ReviewAmerican journal of cancer research2026
Targeting ferroptosis in lung cancer: pharmacological regulation, nanomedicine-based delivery, and AI-enabled translational strategies.
Review in American journal of cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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4 authors.
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Abstract
The problem of lung cancer poses a great threat to the health of people all over the world. This threat is mainly associated with resistance to treatment. There is evidence that shows that ferroptosis fails to take place in lung cancer. There is a high degree of suppression in those that have become resistant to treatment. There seems to be a link between resistance to treatment and suppression of ferroptosis. This led to the development of a strategy to induce ferroptosis. In this review, the mechanisms of ferroptosis in drug-resistant non-small lung cancer cells will be discussed, along with a synopsis of recent studies analyzing combination therapies that involve the pharmacological induction of ferroptosis, thus further validating the notion that the prevention of ferroptosis plays an important role in drug resistance. Based on these principles, we will assess the inadequacies in current ferroptosis-inducing agents regarding their use in the treatment of lung cancer, focusing mainly on the suboptimal targeting of cancer cells and drug delivery efficiency. Conversely, these inadequacies have triggered new approaches in cancer therapy, which include using nanomedicine for improved drug delivery, functional nanomaterials for directly triggering ferroptosis, among others. In addition, we evaluate the validity of nanomaterials for precision medicine in cancer therapy, in addition to utilizing bioactive compounds. Lastly, we discuss the integration with artificial intelligence in single-cell sequence analysis for the development of modeling systems for ferroptosis control.
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