ObjectiveTo investigate the characteristic volatile organic compounds (VOCs) in exhaled breath and their diagnostic value in patients with early stage lung cancer.MethodsSolid-phase micro-extraction combined with gas chromatography mass spectrometry was used to analyze exhaled breath VOCs of 117 patients with early stage lung cancer (54 males and 63 females, with an average age of 61.9±6.8 years) and 130 healthy subjects (79 males and 51 females, with an average age of 63.3±6.6 years. The characteristic VOCs of early stage lung cancer were identified, and a diagnostic model was established.ResultsTen characteristic VOCs of early stage lung cancer were identified, including acetic acid, n-butanol, dimethylsilanol, toluene, 2,3,4-trimethylheptane, 3,4-dimethylbenzoic acid, 5-methyl-3-hexene-2-ketone, n-hexanol, methyl 2-oxoglutarate and 4-methoxyphenol. Gender and the 10 characteristic VOCs were included in the diagnostic model, with a sensitivity of 83.8% and a specificity of 96.2%.ConclusionAnalysis of exhaled breath VOCs is expected to be one of the potential methods used for early stage lung cancer diagnosis.
Being a non-invasive diagnostic technique for molecular biological markers, exhaled breath detection has the most latent capacity and future in the diagnosis and treatment of tumors. The National Key Research and Development Plan named "Strategic Advanced Electronic Materials" in 2020 has laid out the application of exhaled breath detection technology in the medical field, and the scientific research project led by Sichuan Cancer Hospital has been successfully launched. For the moment, as a novel strategy for early detection of lung cancer, exhaled breath detection is being perfected further and popularized or put in clinical practice step by step to reduce the mortality of lung cancer patients.
Early diagnosis of lung cancer is of great significance for reducing mortality and improving survival. Traditional methods of early diagnosis of lung cancer have their own limitations. The exhaled breath can reflect the disease state of the body, which has great potential in the early diagnosis of lung cancer. In this paper, the diagnosis of lung cancer and the application of exhaled breath detection technology in the diagnosis of lung cancer were reviewed.
Objective To explore composition of volatile organic compounds (VOCs) in exhaled breath of low-dose radiation-damaged Sprague-Dawely (SD) rats by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS), and search for the differential metabolites of VOCs in the series of rats after radiation damage, and establish a noninvasive radiation damage detection method. Methods SD rats were randomly divided into five groups (a blank group, a 0.5-Gy group, a 1-Gy group, a 2-Gy group, and a 3-Gy group), with 8 rats in each group. A low-dose radiation injury model was established in rats. After the cobalt source radiation damage was performed, the body weight of rats was recorded, peripheral blood hematology was analyzed, and the exhaled breath of rats was collected on the 1st, 5th, 9th and 13th day. The composition of VOCs in the exhaled breath was analyzed by using the TD30-GC-MS technique, and multivariate statistical analyses were carried out to explore and obtain the differentiated metabolites after the radiation damage. Results After radiation damage, the rats showed a short-term decrease in body weight, peripheral blood and lung tissue sections were different, and the content of VOCs components in the exhaled breath of the damaged rats was significantly different from that of the rats in the blank group. Among them, four VOCs, acetophenone, nonanal, decanal and tetradecane increased, while heptane, chlorobenzene, paraxylene and m-dichlorobenzene decreased. Conclusions Through the GC-MS analysis of the exhaled breath of rats, eight components of VOCs in the exhaled breath of rats can be used as differential metabolites of radiation damage. This study lays a foundation for the establishment of a GC-MS analysis method for the components of VOCs in the exhaled breath of rats, as well as for the development of a nondestructive analytical assay for biological radiation damage.
ObjectiveTo explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). MethodsA systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST).ResultsA total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high bias risk, and the overall applicability was unclear. ConclusionThe exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.