Searching for selected VOCs in human breath samples as potential markers of lung cancer

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.

Recent advances in biosensors detecting biomarkers from exhaled breath and saliva for respiratory disease diagnosis

The global demand for rapid and non-invasive diagnostic methods for respiratory diseases has significantly intensified due to the wide spread of respiratory infectious diseases. Recent advancements in respiratory disease diagnosis through the analysis of exhaled breath and saliva has attracted great attention all over the world. Among various analytical methods, biosensors can offer non-invasive, efficient, and cost-effective diagnostic capabilities, emerging as promising tools in this area. This review intends to provide a comprehensive overview of various biosensors for the detection of respiratory disease related biomarkers in exhaled breath and saliva. Firstly, the characteristics of exhaled breath and saliva, including their generation, composition, and relevant biomarkers are introduced. Subsequently, the design and application of various biosensors for detecting these biomarkers are presented, along with the innovative materials employed as sensitive components. Different types of biosensors are reviewed, including electrochemical, optical, piezoelectric, semiconductor, and other novel biosensors. At last, the challenges, limitations, and future trends of these biosensors are discussed. It is anticipated that biosensors will play a significant role in respiratory disease diagnosis in the future.

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.

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.

Enhancing diagnosis of benign lesions and lung cancer through ensemble text and breath analysis: a retrospective cohort study

Early diagnosis of lung cancer (LC) can significantly reduce its mortality rate. Considering the limitations of the high false positive rate and reliance on radiologists' experience in computed tomography (CT)-based diagnosis, a multi-modal early LC screening model that combines radiology with other non-invasive, rapid detection methods is warranted. A high-resolution, multi-modal, and low-differentiation LC screening strategy named ensemble text and breath analysis (ETBA) is proposed that ensembles radiology report text analysis and breath analysis. In total, 231 samples (140 LC patients and 91 benign lesions [BL] patients) were screened using proton transfer reaction-time of flight-mass spectrometry and CT screening. Participants were randomly assigned to a training set and a validation set (4:1) with stratification. The report section of the radiology reports was used to train a text analysis (TA) model with a natural language processing algorithm. Twenty-two volatile organic compounds (VOCs) in the exhaled breath and the prediction results of the TA model were used as predictors to develop the ETBA model using an extreme gradient boosting algorithm. A breath analysis model was developed based on the 22 VOCs. The BA and TA models were compared with the ETBA model. The ETBA model achieved a sensitivity of 94.3%, a specificity of 77.3%, and an accuracy of 87.7% with the validation set. The radiologist diagnosis performance with the validation set had a sensitivity of 74.3%, a specificity of 59.1%, and an accuracy of 68.1%. High sensitivity and specificity were obtained by the ETBA model compared with radiologist diagnosis. The ETBA model has the potential to provide sensitivity and specificity in CT screening of LC. This approach is rapid, non-invasive, multi-dimensional, and accurate for LC and BL diagnosis.

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.

MicroRNA biosensors in lung cancer

Lung cancer has been one of the leading causes of death over the past century. Unfortunately, the reliance on conventional methods to diagnose the phenotypic properties of tumors hinders early-stage cancer diagnosis. However, recent advancements in identifying disease-specific nucleotide biomarkers, particularly microRNAs, have brought us closer to early-stage detection. The roles of miR-155, miR-197, and miR-182 have been established in stage I lung cancer. Recent progress in synthesizing nanomaterials with higher conductivity has enhanced the diagnostic sensitivity of electrochemical biosensors, which can detect low concentrations of targeted biomarkers. Therefore, this review article focuses on exploring electrochemical biosensors based on microRNA in lung cancer.

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.

Ag-Coated Ternary Layered Double Hydroxide as a High-Performance SERS Sensor for Aldehydes

Volatile organic compounds (VOCs) are common environmental pollutants and important biomarkers for early diagnosis of lung cancer. However, aldehydes are difficult to detect directly due to their small Raman scattering cross-section and gaseous phase. Here, a Ag-coated ternary layered double hydroxide (LDH) was designed for the detection and identification of various aldehydes. The specific surface area of CoNi-LDH was increased by doping Fe3+, which provides abundant active sites to capture gas molecules. Furthermore, the energy band gap (Eg) was decreased due to the local amorphous FeCoNi-LDH with an extended band tail, promoting the excitonic transition of Fe0.07(CoNi)0.93-LDH. In addition, the Fermi level of Ag prevented the recombination of electron-hole pairs of Fe0.07(CoNi)0.93-LDH, providing a new bridge for charge transfer between the substrate and the molecule. Ag/Fe0.07(CoNi)0.93-LDH presented excellent surface-enhanced Raman scattering (SERS) performance for aldehyde VOCs by modification with 4-aminothiophenol (4-ATP) to capture aldehydes and realized the detection of benzaldehyde (BZA) at 10 ppb. The enhancement and Raman shift of the b2 mode indicated the contribution of chemical enhancement to the SERS system, so the substrate presented good uniformity. The recycling of the SERS substrate is realized based on the reversibility of the Schiff base reaction. These results manifested that Ag/FeCoNi-LDH has a wide prospect in the application in the trace detection of aldehydes.

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules

Lung cancer is the leading cause of cancer-related deaths. Early detection and diagnosis are critical, as survival decreases with advanced stages. Approximately 1.6 million nodules are incidentally detected every year on chest CT scan images in the United States. This number of nodules identified is likely much larger after accounting for screening-detected nodules. Most of these nodules, whether incidentally or screening detected, are benign. Despite this, many patients undergo unnecessary invasive procedures to rule out cancer because our current stratification approaches are suboptimal, particularly for intermediate probability nodules. Thus, noninvasive strategies are urgently needed. Biomarkers have been developed to assist through the continuum of lung cancer care and include blood protein-based biomarkers, liquid biopsies, quantitative imaging analysis (radiomics), exhaled volatile organic compounds, and bronchial or nasal epithelium genomic classifiers, among others. Although many biomarkers have been developed, few have been integrated into clinical practice as they lack clinical utility studies showing improved patient-centered outcomes. Rapid technologic advances and large network collaborative efforts will continue to drive the discovery and validation of many novel biomarkers. Ultimately, however, randomized clinical utility studies showing improved patient outcomes will be required to bring biomarkers into clinical practice.

Selectivity of Exhaled Breath Biomarkers of Lung Cancer in Relation to Cancer of Other Localizations

Lung cancer is a leading cause of death worldwide, mostly due to diagnostics in the advanced stage. Therefore, the development of a quick, simple, and non-invasive diagnostic tool to identify cancer is essential. However, the creation of a reliable diagnostic tool is possible only in case of selectivity to other diseases, particularly, cancer of other localizations. This paper is devoted to the study of the variability of exhaled breath samples among patients with lung cancer and cancer of other localizations, such as esophageal, breast, colorectal, kidney, stomach, prostate, cervix, and skin. For this, gas chromatography-mass spectrometry (GC-MS) was used. Two classification models were built. The first model separated patients with lung cancer and cancer of other localizations. The second model classified patients with lung, esophageal, breast, colorectal, and kidney cancer. Mann-Whitney U tests and Kruskal-Wallis H tests were applied to identify differences in investigated groups. Discriminant analysis (DA), gradient-boosted decision trees (GBDT), and artificial neural networks (ANN) were applied to create the models. In the case of classifying lung cancer and cancer of other localizations, average sensitivity and specificity were 68% and 69%, respectively. However, the accuracy of classifying groups of patients with lung, esophageal, breast, colorectal, and kidney cancer was poor.

Metabolic biomarkers in lung cancer screening and early diagnosis (Review)

Late diagnosis is one of the major contributing factors to the high mortality rate of lung cancer, which is now the leading cause of cancer-associated mortality worldwide. At present, low-dose CT (LDCT) screening in the high-risk population, in which lung cancer incidence is higher than that of the low-risk population is the predominant diagnostic strategy. Although this has efficiently reduced lung cancer mortality in large randomized trials, LDCT screening has high false-positive rates, resulting in excessive subsequent follow-up procedures and radiation exposure. Complementation of LDCT examination with biofluid-based biomarkers has been documented to increase efficacy, and this type of preliminary screening can potentially reduce potential radioactive damage to low-risk populations and the burden of hospital resources. Several molecular signatures based on components of the biofluid metabolome that can possibly discriminate patients with lung cancer from healthy individuals have been proposed over the past two decades. In the present review, advancements in currently available technologies in metabolomics were reviewed, with particular focus on their possible application in lung cancer screening and early detection.

Breath testing for SARS-CoV-2 infection

BACKGROUND

From a public health perspective, the identification of individuals with mild respiratory symptoms due to SARS-CoV-2 infection is important to contain the spread of the disease. The objective of this study was to identify volatile organic compounds (VOCs) in exhaled breath common to infection with different variants of the SARS-CoV-2 virus to inform the development of a point-of-care breath test to detect infected individuals with mild symptoms.

METHODS

A prospective, real-world, observational study was conducted on mildly symptomatic out-patients presenting to community test-sites for RT-qPCR SARS-CoV-2 testing when the Alpha, Beta, and Delta variants were driving the COVID-19 pandemic. VOCs in exhaled breath were compared between PCR-positive and negative individuals using TD-GC-ToF-MS. Candidate VOCs were tested in an independent set of samples collected during the Omicron phase of the pandemic.

FINDINGS

Fifty breath samples from symptomatic RT-qPCR positive and 58 breath samples from test-negative, but symptomatic participants were compared. Of the 50 RT-qPCR-positive participants, 22 had breath sampling repeated 8-12 weeks later. PCA-X model yielded 12 distinct VOCs that discriminated SARS-CoV-2 active infection compared to recovery/convalescence period, with an area under the receiver operator characteristic curve (AUROC), of 0.862 (0.747-0.977), sensitivity, and specificity of 82% and 86%, respectively. PCA-X model from 50 RT-qPCR positive and 58 negative symptomatic participants, yielded 11 VOCs, with AUROC of 0.72 (0.604-0.803) and sensitivity of 72%, specificity 65.5%. The 11 VOCs were validated in a separate group of SARS-CoV-2 Omicron positive patients' vs healthy controls demonstrating an AUROC of 0.96 (95% CI 0.827-0.993) with sensitivity of 80% specificity of 90%.

INTERPRETATION

Exhaled breath analysis is a promising non-invasive, point-of-care method to detect mild COVID-19 infection.

FUNDING

Funding for this study was a competitive grant awarded from the Vancouver Coastal Research Institute as well as funding from the BC Cancer Foundation.

Efficient Eu3+-Integrated UiO-66 Probe for Ratiometric Fluorescence Sensing of Styrene and Cyclohexanone

The development of probes with sensitive and prompt detection of volatile organic compounds (VOCs) is of great importance for protecting human health and public security. Herein, we successfully prepared a series of bimetallic lanthanide metal-organic framework (Eu/Zr-UiO-66) by incorporating Eu³⁺ for fluorescence sensing of VOCs (especially styrene and cyclohexanone) using a one-pot method. Based on the multiple fluorescence signal responses of Eu/Zr-UiO-66 toward styrene and cyclohexanone, a ratiometric fluorescence probe using (I₆₁₇/I₃₂₀) and (I₆₁₇/I₃₃₀) as output signals was developed to recognize styrene and cyclohexanone, respectively. Benefitting from the multiple fluorescence response, the limits of detection (LODs) of Eu/Zr-UiO-66 (1:9) for styrene and cyclohexanone were 1.5 and 2.5 ppm, respectively. These are among the lowest reported levels for MOF-based sensors, and this is the first known material for fluorescence sensing of cyclohexanone. Fluorescence quenching by styrene was mainly owing to the large electronegativity of styrene and fluorescence resonance energy transfer (FRET). However, FRET was accounted for fluorescence quenching by cyclohexanone. Moreover, Eu/Zr-UiO-66 (1:9) exhibited good anti-interference ability and recycling performance for styrene and cyclohexanone. More importantly, the visual recognition of styrene and EB vapor can be directly realized with the naked eyes using Eu/Zr-UiO-66 (1:9) test strips. This strategy provides a sensitive, selective, and reliable method for the visual sensing of styrene and cyclohexanone.

Regulating the Electron Depletion Layer of Au/V2O5/Ag Thin Film Sensor for Breath Acetone as Potential Volatile Biomarker

Human exhaled breath has been utilized to identify biomarkers for diseases such as diabetes and cancer. The existence of these illnesses is indicated by a rise in the level of acetone in the breath. The development of sensing devices capable of identifying the onset of lung cancer or diabetes is critical for the successful monitoring and treatment of these diseases. The goal of this research is to prepare a novel breath acetone sensor made of Ag NPs/V₂O₅ thin film/Au NPs by combining DC/RF sputtering and post-annealing as synthesis methods. The produced material was characterized using X-ray diffraction (XRD), UV-Vis, Raman, and atomic force microscopy (AFM). The results revealed that the sensitivity to 50 ppm acetone of the Ag NPs/V₂O₅ thin film/Au NPs sensor was 96%, which is nearly twice and four times greater than the sensitivity of Ag NPs/V₂O₅ and pristine V₂O₅, respectively. This increase in sensitivity can be attributed to the engineering of the depletion layer of V₂O₅ through the double activation of the V₂O₅ thin films with uniform distribution of Au and Ag NPs that have different work function values.

Promising Mass-Productive 4-Inch Commercial SERS Sensor with Particle in Micro-Nano Porous Ag/Si/Ag Structure Using in Auxiliary Diagnosis of Early Lung Cancer

The construction of commercial surface enhanced Raman scattering (SERS) sensors suitable for clinical applications is a pending problem, which is heavily limited by the low production of high-performance SERS bases, because they usually require fine or complicated micro/nano structures. To solve this issue, herein, a promising mass-productive 4-inch ultrasensitive SERS substrate available for early lung cancer diagnosis is proposed, which is designed with a special architecture of particle in micro-nano porous structure. Benefitting from the effective cascaded electric field coupling inside the particle-in-cavity structure and efficient Knudsen diffusion of molecules within the nanohole, the substrate exhibits remarkable SERS performance for gaseous malignancy biomarker, with the limit of detection is 0.1 ppb and the average relative standard deviation value at different scales (from cm² to µm² ) is ≈16.5%. In practical application, this large-sized sensor can be further divided into small ones (1 × 1 cm² ), and more than 65 chips will be obtained from just one 4-inch wafer, greatly increasing the output of commercial SERS sensor. Further, a medical breath bag composed of this small chip is designed and studied in detail here, which suggested high-specificity recognition for lung cancer biomarker in mixed mimetic exhalation tests.

Hybrid volatilomics in cancer diagnosis by HS-GC-FID fingerprinting

Assessing volatile organic compounds (VOCs) as cancer signatures is one of the most promising techniques toward developing non-invasive, simple, and affordable diagnosis. Here, we have evaluated the feasibility of employing static headspace extraction (HS) followed by gas chromatography with flame ionization detector (GC-FID) as a screening tool to discriminate between cancer patients (head and neck-HNC,n= 15; and gastrointestinal cancer-GIC,n= 19) and healthy controls (n= 37) on the basis of a non-target (fingerprinting) analysis of oral fluid and urine. We evaluated the discrimination considering a single bodily fluid and adopting the hybrid approach, in which the oral fluid and urinary VOCs profiles were combined through data fusion. We used supervised orthogonal partial least squares discriminant analysis for classification, and we assessed the prediction power of the models by analyzing the values of goodness of prediction (Q²Y), area under the curve (AUC), sensitivity, and specificity. The individual models HNC urine, HNC oral fluid, and GIC oral fluid successfully discriminated between healthy controls and positive samples (Q²Y = 0.560, 0.525, and 0.559; AUC = 0.814, 0.850, and 0.926; sensitivity = 84.8, 70.2, and 78.6%; and specificity = 82.3; 81.5; 87.5%, respectively), whereas GIC urine was not adequate (Q²Y = 0.292, AUC = 0.694, sensitivity = 66.1%, and specificity = 77.0%). Compared to the respective individual models, Q²Y for the hybrid models increased (0.623 for hybrid HNC and 0.562 for hybrid GIC). However, sensitivity was higher for HNC urine and GIC oral fluid than for hybrid HNC (75.6%) and hybrid GIC (69.8%), respectively. These results suggested that HS-GC-FID fingerprinting is suitable and holds great potential for cancer screening. Additionally, the hybrid approach tends to increase the predictive power if the individual models present suitable quality parameter values. Otherwise, it is more advantageous to use a single body fluid for analysis.

Mapping the landscape of lung cancer breath analysis: A scoping review (ELCABA)

Lung cancer is the leading cause of cancer death worldwide due to its late-stage detection. Lung cancer screening, including low-dose computed tomography (low-dose CT), provides an initial clinical solution. Nevertheless, further innovations and refinements would help to alleviate remaining limitations. The non-invasive, gentle, and fast nature of breath analysis (BA) makes this technology highly attractive to supplement low-dose CT for an improved screening algorithm. However, BA has not taken hold in everyday clinical practice. One reason might be the heterogeneity and variety of BA methods. This scoping review is a comprehensive summary of study designs, breath analytical methods, and suggested biomarkers in lung cancer. Furthermore, this synthesis provides a framework with core outcomes for future studies in lung cancer BA. This work supports future research for evidence synthesis, meta-analysis, and translation into clinical routine workflows.

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.

Volatilomics as an Emerging Strategy to Determine Potential Biomarkers of Female Infertility: A Pilot Study

Due to its high prevalence, infertility has become a prominent public health issue, posing a significant challenge to modern reproductive medicine. Some clinical conditions that lead to female infertility include polycystic ovary syndrome (PCOS), endometriosis, and premature ovarian failure (POF). Follicular fluid (FF) is the biological matrix that has the most contact with the oocyte and can, therefore, be used as a predictor of its quality. Volatilomics has emerged as a non-invasive, straightforward, affordable, and simple method for characterizing various diseases and determining the effectiveness of their current therapies. In order to find potential biomarkers of infertility, this study set out to determine the volatomic pattern of the follicular fluid from patients with PCOS, endometriosis, and POF. The chromatographic data integration was performed through solid-phase microextraction (SPME), followed by gas chromatography–mass spectrometry (GC-MS). The findings pointed to specific metabolite patterns as potential biomarkers for the studied diseases. These open the door for further research into the relevant metabolomic pathways to enhance infertility knowledge and diagnostic tools. An extended investigation may, however, produce a new mechanistic understanding of the pathophysiology of the diseases.

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.

Development and validation of a screening model for lung cancer using machine learning: A large-scale, multi-center study of biomarkers in breath

Objectives

Lung cancer (LC) is the largest single cause of death from cancer worldwide, and the lack of effective screening methods for early detection currently results in unsatisfactory curative treatments. We herein aimed to use breath analysis, a noninvasive and very simple method, to identify and validate biomarkers in breath for the screening of lung cancer.

Materials and methods

We enrolled a total of 2308 participants from two centers for online breath analyses using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). The derivation cohort included 1007 patients with primary LC and 1036 healthy controls, and the external validation cohort included 158 LC patients and 107 healthy controls. We used eXtreme Gradient Boosting (XGBoost) to create a panel of predictive features and derived a prediction model to identify LC. The optimal number of features was determined by the greatest area under the receiver‐operating characteristic (ROC) curve (AUC).

Results

Six features were defined as a breath-biomarkers panel for the detection of LC. In the training dataset, the model had an AUC of 0.963 (95% CI, 0.941–0.982), and a sensitivity of 87.1% and specificity of 93.5% at a positivity threshold of 0.5. Our model was tested on the independent validation dataset and achieved an AUC of 0.771 (0.718–0.823), and sensitivity of 67.7% and specificity of 73.0%.

Conclusion

Our results suggested that breath analysis may serve as a valid method in screening lung cancer in a borderline population prior to hospital visits. Although our breath-biomarker panel is noninvasive, quick, and simple to use, it will require further calibration and validation in a prospective study within a primary care setting.

First-Principles Insight into a B4C3 Monolayer as a Promising Biosensor for Exhaled Breath Analysis

Nanomaterial-based room temperature gas sensors are used as a screening tool for diagnosing various diseases through breath analysis. The stable planar structure of boron carbide (B₄C₃) is utilized as a base material for adsorption of human breath exhaled VOCs, namely formaldehyde, methanol, acetone, toluene along, with interfering gases of carbon dioxide and water. The adsorption energy, charge density, density of states, energy band gap variation, recovery time, sensitivity, and work function of adsorbed molecules on pristine B₄C₃ are analyzed by density functional theory. The computed adsorption energies of VOC are in the range of - 0.176 to - 0.238 eV, and a larger interaction distance validate the physisorption behavior of these VOCs biomarkers on pristine boron carbide monolayer. Minute changes are determined from the electronic band structure of all adsorbed systems conserving the semiconducting nature of the B₄C₃ monolayer. The band gap variation upon adsorption of VOCs and interfering gases is examined between 0.05 and 0.52%. The 13.63 × 10^-9 s recovery time of methanol is slower among VOCs, and 0.556 × 10^-9 s of carbon dioxide (CO₂) is faster for desorption. The results reveal that boron carbide can be utilized as a biosensor at room temperature for the analysis of exhaled VOCs from human breath.

Volatilomics: An Emerging and Promising Avenue for the Detection of Potential Prostate Cancer Biomarkers

Simple Summary

The lack of highly specific and sensitive biomarkers for the early detection of prostate cancer (PCa) is a major barrier to its management. Volatilomics emerged as a non-invasive, simple, inexpensive, and easy-to-use approach for cancer screening, characterization of disease progression, and follow-up of the treatment’s success. We provide a brief overview of the potential of volatile organic metabolites (VOMs) for the establishment of PCa biomarkers from non-invasive matrices. Endogenous VOMs have been investigated as potential biomarkers since changes in these VOMs can be characteristic of specific disease processes. Recent studies have shown that the conjugation of the prostate-specific antigen (PSA) screening with other methodologies, such as risk calculators, biomarkers, and imaging tests, can attenuate overdiagnosis and under-detection issues. This means that the combination of volatilomics with other methodologies could be extremely valuable for the differentiation of clinical phenotypes in a group of patients, providing more personalized treatments.

Abstract

Despite the spectacular advances in molecular medicine, including genomics, proteomics, transcriptomics, lipidomics, and personalized medicine, supported by the discovery of the human genome, prostate cancer (PCa) remains the most frequent malignant tumor and a leading cause of oncological death in men. New methods for prognostic, diagnostic, and therapy evaluation are mainly based on the combination of imaging techniques with other methodologies, such as gene or protein profiling, aimed at improving PCa management and surveillance. However, the lack of highly specific and sensitive biomarkers for its early detection is a major hurdle to this goal. Apart from classical biomarkers, the study of endogenous volatile organic metabolites (VOMs) biosynthesized by different metabolic pathways and found in several biofluids is emerging as an innovative, efficient, accessible, and non-invasive approach to establish the volatilomic biosignature of PCa patients, unravelling potential biomarkers. This review provides a brief overview of the challenges of PCa screening methods and emergent biomarkers. We also focus on the potential of volatilomics for the establishment of PCa biomarkers from non-invasive matrices.

Lipid Peroxidation Produces a Diverse Mixture of Saturated and Unsaturated Aldehydes in Exhaled Breath That Can Serve as Biomarkers of Lung Cancer-A Review

The peroxidation of unsaturated fatty acids is a widely recognized metabolic process that creates a complex mixture of volatile organic compounds including aldehydes. Elevated levels of reactive oxygen species in cancer cells promote random lipid peroxidation, which leads to a variety of aldehydes. In the case of lung cancer, many of these volatile aldehydes are exhaled and are of interest as potential markers of the disease. Relevant studies reporting aldehydes in the exhaled breath of lung cancer patients were collected for this review by searching the PubMed and SciFinderⁿ databases until 25 May 2022. Information on breath test results, including the biomarker collection, preconcentration, and quantification methods, was extracted and tabulated. Overall, 44 studies were included spanning a period of 34 years. The data show that, as a class, aldehydes are significantly elevated in the breath of lung cancer patients at all stages of the disease relative to healthy control subjects. The type of aldehyde detected and/or deemed to be a biomarker is highly dependent on the method of exhaled breath sampling and analysis. Unsaturated aldehydes, detected primarily when derivatized during preconcentration, are underrepresented as biomarkers given that they are also likely products of lipid peroxidation. Pentanal, hexanal, and heptanal were the most reported aldehydes in studies of exhaled breath from lung cancer patients.

Exhaled Breath Volatile Organic Compound Analysis for the Detection of Lung Cancer- A Systematic Review

A rapid and effective diagnostic method is essential for lung cancer since it shows symptoms only at its advanced stage. Research is being carried out in the area of exhaled breath analysis for the diagnosis of various pulmonary diseases including lung cancer. In this method exhaled breath volatile organic compounds (VOC) are analyzed with various techniques such as gas chromatography-mass spectrometry, ion mobility spectrometry, and electronic noses. The VOC analysis is suitable for lung cancer detection since it is non-invasive, fast, and also a low-cost method. In addition, this technique can detect primary stage nodules. This paper presents a systematic review of the various method employed by researchers in the breath analysis field. The articles were selected through various search engines like EMBASE, Google Scholar, Pubmed, and Google. In the initial screening process, 214 research papers were selected using various inclusion and exclusion criteria and finally, 55 articles were selected for the review. The results of the reviewed studies show that detection of lung cancer can be effectively done using the VOC analysis of exhaled breath. The results also show that this method can be used for detecting the different stages and histology of lung cancer. The exhaled breath VOC analysis technique will be popular in the future, bypassing the existing imaging techniques. This systematic review conveys the recent research opportunities, obstacles, difficulties, motivations, and suggestions associated with the breath analysis method for lung cancer detection.

[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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Ultrasensitive SERS detection of exhaled biomarkers of lung cancer using a multifunctional solid phase extraction membrane

The construction and clinical application of a surface-enhanced Raman scattering (SERS) platform for the early diagnosis of lung cancer could improve the survival rate of patients and would be of great significance. Nevertheless, a sensitive and reusable method for the detection of aldehydes, as biomarkers of lung cancer, in exhaled breath is still an enormous challenge. Aldehydes generally have a low cross section in Raman scattering and have a weak specific affinity to plasmonic nanoparticle surfaces, meaning that sensing them at low concentrations is incredibly difficult. Herein, an ultrasensitive SERS strategy, that can be recycled for further use, for the detection of lung cancer biomarkers in the form of aldehydes was realized by fabrication of a multifunctional Ag NPs@ZIF-67/g-C₃N₄ solid phase extraction (SPE) membrane. Based on the change in the vibrational fingerprints of 4-ATP before and after reaction with the aldehydes, the SPE membrane was successfully used for the ultrasensitive detection of aldehydes with a detection limit of 1.35 nM. The excellent SERS performance was attributed to the synergistic effect of the densely and closely distributed Ag NPs (providing SERS "hot spots"), ZIF-67 (concentrating the analyte molecules) and g-C₃N₄ (forming a membrane to prolong the contact time between the aldehydes and the substrate). In addition, recycling of the SPE membrane was achieved by utilizing the self-cleaning ability of the Ag NPs@ZIF-67/g-C₃N₄ membrane originating from the photocatalytic properties of g-C₃N₄. The proposed SERS membrane was easy to operate, rapid and portable, thus providing a potential tool for a point-of-care test in clinical and diagnostic practice.

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.

Zinc Oxide-Based Acetone Gas Sensors for Breath Analysis: A Review

Acetone is one of the toxic, explosive, and harmful gases. It may cause several health hazard issues such as narcosis and headache. Acetone is also regarded as a key biomarker to diagnose several diseases as well as monitor the disorders in human health. Based on clinical findings, acetone concentration in human breath is correlated with many diseases such as asthma, halitosis, lung cancer, and diabetes. Thus, its investigation can become a new approach for health monitoring. Better management at the early stages of such diseases has the potential not only to reduce deaths associated with the disease but also to reduce medical costs. ZnO-based sensors show great potential for acetone gas due to their high chemical stability, simple synthesis process, and low cost. The findings suggested that the acetone sensing performance of such sensors can be significantly improved by manipulating the microstructure (surface area, porosity, etc.), composition, and morphology of ZnO nanomaterials. This article provides a comprehensive review of the state-of-the-art research activities, published during the last five years (2016 to 2020), related to acetone gas sensing using nanostructured ZnO (nanowires, nanoparticles, nanorods, thin films, etc). It focuses on different types of nanostructured ZnO-based acetone gas sensors. Furthermore, several factors such as relative humidity, acetone concentrations, and operating temperature that affects the acetone gas sensing properties- sensitivity, long-term stability, selectivity as well as response and recovery time are discussed in this review. We hope that this work will inspire the development of high-performance acetone gas sensors using nanostructured materials.

Comparison of Targeted and Untargeted Approaches in Breath Analysis for the Discrimination of Lung Cancer from Benign Pulmonary Diseases and Healthy Persons

The aim of the present study was to compare the efficiency of targeted and untargeted breath analysis in the discrimination of lung cancer (Ca+) patients from healthy people (HC) and patients with benign pulmonary diseases (Ca−). Exhaled breath samples from 49 Ca+ patients, 36 Ca− patients and 52 healthy controls (HC) were analyzed by an SPME–GC–MS method. Untargeted treatment of the acquired data was performed with the use of the web-based platform XCMS Online combined with manual reprocessing of raw chromatographic data. Machine learning methods were applied to estimate the efficiency of breath analysis in the classification of the participants. Results: Untargeted analysis revealed 29 informative VOCs, from which 17 were identified by mass spectra and retention time/retention index evaluation. The untargeted analysis yielded slightly better results in discriminating Ca+ patients from HC (accuracy: 91.0%, AUC: 0.96 and accuracy 89.1%, AUC: 0.97 for untargeted and targeted analysis, respectively) but significantly improved the efficiency of discrimination between Ca+ and Ca− patients, increasing the accuracy of the classification from 52.9 to 75.3% and the AUC from 0.55 to 0.82. Conclusions: The untargeted breath analysis through the inclusion and utilization of newly identified compounds that were not considered in targeted analysis allowed the discrimination of the Ca+ from Ca− patients, which was not achieved by the targeted approach.

Breathomics to diagnose systemic sclerosis using thermal desorption and comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry

Systemic sclerosis is a rare autoimmune disease associated with rapidly evolving interstitial lung disease, responsible for the disease severity and mortality. Specific biomarkers enabling the early diagnosis and prognosis associated with the disease progression are highly needed. Volatile organic compounds in exhaled breath are widely available and non-invasive and have the potential to reflect metabolic processes occurring within the body. Comprehensive two-dimensional gas chromatography coupled to high-resolution mass spectrometry was used to investigate the potential of exhaled breath to diagnose systemic sclerosis. The exhaled breath of 32 patients and 30 healthy subjects was analyzed. The high resolving power of this approach enabled the detection of 356 compounds in the breath of systemic sclerosis patients, which was characterized by an increase of mainly terpenoids and hydrocarbons. In addition, the use of 4 complementary statistical approaches (two-tailed equal variance t-test, fold change, partial least squares discriminant analysis, and random forest) resulted in the identification of 16 compounds that can be used to discriminate systemic sclerosis patients from healthy subjects. Receiver operating curves were generated that provided an accuracy of 90%, a sensitivity of 92%, and a specificity of 89%. The chemical identification of eight compounds predictive of systemic sclerosis was validated using commercially available standards. The analytical variations together with the volatile composition of room air were carefully monitored during the timeframe of the study to ensure the robustness of the technique. This study represents the first reported evaluation of exhaled breath analysis for systemic sclerosis diagnosis and provides surrogate markers for such disease.

Exploring Volatile Organic Compounds in Breath for High-Accuracy Prediction of Lung Cancer

(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants' exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

Recognizing lung cancer and stages using a self-developed electronic nose system

Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that could be used as non-invasive biomarkers of lung cancer. Breath-based lung cancer screening has attracted wide attention on account of its convenience, low cost and easy popularization. In this paper, the research of lung cancer detection and staging is conducted by the self-developed electronic nose (e-nose) system. In order to investigate the performance of the device in distinguishing lung cancer patients from healthy controls, two feature extraction methods and two different classification models were adopted. Among all the models, kernel principal component analysis (KPCA) combined with extreme gradient boosting (XGBoost) achieved the best results among 235 breath samples. The accuracy, sensitivity and specificity of e-nose system were 93.59%, 95.60% and 91.09%, respectively. Meanwhile, the device could innovatively classify stages of 90 lung cancer patients (i.e., 44 stage III and 46 stage IV). Experimental results indicated that the recognition accuracy of lung cancer stages was more than 80%. Further experiments of this research also showed that the combination of sensor array and pattern recognition algorithms could identify and distinguish the expiratory characteristics of lung cancer, smoking and other respiratory diseases.

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.

High performance exhaled breath biomarkers for diagnosis of lung cancer and potential biomarkers for classification of lung cancer

Exhaled breath analysis has emerged as a promising non-invasive method for diagnosing lung cancer (LC), whereas reliable biomarkers are lacking. Herein, a standardized and systematic study was presented for LC diagnosis, classification and metabolism exploration. To improve the reliability of biomarkers, a validation group was included, and quality control for breath sampling and analysis, comprehensive pollutants analysis, and strict biomarker screening were performed. The performance of exhaled breath biomarkers was shown to be excellent in diagnosing LC even in early stages (stage I and II) with surpassing 0.930 area under the receiver operating characteristic (ROC) curve (AUC), 90% of sensitivity and 88% of specificity both in the discovery and validation analyses. Meanwhile, in these two groups, diagnosing subtypes of LC attained AUCs over 0.930 and reached 1.00 in the two subtypes of adenocarcinomas. It is demonstrated that the metabolism changes in LC are possibly related to lipid oxidation, gut microbial, cytochrome P450 and glutathione S-transferase, and glutathione pathways change in LC progression. Overall, the reliable biomarkers contribute to the clinical application of breath analysis in screening LC patients as well as those in early stages.

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.

Metal oxide-based gas sensors for detection of exhaled breath markers

Exhaled breath test is a typical disease monitoring method for replacing blood and urine samples that may create discomfort for patients. To monitor exhaled breath markers, gas biomedical sensors have undergone rapid progress for non‐invasive and point‐of‐care diagnostic devices. Among gas sensors, metal oxide‐based biomedical gas sensors have received remarkable attention owing to their unique properties, such as high sensitivity, simple fabrication, miniaturization, portability and real‐time monitoring. Herein, we reviewed the recent advances in chemoresistive metal oxide‐based gas sensors with ZnO, SnO₂ and In₂O₃ as sensing materials for monitoring a range of exhaled breath markers (i.e., NO, H₂, H₂S, acetone, isoprene and formaldehyde). We focused on the strategies that improve the sensitivity and selectivity of metal oxide‐based gas sensors. The challenges to fabricate a functional gas sensor with high sensing performance along with suggestions are outlined.

Analysis of urinary VOCs using mass spectrometric methods to diagnose cancer: A review

The development of non-invasive screening techniques for early cancer detection is one of the greatest scientific challenges of the 21st century. One promising emerging method is the analysis of volatile organic compounds (VOCs). VOCs are low molecular weight substances generated as final products of cellular metabolism and emitted through a variety of biological matrices, such as breath, blood, saliva and urine. Urine stands out for its non-invasive nature, availability in large volumes, and the high concentration of VOCs in the kidneys. This review provides an overview of the available data on urinary VOCs that have been investigated in cancer-focused clinical studies using mass spectrometric (MS) techniques. A literature search was conducted in ScienceDirect, Pubmed and Web of Science, using the keywords "Urinary VOCs", "VOCs biomarkers" and "Volatile cancer biomarkers" in combination with the term "Mass spectrometry". Only studies in English published between January 2011 and May 2020 were selected. The three most evaluated types of cancers in the reviewed studies were lung, breast and prostate, and the most frequently identified urinary VOC biomarkers were hexanal, dimethyl disulfide and phenol; with the latter seeming to be closely related to breast cancer. Additionally, the challenges of analyzing urinary VOCs using MS-based techniques and translation to clinical utility are discussed. The outcome of this review may provide valuable information to future studies regarding cancer urinary VOCs.

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

"Electronic nose" technology, including technical and software tools to analyze gas mixtures, is promising regarding the diagnosis of malignant neoplasms. This paper presents the research results of breath samples analysis from 59 people, including patients with a confirmed diagnosis of respiratory tract cancer. The research was carried out using a gas analytical system including a sampling device with 14 metal oxide sensors and a computer for data analysis. After digitization and preprocessing, the data were analyzed by a neural network with perceptron architecture. As a result, the accuracy of determining oncological disease was 81.85%, the sensitivity was 90.73%, and the specificity was 61.39%.

Identification of salivary volatile organic compounds as potential markers of stomach and colorectal cancer: A pilot study

OBJECTIVES

The purpose of the pilot study was to determine the potential diagnostic capabilities for the analysis of oxygen-containing salivary volatile organic compounds (VOCs) in stomach and colorectal cancer.

METHODS

Saliva samples of 11 patients with stomach cancer, 18 patients with colorectal cancer, and 16 healthy volunteers were analyzed through capillary gas chromatography. The levels of lipid peroxidation products and catalase activity were determined in all samples. To assess saliva diagnostic potential, we constructed a Classification and Regression Tree (CART).

RESULTS

It was shown that the use of a combination of saliva VOCs (acetaldehyde, acetone, propanol-2, and ethanol) allowed classification into Cancer/Control groups with a sensitivity and specificity of 95.7 and 90.9%, respectively. To clarify the location of the tumor, it was necessary to add a methanol level; in this case, the sensitivity for detecting stomach and colorectal cancer was 80.0% and 92.3%, respectively, while the specificity in both cases was 100%. When the lipid peroxidation product content was added to the VOC indicators, they were selected as the main factors for constructing the decision tree. For classification into Cancer/Control groups, only the triene conjugate and Schiff base content in saliva was sufficient. The combination of VOCs in saliva and lipid peroxidation indices improved the sensitivity and specificity for classification to 100%.

CONCLUSION

Preliminary data were obtained on the sensitivity and specificity of the diagnosis of stomach and colorectal cancer, which confirmed the promise of further studies on saliva VOCs for the purpose of clinical laboratory diagnostics.

Recent advances in breath analysis to track human health by new enrichment technologies

Detection of biomarkers in exhaled breath has been gaining increasing attention as a tool for diagnosis of specific diseases. However, rapid and accurate quantification of biomarkers associated with specific diseases requires the use of analytical methods capable of fast sampling and preconcentration from breath matrix. In this regard, solid phase microextraction and needle trap technology are becoming increasingly popular in the field of breath analysis due to the unique benefits imparted by such methods, such as the integration of sampling, extraction, and preconcentration in a single step. This review discusses recent advances in breath analysis using these sample preparation techniques, providing a summary of recent developments of analytical methods based on breath volatile organic compounds analysis, including the successful identification of various biomarkers related to human diseases.

Evolution and Evaluation of GC Columns

A chromatographic column is the fundamental element required for gas-chromatographic analysis. The separation of components coming from complex mixtures, prior to their detection was leading to a prominent revolution in different areas of science. Moreover, current advances in gas chromatographic (GC) columns technology and development have been providing almost unlimited possibilities for analysis employing diverse matrices. We aim through this review article to describe the evolution of chromatographic columns, by pointing the most important stages, as well as the new trends and future perspectives predicted for the new generation of GC columns. Furthermore, it was in our scope to present the main fundamentals regarding the theoretical relationships that describe the chromatographic separation, to introduce concepts related to columns selection in accordance with the required application as well as to discuss the available evaluation parameters for columns efficiency. Consequently, the early stages of first columns preparation up to the development of GC capillary columns used nowadays, together with examples of their applications are also reported and described in detail.