ArticleScientific reports2026
Integrating circulating microRNAs with epidemiological factors enhances breast cancer detection across subtypes: the MCC-Spain study.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
Circulating microRNAs (miRNAs) are promising non-invasive biomarkers for cancer detection; however, their diagnostic performance across breast cancer molecular subtypes and their incremental value beyond demographic and epidemiological variables remain incompletely characterized. We conducted a multicenter case–control study including 317 breast cancer cases and 127 population-based controls. Serum levels of 44 literature-derived miRNAs were quantified by RT-qPCR. Feature selection was performed using LASSO penalization, followed by multivariable logistic regression to estimate odds ratios (ORs) with 95% confidence intervals (CIs). Models were adjusted for demographic and epidemiological covariates. Predictive performance was assessed using repeated fivefold cross-validation and reported as area under the curve (AUC) with bootstrap bias-corrected 95% CIs. Incorporating demographic and epidemiological covariates enhanced discrimination overall (AUC = 0.908 vs. 0.802 unadjusted) and across subtypes. The most notable improvements were observed in Luminal A (0.896 vs. 0.751) and Luminal B (0.894 vs. 0.768), while HER2-positive and Basal-like tumors already showed high performance (AUC = 0.965 and 0.989, respectively). Among the 12 miRNAs selected by LASSO, miR-21-5p and miR-423-3p were consistently elevated in cases, particularly in HER2-positive and Basal-like tumors, whereas miR-101-3p, miR-146a-5p, and miR-29a-3p showed reproducibly lower levels across multiple subtypes, consistent with oncogenic and tumor-suppressive roles, respectively. Circulating miRNA signatures, especially when integrated with demographic and epidemiological information, demonstrate high discriminatory power for breast cancer detection across molecular subtypes. These results support subtype-aware, minimally invasive strategies for screening and risk stratification using miRNA-based models. Prospective validation in independent cohorts is warranted to confirm clinical utility.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.