Five-Marker Tissue Model Distinguishes Benign from Malignant Breast Lesions in Internal Validation

Researchers in China have developed a five-marker molecular model that, measured in breast tissue, separated malignant breast tumors from benign breast lesions with an internal-validation accuracy of 0.844. The study, published in Frontiers in Molecular Biosciences, combined public genomic datasets with laboratory measurements in an independent cohort of 150 breast-lesion samples and identified two pairs of microRNAs and their target genes showing the strongest evidence of inverse expression in breast disease.

The clinical motivation is familiar to breast specialists: when imaging findings are ambiguous, such as in lesions classified as BI-RADS 4, or when radiologic and pathologic results are discordant, deciding whether a lesion is benign or malignant can be difficult. The team, comprising Xiaoyan Zhao, Xingxing Wu and Dan Zhao of Shanxi Provincial Children’s Hospital together with Hui Feng of Taiyuan Maternal and Child Health Hospital, built a tissue-based molecular tool intended to complement, not replace, standard histopathological diagnosis in these challenging cases.

The researchers first mined public microRNA and messenger RNA datasets to discover and prioritize candidate inverse-expression pairs, using those datasets primarily for tumor-versus-normal discovery, while reserving the critical benign-versus-malignant question for their own independently collected tissue cohort. That cohort comprised 150 breast-lesion samples, 68 benign and 82 malignant, in which the team measured ten candidate axes using reverse-transcription quantitative polymerase chain reaction, a laboratory technique that quantifies specific RNA molecules.

Two candidate pairs emerged with the strongest evidence of inverse expression: miR-205-5p paired with HMGB3, and miR-96-5p paired with FOXO1. The study’s contribution lies in these measured expression relationships within a diagnostic context rather than in new functional claims about the genes themselves.

To convert the molecular measurements into a diagnostic tool, the researchers developed a least absolute shrinkage and selection operator, or LASSO, logistic-regression model in a training cohort. The final model retained five markers: miR-205-5p, HMGB3, miR-96-5p, FOXO1 and miR-21-5p.

The performance figures come from an internal-validation design with a notable safeguard. The model was tested in a separate internal-validation cohort without any marker reselection, coefficient refitting or threshold re-optimization, meaning it faced the validation data exactly as it left the training phase. In that validation set, the area under the receiver operating characteristic curve, a standard summary of diagnostic discrimination, was 0.894, with a 95 percent confidence interval of 0.788 to 0.975. Accuracy reached 0.844, sensitivity 0.840 and specificity 0.850.

These numbers indicate that the model correctly identified roughly 84 percent of malignant lesions and 85 percent of benign ones in the held-out samples. In a diagnostically uncertain setting, a molecular adjunct with this level of discrimination could add confidence to clinical decisions. The authors frame the model as adjunctive throughout: it is meant to sit alongside imaging and pathology, not to replace them.

The study’s limitations are stated plainly. The validation is internal, and external validation in independent cohorts, ideally from multiple centers, as well as functional confirmation of the candidate miRNA-mRNA axes, are required before any clinical use can be considered. For now, the finding stands as a promising early step, and whether the miR-205-5p/HMGB3 and miR-96-5p/FOXO1 axes prove useful in the broader population of ambiguous breast lesions will depend on the external studies the authors themselves call for.

Subject of Research: A tissue-based inverse miRNA–mRNA diagnostic model for distinguishing benign from malignant breast lesions

Article Title: Identification of candidate inverse miRNA–mRNA pairs and an internally validated tissue-based diagnostic model for benign and malignant breast lesions

Article References: Zhao, X., Feng, H., Wu, X., & Zhao, D. (2026). Identification of candidate inverse miRNA–mRNA pairs and an internally validated tissue-based diagnostic model for benign and malignant breast lesions. Frontiers in Molecular Biosciences, 13, Article 1952773. https://doi.org/10.3389/fmolb.2026.1952773

Image Credits: AI Generated

DOI: 10.3389/fmolb.2026.1952773

Keywords: breast cancer, benign breast lesions, miRNA, mRNA, RT-qPCR, LASSO logistic regression, diagnostic model, miR-205-5p, HMGB3, molecular diagnostics, BI-RADS 4, Identification