Non-human primates (NHPs) represent the most valuable animals for drug discovery. However, the current main challenge remains that the NHP has not yet been used to develop an efficient translational medicine platform simulating human diseases, such as cancer. This study generated an in situ gene-editing approach to induce efficient loss-of-function mutations of Pten and p53 genes for rapid modeling primary and metastatic liver tumors using the CRISPR/Cas9 in the adult cynomolgus monkey. Under ultrasound guidance, the CRISPR/Cas9 was injected into the cynomolgus monkey liver through the intrahepatic portal vein. The results showed that the ultrasound-guided CRISPR/Cas9 resulted in indels of the Pten and p53 genes in seven out of eight monkeys. The best mutation efficiencies for Pten and p53 were up to 74.71% and 74.68%, respectively. Furthermore, the morbidity of primary and extensively metastatic (lung, spleen, lymph nodes) hepatoma in CRISPR-treated monkeys was 87.5%. The ultrasound-guided CRISPR system could have great potential to successfully pursue the desired target genes, thereby reducing possible side effects associated with hitting non-specific off-target genes, and significantly increasing more efficiency as well as higher specificity of in situ gene editing in vivo, which holds promise as a powerful, yet feasible tool, to edit disease genes to build corresponding human disease models in adult NHPs and to greatly accelerate the discovery of new drugs and save economic costs.
Background
Primary colorectal cancer (PCRC) is one of the most common malignant tumors in clinic, and is characterized by high heterogeneity occurring between tumors and intracellularly. Therefore, this study aimed to explore potential gene targets for the diagnosis and treatment of PCRC via bioinformatic technology.
Methods
Gene Expression Omnibus (GEO) was used to download the data used in this study. Differently expressed genes (DEGs) were identified with GEO2R, and the gene set enrichment analysis (GSEA) was implemented for enrichment analysis. Then, the researchers constructed a protein-protein interaction (PPI) network, a significant module, and a hub genes network.
Results
The GSE81558 dataset was downloaded, and a total of 97 DEGs were found. There were 23 up-regulated DEGs and 74 down-regulated DEGs in the PCRC samples, compared with the control group. The PPI network included a total of 42 nodes and 63 edges. One module network consisted of 11 nodes and 25 edges. Another module network consisted of 4 nodes and 6 edges. The hub genes network was created by cytoHubba using GCG, GUCA2B, CLCA4, ZG16, TMIGD1, GUCA2A, CHGA, PYY, SST, and MS4A12.
Conclusions
Ten hub genes were found from the genomic samples of patients with PCRC and normal controls by bioinformatics analysis. The hub genes might provide novel ideas and evidence for the diagnosis and targeted therapy of PCRC.
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