Stability Of Urinary VOCs And Optimization Of Sample Preparation For Cancer Detection

Cancer is one of the leading causes of death worldwide, (1) however, several cancers can be
cured if they are detected early and treated effectively. (2) In this context, the URIDIAG
(Detection of volatile URInary biomarkers for cancer DIAGnostic) project aims to identify urinary
volatile organic compounds (VOCs) that could be used as cancer biomarkers. This non-invasive
approach could contribute to earlier and more accessible detection of the disease. However,
urine is a complex biological matrix, which requires the use of highly sensitive and high
resolution separation techniques. Comprehensive two-dimensional gas chromatography
coupled with high-resolution mass spectrometry (GC×GC-HRMS) is therefore a major asset for
urinary VOC analysis. Nevertheless, the reliability of the results does not only depend on
instrumental performance: but also on sample stability during transport and storage, as well as
on sample preparation before analysis, which are critical factors. In our study, access to
samples from affected patients is limited; therefore, minimizing experimental bias during the
pre-analytical steps is crucial to ensure reliable results. A study on the stability of urinary VOCs
was conducted by examining key pre-analytical factors. The most critical parameters identified
were the storage temperature before analysis, the time between sample collection and analysis,
and the type of container used after the first thawing step. This work provides a better
understanding of VOC stability in urine and highlights the conditions needed to reduce
experimental bias, thereby improving the robustness of further analyses. Based on these
results, the next step of this study aims to optimize sample preparation in order to achieve the
best sensitivity and reproducibility for urinary VOC analysis. For this purpose, different
extraction techniques like the Solid-Phase Microextraction (SPME), Dynamic Headspace (DHS),
and Stir Bar Sorptive Extraction (SBSE) will be compared to identify the most suitable approach.
The comparison will be based on sensitivity, reproducibility, extraction efficiency, and
robustness, as well as practical aspects such as analysis time and ease of implementation.