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Synergistic Tailoring of Electrostatic and Hydrophobic Interactions for Rapid and Specific Recognition of Lysophosphatidic Acid, an Early-Stage Ovarian Cancer Biomarker.

Ying WangHanwen PeiYan JiaJianhua LiuZelun LiKelong AiZhongyuan LuLehui Lu
Published in: Journal of the American Chemical Society (2017)
Early detection of ovarian cancer, the most lethal type of gynecologic cancer, can dramatically improve the efficacy of available treatment strategies. However, few screening tools exist for rapidly and effectively diagnosing ovarian cancer in early stages. Here, we present a facile "lock-key" strategy, based on rapid, specific detection of plasma lysophosphatidic acid (LPA, an early stage biomarker) with polydiacetylenes (PDAs)-based probe, for the early diagnosis of ovarian cancer. This strategy relies on specifically inserting LPA "key" into the PDAs "lock" through the synergistic electrostatic and hydrophobic interactions between them, leading to conformation transition of the PDA backbone with a concomitant blue-to-red color change. The detailed mechanism underlying the high selectivity of PDAs toward LPA is revealed by comprehensive theoretical calculation and experiments. Moreover, the level of LPA can be quantified in plasma samples from both mouse xenograft tumor models and patients with ovarian cancer. Impressively, this approach can be introduced into a portable point-of-care device to successfully distinguish the blood samples of patients with ovarian cancer from those of healthy people, with 100% accuracy. This work provides a valuable portable tool for early diagnosis of ovarian cancer and thus holds a great promise to dramatically improve the overall survival.
Keyphrases
  • early stage
  • loop mediated isothermal amplification
  • molecular dynamics simulations
  • radiation therapy
  • cancer therapy
  • deep learning
  • big data
  • endometrial cancer