Integrative bioinformatic analyses of an oncogenomic profile reveal the biology of endometrial cancer and guide drug discovery

Henry Sung Ching Wong, Yung-Shun Juan, Mei-Shin Wu, Yan-Feng Zhang, Yu-Wen Hsu, Huang-Hui Chen, Wei-Min Liu, Wei-Chiao Chang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

A major challenge in personalized cancer medicine is to establish a systematic approach to translate huge oncogenomic datasets to clinical situations and facilitate drug discovery for cancers such as endometrial carcinoma. We performed a genomewide somatic mutation-expression association study in a total of 219 endometrial cancer patients from TCGA database, by evaluating the correlation between ~5,800 somatic mutations to ~13,500 gene expression levels (in total, ~78, 500, 000 pairs). A bioinformatics pipeline was devised to identify expression-associated single nucleotide variations (eSNVs) which are crucial for endometrial cancer progression and patient prognoses. We further prioritized 394 biologically risky mutational candidates which mapped to 275 gene loci and demonstrated that these genes collaborated with expression features were significantly enriched in targets of drugs approved for solid tumors, suggesting the plausibility of drug repurposing. Taken together, we integrated a fundamental endometrial cancer genomic profile into clinical circumstances, further shedding light for clinical implementation of genomic-based therapies and guidance for drug discovery.
Original languageEnglish
Pages (from-to)5909-5923
Number of pages15
JournalOncotarget
Volume7
Issue number5
Publication statusPublished - 2016

Keywords

  • Cancer drivers
  • Cancer drug repurposing
  • Cancer genomics
  • Endometrial cancers
  • Expression-associated somatic mutations

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