Identification of the PCA29 gene signature as a predictor in prostate cancer

Jung Yu Lee, Si Yu Lin, Chun-Yu Lin, Yi Huan Chuang, Sing Han Huang, Yu Yao Tseng, Hung Jung Wang, Jinn-Moon Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Prostate cancer (PCa) is the second leading cause of cancer death among men worldwide. About 70% of PCa patients were diagnosed at later stage, and metastasis has been observed. Additionally, the cure rate of PCa closely relies on the early diagnosis with biomarkers. The identification of biomarkers for diagnosis and prognosis is an urgent clinical issue for PCa. Here, we developed a novel scoring strategy, including cluster score (CS) and predicting score (PS), to identify 29 PCa genes (called PCa29) for early diagnostic biomarkers from two datasets in Gene Expression Omnibus. The result indicates that PCa29 can discriminate between normal and tumor tissues and are specific for prostate cancer. To validate PCa29, we found that 97% of PCa29 were consistently significant with these gene expressions in The Cancer Genome Atlas; furthermore, a70% of PCa29 are consensus to the protein expression in The Human Protein Atlas. Finally, we examined 10 genes in PCa29 on three PCa cell lines by real-time quantitative polymerase chain reaction. The experimental results show that the trend of the differential PCa29 expression is consistent with the analyzed results from our novel scoring method. We believe that our method is useful and PCa29 are potential biomarkers that provide the clues to develop targeting therapy for PCa.

Original languageEnglish
Article number1940006
JournalJournal of Bioinformatics and Computational Biology
Issue number3
StatePublished - 1 Jun 2019


  • Prostate cancer
  • biomarkers
  • diagnosis
  • gene expression profiling
  • protein-protein interaction network


Dive into the research topics of 'Identification of the PCA29 gene signature as a predictor in prostate cancer'. Together they form a unique fingerprint.

Cite this