ArticlePeerJ2025
Development of a prognostic model based on the ceRNA network in Triple-Negative Breast cancer.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- State-Aware RNA Biomarkers in Triple-Negative Breast Cancer (TNBC): Integrating Tumor Plasticity, Spatial Architecture, and Temporal Monitoring.International journal of molecular sciences · 2026Review
- Dysregulation of adipokines in the serum of breast cancer patients - role in pathogenesis and potential clinical application (preliminary studies).Frontiers in immunology · 2026Article
- SH3BGRL2 as a vital tumor suppressor and prognostic factor in human esophageal squamous cell carcinoma.Journal of thoracic disease · 2025Article
- Article
- Exploring the Clinical Transformation of circRNA as a Biomarker in Breast Cancer.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with a poor prognosis. Although circular RNAs (circRNAs) have been implicated in cancer progression, their roles in TNBC remain poorly understood. In this study, we aimed to develop a prognostic model for TNBC by constructing a competing endogenous RNA (ceRNA) network. This network integrates circRNAs, long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) to identify potential biomarkers and therapeutic targets for improving clinical outcomes. Methods: Differentially expressed circRNAs, lncRNAs, and mRNAs were identified from GEO datasets (144 samples: 94 TNBC and 50 normal tissues). A ceRNA network was constructed, and key genes were validated using The Cancer Genome Atlas (TCGA) dataset (115 TNBC and 113 para-cancer tissues). Multivariate Cox regression analysis was performed to develop a prognostic model, and Gene Set Enrichment Analysis (GSEA) was performed to identify associated pathways. Results: Nine genes ( Conclusion: This study developed a novel prognostic model based on a ceRNA network analysis, highlighting the critical role of circRNAs and the MAPK signaling pathway in TNBC progression. These findings offer valuable insights into potential biomarkers for TNBC prognosis and reveal promising therapeutic targets for improving patient outcomes.
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Registered trials
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