Application of CRISPR-Cas9 genome editing technology in various fields: A review

Novel molecular classification and prognosis of papillary renal cell carcinoma based on a large-scale CRISPR-Cas9 screening and machine learning

Papillary renal cell carcinoma (PRCC) is a highly heterogeneous cancer, and PRCC patients with advanced/metastatic subgroup showed obviously shorter survival compared to other kinds of renal cell carcinomas. However, the molecular mechanism and prognostic predictors of PRCC remain unclear and are worth deep studying. The aim of this study is to identify novel molecular classification and construct a reliable prognostic model for PRCC. The expression data were retrieved from TCGA, GEO, GTEx and TARGET databases. CRISPR data was obtained from Depmap database. The key genes were selected by the intersection of CRISPR-Cas9 screening genes, differentially expressed genes, and genes with prognostic capacity in PRCC. The molecular classification was identified based on the key genes. Drug sensitivity, tumor microenvironment, somatic mutation, and survival were compared among the novel classification. A prognostic model utilizing multiple machine learning algorithms based on the key genes was developed and tested by independent external validation set. Our study identified three clusters (C1, C2 and C3) in PRCC based on 41 key genes. C2 had obviously higher expression of the key genes and lower survival than C1 and C3. Significant differences in drug sensitivity, tumor microenvironment, and mutation landscape have been observed among the three clusters. By utilizing 21 combinations of 9 machine learning algorithms, 9 out of 41 genes were chosen to construct a robust prognostic signature, which exhibited good prognostic ability. SERPINH1 was identified as a critical gene for its strong prognostic ability in PRCC by univariate and multiple Cox regression analyses. Quantitative real-time PCR and Western blot demonstrated that SERPINH1 mRNA and protein were highly expressed in PRCC cells compared with normal human renal cells. This study exhibited a new molecular classification and prognostic signature for PRCC, which may provide a potential biomarker and therapy target for PRCC patients.

Designing hybrid CRISPR-Cas12 and LAMP detection systems for treatment-resistant Plasmodium falciparum with in silico method

Genes associated with drug resistance of first line drugs for Plasmodium falciparum have been identified and characterized of which three genes most commonly associated with drug resistance are P. falciparum chloroquine resistance transporter gene (PfCRT), P. falciparum multidrug drug resistance gene 1 (PfMDR1), and P. falciparum Kelch protein K13 gene (PfKelch13). Polymorphism in these genes could be used as molecular markers for identifying drug resistant strains. Nucleic acid amplification test (NAAT) along with DNA sequencing is a powerful diagnostic tool that could identify these polymorphisms. However, current NAAT and DNA sequencing technologies require specific instruments which might limit its application in rural areas. More recently, a combination of isothermal amplification and CRISPR detection system showed promising results in detecting mutations at a nucleic acid level. Moreover, the Loop-mediated isothermal amplification (LAMP)-CRISPR systems offer robust and straightforward detection, enabling it to be deployed in rural and remote areas. The aim of this study was to develop a novel diagnostic method, based on LAMP of targeted genes, that would enable the identification of drug-resistant P. falciparum strains. The methods were centered on sequence analysis of P. falciparum genome, LAMP primers design, and CRISPR target prediction. Our designed primers are satisfactory for identifying polymorphism associated with drug resistant in PfCRT, PfMDR1, and PfKelch13. Overall, the developed system is promising to be used as a detection method for P. falciparum treatment-resistant strains. However, optimization and further validation the developed CRISPR-LAMP assay are needed to ensure its accuracy, reliability, and feasibility.