Investigation of different approaches for exhaled breath and tumor tissue analyses to identify lung cancer biomarkers

Considerations regarding the selection, sampling, extraction, analysis, and modelling of biomarkers in exhaled breath for early lung cancer screening

Lung cancer (LC) is the deadliest cancer due to the lack of efficient screening methods that detect the disease early. This review, covering the years 2011 - 2025, summarizes state-of-the-art LC screening through analysis of volatile organic compounds (VOCs) in exhaled breath. All fundamental parts of the methodology are covered, i.e., sampling, analysis, and multivariate data modelling. This review shows that breath is commonly collected in Tedlar® bags and subsequently analysed with solid phase micro-extraction gas chromatography mass spectrometry (SPME-GC-MS) or sensors. Data analysis has been made using multivariate methods like principal component analysis (PCA) or artificial neural networks (ANNs). The VOCs exhaled by LC patients and healthy subjects are in principle the same. However, concentration levels differ between the two groups. Therefore, LC patients are usually separated from healthy controls through multivariate modelling of a set of VOC biomarkers rather than by individual biomarkers. Although most exhaled VOCs are formed endogenously via metabolic processes and oxidative stress, some compounds also have exogenous origins, which must be taken into consideration. More than 200 different VOCs have been reported as potential biomarkers in the breath of LC patients, while the number of biomarkers per study were typically around 10-20 compounds. The 15 most common LC biomarkers were (from high to low frequency) acetone, isoprene, hexanal, benzene, butanone, styrene, ethylbenzene, 1-propanol, 2-propanol, toluene, pentanal, 2-pentanone, cyclohexane, nonanal and decane. Several methods showed, in combination with multivariate data analysis, potential to distinguish between LC patients and healthy controls.

Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review

This paper offers a systematic review of advancements in electronic nose technologies for early cancer detection with a particular focus on the detection and analysis of volatile organic compounds present in biomarkers such as breath, urine, saliva, and blood. Our objective is to comprehensively explore how these biomarkers can serve as early indicators of various cancers, enhancing diagnostic precision and reducing invasiveness. A total of 120 studies published between 2018 and 2023 were examined through systematic mapping and literature review methodologies, employing the PICOS (Population, Intervention, Comparison, Outcome, and Study design) methodology to guide the analysis. Of these studies, 65.83% were ranked in Q1 journals, illustrating the scientific rigor of the included research. Our review synthesizes both technical and clinical perspectives, evaluating sensor-based devices such as gas chromatography-mass spectrometry and selected ion flow tube-mass spectrometry with reported incidences of 30 and 8 studies, respectively. Key analytical techniques including Support Vector Machine, Principal Component Analysis, and Artificial Neural Networks were identified as the most prevalent, appearing in 22, 24, and 13 studies, respectively. While substantial improvements in detection accuracy and sensitivity are noted, significant challenges persist in sensor optimization, data integration, and adaptation into clinical settings. This comprehensive analysis bridges existing research gaps and lays a foundation for the development of non-invasive diagnostic devices. By refining detection technologies and advancing clinical applications, this work has the potential to transform cancer diagnostics, offering higher precision and reduced reliance on invasive procedures. Our aim is to provide a robust knowledge base for researchers at all experience levels, presenting insights on sensor capabilities, metrics, analytical methodologies, and the transformative impact of emerging electronic nose technologies in clinical practice.

Diagnosis of Lung Cancer Through Exhaled Breath: A Comprehensive Study

OBJECTIVES

Exhaled breath analysis is an attractive lung cancer diagnostic tool. However, various factors that are not related to the disease status, comorbidities, and other diseases must be considered to obtain a reliable diagnostic model.

METHODS

Exhaled breath samples from 646 individuals including 273 patients with lung cancer (LC), 90 patients with cancer of other localizations (OC), 150 patients with noncancer lung diseases (NLD), and 133 healthy controls (HC) were analyzed using gas chromatography-mass spectrometry (GC-MS). The samples were collected in Tedlar bags. Volatile organic compounds (VOCs) were preconcentrated on Tenax TA sorbent tubes with subsequent two-stage thermal desorption followed by GC-MS analysis. The influence of age, gender, smoking status, time since last food consumption, and comorbidities on exhaled breath were evaluated. Also, the effect of histology, TNM, tumor localization, treatment status, and the presence of a tumor on VOC profile of patients with lung cancer were assessed. Intergroup statistics were estimated, diagnostic models were created using artificial neural networks (ANNs) and gradient boosted decision trees (GBDTs).

RESULTS

Smoking status and food consumption affect exhaled breath VOC profile: benzene, ethylbenzene, toluene, 1,3-pentadiene 1,4-pentadiene acetonitrile, and some ratios are significantly different in exhaled breath of smokers and nonsmokers; the ratios 2,3-butandione/2-pentanone, 2,3-butandione/dimethylsulfide, and 2-butanone/2-pentanone are affected by time since last food consumption. Exhaled breath of LC is affected by the form of the disease and comorbidities. One-pentanol and 2-butanone were different in exhaled breath of patients with various tumor localization; 2-butanone was different in exhaled breath of patients before and during treatment. Diabetes as a comorbidity affects the pentanal level in exhaled breath; obesity affects the ratios of 2,3-butanedione/dimethylsulfide and 2-butanone/isoprene. Sensitivity and specificity of diagnostic models aimed to discriminate LC and HC, OC, and NLD were 78.7% and 51.0%, 62.2% and 53.4%, and 60.4% and 58.0%, respectively. HC and patients, regardless of the disease, can be classified with sensitivity of 76.6% and specificity of 68.2%.

CONCLUSIONS

The models created to diagnose lung cancer can also classify OC and NLD as patients with lung cancer. Additionally, the influence of comorbidities and factors not related to the disease status must be considered before the creation of diagnostic models to avoid false results.

Exhaled VOC detection in lung cancer screening: a comprehensive meta-analysis

BACKGROUND

Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined.

METHODS

Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies' titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity.

RESULTS

This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research.

CONCLUSION

This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.

Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms

Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, emphysema, and chronic bronchitis. Despite recent attention from researchers, the link between exhaled breath and ozone-induced injury remains poorly understood. This study aimed to identify novel exhaled biomarkers in ozone-exposed mice using ultra-sensitive photoinduced associative ionization time-of-flight mass spectrometry and machine learning. Distinct ion peaks for acetonitrile (m/z 42, 60, and 78), butyronitrile (m/z 70, 88, and 106), and hydrogen sulfide (m/z 35) were detected. Integration of tissue characteristics, oxidative stress-related mRNA expression, and exhaled breath condensate free-radical analysis enabled a comprehensive exploration of the relationship between ozone-induced biological responses and potential biomarkers. Under similar exposure levels, C57BL/6 mice exhibited pulmonary injury characterized by significant inflammation, oxidative stress, and cardiac damage. Notably, C57BL/6 mice showed free radical signals, indicating a distinct susceptibility profile. Immunodeficient non-obese diabetic Prkdc-/-/Il2rg-/- (NPI) mice exhibited minimal biological responses to pulmonary injury, with little impact on the heart. These findings suggest a divergence in ozone-induced damage pathways in the two mouse types, leading to alterations in exhaled biomarkers. Integrating biomarker discovery with comprehensive biopathological analysis forms a robust foundation for targeted interventions to manage health risks posed by ozone exposure.

Investigating the enhancement of lung cancer sensing: the effect of edge halogenation in armchair stanene nanoribbons

In this research, we explore the impact of edge passivation using halogen atoms on armchair stanene nanoribbon (ASNR) for the early detection of lung cancer biomarkers. We employ non-equilibrium green function (NEGF) and density functional theory (DFT) methods to evaluate sensing characteristics. The edges of ASNR are passivated with fluorine, chlorine, bromine, and iodine atoms. Our findings indicate a significant enhancement in sensing performance upon halogenation of ASNR. Notable changes in adsorption energy and current for edge-halogenated ASNR configurations demonstrate improved sensing behavior. Moreover, current curves exhibit greater distinctiveness of halogenated ASNR in comparison to hydrogenated ASNR. The calculations indicate a change in adsorption energy (Eads) of -7.59 eV, -7.6 eV, -8.3 eV, and -8.6 eV for the adsorption by styrene on I-ASnNR, Br-ASnNR, toluene on Cl-ASnNR, and styrene on F-ASnNR, respectively. The corresponding sensitivity improves up to 37.33%, 38.09%, 38.35%, and 45.5%, respectively. These findings highlight that the most significant change occurs with the edge fluorination of ASnNR. Our findings underscore the effectiveness of halogen atom edge passivation in ASNR for heightened sensing performance, making it a promising choice for the development of early-detection lung cancer sensors.

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.

A combined screening study for evaluating the potential of exhaled acetone, isoprene, and nitric oxide as biomarkers of lung cancer

Background: the early lung cancer (LC) screening strategy significantly reduces LC mortality. According to previous studies, lung cancer can be effectively diagnosed by analyzing the concentration of volatile organic compounds (VOCs) in human exhaled breath and establishing a diagnosis model based on the different VOCs. This method, called breath analysis, has the advantage of being rapid and non-invasive. To develop a non-invasive, portable breath detection instrument based on cavity ring-down spectroscopy (CRDS), we explored the feasibility of establishing a model with acetone, isoprene, and nitric oxide (NO) exhaled through human breath, which can be detected on the CRDS instrument. Methods: a total of 511 participants were recruited from the Cancer Institute and Hospital, Tianjin Medical University as the discovery set and randomly split (2 : 1) into training set and internal validation set with stratification. For external validation, 51 participants were recruited from the General Hospital, Tianjin Medical University. Acetone and isoprene from exhaled breath were detected by proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOF-MS), and NO was measured using CRDS. The model was constructed using the ensemble learning method that set eXtreme gradient boosting and logistic regression as the basis model and logistic regression as the senior model. The model was evaluated based on accuracy, sensitivity, and specificity. Results: the model achieved an accuracy of 78.8%, sensitivity of 81.0%, specificity of 70.0%, and area under the receiver operating curve (ROC, AUC) of 0.8341 (95% CI from 0.8055 to 0.8852) in the internal validation set. Furthermore, it attained an accuracy of 66.7%, sensitivity of 68.2%, specificity of 65.5%, and AUC of 0.6834 (95% CI from 0.5259 to 0.7956) in the external validation set. Conclusion: the model, established with acetone, isoprene, and NO as predictors, possesses the ability to identify LC patients from healthy control (HC) participants. The CRDS instrument, which simultaneously detects acetone, isoprene, and NO, is expected to be a non-invasive, rapid, portable, and accurate device for early screening of LC.

HS-SPME-GC-MS Untargeted Analysis of Normal Rat Organs Ex Vivo: Differential VOC Discrimination and Fingerprint VOC Identification

The investigation of volatile organic compounds (VOCs) in human metabolites has been a topic of interest as it holds the potential for the development of non-invasive technologies to screen for organ lesions in vivo. However, it remains unclear whether VOCs differ among healthy organs. Consequently, a study was conducted to analyze VOCs in ex vivo organ tissues obtained from 16 Wistar rats, comprising 12 different organs. The VOCs released from each organ tissue were detected by the headspace-solid phase microextraction-gas chromatography-mass spectrometry technique. In the untargeted analysis of 147 chromatographic peaks, the differential volatiles of rat organs were explored based on the Mann-Whitney U test and fold change (FC > 2.0) compared with other organs. It was found that there were differential VOCs in seven organs. A discussion on the possible metabolic pathways and related biomarkers of organ differential VOCs was conducted. Based on the orthogonal partial least squares discriminant analysis and receiver operating characteristic curve, we found that differential VOCs in the liver, cecum, spleen, and kidney can be used as the unique identification of the corresponding organ. In this study, differential VOCs of organs in rats were systematically reported for the first time. Profiles of VOCs produced by healthy organs can serve as a reference or baseline that may indicate the presence of disease or abnormalities in the organ's function. Differential VOCs can be used as the fingerprint of organs, and future integration with metabolic research may contribute to the development of healthcare.

Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review

Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.

Early detection of lung cancer biomarkers in exhaled breath by modified armchair stanene nanoribbons

In this study, we analyze armchair stanene nanoribbons as excellent sensing substances for the early diagnosis of lung cancer using density functional theory and the non-equilibrium Green function. Four modified configurations of surface- and edge-defected armchair stanene nanoribbons were studied to improve the sensing performance. Our probes indicated that the adsorption energy of armchair stanene nanoribbons is at least five times greater than that of other previously reported substances, such as single-wall carbon nanotubes, phosphorene, and silicene. A noticeable reduction in the current was observed, implying the high sensitivity of our sensing configurations. The adsorption energy and current results suggest that configurations with a single vacancy and edge defects improve the sensitivity and selectivity of the system because of their free dangling bonds. The calculated results demonstrate that the both-side edge defected armchair stanene nanoribbons reduce the adsorption energy to -8.35 eV and increase the sensitivity up to 45% for toluene detection. This reduction in adsorption energy and the surge of sensitivity shows ultra-high sensing performance, yielding a more efficient structure for the future design of early-diagnosis lung cancer sensing applications, thus improving lung cancer patients' survival and life expectancy.

Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities

Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82-88% sensitivity and 80-86% specificity on the test data.

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.

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.

Non-invasive Exhaled Breath and Skin Analysis to Diagnose Lung Cancer: Study of Age Effect on Diagnostic Accuracy

Development of simple, fast, and non-invasive tests for lung cancer diagnostics is essential for clinical practice. In this paper, exhaled breath and skin were studied as potential objects to diagnose lung cancer. The influence of age on the performance of diagnostic models was studied. Gas chromatography in combination with mass spectrometry (MS) was used to analyze the exhaled breath of 110 lung cancer patients and 212 healthy individuals of various ages. Peak area ratios of volatile organic compounds (VOCs) were used for data analysis instead of VOC peak areas. Various machine learning algorithms were applied to create diagnostic models, and their performance was compared. The best results on the test data set were achieved using artificial neural networks (ANNs): classification of patients with lung cancer and young healthy volunteers: 88 ± 4% sensitivity and 83 ± 3% specificity; classification of patients with lung cancer and old healthy individuals: 81 ± 3% sensitivity and 85 ± 1% specificity. The difference between performance of models based on young and old healthy groups was minor. The results obtained have shown that metabolic dysregulation driven by the disease biology is too high, which significantly overlaps the age effect. The influence of tumor localization and histological type on exhaled breath samples of lung cancer patients was studied. Statistically significant differences between some parameters in these samples were observed. A possibility of assessing the disease status by skin analysis in the Zakharyin-Ged zones using an electronic nose based on the quartz crystal microbalance sensor system was evaluated. Diagnostic models created using ANNs allow us to classify the skin composition of patients with lung cancer and healthy subjects of different ages with a sensitivity of 69 ± 2% and a specificity of 68 ± 8% for the young healthy group and a sensitivity of 74 ± 7% and a specificity of 66 ± 6% for the old healthy group. Primary results of skin analysis in the Zakharyin-Ged zones for the lung cancer diagnosis have shown its utility, but further investigation is required to confirm the results obtained.

Trends in the Development of Electronic Noses Based on Carbon Nanotubes Chemiresistors for Breathomics

The remarkable potential of breath analysis in medical care and diagnosis, and the consequent development of electronic noses, is currently attracting the interest of the research community. This is mainly due to the possibility of applying the technique for early diagnosis, screening campaigns, or tracking the effectiveness of treatment. Carbon nanotubes (CNTs) are known to be good candidates for gas sensing, and they have been recently considered for the development of electronic noses. The present work has the aim of reviewing the available literature on the development of CNTs-based electronic noses for breath analysis applications, detailing the functionalization procedure used to prepare the sensors, the breath sampling techniques, the statistical analysis methods, the diseases under investigation, and the population studied. The review is divided in two main sections: one focusing on the e-noses completely based on CNTs and one reporting on the e-noses that feature sensors based on CNTs, along with sensors based on other materials. Finally, a classification is presented among studies that report on the e-nose capability to discriminate biomarkers, simulated breath, and animal or human breath.

Analyses of lung cancer-derived volatiles in exhaled breath and in vitro models

Lung cancer is one of the leading causes of cancer incidence and cancer-related deaths in the world. Early diagnosis of pulmonary tumors results in improved survival compared to diagnosis with more advanced disease, yet early disease is not reliably indicated by symptoms. Despite of the improved testing and monitoring techniques for lung cancer in the past decades, most diagnostic tests, such as sputum cytology or tissue biopsies, are invasive and risky, rendering them unfeasible for large population screening. The non-invasive analysis of exhaled breath has gained attentions as an innovative screening method to measure chemical alterations within the human volatilome profile as a result of oncogenesis. More importantly, volatile organic compounds (VOCs) have been correlated to the pathophysiology of disease since the source of volatile compounds relies mostly on endogenous metabolic processes that are altered as a result of disease onset. Therefore, studying VOCs emitted from human breath may assist lung cancer diagnosis, treatment monitoring, and other surveillance of this devastating disease. In this mini review, we evaluated recent human studies that have attempted to identify lung cancer-derived volatiles in exhaled breath of patients. We also examined reported volatiles in cell cultures of lung cancer to better understand the origins of cancer-associated VOCs. We highlight the metabolic processes of lung cancer that could be responsible for the endogenous synthesis of these VOCs and pinpoint the protein-encoding genes involved in these pathways. Finally, we highlight the potential value of a breath test in lung cancer and propose prominent areas for future research required for the incorporation of VOCs-based testing into clinical settings.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis

BACKGROUND AND AIMS

In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.

MATERIALS AND METHODS

A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.

RESULTS

Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI_95%: 84-100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.

CONCLUSIONS

Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.

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.

Exhaled breath analysis using GC-MS and an electronic nose for lung cancer diagnostics

Exhaled breath analysis is an interesting and promising approach for the diagnostics of various diseases. Being non-invasive, convenient and simple, this approach has tremendous potential utility for further translation into clinical practice. In this study, gas chromatography-mass spectrometry (GC-MS) and quartz microbalance sensor-based "electronic nose" were applied for analysis of the exhaled breath of 40 lung cancer patients and 40 healthy individuals. It was found that the electronic nose was unable to distinguish the samples of different groups. However, the application of GC-MS allowed identifying statistically significant differences in compound peak areas and their ratios for investigated groups. Diagnostic models were created using random forest classifier based on peak areas and their ratios with the sensitivity and specificity of peak areas (ratios) of 85.7-96.5% (75.0-93.1%) and 73.3-85.1% (90.0-92.5%) on training data and 63.6-75.0% (72.7-100.0%) and 50.0-69.2% (76.9-84.6%) on test data, respectively. The exhaled breath samples of lung cancer patients and healthy volunteers could be distinguished by GC-MS with the use of individual compounds, but application of their ratios could help to determine specific differences between investigated groups and the level the influence of individual metabolism features alternating from one person to another as well as daily instrument reproducibility deviations. The electronic nose has to be significantly improved to apply it to lung cancer diagnostics of exhaled breath analysis and the influence of water vapour has to be lowered to increase the sensitivity of the sensors to detect lung cancer biomarkers.

Study of confounding factors influence on lung cancer diagnostics effectiveness using gas chromatography-mass spectrometry analysis of exhaled breath

Aim: The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Patients & methods: Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography-mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Results: Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Conclusion: Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.