Alemayehu Ermiyas, Gedefie Alemu, Debash Habtu, Worede Abebaw, Mulatie Zewudu, Ebrahim Endris, Teshome Mulugeta, Ebrahim Hussen, Belete Melaku Ashagrie
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.
Sci Rep. 2025 Jul 1;15(1):21714. doi: 10.1038/s41598-025-07860-3.
Managing lupus nephritis (LN) has been challenging despite therapeutic advancements, as 30% of patients face end-stage kidney disease. Current laboratory tests for diagnosing LN activity are insufficient. Recognizing the potential for severe complications in patients with LN, there is crucial need to identify innovative biomarkers for early diagnosis of this disease. Hence, the objective of this study was to evaluate diagnostic efficacy of miRNAs for LN. The protocol has been registered on PROSPERO (CRD42023460747). This systematic review and meta-analysis was performed following the PRISMA guidelines. A comprehensive search for relevant literature was carried out across PubMed, Scopus, Embase, and Hinari. The methodological quality of the articles was assessed using the QUADAS-2 tool. Stata 14.0 software was used to calculate pooled sensitivity, specificity, and other diagnostic parameters related to miRNAs for the diagnosis of LN utilizing a random effects model. The assessment of heterogeneity among studies involved the application of the Cochran-Q test and I statistic tests. Subsequently, subgroup analyses and meta-regression analysis were conducted to investigate the primary sources of heterogeneity. To evaluate publication bias, Deeks' funnel plot was used. Additionally, Fagan's nomogram and likelihood ratio scattergram were used to assess the clinical utility of miRNAs for LN. Additionally, a sensitivity analysis was conducted to evaluate the robustness and reliability of the results. In this meta-analysis, 23 studies from 11 publications were included, encompassing a total of 526 LN patients and 490 controls. The selected studies pertained low risk of bias. The overall pooled values for sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.85 (95% CI 0.78-0.89), 0.83 (95% CI 0.75-0.89), 4.93 (95% CI 3.47-7.07), 0.19 (95% CI 0.13-0.26), 26.35 (95% CI 16.39-42.36), and 0.91 (95% CI 0.88-0.93), respectively. Furthermore, miR-181a, miR-223 and miR-146a demonstrated superior diagnostic efficacy with promising clinical utility in excluding and confirming LN. Additionally, findings from the subgroup analysis indicated that miRNA panels, upregulated miRNAs, and miRNAs used in the diagnosis of African patients exhibited superior diagnostic performance. Circulating microRNAs (miRNAs) show promise as noninvasive biomarkers for the early diagnosis of LN. Furthermore, miRNA panels and upregulated miRNAs exhibited improved diagnostic efficacy for LN diagnosis. However, substantiating these findings will necessitate a substantial number of prospective studies and multicenter studies in the near future.
尽管治疗取得了进展，但狼疮性肾炎（LN）的管理仍然具有挑战性，因为30%的患者面临终末期肾病。目前用于诊断LN活动的实验室检查并不充分。认识到LN患者存在严重并发症的可能性，迫切需要识别用于该疾病早期诊断的创新生物标志物。因此，本研究的目的是评估miRNA对LN的诊断效能。该方案已在PROSPERO（CRD42023460747）上注册。本系统评价和荟萃分析按照PRISMA指南进行。在PubMed、Scopus、Embase和Hinari上全面检索了相关文献。使用QUADAS - 2工具评估文章的方法学质量。使用Stata 14.0软件，采用随机效应模型计算与miRNA诊断LN相关的合并敏感性、特异性和其他诊断参数。研究间异质性的评估涉及Cochran - Q检验和I统计检验的应用。随后，进行亚组分析和meta回归分析以探究异质性的主要来源。为评估发表偏倚，使用了Deeks漏斗图。此外，使用Fagan列线图和似然比散点图评估miRNA对LN的临床效用。另外，进行了敏感性分析以评估结果的稳健性和可靠性。在这项荟萃分析中，纳入了来自11篇出版物的23项研究，共包括526例LN患者和490例对照。所选研究的偏倚风险较低。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）的总体合并值分别为0.85（95%可信区间0.78 - 0.89）、0.83（95%可信区间0.75 - 0.89）、4.93（95%可信区间3.47 - 7.07）、0.19（95%可信区间0.13 - 0.26）、26.35（95%可信区间16.39 - 42.36）和0.91（95%可信区间0.88 - 0.93）。此外，miR - 181a、miR - 223和miR - 146a在排除和确诊LN方面表现出卓越的诊断效能和有前景的临床效用。此外，亚组分析结果表明，miRNA组合、上调的miRNA以及用于非洲患者诊断的miRNA表现出卓越的诊断性能。循环微小RNA（miRNA）有望成为LN早期诊断的非侵入性生物标志物。此外，miRNA组合和上调的miRNA在LN诊断中表现出更高的诊断效能。然而，要证实这些发现，在不久的将来需要大量的前瞻性研究和多中心研究。
