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MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery.

Ziling Fan¹, Yuan Zhou², Habtom W Ressom²

¹Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC 20057, USA.

|April 12, 2020

Summary

This summary is machine-generated.

MOTA, a network-based method, identifies more shared metabolite and cancer driver mRNA biomarkers for hepatocellular carcinoma (HCC) by integrating multi-omic data. This approach enhances biomarker discovery compared to traditional statistical methods.

Keywords:

differential network metabolomics multi-omic integration transcriptomics

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Area of Science:

Biomarker Discovery
Systems Biology
Network Medicine

Background:

Omic technologies enable system-level disease biomarker identification.
Integrative analysis of multi-layered biological data aids biomarker candidate ranking.

Purpose of the Study:

Propose MOTA, a network-based method for ranking disease biomarker candidates using multi-omic data.
Evaluate MOTA's ability to identify robust and biologically significant biomarkers.

Main Methods:

Developed MOTA, a network-based approach for multi-omic data integration.
Applied MOTA to three hepatocellular carcinoma (HCC) cohorts with liver cirrhosis.
Compared MOTA's performance against traditional statistical and differential expression methods.

Main Results:

MOTA identified more shared top-ranked metabolite biomarker candidates across cohorts than traditional methods.
Top-ranked mRNA candidates by MOTA included a higher proportion of cancer driver genes.
MOTA facilitates investigation into the biological significance of ranked biomarker candidates.

Conclusions:

MOTA effectively ranks disease biomarker candidates by integrating multi-omic data.
The method shows improved performance in identifying shared metabolite and cancer driver mRNA biomarkers for HCC.
MOTA offers a valuable tool for advancing biomarker discovery in complex diseases.