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Free Radicals in Chemical Biology: from Chemical Behavior to Biomarker Development

Free Radicals in Chemical Biology: from Chemical Behavior to Biomarker Development

Published on: April 15, 2013

A biological function based biomarker panel optimization process.

Min Young Lee¹, Taek-Kyun Kim¹, Kathie-Anne Walters¹

¹Institute for Systems Biology, Seattle, Washington, United States of America.

Scientific Reports

|May 16, 2019

Summary

This summary is machine-generated.

Developing multi-gene biomarker panels for clinical use is challenging. This study presents a method to substitute and reduce genes based on biological function, maintaining diagnostic performance during platform transitions.

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Area of Science:

Biomarker Discovery
Translational Medicine
Molecular Diagnostics

Background:

High-throughput data (microarray, next-generation sequencing) identifies multi-gene biomarker panels.
Transitioning these panels to clinical platforms like quantitative polymerase chain reaction (qPCR) presents technical challenges, including inconsistent measurements.
These inconsistencies can hinder biomarker panel development and clinical utility.

Purpose of the Study:

To describe a systematic process for overcoming technical challenges in biomarker panel development during platform transition.
To demonstrate a method for replacing underperforming genes and reducing panel size without compromising classification performance.
To validate this optimization process using a sepsis diagnostic panel.

Main Methods:

Genes were substituted based on biological function and shared discriminatory power within biological processes.
The number of genes (features) in a diagnostic panel was reduced.
The performance of an optimized sepsis diagnostic panel was assessed after gene substitution and reduction.

Main Results:

Replacing over half the genes and/or reducing gene numbers based on biological processes did not negatively impact the sepsis diagnostic panel's performance.
The proposed systematic gene substitution and reduction process effectively addressed platform transition challenges.
Diagnostic panel classification performance was maintained despite significant changes in panel composition.

Conclusions:

A systematic approach to gene substitution and reduction based on biological function can alleviate challenges in clinical biomarker panel development.
This method facilitates the transition of multi-gene biomarker panels from discovery platforms to clinical settings.
The findings support the reliable clinical translation of molecular diagnostic panels.