Volatile organic compounds in human matrices as lung cancer biomarkers: a systematic review

Identification of volatile biomarkers for lung cancer from different histological sources: A comprehensive study

The identification of noninvasive volatile biomarkers for lung cancer is a significant clinical challenge. Through in vitro studies, the recognition of altered metabolism in cell volatile organic compound (VOC) emitting profile, along with the occurrence of oncogenesis, provides insight into the biochemical pathways involved in the production and metabolism of lung cancer volatile biomarkers. In this research, for the first time, a comprehensive comparative analysis of the volatile metabolites in NSCLS cells (A549), SCLC cells (H446), lung normal cells (BEAS-2B), as well as metabolites in both the oxidative stress (OS) group and control group. Specifically, the combination of eleven VOCs, including n-dodecane, acetaldehyde, isopropylbenzene, p-ethyltoluene and cis-1,3-dichloropropene, exhibited potential as volatile biomarkers for lung cancer originating from two different histological sources. Furthermore, the screening process in A549 cell lines resulted in the identification of three exclusive biomarkers, isopropylbenzene, formaldehyde and bromoform. Similarly, the exclusive biomarkers 1,2,4-trimethylbenzene, p-ethyltoluene, and cis-1,3-dichloropropene were present in the H446 cell line. Additionally, significant changes in trans-2-pentene, acetaldehyde, 1,2,4-trimethylbenzene, and bromoform were observed, indicating a strong association with OS. These findings highlight the potential of volatile biomarkers profiling as a means of noninvasive identification for lung cancer diagnosis.

Research on the kinetics and degradation pathways of gaseous acetic acid ester organics

ABSTRACTDesigned to meet the specific needs of the printing industry exhaust gas emissions, this paper proposes a method for the degradation of gaseous acetic acid ester organics that is environmentally friendly, safe, and simple to use: micro-nano cavitation technology. In the process of using micro-nano cavitation technology to degrade acetic acid ester organics, the products in the degradation process were analyzed by gas chromatography-mass (GC-MS) spectrometry, and the degradation pathways of acetic acid ester organics were identified. Under high temperatures and high pressure caused by cavitation collapse, the C-C bond and C-O bond on the main chain of organic matter are cleaved to form low molecular products. Low-molecular intermediate products are continuously produced as the reaction advances, and these intermediate products are further oxidized and decomposed into carbon dioxide and water. Besides, the factors that influence the degradation rate of acetic acid ester organics were investigated. Based on the experimental data, acetic acid esters can degrade with the greatest efficiency when their initial concentration is 200 ± 50 mg/m3 and their treatment time is 20∼30 min. Moreover, the experiment was optimized using the response surface method. The results suggested that for an initial concentration of 155.544 mg/m3 and a reaction time of 21.961 min, the best degradation rate was 0.251 min-1. Micro-nano cavitation technology is a novel and promising technology for the degradation of volatile organic compounds, with a wide range of practical applications.

Highly sensitive serum volatolomic biomarkers for pancreatic cancer diagnosis

The discovery of new diagnostic tools for the early detection of diseases with poor prognosis such as pancreatic adenocarcinoma (PAC) is of high importance. The results from a control-case study (20 PAC patients, 19 healthy controls) for the search of new biomarkers of pancreatic cancer based in differences in the serum volatolome are presented in this work. Volatolomics were performed following a non-targeted HS-SPME-GC/MS approach, and a total of 433 volatile organic compounds (VOCs) was detected in the human serum samples. Of these, 125 VOC indexes showed a significant variation when controls and patients were compared (p-value < 0.05). Bonferroni corrected p-values < 0.05 were found for 40 features. PCA analysis showed the control-PAC discrimination capability of VOCs in serum, and PLS-DA was performed to select the best candidate biomarkers for the diagnosis of PAC. For the 40 selected VOCs, calculated areas under the curve (AUC) ranged from 0.98 to 0.85, and 11 of them were successfully validated using an independent set of samples (5 PAC patients, 5 healthy controls). Four of the proposed PAC biomarkers were identified as toluene, 2-ethyl-1-hexanol, pentylbenzene, and butoxymethylbenzene. Combinations of the identified PAC biomarkers were tested and showed AUC > 0.90, with the more promising candidate being butoxymethylbenzene (AUC = 0.98).

Exhaled breath and urinary volatile organic compounds (VOCs) for cancer diagnoses, and microbial-related VOC metabolic pathway analysis: a systematic review and meta-analysis

BACKGROUND

The gradual evolution of the detection and quantification of volatile organic compounds (VOCs) has been instrumental in cancer diagnosis. The primary objective of this study was to assess the diagnostic potential of exhaled breath and urinary VOCs in cancer detection. As VOCs are indicative of tumor and human metabolism, our work also sought to investigate the metabolic pathways linked to the development of cancerous tumors.

MATERIALS AND METHODS

An electronic search was performed in the PubMed database. Original studies on VOCs within exhaled breath and urine for cancer detection with a control group were included. A meta-analysis was conducted using a bivariate model to assess the sensitivity and specificity of the VOCs for cancer detection. Fagan's nomogram was designed to leverage the findings from our diagnostic analysis for the purpose of estimating the likelihood of cancer in patients. Ultimately, MetOrigin was employed to conduct an analysis of the metabolic pathways associated with VOCs in relation to both human and/or microbiota.

RESULTS

The pooled sensitivity, specificity and the area under the curve for cancer screening utilizing exhaled breath and urinary VOCs were determined to be 0.89, 0.88, and 0.95, respectively. A pretest probability of 51% can be considered as the threshold for diagnosing cancers with VOCs. As the estimated pretest probability of cancer exceeds 51%, it becomes more appropriate to emphasize the 'ruling in' approach. Conversely, when the estimated pretest probability of cancer falls below 51%, it is more suitable to emphasize the 'ruling out' approach. A total of 14, 14, 6, and 7 microbiota-related VOCs were identified in relation to lung, colorectal, breast, and liver cancers, respectively. The enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in the aforementioned tumor types.

CONCLUSIONS

The analysis of exhaled breath and urinary VOCs showed promise for cancer screening. In addition, the enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in four tumor types, namely lung, colorectum, breast and liver. These findings hold significant implications for the prospective clinical application of multiomics correlation in disease management and the exploration of potential therapeutic targets.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

Effect of chemotherapy on urinary volatile biomarkers for lung cancer by HS-SPME-GC-MS and chemometrics

BACKGROUND

Volatile organic compounds (VOCs) have been studied as possible biomarkers in several diseases, including lung cancer. Early detection of cancer can improve long-term survival rates and the quality of life, so the study of VOCs in exhaled breath and urine has been increasing in recent years. This study aimed to assess the urinary VOCs that are modified after chemotherapy to identify those with the potential to be lung cancer biomarkers that can be monitored during treatment.

METHODS

Three urine samples from 10 men with stage IV lung adenocarcinoma were collected, as well as urine samples from 14 men with other types of cancer (control group). All samples were analyzed by headspace-solid phase microextraction gas chromatography coupled with mass spectrometry.

RESULTS

A total of 21 urinary VOCs were found with different levels after the administration of chemotherapy, with 2-pentanone being one of those that significantly decreased. Furthermore, 2-pentanone and 3-hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate showed statistically significant differences with the control group.

CONCLUSIONS

Chemotherapy administered to patients with advanced lung adenocarcinoma modified the volatile profile of urine. 2-Pentanone, a final product of the increased rate of fatty acid oxidation and protein hypermetabolism, significantly decreased after chemotherapy. Therefore, monitoring its urinary excretion could be very useful since its decrease over time could indicate an adequate response to chemotherapy and arrest of cancer development. Another VOC that could be a potential lung cancer biomarker is 3 hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate, whose origin may be due to inhibition of the propanoic acid metabolic pathway or increased aldehyde dehydrogenase activity.

A PVDF-based colorimetric sensor array for noninvasive detection of multiple disease-related volatile organic compounds

Detection of human-generated volatile organic compounds (VOCs) is a new pathway for assessing health. Herein, a polyvinylidene fluoride (PVDF)-based colorimetric sensor array was designed for detecting disease-related VOCs (DVOCs) within 15 min, using a complex of Cu metal-organic framework, graphene aerogel, and dyes as response materials. Fingermaps derived from 28 DVOCs were obtained for further data processing. Pattern recognition was successfully employed in the correct discrimination of 28 DVOCs in low (10 μM), medium (100 μM), and high (300 μM) concentrations. Importantly, the sensor array also presented excellent discrimination ability and application potential when detecting VOCs produced by human cancer and normal cells. In general, VOC acquisition is noninvasive and harmless, and the PVDF-based sensor arrays are simple and visual. Such advantages expand their further application potential.

Critical appraisal tools used in systematic reviews of in vitro cell culture studies: A methodological study

Systematic reviews (SRs) of preclinical studies are marked with poor methodological quality. In vitro studies lack assessment tools to improve the quality of preclinical research. This methodological study aimed to identify, collect, and analyze SRs based on cell culture studies to highlight the current appraisal tools utilized to support the development of a validated critical appraisal tool for cell culture in vitro research. SRs, scoping reviews, and meta-analyses that included cell culture studies and used any type of critical appraisal tool were included. Electronic search, study selection, data collection and methodological quality (MQ) assessment tool were realized. Further, statistical analyses regarding possible associations and correlations between MQ and collected data were performed. After the screening process, 82 studies remained for subsequent analysis. A total of 32 different appraisal tools were identified. Approximately 60% of studies adopted pre-structured tools not designed for cell culture studies. The most frequent instruments were SYRCLE (n = 14), OHAT (n = 9), Cochrane Collaboration's tool (n = 7), GRADE (n = 6), CONSORT (n = 5), and ToxRTool (n = 5). The studies were divided into subgroups to perform statistical analyses. A significant association (OR = 5.00, 95% CI = 1.54-16.20, p = 0.008) was found between low MQ and chronic degenerative disorders as topic of SR. Several challenges in collecting information from the included studies led to some modifications related to the previously registered protocol. These results may serve as a basis for further development of a critical appraisal tool for cell culture studies capable of capturing all the essential factors related to preclinical research, therefore enhancing the practice of evidence-based.

Methanethiol: A Scent Mark of Dysregulated Sulfur Metabolism in Cancer

In order to cope with increased demands for energy and metabolites as well as to enhance stress resilience, tumor cells develop various metabolic adaptations, representing a hallmark of cancer. In this regard, the dysregulation of sulfur metabolism that may result in elevated levels of volatile sulfur compounds (VSCs) in body fluids, breath, and/or excretions of cancer patients has recently gained attention. Besides hydrogen sulfide (H2S), methanethiol is the predominant cancer-associated VSC and has been proposed as a promising biomarker for non-invasive cancer diagnosis. Gut bacteria are the major exogenous source of exposure to this foul-smelling toxic gas, with methanethiol-producing strains such as Fusobacterium nucleatum highly abundant in the gut microbiome of colorectal carcinoma (CRC) patients. Physiologically, methanethiol becomes rapidly degraded through the methanethiol oxidase (MTO) activity of selenium-binding protein 1 (SELENBP1). However, SELENBP1, which is considered a tumor suppressor, is often downregulated in tumor tissues, and this has been epidemiologically linked to poor clinical outcomes. In addition to impaired removal, an increase in methanethiol levels may derive from non-enzymatic reactions, such as a Maillard reaction between glucose and methionine, two metabolites enriched in cancer cells. High methionine concentrations in cancer cells may also result in enzymatic methanethiol production in mitochondria. Moreover, enzymatic endogenous methanethiol production may occur through methyltransferase-like protein 7B (METTL7B), which is present at elevated levels in some cancers, including CRC and hepatocellular carcinoma (HCC). In conclusion, methanethiol contributes to the scent of cancer as part of the cancer-associated signature combination of volatile organic compounds (VOCs) that are increasingly being exploited for non-invasive early cancer diagnosis.

Does the last 20 years paradigm of clinical research using volatile organic compounds to non-invasively diagnose cancer need to change? Challenges and future direction

PURPOSE

Volatile organic compounds (VOCs) have shown great potential as novel biomarkers for cancer detection; however, comprehensive quantitative analysis is lacking. In this study, we performed a bibliometric analysis of non-invasive cancer diagnosis using VOCs to better characterise international trends and to predict future hotspots in this field, and then we focussed on human studies to analyse clinical characteristics for presenting the current controversies and future perspectives of further clinical work.

METHODS

Publications, from 2002 to 2022, were retrieved from the Web of Science Core Collection database. CiteSpace and VOSviewer were used to generate network maps and identify the annual publications, top countries, authors, institutions, journals, references, and keywords. Then, we further screened clinical trials, and the key information was extracted into Microsoft Excel for further systematical analysis.

RESULTS

Six hundred and forty-one articles were identified to evaluate research trends, of which 301 clinical trials were selected for further systematical analysis. Overall, the annual publications in this area increased, with an overall upward trend, while the quality of clinical research remains remarkably uneven.

CONCLUSION

The study of non-invasive cancer diagnosis using VOCs would continue to be an active field. However, without stringent clinical design criteria, most suitable acquisition and analysis devices and statistical approaches, a list of exclusive, specific, reliable and reproducible VOCs to identify a disease and these VOCs appearing in a breath at detectable levels at early stage disease, the clinical utility of VOC tests will be difficult to have any breakthroughs.

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.

Screening of Breast Cancer from Sweat Samples Analyzed by 2-Dimensional Gas Chromatography-Mass Spectrometry: A Preliminary Study

Breast cancer (BC) remains one of the most commonly diagnosed malignancies in women. There is increasing interest in the development of non-invasive screening methods. Volatile organic compounds (VOCs) emitted through the metabolism of cancer cells are possible novel cancer biomarkers. This study aims to identify the existence of BC-specific VOCs in the sweat of BC patients. Sweat samples from the breast and hand area were collected from 21 BC participants before and after breast tumor ablation. Thermal desorption coupled with two-dimensional gas chromatography and mass spectrometry was used to analyze VOCs. A total of 761 volatiles from a homemade human odor library were screened on each chromatogram. From those 761 VOCs, a minimum of 77 VOCs were detected within the BC samples. Principal component analysis showed that VOCs differ between the pre- and post-surgery status of the BC patients. The Tree-based Pipeline Optimization Tool identified logistic regression as the best-performing machine learning model. Logistic regression modeling identified VOCs that distinguish the pre-and post-surgery state in BC patients on both the breast and hand area with sensitivities close to 1. Further, Shapley additive explanations and the probe variable method identified the most important and pertinent VOCs distinguishing pre- and post-operative status which are mostly of distinct origin for the hand and breast region. Results suggest the possibility to identify endogenous metabolites linked to BC, hence proposing this innovative pipeline as a stepstone to discovering potential BC biomarkers. Large-scale studies in a multi-centered VOC analysis setting must be carried out to validate obtained findings.

Smoking is associated with elevated blood level of volatile organic compounds: a population-based analysis of NHANES 2017-2018

BACKGROUND

The study aims to explore the association between cigarette smoking with blood exposure to volatile organic compounds using population data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018.

METHODS

Based on the data of NHANES 2017-2018, we identified 1117 participants aged 18 to 65 years, who had complete VOCs testing data and filled out the Smoking-Cigarette Use and Volatile Toxicant questionnaires. The participants consisted of 214 dual-smoking persons, 41 E-cigarette smokers, 293 combustible-cigarette smokers and 569 non-smokers. We used One-way ANOVA and Welch's ANOVA to compare differences of VOCs concentration among 4 groups and multivariable regression model to confirm the factors associated with VOCs concentration.

RESULTS

In dual-smoking and combustible-cigarette smokers, blood concentration of 2,5-Dimethylfuran, Benzene, Benzonitrile, Furan, Isobutyronitrile were higher than non-smokers. When compared with people who never smoked, E-cigarette smokers had similar blood concentrations of VOCs. Blood concentrations of Benzene, Furan, and Isobutyronitrile were significant higher in combustible-cigarette smokers than in E-cigarette smokers. In the multivariable regression model, dual-smoking and combustible-cigarette smoking were associated with elevated blood concentrations of several VOCs except 1,4-Dichlorobenzene, while E-cigarette smoking was only associated with elevated 2,5-Dimethylfuran concentration.

CONCLUSIONS

Smoking, mainly dual-smoking and combustible-cigarette smoking, is associated with elevated blood concentration of VOCs, while the effect is weak in E-cigarette smoking.

Fluorosensing of benzaldehydes by CuI-graphene: A spectroscopy, thermodynamics and docking supported phenomenon

Benzaldehyde and 4-methyl benzaldehyde constitute a major part of the harmful volatile organic compounds (VOCs) found in the environment. Hence, rapid and selective detection of benzaldehyde derivatives are required to minimize the environmental degradation as well as the potential hazards on human health. In this study, the surface of the graphene nanoplatelets were functionalized with CuI nanoparticles for specific and selective detection of benzaldehyde derivatives by fluorescence spectroscopy. CuI-Gr nanoparticles exhibited higher efficiency towards the detection of benzaldehyde derivatives as compared to pristine CuI nanoparticles with detection limit (LOD) 2 ppm and 6 ppm for benzaldehyde and 4-methyl benzaldehyde respectively in aqueous medium. The LOD values for the detection of benzaldehyde and 4-methyl benzaldehyde by pristine CuI nanoparticles were poor and found to be 11 ppm and 15 ppm respectively. Fluorescence intensity of CuI-Gr nanoparticles were found to be quenched with increasing concentration (0-0.01 mg/mL) of the benzaldehyde and 4-methyl benzaldehyde. This novel graphene-based sensor was also found to be highly selective for the benzaldehyde derivatives as no changes in signal were detected in presence of other VOCs like formaldehyde and acetaldehyde.

Prognostic and predictive biomarkers for immunotherapy in advanced renal cell carcinoma

The therapeutic algorithm of renal cell carcinoma has been revolutionized by the approval of immunotherapy agents by regulatory agencies. However, objective and durable responses are still not observed in a large number of patients, and prognostic and predictive biomarkers for immunotherapy response are urgently needed. Prognostic models used in clinical practice are based on clinical and laboratory factors (such as hypercalcaemia, neutrophil count or Karnofsky Performance Status), but, with progress in molecular biology and genome sequencing techniques, new renal cell carcinoma molecular features that might improve disease course and outcomes prediction have been highlighted. An implementation of current models is needed to improve the accuracy of prognosis in the immuno-oncology era. Moreover, several potential biomarkers are currently under evaluation, but effective markers to select patients who might benefit from immunotherapy and to guide therapeutic strategies are still far from validation.

Simultaneous Sensing of Multiplex Volatile Organic Compounds by Adsorption and Plasmon Dual-Induced Raman Enhancement Technique

Developing highly efficient gas sensors with excellent performance for rapid and sensitive detection of volatile organic compounds (VOCs) is of critical importance for the protection of human health, ecological environment, and other factors. Here, a robust gas sensor based on Raman technology was constructed by an in situ grown 2D covalent organic framework (COF) on Au nanoparticles' surface in the microchannel. Dual enhancement effects are included for the as-prepared microfluidic sensor. First, acting as a gas confinement chamber, the 2D COF could effectively capture gas molecules with high adsorption capacity and fast adsorption kinetics, resulting in VOCs' preconcentration at a high level in the COF layer. At the same time, after being stacked in the microchannel, abundant hot spots were generated among the nanogaps of Au@COF NPs. The local surface plasmon resonance effect could effectively enhance the Raman intensity. Both factors contribute to the improved detection sensitivity of VOCs. As a demonstration, several representative VOCs with different functional groups were tested. The resultant Raman spectra were subjected to the statistical principal component analysis. Varied VOCs can be successfully detected with a detection limit as low as ppb level and distinguished with 95% confidence interval. The present microfluidic platform provides a simple, sensitive, and fast method for VOCs' sensing and distinguishing, which is expected to hold potential applications in the fields of health, agricultural, and environmental research.

Harnessing insect olfactory neural circuits for detecting and discriminating human cancers

There is overwhelming evidence that presence of cancer alters cellular metabolic processes, and these changes are manifested in emitted volatile organic compound (VOC) compositions of cancer cells. Here, we take a novel forward engineering approach by developing an insect olfactory neural circuit-based VOC sensor for cancer detection. We obtained oral cancer cell culture VOC-evoked extracellular neural responses from in vivo insect (locust) antennal lobe neurons. We employed biological neural computations of the antennal lobe circuitry for generating spatiotemporal neuronal response templates corresponding to each cell culture VOC mixture, and employed these neuronal templates to distinguish oral cancer cell lines (SAS, Ca9-22, and HSC-3) vs. a non-cancer cell line (HaCaT). Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line. By using high-dimensional population neuronal response analysis and leave-one-trial-out methodology, our approach yielded high classification success for each cell line tested. Our analyses achieved 76-100% success in identifying cell lines by using the population neural response (n = 194) collected for the entire duration of the cell culture study. We also demonstrate this cancer detection technique can distinguish between different types of oral cancers and non-cancer at different time-matched points of growth. This brain-based cancer detection approach is fast as it can differentiate between VOC mixtures within 250 ms of stimulus onset. Our brain-based cancer detection system comprises a novel VOC sensing methodology that incorporates entire biological chemosensory arrays, biological signal transduction, and neuronal computations in a form of a forward-engineered technology for cancer VOC detection.

Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.

Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study

INTRODUCTION

The clinical application of lung cancer detection based on breath test is still challenging due to lack of predictive molecular markers in exhaled breath. This study explored potential lung cancer biomarkers and their related pathways using a typical process for metabolomics investigation.

MATERIAL AND METHODS

Breath samples from 60 lung cancer patients and 176 healthy people were analyzed by GC-MS. The original data were GC-MS peak intensity removing background signal. Differential metabolites were selected after univariate statistical analysis and multivariate statistical analysis based on OPLS-DA and Spearman rank correlation analysis. A multivariate PLS-DA model was established based on differential metabolites for pattern recognition. Subsequently, pathway enrichment analysis was performed on differential metabolites.

RESULTS

The discriminant capability was assessed by ROC curve of whom the average AUC and average accuracy in 100-fold cross validations were 0.871 and 0.787, respectively. Eight potential biomarkers were involved in a total of 18 metabolic pathways. Among them, 11 metabolic pathways have p-value smaller than .1.

DISCUSSION

Some pathways among them are related to risk factors or therapies of lung cancer. However, more of them are dysregulated pathways of lung cancer reported in studies based on genome or transcriptome data.

CONCLUSION

We believe that it opens the possibility of using metabolomics methods to analyze data of exhaled breath and promotes involvement of knowledge dataset to cover more volatile metabolites.

CLINICAL SIGNIFICANCE

Although a series of related research reported diagnostic models with highly sensitive and specific prediction, the clinical application of lung cancer detection based on breath test is still challenging due to disease heterogeneity and lack of predictive molecular markers in exhaled breath. This study may promote the clinical application of this technique which is suitable for large-scale screening thanks to its low-cost and non-invasiveness. As a result, the mortality of lung cancer may be decreased in future.Key messagesIn the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer.We found that it is possible to apply metabolomics methods in analysis of data from breath test, which is meaningful to discover convinced volatile markers with definite pathological and histological significance.

The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime?

Breath analysis is a promising non-invasive method for the detection and management of lung cancer. Exhaled breath contains a complex mixture of volatile and non-volatile organic compounds that are produced as end-products of metabolism. Several studies have explored the patterns of these compounds and have postulated that a unique breath signature is emitted in the setting of lung cancer. Most studies have evaluated the use of gas chromatography and mass spectrometry to identify these unique breath signatures. With recent advances in the field of analytical chemistry and machine learning gaseous chemical sensing and identification devices have also been created to detect patterns of odorant molecules such as volatile organic compounds. These devices offer hope for a point-of-care test in the future. Several prospective studies have also explored the presence of specific genomic aberrations in the exhaled breath of patients with lung cancer as an alternative method for molecular analysis. Despite its potential, the use of breath analysis has largely been limited to translational research due to methodological issues, the lack of standardization or validation and the paucity of large multi-center studies. It is clear however that it offers a potentially non-invasive alternative to investigations such as tumor biopsy and blood sampling.

Volatolomic urinary profile analysis for diagnosis of the early stage of lung cancer

Nowadays in clinical practice there is a pressing need for potential biomarkers that can identify lung cancer at early stage before becoming symptomatic or detectable by conventional means. Several researchers have independently pointed out that the volatile organic compounds (VOCs) profile can be considered as a lung cancer fingerprint useful for diagnosis. In particular, 16% of volatiles contributing to the human volatilome are found in urine, which is therefore an ideal sample medium. Its analysis through non-invasive, relatively low-cost and straightforward techniques could offer great potential for the early diagnosis of lung cancer. In this study, urinary VOCs were analysed with a gas chromatography-ion mobility spectrometer (GC-IMS) and an electronic nose (e-nose) made by a matrix of twelve quartz microbalances (QMBs) complemented by a photoionization detector (PID). This clinical prospective study involved 127 individuals, divided into two groups: 46 with lung cancer stage I-II-III confirmed by computerized tomography (CT) or positron emission tomography-(PET) imaging techniques and histology (biopsy), and 81 healthy controls. Both instruments provided a multivariate signal which, after being analysed by a machine learning algorithm, identified eight VOCs that could distinguish lung cancer patients from healthy ones. The eight VOCs are 2-pentanone, 2-hexenal, 2-hexen-1-ol, hept-4-en-2-ol, 2-heptanone, 3-octen-2-one, 4-methylpentanol, 4-methyl-octane. Results show that GC-IMS identifies lung cancer with respect to the control group with a diagnostic accuracy of 88%. Sensitivity resulted as being 85%, and specificity was 90% - Area Under the Receiver Operating Characteristics (AUROC): 0.91. The contribution made by the e-nose was also important, even though the results were slightly less sensitive with an accuracy of 71.6%. Moreover, of the eight VOCs identified as potential biomarkers, five VOCs had a high sensitivity (p≤ 0.06) for early stage (stage I) lung cancer.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

Headspace Volatile Organic Compound Profiling of Pleural Mesothelioma and Lung Cancer Cell Lines as Translational Bridge for Breath Research

Introduction

Malignant pleural mesothelioma (MPM) is a lethal cancer for which early-stage diagnosis remains a major challenge. Volatile organic compounds (VOCs) in breath proved to be potential biomarkers for MPM diagnosis, but translational studies are needed to elucidate which VOCs originate from the tumor itself and thus are specifically related to MPM cell metabolism.

Methods

An in vitro model was set-up to characterize the headspace VOC profiles of six MPM and two lung cancer cell lines using thermal desorption-gas chromatography-mass spectrometry. A comparative analysis was carried out to identify VOCs that could discriminate between MPM and lung cancer, as well as between the histological subtypes within MPM (epithelioid, sarcomatoid and biphasic).

Results

VOC profiles were identified capable of distinguishing MPM (subtypes) and lung cancer cells with high accuracy. Alkanes, aldehydes, ketones and alcohols represented many of the discriminating VOCs. Discrepancies with clinical findings were observed, supporting the need for studies examining breath and tumor cells of the same patients and studying metabolization and kinetics of in vitro discovered VOCs in a clinical setting.

Conclusion

While the relationship between in vitro and in vivo VOCs is yet to be established, both could complement each other in generating a clinically useful breath model for MPM.

Organic compound and particle emissions of additive manufacturing with photopolymer resins and chemical outgassing of manufactured resin products

Photopolymer resins are applied at an increasing rate in additive manufacturing (AM) industry as vat photopolymerization (VP) and material jetting (MJ) methods gain more popularity. The aim of this study was to measure volatile organic compound (VOC), carbonyl compound, ultrafine particle (UFP), and particulate matter (PM₁₀) air concentrations emitted in 3D printer operations. Individual chemicals were identified when multiple photopolymer resin feedstocks were used in various VP and MJ printers. The size distributions of UFPs, and indoor air parameters were also monitored. Finally, the VOC outgassing of the cured resin materials was determined over 84 days. The data demonstrated that 3D printer operators were exposed to low concentrations of airborne exposure agents as follows: average concentrations of VOCs were between 41 and 87 µg/m³, UFP number levels ranged between 0.19 and 3.62 × 10³ number/cm³; however, no impact was detected on air parameters or PM₁₀ concentrations. A majority of the UFPs existed in the 10-45 nm size range. The identified compounds included hazardous species included sensitizing acrylates and carcinogenic formaldehyde. The outgassed products included similar compounds that were encountered during the AM processes, and post-processing solvents. Products heated to 37°C emitted 1.4‒2.9-fold more VOCs than at room temperature. Total emissions were reduced by 84‒96% after 28 days roughly from 3000-14000 to 100-1000 µg/m²/hr. In conclusion, resin printer operators are exposed to low concentrations of hazardous emissions, which might result in adverse health outcomes during prolonged exposure. Manufactured resin products are suggested to be stored for 4 weeks after their production to reduce potential consumer VOC hazards.

Paving the Way for a Green Transition in the Design of Sensors and Biosensors for the Detection of Volatile Organic Compounds (VOCs)

The efficient and selective detection of volatile organic compounds (VOCs) provides key information for various purposes ranging from the toxicological analysis of indoor/outdoor environments to the diagnosis of diseases or to the investigation of biological processes. In the last decade, different sensors and biosensors providing reliable, rapid, and economic responses in the detection of VOCs have been successfully conceived and applied in numerous practical cases; however, the global necessity of a sustainable development, has driven the design of devices for the detection of VOCs to greener methods. In this review, the most recent and innovative VOC sensors and biosensors with sustainable features are presented. The sensors are grouped into three of the main industrial sectors of daily life, including environmental analysis, highly important for toxicity issues, food packaging tools, especially aimed at avoiding the spoilage of meat and fish, and the diagnosis of diseases, crucial for the early detection of relevant pathological conditions such as cancer and diabetes. The research outcomes presented in the review underly the necessity of preparing sensors with higher efficiency, lower detection limits, improved selectivity, and enhanced sustainable characteristics to fully address the sustainable manufacturing of VOC sensors and biosensors.

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.

Volatolomics in healthcare and its advanced detection technology

Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.

Diagnostic Application of Volatile Organic Compounds as Potential Biomarkers for Detecting Digestive Neoplasia: A Systematic Review

Volatile organic compounds (VOCs) are part of the exhaled breath that were proposed as non-invasive breath biomarkers via different human discharge products like saliva, breath, urine, blood, or tissues. Particularly, due to the non-invasive approach, VOCs were considered as potential biomarkers for non-invasive early cancer detection. We herein aimed to review the data over VOCs utility in digestive neoplasia as early diagnosis or monitoring biomarkers. A systematic literature search was done using MEDLINE via PubMed, Cochrane Library, and Thomson Reuters' Web of Science Core Collection. We identified sixteen articles that were included in the final analysis. Based on the current knowledge, we cannot identify a single VOC as a specific non-invasive biomarker for digestive neoplasia. Several combinations of up to twelve VOCs seem promising for accurately detecting some neoplasia types. A combination of different VOCs breath expression are promising tools for digestive neoplasia screening.

[Advances on Collection and Analysis of Volatile Organic Compounds in the Diagnosis of Lung Cancer]

Lung cancer is a leading cause of cancer-related morbidity and mortality globally, which is the biggest menace to the health and life of the population. Screening and early detection of lung cancer are effective in reducing its mortality, and the measurement of volatile organic compounds (VOCs) has become a promising clinical means for early detection, course detection and prognosis management of lung cancer, with advantages of rapid speed, non-invasiveness and convenience. Now, a variety of VOCs collection ways and analysis methods have emerged at home and abroad. This report summarized three aspects, including VOCs collection, multiple methods of analysis and progress in the diagnosis and treatment of lung cancer. At last, we discussed the limitations and prospects of VOCs analysis.
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An Update on the Use of Exhaled Breath Analysis for the Early Detection of Lung Cancer

Lung cancer has historically been the main responsible for cancer associated deaths. Owing to this is our current inability to screen for and diagnose early pathological findings, preventing us from a timely intervention when cure is still achievable. Over the last decade, together with the extraordinary progress in therapeutical alternatives in the field, there has been an ongoing search for a biomarker that would allow for this. Numerous technologies have been developed but their clinical application is yet to come. In this review, we provide an update on volatile organic compounds, a non-invasive method that can hold the key for detecting early metabolic pathway changes in carcinogenesis. For its compilation, web-based search engines of scientific literature such as PubMed were explored and reviewed, using articles, research, and papers deemed meaningful by authors discretion. After a brief description, we depict how this technique can complement current methods and present the value of electronic noses in the identification of the “breathprint”. Lastly, we bring some of the latest updates in the field together with the current limitations and final remarks.

Coating-Based Quartz Crystal Microbalance Detection Methods of Environmentally Relevant Volatile Organic Compounds

Volatile organic compounds (VOCs) that evaporate under standard atmospheric conditions are of growing concern. This is because it is well established that VOCs represent major contamination risks since release of these compounds into the atmosphere can contribute to global warming, and thus, can also be detrimental to the overall health of worldwide populations including plants, animals, and humans. Consequently, the detection, discrimination, and quantification of VOCs have become highly relevant areas of research over the past few decades. One method that has been and continues to be creatively developed for analyses of VOCs is the Quartz Crystal Microbalance (QCM). In this review, we summarize and analyze applications of QCM devices for the development of sensor arrays aimed at the detection of environmentally relevant VOCs. Herein, we also summarize applications of a variety of coatings, e.g., polymers, macrocycles, and ionic liquids that have been used and reported in the literature for surface modification in order to enhance sensing and selective detection of VOCs using quartz crystal resonators (QCRs) and thus QCM. In this review, we also summarize novel electronic systems that have been developed for improved QCM measurements.

Application of chemoresistive gas sensors and chemometric analysis to differentiate the fingerprints of global volatile organic compounds from diseases. Preliminary results of COPD, lung cancer and breast cancer

BACKGROUND

Analysis of volatile organic compounds (VOCs) in exhaled breath has been proposed as a screening method that discriminates between disease and healthy subjects, few studies evaluate whether these chemical fingerprints are specific when compared between diseases. We evaluated global VOCs and their discrimination capacity in chronic obstructive pulmonary disease (COPD), lung cancer, breast cancer and healthy subjects by chemoresistive sensors and chemometric analysis.

METHODS

A cross-sectional study was conducted with the participation of 30 patients with lung cancer, 50 with breast cancer, 50 with COPD and 50 control subjects. Each participant's exhaled breath was analyzed with the electronic nose. A multivariate analysis was carried: principal component analysis (PCA) and, canonical analysis of principal coordinates (CAP). Twenty single-blind samples from the 4 study groups were evaluated by CAP.

RESULTS

A separation between the groups of patients to the controls was achieved through PCA with explanations of >90% of the data and with a correct classification of 100%. In the CAP of the 4 study groups, discrimination between the diseases was obtained with 2 canonical axes with a correct general classification of 91.35%. This model was used for the prediction of the single-blind samples resulting in correct classification of 100%.

CONCLUSIONS

The application of chemoresistive gas sensors and chemometric analysis can be used as a useful tool for a screening test for lung cancer, breast cancer and COPD since this equipment detects the set of VOCs present in the exhaled breath to generate a characteristic chemical fingerprint of each disease.

Calculated indices of volatile organic compounds (VOCs) in exhalation for lung cancer screening and early detection

BACKGROUND

Breath analysis is a promising noninvasive technique that offers a wide range of opportunities to facilitate early diagnosis of lung cancer (LC).

METHOD

Exhaled breath samples of 352 subjects including 160 with lung cancer (LC), 70 with benign pulmonary nodule (BPN) and 122 healthy controls (HC) were analyzed through thermal desorption coupled with gas chromatography-mass spectrometry (TD-GC-MS) to obtain the metabolic information from volatile organic compounds (VOCs). Statistical classification models were used to find diagnostic clusters of VOCs for the discrimination of HC, BPN and LC patients' early and advanced stages, as well as subtypes of LC. Receiver operator characteristics (ROC) curves with 5-fold validations were used to evaluate the accuracy of these models.

RESULTS

The analysis revealed that 20, 19, 19, and 20 VOCs discriminated LC from HC, LC from BPN, histology and LC stages respectively. The calculated diagnostic indices showed a large area under the curve (AUC) to distinguish HC from LC (AUC: 0.987, 95 % confidence interval (CI): 0.976-0.997), BPN from LC (AUC: 0.809, 95 % CI: 0.758-0.860), NSCLC from SCLC (AUC: 0.939, 95 % CI: 0.875-0.995) and Stage III from stage III-IV (AUC: 0.827, 95 % CI: 0.768-0.886). The comparison between the high-risk groups (BPN and HC smokers) and early stages LC resulted in the AUC of 0.756 (95 %CI: 0.681-0.817) for BPN vs. early stage LC and AUC of 0.986 (95 % CI: 0.972-0.994) for HC smoker vs. early stage LC.

CONCLUSION

Volatome of breath of the LC patients was significantly different from that of both BPN patients and HC and showed an ability of distinguishing early from advance stage LC and NSCLC from SCLC. We conclude that the volatome has a potential to help improve early diagnosis of LC.

Breath profile as composite biomarkers for lung cancer diagnosis

OBJECTIVES

Lung cancer is continuously the leading cause of cancer related death, resulting from the lack of specific symptoms at early stage. A large-scale screening method may be the key point to find asymptomatic patients, leading to the reduction of mortality.

METHODS

An alternative method combining breath test and a machine learning algorithm is proposed. 236 breath samples were analyzed by TD-GCMS. Breath profile of each sample is composed of 308 features extracted from chromatogram. Gradient boost decision trees algorithm was employed to recognize lung cancer patients. Bootstrap is performed to simulate real diagnostic practice, with which we evaluated the confidence of our methods.

RESULTS

An accuracy of 85 % is shown in 6-fold cross validations. In statistical bootstrap, 72 % samples are marked as "confident", and the accuracy of confident samples is 93 % throughout the cross validations.

CONCLUSION

We have proposed such a non-invasive, accurate and confident method that might contribute to large-scale screening of lung cancer. As a consequence, more asymptomatic patients with early lung cancer may be detected.

Volatile Organic Compounds in Exhaled Breath as Fingerprints of Lung Cancer, Asthma and COPD

Lung cancer, chronic obstructive pulmonary disease (COPD) and asthma are inflammatory diseases that have risen worldwide, posing a major public health issue, encompassing not only physical and psychological morbidity and mortality, but also incurring significant societal costs. The leading cause of death worldwide by cancer is that of the lung, which, in large part, is a result of the disease often not being detected until a late stage. Although COPD and asthma are conditions with considerably lower mortality, they are extremely distressful to people and involve high healthcare overheads. Moreover, for these diseases, diagnostic methods are not only costly but are also invasive, thereby adding to people’s stress. It has been appreciated for many decades that the analysis of trace volatile organic compounds (VOCs) in exhaled breath could potentially provide cheaper, rapid, and non-invasive screening procedures to diagnose and monitor the above diseases of the lung. However, after decades of research associated with breath biomarker discovery, no breath VOC tests are clinically available. Reasons for this include the little consensus as to which breath volatiles (or pattern of volatiles) can be used to discriminate people with lung diseases, and our limited understanding of the biological origin of the identified VOCs. Lung disease diagnosis using breath VOCs is challenging. Nevertheless, the numerous studies of breath volatiles and lung disease provide guidance as to what volatiles need further investigation for use in differential diagnosis, highlight the urgent need for non-invasive clinical breath tests, illustrate the way forward for future studies, and provide significant guidance to achieve the goal of developing non-invasive diagnostic tests for lung disease. This review provides an overview of these issues from evaluating key studies that have been undertaken in the years 2010–2019, in order to present objective and comprehensive updated information that presents the progress that has been made in this field. The potential of this approach is highlighted, while strengths, weaknesses, opportunities, and threats are discussed. This review will be of interest to chemists, biologists, medical doctors and researchers involved in the development of analytical instruments for breath diagnosis.

Optimisation of Urine Sample Preparation for Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry: Altering Sample pH, Sulphuric Acid Concentration and Phase Ratio

Headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) can be used to measure volatile organic compounds (VOCs) in human urine. However, there is no widely adopted standardised protocol for the preparation of urine samples for analysis resulting in an inability to compare studies reliably between laboratories. This paper investigated the effect of altering urine sample pH, volume, and vial size for optimising detection of VOCs when using HS-SPME-GC-MS. This is the first, direct comparison of H₂SO₄, HCl, and NaOH as treatment techniques prior to HS-SPME-GC-MS analysis. Altering urine sample pH indicates that H₂SO₄ is more effective at optimising detection of VOCs than HCl or NaOH. H₂SO₄ resulted in a significantly larger mean number of VOCs being identified per sample (on average, 33.5 VOCs to 24.3 in HCl or 12.2 in NaOH treated urine) and more unique VOCs, produced a more diverse range of classes of VOCs, and led to less HS-SPME-GC-MS degradation. We propose that adding 0.2 mL of 2.5 M H₂SO₄ to 1 mL of urine within a 10 mL headspace vial is the optimal sample preparation prior to HS-SPME-GC-MS analysis. We hope the use of our optimised method for urinary HS-SPME-GC-MS analysis will enhance our understanding of human disease and bolster metabolic biomarker identification.