Prostate cancer screening using chemometric processing of GC–MS profiles obtained in the headspace above urine samples

Deev, V. A.; Solovieva, Svetlana; Andreev, Evgeny; Protoshchak, Vladimir V.; Karpushchenko, Evgeny; Sleptsov, Aleksander; Карцова, Л. А.; Бессонова, Е. А.; Legin, Andrey; Kirsanov, Dmitry

doi:10.1016/j.jchromb.2020.122298

Cited by 18 publications

(12 citation statements)

References 40 publications

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“…Production of VOCs by the human body differs greatly by individual, since VOCs are influenced by genetic background, environmental factors, and medical history, such as a comorbidity [14]. However, VOCs have been associated with many cancer types [15][16][17][18][19][20][21][22][23][24][25][26][27], and it has been suggested that they can be used as alternative cancer screening markers. Canine cancer detection, as well as the recent Caenorhabditis elegans cancer detection method, are believed to be based on these cancer-related VOCs [18,28].…”

Section: Discussionmentioning

confidence: 99%

The Trained Sniffer Dog Could Accurately Detect the Urine Samples from the Patients with Cervical Cancer, and Even Cervical Intraepithelial Neoplasia Grade 3: A Pilot Study

Yamamoto

Kamoi

Kurose

et al. 2020

Cancers

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(1) Background: Previous reports have indicated that cancers of the stomach, lung, and pancreas can be detected by dog sniffing, but results have been varied. Here, a highly trained dog was used to determine whether urine from patients with cervical premalignant lesions and malignant tumors have a cancer-specific scent. (2) Methods: A total of 195 urine samples were collected from patients with cervical cancer, cervical intraepithelial neoplasia grade 3 (CIN3), benign uterine diseases, and healthy volunteers. Each test was performed using one urine sample from a cancer patient and four samples from different controls. Each of the five urine samples was placed in a separate box. When the cancer sniffing dog stopped and sat in front of the box with a sample from a cancer patient, the test was considered as positive. (3) Results: 83 patients with cervical cancer (34 cases of cervical cancer and 49 cases of cervical intraepithelial neoplasia grade 3), 49 patients with uterine benign diseases, and 63 healthy volunteers were enrolled, and their urine samples were collected. In 83 times out of 83 runs in a double-blind test, the trained dog could correctly identify urine samples of cervical cancer patients. (4) Conclusion: A trained dog could accurately distinguish the urine of all patients with cervical cancer or CIN3, regardless of the degree of cancer progression.

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Section: Discussionmentioning

confidence: 99%

The Trained Sniffer Dog Could Accurately Detect the Urine Samples from the Patients with Cervical Cancer, and Even Cervical Intraepithelial Neoplasia Grade 3: A Pilot Study

Yamamoto

Kamoi

Kurose

et al. 2020

Cancers

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“…Raja et al 26 conducted a metabolomics study based on 1 H NMR for cancer targeting and metabolic engineering, using traditional chemometric methods such as principal component analysis (PC) and partial least-squares discriminant analysis (PLS-DA) to examine metabolic differences. Deev et al 27 developed prostate cancer screening methodologies using chemometric processing of GC−MS profiles obtained in the headspace above urine samples, where they applied the PC for exploratory data analysis, the methods k-nearest neighbors (KNN), and the PLS-DA to make the models allowing attribution of a urine sample to the particular class. Drivelos et al 28 developed methodologies for identifying the geographical origin and botanical type honey authentication through elemental metabolomics via chemometrics.…”

Section: ■ Introductionmentioning

confidence: 99%

Data-Driven Soft Independent Modeling of Class Analogy in Paper Spray Ionization Mass Spectrometry-Based Metabolomics for Rapid Detection of Prostate Cancer

et al. 2022

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Sensitive, rapid, and meaningful diagnostic tools for prostate cancer (PC) screening are urgently needed. Paper spray ionization mass spectrometry (PSI-MS) is an emerging rapid technology for detecting biomarker and disease diagnoses. Due to lack of chromatography and difficulties in employing tandem MS, PSI-MS-based untargeted metabolomics often suffers from increased ion suppression and subsequent feature detection, affecting chemometric methods for disease classification. This study first evaluated the data-driven soft independent modeling of class analogy (DD-SIMCA) model to analyze PSI-MS-based global metabolomics of a urine data matrix to classify PC. The efficiency of DD-SIMCA was analyzed based on the sensitivity and specificity parameters that showed 100% correct classification of the training set, based on only PC and test set samples, based on normal and PC. This analytical methodology is easy to interpret and efficient and does not require any prior information from the healthy individual. This new application of DD-SIMCA in PSI-MS-based metabolomics for PC disease classification could also be extended to other diseases and opens a rapid strategy to discriminate against health problems.

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“…The most used method to enhance compounds to headspace in urine is based on salt addition, which produces a variation in the equilibrium where neutral organic compounds move to the gas phase [ 28 ]. Studies that tested the salt addition found an increase in the number of compounds extracted in the headspace[ 27 , 29 – 32 ]. Nevertheless, salt saturation can be contra-productive due to opposite effects: a decrease in the number of compounds [ 30 , 33 ], and an increase in the degraded compounds [ 34 ].…”

Section: Urine Pretreatmentmentioning

confidence: 99%

The untargeted urine volatilome for biomedical applications: methodology and volatilome database

2022

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Chemically diverse in compounds, urine can give us an insight into metabolic breakdown products from foods, drinks, drugs, environmental contaminants, endogenous waste metabolites, and bacterial by-products. Hundreds of them are volatile compounds; however, their composition has never been provided in detail, nor has the methodology used for urine volatilome untargeted analysis. Here, we summarize key elements for the untargeted analysis of urine volatilome from a comprehensive compilation of literature, including the latest reports published. Current achievements and limitations on each process step are discussed and compared. 34 studies were found retrieving all information from the urine treatment to the final results obtained. In this report, we provide the first specific urine volatilome database, consisting of 841 compounds from 80 different chemical classes.

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Prostate cancer screening using chemometric processing of GC–MS profiles obtained in the headspace above urine samples

Cited by 18 publications

References 40 publications

The Trained Sniffer Dog Could Accurately Detect the Urine Samples from the Patients with Cervical Cancer, and Even Cervical Intraepithelial Neoplasia Grade 3: A Pilot Study

The Trained Sniffer Dog Could Accurately Detect the Urine Samples from the Patients with Cervical Cancer, and Even Cervical Intraepithelial Neoplasia Grade 3: A Pilot Study

Data-Driven Soft Independent Modeling of Class Analogy in Paper Spray Ionization Mass Spectrometry-Based Metabolomics for Rapid Detection of Prostate Cancer

The untargeted urine volatilome for biomedical applications: methodology and volatilome database

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