AI Insight
This systematic review and meta-analysis of 32 studies involving 6,935 participants found that circulating microRNAs and lipidomic biomarkers in blood showed high diagnostic accuracy for detecting breast cancer, with pooled sensitivity of 87% and specificity of 84%. However, substantial variability between studies and potential publication bias were identified, and most studies used retrospective case-control designs rather than prospective screening populations, limiting immediate clinical applicability.
Why it matters
These blood-based biomarkers could potentially complement mammography screening, particularly in women with dense breast tissue where imaging performance is limited. However, the findings indicate these tests are not yet ready to replace current screening methods and require validation in large prospective studies before clinical implementation.
Understand the Science
by Heba Mohammed Arafat, Akbar Ali, Tengku Ahmad Damitri Al Astani Tengku Din, Ohood Mohammed Shamallakh, Rashid Jusoh, Maya Mazuwin Yahya, Wan Zainira Wan Zain, Wan Faiziah Wan Abdul Rahman
Background
Breast cancer outcomes improve substantially with earlier detection, yet mammography performance can be limited in dense breasts and may lead to false-positive investigations. Circulating microRNAs (miRNAs) and lipidomic/metabolomic signatures have emerged as promising minimally invasive biomarkers that could complement imaging for early-stage detection. The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of circulating microRNAs and lipidomic/metabolomic biomarkers for early breast cancer detection and to explore between-study heterogeneity.
Materials and methods
A PRISMA 2020–compliant systematic review and meta-analysis were conducted. MEDLINE (PubMed), Web of Science, Scopus, Springer, ScienceDirect, and the Cochrane Library were searched for eligible diagnostic studies assessing circulating microRNAs and lipidomic/metabolomic biomarkers measured in serum or plasma. No language restrictions were applied. Non-English reports were screened and, when potentially eligible, translated for full-text assessment and data extraction. Studies using whole blood were excluded. Study quality was assessed using QUADAS-2. Random-effects models (DerSimonian–Laird) pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), and summary ROC (SROC) area under the curve (AUC). Heterogeneity was evaluated using Q and I2 statistics, and small-study effects were assessed using Deeks’ test.
Results
Thirty-two studies (2015–2025) comprising 6,935 participants (3,697 breast cancer cases; 3,238 controls) were included. Pooled sensitivity was 0.87 (95% CI [0.83, 0.90]) and pooled specificity was 0.84 (95% CI [0.79, 0.88]), with pooled DOR 46.10 (95% CI [27.80, 76.70]), PLR 5.17 (95% CI [3.99, 6.68]), NLR 0.16 (95% CI [0.13, 0.21]), and SROC AUC 0.92 (95% CI [0.87, 0.94]). Heterogeneity was substantial (I2 = 88.29% for sensitivity; I2 = 90.20% for specificity). In subgroup analyses, serum-based studies showed higher pooled specificity than plasma-based studies. A formal threshold effect assessment did not reveal a statistically significant correlation between sensitivity and false-positive rate (Spearman ρ = −0.334, p = 0.062). Deeks’ test suggested potential small-study effects (p = 0.043).
Conclusions
Circulating microRNA and lipidomic/metabolomic biomarkers demonstrate strong overall diagnostic performance for breast cancer detection; however, substantial heterogeneity and potential small-study effects limit their immediate clinical translation. Given that most included studies used retrospective case-control designs rather than prospective screening cohorts, these biomarkers are best regarded as investigational, complementary tools rather than replacements for mammography at this stage. Future large, prospective, standardized studies with harmonized pre-analytics and prespecified thresholds are needed to support the implementation of screening or triage pathways.