4.5 Review

A review of omics approaches to study preeclampsia

期刊

PLACENTA
卷 92, 期 -, 页码 17-27

出版社

W B SAUNDERS CO LTD
DOI: 10.1016/j.placenta.2020.01.008

关键词

Preeclampsia; Big data; Omics; Epigenetics; Proteomics; Transcriptomics; Metabolomics; Multi-omics; Integration; Network; Pathway; Biomarker

资金

  1. National Insitute of Enviromental Health Sciences through trans-National Insitute of Health (NIH) Big Data to Knowledge (BD2K) initiative [K01ES025434]
  2. National Insitute of General Medical Sciences (NIGMS) [P20 COBRE GM103457]
  3. National Library of Medicine (NLM) [R01 LM012373, LM 012907]
  4. National Institute of Child Health and Human Development (NICHD) [R01 HD084633]
  5. Department of Obstetrics and Gynecology, University of Hawaii

向作者/读者索取更多资源

Preeclampsia is a medical condition affecting 5-10% of pregnancies. It has serious effects on the health of the pregnant mother and developing fetus. While possible causes of preeclampsia are speculated, there is no consensus on its etiology. The advancement of big data and high-throughput technologies enables to study preeclampsia at the new and systematic level. In this review, we first highlight the recent progress made in the field of preeclampsia research using various omics technology platforms, including epigenetics, genome-wide association studies (GWAS), transcriptomics, proteomics and metabolomics. Next, we integrate the results in individual omic level studies, and show that despite the lack of coherent biomarkers in all omics studies, inhibin is a potential preeclamptic biomarker supported by GWAS, transcriptomics and DNA methylation evidence. Using network analysis on the biomarkers of all the literature reviewed here, we identify four striking sub-networks with clear biological functions supported by previous molecular-biology and clinical observations. In summary, omics integration approach offers the promise to understand molecular mechanisms in preeclampsia.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据