ReviewCells2020
Metabolic Constrains Rule Metastasis Progression.
Review in Cells, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Post-translational modifications and the reprogramming of tumor metabolism.Discover oncology · 2025Review
- Breast cancers as ecosystems: a metabolic perspective.Cellular and molecular life sciences : CMLS · 2023Review
- Metabolomics-Guided Identification of a Distinctive Hepatocellular Carcinoma Signature.Cancers · 2023Article
- Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma.Frontiers in endocrinology · 2023Article
- Genetic Contribution of the Adrenergic, Cholinergic, and Serotonergic Systems to Leiomyoma Development and Treatment.International journal of molecular and cellular medicine · 2023Article
- Lipid Metabolism Heterogeneity and Crosstalk with Mitochondria Functions Drive Breast Cancer Progression and Drug Resistance.Cancers · 2022Review
- Beyond Genetics: Metastasis as an Adaptive Response in Breast Cancer.International journal of molecular sciences · 2022Review
- Blood Vessels and Peripheral Nerves as Key Players in Cancer Progression and Therapy Resistance.Cancers · 2021Review
- Article
- Metabolic Classification and Intervention Opportunities for Tumor Energy Dysfunction.Metabolites · 2021Review
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
Metastasis formation accounts for the majority of tumor-associated deaths and consists of different steps, each of them being characterized by a distinctive adaptive phenotype of the cancer cells. Metabolic reprogramming represents one of the main adaptive phenotypes exploited by cancer cells during all the main steps of tumor and metastatic progression. In particular, the metabolism of cancer cells evolves profoundly through all the main phases of metastasis formation, namely the metastatic dissemination, the metastatic colonization of distant organs, the metastatic dormancy, and ultimately the outgrowth into macroscopic lesions. However, the metabolic reprogramming of metastasizing cancer cells has only recently become the subject of intense study. From a clinical point of view, the latter steps of the metastatic process are very important, because patients often undergo surgical removal of the primary tumor when cancer cells have already left the primary tumor site, even though distant metastases are not clinically detectable yet. In this scenario, to precisely elucidate if and how metabolic reprogramming drives acquisition of cancer-specific adaptive phenotypes might pave the way to new therapeutic strategies by combining chemotherapy with metabolic drugs for better cancer eradication. In this review we discuss the latest evidence that claim the importance of metabolic adaptation for cancer progression.
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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.