Exosome biomarkers in breast cancer: Systematic review and meta-analysis

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-04-29 DOI:10.1016/j.cca.2025.120342

Yurou Kang , Xiaoqing Cao , Yujing Fan , Yimin Li , Tao Xu , Qing Zhou , Bangshun He

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引用次数: 0

Abstract

Background

Breast cancer (BC) has become the primary cancer that threatens women’s health and life expectancy. Early diagnosis is crucial for effective treatment and favourable prognosis. As a non-invasive and valuable liquid biopsy method, exosomes are promising for the diagnosis and prognosis of BC. The aim of this meta-analysis is to evaluate the diagnostic and prognostic value of exosome biomarkers in BC.

Methods

A systematic search of relevant English literature was conducted in PubMed, Web of Science, and Cochrane library until August 2024 (diagnosis) and October 2024 (prognosis). QUADAS-2 and QUAPAS were used to assess the quality of the literature. Summary statistics and analyses of relevant effect sizes were conducted using STATA software. Subgroup analysis and sensitivity analysis were performed to identify potential sources of heterogeneity.

Results

For diagnosis, a total of 31 articles with 3,778 patients and 2,722 controls were included, the pooled sensitivity (SEN), specificity (SPE), and area under the receiver operating characteristic curve (AUC) of overall exosome biomarkers were 0.89 (95 %CI: 0.86–0.91), 0.87 (95 %CI: 0.85–0.90), and 0.94 (95 %CI: 0.92–0.96), respectively, indicating a high diagnostic value of exosomes in BC patients. Subgroup analysis suggested that miRNAs in exosomes exhibited better diagnostic value compared to proteins and non-miRNAs, the SEN, SPE, and AUC were 0.89 (95 %CI: 0.82–0.93), 0.86 (95 %CI: 0.80–0.90), and 0.92 (95 %CI: 0.90–0.94), respectively. Among all miRNAs, the pooled SEN, SPE, and AUC of miR-21 were 0.86 (95 %CI: 0.67–0.95), 0.90 (95 %CI: 0.78–0.96), and 0.95 (95 %CI: 0.92–0.96), respectively. The diagnostic efficiency was improved when biomarkers were combined as a panel (SEN 0.91 versus 0.87, SPE 0.89 versus 0.86, AUC 0.96 versus 0.91).

In terms of prognosis, we retrieved 14 articles with 2,781 patients. The pooled HR of overall survival (OS) and progression-free survival (PFS) were 1.41 (95 %CI: 0.92–1.90) and 4.39 (95 %CI: 1.87–6.91), respectively, indicating exosome biomarkers like soluble HLA-G, miR-1246, miR-155, and PSMA were a predictor of poor PFS in BC patients. Subgroup analysis in OS group revealed a significant association between the overexpression of exosome proteins (soluble HLA-G, AnxA2, NGF, CXCL13) and worse OS in BC patients (HR = 2.91, 95 %CI: 1.36–4.47). Similarly, the overexpression of miR-1246 and miR-155 was associated with worse PFS in BC patients (HR = 4.13, 95 %CI: 1.24–7.03). Moreover, when biomarkers were combined as a panel, the prognostic efficiency significantly improved in OS (HR = 4.05, 95 %CI: 2.26–5.84) outcome.

Conclusion

The meta-analysis revealed that exosome miR-21 might serve as a promising diagnostic biomarker in BC. Dysregulated exosome proteins and miRNAs could predict poor OS and PFS outcomes, respectively.

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乳腺癌的外泌体生物标志物：系统回顾和荟萃分析

乳腺癌（BC）已成为威胁妇女健康和预期寿命的主要癌症。早期诊断对有效治疗和良好预后至关重要。外泌体作为一种无创、有价值的液体活检方法，在BC的诊断和预后方面具有广阔的应用前景。本荟萃分析的目的是评估BC中外泌体生物标志物的诊断和预后价值。方法系统检索PubMed、Web of Science、Cochrane图书馆相关英文文献，检索时间截止到2024年8月（诊断）、2024年10月（预后）。采用QUADAS-2和QUAPAS评估文献质量。采用STATA软件进行汇总统计和相关效应量分析。进行亚组分析和敏感性分析以确定异质性的潜在来源。结果共纳入31篇文献，3778例患者和2722例对照，总体外泌体生物标志物的综合敏感性（SEN）、特异性（SPE）和受者工作特征曲线下面积（AUC）分别为0.89 （95% CI: 0.86 ~ 0.91）、0.87 （95% CI: 0.85 ~ 0.90）和0.94 (95% CI: 0.92 ~ 0.96)，表明外泌体在BC患者中具有较高的诊断价值。亚组分析表明，与蛋白质和非mirna相比，外泌体中的mirna具有更好的诊断价值，SEN， SPE和AUC分别为0.89 (95% CI: 0.82-0.93), 0.86 （95% CI: 0.80-0.90）和0.92 （95% CI: 0.90-0.94）。在所有mirna中，miR-21的汇总SEN、SPE和AUC分别为0.86 （95% CI: 0.67-0.95）、0.90 （95% CI: 0.78-0.96）和0.95 （95% CI: 0.92-0.96）。当生物标志物联合使用时，诊断效率得到提高（SEN 0.91 vs 0.87, SPE 0.89 vs 0.86, AUC 0.96 vs 0.91）。在预后方面，我们检索了14篇文章，涉及2781例患者。总生存期（OS）和无进展生存期（PFS）的总风险比分别为1.41 （95% CI: 0.92-1.90）和4.39 (95% CI: 1.87-6.91)，表明可溶性HLA-G、miR-1246、miR-155和PSMA等外泌体生物标志物是BC患者不良PFS的预测因子。OS组的亚组分析显示，BC患者外显体蛋白（可溶性HLA-G、AnxA2、NGF、CXCL13）的过表达与更严重的OS存在显著相关性（HR = 2.91, 95% CI: 1.36-4.47）。同样，miR-1246和miR-155的过表达与BC患者更差的PFS相关（HR = 4.13, 95% CI: 1.24-7.03）。此外，当生物标志物联合作为一个整体时，OS预后效率显著提高（HR = 4.05, 95% CI: 2.26-5.84）。荟萃分析显示，外泌体miR-21可能是一种有前景的BC诊断生物标志物。失调的外泌体蛋白和mirna分别可以预测不良的OS和PFS结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.