Electronic noses (e-nose) offer potential for the detection of cancer in its early stages. The ability to analyse samples in real time, at a low cost, applying easy–to-use and portable equipment, gives e-noses advantages over other technologies, such as Gas Chromatography-Mass Spectrometry (GC-MS). For diseases such as cancer with a high mortality, a technology that can provide fast results for use in routine clinical applications is important. Colorectal cancer (CRC) is among the highest occurring cancers and has high mortality rates, if diagnosed late. In our study, we investigated the use of portable electronic nose (PEN3), with further analysis using GC-TOF-MS, for the analysis of gases and volatile organic compounds (VOCs) to profile the urinary metabolome of colorectal cancer. We also compared the different cancer stages with non-cancers using the PEN3 and GC-TOF-MS. Results obtained from PEN3, and GC-TOF-MS demonstrated high accuracy for the separation of CRC and non-cancer. PEN3 separated CRC from non-cancerous group with 0.81 AUC (Area Under the Curve). We used data from GC-TOF-MS to obtain a VOC profile for CRC, which identified 23 potential biomarker VOCs for CRC. Thus, the PEN3 and GC-TOF-MS were found to successfully separate the cancer group from the non-cancer group.
Hepatocellular carcinoma (HCC) biomarkers are lacking in clinical practice. We therefore explored the pattern and composition of urinary volatile organic compounds (VOCs) in HCC patients. This was done in order to assess the feasibility of a potential non-invasive test for HCC, and to enhance our understanding of the disease. This pilot study recruited 58 participants, of whom 20 were HCC cases and 38 were non-HCC cases. The non-HCC cases included healthy individuals and patients with various stages of non-alcoholic fatty liver disease (NAFLD), including those with and without fibrosis. Urine was analysed using gas chromatography–ion mobility spectrometry (GC–IMS) and gas chromatography–time-of-flight mass spectrometry (GC–TOF-MS). GC–IMS was able to separate HCC from fibrotic cases with an area under the curve (AUC) of 0.97 (0.91–1.00), and from non-fibrotic cases with an AUC of 0.62 (0.48–0.76). For GC-TOF-MS, a subset of samples was analysed in which seven chemicals were identified and tentatively linked with HCC. These include 4-methyl-2,4-bis(p-hydroxyphenyl)pent-1-ene (2TMS derivative), 2-butanone, 2-hexanone, benzene, 1-ethyl-2-methyl-, 3-butene-1,2-diol, 1-(2-furanyl)-, bicyclo(4.1.0)heptane, 3,7,7-trimethyl-, [1S-(1a,3β,6a)]-, and sulpiride. Urinary VOC analysis using both GC–IMS and GC-TOF-MS proved to be a feasible method of identifying HCC cases, and was also able to enhance our understanding of HCC pathogenesis.
Bladder cancer (BCa) and prostate cancer (PCa) are some of the most common cancers in the world. In both BCa and PCa, the diagnosis is often confirmed with an invasive technique that carries a risk to the patient. Consequently, a non-invasive diagnostic approach would be medically desirable and beneficial to the patient. The use of volatile organic compounds (VOCs) for disease diagnosis, including cancer, is a promising research area that could support the diagnosis process. In this study, we investigated the urinary VOC profiles in BCa, PCa patients and non-cancerous controls by using gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) to analyse patient samples. GC-IMS separated BCa from PCa (area under the curve: AUC: 0.97 (0.93–1.00)), BCa vs. non-cancerous (AUC: 0.95 (0.90–0.99)) and PCa vs. non-cancerous (AUC: 0.89 (0.83–0.94)) whereas GC-TOF-MS differentiated BCa from PCa (AUC: 0.84 (0.73–0.93)), BCa vs. non-cancerous (AUC: 0.81 (0.70–0.90)) and PCa vs. non-cancerous (AUC: 0.94 (0.90–0.97)). According to our study, a total of 34 biomarkers were found using GC-TOF-MS data, of which 13 VOCs were associated with BCa, seven were associated with PCa, and 14 VOCs were found in the comparison of BCa and PCa.
Bladder cancer (BLC) and Prostate cancer (PC) are some of the most common cancers in the world, both having low five-year survival rates. In both BLC and PC, the diagnosis is often confirmed with an invasive technique that carries a risk to the patient. Consequently, a non-invasive diagnostic approach in these cancers would be medically desirable. The use of volatile organic compounds (VOCs) for disease diagnosis, including cancer, is a promising research area that could support the diagnosis process. In this study, we investigated the urinary VOC profiles in BLC, PC patients and non-cancerous controls by using gas chromatography–ion mobility spectrometry (GC–IMS) and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) to analyse patient samples. GC-IMS separated BLC from PC (AUC: 0.97 (0.93-1.00)), BLC Vs Non-cancerous (AUC: 0.95 (0.90-0.99).) and PC Vs Non-cancerous (AUC: 0.89 (0.83-0.94)) whereas GC-TOF-MS differentiated BLC from PC (AUC: 0.84 (0.73-0.93) BLC Vs Non-cancerous (AUC: 0.81 (0.70-0.90)) and PC vs Non-cancerous (AUC: 0.94 (0.90-0.97)). According to our study, a total of 34 biomarkers were found using GC-TOF-MS data, of which 13 VOCs were associated with BLC, 7 were associated with PC and 14 VOCs were found in the comparison of BLC and PC.
This study outlines the use of an electronic nose as a method for the detection of VOCs as biomarkers of bladder cancer. Here, an AlphaMOS FOX 4000 electronic nose was used for the analysis of urine samples from 15 bladder cancer and 41 non-cancerous patients. The FOX 4000 consists of 18 MOS sensors that were used to differentiate the two groups. The results obtained were analysed using s MultiSens Analyzer and RStudio. The results showed a high separation with sensitivity and specificity of 0.93 and 0.88, respectively, using a Sparse Logistic Regression and 0.93 and 0.76 using a Random Forest classifier. We conclude that the electronic nose shows potential for discriminating bladder cancer from non-cancer subjects using urine samples.
About Me
Heena Tyagi is a 3rd year Ph.D. student in Biomedical sensors in School of Engineering at University of Warwick.
Heena Tyagi is a 3rd year Ph.D. student in Biomedical sensors in School of Engineering at University of Warwick.