Development of a computational promoter with highly efficient expression in tumors

• Main Authors: Shu-Yi Ho, Bo-Hau Chang, Chen-Han Chung, Yu-Ling Lin, Cheng-Hsun Chuang, Pei-Jung Hsieh, Wei-Chih Huang, Nu-Man Tsai, Sheng-Chieh Huang, Yen-Ku Liu, Yu-Chih Lo, Kuang-Wen Liao
• Format: Article
• Language: English
• Published: BMC 2018-04-01
• Series: BMC Cancer
• Subjects: Gene therapy; Transcription factor; HIF-1α; NF-κB and CREB
• Online Access: http://link.springer.com/article/10.1186/s12885-018-4421-7

Abstract Background Gene therapy is a potent method to increase the therapeutic efficacy against cancer. However, a gene that is specifically expressed in the tumor area has not been identified. In addition, nonspecific expression of therapeutic genes in normal tissues may cause side effects that can harm the patients’ health. Certain promoters have been reported to drive therapeutic gene expression specifically in cancer cells; however, low expression levels of the target gene are a problem for providing good therapeutic efficacy. Therefore, a specific and highly expressive promoter is needed for cancer gene therapy. Methods Bioinformatics approaches were utilized to analyze transcription factors (TFs) from high-throughput data. Reverse transcription polymerase chain reaction, western blotting and cell transfection were applied for the measurement of mRNA, protein expression and activity. C57BL/6JNarl mice were injected with pD5-hrGFP to evaluate the expression of TFs. Results We analyzed bioinformatics data and identified three TFs, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), cyclic AMP response element binding protein (CREB), and hypoxia-inducible factor-1α (HIF-1α), that are highly active in tumor cells. Here, we constructed a novel mini-promoter, D5, that is composed of the binding sites of the three TFs. The results show that the D5 promoter specifically drives therapeutic gene expression in tumor tissues and that the strength of the D5 promoter is directly proportional to tumor size. Conclusions Our results show that bioinformatics may be a good tool for the selection of appropriate TFs and for the design of specific mini-promoters to improve cancer gene therapy.