A systematic DoE approach for optimizing urinary LC-HRMS metabolomics: enhancing reliability in bladder cancer...

Area of Science:

• Analytical Chemistry
• Biochemistry
• Oncology

Background:

• Untargeted metabolomics faces challenges in reproducibility and interpretation for clinical oncology.
• Bladder cancer (BCa) biomarker discovery requires robust and reproducible analytical methods.

Purpose of the Study:

• To systematically optimize a liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflow for bladder cancer biomarker discovery.
• To improve the reproducibility and biological interpretation of untargeted urinary metabolomics.

Main Methods:

• A two-stage design of experiments (DoE) approach was used to optimize metabolite extraction parameters.
• Instrumental stability and injection precision were assessed using quality control (QC) samples.
• Principal component analysis (PCA) was employed to evaluate the reproducibility of instrumental performance.

Main Results:

• A comprehensive dataset of 15,344 metabolic signals was generated, with 854 compounds putatively identified.
• Reproducible instrumental performance was confirmed by tight QC sample clustering.
• Distinct disease-specific clustering was observed, revealing perturbations in tryptophan metabolism, lipid remodeling, and proteolytic activity in BCa patients.

Conclusions:

• The optimized LC-HRMS workflow provides a reliable analytical approach for non-invasive bladder cancer biomarker discovery.
• This methodology supports the development of diagnostic panels and efficient laboratory workflows aligned with Analytics 5.0 principles.