ReviewCellular and molecular life sciences : CMLS2023
Breast cancers as ecosystems: a metabolic perspective.
Review in Cellular and molecular life sciences : CMLS, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 10 citations in OpenAlex.
- Racial Disparity in Ductal Carcinoma in Situ: Risk-Predictive and Actionable Biomarkers for Early Intervention.Cancers · 2026Review
- Host-microbiome interactions in breast cancer progression and treatment response.Frontiers in medicine · 2026Review
- Minimal Residual Disease in Breast Cancer: Tumour Microenvironment Interactions, Detection Methods and Therapeutic Approaches.International journal of molecular sciences · 2025Review
- TransBreastNet a CNN transformer hybrid deep learning framework for breast cancer subtype classification and temporal lesion progression analysis.Scientific reports · 2025Article
- Breathomics: A Non-Invasive Approach for the Diagnosis of Breast Cancer.Bioengineering (Basel, Switzerland) · 2025Review
- Current state of cancer immunity cycle: new strategies and challenges of using precision hydrogels to treat breast cancer.Frontiers in immunology · 2025Review
- Harnessing Oncolytic Viruses for Targeted Therapy in Triple-Negative Breast Cancer.International journal of medical sciences · 2025Review
- TBC1 domain-containing proteins are frequently involved in triple-negative breast cancers in connection with the induction of a glycolytic phenotype.Cell death & disease · 2024Article
- A PET-Surrogate Signature for the Interrogation of the Metabolic Status of Breast Cancers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Onco-Breastomics: An Eco-Evo-Devo Holistic Approach.International journal of molecular sciences · 2024Review
- The influence of diabetes mellitus on blood vessels' amounts and immune status in case of breast cancer.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Breast cancer (BC) is the most frequently diagnosed cancer and one of the major causes of cancer death. Despite enormous progress in its management, both from the therapeutic and early diagnosis viewpoints, still around 700,000 patients succumb to the disease each year, worldwide. Late recurrency is the major problem in BC, with many patients developing distant metastases several years after the successful eradication of the primary tumor. This is linked to the phenomenon of metastatic dormancy, a still mysterious trait of the natural history of BC, and of several other types of cancer, by which metastatic cells remain dormant for long periods of time before becoming reactivated to initiate the clinical metastatic disease. In recent years, it has become clear that cancers are best understood if studied as ecosystems in which the impact of non-cancer-cell-autonomous events-dependent on complex interaction between the cancer and its environment, both local and systemic-plays a paramount role, probably as significant as the cell-autonomous alterations occurring in the cancer cell. In adopting this perspective, a metabolic vision of the cancer ecosystem is bound to improve our understanding of the natural history of cancer, across space and time. In BC, many metabolic pathways are coopted into the cancer ecosystem, to serve the anabolic and energy demands of the cancer. Their study is shedding new light on the most critical aspect of BC management, of metastatic dissemination, and that of the related phenomenon of dormancy and fostering the application of the knowledge to the development of metabolic therapies.
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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.