Development of a computational promoter with highly efficient expression in tumors

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number480
JournalBMC cancer
Issue number1
Publication statusPublished - 2018 Apr 27

All Science Journal Classification (ASJC) codes

  • Oncology
  • Genetics
  • Cancer Research


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