ArticleJournal of molecular cell biology2019
Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers.
Article in Journal of molecular cell biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Detection of pre-transition phases during biological development using single-sample network entropy (SNE).NPJ systems biology and applications · 2025Article
- Identifying critical States of complex diseases by local network Wasserstein distance.Scientific reports · 2025Article
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- Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE).Research (Washington, D.C.) · 2024Article
- Trehalose metabolism coordinates transcriptional regulatory control and metabolic requirements to trigger the onset of cassava storage root initiation.Scientific reports · 2023Article
- Review
- Proteomic time course of breast cancer cells highlights enhanced sensitivity to Stat3 and Src inhibitors prior to endocrine resistance development.Cancer gene therapy · 2023Article
- Dynamic network biomarker factors orchestrate cell-fate determination at tipping points during hESC differentiation.Innovation (Cambridge (Mass.)) · 2023Article
- Dynamic Network Biomarker Analysis Reveals the Critical Phase Transition of Fruit Ripening in Grapevine.Genes · 2022Article
- A novel method to detect the early warning signal of COVID-19 transmission.BMC infectious diseases · 2022Article
- Single-cell entropy network detects the activity of immune cells based on ribosomal protein genes.Computational and structural biotechnology journal · 2022Article
- Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.Computational and structural biotechnology journal · 2022Review
- Genome-wide dynamic network analysis reveals the potential genes for MeJA-induced growth-to-defense transition.BMC plant biology · 2021Article
- Single-cell transcriptomics revealMolecular therapy oncolytics · 2021Article
- Impact of Stilbenes as Epigenetic Modulators of Breast Cancer Risk and Associated Biomarkers.International journal of molecular sciences · 2021Review
- A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms.Scientific reports · 2021Article
- Random distributed logistic regression framework for predicting potential lncRNA‒disease association.Journal of molecular cell biology · 2021Article
- A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery.Journal for immunotherapy of cancer · 2021Article
- Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis.PLoS computational biology · 2021Article
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Authors and funding
10 authors.
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Abstract
Acquired drug resistance is the major reason why patients fail to respond to cancer therapies. It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules. Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance. We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules. The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients. The results provided the detection for the pre-resistance state or early signs of endocrine resistance. Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.
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