Urinary volatile compounds as biomarkers for lung cancer: a proof of principle study using odor signatures in mouse models of lung cancer

Urinary VOCs as biomarkers of early stage lung tumour development in mice

BACKGROUND

Lung cancer is the primary cause of cancer-induced death. In addition to prevention and improved treatment, it has increasingly been established that early detection is critical to successful remission.

OBJECTIVE

The aim of this study was to identify volatile organic compounds (VOCs) in urine that could help diagnose mouse lung cancer at an early stage of its development.

METHODS

We analysed the VOC composition of urine in a genetically engineered lung adenocarcinoma mouse model with oncogenic EGFR doxycycline-inducible lung-specific expression. We compared the urinary VOCs of 10 cancerous mice and 10 healthy mice (controls) before and after doxycycline induction, every two weeks for 12 weeks, until full-blown carcinomas appeared. We used SPME fibres and gas chromatography - mass spectrometry to detect variations in cancer-related urinary VOCs over time.

RESULTS

This study allowed us to identify eight diagnostic biomarkers that help discriminate early stages of cancer tumour development (i.e., before MRI imaging techniques could identify it).

CONCLUSION

The analysis of mice urinary VOCs have shown that cancer can induce changes in odour profiles at an early stage of cancer development, opening a promising avenue for early diagnosis of lung cancer in other models.

Investigating the effects of storage conditions on urinary volatilomes for their reliability in disease diagnosis

BACKGROUND

Cancer detection presents challenges regarding invasiveness, cost, and reliability. As a result, exploring alternative diagnostic methods holds significant clinical importance. Urinary metabolomic profiling has emerged as a promising avenue; however, its application for cancer diagnosis may be influenced by sample preparation or storage conditions.

OBJECTIVE

This study aimed to assess the impact of sample storage and processing conditions on urinary volatile organic compounds (VOCs) profiles and establish a robust standard operating procedure (SOP) for such diagnostic applications.

METHODS

Five key variables were investigated: storage temperatures, durations, freeze-thaw cycles, sample collection conditions, and sample amounts. The analysis of VOCs involved stir bar sorptive extraction coupled with thermal desorption-gas chromatography/mass spectrometry (SBSE-TD-GC-MS), with compound identification facilitated by the National Institute of Standards and Technology Library (NIST). Extensive statistical analysis, including combined scatterplot and response surface (CSRS) plots, partial least squares-discriminant analysis (PLS-DA), and probability density function plots (PDFs), were employed to study the effects of the factors.

RESULTS

Our findings revealed that urine storage duration, sample amount, temperature, and fasting/non-fasting sample collection did not significantly impact urinary metabolite profiles. This suggests flexibility in urine sample collection conditions, enabling individuals to contribute samples under varying circumstances. However, the influence of freeze-thaw cycles was evident, as VOC profiles exhibited distinct clustering patterns based on the number of cycles. This emphasizes the effect of freeze-thaw cycles on the integrity of urinary profiles.

CONCLUSIONS

The developed SOP integrating SBSE-TD-GC-MS and statistical analyses can serve as a valuable tool for analyzing urinary organic compounds with minimal preparation and sensitive detection. The findings also support that urinary VOCs for cancer screening and diagnosis could be a feasible alternative offering a robust, non-invasive, and sensitive approach for cancer screening.

A Novel Urine Test Biosensor Platform for Early Lung Cancer Detection

Lung cancer is the leading cause of cancer-related mortality worldwide. Early detection is essential to achieving a better outcome and prognosis. Volatile organic compounds (VOCs) reflect alterations in the pathophysiology and body metabolism processes, as shown in various types of cancers. The biosensor platform (BSP) urine test uses animals' unique, proficient, and accurate ability to scent lung cancer VOCs. The BSP is a testing platform for the binary (negative/positive) recognition of the signature VOCs of lung cancer by trained and qualified Long-Evans rats as biosensors (BSs). The results of the current double-blind study show high accuracy in lung cancer VOC recognition, with 93% sensitivity and 91% specificity. The BSP test is safe, rapid, objective and can be performed repetitively, enabling periodic cancer monitoring as well as an aid to existing diagnostic methods. The future implementation of such urine tests as routine screening and monitoring tools has the potential to significantly increase detection rate as well as curability rates with lower healthcare expenditure. This paper offers a first instructive clinical platform utilizing VOC's in urine for detection of lung cancer using the innovative BSP to deal with the pressing need for an early lung cancer detection test tool.

Ants act as olfactory bio-detectors of tumours in patient-derived xenograft mice

Early detection of cancer is critical in medical sciences, as the sooner a cancer is diagnosed, the higher are the chances of recovery. Tumour cells are characterized by specific volatile organic compounds (VOCs) that can be used as cancer biomarkers. Through olfactory associative learning, animals can be trained to detect these VOCs. Insects such as ants have a refined sense of smell, and can be easily and rapidly trained with olfactory conditioning. Using urine from patient-derived xenograft mice as stimulus, we demonstrate that individual ants can learn to discriminate the odour of healthy mice from that of tumour-bearing mice and do so after only three conditioning trials. After training, they spend approximately 20% more time in the vicinity of the learned odour than beside the other stimulus. Chemical analyses confirmed that the presence of the tumour changed the urine odour, supporting the behavioural results. Our study demonstrates that ants reliably detect tumour cues in mice urine and have the potential to act as efficient and inexpensive cancer bio-detectors.

The neglected potential of invertebrates in detecting disease via olfaction

Agents that cause disease alter the cell metabolism of their hosts. Cells with an altered metabolism produce particular profiles of biomolecules, which are different from those of healthy cells. Such differences may be detected by olfaction. Historically, physicians used olfactory cues to diagnose sickness by smelling the breath or the urine of patients. However, other species have been shown to possess excellent olfactory abilities. Dogs, for instance, have been frequently used as biodetectors of human diseases, including cancer, viral and bacterial infections. Other mammalian species, such as rats, have been trained to perform similar tasks, but their disease detection abilities remain poorly explored. Here, we focus on the overlooked potential of invertebrate species and we review the current literature on olfactory detection of diseases by these animals. We discuss the possible advantages of exploring further the abilities of invertebrates as detection tools for human disease.

The Versatility and Diagnostic Potential of VOC Profiling for Noninfectious Diseases

A variety of volatile organic compounds (VOCs) are produced and emitted by the human body every day. The identity and concentration of these VOCs reflect an individual's metabolic condition. Information regarding the production and origin of VOCs, however, has yet to be congruent among the scientific community. This review article focuses on the recent investigations of the source and detection of biological VOCs as a potential for noninvasive discrimination between healthy and diseased individuals. Analyzing the changes in the components of VOC profiles could provide information regarding the molecular mechanisms behind disease as well as presenting new approaches for personalized screening and diagnosis. VOC research has prioritized the study of cancer, resulting in many research articles and reviews being written on the topic. This review summarizes the information gained about VOC cancer studies over the past 10 years and looks at how this knowledge correlates with and can be expanded to new and upcoming fields of VOC research, including neurodegenerative and other noninfectious diseases. Recent advances in analytical techniques have allowed for the analysis of VOCs measured in breath, urine, blood, feces, and skin. New diagnostic approaches founded on sensor-based techniques allow for cheaper and quicker results, and we compare their diagnostic dependability with gas chromatography- and mass spectrometry-based techniques. The future of VOC analysis as a clinical practice and the challenges associated with this transition are also discussed and future research priorities are summarized.

Ability of animals to detect cancer odors

The olfactory capacity of animals has long been used by humans to help with various activities, e.g., hunting, detecting mines, locating people, and diagnosing diseases. Cancer is among the leading diseases causing death worldwide. Several recent studies have underscored the benefit of using scent to detect cancer, and this paper will review the studies using animals to detect tumor scents. A large variety of animals have been used for this purpose-dogs, rodents, insects, and nematodes-and have shown their capacity to detect cancer, with a success rate close to 90%. Here we discuss these studies, their methodologies, and the animal models used. Finally, we discuss the medical perspectives for cancer diagnosis using odors.

Bile-volatile organic compounds in the diagnostics of pancreatic cancer and biliary obstruction: A prospective proof-of-concept study

OBJECTIVES

Detection of volatile organic compounds (VOCs) from bodily fluids with field asymmetric waveform ion mobility spectrometry (FAIMS) and related methods has been studied in various settings. Preliminary results suggest that it is possible to detect prostate, colorectal, ovarian and pancreatic cancer from urine samples. In this study, our primary aim was to differentiate pancreatic cancer from pancreatitis and benign tumours of the pancreas by using bile samples obtained during endoscopic retrograde cholangiopancreatography (ERCP). Secondarily, we aimed to differentiate all pancreatic region malignancies from all other kinds of benign causes of biliary obstruction.

METHODS

A bile sample was successfully aspirated from 94 patients during ERCP in Tampere University Hospital. Hospital and patient records were prospectively followed up for at least two years after ERCP. Bile samples were analysed using a Lonestar chemical analyser (Owlstone, UK) using an ATLAS sampling system and a split-flow box. Diagnoses and corresponding data from the analyses were matched and divided into two subcategories for comparison. Statistical analysis was performed using linear discriminant analysis, support vector machines, and 5-fold cross-validation.

RESULTS

Pancreatic cancers (n=8) were differentiated from benign pancreatic lesions (n=9) with a sensitivity of 100%, specificity of 77.8%, and correct rate of 88%. All pancreatic region cancers (n=19) were differentiated from all other kinds of benign causes of biliary obstruction (n=75) with corresponding values of 21.1%, 94.7%, and 80.7%. The sample size was too small to try to differentiate pancreatic cancers from adjacent cancers.

CONCLUSION

Analysing bile VOCs using FAIMS shows promising capability in detecting pancreatic cancer and other cancers in the pancreatic area.

Non-invasive cancer detection in canine urine through Caenorhabditis elegans chemotaxis

Cancer is the leading cause of death in companion animals, and successful early treatment has been a challenge in the veterinary field. We have developed the Non-Invasive Cancer Screening (N.C.S.) Study to perform cancer detection through the analysis of canine urine samples. The test makes use of the strong olfactory system of the nematode Caenorhabditis elegans, which was previously shown to positively respond to urine samples from human cancer patients. We performed a proof-of-concept study to optimize the detection capability in urine samples obtained from dogs with naturally occurring cancers. In this study, we established a scale for identifying the cancer risk based on the magnitude of the chemotaxis index of C. elegans toward a canine urine sample. Through validation, the N.C.S. Study achieved a sensitivity of 85%, showing that it is highly sensitive to indicate the presence of cancer across multiple types of common canine cancers. The test also showed a 90% specificity to cancer samples, indicating a low rate of over-identifying cancer risk. From these results, we have demonstrated the ability to perform low-cost, non-invasive cancer detection in companion animals—a method that can increase the ability to perform cancer diagnosis and treatment.

Changes in the urine volatile metabolome throughout growth of transplanted hepatocarcinoma

Trained detection dogs distinguish between urine samples from healthy organisms and organisms with malignant tumors, suggesting that the volatile urine metabolome contains information about tumor progression. The aim of this study was to determine whether the stage of tumor growth affects the chemical differences in the urine of mice and to what extent the "olfactory image of disease" perceived by dogs coincides with the "image of disease" recorded by the mass spectrometer. We used a novel laser ionization mass spectrometry method and propose a mass spectrometric analysis without detailed interpretation of the spectrum of volatile metabolomes in urine. The mass spectrometer we use works without sample preparation and registers volatile organic compounds in air at room temperature without changing the pH of the sample, i.e. under conditions similar to those in which dogs solve the same problem. The experimental cancer models were male BDF-f1 hybrid mice transplanted with hepatocarcinoma tissue, and similar mice transplanted with healthy liver tissue were used as controls. Our data show that both dogs and our proposed laser mass spectrometry method are able to detect both the entire spectrum of volatile organic compounds associated with the disease and minor changes in this spectrum during its course.

Hierarchical Elemental Odor Coding for Fine Discrimination Between Enantiomer Odors or Cancer-Characteristic Odors

Odors trigger various emotional responses such as fear of predator odors, aversion to disease or cancer odors, attraction to male/female odors, and appetitive behavior to delicious food odors. Odor information processing for fine odor discrimination, however, has remained difficult to address. The olfaction and color vision share common features that G protein-coupled receptors are the remote sensors. As different orange colors can be discriminated by distinct intensity ratios of elemental colors, such as yellow and red, odors are likely perceived as multiple elemental odors hierarchically that the intensities of elemental odors are in order of dominance. For example, in a mixture of rose and fox-unique predator odors, robust rose odor alleviates the fear of mice to predator odors. Moreover, although occult blood odor is stronger than bladder cancer-characteristic odor in urine samples, sniffer mice can discriminate bladder cancer odor in occult blood-positive urine samples. In forced-choice odor discrimination tasks for pairs of enantiomers or pairs of body odors vs. cancer-induced body odor disorders, sniffer mice discriminated against learned olfactory cues in a wide range of concentrations, where correct choice rates decreased in the Fechner's law, as perceptual ambiguity increased. In this mini-review, we summarize the current knowledge of how the olfactory system encodes and hierarchically decodes multiple elemental odors to control odor-driven behaviors.

Odours of cancerous mouse congeners: detection and attractiveness

Chemical communication plays a major role in social interactions. Cancer, by inducing changes in body odours, may alter interactions between individuals. In the framework of research targeting non-invasive methods to detect early stages of cancer development, this study asked whether untrained mice could detect odour changes in cancerous congeners. If yes, were they able to detect cancer at an early developmental stage? Did it influence female preference? Did variations in volatile organic components of the odour source paralleled mice behavioural responses? We used transgenic mice strains developing or not lung cancer upon antibiotic ingestion. We sampled soiled bedding of cancerous mice (CC) and not cancerous mice (NC), at three experimental conditions: before (T0), early stage (T2) and late stage (T12) of cancer development. Habituation/generalisation and two-way preference tests were performed where soiled beddings of CC and NC mice were presented to wild-derived mice. The composition and relative concentration of volatile organic components (VOC) in the two stimuli types were analysed. Females did not show directional preference at any of the experimental conditions, suggesting that cancer did not influence their choice behaviour. Males did not discriminate between CC and NC stimuli at T0 but did so at T2 and T12, indicating that wild-derived mice could detect cancer at an early stage of development. Finally, although the VOC bouquet differed between CC and NC it did not seem to parallel the observed behavioural response suggesting that other types of odorant components might be involved in behavioural discrimination between CC and NC mice.

Summary: Male mice could discriminate the smell of cancerous congeners even when the tumour was hardly detectable by other means; however, females did not discriminate against the smell of males carrying cancerous tumours. Odorant molecules other than volatile organic compounds analysed here might explain the observed behaviour.

Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer

The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to identify urinary volatile organic metabolites (uVOMs) to discriminate among LC patients from CTRL. Overall, 147 VOMs of several chemistries were identified in the intervention groups-including naphthalene derivatives, phenols, and organosulphurs-augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.

Changes in gaseous concentration of alkylpyrazine analogs affect mouse avoidance behavior

We developed a rapid and accurate method for quantifying gaseous phase odorants using headspace solid-phase microextraction (HS-SPME) in conjunction with GC-MS and used our system to quantify alkylpyrazine analogs in the Y-maze. Rapid extraction of volatile compounds in the vapor phase achieved accurate quantitative analysis of gaseous alkylpyrazine analogs at several locations in the Y-maze. We also used a series of three SPME fibers to quantify changes in the concentration over time. We conducted a behavioral test of mice in response to these alkylpyrazines and identified a positive relationship between the rate of increase in gaseous concentration and the avoidance rate induced. Our results demonstrate that the Y-maze is a simple but reliable apparatus for behavioral studies of olfaction. The HS-SPME fast extraction method can quantify how gaseous concentrations of alkylpyrazines change over time, and the time-dependent increase of alkylpyrazine concentration is correlated with induction of aversive behavior in mice.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Identification of odor biomarkers in irradiation injury urine based on headspace SPME-GC-MS

PURPOSE

The threat of population exposure to ionizing radiation is increasing rapidly worldwide. Such exposure, especially at high-dose, is known to cause acute radiation syndrome (ARS). Hence, it is necessary to develop specific and sensitive biomarkers to accurately diagnose radiation injury and evaluate medical countermeasures.

MATERIALS AND METHODS

Caenorhabditis elegans (C. elegans), a model organism with a fine and sound olfactory system, was used to examine the odor of urine samples collected from irradiation-injured rats, and compared with those from un-irradiated control rats to investigate the 'special odor' of radiation injury. Subsequently, headspace SPME-GC-MS was applied for non-targeted metabolomic analysis of volatile organic compounds (VOCs) in urine, with the aim to discover changes of small molecule metabolites and identify odor biomarkers of irradiation injury.

RESULTS

C. elegans showed significant attraction to the urine of total body irradiation (TBI) rats compared with control rats, indicating that irradiation injury can emit 'special odor' and the metabolites in urine VOCs were changed. Using metabolomics based on headspace SPME-GC-MS for metabolic profiles analysis, we screened 63 differentially expressed metabolites. Among them, 10 metabolites including p-Cresol with excellent diagnostic ability were identified as odor biomarkers according to receiver operating characteristic (ROC) curve analysis.

CONCLUSIONS

This study confirmed the 'special odor' induced by irradiation injury, and identified biomarkers through urine VOCs analysis for the first time, which can provide a novel approach and insight to evaluate irradiation injury noninvasively, accurately and conveniently.[Figure: see text].

C. elegans-based chemosensation strategy for the early detection of cancer metabolites in urine samples

Chemosensory receptors play a crucial role in distinguishing the wide range of volatile/soluble molecules by binding them with high accuracy. Chemosensation is the main sensory modality in organisms lacking long-range sensory mechanisms like vision/hearing. Despite its low number of sensory neurons, the nematode Caenorhabditis elegans possesses several chemosensory receptors, allowing it to detect about as many odorants as mammals. Here, we show that C. elegans displays attraction towards urine samples of women with breast cancer, avoiding control ones. Behavioral assays on animals lacking AWC sensory neurons demonstrate the relevance of these neurons in sensing cancer odorants: calcium imaging on AWC increases the accuracy of the discrimination (97.22%). Also, chemotaxis assays on animals lacking GPCRs expressed in AWC allow to identify receptors involved in binding cancer metabolites, suggesting that an alteration of a few metabolites is sufficient for the cancer discriminating behavior of C. elegans, which may help identify a fundamental fingerprint of breast cancer.

Urinary malate dehydrogenase 2 is a new biomarker for early detection of non-small-cell lung cancer

Reliable and noninvasive biomarkers for the early diagnosis of non‐small‐cell lung cancer (NSCLC) are an unmet need. This study aimed to screen and validate potential urinary biomarkers for the early diagnosis of NSCLC. Using protein mass spectrometry, urinary MDH2 was found to be abundant both in patients with lung cancer and lung cancer model mice compared with controls. Urine samples obtained as retrospective and prospective cohorts including 1091 NSCLC patients and 736 healthy controls were measured using ELISA. Patients with stage I NSCLC had higher urinary MDH2 compared with healthy controls. The area under the receiver‐operating characteristic curve (AUC) for the urinary MDH2 was 0.7679 and 0.7234 in retrospective and prospective cohorts to distinguish stage I cases from controls. Urinary MDH2 levels correlated with gender and smoking history. MDH2 expression levels were elevated in lung cancer tissues. MDH2 knockdown using shRNA inhibited the proliferation of lung cancer cells. Our study demonstrated that urinary MDH2 concentration was higher in early‐stage NSCLC patients compared with that in controls and that MDH2 could serve as a potential biomarker for early detection of NSCLC.

Cancer and mosquitoes - An unsuspected close connection

Cancer is a major public health issue and represents a significant burden in countries with different levels of economic wealth. In parallel, mosquito-borne infectious diseases represent a growing problem causing significant morbidity and mortality worldwide. Acknowledging that these two concerns are both globally distributed, it is essential to investigate whether they have a reciprocal connection that can fuel their respective burdens. Unfortunately, very few studies have examined the link between these two threats. This review provides an overview of the possible links between mosquitoes, mosquito-borne infectious diseases and cancer. We first focus on the impact of mosquitoes on carcinogenesis in humans including the transmission of oncogenic pathogens through mosquitoes, the immune reactions following mosquito bites, the presence of non-oncogenic mosquito-borne pathogens, and the direct transmission of cancer cells. The second part of this review deals with the direct or indirect consequences of cancer in humans on mosquito behaviour. Thirdly, we discuss the potential impacts that natural cancers in mosquitoes can have on their life history traits and therefore on their vector capacity. Finally, we discuss the most promising research avenues on this topic and the integrative public health strategies that could be envisioned in this context.

Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile

Current available methods for the clinical diagnosis of urinary tract infection (UTI) rely on a urine dipstick test or culturing of pathogens. The dipstick test is rapid (available in 1-2 min), but has a low positive predictive value, while culturing is time-consuming and delays diagnosis (24-72 h between sample collection and pathogen identification). Due to this delay, broad-spectrum antibiotics are often prescribed immediately. The over-prescription of antibiotics should be limited, in order to prevent the development of antimicrobial resistance. As a result, there is a growing need for alternative diagnostic tools. This paper reviews applications of chemical-analysis instruments, such as gas chromatography-mass spectrometry (GC-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS) and electronic noses (eNoses) used for the diagnosis of UTI. These methods analyse volatile organic compounds (VOCs) that emanate from the headspace of collected urine samples to identify the bacterial pathogen and even determine the causative agent's resistance to different antibiotics. There is great potential for these technologies to gain wide-spread and routine use in clinical settings, since the analysis can be automated, and test results can be available within minutes after sample collection. This could significantly reduce the necessity to prescribe broad-spectrum antibiotics and allow the faster and more effective use of narrow-spectrum antibiotics.

Identification of characteristic compounds of moderate volatility in breast cancer cell lines

In this study, we were challenging to identify characteristic compounds in breast cancer cell lines. GC analysis of extracts from the culture media of breast cancer cell lines (MCF-7, SK-BR-3, and YMB-1) using a solid-phase Porapak Q extraction revealed that two compounds of moderate volatility, 1-hexadecanol and 5-(Z)-dodecenoic acid, were detected with markedly higher amount than those in the medium of fibroblast cell line (KMST-6). Furthermore, LC-TOF/MS analysis of the extracts clarified that in addition to the above two fatty acids, the amounts of five unsaturated fatty acids [decenoic acid (C10:1), decadienoic acid (C10:2), 5-(Z)-dodecenoic acid (C12:1), 5-(Z)-tetradecenoic acid (C14:1), and tetradecadienoic acid (C14:2)] in MCF-7 medium were higher than those in medium of KMST-6. Interestingly, H2O2-oxidation of 5-(Z)-dodecenoic acid and 5-(Z)-tetradecenoic acid produced volatile aldehydes that were reported as specific volatiles in breath from various cancer patients, such as heptanal, octanal, nonanal, decanal, 2-(E)-nonenal, and 2-(E)-octenal. Thus, we concluded that these identified compounds over-produced in breast cancer cells in this study could serve as potential precursors producing reported cancer-specific volatiles.

Gut metabolomics profiling of non-small cell lung cancer (NSCLC) patients under immunotherapy treatment

BACKGROUND

Despite the efficacy of immune checkpoint inhibitors (ICIs) only the 20-30% of treated patients present long term benefits. The metabolic changes occurring in the gut microbiota metabolome are herein proposed as a factor potentially influencing the response to immunotherapy.

METHODS

The metabolomic profiling of gut microbiota was characterized in 11 patients affected by non-small cell lung cancer (NSCLC) treated with nivolumab in second-line treatment with anti-PD-1 nivolumab. The metabolomics analyses were performed by GC-MS/SPME and ¹H-NMR in order to detect volatile and non-volatile metabolites. Metabolomic data were processed by statistical profiling and chemometric analyses.

RESULTS

Four out of 11 patients (36%) presented early progression, while the remaining 7 out of 11 (64%) presented disease progression after 12 months. 2-Pentanone (ketone) and tridecane (alkane) were significantly associated with early progression, and on the contrary short chain fatty acids (SCFAs) (i.e., propionate, butyrate), lysine and nicotinic acid were significantly associated with long-term beneficial effects.

CONCLUSIONS

Our preliminary data suggest a significant role of gut microbiota metabolic pathways in affecting response to immunotherapy. The metabolic approach could be a promising strategy to contribute to the personalized management of cancer patients by the identification of microbiota-linked "indicators" of early progressor and long responder patients.

Untargeted Urinary 1H NMR-Based Metabolomic Pattern as a Potential Platform in Breast Cancer Detection

Breast cancer (BC) remains the second leading cause of death among women worldwide. An emerging approach based on the identification of endogenous metabolites (EMs) and the establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the highest contribution towards discriminating BC patients from healthy controls (variable importance in projection (VIP) >1, p < 0.05). The discrimination efficiency and accuracy of the urinary EMs were ascertained by receiver operating characteristic curve (ROC) analysis that allowed the identification of some metabolites with the highest sensitivities and specificities to discriminate BC patients from healthy controls (e.g. creatine, glycine, trimethylamine N-oxide, and serine). The metabolomic pathway analysis indicated several metabolism pathway disruptions, including amino acid and carbohydrate metabolisms, in BC patients, namely, glycine and butanoate metabolisms. The obtained results support the high throughput potential of NMR-based urinary metabolomics patterns in discriminating BC patients from CTL. Further investigations could unravel novel mechanistic insights into disease pathophysiology, monitor disease recurrence, and predict patient response towards therapy.

Application of Urinary Volatile Organic Compounds (VOCs) for the Diagnosis of Prostate Cancer

BACKGROUND

Prostate cancer (PCa) screening using serum prostate-specific antigen (PSA) testing has caused unnecessary biopsies and overdiagnosis owing to its low accuracy and reliability. Therefore, there is an increased interest in identifying better PCa biomarkers. Studies showed that trained dogs can discriminate patients with PCa from unaffected men by sniffing urine. We hypothesized that urinary volatile organic compounds (VOCs) may be the source of that odor and could be used to develop urinary VOC PCa diagnosis models.

PATIENTS AND METHODS

Urine samples from 55 and 53 biopsy proven PCa-positive and -negative patients respectively were initially obtained for diagnostic model development. Urinary metabolites were analyzed by gas chromatography-mass spectrometry. A PCa diagnosis model was developed and validated using innovative statistical machine-learning techniques. A second set of samples (53 PCa-positive and 22 PCa-negative patients) were used to evaluate the previously developed PCa diagnosis model.

RESULTS

The analysis resulted in 254 and 282 VOCs for their significant association (P < .05) with either PCa-positive or -negative samples respectively. Regularized logistic regression analysis and the Firth method were then applied to predict PCa prevalence, resulting in a final model that contains 11 VOCs. Under cross-validation, the area under the receiver operating characteristic curve (AUC) for the final model was 0.92 (sensitivity, 0.96; specificity, 0.80). Further evaluation of the developed model using a testing cohort yielded an AUC of 0.86. As a comparison, the PSA-based diagnosis model only rendered an AUC of 0.54.

CONCLUSION

The study describes the development of a urinary VOC-based model for PCa detection.

Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data

The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.e., the biomarkers (e.g., gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets. The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation. BioDiscML is delivered as a stand-alone program and is available for download at https://github.com/mickaelleclercq/BioDiscML.

Detection of Volatile Organic Compounds (VOCs) in Urine via Gas Chromatography-Mass Spectrometry QTOF to Differentiate Between Localized and Metastatic Models of Breast Cancer

Breast cancer is the most common cancer detected in women and current screening methods for the disease are not sensitive. Volatile organic compounds (VOCs) include endogenous metabolites that provide information about health and disease which might be useful to develop a better screening method for breast cancer. The goal of this study was to classify mice with and without tumors and compare tumors localized to the mammary pad and tumor cells injected into the iliac artery by differences in VOCs in urine. After 4T1.2 tumor cells were injected into BALB/c mice either in the mammary pad or into the iliac artery, urine was collected, VOCs from urine headspace were concentrated by solid phase microextraction and results were analyzed by gas chromatography-mass spectrometry quadrupole time-of-flight. Multivariate and univariate statistical analyses were employed to find potential biomarkers for breast cancer and metastatic breast cancer in mice models. A set of six VOCs classified mice with and without tumors with an area under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00]) via five-fold cross validation. Classification of mice with tumors in the mammary pad and iliac artery was executed utilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]).

Machine Learning in Human Olfactory Research

The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data-driven approaches that complement hypothesis-driven research. Contemporary developments in computational and data science, with its currently most popular implementation as machine learning, facilitate complex data-driven research approaches. The use of machine learning in human olfactory research included major approaches comprising 1) the study of the physiology of pattern-based odor detection and recognition processes, 2) pattern recognition in olfactory phenotypes, 3) the development of complex disease biomarkers including olfactory features, 4) odor prediction from physico-chemical properties of volatile molecules, and 5) knowledge discovery in publicly available big databases. A limited set of unsupervised and supervised machine-learned methods has been used in these projects, however, the increasing use of contemporary methods of computational science is reflected in a growing number of reports employing machine learning for human olfactory research. This review provides key concepts of machine learning and summarizes current applications on human olfactory data.

Identification of volatile metabolites in human saliva from patients with oral squamous cell carcinoma via zeolite-based thin-film microextraction coupled with GC-MS

In recent years, volatile organic compounds (VOCs) discharged from the human body, of which some compounds exhibit strong correlations with pathological conditions, have attracted attention as a new means of disease diagnosis technology. The aim of this study was to establish the salivary metabolomic profiles of oral squamous cell carcinoma (OSCC) patients and healthy volunteers (control group) and to investigate VOCs as potential biomarkers in the diagnosis of oral cancer. We have demonstrated a method combining thin-film microextraction based on a ZSM-5/polydimethylsiloxane hybrid film coupled with gas chromatography-mass spectrometry and carried out a comparative analysis of salivary VOC profiles between OSCC patients and healthy controls. The results depicted that 42 and 73 VOCs were detected and identified in samples from the healthy control group (n = 50) and oral cancer group (n = 24), respectively. Among them, twenty-seven VOCs (ten were decreased, seven disappeared, and ten were newly produced in the oral cancer group) depict significant differences between both the sample groups, and they have relevance as candidate biomarkers for OSCC. Twelve salivary VOCs that were characteristic of oral cancer patients were finally extracted and used for pattern recognition analyses for oral cancer diagnosis. The proposed TFME approach for analyzing human saliva on the basis of a ZSM-5-loaded PDMS hybrid thin film has been performed for the very first time in the field of dentistry.

Exploring the potential of NTME/GC-MS, in the establishment of urinary volatomic profiles. Lung cancer patients as case study

The growing cancer incidence and mortality worldwide claims for the development of novel diagnostic strategies. In this study we aimed to explore the potential of an innovative methodology, based on a needle trap microextraction (NTME), combined with gas chromatography-mass spectrometry (GC-MS), as new approach to isolate and profile urinary volatile organic metabolites (VOMs) from lung cancer (LC) patients and healthy individuals (CTRL). In this context, different experimental parameters with influence of NTME extraction efficiency including, temperature, equilibration time, headspace volume, ionic strength, pH, effects of sample volume and stirring, were investigated and optimized. For the DVB/CarX/Car1000 needle trap device (NTD), the best results were obtained using 40 mL headspace of a 4-mL acidified (pH = 2) urine sample with 20% NaCl and an extraction temperature of 50 °C for 40 min of equilibration time. The stability of the isolated VOMs was investigated up to 72 h after extraction. From the VOMs identified, belonging namely to ketones, sulphur and benzene derivatives, 98 presented a frequency of occurrence above 90%. Data were processed by discriminant analysis, retrieving differentiated clusters for LC and CTRL groups. As far we are aware, this is the first study using NTME/GC-MS to establish urinary volatomic profiles. Preliminary results are very promising, as broad and comprehensive volatile profiles were obtained. Moreover, the extended storage stability of the NTD devices opens new opportunities for sampling other matrices in a wide range of applications.

Prospects and Challenges of Volatile Organic Compound Sensors in Human Healthcare

The chemical signatures of volatile organic compounds (VOCs) in humans can be utilized for point-of-care (POC) diagnosis. Apart from toxic exposure studies, VOCs generated in humans can provide insights into one's healthy and diseased metabolic states, acting as a biomarker for identifying numerous diseases noninvasively. VOC sensors and the technology of e-nose have received significant attention for continuous and selective monitoring of various physiological and pathophysiological conditions of an individual. Noninvasive detection of VOCs is achieved from biomatrices of breath, sweat and saliva. Among these, detection from sweat and saliva can be continuous in real-time. The sensing approaches include optical, chemiresistive and electrochemical techniques. This article provides an overview of such techniques. These, however, have limitations of reliability, precision, selectivity, and stability in continuous monitoring. Such limitations are due to lack of sensor stability and complexity of samples in a multivariate environment, which can lead to false readings. To overcome selectivity barriers, sensor arrays enabling multimodal sensing, have been used with pattern recognition techniques. Stability and precision issues have been addressed through advancements in nanotechnology. The use of various forms of nanomaterial not only enhance sensing performance, but also plays a major role in detection on a miniaturized scale. The rapid growth in medical Internet of Things (IoT) and artificial intelligence paves a pathway for improvements in human theranostics.

Menthol, a unique urinary volatile compound, is associated with chronic inflammation in interstitial cystitis

Chronic inflammation is a potential systemic risk factor for many bladder dysfunctions, including interstitial cystitis (IC). However, the underlying mechanism through which a healthy bladder protects itself from inflammatory triggers remains unknown. In this study, we identified odor compounds in urine obtained from IC patients and healthy controls. Using comprehensive solid-phase microextraction-gas chromatography-time-of-flight-mass spectrometry (SPME-GC-TOF-MS) profiling and bioinformatics, we found that levels of urinary volatile metabolites, such as menthol, were significantly reduced in IC patients, compared to healthy controls. In an attempt to understand the mechanistic meaning of our volatile metabolites data and the role of menthol in the immune system, we performed two independent experiments: (a) cytokine profiling, and (b) DNA microarray. Our findings suggest that lipopolysaccharide (LPS)-stimulated inflammatory events, such as the production and secretion of inflammatory cytokines (e.g., TNF-α, IL-6, and IL-1β) and the activation of NF-κB and associated proteins within a large signaling network (e.g., Akt, TLR1, TNFAIP3, and NF-κB), are suppressed by the presence of menthol. These findings broaden our knowledge on the role of urinary menthol in suppressing inflammatory events and provide potential new strategies for alleviating both the odor and inflammation associated with IC.

The volatilome - investigation of volatile organic metabolites (VOM) as potential tumor markers in patients with head and neck squamous cell carcinoma (HNSCC)

BACKGROUND

Different organisms such as bacteria, molds and humans produce and release a relative unknown class of metabolites which are responsible for the individual olfactory pattern. These volatile organic metabolites (VOM) represent a kind of biosignature that reflects the sum of all multifactorial influences, including genetics, environmental factors, nutritional and disease status. As a result of pathological processes the individual body odor can be influenced by newly produced or altered compositions of the VOM. Until now, human VOM have been detected in various body media, such as on the skin, in the exhaled air as well as in body fluids such as saliva, mother's milk, sweat, blood and urine.

METHODS

In this retrospective case-control study urinary VOM of 53 therapy-naive patients with head and neck squamous cell carcinoma (HNSCC) and 82 healthy controls were semi-quantified by headspace solid-phase micro extraction (SPME) gas chromatography (GC) mass spectrometry (MS). At first, the procedure was optimized in respect to the extraction parameters. By using Student's t-test significant differences in the VOM pattern with the corresponding p-values were obtained. For multivariate metabolite pattern recognition the hierarchical cluster analysis by Ward was applied, followed by the supervised partial least squares-discriminant analysis (PLS-DA).

RESULTS

In total 81 VOMs could identified in the urine samples of all study participants, of which 25 were significantly increased and of which were 10 significantly reduced in HNSCC-patients compared to the controls. In addition, the multivariate statistics confirmed that on the basis of the renal excreted pattern of the volatile metabolites a high discrimination can be carried out between patients with a tumor in the head and neck region and controls. The substance group of the saturated, unbranched aldehydes makes a substantial contribution in this context.

CONCLUSIONS

The systematic pattern analysis of urinary VOMs appears to have potential clinical application as a diagnostic tool for cancer, especially HNSCC.

A Proof of Concept: Are Detection Dogs a Useful Tool to Verify Potential Biomarkers for Lung Cancer?

Early and reliable diagnostic test is essential for effective therapy of lung cancer. Volatile organic compounds that are characteristic for cancer could serve as valuable biomarkers in cancer diagnosis. Both trace analytical and detection dog approaches give some evidence for the existence of such biomarkers. In this proof of concept, study dogs and trace analysis were implemented in combination to gain more information concerning cancer biomarkers. Two dogs were trained to distinguish between absorbed breath samples of lung cancer patients and healthy persons and succeeded with correct identification of patients with 9/9 and 8/9 and correct negative indications from of 8/10 and 4/10 samples from healthy individuals. A recent observational study found that breath samples from lung cancer patients showed an increase in 1-butanol, 2-butanone, 2-pentanone, and hexanal. Synthetic air samples were therefore fortified with these compounds and adsorbed to a fleece. Tested against breath samples from healthy probands, on presentation to the dogs these synthetic samples provoked an indication in three out of four samples. We were able to demonstrate that a combination of the natural nose of a dog and a trace analytic technique can be a valuable concept in the search for cancer biomarkers.

Thermotropic Liquid Crystal-Assisted Chemical and Biological Sensors

In this review article, we analyze recent progress in the application of liquid crystal-assisted advanced functional materials for sensing biological and chemical analytes. Multiple research groups demonstrate substantial interest in liquid crystal (LC) sensing platforms, generating an increasing number of scientific articles. We review trends in implementing LC sensing techniques and identify common problems related to the stability and reliability of the sensing materials as well as to experimental set-ups. Finally, we suggest possible means of bridging scientific findings to viable and attractive LC sensor platforms.

Sniffer mice discriminate urine odours of patients with bladder cancer: A proof-of-principle study for non-invasive diagnosis of cancer-induced odours

Similar to fingerprints, humans have unique, genetically determined body odours. In case of urine, the odour can change due to variations in diet as well as upon infection or tumour formation. We investigated the use of mice in a manner similar to “sniffer dogs” to detect changes in urine odour in patients with bladder cancer. We measured the odour discrimination thresholds of mice in a Y-maze, using urine mixtures from patients with bladder cancer (Stage I) and healthy volunteers (dietary variations) as well as occult blood- or antibiotic drug metabolite-modulated samples. Threshold difference indicated that intensities of urinary olfactory cues increase in the following order: dietary variation < bladder cancer < occult blood < antibiotic drug metabolites. After training with patient urine mixtures, sniffer mice discriminated between urine odours of pre- and post-transurethral resection in individual patients with bladder cancer in an equal-occult blood diluted condition below the detection level of dietary variations, achieving a success rate of 100% (11/11). Furthermore, genetic ablation of all dorsal olfactory receptors elevated the discrimination thresholds of mice by ≥ 10⁵-fold. The marked reduction in discrimination sensitivity indicates an essential role of the dorsal olfactory receptors in the recognition of urinary body odours in mice.

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

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.

Targeting Bcl-2 stability to sensitize cells harboring oncogenic ras

The pro-survival factor Bcl-2 and its family members are critical determinants of the threshold of the susceptibility of cells to apoptosis. Studies are shown that cells harboring an oncogenic ras were extremely sensitive to the inhibition of protein kinase C (PKC) and Bcl-2 could antagonize this apoptotic process. However, it remains unrevealed how Bcl-2 is being regulated in this apoptotic process. In this study, we investigate the role of Bcl-2 stability in sensitizing the cells harboring oncogenic K-ras to apoptosis triggered by PKC inhibitor GO6976. We demonstrated that Bcl-2 in Swiss3T3 cells ectopically expressing or murine lung cancer LKR cells harboring K-ras rapidly underwent ubiquitin-dependent proteasome pathway after the treatment of GO6976, accompanied with induction of apoptosis. In this process, Bcl-2 formed the complex with Keap-1 and Cul3. The mutation of serine-17 and deletion of BH-2 or 4 was required for Bcl-2 ubiquitination and degradation, which elevate the signal threshold for the induction of apoptosis in the cells following PKC inhibition. Thus, Bcl-2 appears an attractive target for the induction of apoptosis by PKC inhibition in cancer cells expressing oncogenic K-ras.

Brain Injury Alters Volatile Metabolome

Chemical signals arising from body secretions and excretions communicate information about health status as have been reported in a range of animal models of disease. A potential common pathway for diseases to alter chemical signals is via activation of immune function-which is known to be intimately involved in modulation of chemical signals in several species. Based on our prior findings that both immunization and inflammation alter volatile body odors, we hypothesized that injury accompanied by inflammation might correspondingly modify the volatile metabolome to create a signature endophenotype. In particular, we investigated alteration of the volatile metabolome as a result of traumatic brain injury. Here, we demonstrate that mice could be trained in a behavioral assay to discriminate mouse models subjected to lateral fluid percussion injury from appropriate surgical sham controls on the basis of volatile urinary metabolites. Chemical analyses of the urine samples similarly demonstrated that brain injury altered urine volatile profiles. Behavioral and chemical analyses further indicated that alteration of the volatile metabolome induced by brain injury and alteration resulting from lipopolysaccharide-associated inflammation were not synonymous. Monitoring of alterations in the volatile metabolome may be a useful tool for rapid brain trauma diagnosis and for monitoring recovery.

Alterations of the volatile metabolome in mouse models of Alzheimer's disease

In the present study, we tested whether the volatile metabolome was altered by mutations of the Alzheimer's disease (AD)-implicated amyloid precursor protein gene (APP) and comprehensively examined urinary volatiles that may potentially serve as candidate biomarkers of AD. Establishing additional biomarkers in screening populations for AD will provide enhanced diagnostic specificity and will be critical in evaluating disease-modifying therapies. Having strong evidence of gross changes in the volatile metabolome of one line of APP mice, we utilized three unique mouse lines which over-express human mutations of the APP gene and their respective non-transgenic litter-mates (NTg). Head-space gas chromatography/mass spectrometry (GC/MS) of urinary volatiles uncovered several aberrant chromatographic peak responses. We later employed linear discrimination analysis and found that the GC/MS peak responses provide accurate (>84%) genotype classification of urinary samples. These initial data in animal models show that mutant APP gene expression entails a uniquely identifiable urinary odor, which if uncovered in clinical AD populations, may serve as an additional biomarker for the disease.

Accuracy of volatile urine biomarkers for the detection and characterization of lung cancer

Background

The mixture of volatile organic compounds in the headspace gas of urine may be able to distinguish lung cancer patients from relevant control populations.

Methods

Subjects with biopsy confirmed untreated lung cancer, and others at risk for developing lung cancer, provided a urine sample. A colorimetric sensor array was exposed to the headspace gas of neat and pre-treated urine samples. Random forest models were trained from the sensor output of 70 % of the study subjects and were tested against the remaining 30 %. Models were developed to separate cancer and cancer subgroups from control, and to characterize the cancer. An additional model was developed on the largest clinical subgroup.

Results

90 subjects with lung cancer and 55 control subjects participated. The accuracies, reported as C-statistics, for models of cancer or cancer subgroups vs. control ranged from 0.795 – 0.917. A model of lung cancer vs. control built using only subjects from the largest available clinical subgroup (30 subjects) had a C-statistic of 0.970. Models developed and tested to characterize cancer histology, and to compare early to late stage cancer, had C-statistics of 0.849 and 0.922 respectively.

Conclusions

The colorimetric sensor array signature of volatile organic compounds in the urine headspace may be capable of distinguishing lung cancer patients from clinically relevant controls. The incorporation of clinical phenotypes into the development of this biomarker may optimize its accuracy.

Electronic Olfactory Sensor Based on A. mellifera Odorant-Binding Protein 14 on a Reduced Graphene Oxide Field-Effect Transistor

An olfactory biosensor based on a reduced graphene oxide (rGO) field-effect transistor (FET), functionalized by the odorant-binding protein 14 (OBP14) from the honey bee (Apis mellifera) has been designed for the in situ and real-time monitoring of a broad spectrum of odorants in aqueous solutions known to be attractants for bees. The electrical measurements of the binding of all tested odorants are shown to follow the Langmuir model for ligand-receptor interactions. The results demonstrate that OBP14 is able to bind odorants even after immobilization on rGO and can discriminate between ligands binding within a range of dissociation constants from K(d)=4 μM to K(d)=3.3 mM. The strongest ligands, such as homovanillic acid, eugenol, and methyl vanillate all contain a hydroxy group which is apparently important for the strong interaction with the protein.

Volatile Organic Metabolites Identify Patients with Mesangial Proliferative Glomerulonephritis, IgA Nephropathy and Normal Controls

Urinary volatile organic compounds (VOCs) analysis for kidney diseases has attracted a large amount of scientific interest recently, and urinary metabolite analysis has already been applied to many diseases. Urine was collected from 15 mesangial proliferative glomerulonephritis (MsPGN) patients, 21 IgA nephropathy (IgAN) patients and 15 healthy controls. Solid phase microextraction-chromatography- mass spectrometry (SPME-GC-MS) was used to analyse the urinary metabolites. The statistical methods principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLSDA) were performed to process the final data. Five metabolites were significantly greater in the group of MsPGN patients than in the normal control group (P < 0.05) while three metabolites were found at increased levels in the group of IgAN patients compared with the normal controls (P < 0.05). In addition, five metabolites were significantly increased in the group of IgAN patients compared with the MsPGN patients (P < 0.05). These five metabolites may be specific biomarkers for distinguishing between MsPGN and IgAN. The analysis of urinary VOCs appears to have potential clinical applications as a diagnostic tool.