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首页> 外文期刊>RSC Advances >Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks
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Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks

机译：使用GOLD纳米颗粒评估抗EGFR-iRGD重组蛋白：使用优化的深度神经网络对抗肿瘤效率的协同作用

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The epidermal growth factor receptor, also known as EGFR, is a tyrosine kinase receptor commonly found in epithelial tumors. As part of the first target for cancer treatment, EGFR has been the subject of intense research for more than 20 years; as a result, there are a number of anti-EGFR agents currently available. More recently, with our basic understanding of mechanisms related to receptor activation and function, both the secondary and primary forms of EGFR somatic mutations have led to the discovery of new anti-EGFR agents aimed at providing new insights into the clinical targeting of this receptor and possibly acting as an ideal model for developing strategies to target other types of receptors. In this study, we use genomic pattern to prove that EGFR is most frequently altered in GBM, glioma and astrocytoma; and analysed the prognostic potentiality of EGFR in glioma, which is a major type of brain tumor. Further we proposed a new screening technique for EGFR inhibitors by employing an in silico optimized deep neural network approach. This method was applied to screen a nanoparticle (NP) library, and it was concluded that gold NPs (AuNPs) induced significant inhibition of EGFR compared with other selected NPs. These findings were further analyzed by molecular docking, systems biology, time course simulations and synthetic biology (biological circuits), revealing that anti-EGFR-iRGD and AuNP showed potential inhibition against tumors caused by EGFR.
机译：表皮生长因子受体，也称为EGFR，是一种常见于上皮肿瘤中的酪氨酸激酶受体。作为第一个癌症治疗靶标的一部分，EGFR一直是20多年来的研究热点。结果，目前有许多抗EGFR药物。最近，凭借对受体激活和功能相关机制的基本了解，EGFR体细胞突变的次级和初级形式都导致发现新的抗EGFR药物，旨在为该受体的临床靶向提供新的见解。可能是开发针对其他类型受体的策略的理想模型。在这项研究中，我们使用基因组模式来证明EGFR在GBM，神经胶质瘤和星形细胞瘤中最常见。并分析了EGFR在脑胶质瘤（一种脑肿瘤的主要类型）中的预后潜力。此外，我们通过采用计算机优化的深度神经网络方法，提出了一种针对EGFR抑制剂的新筛选技术。该方法用于筛选纳米颗粒（NP）库，得出的结论是，与其他选定的NPs相比，金NPs（AuNPs）可以显着抑制EGFR。通过分子对接，系统生物学，时程模拟和合成生物学（生物回路）进一步分析了这些发现，发现抗EGFR-iRGD和AuNP对EGFR引起的肿瘤具有潜在的抑制作用。

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