Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks

Abstract

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.

Fig. 1. Pan-cancer genomic and transcriptomic profile of EGFR and KM plots. (A) Pan-cancer expressional profile of EGFR. “T” stands for tumor tissue and “N” stands for paired normal tissue. The expression abundance is measured by log-normalized transcripts per million (TPM). The green color of cancer type means that the expression of EGFR is significantly down-regulated in cancer tissue compared to paired normal tissue. The red color of cancer type means that the expression of EGFR is significantly up-regulated in cancer tissue compared to paired normal tissue. (B) Pan-cancer genomic alternation rate of EGFR. (C) KM plot for the EGFR mutation groups. (D) Plot for cut point determination of EGFR expression value. The optimal cut point is the expression value with the highest standardized log-rank statistics. (E) KM plots for the EGFR groups based on its expression.

Fig. 2. Anti-EGFR-IRGD docking pose with AuNP, where upper compartments represents the interaction with AuNP with molecular interaction view and lower compartment represents the AuNP interaction with anti-EGFR-iRGD.

Fig. 3. Figure depicts the anti-EGFR-iRGD endeavoured with NP (b) and without NP (a), anti-EGFR-iRGD with GOLD NP was to restrain the direction of malignancy where lines in figure speak to the 0 and 1 supply for each connecting entities. Blue delineate the dynamic shape and red show inert type of biological circuits.