ArticleCancer diagnosis & prognosis
Circulating miRNA Expression Profiling in Breast Cancer Molecular Subtypes: Applying Machine Learning Analysis in Bioinformatics.
Article in Cancer diagnosis & prognosis. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The emerging role of machine learning-based methods in cancer classification using microRNA.Biochemistry and biophysics reports · 2026Review
- Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning Methods.Biotech (Basel (Switzerland)) · 2025Review
- The assessment of breast cancer biomarkers in diagnosis, prognosis and treatment monitoring: integrated analysis.Journal of cancer research and clinical oncology · 2025Review
- Article
- MicroRNA-98: the multifaceted regulator in human cancer progression and therapy.Cancer cell international · 2024Review
- A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data.Scientific reports · 2023Article
- An Overview of Circulating Cell-Free Nucleic Acids in Diagnosis and Prognosis of Triple-Negative Breast Cancer.International journal of molecular sciences · 2023Review
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Authors and funding
7 authors.
Funding
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Abstract
BACKGROUND/
aimBreast cancer is a leading worldwide cause of female cancer-related morbidity and mortality. Since molecular characteristics increasingly guide disease management, demystifying breast tumor miRNA signature emerges as an essential step toward personalized care. This study aimed to investigate the variations in circulating miRNA expression profiles between breast cancer subtypes and healthy controls and to identify relevant target genes and molecular functions. MATERIALS AND
methodsMiRNA expression was tested by miScript™ miRNA PCR Array Human Cancer Pathway Finder kit, and subsequently, a machine learning approach was applied for miRNA profiling of the various breast cancer molecular subtypes.
resultsSerum samples from patients with primary breast cancer (n=66) and healthy controls (n=16) were analyzed. MiR-21 was the single common molecule among all breast cancer subtypes. Furthermore, several miRNAs were found to be differentially expressed explicitly in the different subtypes; luminal A (miR-23b, miR-142, miR-29a, miR-181d, miR-16, miR-29b, miR-155, miR-181c), luminal B (miR-148a, let-7d, miR-92a, miR-34c, let-7b, miR-15a), HER2+ (miR-125b, miR-134, miR-98, miR-143, miR-138, miR-135b) and triple negative breast cancer (miR-17, miR-150, miR-210, miR-372, let-7f, miR-191, miR-133b, miR-146b, miR-7). Finally, miRNA-associated target genes and molecular functions were identified.
conclusionApplying a machine learning approach to delineate miRNA signatures of various breast cancer molecular subtypes allows further understanding of molecular disease characteristics that can prove clinically relevant.
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