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Identification of Novel Biomarkers and Candidate Drug in Ovarian Cancer.

Giou-Teng YiangLi-Te LinPei-Yi ChuAn-Jen ChiangHsiao-Wen TsaiYi-Han ChiuMei-Shu HuangZhi-Hong WenKuan-Hao Tsui
Published in: Journal of personalized medicine (2021)
This paper investigates the expression of the CREB1 gene in ovarian cancer (OV) by deeply excavating the gene information in the multiple databases and the mechanism thereof. In short, we found that the expression of the CREB1 gene in ovarian cancer tissue was significantly higher than that of normal ovarian tissue. Kaplan-Meier survival analysis showed that the overall survival was significantly shorter in patients with high expression of the CREB1 gene than those in patients with low expression of the CREB1 gene, and the prognosis of patients with low expression of the CREB1 gene was better. The CREB1 gene may play a role in the occurrence and development of ovarian cancer by regulating the process of protein. Based on differentially expressed genes, 20 small-molecule drugs that potentially target CREB1 with abnormal expression in OV were obtained from the CMap database. Among these compounds, we found that naloxone has the greatest therapeutic value for OV. The high expression of the CREB1 gene may be an indicator of poor prognosis in ovarian cancer patients. Targeting CREB1 may be a potential tool for the diagnosis and treatment of OV.
Keyphrases
  • poor prognosis
  • genome wide
  • long non coding rna
  • genome wide identification
  • copy number
  • small molecule
  • binding protein
  • healthcare
  • gene expression
  • transcription factor
  • machine learning
  • climate change
  • deep learning