive approach to fibrosis assessment. MicroRNA-15a/16-1 (miR-15a/16) was reported to be implicated in fibrosis development, but the role of plasma miR-15a/16 in liver fibrosis assessment remains poorly understood. This study explored the importance of plasma miR-15a/16 in assessing liver fibrosis severity of CHB patients.

Methods

Quantitative PCR was utilized to measure the levels of plasma miR-15a/16 in 435 patients with CHB and 74 healthy controls. We assessed the correlation between plasma miR-15a/16 levels and liver fibrosis and cirrhosis using Pearson correlation coefficients, multivariate linear and logistic regression models, and smooth curve fitting. Utilizing the receiver operating characteristic (ROC) curve, we examined the diagnostic potential of plasma miR-15a/16 in severe fibrosis and cirrhosis.

Results

Plasma levels of miR-15a/16 in patients with CHB were significantly reduced compared to those in healthy controls. In the CHB cohort, levels were notably decreased in individuals with severe fibrosis or cirrhosis compared to those without severe fibrosis or cirrhosis. Plasma miR-15a/16 levels exhibited a negative relationship with the severity of liver fibrosis, gradually decreasing as the histological fibrosis stage progressed from S0 to S4. Reduced levels of plasma miR-15a/16 were linked to an elevated risk of severe liver fibrosis (miR-15a: odds ratio [OR] = 0.243; 95 % confidence interval [CI]: 0.138, 0.427; miR-16: OR = 0.201; 95 % CI: 0.097, 0.417) and cirrhosis (miR-15a: OR = 0.153; 95 % CI: 0.079, 0.298; miR-16: OR = 0.064; 95 % CI: 0.025, 0.162). MiR-15a achieved an area under the ROC curve of 0.886 and 0.832 for detecting moderate-to-severe fibrosis (S2-S4) and cirrhosis, respectively. MiR-16 demonstrated similar diagnostic values.

Conclusion

Plasma miR-15a/16 levels were negatively correlated with the severity of liver fibrosis in CHB patients and could serve as a new non-invasive indicator in evaluating liver fibrosis.

Keywords

microRNA-15a
microRNA-16-1
Biomarker
Liver fibrosis
Cirrhosis
Chronic hepatitis B (CHB)
==== Body
pmc1 Introduction

Liver fibrosis, a progressive condition, can advance to cirrhosis and ultimately lead to hepatocellular carcinoma (HCC), substantially augmenting the global disease burden [1]. Chronic inflammation and hepatic injury resulting from persistent infection with hepatitis B virus (HBV) are substantial risk factors for liver fibrosis [2]. Therefore, assessing hepatic fibrosis in individuals with chronic hepatitis B (CHB) is important for identifying patients prone to cirrhosis. Furthermore, the assessment of liver fibrosis in CHB patients is recommended by the American Association for the Study of Liver Diseases guidelines (2018) [3].

Liver biopsy remains the gold standard for evaluating liver fibrosis. However, its application is currently limited due to invasiveness [1]. Moreover, liver biopsy samples only a minute portion of the entire organ, potentially lacking a comprehensive representation of hepatic fibrosis due to the heterogeneous distribution of fibrotic lesions [4]. Ultrasonography and standard liver biochemistry are not recommended for assessing liver fibrosis because of their low sensitivity and specificity [5,6]. Transient elastography, a technique that measures liver stiffness, has shown good accuracy in evaluating fibrosis and cirrhosis [7]. However, the accuracy of transient elastography may be influenced by factors such as inflammation, biliary obstruction, and hepatic congestion in patients [7,8]. While magnetic resonance imaging (MRI) techniques may offer superior efficacy in hepatic fibrosis assessment compared to the tests mentioned above, they are characterized by higher costs and limited accessibility [9]. Therefore, improvements in liver fibrosis evaluation, especially in identifying reliable non-invasive diagnostic biomarkers, are urgently needed.

MicroRNAs (miRNAs), short non-coding RNAs about 22 nucleotides long, are involved in numerous biological processes [10]. MiRNA-15a/16-1 (miR-15a/16) is a cluster of miRNA located in the 13q14 chromosomal area in humans [11]. Our previous study found that HBV infection inhibited the miR-15a/16 expression in HCC cells [12]. Consistently, several studies have also demonstrated that the transcripts and proteins of HBV downregulate the expression of miR-15a/16 in hepatocytes in vitro [13,14]. Furthermore, dysregulation of miR-15a/16 is associated with the activation and proliferation of hepatic stellate cells (HSC), contributing to fibrosis [[15], [16], [17]]. These findings indicated their significant role in the initiation and advancement of liver fibrosis. However, the relationship between plasma miR-15a/16 and liver fibrosis remains unclear. Given that circulating miRNAs can be stably detected in human peripheral blood and have reproducible concentrations among individuals [18], and coupled with our previous demonstration of plasma miR-15a/16 as a biomarker for HBV-related HCC [19], we aimed to investigate whether plasma miR-15a/16 could also potentially serve as a biomarker for assessing liver fibrosis in individuals with CHB. Thus, in this study, we explored the association between plasma miR-15a/16 and liver fibrosis, severe liver fibrosis, and cirrhosis in CHB patients using a large clinical cohort. Furthermore, we evaluated the potential of plasma miR-15a/16 as a circulating indicator for assessing liver fibrosis.

2 Materials and methods

2.1 Participants

Between May 2021 and December 2023, we enrolled 509 participants from The Third Affiliated Hospital of Sun Yat-sen University, comprising 74 healthy controls (HC) and 435 individuals with chronic hepatitis B (CHB). The inclusion criteria were adults aged 18 to 90, both men and women. Healthy controls were required to be in good health with no liver or systemic ailments, while CHB patients needed to test positive for hepatitis B surface antigen for over 6 months [3]. The exclusion criteria for all groups included prior or ongoing infection with hepatitis viruses other than HBV and the presence of non-HBV-related systemic disorders.

During the first visit, data on the medical history and test results of every patient were gathered from the record system. The histological liver fibrosis stage (S0-S4) and inflammation grade (G0-G4) of the patients were confirmed by two independent pathologists. We also defined severe liver fibrosis using either the aspartate aminotransferase (AST) to platelet (PLT) ratio index (APRI) [20] or the fibrosis index based on four factors (FIB-4), which includes age, AST, alanine aminotransferase (ALT), and PLT [21]. Calculation of FIB-4 and APRI scores followed established methodologies as previously described [20,21]. Severe liver fibrosis was categorized as an APRI score ≥1.5 or a FIB-4 score ≥3.25 [20,21]. The diagnosis of cirrhosis relied on either biopsy or the concurrence of two imaging examinations (ultrasound coupled with computed tomography and/or MRI) [22].

We received informed consent from all participants, and the study was approved by the Institute Research Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (Ethical approval No. [2021] 02-274-01).

2.2 Plasma sample collection

We drew 5 mL of peripheral blood from all participants into tubes containing ethylenediaminetetraacetic acid for plasma collection. Within half an hour, the blood was centrifuged at 4 °C for 10 min at 1000 g. The supernatant was then subjected to a second centrifugation at 4 °C for 10 min at 1000 g to remove residual cellular debris. Finally, we aliquoted the resultant supernatant into 200 μL portions and stored it at −80 °C until further use.

2.3 Plasma RNA extraction

We used TRIzol reagent (Invitrogen Carlsbad, USA) to extract total RNA, including microRNA, from 200 μL of plasma. Before isolating RNA, synthetic cel-miR-39-3p (RiboBio, Guangzhou, China) from Caenorhabditis elegans was added to the plasma samples at a concentration of 0.2 nM to serve as a reference. We also added glycogen (Ambion/Applied Biosystems, Foster City, CA, USA) to aid RNA precipitation, with a final concentration of 100 μg/mL during isopropanol precipitation. Afterward, the RNA pellet was air-dried at room temperature and re-dissolved with 10 μL of RNase-free water.

2.4 MiRNA quantitation by real-time quantitative PCR

Plasma miRNA levels were measured using real-time quantitative PCR (RT-qPCR). Briefly, the extracted plasma RNA underwent poly(A) tailing and reverse transcription into complementary DNA using Escherichia coli poly(A) polymerase (New England Biolabs, Ipswich, MA, USA) and the GoScript™ Reverse Transcription System (Promega, Madison, USA), respectively. Platinum SYBR Green qPCR SuperMix-UDG (Invitrogen, Grand Island, USA) was used for RT-qPCR analysis of miR-15a/16 levels on a LightCycler 480 system (Roche, Indianapolis, USA). Primer sequences for miRNA RT-qPCR amplification were designed as previously described [23], with details provided in Supplementary Table 1. The primers of this investigation were purchased from RuiBiotech in Beijing, China. The levels of plasma miRNA were standardized to cel-miR-39 and calculated using the 2−ΔCt method, with ΔCt being defined as the value of Cttarget minus Ctreference.

2.5 Statistical analysis

Statistical analyses were performed using software of SPSS 26.0 (IBM Inc., Chicago, IL, USA), GraphPad Prism 9.0 (San Diego, CA, USA), R (version 4.0.2; http://www.R-project.org), and EmpowerStats (https://www.empowerstats.net/en/). Statistical tests were conducted as two-sided, with P-values below 0.05 considered statistically significant. We presented continuous variables as mean ± standard deviation, while categorical variables were shown as the number of cases with corresponding percentages. Contingency tables were used for categorical variables, and the chi-square test was employed to assess distribution variability among clinical indicators. Continuous variables were analyzed using Student's t-test to compare means between two groups. Pearson's correlation coefficient was used to assess the linear correlation between two variables. To investigate the correlation between plasma miR-15a/16-1 levels and liver fibrosis, we stratified CHB patients into four distinct groups based on the quartiles of their miR-15a or miR-16-1 plasma levels. The first group, quartile 1 (Q1), comprised patients with miR-15a/16-1 levels below the lower quartile. The second group, quartile 2 (Q2), included those with levels between the lower quartile and the median. The third group, quartile 3 (Q3), consisted of patients with levels between the median and the upper quartile. Finally, the fourth group, quartile 4 (Q4), encompassed patients with miR-15a/16-1 levels above the upper quartile. Furthermore, multivariable linear and multivariable logistic regression models were constructed to investigate the correlation of plasma miR-15a/16 with liver fibrosis, severe liver fibrosis, or cirrhosis. We performed smooth curve fitting to address nonlinearity associations. The diagnostic efficacy of plasma miR-15a/16 was analyzed using receiver operating characteristic (ROC) curves.

3 Results

3.1 Clinical features of the participants

In Table 1, we display the clinical characteristics of participants by subgroup. Our cohort comprised 435 CHB patients and 74 healthy controls. Of the CHB patients, 179 (41.1 %) had cirrhosis, while the remaining 256 (58.9 %) did not. There were no significant differences in gender distribution or the mean levels of AST and total bilirubin (TBIL) between the cirrhosis and non-cirrhosis groups. Compared to the non-cirrhosis cohort, the cirrhosis group exhibited higher age and HBV-DNA levels, alongside lower levels of ALT, albumin (ALB), albumin to globulin ratio (AGR), white blood cells (WBC), and PLT.Table 1 Clinical features of the participants.

Table 1Variables	HC	CHB	P value	
(n = 74)	Non-cirrhosis (n = 256)	Cirrhosis (n = 179)	
Gender	
 Female	23.0 (31.1 %)	59.0 (23.0 %)	35.0 (19.6 %)	0.140	
 Male	51.0 (68.9 %)	197.0 (77.0 %)	144.0 (80.4 %)	
Age (years)	42.8 ± 10.9	41.8 ± 10.5	51.6 ± 8.9	<0.001	
AST (U/L)	20.2 ± 4.0	86.7 ± 234.0	68.0 ± 100.9	0.314	
ALT (U/L)	19.1 ± 6.4	127.4 ± 338.0	58.7 ± 79.4	0.008	
ALB (g/L)	NA	45.2 ± 4.2	38.7 ± 7.5	<0.001	
AGR	NA	1.6 ± 0.3	1.3 ± 0.4	<0.001	
TBIL (umol/L)	NA	28.1 ± 64.1	41.7 ± 84.6	0.057	
WBC ( × 109/L)	NA	6.0 ± 2.0	5.2 ± 2.3	<0.001	
PLT ( × 109/L)	NA	203.8 ± 75.4	109.6 ± 59.4	<0.001	
HBV-DNA (IU/mL)	NA	(1.6E + 07) ± (4.5E + 07)	(2.1E + 06) ± (1.4E + 07)	<0.001	
	
Inflammation grades (G0/G1/G2/G3/G4/NA)	NA	(3/12/13/2/0/226)	(0/0/2/1/0/176)	NA	
Fibrosis stages (S0/S1/S2/S3/S4/NA)	NA	(6/11/9/4/0/226)	(0/0/0/0/3/176)	NA	
Data are mean ± standard deviation or n (percentages). The P-values for the difference between non-cirrhosis and cirrhosis groups in CHB cohorts. HC, healthy control; CHB, chronic hepatitis B; AST, aspartate aminotransferase; ALT, alanine aminotransferase; ALB, albumin; AGR, albumin to globulin ratio; TBIL, total bilirubin; WBC, white blood cells; PLT, platelet; HBV-DNA, hepatitis B virus deoxyribonucleic acid; NA, not available.

3.2 Plasma miR-15a/16 levels in CHB patients with different fibrosis stages

We first compared plasma miR-15a/16 levels between CHB patients and healthy controls. The results showed that plasma miR-15a/16 levels were significantly lower in CHB patients than in healthy controls (P < 0.0001 for all, Fig. 1A and B). We then investigated whether miR-15a/16 plasma levels differed by fibrosis stage. We observed that plasma levels of miR-15a/16 gradually decreased as the histological fibrosis stage progressed from S0 to S4 (Fig. 1C and D). Plasma miR-15a/16 levels were also significantly lower in inflammation grades G2-G3 compared to G0-G1 (Supplementary Fig. 1A and B, P < 0.01 for all). Furthermore, patients with severe liver fibrosis had significantly lower plasma miR-15a/16 levels compared to those without severe liver fibrosis when stratified by FIB-4 (P < 0.0001 for all; Fig. 1A and B) or APRI (P < 0.01 for all, Supplementary Figs. 2A and B). Additionally, compared with non-cirrhosis patients, those with cirrhosis exhibited significantly reduced plasma miR-15a/16 levels. (P < 0.0001 for all; Supplementary Figs. 2C and D).Fig. 1 Plasma levels of miR-15a/16 in CHB patients with different fibrosis stages. Plasma miR-15a (A) and miR-16 (B) levels of HC and CHB, where CHB were classified into non-severe fibrosis (FIB-4 < 3.25) and severe fibrosis (FIB-4 ≥ 3.25). Plasma levels of miR-15a (C) and miR-16 (D) in individual histological liver fibrosis stages (S0-S4) of CHB patients. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; ns, not significant. HC, healthy controls. CHB, chronic hepatitis B. FIB-4, fibrosis index based on four factors (incorporating age, aspartate aminotransferase, alanine aminotransferase, and platelet).

Fig. 1

3.3 Association between plasma miR-15a/16 levels and severity of liver fibrosis

Simple linear correlation analysis showed moderately significant negative associations between plasma miR-15a/16 levels and FIB-4 scores (Fig. 2A and B), and weak but significant negative correlations between plasma miR-15a/16 levels and APRI scores (Fig. 2C and D). Subsequently, multivariate linear regression analysis was conducted to explore the relationship between miR-15a/16 and liver fibrosis. We performed three distinct models to assess the beta (β) coefficient values (95 % CI) of miR-15a/16. The variables used to calculate FIB-4 or APRI scores were not adjusted in the corresponding models. Therefore, for the three models of FIB-4: model 1, without any covariate adjustments; model 2, adjusted for gender; model 3, gender, ALB, AGR, TBIL, WBC, and HBV-DNA were adjusted. For the three models of APRI: model 1, without any covariate adjustments; model 2, adjusted for gender and age; model 3, gender, age, ALT, ALB, AGR, TBIL, WBC, and HBV-DNA were adjusted.Fig. 2 Association between plasma levels of miR-15a/16 and liver fibrosis (FIB-4 and APRI) in CHB patients by Pearson's correlation analysis. Plasma levels of miR-15a (A) and miR-16 (B) exhibited a negative correlation with FIB-4. Plasma levels of miR-15a (C) and miR-16 (D) exhibited a negative correlation with APRI. FIB-4, fibrosis index based on four factors (incorporating age, aspartate aminotransferase, alanine aminotransferase, and platelet). APRI, aspartate aminotransferase to platelet ratio index. CHB, chronic hepatitis B.

Fig. 2

As shown in Table 2, the results indicated that miR-15a was significantly negatively associated with FIB-4 in all the multivariate linear regression models. Specifically, in model 1, the beta coefficient was −0.259 (95 % CI: 2.623, −1.255); in model 2, the beta coefficient was −0.258 (95 % CI: 2.622, −1.255); and in model 3, the association persisted with a slightly reduced beta coefficient of −0.135 (95 % CI: 1.579, −0.443). The strength of this association increased progressively from the lowest quartile group (Q1) to the highest quartile group (Q4) of miR-15a (P for trend <0.001). Similarly, significant negative associations and trends were observed between miR-16 and FIB-4 in models 1 and 2. The beta coefficient in model 1 was −0.257 (95 % CI: 3.971, −1.887). In model 2, the beta coefficient was −0.252 (95 % CI: 3.927, −1.835). However, this association was not observed in model 3, where the beta coefficient was −0.068 (95 % CI: 1.650, 0.102). Upon conducting subgroup analysis by gender, negative correlations between miR-15a/16 and FIB-4 persisted across all models in males. However, in females, significant correlations were only found in model 1. On the other hand, miR-15a/16 demonstrated no correlations with APRI in multivariable linear analysis (Supplementary Table 2). Furthermore, smooth curve fitting was employed to examine the nonlinear relationship, confirming that miR-15a/16 levels decreased with increasing FIB-4 (Supplementary Figs. 3A–F) and APRI (Supplementary Figs. 4A–F).Table 2 Analysis of the correlation between plasma miR-15a/16 and liver fibrosis (FIB-4) in CHB patients by multivariate linear regression.

Table 2Variables	Model 1
β (95 % CI) P value
(n = 435)	Model 2
β (95 % CI) P value
(n = 435)	Model 3
β (95 % CI) P value
(n = 435)	
miR-15a	−0.259 (−2.623, −1.255)
4.5E-08	−0.258 (−2.622, −1.255)
4.4E-08	−0.135 (−1.579, −0.443)
0.0005	
Quartile groups of miR-15a	
 Q1	Reference	Reference	Reference	
 Q2	−0.077 (−2.217, 0.400)
0.1730	−0.075 (−2.191, 0.421)
0.1840	−0.055 (−1.656, 0.374)
0.2153	
 Q3	−0.188 (−3.508, −0.898)
0.0010	−0.184 (−1.732, −0.429)
0.0012	−0.126 (−2.514, −0.432)
0.0057	
 Q4	−0.319 (−5.048, −2.438)
3.1E-08	−0.321 (−1.688, −0.819)
2.6E-08	−0.166 (−3.033, −0.864)
0.0005	
 P for trend	4.9E-09	4.3E-09	0.0002	
miR-16	−0.257 (−3.971, −1.887)
5.7E-08	−0.252 (−3.927, −1.835)
1.0E-07	−0.068 (−1.650, 0.102)
0.0831	
Quartile groups of miR-16	
 Q1	Reference	Reference	Reference	
 Q2	0.001 (−1.311, 1.323)
0.9929	0.009 (−1.215, 1.431)
0.8724	0.018 (−0.834, 1.265)
0.6866	
 Q3	−0.184 (−3.474, −0.846)
0.0013	−0.181 (−3.436, −0.810)
0.0016	−0.035 (−1.469, 0.660)
0.4554	
 Q4	−0.248 (−4.221, −1.593)
1.7E-05	−0.241 (−4.143, −1.508)
3.0E-05	−0.043 (−1.600, 0.583)
0.3610	
 P for trend	4.2E-07	6.3E-07	0.2221	
Stratified by gendera	
 miR-15a	
 Female	−0.241 (−4.129, −0.375)
0.0192	NAa	−0.086 (−2.035, 0.420)
0.1946	
 Male	−0.271 (−2.536, −1.141)
3.8E-07	NAa	−0.154 (−1.643, −0.445)
0.0007	
 miR-16	
 Female	−0.216 (−6.227, −0.206)
0.0365	NAa	0.033 (−1.485, 2.481)
0.6187	
 Male	−0.272 (−3.838, −1.734)
3.3E-07	NAa	−0.098 (−1.909, −0.089)
0.0316	
Model 1: without any covariate adjustments. Model 2: adjusted for gender. Model 3: gender, albumin, albumin to globulin ratio, total bilirubin, white blood cells, and hepatitis B virus DNA were adjusted.

CHB, chronic hepatitis B; FIB-4, fibrosis index based on four factors (incorporating age, aspartate aminotransferase, alanine aminotransferase, and platelet); CI, confidence intervals; NA, not available.

a The models of gender stratification excluded gender as an adjustment variable.

3.4 Plasma levels of miR-15a/16 was correlated with the risk of severe liver fibrosis

We performed multivariate logistic regression analysis to further investigate the connection between miR-15a/16 and severe liver fibrosis. In this analysis, we adjusted the same variables as in the three models used in the multivariable analysis of liver fibrosis in section 3.3.

As shown in Table 3 based on FIB-4 stratification, a consistent negative association was found between miR-15a/16 and the prevalence of severe liver fibrosis across all models. For miR-15a, the odds ratios (ORs) were as follows: model 1, 0.214 (95 % CI: 0.140, 0.328); model 2, 0.213 (95 % CI: 0.139, 0.327); model 3, 0.243 (95 % CI: 0.138, 0.427). Similarly, for miR-16, the ORs were: model 1, 0.108 (95 % CI: 0.061, 0.193); model 2, 0.110 (95 % CI: 0.062, 0.196); model 3, 0.201 (95 % CI: 0.097, 0.417). Remarkably, upon comprehensive adjustment for potential confounders in model 3, each unit decrease in miR-15a or miR-16 was associated with a 75.7 % or 79.9 % increased risk of severe liver fibrosis, respectively. Furthermore, a notable decreasing trend in the risk of severe liver fibrosis was observed across the increasing quartile groups (Q1-Q4) of miR-15a/16 levels (P for trend <0.01). Significant negative associations were also observed in both males and females in all models. According to APRI stratification, similar results were obtained in the three models of miR-15a and in models 1 and 2 of miR-16 and their corresponding male subgroups (Supplementary Table 3). However, in females, a significant negative correlation was found only in model 1 for miR-15a (Supplementary Table 3).Table 3 Analysis of the correlation between plasma miR-15a/16 and severe liver fibrosis (FIB-4 ≥ 3.25) in CHB patients by multivariate logistic regression.

Table 3Variables	Model 1 OR (95 % CI) P value
(n = 435)	Model 2 OR (95 % CI) P value
(n = 435)	Model 3 OR (95 % CI) P value
(n = 435)	
miR-15a	0.214 (0.140, 0.328)
1.4E-12	0.213 (0.139, 0.327)
1.4E-12	0.243 (0.138, 0.427)
8.7E-07	
Quartile groups of miR-15a	
 Q1	Reference	Reference	Reference	
 Q2	14.897 (6.611, 33.572)
7.2E-11	15.288 (6.761, 34.572)
5.7E-11	11.589 (3.997, 33.601)
6.5E-06	
 Q3	5.555 (2.427, 12.72)
4.9E-05	5.721 (2.491, 13.14)
3.9E-05	3.407 (1.164, 9.971)
0.0253	
 Q4	4.157 (1.793, 9.639)
0.0009	4.313 (1.845, 10.036)
0.0007	3.411 (1.135, 10.248)
0.0288	
 P for trend	1.2E-12	1.4E-07	2.8E-06	
miR-16	0.108 (0.061, 0.193)
4.7E-14	0.110 (0.062, 0.196)
7.5E-14	0.201 (0.097, 0.417)
1.6E-05	
Quartile groups of miR-16	
 Q1	Reference	Reference	Reference	
 Q2	9.766 (4.717, 20.220)
8.4E-10	9.551 (4.606, 19.803)
1.3E-09	4.218 (1.693, 10.507)
0.0020	
 Q3	4.643 (2.217, 9.723)
4.7E-05	4.690 (2.237, 9.831)
4.3E-05	2.127 (0.823, 5.459)
0.1166	
 Q4	2.253 (1.034, 4.911)
0.0411	2.221 (1.018, 4.846)
0.0450	1.828 (0.697, 4.794)
0.2199	
 P for trend	6.5E-12	9.5E-12	0.0015	
Stratified by gendera	
 miR-15a	
 Female	0.204 (0.089, 0.464)
0.0002	NAa	0.286 (0.090, 0.905)
0.0333	
 Male	0.217 (0.131, 0.358)
2.2E-09	NAa	0.206 (0.103, 0.411)
7.7E-06	
 miR-16	
 Female	0.076 (0.022, 0.259)
3.9E-05	NAa	0.100 (0.014, 0.710)
0.0213	
 Male	0.124 (0.064, 0.238)
3.9E-10	NAa	0.200 (0.088, 0.457)
0.0001	
Model 1: without any covariate adjustments. Model 2: adjusted for gender. Model 3: gender, albumin, albumin to globulin ratio, total bilirubin, white blood cells, and hepatitis B virus DNA were adjusted.

FIB-4, fibrosis index based on four factors (incorporating age, aspartate aminotransferase, alanine aminotransferase, and platelet); CHB, chronic hepatitis B; OR, odds ratio; CI, confidence intervals; NA, not available.

a The models of gender stratification excluded gender as an adjustment variable.

3.5 Plasma levels of miR-15a/16 was correlated with the risk of cirrhosis

We observed a significant reduction in plasma miR-15a/16 levels among cirrhosis patients (Supplementary Figs. 2C and D). Therefore, we performed similar multivariate logistic regression analyses for miR-15a/16 in relation to cirrhosis. As shown in Table 4, miR-15a/16 exhibited a negative correlation with cirrhosis risk across all three models (miR-15a: model 1, OR = 0.078; 95 % CI: 0.047, 0.131; model 2: OR = 0.098; 95 % CI: 0.057, 0.169; model 3: OR = 0.153; 95 % CI: 0.079, 0.298. miR-16: model 1, OR = 0.035; 95 % CI: 0.018, 0.069; model 2: OR = 0.038; 95 % CI: 0.018, 0.080; model 3: OR = 0.064; 95 % CI: 0.025, 0.162). Notably, in model 3, following comprehensive adjustment for potential confounders, each unit decrease in miR-15a or miR-16 was associated with an increased risk of cirrhosis by 84.7 % or 93.6 %, respectively. Furthermore, the risk of cirrhosis gradually decreased with the elevation of miR-15a/16 quartile groups from Q1 to Q4 (P for trend <0.0001). Upon stratification by gender, the negative associations between miR-15a/16 and cirrhosis prevalence persisted in both males and females in models 1 and 2. However, in model 3, the negative associations were more significant in males.Table 4 Analysis of the correlation between plasma miR-15a/16 and cirrhosis in CHB patients by multivariate logistic regression.

Table 4Variables	Model 1 OR (95 % CI) P value
(n = 435)	Model 2 OR (95 % CI) P value
(n = 435)	Model 3 OR (95 % CI) P value
(n = 435)	
miR-15a	0.078 (0.047, 0.131)
6.7E-22	0.098 (0.057, 0.169)
6.2E-17	0.153 (0.079, 0.298)
3.3E-08	
Quartile groups of miR-15a	
 Q1	Reference	Reference	Reference	
 Q2	37.333 (16.521, 84.364)
3.2E-18	23.019 (9.884, 53.611)
3.6E-13	9.117 (3.284, 25.313)
2.0E-05	
 Q3	14.421 (6.604, 31.490)
2.1E-11	9.255 (4.093, 20.929)
9.1E-08	4.869 (1.856, 12.773)
0.0013	
 Q4	3.307 (1.463, 7.473)
0.0040	2.294 (0.973, 5.406)
0.0577	1.045 (0.356, 3.064)
0.9360	
 P for trend	4.6E-24	1.6E-17	1.0E-07	
miR-16	0.035 (0.018, 0.069)
2.9E-22	0.038 (0.018, 0.080)
6.3E-18	0.064 (0.025, 0.162)
6.2E-09	
Quartile groups of miR-16	
 Q1	Reference	Reference	Reference	
 Q2	31.553 (14.602, 68.182)
1.6E-18	27.119 (11.774, 62.459)
9.0E-15	13.989 (4.900, 39.938)
8.3E-07	
 Q3	7.971 (3.846, 16.521)
2.7E-08	6.226 (2.846, 13.619)
4.7E-06	5.034 (1.853, 13.675)
0.0015	
 Q4	3.702 (1.754, 7.816)
0.0006	3.482 (1.560, 7.770)
0.0023	3.471 (1.277, 9.435)
0.0147	
 P for trend	1.2E-21	1.3E-16	3.4E-07	
Stratified by gendera	
 miR-15a	
 Female	0.083 (0.028, 0.243)
5.7E-06	0.081 (0.023, 0.280)
7.3E-05	0.240 (0.043, 1.325)
0.1016	
 Male	0.076 (0.042, 0.138)
1.9E-17	0.102 (0.055, 0.188)
2.4E-13	0.145 (0.069, 0.307)
4.1E-07	
 miR-16	
 Female	0.047 (0.013, 0.178)
6.3E-06	0.041 (0.009, 0.196)
6.0E-05	0.060 (0.004, 1.007)
0.0505	
 Male	0.028 (0.013, 0.063)
4.3E-18	0.036 (0.016, 0.085)
2.4E-14	0.061 (0.022, 0.170)
8.9E-08	
Model 1: without any covariate adjustments. Model 2: adjusted for age and gender. Model 3: age, gender, aspartate aminotransferase, alanine aminotransferase, albumin, albumin to globulin ratio, total bilirubin, white blood cells, platelet count, and hepatitis B virus DNA were adjusted.

CHB, chronic hepatitis B; OR, odds ratio; CI, confidence intervals.

a The models of gender stratification excluded gender as an adjustment variable.

3.6 Plasma miR-15a/16 could be a diagnostic indicator for severe liver fibrosis and cirrhosis

We performed ROC cove analysis to assess the diagnostic efficacy of plasma miR-15a/16 for severe liver fibrosis and cirrhosis. The findings show that, in detecting moderate-to-severe fibrosis (S2-S4), miR-15a (area under the curve [AUC] 0.886, sensitivity 87.5 %, and specificity 82.4 %), miR-16 (AUC 0.884, sensitivity 93.8 %, and specificity 76.5 %), and the combined miR-15a and miR-16 (AUC 0.893, sensitivity 75.0 %, and specificity 88.2 %) demonstrated superior diagnostic power compared to FIB-4 (AUC 0.761, sensitivity 75.0 %, and specificity 76.5 %) and APRI (AUC 0.610, sensitivity 62.5 %, and specificity 76.5 %) (Fig. 3A, Table 5). When diagnosing severe liver fibrosis defined by FIB-4, miR-15a had an AUC of 0.744, with a sensitivity of 86.6 % and a specificity of 53.3 %; miRNA-16 yielded an AUC of 0.747, with a sensitivity of 63.0 % and a specificity of 77.6 %; and the combined miR-15a and miR-16 had an AUC of 0.762, with a sensitivity of 79.5 % and a specificity of 62.0 % (Fig. 3C–Table 5). However, these biomarkers showed no diagnostic value for severe liver fibrosis when stratified by APRI (Fig. 3D–Table 5).Fig. 3 Performance of plasma miR-15a/16 in detecting moderate-to-severe fibrosis (S2-S4), severe liver fibrosis, and cirrhosis. ROC curves of miR15a, miR16, FIB-4, APRI, and biomarker combinations for diagnosing patients with moderate-to-severe liver fibrosis (S2-S4) (A), patients with cirrhosis (B). The ROC curves of miR15a, miR-16, or their combination in detecting severe liver fibrosis patients under FIB-4 (C) or APRI (D) stratification. FIB-4, fibrosis index based on four factors (incorporating age, aspartate aminotransferase, alanine aminotransferase, and platelet), APRI, aspartate aminotransferase to platelet ratio index. AUC, area under the receiver operating characteristic curve. ROC, receiver operating characteristic.

Fig. 3

Table 5 Diagnostic power of the indicated variables in detecting moderate-to-severe fibrosis (S2-S4), severe liver fibrosis, and cirrhosis.

Table 5Variables	AUC (95 % CI)	Sensitivity (%)	Specificity (%)	P valuea	
histological fibrosis stage S0-S1 vs. S2-S4	
miR-15a	0.886 (0.770, 1.000)	87.5	82.4	0.0002	
miR-16	0.884 (0.772, 0.996)	93.8	76.5	0.0002	
FIB-4	0.761 (0.594, 0.928)	75.0	76.5	0.0105	
APRI	0.610 (0.402, 0.819)	62.5	76.5	0.2798	
miR-15a + miR-16	0.893 (0.789, 0.998)	75.0	88.2	0.0001	
cirrhosis vs. non-cirrhosis	
miR-15a	0.832 (0.794, 0.870)	76.5	79.7	<0.0001	
miR-16	0.815 (0.776, 0.855)	87.2	59.8	<0.0001	
FIB-4	0.857 (0.822, 0.891)	81.0	76.6	<0.0001	
APRI	0.768 (0.724, 0.812)	73.7	71.5	<0.0001	
miR-15a + miR-16	0.847 (0.811, 0.883)	88.8	68.8	<0.0001	
miR-15a + miR-16 + FIB-4	0.885 (0.855, 0.916)	88.8	72.7	<0.0001	
severe vs. non-severe (liver fibrosis was stratified by FIB-4)	
miR-15a	0.744 (0.695, 0.793)	86.6	53.3	<0.0001	
miR-16	0.747 (0.696, 0.797)	63.0	77.6	<0.0001	
miR-15a + miR-16	0.762 (0.714, 0.809)	79.5	62.0	<0.0001	
severe vs. non-severe (liver fibrosis was stratified by APRI)	
miR-15a	0.609 (0.550, 0.669)	75.7	46.7	0.0008	
miR-16	0.615 (0.555, 0.676)	47.6	73.2	0.0004	
miR-15a + miR-16	0.621 (0.562, 0.680)	74.8	47.3	0.0002	
AUC, area under the receiver operating characteristic curve; CI, confidence intervals; FIB-4, fibrosis index based on four factors (incorporating age, aspartate aminotransferase, alanine aminotransferase, and platelet); APRI, aspartate aminotransferase to platelet ratio index.

a Null hypothesis: true area = 0.5.

In ROC coves plotted between cirrhosis and non-cirrhosis, the diagnostic performance of miR-15a (AUC = 0.832, sensitivity = 76.5 %, specificity = 79.7 %), miR-16 (AUC = 0.815, sensitivity = 87.2 %, specificity = 59.8 %), and the combined miR-15a and miR-16 (AUC = 0.847, sensitivity = 88.8 %, specificity = 68.8 %) were comparable to that of FIB-4 (AUC = 0.857, sensitivity = 81.0 %, specificity = 76.6 %) (Fig. 3B–Table 5). On the other hand, miR-15a, the combination of miR-15a and miR-16, and FIB-4 were outperformed APRI (AUC = 0.768, sensitivity = 73.7 %, specificity = 71.5 %) (Fig. 3B–Table 5). Furthermore, the combination of miR-15a/16 with FIB-4 exhibited the highest AUC of 0.885, with a sensitivity of 88.8 % and a specificity of 72.7 % (Fig. 3B–Table 5). Including healthy controls in the ROC curve plots enhanced the diagnostic parameters of miR-15a, miR-16, and their combination (Supplementary Fig. 5, Supplementary Table 4).

4 Discussion

The disease burden caused by progressive liver fibrosis and the invasive nature of liver biopsy emphasized the urgent need to develop non-invasive diagnostic tests for evaluating liver fibrosis [1,4]. The current investigation explores the correlation between plasma miR-15a/16 and liver fibrosis, severe liver fibrosis, and cirrhosis in CHB patients. Our findings reveal a negative association between the levels of plasma miR-15a/16 and the severity of liver fibrosis, evidenced by lower plasma miR-15a/16 levels in individuals with severe fibrosis compared to those without severe fibrosis, and lower levels in the cirrhosis group compared to the non-cirrhosis group. Moreover, plasma miR-15a/16 levels exhibited a gradual decline with the advancement of the histological fibrosis stage from S0 to S4. These trends were consistently observed in Pearson's correlation analysis and multivariable linear regressions. Furthermore, multivariable logistic regressions demonstrated that reduced plasma miR-15a/16 levels are linked to an increased prevalence of severe liver fibrosis and cirrhosis, especially within the male subgroup. These findings suggest that measuring plasma miR-15a/16 levels could be a simple and convenient method for assessing liver fibrosis and cirrhosis.

The involvement of miRNAs in liver metabolism, injury, fibrosis, and HCC development emphasizes their significance in liver diseases [24,25]. Hepatic stellate cells (HSCs) activation and phenotypic transformation are significant factors in liver fibrosis development [26]. Microarray analysis has shown a notable decrease in miR-15b/16 levels in activated HSCs [27]. Molecularly, miR-16 modulated the TGF-β and Wnt pathways by targeting genes such as smad2/3, Wnt3a, and lysine oxidase-like 1(LOXL1), which inhibits hepatic stellate cell activation, eliminates the myofibroblastic phenotype, reducing collagen production, and improves liver fibrosis repair [28,29]. Additionally, the miR-15/16 family targets Bcl-2, a key regulator of the mitochondrial apoptosis pathway, thereby inhibiting apoptotic resistance in HSCs through downstream caspases-3/8/9 [27]. Downregulation of miR-16 in HSCs leads to the overexpression of Guanine nucleotide-binding α-subunit 12, which promotes autophagy by coupling with the autophagy-related gene ATG12-5, thereby contributing to liver fibrosis through activating HSCs [15]. The involvement of miR-16 in liver fibrosis may also be related to its participation in the trans-differentiation of HSCs to myofibroblasts [28]. MiR-15a knockdown promoted HSCs activation and proliferation [16]. In summary, miR-15a/16 can regulate the activation of HSCs by integrating signaling pathways such as TGF-β, mitochondrial apoptosis, and cell autophagy, thereby affecting the progression of liver fibrosis. On the other hand, previous studies have reported that HBV components [13,14,30], non-coding RNAs [31,32], and chromosome deletion [33,34] were partly account for the decrease of miR-15a/16. These factors may also contribute to the gradual downregulation of plasma miR-15a and miR-16 observed in the progression of liver fibrosis. Besides, studies have indicated that miR-15a/16 can attenuate fibrosis in peritoneal mesothelial cells and skin fibrosis [35,36]. And miR-15a has been linked to fibroblast activation and lung fibrosis [37]. Collectively, these investigations highlight a close relationship between miR-15a/16 and various forms of organ or tissue fibrosis, particularly liver fibrosis, which supports the results of our study.

Cellular miRNAs can be released into circulation through passive release, lipoprotein complexes, or nanoparticles termed extracellular vesicles, particularly exosomes [18]. Therefore, circulating miRNAs may provide valuable information about the disease and serve as promising diagnostic markers for early-stage, pre-symptomatic patients. Our study found that decreased plasma levels of miR-15a and miR-16 were correlated with an elevated prevalence of severe liver fibrosis and cirrhosis, indicating their diagnostic potential for these conditions. When detecting moderate-to-severe fibrosis (S2-S4), miR-15a, miR-16, and their combination exhibited superior diagnostic performance compared to FIB-4 and APRI. Notably, the combination of miR-15a with miR-16 yielded an AUC of 0.893, demonstrating higher accuracy than the reported liver fibrosis-associated miRNA-122 (AUC 0.61) and a miRNA-122-based model (AUC 0.85) [38,39]. Furthermore, miR-15a/16 demonstrated diagnostic efficacy for severe liver fibrosis as defined by FIB-4. In distinguishing cirrhosis, the ROC analysis showed that miR-15a/16 was as valuable as FIB-4 and significantly superior to APRI. Noteworthy, the combination of miR-15a/16 with FIB-4 exhibited significantly superior diagnostic performance compared to other markers in this study, achieving an AUC of 0.885. This performance surpasses the accuracy of the reported ‘Complex’ biomarker blood tests such as Fibrotest/Fibrosure (comprising age, sex, bilirubin, γ-glutamyltransferase, α2-macroglobulin, haptoglobin, and apolipoprotein-A1) and FibroMeterv2G (comprising age, sex, platelets, ALT, AST, γ-glutamyltransferase, prothrombin index, urea, and α2-macroglobulin), which have AUCs ranging from 0.80 to 0.84 [40]. Given the routine application of plasma miRNA detection techniques in clinical settings, measuring plasma levels of miR-15a/16 for monitoring liver fibrosis holds promise for clinical implementation.

It should be noted that the number of patients who underwent liver biopsy in the present study was relatively small. Furthermore, the involvement of plasma miR-15a/16 in liver fibrosis may extend beyond HBV as the primary etiology. Thus, we look forward to evaluating plasma miR-15a/16 as an indicator for liver fibrosis assessment across different primary etiologies. Ideally, a larger longitudinal cohort encompassing a variety of patient types would be preferable to further validate the results of our study.

5 Conclusion

Our study demonstrated that plasma miR-15a/16 is independently correlated with liver fibrosis in CHB patients and may serve as a new indicator for assessing liver fibrosis and predicting severe fibrosis and cirrhosis. Future studies will be needed to monitor plasma miR-15a/16 levels in patients during liver disease development and progression using multicenter, prospective, and longitudinal samples.

Funding

This study was supported by 10.13039/501100003453 Natural Science Foundation of Guangdong Province (grant number: 2024A1515013208 ), 10.13039/501100021171 Guangdong Basic and Applied Basic Research Foundation (grant number: 2023A1515220089 ), and General Planned Project of Guangzhou Science and Technology (grant numbers: 202201010950 and 202201020422 ).

Data availability statement

The data of this study can be obtained by contacting the corresponding author. Privacy or ethical restrictions prevent the public availability of the data.

CRediT authorship contribution statement

Huan Wei: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Data curation. Yanhua Bi: Writing – original draft, Validation, Investigation, Funding acquisition, Data curation. Chunhong Liao: Investigation, Data curation. Yuehua Huang: Writing – review & editing, Supervision, Resources, Project administration, Conceptualization. Yifan Lian: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that none of the authors had a conflict of interest.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.ncrna.2024.08.004.
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