Search
Close this search box.

Urinary cancer detection by the target urine volatile organic compounds biosensor platform – Scientific Reports

Participants’ characteristics

The authors assessed urinary volatile organic compounds (VOCs) in a cohort of 64 participants between December 2021 and September 2023 at Suranaree University of Technology Hospital, Thailand (Table 1). The participants were divided into two groups: a cancer group and a non-cancer group. The genitourinary cancer group included 32 patients, consisting of renal cell carcinoma (3.1%), transitional cell carcinoma of the bladder (46.9%), and prostate cancer (adenocarcinoma) (50%). The non-cancer group also had 32 patients, with 9 being healthy subjects and 23 having other genitourinary diseases, such as lupus nephritis (2 patients), primary glomerular diseases, including membranous nephropathy, IgA nephropathy, minimal change disease (5 patients), and benign prostatic hypertrophy (16 patients).

Table 1 Clinical characteristics of participants.

Among the participants, the mean age at the time of evaluation was 70 years, with 42.2% being male. The estimated glomerular filtration rate (eGFR) of the study population was 82 mL/min. The researchers specified that all participants must have an eGFR greater than 60 mL/min to prevent the potential influence of impaired GFR due to advanced chronic kidney disease or acute kidney injury on the analysis of urine VOCs.

Participants who were not normal subjects required confirmation of their diagnosis through patho-histology to be included in the cancer group. Within the cancer group, 35 patients were in stages 1–2, while patients with primary glomerular diseases and lupus nephritis received standard medications, including corticosteroids, mycophenolate mofetil, and calcineurin inhibitors (CNIs; cyclosporine or tacrolimus), along with cyclophosphamide. In the cancer group, patients who were selected were those who had not yet responded to treatment and were undergoing chemotherapy, including Cisplatin and Gemcitabine. Importantly, patients in the cancer group had to have no comorbidities that present in the non-cancer group and had to have cancer in the same organ only, ensuring precise diagnostic interpretation.

When comparing the general characteristics of both groups, as shown in Table 1, it was observed that the patients in the cancer group were significantly older than those in the non-cancer group (P < 0.001). However, no other statistically significant clinical characteristics were identified between the study groups.

Potential Urine VOCs determine genitourinary cancer

In the study’s objectives, which aimed to find comparative data for screening urine VOCs in patients with genitourinary cancers (KUB) and compare them with VOCs in a normal population that may also have other non-cancer-related diseases, the researchers examined to identify significant variations in electrical resistance changes within semiconductors, following the methodology protocol, among three distinct groups: cancer patients, non-cancer patients (with other renal diseases), and normal subjects.

The findings revealed significant differences in the alteration of electrical resistance, particularly in sensors S2 and S4, for specific parameters. However, no significant differences were observed between the population groups in S1, S3, and S5 across all parameters (details are provided in the Supplementary data). It was observed that the electrical resistance of VOC samples from patients differed from those of non-cancer patients and normal subjects in sensors 2 (methane, iso-butane, hydrogen, ethanol). This variation was evident when applying a voltage heater at 2500 mV and measuring parameters called “min” (the lowest point of the electrical resistance), “gap” (the delta resistance changes), “t” (time from the highest to the lowest point of the electrical resistance), and “SL” (the slope of the graph plotting time against the decrease in electrical resistance). Furthermore, at 3500 mV in sensor 2, differences were identified in the parameters “gap,” “t,” and “SL,” while at 4500 mV, differences were observed in all three study groups, particularly in the “SL” parameter.

Additionally, for sensor 4 (Ethanol, Hydrogen, Methane, Isobutane, Propane) at the 2000 mV level, the “min” parameter was significant. For sensors 1, 3, 5, and other parameters of S2 and 4, no significant differences were found among the study groups.

Receiver operating characteristic (ROC) analysis for predicting KUB cancers from others (non-cancer disease and normal subjects)

To achieve the study’s objectives, which involved utilizing urine VOCs as a screening biomarker tool to distinguish KUB cancers from non-cancer diseases and normal subjects, the researchers calculated the area of ROC analysis for sensor 2 in all parameters. This choice was made based on the results of the T-test, which indicated significant differences in sensor 2 across several parameters (gap, t, SL). In contrast, for the other sensors, only sensor 4 parameter “min” at the 2000 mV voltage heater level was considered. To facilitate the analysis, the non-cancer disease and normal subject groups were combined into a single “non-cancer group,” enabling binary data to be used in ROC analysis (Table 2).

Table 2 Receiver operating characteristic (ROC) analysis

It was found that sensor 2, which detected VOCs in the methane, iso-butane, hydrogen, and ethanol groups, at a voltage heater of 2000 mV with parameters gap (the delta resistance changes) and Sl (the slope of the graph plotting time against the decrease of electrical resistance) showed a significant area under the ROC curve (AUC) that could be used to distinguish cancer from non-cancer. In contrast, there was no significant AUC for sensor 4 at parameter min (the lowest point of electrical resistance) when using a voltage heater of 2000 mV to differentiate cancer from non-cancer. This observation was made through ROC analysis when diagnosing cancer compared to the non-disease group, as previously observed in the t-test analysis.

The cut point of electrical resistance of VOCs to differentiate KUB cancer

For determining the Cut Point of Electrical Resistance of VOCs to distinguish KUB cancer, further analysis was conducted using ROC to find the cut-off point based on the Youden’s index, which captured the performance of a dichotomous diagnostic test. From the VOC parameters with significant AUC in distinguishing cancer from non-cancer, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of each parameter were determined. These cut-off points from the Youden’s index were selected from the potential parameters that demonstrated the best performance in the diagnostic test, providing the highest accuracy for each parameter, as presented in Table 3.

Table 3 Receiver operating characteristic (ROC) analysis

The target urine VOCs for screening genito-urinary cancer

The researchers utilized the cut-off points obtained from Youden’s index, as presented in Table 3, to perform logistic regression analysis. The analysis revealed that all three potential VOC parameters displayed statistically significant correlations when predicting genito-urinary cancer in patients. Notably, the parameters gap1S2 and SL1S2 exhibited collinearity, meaning they had similar characteristics, as evidenced by the consistent results from logistic regression and ROC analysis. Consequently, the researchers chose to incorporate gap1S2 with a cut-off point of ≥ 30 and gap4S2 with a cut-off point of ≥ 862 to create a new biomarker for analysis. These new biomarkers were used to find a significant association when predicting genito-urinary cancer in patients, and its area under the ROC curve (AUC) was greater compared to using individual VOC parameters for the diagnosis of genito-urinary cancer, as shown in Table 4 and Fig. 2. When comparing the use of the combined parameters gap1S2 and gap4S2 with the individual parameters gap1S2 and gap4S2, the AUC was found to be 0.72, 0.64, and 0.67, respectively. All three of these parameters were statistically significant in their predictive capabilities (P < 0.05).

Table 4 Logistic regression and ROC analysis of potential urine VOCs for diagnosis of the genito-urinary cancer.
Figure 2
figure 2

Comparing the areas under the ROC curve (AUC); It is found that using a combination of parameters, which were “gap1S2” with a cut point ≥ 30 and “gap4S2” with a cut point ≥ 862, yields a higher AUC (0.72) compared to using VOC parameters alone in predicting the occurrence of genitourinary cancer. However, the three parameters, including “gap1S2,” “gap4S2,” and the combined parameter “gap1S2 + gap4S2,” showed statistical significance in predicting the occurrence of the disease, with p < 0.05.