Tumor cell metabolism is a research paper published in Cancer Biology & Therapy (2011). On theSindex it has a DataRank of 0.818. It has been cited 233 times. Its calibrated FAIR score is 13/100.
Cancer is a genetic disease that is caused by mutations in oncogenes, tumor suppressor genes and stability genes. The fact that the metabolism of tumor cells is altered has been known for many years. However, the mechanisms and consequences of metabolic reprogramming have just begun to be understood. In this review, an integral view of tumor cell metabolism is presented, showing how metabolic pathways are reprogrammed to satisfy tumor cell proliferation and survival requirements. In tumor cells, glycolysis is strongly enhanced to fulfill the high ATP demands of these cells; glucose carbons are the main building blocks in fatty acid and nucleotide biosynthesis. Glutaminolysis is also increased to satisfy NADPH regeneration, whereas glutamine carbons replenish the Krebs cycle, which produces metabolites that are constantly used for macromolecular biosynthesis. A characteristic feature of the tumor microenvironment is acidosis, which results from the local increase in lactic acid production by tumor cells. This phenomenon is attributed to the carbons from glutamine and glucose, which are also used for lactic acid production. Lactic acidosis also directs the metabolic reprogramming of tumor cells and serves as an additional selective pressure. Finally, we also discuss the role of mitochondria in supporting tumor cell metabolism.
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Base Score Contribution
0.818
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.