Melanoma Volatile Fingerprint with a Gas Sensor Array: In Vivo and In Vitro Study

Pennazza, Giorgio; Santonico, Marco; Bartolazzi, Armando; Martinelli, M.; Paolesse, Roberto; Natale, Corrado Di; Bono, R.; Tamburrelli, Vincenzopio; Cristina, S.; D’Amico, A.

doi:10.1016/j.proche.2009.07.248

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…In the present study, the volatile profile of human urine was studied, in an attempt to reveal a VOC‐biomarker panel for non‐invasive detection of this cancer. Despite the potential of VOCs for the discrimination between cancer and control samples being currently acknowledged , concerning RCC, the potentialities of human urine volatilome for that purpose are scarcely exploited .…”

Section: Discussionmentioning

confidence: 99%

“…Regarding this, the analysis of the volatile profile (volatilome), through the analysis of VOCs, is one of the most promising metabolomics‐derived approaches and, in fact, different volatile patterns have been correlated with a plethora of diseases, including cancer . Moreover, after trained dogs successfully discriminate patients with cancer from controls on the basis of urine odour , the potential of VOCs for the analysis of cancer was even more sustained by the development of some promising sensors array which function is dependent on the detection of VOCs emanating directly from cancer lesions , breath and urine . These studies provide a convincing support that the presence of cancer cells in the body leads to the alteration of VOCs emanating from biological samples, particularly of those closely correlated with tumoral tissues.…”

Section: Introductionmentioning

confidence: 99%

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GC‐MS metabolomics‐based approach for the identification of a potential VOC‐biomarker panel in the urine of renal cell carcinoma patients

Monteiro

Moreira

Pinto

et al. 2017

J Cellular Molecular Medi

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The analysis of volatile organic compounds (VOCs) emanating from biological samples appears as one of the most promising approaches in metabolomics for the study of diseases, namely cancer. In fact, it offers advantages, such as non‐invasiveness and robustness for high‐throughput applications. The purpose of this work was to study the urinary volatile metabolic profile of patients with renal cell carcinoma (RCC) (n = 30) and controls (n = 37) with the aim of identifying a potential specific urinary volatile pattern as a non‐invasive strategy to detect RCC. Moreover, the effect of some confounding factors such as age, gender, smoking habits and body mass index was evaluated as well as the ability of urinary VOCs to discriminate RCC subtypes and stages. A headspace solid‐phase microextraction/gas chromatography–mass spectrometry‐based method was performed, followed by multivariate data analysis. A variable selection method was applied to reduce the impact of potential redundant and noisy chromatographic variables, and all models were validated by Monte Carlo cross‐validation and permutation tests. Regarding the effect of RCC on the urine VOCs composition, a panel of 21 VOCs descriptive of RCC was defined, capable of discriminating RCC patients from controls in principal component analysis. Discriminant VOCs were further individually validated in two independent samples sets (nine RCC patients and 12 controls, seven RCC patients with diabetes mellitus type 2) by univariate statistical analysis. Two VOCs were found consistently and significantly altered between RCC and controls (2‐oxopropanal and, according to identification using NIST14, 2,5,8‐trimethyl‐1,2,3,4‐tetrahydronaphthalene‐1‐ol), strongly suggesting enhanced potential as RCC biomarkers. Gender, smoking habits and body mass index showed negligible and age‐only minimal effects on the urinary VOCs, compared to the deviations resultant from the disease. Moreover, in this cohort, the urinary volatilome did not show ability to discriminate RCC stages and histological subtypes. The results validated the value of urinary volatilome for the detection of RCC and advanced with the identification of potential RCC urinary biomarkers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GC‐MS metabolomics‐based approach for the identification of a potential VOC‐biomarker panel in the urine of renal cell carcinoma patients

Monteiro

Moreira

Pinto

et al. 2017

J Cellular Molecular Medi

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“…[357][358][359] Among the very few studies of skin-related VOCs published in the literature, a series of off-line analyses (i.e., collecting the samples by a specific collection method and then transferring the sample to as separate instrument for analysis) have examined the headspace of the skin of healthy subjects, 360,361 or of biopsy samples, taken by invasive surgical procedures from skin areas with melanoma. [362][363][364][365] In all of these studies, heavy, time-consuming and expensive spectrometry and/or electronic nose devices were used. To overcome these challenges, on-skin devices have been designed for sensing the disease VOCs and to continuously monitor the changes in the body in a non-invasive manner.…”

Section: Skin Volatolomicsmentioning

confidence: 99%

Skin bioelectronics towards long-term, continuous health monitoring

et al. 2022

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“…By developing technology and increasing demand for non-invasive methods for diagnosis diseases, the electronic nose is becoming a promising instrument in the medical domain. Commercial and experimental electronic noses have been developed for diagnosis of a wide range of diseases such as lung cancer [4], breast cancer [5], brain cancer [6] and melanoma [7], prostate cancer [8], colorectal cancer [2], asthma [9], and many other diseases. There are only few studies on diagnosing diabetes using urinary VOCs with eNose instruments [10,11].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Diagnosis of Diabetes by Volatile Organic Compounds in Urine Using FAIMS and Fox4000 Electronic Nose

et al. 2018

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The electronic nose (eNose) is an instrument designed to mimic the human olfactory system. Usage of eNose in medical applications is more popular than ever, due to its low costs and non-invasive nature. The eNose sniffs the gases and vapours that emanate from human waste (urine, breath, and stool) for the diagnosis of variety of diseases. Diabetes mellitus type 2 (DM2) affects 8.3% of adults in the world, with 43% being underdiagnosed, resulting in 4.9 million deaths per year. In this study, we investigated the potential of urinary volatile organic compounds (VOCs) as novel non-invasive diagnostic biomarker for diabetes. In addition, we investigated the influence of sample age on the diagnostic accuracy of urinary VOCs. We analysed 140 urine samples (73 DM2, 67 healthy) with Field-Asymmetric Ion Mobility Spectrometry (FAIMS); a type of eNose; and FOX 4000 (AlphaM.O.S, Toulouse, France). Urine samples were collected at UHCW NHS Trust clinics over 4 years and stored at −80 °C within two hours of collection. Four different classifiers were used for classification, specifically Sparse Logistic Regression, Random Forest, Gaussian Process, and Support Vector on both FAIMS and FOX4000. Both eNoses showed their capability of diagnosing DM2 from controls and the effect of sample age on the discrimination. FAIMS samples were analysed for all samples aged 0–4 years (AUC: 88%, sensitivity: 87%, specificity: 82%) and then sub group samples aged less than a year (AUC (Area Under the Curve): 94%, Sensitivity: 92%, specificity: 100%). FOX4000 samples were analysed for all samples aged 0–4 years (AUC: 85%, sensitivity: 77%, specificity: 85%) and a sub group samples aged less than 18 months: (AUC: 94%, sensitivity: 90%, specificity: 89%). We demonstrated that FAIMS and FOX 4000 eNoses can discriminate DM2 from controls using urinary VOCs. In addition, we showed that urine sample age affects discriminative accuracy.

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Melanoma Volatile Fingerprint with a Gas Sensor Array: In Vivo and In Vitro Study

Cited by 7 publications

References 6 publications

GC‐MS metabolomics‐based approach for the identification of a potential VOC‐biomarker panel in the urine of renal cell carcinoma patients

GC‐MS metabolomics‐based approach for the identification of a potential VOC‐biomarker panel in the urine of renal cell carcinoma patients

Skin bioelectronics towards long-term, continuous health monitoring

Non-Invasive Diagnosis of Diabetes by Volatile Organic Compounds in Urine Using FAIMS and Fox4000 Electronic Nose

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