ArticlePeerJ2018
Breast cancer identification via modeling of peripherally circulating miRNAs.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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
22 citing papers in PubMed.
- Exosome-mimetic nanocarriers in cancer diagnosis and drug delivery: engineering strategies, functional biomimicry, safety considerations, and clinical translation.Journal of nanobiotechnology · 2026Review
- FT-MDNNMDs: early detection of breast cancer using fine-tuned multi-deep neural networks with TCGA and clinical image datasets.Scientific reports · 2026Article
- Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach.BMC medical genomics · 2025Article
- Impact of dcEF on microRNA profiles in glioblastoma and exosomes using a novel microfluidic bioreactor.Biomicrofluidics · 2024Article
- Personalized treatment approach for HER2-positive metastatic breast cancer.Medical oncology (Northwood, London, England) · 2024Review
- Identification of serum miR-1246 and miR-150-5p as novel diagnostic biomarkers for high-grade serous ovarian cancer.Scientific reports · 2023Article
- Article
- Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Isolation and Characterization of Urinary Extracellular Vesicles from Healthy Donors and Patients with Castration-Resistant Prostate Cancer.International journal of molecular sciences · 2022Article
- Review
- Potential utility of miRNAs for liquid biopsy in breast cancer.Frontiers in oncology · 2022Review
- A Review on the Role of miR-1246 in the Pathoetiology of Different Cancers.Frontiers in molecular biosciences · 2021Review
- Identifying the Signatures and Rules of Circulating Extracellular MicroRNA for Distinguishing Cancer Subtypes.Frontiers in genetics · 2021Article
- Identification of potential microRNA panels for pancreatic cancer diagnosis using microarray datasets and bioinformatics methods.Scientific reports · 2020Article
- Emergence of Circulating MicroRNAs in Breast Cancer as Diagnostic and Therapeutic Efficacy Biomarkers.Molecular diagnosis & therapy · 2020Review
- RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction.Frontiers in genetics · 2020Article
- Analysis of miRNA signatures in CSF identifies upregulation of miR-21 and miR-146a/b in patients with multiple sclerosis and active lesions.Journal of neuroinflammation · 2019Article
- First Attempts of the Use ofMolecules (Basel, Switzerland) · 2019Article
- Development and Validation of an Esophageal Squamous Cell Carcinoma Detection Model by Large-Scale MicroRNA Profiling.JAMA network open · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prolonged life expectancy in humans has been accompanied by an increase in the prevalence of cancers. Breast cancer (BC) is the leading cause of cancer-related deaths. It accounts for one-fourth of all diagnosed cancers and affects one in eight females worldwide. Given the high BC prevalence, there is a practical need for demographic screening of the disease. In the present study, we re-analyzed a large microRNA (miRNA) expression dataset (GSE73002), with the goal of optimizing miRNA biomarker selection using neural network cascade (NNC) modeling. Our results identified numerous candidate miRNA biomarkers that are technically suitable for BC detection. We combined three miRNAs (miR-1246, miR-6756-5p, and miR-8073) into a single panel to generate an NNC model, which successfully detected BC with 97.1% accuracy in an independent validation cohort comprising 429 BC patients and 895 healthy controls. In contrast, at least seven miRNAs were merged in a multiple linear regression model to obtain equivalent diagnostic performance (96.4% accuracy in the independent validation set). Our findings suggested that suitable modeling can effectively reduce the number of miRNAs required in a biomarker panel without compromising prediction accuracy, thereby increasing the technical possibility of early detection of BC.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.