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Front. Genet. | doi: 10.3389/fgene.2019.00830

MPIC: Molecular Prognostic Indicators in Cirrhosis database for clinical context-specific in silico prognostic biomarker validation

Shun H. Yip1,  Naoto Fujiwara1,  Jason Burke2,  Anand Shetler1, Celina Peralta1, Tongqi Qian3, Hiroki Hoshida1,  Shijia Zhu1* and Yujin Hoshida1*
  • 1UT Southwestern Medical Center, United States
  • 2Broad Institute, United States
  • 3Icahn School of Medicine at Mount Sinai, United States

Prognostic biomarker is vital in the management of progressive chronic diseases such as liver cirrhosis, affecting 1-2% of global population and causing over 1 million deaths every year. Despite numerous candidate biomarkers in literature, costly and lengthy process of validation hampers their clinical translation. Existing omics databases are not suitable for in silico validation due to ignorance of critical factors, i.e., study design, clinical context of biomarker application, and statistical power. To address the unmet need, we have developed Molecular Prognostic Indicators in Cirrhosis (MPIC) database as a representative example of omics database tailored for prognostic biomarker validation. MPIC consists of (i) molecular and clinical database structured by defined disease context and specific clinical outcome and annotated with employed study design and anticipated statistical power by disease domain experts, (ii) bioinformatics analysis engine for user-provided gene-signature- or gene-based prognostic prediction, and (iii) user interface for interactive exploration of relevant clinical cohort/scenario and assessment of significance and reliability of the result for prognostic prediction. MPIC assists cost-effective prognostic biomarker development by facilitating the process of validation, and will transform the care of chronic diseases such as cirrhosis. MPIC is freely available at www.mpic-app.org. The website is implemented in Java, Apache, and MySQL with all major browsers supported.

Keywords: cirrhosis, Chronic Disease, Molecular signature, Study Design, Prognostic prediction

Received: 20 Jun 2019; Accepted: 12 Aug 2019.

Edited by:

Yudong Cai, School of Life Sciences, Shanghai University, China

Reviewed by:

Quan Zou, University of Electronic Science and Technology of China, China
Yu Wang, Jilin University, China  

Copyright: © 2019 Yip, Fujiwara, Burke, Shetler, Peralta, Qian, Hoshida, Zhu and Hoshida. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Shijia Zhu, UT Southwestern Medical Center, Dallas, 75390, Texas, United States, shijia.zhu@utsouthwestern.edu
Prof. Yujin Hoshida, UT Southwestern Medical Center, Dallas, 75390, Texas, United States, yujin.hoshida@utsouthwestern.edu