CRISPR/Cas9 (D10A) nickase-mediated Hb CS gene editing and genetically modified fibroblast identification.

This study investigated whether CRISPR/Cas9 (D10A) nickase-mediated gene editing can correct the aberrant Hb Constant Spring mutation (Hb CS or HBA2: c.427 T > C) in fibroblasts. Vectors for repairing the α-globin-encoding gene, HBA2:c.427 T > C mutation, includingthe CRISPR/Cas9(D10A)-sg plasmid and donor with homology arms, were constructed and used to perform gene editing in patient-derived fibroblasts. We subsequently analyzed the genetic correction, the gene editing efficiency and off-target effect. Sequencing analysis and the BamHI assay showed that HB CS mutant cells were repaired with Hb CS point mutations, the editing efficiency was 4.18%~9.34% and no off-target effects were detected. The results indicate that the HB CS mutant gene is effectively repaired by the CRISPR/Cas9 (D10A)system, which may enable truly personalized therapy for precise repair of α-thalassemia.

Functional classification of prostate cancer­associated miRNAs through CRISPR/Cas9­mediated gene knockout.

The aim of the present study was to use the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR­associated (Cas) 9­mediated gene knockout technology for the rapid classification of the differential function of micro (mi)RNAs screened using miRNA expression profiling by microarray. The rational design of single guide RNAs for the CRISPR/Cas9 system was verified to function in human LNCaP cells with rapid and efficient target gene editing. miRNA (miR)­205, miR­221, miR­222, miR­30c, miR­224, miR­455­3p, miR­23b and miR­505 were downregulated in patients with prostate cancer (PCa) and were experimentally validated to function as tumor suppressors in prostate cancer cells, affecting tumor proliferation, invasion and aerobic glycolysis. In addition, the data of the present study suggested that miR­663a and mfiR­1225­5p were upregulated in prostate cancer tissues and cell proliferation of miR­663a and miR­1225­5p knockout PCa cells was significantly lower compared with miR­NC cells. Furthermore, knockout of miR­1225­5p and miR­663a significantly decreased the lactate production in LNCaP cells in vitro. In conclusion, the present study offered a simple and efficient method for rapidly classifying miRNA function by applying CRISPR/Cas9 in LNCaP cells. The present study suggested, for the first time to the best of the authors' knowledge, that the aberrant expression of miR­663a and miR­1225­5p may be involved with the progression of prostate cancer, implying their potential as candidate markers for this type of cancer. However, the precise role of miR­663a and miR­1225­5p in accelerating the development of prostate cancer and promoting tumor progression remains to be elucidated.

The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks.

BACKGROUND: Prostate cancer (PCa) is the most common malignancy seen in men and the second leading cause of cancer-related death in males. The incidence and mortality associated with PCa has been rapidly increasing in China recently. METHODS: Multiple diagnostic models of human PCa were developed based on Taylor database by combining the artificial neural networks (ANNs) to enhance the ability of PCa diagnosis. Genetic algorithm (GA) is used to select feature genes as numerical encoded parameters that reflect cancer, metastatic, or normal samples. Back propagation (BP) neural network and learning vector quantization (LVQ) neural network were used to build different Cancer/Normal, Primary/Metastatic, and Gleason Grade diagnostic models. RESULTS: The performance of these modeling approaches was evaluated by predictive accuracy (ACC) and area under the receiver operating characteristic curve (AUC). By observing the statistically significant parameters of the three training sets, our Cancer/Normal, Primary/Metastatic, and Gleason Grade models' with ACC and AUC can be drawn (97.33%, 0.9832), (99.17%, 0.9952), and (90.48%, 0.8742), respectively. CONCLUSION: These results indicated that our diagnostic models of human PCa based on Taylor database combining the feature gene expression profiling data and artificial intelligence algorithms might act as a powerful tool for diagnosing PCa. Gleason Grade diagnostic models were used as novel prognostic diagnosis models for biochemical recurrence-free survival and overall survival, which might be helpful in the prognostic diagnosis of PCa in patients.

Increasing of FKBP9 can predict poor prognosis in patients with prostate cancer.

BACKGROUND: FK506 binding protein 9 (FKBP9) has been reported and identified for a long time, but its relationship with cancer is rarely studied. For example, the role of FK506 binding protein 9 in prostate cancer (PCa) is still unclear. Therefore, we decided to detect the expression level of FKBP9 in PCa and explore its clinical significance. METHODS: The expression level of FKBP9 protein was detected by immunohistochemistry. In addition, it was demonstrated by high-throughput sequencing of mRNA levels in the TCGA (cancer genome atlas) dataset of 499 patients. Kaplan-meier analysis and Cox proportional hazard regression model were used to evaluate the relationship between FKBP9 expression and survival in prostate cancer patients. RESULTS: The expression of FKBP9 was localized in the cytoplasm, which in normal prostate tissues was obviously lower than that in PCa tissues (P = 0.001). High expression of FKBP9 was related with lymph node metastasis (P = 0.022) and distant metastasis (P = 0.028). Kaplan-Meier survival analysis revealed that the BCR-free survival of PCa patients with high FKBP9 level was significantly shortened (P=0.041). CONCLUSIONS: FKBP9 may be a cancer promoter that enhances PCa progression, and the level of FKBP9 may be used as an independent precursor of PCa patients.