Reproducibility challenges for biomarker detection with uncertain but informative experimental data.

Area of Science:

• Biomarker Discovery
• Molecular Diagnostics
• Biostatistics

Background:

• Circulating microRNAs show promise as biomarkers for disease and toxicity detection.
• Quantitative real-time polymerase chain reaction (qPCR) is a standard method for measuring microRNA levels.
• Incomplete or uncertain qPCR data is frequently encountered in experimental settings.

Purpose of the Study:

• To evaluate the impact of commonly used methods for handling incomplete qPCR data on biomarker detection.
• To highlight the potential for biased inference and decreased reproducibility caused by these methods.

Main Methods:

• Biostatistical analysis of published studies.
• Review of common data handling techniques for incomplete qPCR observations.
• Assessment of the effects of imputation and exclusion methods on inference.

Main Results:

• Three prevalent methods for addressing incomplete qPCR data were found to introduce bias.
• These methods significantly decrease the reproducibility of circulating microRNA biomarker detection.
• Incomplete data handling poses a challenge to reliable biomarker identification.

Conclusions:

• Current approaches to managing uncertain qPCR data are inadequate for robust biomarker discovery.
• Further research and development of advanced statistical methods are crucial.
• Addressing data uncertainty is essential for identifying reliable circulating biomarkers in liquid biopsies.