Screening and Application of Exhaled Breath Biomarkers for Early Lung Cancer Diagnosis Based on HPPI-TOFMS

Abstract

The present invention discloses the screening of exhaled breath markers for early diagnosis of lung cancer using HPPI-TOFMS and their applications. The present invention provides the application of 16 volatile organic compounds as exhaled breath biomarkers for lung cancer in the development of products for diagnosing or assisting in the diagnosis of lung cancer. The 16 volatile organic compounds are: acetaldehyde, 2-hydroxyacetaldehyde, isoprene, valeraldehyde, butyric acid, toluene, 2,5-dimethylfuran, cyclohexanone, hexanal, heptanal, acetophenone, propylcyclohexane, octanal, nonanal, decanal and 2,2-dimethyldecane. The inventors of the present invention identified 16 exhaled breath biomarkers for lung cancer by performing perioperative exhaled omics testing using HPPI-TOFMS. The combination of these 16 exhaled breath biomarkers can distinguish between lung cancer patients and healthy people, and can be used in clinical practice to optimize lung cancer screening programs.

Description

Technical Field

[0001] The present invention belongs to the field of medical diagnosis and relates to the screening of exhaled breath biomarkers for early lung cancer diagnosis based on HPPI-TOFMS and their application. It specifically provides the development of 16 volatile organic compounds (VOCs) as exhaled breath biomarkers for lung cancer and their application in the diagnosis of lung cancer. Background Art

[0002] Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis and treatment are crucial to improving survival rates for lung cancer. However, early detection and diagnosis of lung cancer remain challenging due to the lack of early clinical manifestations and specific biomarkers. Annual low-dose computed tomography (LDCT) screening can significantly reduce lung cancer-specific mortality. However, the application of LDCT screening is challenging in many aspects. High cost, radiation exposure, and high false-positive rates hinder the application of LDCT in large-scale screening. Currently, there is an urgent need for a highly accurate and non-invasive lung cancer screening method.

[0003] Breathomics is considered a promising method for lung cancer screening. Genomic and transcriptomic changes during carcinogenesis and cancer progression lead to metabolic pathway dysregulation and the accumulation of abnormal metabolites. Among the numerous metabolites, cancer-derived volatile organic compounds (VOCs) can diffuse into the alveoli and be detected in exhaled breath. The concentration of VOCs in exhaled breath is well correlated with their blood concentration. Gas chromatography-mass spectrometry (GC-MS) is currently considered the "gold standard" for the identification and quantification of exhaled breath biomarkers.

[0004] Although GC-MS technology is mature, its cumbersome pretreatment steps and time-consuming detection process limit its application. Other direct mass spectrometry detection methods, such as secondary electrospray ionization, selected ion flow tubes, and proton transfer reactions, have also been used for rapid detection of exhaled breath; however, the large amount of water vapor in exhaled breath complicates the ionization process and increases the complexity of data analysis. In contrast, high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) has the advantages of high sensitivity, no need for pretreatment, and good moisture resistance, and has attracted much attention in exhaled breath detection. In previous studies, HIPPI-TOFMS has been used to monitor exhaled propofol concentrations during surgery, detect volatile metabolites in urine, and distinguish mixed flavor compounds. Summary of the Invention

[0005] The technical problem addressed by the present invention is lung cancer diagnosis. This invention provides HPPI-TOFMS-based exhaled breath biomarker screening for early lung cancer diagnosis and its application. Specifically, it provides the development of 16 volatile organic compounds (VOCs) as lung cancer exhaled breath biomarkers and their application in lung cancer diagnosis.

[0006] The present invention provides the use of 16 volatile organic compounds as lung cancer exhaled breath biomarkers in developing products for diagnosing or assisting in the diagnosis of lung cancer.

[0007] In the application, the abundance of the 16 volatile organic compounds is used as a breath biomarker for lung cancer.

[0008] The abundances of the 16 volatile organic compounds refer to the abundances of the 16 volatile organic compounds in the breath samples of the subjects.

[0009] In the application, the method for diagnosing or assisting in the diagnosis of lung cancer is as follows: substituting the abundances of 16 volatile organic compounds into a model formula to obtain a P value, and then performing a diagnosis or assisting in the diagnosis according to the diagnostic criteria.

[0010] The present invention also provides the use of a substance for detecting 16 volatile organic compounds in the preparation of a product for diagnosing or assisting in the diagnosis of lung cancer.

[0011] The substance for detecting 16 volatile organic compounds may specifically be a substance for detecting the abundance of 16 volatile organic compounds.

[0012] The substance for detecting 16 volatile organic compounds may specifically be a substance for detecting the abundance of 16 volatile organic compounds in a breath sample of a subject.

[0013] In the application, the method for diagnosing or assisting in the diagnosis of lung cancer is as follows: substituting the abundances of 16 volatile organic compounds into a model formula to obtain a P value, and then performing a diagnosis or assisting in the diagnosis according to the diagnostic criteria.

[0014] The present invention also provides a product for diagnosing or assisting in the diagnosis of lung cancer, comprising substances for detecting 16 volatile organic compounds.

[0015] The substance for detecting 16 volatile organic compounds may specifically be a substance for detecting the abundance of 16 volatile organic compounds.

[0016] The substance for detecting 16 volatile organic compounds may specifically be a substance for detecting the abundance of 16 volatile organic compounds in a breath sample of a subject.

[0017] The product also includes a carrier recording the model formula and diagnostic criteria.

[0018] The model formula is as follows:

[0019] P=1 / (1+e -(-0.01x1+0.001x2+0.001x3+0.01x4-0.001x5+0.001x6-0.027x7-0.004x8+0.022x9+0.005x10-0.007x11+0.001x12-0.005x13+0.034x1 4+0.073x15+0.007x16-5.181) ).

[0020] In the model formula, x1 to x16 correspond to the spectral peaks of acetaldehyde, 2-hydroxyacetaldehyde, isoprene, valeraldehyde, butyric acid, toluene, 2,5-dimethylfuran, cyclohexanone, hexanal, heptanal, acetophenone, propylcyclohexane, octanal, nonanal, decanal and 2,2-dimethyldecane, respectively.

[0021] Diagnostic criteria: P = 0.267 is the threshold value. If it is greater than or equal to the threshold value, it is diagnosed as lung cancer; if it is less than the threshold value, it is diagnosed as non-lung cancer.

[0022] Any of the above 16 volatile organic compounds are: acetaldehyde, 2-hydroxyacetaldehyde, isoprene, valeraldehyde, butyric acid, toluene, 2,5-dimethylfuran, cyclohexanone, hexanal, heptanal, acetophenone, propylcyclohexane, octanal, nonanal, decanal and 2,2-dimethyldecane.

[0023] The abundance may specifically be a spectrum peak.

[0024] The abundance may specifically be a spectrum peak of the volatile organic compound identified based on the m / z value and the ionization model of HPPI-TOFMS.

[0025] The substance used to detect the 16 volatile organic compounds can be a device or a reagent combination.

[0026] The material used to detect 16 volatile organic compounds can be a HPPI-TOFMS instrument.

[0027] The materials used for detecting 16 volatile organic compounds may be Tedlar bags and HPPI-TOFMS instruments.

[0028] The substances used to detect 16 volatile organic compounds can be the reagents required for HPPI-TOFMS.

[0029] The materials used for detecting 16 volatile organic compounds may be Tedlar bags and reagents required for HPPI-TOFMS.

[0030] The present invention provides the use of eight volatile organic compounds as lung cancer exhaled breath biomarkers in developing products for diagnosing or assisting in the diagnosis of lung cancer.

[0031] In the application, the abundance of the eight volatile organic compounds is used as a breath biomarker for lung cancer.

[0032] The abundances of the eight volatile organic compounds refer to the abundances of the eight volatile organic compounds in the breath samples of the subjects.

[0033] In the application, the method for diagnosing or assisting in the diagnosis of lung cancer is as follows: the abundances of eight volatile organic compounds are substituted into a model formula to obtain a P value, and then a diagnosis or assisting diagnosis is performed according to the diagnostic criteria.

[0034] The present invention also provides the use of a substance for detecting eight volatile organic compounds in the preparation of a product for diagnosing or assisting in the diagnosis of lung cancer.

[0035] The substance for detecting eight volatile organic compounds may specifically be a substance for detecting the abundance of eight volatile organic compounds.

[0036] The substance for detecting eight volatile organic compounds may specifically be a substance for detecting the abundance of eight volatile organic compounds in a breath sample of a subject.

[0037] In the application, the method for diagnosing or assisting in the diagnosis of lung cancer is as follows: the abundances of eight volatile organic compounds are substituted into a model formula to obtain a P value, and then a diagnosis or assisting diagnosis is performed according to the diagnostic criteria.

[0038] The present invention also provides a product for diagnosing or assisting in the diagnosis of lung cancer, comprising substances for detecting eight volatile organic compounds.

[0039] The substance for detecting eight volatile organic compounds may specifically be a substance for detecting the abundance of eight volatile organic compounds.

[0040] The substance for detecting eight volatile organic compounds may specifically be a substance for detecting the abundance of eight volatile organic compounds in a breath sample of a subject.

[0041] The product also includes a carrier recording the model formula and diagnostic criteria.

[0042] The model formula is as follows:

[0043] P=1 / (1+e -(0.001x1+0.002x2+0.004x3-0.001x4+0.019x5+0.021x6-0.004x7-0.011x8-6.454) ).

[0044] In the model formula, x1 to x8 correspond to the spectral peaks of isoprene, hexanal, valeraldehyde, propylcyclohexane, nonanal, 2,2-dimethyldecane, heptanal, and decanal, respectively.

[0045] Diagnostic criteria: P = 0.236 is the threshold value. If it is greater than or equal to the threshold value, it is diagnosed as lung cancer; if it is less than the threshold value, it is diagnosed as non-lung cancer.

[0046] Any of the above eight volatile organic compounds are: isoprene, hexanal, valeraldehyde, propylcyclohexane, nonanal, 2,2-dimethyldecane, heptaldehyde and decanal.

[0047] The abundance may specifically be a spectrum peak.

[0048] The abundance may specifically be a spectrum peak of the volatile organic compound identified based on the m / z value and the ionization model of HPPI-TOFMS.

[0049] The substance used to detect the eight volatile organic compounds can be a device or a reagent combination.

[0050] The material used to detect the eight volatile organic compounds can be a HPPI-TOFMS instrument.

[0051] The materials used for detecting the eight volatile organic compounds may be a Tedlar bag and an HPPI-TOFMS instrument.

[0052] The substances used to detect eight volatile organic compounds can be the reagents required for HPPI-TOFMS.

[0053] The materials used for detecting the eight volatile organic compounds may be Tedlar bags and reagents required for HPPI-TOFMS.

[0054] The present invention also protects the use of isoprene as a lung cancer breath biomarker in developing products for diagnosing or assisting in the diagnosis of lung cancer.

[0055] In the application, the abundance of isoprene is used as a breath biomarker for lung cancer.

[0056] The abundance of isoprene refers to the abundance of isoprene in the breath samples of the subjects.

[0057] The abundance may specifically be a spectrum peak.

[0058] The abundance can specifically be the spectral peak of isoprene identified based on the m / z value and the ionization model of HPPI-TOFMS.

[0059] The present invention also protects the use of a substance for detecting isoprene in the preparation of a product for diagnosing or assisting in the diagnosis of lung cancer.

[0060] In the application, the abundance of isoprene is used as a breath biomarker for lung cancer.

[0061] The abundance of isoprene refers to the abundance of isoprene in the breath samples of the subjects.

[0062] The abundance may specifically be a spectrum peak.

[0063] The abundance can specifically be the spectral peak of isoprene identified based on the m / z value and the ionization model of HPPI-TOFMS.

[0064] The substance used to detect isoprene can be a device or a reagent combination.

[0065] The material used to detect isoprene can be an HPPI-TOFMS instrument.

[0066] Materials used for detecting isoprene can be Tedlar bags and HPPI-TOFMS instruments.

[0067] The substance used to detect isoprene can be a reagent required for HPPI-TOFMS.

[0068] Materials used for detecting isoprene can include a Tedlar bag and reagents required for HPPI-TOFMS.

[0069] The present invention also protects the use of hexanal as a lung cancer breath biomarker in developing products for diagnosing or assisting in the diagnosis of lung cancer.

[0070] In the application, the abundance of hexanal is used as a breath biomarker for lung cancer.

[0071] The abundance of hexanal refers to the abundance of hexanal in the breath sample of the subject.

[0072] The abundance may specifically be a spectrum peak.

[0073] The abundance can specifically be the spectrum peak of hexanal identified based on the m / z value and the ionization model of HPPI-TOFMS.

[0074] The present invention also protects the use of a substance for detecting hexanal in preparing a product for diagnosing or assisting in the diagnosis of lung cancer.

[0075] In the application, the abundance of hexanal is used as a breath biomarker for lung cancer.

[0076] The abundance of hexanal refers to the abundance of hexanal in the breath sample of the subject.

[0077] The abundance may specifically be a spectrum peak.

[0078] The abundance can specifically be the spectrum peak of hexanal identified based on the m / z value and the ionization model of HPPI-TOFMS.

[0079] The substance for detecting hexanal may be a device or a reagent combination.

[0080] The material used to detect hexanal can be a HPPI-TOFMS instrument.

[0081] The materials used for detecting hexanal can be Tedlar bags and HPPI-TOFMS instruments.

[0082] The substance used to detect hexanal can be a reagent required for HPPI-TOFMS.

[0083] The materials used for detecting hexanal can be Tedlar bags and reagents required for HPPI-TOFMS.

[0084] The inventors of the present invention used HPPI-TOFMS to perform perioperative breath omics testing and identified 16 breath biomarkers for lung cancer, the combination of which can distinguish lung cancer patients from healthy people and can be used in clinical practice to optimize lung cancer screening programs. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 Schematic diagram of the discovery research process.

[0086] Figure 2 Examples of mass spectra in Example 1. A: Mass spectra of the patient before surgery (left) and 4 weeks after surgery (right). B: Mass spectrometry identification of 16 VOCs before surgery. Patient characteristics: Female, 52 years old, stage IA3.

[0087] Figure 3 Figure 2. Perioperative dynamic changes in 16 VOCs in exhaled breath of lung cancer patients. P values represent the difference in peak intensity between preoperatively and 4 weeks postoperatively, derived from a Wilcoxon matched-pairs signed-rank test. PO: postoperatively.

[0088] Figure 4 Schematic diagram of the confirmatory study process.

[0089] Figure 5 Comparison of VOC spectra peak intensities between lung cancer patients and healthy controls. P values are derived from the Mann–Whitney U test. HI: healthy controls; LC: lung cancer.

[0090] Figure 6 Figure 1 shows a combination of exhaled VOCs and lung cancer. A: Volcano plot showing the changes and differences in exhaled VOC peak intensity between lung cancer patients and healthy subjects. B: Correlation analysis of 16 VOCs in healthy subjects and lung cancer patients. D: Performance of 16 VOCs in lung cancer diagnosis. E: Performance of combinations of 16 VOCs in lung cancer diagnosis. F: Performance of 8 VOC combinations in lung cancer diagnosis. DETAILED DESCRIPTION

[0091] The present invention will be further described in detail below in conjunction with specific embodiments. The examples provided are only for illustrating the present invention and are not intended to limit the scope of the present invention. The examples provided below can serve as a guide for further improvements by those skilled in the art and are not intended to limit the present invention in any way.

[0092] Unless otherwise specified, the experimental methods in the following examples are conventional methods and were performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials and reagents used in the following examples, unless otherwise specified, were all commercially available.

[0093] Unless otherwise specified, the quantitative tests in the following examples were performed three times, and the results were averaged.

[0094] In the embodiment, categorical data are expressed as frequency (percentage), and continuous data are expressed as mean ± standard deviation or median (interquartile range). The Wilcoxon paired signed rank test was used to evaluate the changes in the peak intensity of VOCs during the perioperative period. The evaluation methods for the differences between the groups included analysis of variance, Pearson chi-square test, Mann-Whitney U test and Kruskal-Wallis test. The relationship between lung cancer and VOCs peak intensity was studied using a multivariate logistic regression model and a conditional backward method. A clinical prediction model based on multivariate logistic regression was established to evaluate the performance of VOCs for the diagnosis of lung cancer. In the confirmatory study, sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated to evaluate the diagnostic performance of exhaled VOCs for lung cancer. The receiver operating characteristic curve was drawn, and the area under the curve (AUC) was calculated. In the difference test, a two-tailed P value <0.05 was considered statistically significant. All analyses were performed using IBM SPSS statistical software for Windows (version 24.0, IBM Corp., Armonk, NY, USA) and R tools (version 4.1.2).

[0095] Example 1. Screening of biomarkers (discovery study)

[0096] The process diagram of discovery research is as follows: Figure 1 shown.

[0097] 1. Subject Screening and Subject Information

[0098] The discovery study was conducted at Peking University People's Hospital from September to December 2020. Breath samples were collected at three time points, namely the morning of the day of surgery (preoperatively), 3 days after surgery, and 4 weeks after surgery. This study was approved by the Ethics Committee of Peking University People's Hospital (2019PHB095-01). All patients were informed of the study protocol and written consent was obtained before entering the study. The study was conducted in accordance with the Standards for Reporting Diagnostic Accuracy (STARD) reporting guidelines (Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015; 351: h5527. DOI: 10.1136 / bmj.h5527.).

[0099] The inclusion criteria for the discovery study were patients with suspected lung lesions, aged ≥18 years, who were scheduled for surgical resection. Preoperative exclusion criteria were as follows: (1) a history of cancer within 5 years before surgery; (2) preoperative anti-tumor treatment; (3) active infection; (4) liver or kidney dysfunction; (5) lack of written informed consent for participation. Postoperative exclusion criteria were as follows: (1) pathologically confirmed benign lung disease; (2) lack of planned breath sampling; (3) severe perioperative complications that may affect breath omics detection. During the discovery study, a total of 112 patients underwent lung surgery. 18 patients were excluded based on preoperative exclusion criteria. Six patients were excluded because of benign disease based on postoperative pathological diagnosis. Another four patients were excluded due to lack of breath sampling 4 weeks after surgery. No serious complications or deaths were observed during the study. Finally, 84 lung cancer patients (including 27 men and 57 women) were included in the data analysis (patient characteristics are shown in Table 1). The average age was 55.0±10.2 years. Most patients are diagnosed with early-stage adenocarcinoma. Wedge resection is the most common procedure, followed by lobectomy and segmentectomy.

[0100] Table 1 Characteristics of lung cancer patients

[0101] feature Data (N=84) Sociodemographic data Age, years old 55.0±10.2 Gender (female) 57(67.9) Body Mass Index 24.1(22.9-26.3) Smoking history 12(14.3) Preoperative complications diabetes 9(10.7) cardiovascular disease 22(26.2) Cerebrovascular disease 3(3.6) respiratory diseases 2(2.4) Thyroid disease 10(11.9) Surgical method Lobectomy 35(41.7) Segmentectomy 4(4.8) wedge resection 45(53.6) Pathological data Tumor pathological type Adenocarcinoma 80(95.2) squamous cell carcinoma 1(1.2) Small cell carcinoma 3(3.6) Pathological staging IA1 / IA2 / IA3 / II-III 47 / 18 / 10 / 9(56.0 / 21.4 / 11.9 / 10.7) Lymph node metastasis 9(10.7) Multiple primary cancers 10(11.9)

[0102] 2. Preliminary Screening of Biomarkers

[0103] Considering the wide variety of volatile organic compounds (VOCs) in exhaled breath, the inventors reviewed existing research to identify potential exhaled breath biomarkers for lung cancer. VOCs associated with lung cancer reported in at least two original studies were selected as potential exhaled breath biomarkers. After preliminary research, 28 VOCs were initially selected for further validation. Information on these 28 VOCs is shown in Table 2. These VOCs primarily consist of hydrocarbons (aromatic and aliphatic) and oxygenates (aldehydes, alcohols, phenols, carboxylic acids, ethers, and furans).

[0104] Table 2

[0105]

[0106]

[0107] 3. Methods for collecting breath samples and detecting the abundance of biomarkers in breath samples

[0108] 1. Breath collection plan

[0109] All exhaled breath samples were collected by trained investigators using prepared Tedlar bags. The evening before breath collection, the Tedlar bags were baked at 60°C for 3 hours to completely release any interfering substances and then purged four times with high-purity nitrogen. Breath samples were collected in a fixed room, with corresponding ambient air. Participants first rinsed their mouths with purified water, then took a single deep breath and exhaled it completely into the Tedlar bag through their mouths. A total of 1000 ml of exhaled air was collected. A carbon dioxide sensor ensured that alveolar air was collected: exhaled breath collection only began once the carbon dioxide sensor detected a carbon dioxide concentration exceeding 4%. All participants were asked to fast for at least 8 hours and to refrain from consuming spicy foods, alcohol, or coffee the evening before breath collection.

[0110] 2. Detecting the abundance of biomarkers in breath samples

[0111] Identification of VOCs spectral peaks based on the m / z values of various biomarkers and the ionization model of HPPI-TOFMS (Jiang D, Li E, Zhou Q, Wang X, Li H, Ju B, et al. Online Monitoring of Intraoperative Exhaled Propofol by Acetone-Assisted Negative Photoionization Ion Mobility Spectrometry Coupled with Time-Resolved Purge Introduction. Anal Chem. 2018; 90(8): 5280-9. DOI: 10.1021 / acs.analchem.8b00171; Wang Y, Jiang J, Hua L, Hou K, Xie Y, Chen P, et al. High-Pressure Photon Ionization Source for TOFMS and Its Application for Online Breath Analysis. Anal Chem. 2016; 88(18): 9047-55. DOI: 10.1021 / acs.analchem.6b01707) to obtain the peak intensity of each VOC in the breath sample.

[0112] HPPI-TOFMS consists of a vacuum ultraviolet lamp-based HPPI ion source and an orthogonal acceleration time-of-flight (TOF) mass analyzer. The TOF mass analyzer uses a 0.4-meter field-free drift tube to achieve a mass resolution of 4000 (half the full-width maximum) at a mass-to-charge ratio (m / z) of 92. The gaseous exhaled gas sample is introduced directly from the gas bag into the ionization region through a stainless steel capillary. In order to eliminate the condensation of VOCs in the exhaled breath and minimize surface adsorption, the stainless steel capillary is heated to 100°C and the HPPI ion source is heated to 60°C. The TOF signal is recorded with a 400 picosecond time-to-digital converter at 25kHz, and all mass spectra are accumulated for 60 seconds. All mass spectrometers need to be mass calibrated before being put into use. Typically, 1,2-dichloroethylene, tetrachloroethylene, and hexachloro-1,3-butadiene, as well-known m / z value substances, are evenly distributed in the required mass range. Calibration formula y = ax 2+bx+c is used to convert time-of-flight to m / z. This calibration is performed by the mass spectrometer software. HPPI-TOFMS peaks with m / z less than 500 are recorded. Mass spectrometric data are preprocessed for further noise reduction, baseline correction, and VOC signature detection. Ambient background air data are subtracted from the exhaled breath sample, and the resulting data are used for further analysis.

[0113] The result obtained in step 3 is the characteristic spectrum peak of each volatile organic compound (VOC), that is, the spectrum peak, which is used to characterize the abundance of the VOC.

[0114] 4. Results Analysis

[0115] The spectral peaks of 28 VOCs at three time points are shown in Table 3. The spectral peaks of most VOCs showed fluctuating changes between the three time points. Considering that perioperative drug metabolism and surgical stress may affect the breath omics detection on the third day after surgery. According to the inventor's clinical practice, the patient's physiological state basically returned to normal 4 weeks after surgery. Therefore, the inventor focused on comparing the breath omics results before and after surgery. Examples of different mass spectra at two time points are shown in Figure 3. Figure 2 As shown in A (the left picture is before surgery, and the right picture is 4 weeks after surgery). Based on the Wilcoxon paired signed rank test, the spectral peak intensities of 15 VOCs decreased significantly during this period. They are: 2-hydroxyacetaldehyde, isoprene, valeraldehyde, butyric acid, toluene, 2,5-dimethylfuran, cyclohexanone, hexanal, heptanal, acetophenone, propylcyclohexane, octanal, nonanal, decanal and 2,2-dimethyldecane. However, the spectral peak intensity of acetaldehyde increased significantly four weeks after surgery. Examples of identification of 16 VOCs in mass spectrometry are shown below. Figure 2 B. The dynamic changes of the spectral peak intensity of these volatile organic compounds at three time points are shown in Figure 3 These 16 volatile organic compounds were selected as possible lung cancer breath biomarkers for further analysis. Figure 2 The corresponding patient information is: female, 52 years old, IA3 stage.

[0116] Table 3 Changes in volatile organic compounds in exhaled air of lung cancer patients before and after surgery

[0117]

[0118]

[0119] Example 2: Confirmation and evaluation of biomarkers (confirmatory study)

[0120] The flowchart of the confirmatory study is as follows Figure 4 shown.

[0121] 1. Subject Screening and Subject Information

[0122] The above-mentioned lung cancer breath markers were validated at the First Affiliated Hospital of Zhengzhou University.

[0123] The validation study included lung cancer patients with the same eligibility criteria as the discovery study; breath samples were collected on the day of surgery or biopsy (preoperatively). The validation study included healthy individuals who underwent a physical examination without positive LDCT results; breath samples were collected on the same day. All participants were asked to fast for at least 8 hours and to refrain from consuming spicy foods, alcohol, or coffee the night before breath collection.

[0124] The validation study included 157 lung cancer patients and 368 healthy controls. Subject characteristics are shown in Table 4. Lung cancer patients were older and had a higher prevalence of cardiovascular disease than healthy controls. A history of smoking and alcohol consumption was more common among healthy controls.

[0125] Table 4 Subject characteristics

[0126]

[0127] 2. Methods for collecting breath samples and detecting the abundance of biomarkers in breath samples

[0128] Same as step 3 of Example 1.

[0129] 3. Results Analysis

[0130] The spectral peak intensities of 16 VOCs in the breath samples of the two groups of people are as follows: Figure 5 shown.

[0131] 1. Individual diagnostic performance

[0132] The comparison of the spectral peak intensities of the above 16 VOCs between the two groups is shown in Figure 5 (HI represents healthy people, LC represents lung cancer people). Compared with healthy people, the spectrum peak intensity of all these VOCs in lung cancer patients is significantly increased. Volcano plot ( Figure 6 A) shows the fold changes and differences of 16 VOCs between lung cancer patients and healthy individuals. Correlation analysis showed that the enrichment of VOCs in lung cancer patients and healthy individuals was different (respectively Figure 6 B and Figure 6 C), indicating that the association patterns of VOCs in the two groups were different.

[0133] After adjusting for confounding factors (including age, sex, smoking history, drinking history, and comorbidities), lung cancer status (compared with healthy controls) remained an independent correlate of increased VOC spectral peak intensity (see Table 6).

[0134] Table 6 Multivariate analysis of the relationship between lung cancer and increased VOC spectrum peak intensity

[0135]

[0136]

[0137] Figure 6 Table D shows the individual diagnostic performance of 16 VOCs for lung cancer, and the data are summarized in Table 7. Isoprene and hexanal had the highest diagnostic AUCs, with an AUC of 0.859 for isoprene and 0.843 for hexanal.

[0138] Table 7 Performance of 16 VOCs in differential diagnosis of lung cancer patients and healthy subjects

[0139]

[0140] 2. Diagnostic performance of 16 VOCs

[0141] A clinical prediction model based on multivariate logistic regression was established.

[0142] The model formula is as follows:

[0143] P=1 / (1+e -(-0.01x1+0.001x2+0.001x3+0.01x4-0.001x5+0.001x6-0.027x7-0.004x8+0.022x9+0.005x10-0.007x11+0.001x12-0.005x13+0.034x1 4+0.073x15+0.007x16-5.181) ).

[0144] In the model formula, x1 to x16 correspond to the spectral peaks of acetaldehyde, 2-hydroxyacetaldehyde, isoprene, valeraldehyde, butyric acid, toluene, 2,5-dimethylfuran, cyclohexanone, hexanal, heptanal, acetophenone, propylcyclohexane, octanal, nonanal, decanal and 2,2-dimethyldecane, respectively.

[0145] Diagnostic criteria: P = 0.267 is the threshold value. If it is greater than or equal to the threshold value, it is diagnosed as lung cancer; if it is less than the threshold value, it is diagnosed as healthy (non-lung cancer).

[0146] The peak values of the 16 VOCs in the breath samples of all subjects were substituted into the above model formula to obtain the diagnosis results. The diagnosis results of the subjects were compared with the actual disease conditions of the subjects. The diagnosis AUC was 0.952, the sensitivity was 89.2%, the specificity was 89.1%, and the accuracy was 89.1% ( Figure 6 E).

[0147] 3. Diagnostic performance of 8 VOCs

[0148] The diagnostic performance of the combination of the top eight VOCs by AUC in Table 7 was evaluated. The top eight VOCs by AUC are isoprene, hexanal, valeraldehyde, propylcyclohexane, nonanal, 2,2-dimethyldecane, heptaldehyde, and decanal.

[0149] A clinical prediction model based on multivariate logistic regression was established.

[0150] The model formula is as follows:

[0151] P=1 / (1+e -(0.001x1+0.002x2+0.004x3-0.001x4+0.019x5+0.021x6-0.004x7-0.011x8-6.454) ).

[0152] In the model formula, x1 to x8 correspond to the spectral peaks of isoprene, hexanal, valeraldehyde, propylcyclohexane, nonanal, 2,2-dimethyldecane, heptanal, and decanal, respectively.

[0153] Diagnostic criteria: P = 0.236 is the threshold value. If it is greater than or equal to the threshold value, it is diagnosed as lung cancer; if it is less than the threshold value, it is diagnosed as healthy (non-lung cancer).

[0154] The peak values of the eight VOCs in the breath samples of all subjects were substituted into the above model formula to obtain the diagnosis results. The diagnosis results of the subjects were compared with their actual disease conditions. The diagnosis AUC was 0.931, the sensitivity was 86.0%, the specificity was 87.2%, and the accuracy was 86.9% ( Figure 6 F).

[0155] The present invention has been described in detail above. It will be apparent to those skilled in the art that the present invention may be practiced over a wide range of parameters, concentrations, and conditions without departing from the spirit and scope of the present invention and without unnecessary experimentation. Although specific embodiments have been given herein, it should be understood that further modifications may be made to the present invention. In summary, this application is intended to encompass any variations, uses, or improvements to the present invention, including those made by conventional techniques known in the art that depart from the scope of the present invention. Applications of the essential features may be made within the scope of the following claims.

Claims

1. Use of a combination of 16 volatile organic compounds as exhaled breath biomarkers for lung cancer in the development of products for diagnosing or assisting in the diagnosis of lung cancer; the combination of 16 volatile organic compounds consists of acetaldehyde, 2-hydroxyacetaldehyde, isoprene, valeraldehyde, butyric acid, toluene, 2,5-dimethylfuran, cyclohexanone, hexanal, heptanal, acetophenone, propylcyclohexane, octanal, nonanal, decanal, and 2,2-dimethyldecane.

2. Application of a combination of eight volatile organic compounds as exhaled breath biomarkers for lung cancer in the development of products for diagnosing or assisting in the diagnosis of lung cancer; the combination of eight volatile organic compounds consists of isoprene, hexanal, valeraldehyde, propylcyclohexane, nonanal, 2,2-dimethyldecane, heptaldehyde, and decanal.

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