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Updated: Dec 6, 2025
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Reproducibility challenges for biomarker detection with uncertain but informative experimental data
Wei Zhuang1, Luísa Camacho2, Camila S Silva2
1Division of Bioinformatics & Biostatistics, NCTR, US FDA, Jefferson, AR 72079, USA.
Abstract:
Recent studies have revealed that circulating microRNAs are promising biomarkers for detecting toxicity or disease. Quantitative real-time polymerase chain reaction (qPCR) is often used to measure the levels of microRNAs. Besides complete and certain data, investigators inevitably have observed technically incomplete or uncertain qPCR data. Investigators usually set incomplete observations equal to the maximum quality number of qPCR cycles, apply the complete-observation method, or choose not to analyze targets with incomplete observations. Using biostatistical knowledge and published studies, we show that three commonly applied methods tend to cause biased inference and decrease reproducibility in biomarker detection. More efforts are needed to address the challenges to identify and detect reliable, novel circulating biomarkers in liquid biopsies.
Insights
Quantitative real-time PCR (qPCR) data for circulating microRNAs can be incomplete. Current methods for handling this uncertain data lead to biased results and reduced reproducibility in biomarker detection.
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.
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