## 3 Results and Discussion  
### Characterisation of Urinary Volatile Metabolites  
VOMs have been described as a promising class of biomarkers for specific diseases through the definition of volatilomic biosignatures. These sets of VOMs have the potential to be used in early detection, as diagnostic tools, and to monitor therapeutic efficacy and disease follow-up [41, 42]. This study aimed to establish a urinary volatilomic profile of PCa to identify putative biomarkers for PCa diagnosis. The volatile composition of urine samples from the PCa patients (\(n\) = 26) and healthy subjects without any known pathology \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & **Control** & **Prostate Cancer** \\ \hline \multicolumn{1}{c}{**Number of subjects**} & 30 & 26 \\ \multicolumn{1}{c}{**Mean age \(\pm\) SD (years)**} & 46.21 \(\pm\) 11.58 & 66.92 \(\pm\) 9.14 \\ \multicolumn{1}{c}{**BMI (kg/m\({}^{2}\)) \(\pm\) SD**} & 27.67 \(\pm\) 3.78 & 27.34 \(\pm\) 3.40 \\ \multicolumn{1}{c}{**Smoking habits**} & & \\ \multicolumn{1}{c}{Ever smokers} & 6 & 16 \\ \multicolumn{1}{c}{Never smokers} & 19 & 10 \\ \multicolumn{1}{c}{Unknown} & 5 & 0 \\ \multicolumn{1}{c}{**PSA (ng/mL), \(n\) (\%)**} & & \\ \(<\)4 & 30 (100\%) & 1 (3.85\%) \\ \(4\)–10 & - & 13 (50.00\%) \\ \(>\)10 & - & 12 (46.15\%) \\ \multicolumn{1}{c}{**Gleason score, \(n\) (\%)**} & & \\ \(\leq\)6 & - & 4 (15.38\%) \\ \(7\) & - & 12 (46.15\%) \\ \(\geq\)8 & - & 10 (38.46\%) \\ \multicolumn{1}{c}{**Grade group, \(n\) (\%)**} & & \\ \(1\) & - & 4 (15.38\%) \\ \(2\) & - & 8 (30.77\%) \\ \(3\) & - & 4 (15.38\%) \\ \(4\) & - & 9 (34.62\%) \\ \(5\) & - & 1 (3.85\%) \\ \hline \hline \end{tabular} Legend: BMI: body mass index; SD: standard deviation.  
\end{table} 
Table 2: Demographic and clinical data of the cancer-free controls and prostate cancer patients included in this study. (control group, \(n=30\)) (Table 1) was established using HS-SPME/GC-MS, according to the experimental procedure described. Following the HS-SPME/GC-MS analysis of the urine samples of the 56 recruited subjects, different chromatographic profiles were obtained from the control group and the PCa patients (Figure 2). Overall, 147 VOMs were identified in the analysed samples, belonging to different chemical families, which included 13 ketones, five aldehydes, three esters, one alcohol, three carboxylic acids, seven sulfur compounds, 16 benzene derivatives, five naphthalene derivatives, 11 phenolic compounds, seven furanic compounds, 15 hydrocarbons, four heterocyclic hydrocarbons, 35 terpen, 19 norisoprenoids, and three sesquiterpenes (Table S1, Supplementary Materials). Detailed analysis of each sample group showed differences in terms of areas for the different chemical families (Figure 3). As a result of bacterial activity, metabolism, pH changes, or breakdown of urine constituents, the human urinary profile changes over time. It is also influenced by external factors, including health status, dietary habits, physical stress, and environmental exposure, which along with exogenous compounds, contribute to an individual's volatilomic profile [11]. Due to these factors, the human metabolism is very complex, and cancer development and progression make it even more difficult to understand all the metabolic processes that may contribute to an increase or decrease in certain metabolites [35, 43, 44]. Thus, it is crucial to establish a relationship between the identified VOMs and their potential endogenous origin; however, the origin of many VOMs has not been clearly defined [8]. Terpenes, phenolic compounds, and norisoprenoids were the chemical families that contributed the most to the volatilomic pattern of the studied groups (Figure 3). Norisoprenoids, phenolic, and terpenic compounds can be easily found in different exogenous sources such as food [45, 46].