ArticleCancer research and treatment2025
Machine Learning-Based Prognostic Gene Signature for Early Triple-Negative Breast Cancer.
Article in Cancer research and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Navigating the Metabolic-Genomic Paradigm: Mitochondrial Reprogramming as a Driver of Cancer Plasticity.Oncology research · 2026Review
- Development of predictive models for the prognosis of triple-negative breast cancer using multiple transcriptomic analyses.PloS one · 2026Article
- A systems biology approach to unveil shared therapeutic targets and pathological pathways across major human cancers.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeThis study aimed to develop a machine learning-based approach to identify prognostic gene signatures for early-stage triple-negative breast cancer (TNBC) using next-generation sequencing data from Asian populations. Materials and Methods: We utilized next-generation sequencing data to analyze gene expression profiles and identify potential biomarkers. Our methodology involved integrating various machine learning techniques, including feature selection and model optimization. We employed logistic regression, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curves to validate the identified gene signatures.
resultsWe identified a gene signature significantly associated with relapse in TNBC patients. The predictive model demonstrated robustness and accuracy, with an area under the ROC curve of 0.9087, sensitivity of 0.8750, and specificity of 0.9231. The Kaplan-Meier survival analysis revealed a strong association between the gene signature and patient relapse, further validated by logistic regression analysis.
conclusionThis study presents a novel machine learning-based prognostic tool for TNBC, offering significant implications for early detection and personalized treatment. The identified gene signature provides a promising approach for improving the management of TNBC, contributing to the advancement of precision oncology.
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Registered trials
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