ArticlePloS one2012
Met kinetic signature derived from the response to HGF/SF in a cellular model predicts breast cancer patient survival.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed, 17 citations in OpenAlex.
- MET-associated immune prognostic signature predicts survival and guides personalized therapy in glioma.Acta neuropathologica communications · 2026Article
- Boswellia frereana suppresses HGF-mediated breast cancer cell invasion and migration through inhibition of c-Met signalling.Journal of translational medicine · 2018Article
- Analysis of circulating adipokines in patients newly diagnosed with solid cancer: Associations with measures of adiposity and tumor characteristics.Oncology letters · 2017Article
- Activation of the c-Met Pathway Mobilizes an Inflammatory Network in the Brain Microenvironment to Promote Brain Metastasis of Breast Cancer.Cancer research · 2016Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To determine the signaling pathways leading from Met activation to metastasis and poor prognosis, we measured the kinetic gene alterations in breast cancer cell lines in response to HGF/SF. Using a network inference tool we analyzed the putative protein-protein interaction pathways leading from Met to these genes and studied their specificity to Met and prognostic potential. We identified a Met kinetic signature consisting of 131 genes. The signature correlates with Met activation and with response to anti-Met therapy (p<0.005) in in-vitro models. It also identifies breast cancer patients who are at high risk to develop an aggressive disease in six large published breast cancer patient cohorts (p<0.01, N>1000). Moreover, we have identified novel putative Met pathways, which correlate with Met activity and patient prognosis. This signature may facilitate personalized therapy by identifying patients who will respond to anti-Met therapy. Moreover, this novel approach may be applied for other tyrosine kinases and other malignancies.
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Registered trials
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