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循环微小RNA作为肝细胞癌有前景的诊断生物标志物:一项系统评价和荟萃分析

Circulating microRNAs as promising diagnostic biomarkers for hepatocellular carcinoma: a systematic review and meta-analysis.

作者信息

Alemayehu Ermiyas, Fasil Alebachew, Ebrahim Hussen, Mulatie Zewudu, Bambo Getachew Mesfin, Gedefie Alemu, Teshome Mulugeta, Worede Abebaw, Belete Melaku Ashagrie

机构信息

Department of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.

Department of Clinical Chemistry, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.

出版信息

Front Mol Biosci. 2024 May 14;11:1353547. doi: 10.3389/fmolb.2024.1353547. eCollection 2024.

Abstract

Hepatocellular carcinoma (HCC), the most common type of liver cancer, is a major global health problem, ranking as the third leading cause of cancer-related death worldwide. Early identification and diagnosis of HCC requires the discovery of reliable biomarkers. Therefore, the study aimed to assess the diagnostic accuracy of miRNAs for HCC. The protocol was registered on PROSPERO website with the registration number CRD42023417494. A literature search was conducted in PubMed, Scopus, Embase, Wiley Online Library, and Science Direct databases to identify pertinent articles published between 2018 and 30 July 2023. Stata 17.0 software was employed to determine the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic ratio (DOR), and area under the curve (AUC) for evaluating the accuracy of miRNAs in diagnosing HCC. The assessment of heterogeneity among studies involved the use of the Cochran-Q test and I statistic tests. Due to the observed significant heterogeneity, the random-effect model was chosen. Subgroup analysis and meta-regression analysis were also undertaken to explore potential sources contributing to heterogeneity. Deeks' funnel plot was used to assess publication bias. In addition, Fagan's nomogram and likelihood ratio scattergram were utilized to assess the clinical validity of miRNAs for HCC. Twenty-four articles were included, involving 1,668 individuals diagnosed with HCC and 1,236 healthy individuals. The findings revealed pooled sensitivity of 0.84 (95% CI: 0.80-0.88), specificity of 0.81 (95% CI: 0.77-0.84), PLR of 4.36 (95% CI: 3.59-5.30), NLR of 0.19 (95% CI: 0.15-0.25), DOR of 22.47 (95% CI: 14.47-32.64), and an AUC of 0.89 (95% CI: 0.86-0.91) for the diagnosis of HCC using miRNAs. Furthermore, results from the subgroup analysis demonstrated that superior diagnostic performance was observed when utilizing plasma miRNAs, a large sample size (≥100), and miRNA panels. Hence, circulating miRNAs demonstrate substantial diagnostic utility for HCC and can serve as effective non-invasive biomarkers for the condition. Additionally, miRNA panels, miRNAs derived from plasma, and miRNAs evaluated in larger sample sizes (≥100) demonstrate enhanced diagnostic efficacy for HCC diagnosis. Nevertheless, a large pool of prospective studies and multi-center research will be required to confirm our findings in the near future.

摘要

肝细胞癌(HCC)是最常见的肝癌类型,是一个重大的全球健康问题,在全球癌症相关死亡原因中排名第三。早期识别和诊断HCC需要发现可靠的生物标志物。因此,本研究旨在评估miRNA对HCC的诊断准确性。该方案已在PROSPERO网站注册,注册号为CRD42023417494。在PubMed、Scopus、Embase、Wiley Online Library和Science Direct数据库中进行文献检索,以识别2018年至2023年7月30日期间发表的相关文章。使用Stata 17.0软件确定合并敏感性、特异性、阳性似然比(PLR)、阴性似然比(NLR)、诊断比值比(DOR)和曲线下面积(AUC),以评估miRNA在诊断HCC中的准确性。研究间异质性评估采用Cochran-Q检验和I统计检验。由于观察到显著的异质性,选择了随机效应模型。还进行了亚组分析和元回归分析,以探索导致异质性的潜在来源。使用Deeks漏斗图评估发表偏倚。此外,利用Fagan列线图和似然比散点图评估miRNA对HCC的临床有效性。纳入了24篇文章,涉及1668例被诊断为HCC的个体和1236例健康个体。研究结果显示,使用miRNA诊断HCC的合并敏感性为0.84(95%CI:0.80-0.88),特异性为0.81(95%CI:0.77-0.84),PLR为4.36(95%CI:3.59-5.30),NLR为0.19(95%CI:0.15-0.25),DOR为22.47(95%CI:14.47-32.64),AUC为0.89(95%CI:0.86-0.91)。此外,亚组分析结果表明,使用血浆miRNA、大样本量(≥100)和miRNA panel时观察到更好的诊断性能。因此,循环miRNA对HCC具有显著的诊断效用,可作为该疾病有效的非侵入性生物标志物。此外,miRNA panel、源自血浆的miRNA以及在更大样本量(≥100)中评估的miRNA对HCC诊断具有更高的诊断效能。然而,在不久的将来需要大量的前瞻性研究和多中心研究来证实我们的发现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df93/11130514/88308dcab069/fmolb-11-1353547-g001.jpg

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