Identification of candidate inverse miRNA–mRNA pairs and an internally validated tissue-based diagnostic model for benign and malignant breast lesions
Abstract
BackgroundDifferentiating malignant breast tumors from benign breast lesions remains clinically important when imaging or pathology is indeterminate. We evaluated candidate inverse miRNA–mRNA expression pairs as a tissue-based adjunctive molecular approach intended to complement, not replace, histopathological diagnosis, with particular relevance to diagnostically challenging lesions such as BI-RADS four or radiologic–pathologic discordant cases.MethodsPublic microRNA (miRNA) and messenger RNA (mRNA) datasets were used for candidate discovery and prioritization. An independent reverse-transcription quantitative polymerase chain reaction (RT-qPCR) tissue cohort included 150 breast-lesion samples (68 benign and 82 malignant). Ten candidate axes were measured. A least absolute shrinkage and selection operator (LASSO) logistic-regression model was developed in the training cohort and tested without marker reselection, coefficient refitting, or threshold re-optimization in the internal-validation cohort. Public datasets were used primarily for tumor-versus-normal discovery, whereas benign-versus-malignant evaluation was performed in the independent tissue cohort.ResultsmiR-205-5p/HMGB3 and miR-96-5p/FOXO1 showed the strongest inverse-expression candidate evidence. The final model retained miR-205-5p, HMGB3, miR-96-5p, FOXO1, and miR-21-5p. In internal validation, the area under the receiver operating characteristic curve (AUC) was 0.894 (95% confidence interval 0.788–0.975), with accuracy 0.844, sensitivity 0.840, and specificity 0.850.ConclusionA five-marker tissue model discriminated benign from malignant breast lesions in internal validation. miR-205-5p/HMGB3 and miR-96-5p/FOXO1 were the leading inverse-expression candidates. External validation and functional confirmation are required before clinical use.
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BibTeX
@article{zhao2026identification,
title = {{Identification of candidate inverse miRNA–mRNA pairs and an internally validated tissue-based diagnostic model for benign and malignant breast lesions}},
author = {Xiaoyan Zhao and Hui Feng and Xingxing Wu and Dan Zhao},
journal = {Frontiers in Molecular Biosciences},
year = {2026},
month = oct,
doi = {10.3389/fmolb.2026.1952773},
url = {https://www.frontiersin.org/articles/10.3389/fmolb.2026.1952773},
note = {CC BY},
}
RIS
TY - JOUR
TI - Identification of candidate inverse miRNA–mRNA pairs and an internally validated tissue-based diagnostic model for benign and malignant breast lesions
AU - Xiaoyan Zhao
AU - Hui Feng
AU - Xingxing Wu
AU - Dan Zhao
PY - 2026
DA - 2026/10/23
JO - Frontiers in Molecular Biosciences
DO - 10.3389/fmolb.2026.1952773
UR - https://www.frontiersin.org/articles/10.3389/fmolb.2026.1952773
AB - BackgroundDifferentiating malignant breast tumors from benign breast lesions remains clinically important when imaging or pathology is indeterminate. We evaluated candidate inverse miRNA–mRNA expression pairs as a tissue-based adjunctive molecular approach intended to complement, not replace, histopathological diagnosis, with particular relevance to diagnostically challenging lesions such as BI-RADS four or radiologic–pathologic discordant cases.MethodsPublic microRNA (miRNA) and messenger RNA (mRNA) datasets were used for candidate discovery and prioritization. An independent reverse-transcription quantitative polymerase chain reaction (RT-qPCR) tissue cohort included 150 breast-lesion samples (68 benign and 82 malignant). Ten candidate axes were measured. A least absolute shrinkage and selection operator (LASSO) logistic-regression model was developed in the training cohort and tested without marker reselection, coefficient refitting, or threshold re-optimization in the internal-validation cohort. Public datasets were used primarily for tumor-versus-normal discovery, whereas benign-versus-malignant evaluation was performed in the independent tissue cohort.ResultsmiR-205-5p/HMGB3 and miR-96-5p/FOXO1 showed the strongest inverse-expression candidate evidence. The final model retained miR-205-5p, HMGB3, miR-96-5p, FOXO1, and miR-21-5p. In internal validation, the area under the receiver operating characteristic curve (AUC) was 0.894 (95% confidence interval 0.788–0.975), with accuracy 0.844, sensitivity 0.840, and specificity 0.850.ConclusionA five-marker tissue model discriminated benign from malignant breast lesions in internal validation. miR-205-5p/HMGB3 and miR-96-5p/FOXO1 were the leading inverse-expression candidates. External validation and functional confirmation are required before clinical use.
SN - 2296-889X
ER -