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Achieving highly sensitive and precise detection and imaging of cancer cells within the body is crucial for providing pathological information support in disease diagnosis and treatment. However, due to the existence of factors such as non-specific activation in local areas, membrane barriers, and the complexity of the intracellular environment, the tumor imaging strategies based on a single biomarker are subject to interference from false positive signals, which restricts the precise distinction between tumor tissues and normal tissues. To address these problems, we employ an “AND” logic gating mechanism, integrating the high ATP content and high expression of FEN1 within cancer cells as multiple targets for coordinated responses, to construct an ATP-mediated strategy for activating a self-assembled nanoprobe (named AFG probe) with FEN1-responsive properties for selective recognition and precise imaging of cancer cells. The design leverages the conformational adaptability of ATP aptamers to hybridize and assemble DNA nanoprobes with tunable sizes through the adjustment of the monomer. Once internalized within cancer cells, the high ATP concentrations present trigger a conformational shift of the aptamer, leading to the formation of substrate structures that can be specifically recognized by the intracellular target FEN1. These substrates undergo cleavage under the enzymatic action of FEN1, which leads to the dissociation of the nanoprobe and generates fluorescence signals. The results demonstrate that the programmable AFG probe exhibits high sensitivity, specificity, and stability. Furthermore, we verified the ability of the AFG probe to dynamically monitor the changes of FEN1 within cancer cells, and systematically evaluated its imaging performance in breast cancer cells, lung cancer cells, normal cells, lung cancer tissues and normal tissues, as well as in live mice. All these findings indicate that this probe, by utilizing the high concentration of ATP and high expression of FEN1 in cancer cells, can effectively distinguish tumor tissues from normal tissues. This strategy provides a idea for precise imaging of FEN1-related tumors and offers technical support for overcoming the problem of false positives based on a single biomarker.
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Tang et al. (2026) studied this question.
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