Diagnostic accuracy of circulating microRNA in hepatitis B virus-related hepatocellular carcinoma: a meta-analysis based on Asian data

1130-0108/2022/114/5/280-288 • REVISTA ESPAÑOLA DE ENFERMEDADES DIGESTIVAS © Copyright 2022. SEPD y © ARÁN EDICIONES, S.L. REV ESP ENFERM DIG 2022:114(5):280-288 DOI: 10.17235/reed.2021.8139/2021 Diagnostic accuracy of circulating microRNA in hepatitis B virus-related hepatocellular carcinoma: a meta-analysis based on Asian data Wen-Ting Zhang1,3, Antonio Gil-Gómez1,2, Chang-Hai Liu4, Shuai-Shuai Gao3, and Manuel Romero-Gómez1,2 1Unit of Digestive Diseases and Ciberehd. Hospital Universitario Virgen del Rocío. Sevilla, Spain. 2Instituto de Biomedicina de Sevilla. Sevilla, Spain. 3International Doctoral School. Universidad de Sevilla. Sevilla, Spain. 4Center of Infectious Diseases. West China Hospital of Sichuan University. Chengdu, People’s Republic of China Received: 10/06/2021 · Accepted: 26/07/2021 Correspondence: Manuel Romero-Gómez. Unit for the Clinical Management of Digestive Diseases. Hospital Universitario Virgen del Rocío. Av. Manuel Siurot, s/n. 41013 Sevilla, Spain. e-mail: [email protected] ABSTRACT Background and aim: hepatitis B virus (HBV) is the main risk factor for hepatocellular carcinoma (HCC). We performed a meta-analysis based on Asian data to evaluate the diagnostic accuracy of circulating microRNA as a non-invasive biomarker in the diagnosis of HBV-related HCC. Methods: a comprehensive literature search (updated to May 12, 2021) in PubMed, Embase, Web of Science, Wanfang Database, and China National Knowledge Infrastructure (CNKI) was performed to identify eligible studies. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) for diagnosing HBV-related HCC were pooled in this meta-analysis. A subgroup analysis was performed to explore heterogeneity, and Deeks’ funnel plot was used to assess publication bias. Results: a total of 19 articles including 32 studies were included in the current meta-analysis. The overall sensitivity, specificity, PLR, NLR, DOR and AUC were 0.83 (95 % CI: 0.79 to 0.87), 0.78 (95 % CI: 0.73 to 0.83), 3.9 (95 % CI: 3.0 to 4.9), 0.21 (95 % CI: 0.16 to 0.27), 18 (95 % CI: 12 to 27) and 0.88 (95 % CI: 0.85 to 0.91), respectively. Subgroup analysis shows that miRNA clusters with a large sample size showed better diagnostic accuracy. Although there is no publication bias, the research still has some limitations. REVIEW Zhang W-T, Gil-Gómez A, Liu C-H, Gao S-S, Romero-Gómez M. Diagnostic accuracy of circulating microRNA in hepatitis B virus-related hepatocellular carcinoma: a meta-analysis based on Asian data. Rev Esp Enferm Dig 2022;114(5):280-288 DOI: 10.17235/reed.2021.8139/2021 Declaration of conflicting interests: the authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Ethics statement: our study did not require an ethical board approval because it was a meta-analysis and did not contain human or animal trials. Funding: the authors received no financial support for the research, authorship, and/ or publication of this article. Conclusions: circulating miRNAs could serve as a potential non-invasive biomarker in diagnosing HBV-related HCC in Asian populations. Keywords: Hepatocellular carcinoma. Hepatitis B virus. MicroRNA. Biomarkers. Meta-analysis. INTRODUCTION Hepatocellular carcinoma (HCC) is the fifth most common cancer and the third leading cause of deaths globally (1). Its main risk factor is hepatitis B virus (HBV) infection, which accounts for approximately 50 % of HCC cases and almost all liver cancers in childhood (2). The majority of HBV-relat- ed HCC cases worldwide occur in the Asia-Pacific region. Unfortunately, early HCC is generally asymptomatic and the tumor progresses rapidly. The tumor is often found after metastasis have occurred, a time when it cannot be locally ablated or surgically removed, causing a high mortality rate. The diagnosis of HCC usually relies on radiological imaging techniques (such as ultrasound, CT, and MRI) in combination with AFP dosing and histological analysis of liver biopsies. Although the imaging method is non-inva- sive, it cannot detect small lesions due to its low sensitivity (3). Alpha-fetoprotein (AFP) is a standard serum biomarker for the diagnosis of HCC, and its higher level of expression is closely related to the clinicopathological characteristics Diagnostic accuracy of circulating microRNA in hepatitis B virus-related hepatocellular carcinoma: a meta-analysis based on Asian data REV ESP ENFERM DIG 2022:114(5):280-288 DOI: 10.17235/reed.2021.8139/2021 281 of patients with HCC (4). Although AFP has been widely used in the clinical diagnosis and prognostic monitoring of HCC, it has limited sensitivity and specificity. Some liver nodules may not release AFP, and patients with active chronic hepatitis or liver cirrhosis may have high levels of AFP (5). Liver biopsy, the gold standard for the diagnosis and stage assessment of HCC, depends on tumor size and cirrhosis status. Due to its invasive nature, various complications, insufficient sampling, and interpretation variability the application of liver biopsy to the management of HCC is limited. More powerful biomarkers are needed to compensate for the shortcomings that existing diagnostic tools have for the detection of HCC. Circulating microRNA (miRNA) has been widely used in recent years in different cancer clinical settings, including early detection, prediction, monitoring of disease progression, and response to treatment (6). A recent meta-anal- ysis showed that circulating miR-122 can be used as a diagnostic marker for HCC (7). In addition, miRNA plays a crucial role in HBV-associated hepato-carcinogenesis. They can act as tumor suppressors by negatively regulating the expression of oncogenes or as tumor promoters by limiting the expression of onco-suppressor proteins, depending on the cellular function of the target genes (6). Several studies reported on the possibility of using miR- NA as a non-invasive biomarker for HBV-related HCC, and their results were satisfactory but inconsistent. Therefore, this meta-analysis aims to evaluate the diagnostic value of circulating miRNA as a non-invasive biomarker for the diagnosis of HBV-related HCC. MATERIALS AND METHODS Search strategy and literature selection This meta-analysis was performed according to the PRISMA statement (8). Two investigators independently conducted a comprehensive search of PubMed, Embase, Web of Science, Wanfang Database, and China National Knowledge Infrastructure (CNKI) up to May 12, 2021. MeSH keywords and terms were used, and search terms were as follows: “hepatocellular carcinoma” or “liver cancer” or “HCC” and “microRNA” or “miRNA” or “miR” and “hepatitis b virus” or “HBV” or “chronic hepatitis”. The searches were limited to publications with human subjects, without language restrictions. A manual search was performed using the references listed in the original articles and review articles retrieved. Inclusion and exclusion criteria We screened the literature according to the inclusion criteria: 1) clinical studies aimed to assess the diagnostic accuracy of circulating miRNAs for the diagnosis of HBV-related HCC; all patients were diagnosed by means of histopathology examinations; 2) the control group was composed of HBV-infected individuals without HCC, including chronic hepatitis B (CHB) and HBV-related liver cirrhosis (LC); 3) all HBV-related HCC cases had received no treatment (surgery, radiotherapy, or chemotherapy) before being collected; 4) the miRNAs obtained were measured in serum or plasma samples; 5) the studies contained sufficient data on sensitivity, specificity, and sample size to construct a two-by-two diagnostic table. In contrast, the exclusion criteria were: 1) duplicate reports or publications with incomplete information; 2) miRNAs obtained from cell lines, animals, liver tissue, or urine; 3) comments, reviews, case reports, letters to the editors, and 4) systematic reviews or meta-analyses. Data extraction and quality assessment Relevant studies were selected based on title, abstract, and full text, which were reviewed for further assessment if the study was collected by one of the two investigators. We extracted the following data from each eligible study: first author’s name, publication year, miRNA profile, regulation mode, sample size, specimen source, relevant statistical data required, and methodological quality information. In addition, we assessed the quality of the included studies using the Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool (9). A third investigator resolved any disagreements. Statistical analysis The number of true positives, false positives, false negatives, and true negatives among patients in each study was extracted. Heterogeneity was assessed using I2 statistics. Significant heterogeneity was indicated by an I2 value greater than 50 %, and then a random-effects model was performed. The potential sources of heterogeneity were explored by subgroup analysis. We calculated sensitivity (SEN), specificity (SPE), the positive likelihood ratio (PLR), the negative likelihood ratio (NLR), and the diagnostic odds ratio (DOR). Besides, we generated the summary receiver operating characteristics (SROC) curve and calculated the area under the SROC curve (AUC) for both the overall and subgroup analyses. Assessment criteria for diagnostic efficacy included: AUC = 1.00 is perfect, AUC > 0.90 is excellent, AUC > 0.80 is good, AUC < 0.80 is medium (10). Finally, we assessed the potential publication bias by using Deeks’ funnel plot asymmetry test, in which p < 0.05 indicated statistical significance. All statistical analyses were performed using Review Manager 5.2 and STATA version 13.0. RESULTS Study selection and literature characteristics As shown in figure 1, the flow chart of the article selection process, a total of 3764 articles were initially identified from the primary literature search strategy, of which 818 were from PubMed, 1792 were from Embase, 887 were from Web of Science, 120 were from the Wan-fang Databases, and 147 were from Chinese National Knowledge Infrastructure (CKNI). A total of 3117 articles were left for screening after 647 duplicates were removed. After reviewing titles and abstracts manually, 3055 articles were excluded because they reported irrelevant studies, animal experiments or cell line studies, or were review articles or letters. Subsequently, the full texts of the remaining 62 articles were read to assess eligibility, and 43 articles were excluded. Finally, 32 studies from 19 articles (11-29) were included in the current meta-analysis. The main characteristics of the 19 articles included are summarized in table 1, in order by year of publication, W.-T. Zhang et al. REV ESP ENFERM DIG 2022:114(5):280-288 DOI: 10.17235/reed.2021.8139/2021 282 ranging from 2010 to 2021. In total, 2590 HBV-related HCC patients and 1963 controls were included. In total also, 15 articles referred to a single miRNAs, and four articles focused on miRNA clusters. In addition, real-time quantitative reverse transcription PCR (qRT-PCR) was used to detect miRNA expression levels in 15 serum and four plasma specimens. Most of the studies came from China, and the dominant ethnicity of the study subjects was Asian. The methodological quality assessment graph is shown in figure 2. Diagnostic accuracy of circulating miRNAs in HBV-related HCC The sensitivities and specificities of miRNAs in 32 studies that included 2590 patients with HBV-related HCC and 1963 controls (CHB and HBV-related LC) were analyzed using a forest plot. There was significant heterogeneity among studies overall (I2 = 83.25 % for sensitivity and I2 = 82.34 % for specificity), and therefore a random-effects model was selected in our meta-analysis. Pooled results were as follows: sensitivity, 0.83 (95 % CI: 0.79 to 0.87); specificity, 0.78 (95 % CI: 0.73 to 0.83); PLR, 3.9 (95 % CI: 3.0 to 4.9); NLR, 0.21 (95 % CI: 0.16 to 0.27); DOR, 18 (95 % CI: 12 to 27), and AUC, 0.88 (95 % CI: 0.85 to 0.91) (Fig. 3). The results showed that circulating miRNAs had excellent diagnostic accuracy for HBV-related HCC. Diagnostic value of miR-125b for HBV-related HCC There were 6 studies accessing miR-125b as a diagnostic biomarker for HBV-related HCC, including 391 HBV-related HCC patients and 454 controls. The combined sensitivity was 0.89 (95 % CI: 0.82 to 0.94), specificity was 0.85 (95 % CI: 0.74 to 0.92), PLR was 5.5 (95 % CI: 3.9 to 9.9), NLR was 0.13 (95 % CI: 0.08 to 0.21), DOR was 47 (95 % CI: 26 to 83), and the AUC was 0.94 (95 % CI: 0.91 to 0.96) (Fig. 4). Subgroup analysis We conducted a subgroup analysis to find probable sources of heterogeneity, which was performed according to country, miRNA profiling, regulation mode, type of control, sample size, and specimen types. The detailed results of all subgroup analyses are summarized in table 2. We found that studies in a Chinese population showed a better diagnostic value when compared to those in other populations: sensitivity (0.85 vs. 0.64), specificity (0.80 vs. 0.71), PLR (4.2 vs. 2.2), NLR (0.19 vs. 0.51), DOR (22 vs. 4), and AUC (0.90 vs. 0.72). In all, miRNA clusters exhibited a better diagnostic value than single miRNAs: sensitivity (0.83 vs. 0.84), specificity (0.89 vs. 0.76), PLR (7.3 vs. 3.5), NLR (0.20 vs. 0.21), DOR (37 vs. 16), and AUC (0.91 vs. 0.87). Furthermore, the ability of miRNA to distinguish HBV-re- lated HCC from HBV-related LC is better than for CHB: Records identified through database searching (n = 3764) • PubMed (n = 818) • Embase (n = 1792) • Web of Science (n = 887) • Wan-fang Databases (n = 120) • Chinese National Knowledge Infrastructure (n = 147) Records after duplicates removed (n = 647) Literature search: examination of abstracts Full-text articles assessed for eligibility (n = 62) Final articles included in qualitative synthesis (n = 19) Records excluded (n = 3055) • Irrelevant studies (n = 2197) • Not human studies (n = 543) • Reviews, letters, case reports (n = 315) Full-text articles excluded (n = 43) • Not a case-control study (n = 17) • Without sufficient data (n = 26) Studies included in meta-analysis (n = 32) Fig. 1. The flow chart of this meta-analysis to identify inclusion studies. Diagnostic accuracy of circulating microRNA in hepatitis B virus-related hepatocellular carcinoma: a meta-analysis based on Asian data REV ESP ENFERM DIG 2022:114(5):280-288 DOI: 10.17235/reed.2021.8139/2021 283 sensitivity (0.85 vs. 0.83), specificity (0.85 vs. 0.76), PLR (5.6 vs. 3.5), NLR (0.17 vs. 0.22), DOR (31 vs. 16), and AUC (0.91 vs. 0.87). On the other hand, studies with a sample size greater than 100 performed better: sensitivity (0.85 vs. 0.77), specificity (0.82 vs. 0.66), PLR (4.6 vs. 2.2), NLR (0.19 vs. 0.35), DOR (24 vs. 6), and AUC (0.90 vs. 0.77). Serum specimens had a slightly higher diagnostic value when compared to plasma: sensitivity (0.84 vs. 0.80), specificity (0.78 vs. 0.78), PLR (3.9 vs. 3.7), NLR (0.20 vs. 0.26), DOR (20 vs. 14), and AUC (0.89 vs. 0.86). Furthermore, down-regu- lated miRNAs have better diagnostic accuracy than up-reg- ulated miRNAs: sensitivity (0.88 vs. 0.79), specificity (0.78 vs. 0.79), PLR (4.0 vs. 3.8), NLR (0.15 vs. 0.26), DOR (26 vs. 14), and AUC (0.91 vs. 0.86). Table 1. Characteristics of the included studies Author Year Country microRNAs Regulation mode Sample size Specimen Diagnostic power Case No. Control No. Sen (%) Spe (%) AUC Single miRNA Qi, P. 2011 China miR-122 Up HCC 70 CHB 48 Serum 0.78 0.58 0.63 Li, L. 2012 China miR-18a Up HCC 101 CHB + LC 30 Serum 0.77 0.70 0.78 Xie, Y. 2014 China miR-101 Down HCC 67 LC 61 Serum 0.96 0.90 0.98 Yu, F. 2015 China miR-150 Down HCC 120 CHB 110 Serum 0.79 0.77 0.88 Chen, Y. 2015 China miR-96 Up HCC 104 CHB 100 Serum 0.78 0.75 0.80 Ghosh, A. 2016 Indian miR-126 Up HCC 49 CHB + LC 38 Plasma 0.63 0.58 0.67 Ghosh, A. 2016 Indian miR-142-3p Up HCC 49 CHB + LC 38 Plasma 0.32 0.91 0.55 Xiong, F. 2016 China miR-99a Down HCC 32 CHB 30 Serum 0.84 0.57 0.69 Xiong, F. 2016 China miR-125b Down HCC 32 CHB 30 Serum 0.91 0.57 0.70 Xiong, F. 2016 China miR-99a Up HCC 32 LC 30 Serum 0.97 0.56 0.70 Lin, L. 2016 China miR-224 Up HCC 122 CHB + LC 135 Serum 0.87 0.71 0.84 Lin, L. 2016 China miR-224 Up HCC 122 LC 61 Serum 0.87 0.67 0.83 Lin, L. 2016 China miR-224 Up HCC 122 CHB 74 Serum 0.87 0.75 0.85 Chen, S. 2017 China miR-125b Down HCC 64 CHB 63 Plasma 0.94 0.86 0.96 Chen, S. 2017 China miR-125b Down HCC 64 LC 59 Plasma 0.89 0.88 0.96 Chen, S. 2017 China miR-125b Down HCC 31 CHB + LC 102 Plasma 1.00 0.76 0.94 Zhao, Q. 2018 China miR-143 Down HCC 85 CHB 50 Serum 0.78 0.86 0.81 Zhao, Q. 2018 China miR-145 Down HCC 85 CHB 50 Serum 0.88 0.78 0.85 Xu, LJ. 2018 China miR-125b Up HCC 100 CHB 100 Serum 0.81 0.87 0.80 Xu, LJ. 2018 China miR-125b Up HCC 100 LC 100 Serum 0.78 0.96 0.91 Moradi, N. 2019 Iran miR-214 Down HCC 23 CHB 25 Plasma 0.85 0.43 0.52 Moradi, N. 2019 Iran miR-6510 Down HCC 23 CHB 25 Plasma 0.81 0.39 0.53 Moradi, N. 2019 Iran miR-5193 Down HCC 23 CHB 25 Plasma 0.80 0.82 0.82 Moradi, N. 2019 Iran miR-34a Down HCC 23 CHB 25 Plasma 0.40 0.87 0.62 Zhang, W. 2020 China miR-375 Down HCC 63 CHB 74 Serum 0.94 0.64 0.77 Cao, X. 2020 China miR-487b Up HCC 87 CHB 68 Serum 0.76 0.90 0.86 Li, X. 2021 China miR-487b Up HCC 116 CHB 66 Serum 0.84 0.67 0.82 miRNA clusters Li, LM. 2010 China miRNA clusters Down HCC 65 CHB 75 Serum 0.96 0.99 0.99 Zhou, J. 2011 China miRNA clusters Up HCC 196 CHB 72 Plasma 0.79 0.76 0.84 Zhou, J. 2011 China miRNA clusters Up HCC 196 LC 56 Plasma 0.75 0.91 0.88 Tan, Y. 2014 China miRNA clusters Up HCC 103 LC 78 Serum 0.82 0.85 0.89 Zhu, HT. 2017 China miRNA clusters Up HCC 121 LC 63 Serum 0.79 0.79 0.86 HCC: hepatitis B virus-related hepatocellular carcinoma; CHB: chronic hepatitis B; LC: hepatitis B virus-related liver cirrhosis; Up: upregulated; Down: downregulated; Sen: sensitivity; Spe: specificity; AUC: area under the curve. W.-T. Zhang et al. REV ESP ENFERM DIG 2022:114(5):280-288 DOI: 10.17235/reed.2021.8139/2021 284 Publication bias The publication bias of the included studies was verified using Deeks’ funnel plot test. The pooled Deeks’ test result for the overall study was P = 0.35 (Fig. 3D), and for miR- 125b was P = 0.88 (Fig. 4D), indicating no significant publication bias in this meta-analysis. DISCUSSION HCC is one of the most prevalent malignant tumors worldwide, and chronic HBV infection continues to be one of the main causes of its pathogenesis. Several studies have reported the possibility of using miRNAs as non-invasive biomarkers for the diagnosis of HCC-HBV, but the results are inconsistent. Therefore, we performed this meta-anal- ysis. Multiple databases were searched, and finally 32 studies from 19 articles with 4553 patients (2590 HBV-related HCC subjects and 1963 controls) on the diagnostic value of circulating miRNAs for HBV-related HCC were included. In this meta-analysis, the pooled sensitivity was 0.83 (95 % CI: 0.79 to 0.87), and pooled specificity was 0.78 (95 % CI: 0.73 to 0.83); we also plotted the ROC curve and calculated the corresponding AUC, which was 0.88 (95 % CI: 0.85 to 0.91), which means that circulating miRNA has an excellent diagnostic precision for HBV-related HCC. We also calculated PLR, NLR, and DOR to test miRNAs discrimination ability, providing more meaningful references for clinical use. The combined PLR, NLR and DOR were 3.9 (95 % CI: 3.0 to 4.9), 0.21 (95 % CI: 0.16 to 0.27), and 18 (95 % CI: 12 to 27), respectively. This finding shows that the probability of a correct diagnosis of patients with HBV-related HCC is 18 times higher than that of a false negative diagnosis of controls. However, PLR is less than ten and NLR is greater than 0.1, which does not meet the general award criteria or exclusion decision (30). In addition, a recent meta-analysis also confirmed that circulating miRNAs showed promising potential for the diagnosis of HBV-related HCC patients with low AFP levels (31). At the same time, we found that miR-125b had a higher diagnostic value for HBV-related HCC than other single miRNAs. It is reported that miR-125b plays an important role as an oncogene or tumor suppressor in HBV-related hepatocarcinogenesis and tumor progression (32). It can inhibit tumorigenesis by attacking Mcl-1 and IL-6R (33). Additionally, miR-125b suppresses hepatocyte migration and invasion by directly targeting the LIN28B oncogene and the transcriptional coactivator with the PDZ-binding motif (34). Moreover, miR-125b also inhibits cell proliferation, migration, and invasion in hepatocellular carcinoma by targeting thioredoxin reductase 1 (TXNRD1) (35). By monitoring the level of miR-125b in the blood, it may be possible to detect HBV infection with a higher risk of HCC in order to improve survival. Furthermore, Zheng and colleagues revealed that serum miR-125a-5p could be used as a non-invasive biomarker for monitoring liver disease progression (36). A subgroup analysis was performed according to country, miRNA profiling, regulation mode, type of control, sample size, and specimen type to explore possible sources of heterogeneity among studies. It suggested that miRNA clusters had better diagnostic value than single miRNAs. A single miRNA lacks specificity because it not only detects cancer but also other infectious diseases, nonspecific inflammations, and acute lesions. In other words, miRNA clusters have more complex molecular mechanisms, such as competing endogenous RNA (ceRNAs) networks that intersect during tumorigenesis (such as the appearance and development of severe tumors), an association that may be valuable for the detection of HCC. Furthermore, we found that the ability of miRNA to distinguish HBV-related HCC from HBV-related LC is better than using another control group, which still requires large-scale prospective studies to consol- Fig. 2. Quality evaluation according to the QUADAS-2 criteria. Diagnostic accuracy of circulating microRNA in hepatitis B virus-related hepatocellular carcinoma: a meta-analysis based on Asian data REV ESP ENFERM DIG 2022:114(5):280-288 DOI: 10.17235/reed.2021.8139/2021 285 idate the results. Other than that, the results with a sample size greater than 100 showed a better diagnostic accuracy when compared to studies with smaller ones, which supports the use of larger sample studies in the future. This meta-analysis has several advantages. Compared to a previous meta-analysis (37), we include the miRNAs detected in the latest studies, and exhaustively evaluate the diagnostic efficacy of circulating miRNAs for HBV-re- lated HCC. Furthermore, the included studies were selected based on strict inclusion and exclusion criteria. Although we have tried our best to avoid bias, there were still some limitations. First, several valuable studies may have been missed despite the comprehensive search strategy during Fig. 3. Forest plot of (A) sensitivity, (B) specificity, (C) area under the curve (AUC), and (D) Deeks’ funnel plot of circulating miRNAs for diagnosing HBV-related HCC.   Fig. 3. Forest plot of (A) sensitivity, (B) specificity, (C) area under the curve (AUC), and (D) Deeks’ funnel plot of circulating miRNAs for diagnosing HBV-related HCC. A B C D W.-T. Zhang et al. REV ESP ENFERM DIG 2022:114(5):280-288 DOI: 10.17235/reed.2021.8139/2021 286 our literature search. Second, due to limited research data and differences in criteria, we did not extract cut-off values. Different cut-off values may lead to inconsistent conclusions. Third, a relatively small number of patients were included in the individual studies, thus limiting the strength of the conclusions of our meta-analysis. Fourth, it is important to determine the diagnostic value of miRNA in HBV-related HCC based on the size and characteristics of the tumor. Finally, all of the included studies were from Asia and mainly from China. The applicability of circulating miRNAs in other countries and regions still remains unknown. Therefore, a series of large-scale, multicenter, and multi-country clinical studies is needed to provide high-quality evidence.   Fig. 4. Forest plot of (A) sensitivity, (B) specificity, (C) area under the curve (AUC), and (D) Deeks’ funnel plot of miR-125b for diagnosing HBV-related HCC. Fig. 4. Forest plot of (A) sensitivity, (B) specificity, (C) area under the curve (AUC), and (D) Deeks’ funnel plot of miR- 125b for diagnosing HBV-related HCC. A B   Fig. 4. Forest plot of (A) sensitivity, (B) specificity, (C) area under the curve (AUC), and (D) Deeks’ funnel plot of miR-125b for diagnosing HBV-related HCC. C   Fig. 4. Forest plot of (A) sensitivity, (B) specificity, (C) area under the curve (AUC), and (D) Deeks’ funnel plot of miR-125b for diagnosing HBV-related HCC. D Diagnostic accuracy of circulating microRNA in hepatitis B virus-related hepatocellular carcinoma: a meta-analysis based on Asian data REV ESP ENFERM DIG 2022:114(5):280-288 DOI: 10.17235/reed.2021.8139/2021 287 CONCLUSION In summary, circulating miRNAs could serve as a potential non-invasive biomarker for diagnosing HBV-related HCC in Asian populations. Additionally, using miRNA clusters and increasing sample size can improve diagnostic accuracy. In the future, large-scale multicenter clinical studies are still needed to verify our conclusions. REFERENCES 1. Zhang L, Fan Y, Wang X, et al. 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Summary estimates of diagnostic power and their 95 % confidence intervals Subgroup Sen (95 % CI) Spe (95 % CI) PLR (95 % CI) NLR (95 % CI) DOR (95 % CI) AUC (95 % CI) Country: China 0.85 (0.82-0.88) 0.80 (0.74-0.84) 4.2 (3.3-5.3) 0.19 (0.15-0.23) 22 (15-32) 0.90 (0.87-0.92) Non-China 0.64 (0.45-0.80) 0.71 (0.49-0.86) 2.2 (1.4-3.4) 0.51 (0.36-0.71) 4 (3-7) 0.72 (0.68-0.76) miRNA profiling: Single miRNA 0.84 (0.78-0.88) 0.76 (0.70-0.81) 3.5 (2.8-4.4) 0.21 (0.16-0.28) 16 (11-24) 0.87 (0.84-0.90) miRNA clusters 0.83 (0.74-0.89) 0.89 (0.75-0.95) 7.3 (3.0-17.7) 0.20 (0.12-0.33) 37 (10-144) 0.91 (0.88-0.93) Regulation mode: Up-regulated 0.79 (0.74-0.84) 0.79 (0.72-0.84) 3.8 (2.9-4.9) 0.26 (0.21-0.33) 14 (10-21) 0.86 (0.83-0.89) Down-regulated 0.88 (0.82-0.93) 0.78 (0.68-0.86) 4.0 (2.6-6.1) 0.15 (0.09-0.24) 26 (12-57) 0.91 (0.88-0.93) Control type: CHB 0.83 (0.79-0.87) 0.76 (0.68-0.83) 3.5 (2.6-4.8) 0.22 (0.17-0.28) 16 (10-26) 0.87 (0.84-0.90) LC 0.85 (0.79-0.90) 0.85 (0.75-0.91) 5.6 (3.4-9.1) 0.17 (0.12-0.24) 31 (18-58) 0.91 (0.89-0.94) CHB + LC 0.79 (0.49-0.94) 0.75 (0.64-0.83) 3.1 (2.1-4.7) 0.28 (0.10-0.81) 11 (3-42) 0.80 (0.77-0.84) Sample size: < 100 0.77 (0.60-0.88) 0.66 (0.52-0.78) 2.2 (1.7-3.0) 0.35 (0.22-0.57) 6 (4-11) 0.77 (0.73-0.80) ≥ 100 0.85 (0.81-0.87) 0.82 (0.77-0.86) 4.6 (3.6-6.0) 0.19 (0.16-0.23) 24 (16-36) 0.90 (0.87-0.92) Specimen type: Serum 0.84 (0.81-0.87) 0.78 (0.72-0.84) 3.9 (2.9-5.2) 0.20 (0.16-0.24) 20 (13-30) 0.89 (0.85-0.91) Plasma 0.80 (0.65-0.89) 0.78 (0.67-0.87) 3.7 (2.4-5.7) 0.26 (0.15-0.46) 14 (6-32) 0.86 (0.82-0.88) CHB: chronic hepatitis B; LC: hepatitis B virus-related liver cirrhosis; Sen: sensitivity; Spe: specificity; PLR: positive likelihood ratios; NLR: negative likelihood ratios; DOR: diagnostic odds ratio; AUC: area under the curve; CI: confidence interval. 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