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ORIGINAL ARTICLE Exhaled breath volatile biomarker analysis for thyroid cancer Q17
LEI GUO, CHANGSONG WANG, CHUNJIE CHI, XIAOYANG WANG, SHANSHAN LIU, WEI ZHAO, CHAOFU KE, GUOWANG XU, and ENYOU LI HARBIN AND DALIAN, CHINA
Compared with other types of cancer, thyroid cancer incidence rates have increased rapidly worldwide in the past few decades. In recent years, potential thyroid cancer biomarkers have been studied, but these biomarkers have neither specificity nor good positive predictive value. Exhaled breath analysis is a recently developed convenient and noninvasive method for screening and diagnosing the disease. In this study, potential thyroid cancer biomarkers in volatile organic compounds (VOCs) were detected. Exhaled breath was collected from 64 patients with histologically confirmed cases of thyroid disease (including 39 individuals with papillary thyroid carcinoma and 25 individuals with nodular goiters) and 32 healthy volunteers. Solid-phase microextraction–gas chromatography and mass spectrometry was used to assess the exhaled VOCs of the study participants. The statistical methods of principal component analysis and partial least-squares discriminant analysis were performed to process the final data. The VOCs exhibited significant differences between nodular goiter patients and normal controls, papillary thyroid carcinoma patients and normal controls, and papillary thyroid carcinoma patients and nodular goiter patients; 7, 7, and 3 characteristic metabolites played decisive roles in sample classification, respectively. Breath analysis may provide a new, noninvasive, and directly qualitative method for the clinical diagnosis of thyroid disease. (Translational Research 2015;-:1–8)
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Abbreviations: FNAB ¼ fine-needle aspiration biopsy; GABA ¼ gamma aminobutyric acid; GC/ MS ¼ gas chromatography and mass spectrometry; GHB ¼ g-hydroxybutyric acid; IARC ¼ International Agency for Research on Cancer; PCA ¼ principal component analysis; PLS-DA ¼ partial least-squares discriminant analysis; PTC ¼ papillary thyroid carcinoma; VIP ¼ variable importance in the projection; VOCs ¼ volatile organic compounds
From the Department of Anesthesiology, The First Affiliated Hospital of Harbin Medical University, Harbin, China; Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China. Submitted for publication March 11, 2014; revision submitted December 31, 2014; accepted for publication January 15, 2015.
Reprint requests: Enyou Li, Department of Anesthesiology, The First Affiliated Hospital of Harbin Medical University, No. 23 Youzheng Street, Nangang District, Harbin, Heilongjiang 150001, China; e-mail: [email protected]. 1931-5244/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.trsl.2015.01.005
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AT A GLANCE COMMENTARY Guo L, et al. Background
Compared with other types of cancer, the growth rate of thyroid cancer is very fast. In recent years, potential thyroid cancer biomarkers have been studied, but these biomarkers have neither specificity nor good positive predictive value. Exhaled breath analysis is a recently developed convenient and noninvasive method of screening and diagnosing the disease. Translational Significance
In this study, potential thyroid cancer biomarkers in volatile organic compounds were detected. Breath analysis may provide a new, noninvasive, and directly qualitative method for the clinical diagnosis of thyroid disease.
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INTRODUCTION
The incidence of thyroid cancer has been increasing dramatically. Compared with other types of cancer in males and females, thyroid cancer incidence rates have increased rapidly worldwide in the past few decades.1 With the growing application of highresolution ultrasound in clinical practice, a growing number of thyroid nodules are being detected. Although most nodules are benign, 5%–10% of them are malignant.2 Identifying the nature of the thyroid nodules and developing subsequent treatment have become difficult issues for clinicians and patients. The most common diagnostic method is fine-needle aspiration biopsy, which has the advantage of direct characterization and the disadvantage of invasive operation. Additionally, the success of this technique is highly dependent on the strength of the technological development and the experience of the clinician. It has a specificity of less than 75%.3-5 In recent years, potential thyroid cancer biomarkers such as galectin-3, cytokeratin-19, and fibronectin-1 have been studied, but these biomarkers have neither specificity nor good positive predictive value.6,7 Accordingly, the exploration of a direct and noninvasive diagnostic method is still needed to guide clinical diagnoses and the development of treatment plans. Metabolomic approaches can identify diseasespecific metabolic patterns by measuring in vivo metabolites. This technique has been widely used in studies on a variety of disease-specific biomarkers.8-10 Yao et al
used liquid chromatography coupled to an LTQ Orbitrap XL (Thermo Fisher) hybrid mass Q3 spectrometer combined with a metabolomics analysis method to study metabolic changes in the serum of healthy subjects, patients with nodular goiter, and patients with papillary thyroid carcinoma (PTC).11 The analysis showed that the main components that changed in the serum were free fatty acids such as arachidonic acid and linolenic acid, phospholipids such as lysophospholipids, and amino acids such as tryptophan, serotonin, and lysine. However, this method is invasive because it requires collecting the patient’s blood. Exhaled breath analysis is a recently developed method of screening and diagnosing the disease. Because of its convenience, noninvasiveness, good tolerance, and easy acceptability by patients, this technique has drawn increasing attention from researchers and clinicians.12,13 Compared with normal tissue, cancerous tissue may exhibit specific metabolic patterns, and the resulting volatile metabolites will be discharged in the blood, excreted by the alveolae, and exhaled in the breath. These specific volatile metabolites can be detected and analyzed using gas chromatography and mass spectrometry (GC/MS).14 Studies have demonstrated the specificity of volatile metabolites from patients with different cancers such as lung cancer, colorectal cancer, gastric cancer, and breast cancer.15-18 In the present study, we used the multivariate data analysis method with GC/MS and metabolomics to compare volatile organic compounds in the exhaled breath from healthy subjects, patients with nodular goiter, and patients with PTC. MATERIALS AND METHODS
This study was approved by the Ethics Committee of Harbin Medical University (No. 201314). A total of 64 patients who were admitted to the First Affiliated Hospital of Harbin Medical University between April and October 2012 were included in this study. To screen and identify possible noninvasive biomarkers of malignant thyroid nodules, breath samples from 39 patients with PTC (the most common type of malignant thyroid nodule) and 25 patients with nodular goiter (benign nodule), as well as 32 age and gender-matched healthy subjects, were collected, as shown in Table I. All the participants were recruited in accordance with the strict inclusion and exclusion criteria established by our research institution. In particular, the following inclusion criteria were used: (1) an age of 25–70 years and (2) agreement to participate in the study, as indicated by a signed informed consent form. The following exclusion criteria were used: (1) pregnancy, lactation,
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Table I. Demographic characteristics of the study subjects Q14
Control group
Nodular goiters
Papillary thyroid carcinoma
Subjects (n) 32 25 39 Age (mean 6 SD) 39.8 6 13.4 51.8 6 12.1* 46.2 6 11.4* Sex Male 6 6 5 Female 26 19 34 Smokers (n) 4 6 7 Diabetes 0 2 2 *Statistically significant difference (P , 0.05).
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or the possibility of pregnancy; (2) the presence of a confirmed congenital disease; (3) a family history of mental illness; (4) the presence of a chronic inflammatory disease; (5) symptoms of an acute disease during the 2 weeks before the study enrollment; and (6) a history of infectious disease. Breath collection. Breath gas sampling was performed in conjunction with the parallel collection of ambient air within 24 hours after the subjects had fasted overnight. The alveolar breath sampling procedure was performed in accordance with previous studies.19,20 Samples were collected with carbon dioxide monitoring. Once the carbon dioxide plateau was reached, the samples were collected. Briefly, 20-mL samples of exhaled gas were drawn into gas-tight syringes (50 mL) (Agilent Inc). These samples were immediately transferred into 20mL glass vials (Supelco Inc). To remove any residual contaminants, all these vials were thoroughly cleaned by flushing with nitrogen gas (99.99% purity; Liming Gas Inc) and subsequently evacuated for breath sample collection.21 All the samples were analyzed within 3 hours of sampling. Solid-phase microextraction. A manual solid-phase microextraction holder with 75 mm thick carboxen/ polydimethylsiloxane fibers was purchased from Supelco (Bellefonte, Pennsylvania). A solid-phase microextraction fiber was inserted into each vial and exposed to the gaseous sample for 20 minutes at 40 C. Subsequently, desorption of the volatiles occurred in a hot GC injector at 200 C for 2 minutes. GC/MS analyses. The analysis was performed on a GC/MS instrument (Shimadzu GC-MS QP 2010; Shimadzu) equipped with a DB-5MS (length 30 m 3 inner diameter 0.25 mm 3 film thickness 0.25 mm; Agilent Technologies Inc) porous-layer opentubular PLOT column. The injections were performed in the splitless mode with a splitless time of 1 minute. The temperature of the injector was 200 C. The flow rate of the helium (99.99%) carrier gas was maintained at a constant rate of 2 mL/min. The column
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temperature was held at 40 C for 2 minutes to concentrate the hydrocarbons at the head of the column; subsequently, the column temperature was increased by 70 C/min to 200 C for 1 minute, ramped by 20 C/min to 230 C, and held at 230 C for 3 minutes. The MS analyses were performed in a full scan mode, with a scan range of 35–200 amu. The ion source was maintained at 200 C, and an ionization energy of 70 eV was used for each measurement. The extraction and pretreatment of raw GC/MS data. The raw GC/MS data were converted into CDF
format (NetCDF) files by the Shimadzu GCMS Postrun Analysis software package and subsequently processed using the XCMS toolbox (http://metlin.scripps.edu/ download). The default XCMS parameters were used, Q10 with the following exceptions: xcmsSet (fwhm 5 8, snthresh 5 6, max 5 200); retcor (method 5 ‘‘linear,’’ family 5 ‘‘gaussian,’’ plottype 5 ‘‘mdevden’’), a bandwidth of 8 for the first grouping command and 4 for the second grouping command.22 The data set of aligned mass ions was exported from XCMS for further processing with Microsoft Excel, which was used to normalize the data before the multivariate analyses. Statistical analyses. Each sample was normalized to the total peak area before the statistical analyses. The normalized data were subsequently exported to SIMCA-P 11.5 for principal component analysis (PCA) to detect grouping trends and outliers. Partial Q11 least-squares discriminant analysis (PLS-DA) was then performed, using the default 7-round cross-validation approach, and the values of the corresponding variable importance in the projection (VIP) in the PLS-DA model were calculated. To prevent overfitting, permutation tests with 100 iterations were performed to validate the supervised model. In addition, the nonparametric Kruskal-Wallis rank sum test was performed to determine the significance of each metabolite. Potential metabolic biomarkers were selected based on the VIP values and the nonparametric P values, with thresholds of 1.2 and 0.01, respectively. RESULTS Nodule goiters vs normal controls. A total of 149 metabolites were consistently detected in 50% of the samples from the patients with nodular goiters and the control patients. Although the 2-dimensional PCA score plot displayed a good separation trend (Fig 1, A), the PLS-DA score plot demonstrated excellent separation between the patients with nodular goiters and the controls using 4 components (R2X 5 0.656, R2Y 5 0.963, Q2 5 0.92; Fig 1, B). Moreover, all the R2 and Q2 values calculated from the permutated data
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Fig 1. (A) PCA score plot (10 principal components, R2X 5 0.878, Q2 5 0.723). (B) PLS-DA score plot (4 principal components, R2X 5 0.656, R2Y 5 0.963, Q2 5 0.92). (C) PLS-DA validation plot intercepts: R2 5 (0.0, 0.39), Q2 5 (0.0, 20.43). PCA, principal component analysis; PLS-DA, partial least-squares discriminant analysis.
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were lower than the original values in the validation plot, which confirmed the validity of the supervised model (Fig 1, C). PTCs vs normal controls. A total of 167 metabolites were consistently detected in 50% of the samples from the patients with PTC and the controls. Although the 2-dimensional PCA score plot displayed good separation (Fig 2, A), the PLS-DA score plot demonstrated excellent separation between the patients with PTC and the controls using 4 components (R2X 5 0.648, R2Y 5 0.963, Q2 5 0.916; Fig 2, B). Moreover, all the R2 and Q2 values calculated from the permutated data were lower than the original values in the validation plot, which confirmed the validity of the supervised model (Fig 2, C). Nodule goiters vs PTC. Although the results of the PCA analysis did not distinguish between the nodule goiters and PTC (Fig 3, A), a good distinction was achieved after the PLS-DA analysis (Fig 3, B). Moreover, all the R2 and Q2 values calculated from the permutated data were lower than the original values in the validation plot, which confirmed the validity of the supervised model (Fig 3, C). Standard measures of accuracy (AUC, sensitivity, specificity) were computed based on PLS-DA crossvalidation.23 Metabolic signatures for nodule goiters vs normal controls and PTCs vs normal controls re-
sulted in an AUC of 1.0000, with both sensitivity and specificity of 1.0000 at the best cutoff point. For nodular goiter vs papillary thyroid cancer, the AUC value was 0.9015, with a sensitivity of 0.9200 and a specificity of 0.8205 at the best cutoff point. Potential biomarkers. Among the significantly altered metabolites identified using the VIP values in the PLSDA model and the FDR values, 12 were annotated using the NIST 11 database with a similarity threshold of 75% (Table II). Q13 DISCUSSION
The standard diagnosis of thyroid diseases includes the patient’s history, a clinical examination, laboratory tests, scintigraphy, thyroid ultrasound, and fine-needle aspiration biopsy. Although a series of molecular markers could help to predict malignancy, molecular biomarkers are still in the research phase.24 Although exhalation research is an emerging field, it is relatively advanced for the detection of Helicobacter pylori. Applications to lung cancer, breast cancer, liver cancer, and colorectal cancer also have been reported, but the use of exhaled breath has not been examined for thyroid diseases. Thus, we conducted this study. The exhaled breath approach can be used to screen or diagnose thyroid diseases, monitor the recurrence of
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Fig 2. (A) PCA score plot (10 principal components, R2X 5 0.883, Q2 5 0.758). (B) PLS-DA score plot (4 principal components, R2X 5 0.648, R2Y 5 0.963, Q2 5 0.916). (C) PLS-DA validation plot intercepts: R2 5 (0.0, 0.28), Q2 5 (0.0, 20.42). PCA, principal component analysis; PLS-DA, partial least-squares discriminant analysis; PTC, papillary thyroid carcinoma.
thyroid disease, and assess the effect of anticancer drugs. Although the method has many deficiencies at present study, it offers a promising new way to diagnose thyroid disease. Even if it only plays a supporting role in the diagnosis at present, it is a meaningful start. Compared with the samples from healthy subjects, the samples from the patients in the nodular goiter and the PTC groups showed declines in the concentrations of many substances. This result is similar to that found for blood by Yao et al.11 It may be because of the consumption of nutrients and metabolic energy for tumor growth. Meanwhile, by culturing the cancer cell line CALU-1, Filipiak et al25 analyzed the composition in the head space and found that during cancer cell growth, the levels of volatile biomarkers such as butyl acetate and 2-methylpropanal decreased, indicating that the tumor cells had consumed them. In this study, decreases in the concentrations of the metabolites sulfurous acid, cyclohexylmethyl hexyl ester, cyclohexanone, 4-hydroxybutyric acid, 2,2-dimethyldecane, ethylhexanol, and (3-methyl-oxiran-2-yl)-methanol were detected, which could be explained by the oncology consumption mechanism. However, the experimental procedures were different from ours, and the differences in the results may have occurred because of the different sources of cancer cells.
In this study, compared with the samples from healthy controls, the concentration of ethylhexanol in the exhaled gas from the patients with PTC was significantly lower. This result likely occurred because of differences in the cytochrome P450 enzymes, which hydroxylate lipid peroxidation biomarkers to produce alcohol. This result may have occurred because of the regulation of lipid metabolism by thyroid hormone. In addition to having exogenous origins, such as contact with liquid phenol or drinking water contaminated with phenol, the phenol in the human body may also come from the metabolism of benzene. Phenol content in urine is commonly used to monitor people who have come into contact with benzene and can be used to accurately measure the duration of short-term benzene exposure. First, benzene is oxidized by cytochrome P450 monooxygenase in the liver to produce styrene, which is toxic. It can then be combined with glutathione to form phenylthio uric acid, which will continue to be metabolized to phenol, catechol, and hydroquinone and can be excreted as glucuronide or sulfate conjugates.26 In this study, the samples of exhaled gas were collected along with control samples from the background environment using the same method, so the exhaled phenol is likely to have come from the metabolism of benzene. In addition to entering the
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Fig 3. (A) PCA score plot (8 principal components, R2X 5 0.897, Q2 5 0.799). (B) PLS-DA score plot (2 principal components, R2X 5 0.577, R2Y 5 0.54, Q2 5 0.458). (C) PLS-DA validation plot intercepts: R2 5 (0.0, 0.124), Q2 5 (0.0, 20.182). PCA, principal component analysis; PLS-DA, partial least-squares discriminant analysis; PTC, papillary thyroid carcinoma.
body through direct contact, benzene can enter the body during smoking and be stored in the body before being slowly metabolized and excreted. Benzene has long been confirmed as a cancer-inducing substance by the International Agency for Research on Cancer. In the present study, compared with the healthy controls, the phenol concentrations in exhaled breath from the patients with either nodular goiter or PTC were increased. Therefore, a higher in vivo phenol level indicates a higher level of benzene and thus a greater degree and chance of harm to the human body by benzene. This hypothesis requires further investigation. 4-Hydroxybutanoic acid, also known as g-hydroxybutyric acid (GHB), is a naturally existing substance found in the human central nervous system that is involved in the exchange of information. It interacts with other neurotransmitters, such as gamma aminobutyric acid. GHB is produced by the reduction of succinic semialdehyde by succinic semialdehyde dehydrogenase. GHB exhibits neuroprotective effects and protects cells from hypoxia.27 Furthermore, it can induce the accumulation of tryptophan derivatives or tryptophan itself in the extracellular space and possibly overcome the blood-brain barrier by increasing the transport of tryptophan. An increase in GHB levels also causes increases in the levels of certain amino acids, including trypto-
phan, in blood.28 This study found that, compared with the control group, the levels of exhaled GHB from patients with nodular goiter and PTC were significantly reduced. Thus, it can be inferred that when GHB decreases, the level of tryptophan in the blood induced by GHB decreases. This inference is consistent with the findings by Yao et al. Their study showed that the levels of tryptophan and 5-hydroxytryptamine in the blood of a nodular goiter group and a PTC group were significantly lower than those of the control group. The reduction in GHB in the blood leads to reductions in tryptophan and 5-hydroxytryptamine, which lead to the weakening of the 5-hydroxytryptamine system. Because the 5-hydroxytryptamine system is involved in the regulation of sleep, mood, and anxiety, people with lower levels of 5-hydroxytryptamine are more prone to depression, impulsive behavior, alcoholism, suicide, attacks, and violence. CONCLUSIONS
This study used a gas chromatograph mass spectrometer to detect volatile organic compounds in the exhaled breath from healthy subjects, patients with nodular goiter, and patients with PTC. A statistical analysis method with a metabolomic focus was used to identify
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Table II. Significantly changed metabolites among different group Nodule goiters vs normal controls P value
Potential biomarker Q15
Sulfurous acid, cyclohexylmethyl hexyl ester Isolongifolene-5-ol 3,5-Decadien-7-yne, 6-tbutyl-2,2,9,9-tetramethyl Cyclohexanone 4-Hydroxybutyric acid Phenol 2,2-Dimethyldecane Ethylhexanol Ethyleneglycol mono vinyl ester Cyclopropane, 1-bromo1-(3-methyl-1-pentenylidene)2,2,3,3-tetramethyl (3-Methyl-oxiran-2-yl)-methanol Cyclopentane, 1,1,3-trimethyl3-(2-methyl-2-propenyl) trans-2-Dodecen-1-ol
FC
VIP
1.14 3 206
23.77
1.2055
5.96 3 208 2.30 3 210
3.22 4.76
1.2703 1.5584
2.43 3 209 1.51 3 208 2.12 3 210 1.25 3 210
23.78 23 1.89 22.94
1.4462 1.5193 1.5701 1.3183
PTC vs normal controls P value
FC
VIP
1.13 3 210 1.22 3 210 2.72 3 212 1.65 3 212 4.99 3 209 6.94 3 209 8.46 3 213
23.34 22.96 2.65 22.86 22.16 5.43 3.57
1.4577 1.5217 1.5406 1.3543 1.2644 1.2088 1.5689
PTC vs nodule goiter P value
FC
VIP
0.000314 9.97 3 209
23.27 1.28
1.6851 1.4034
1.17 3 208
1.07
1.4541
Abbreviations: PTC, papillary thyroid carcinoma; VIP, variable importance in the projection.
substances that were present at different levels between the groups. It demonstrated the effectiveness of the method and provided a new, noninvasive, and directly qualitative method for the clinical diagnosis and treatment of thyroid disease.
ACKNOWLEDGMENTS
Conflicts of Interest: All authors have read the journal’s policy on disclosure of potential conflicts of interest and have none to declare. Financial support from grants from the National Natural Science Foundation of China (No.30972839), the China Postdoctoral Science Foundation (No.2013M531069), the Foundation of Heilongjiang Educational Committee (No.12531245), the Science and Technology Planning Project of Heilongjiang Province (No.GC12C305-5), and the Doctoral Fund of the First Affiliated Hospital of Harbin Medical University (No.2012B006) is gratefully acknowledged. All authors have read the journal’s authorship agreement.
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16. Phillips M, Cataneo RN, Ditkoff BA, et al. Volatile markers of breast cancer in the breath. Breast J 2003;9:184–91. 17. Altomare DF, Di Lena M, Porcelli F, et al. Exhaled volatile organic compounds identify patients with colorectal cancer. Br J Surg 2013;100:144–50. 18. Xu ZQ, Broza YY, Ionsecu R, et al. A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions. Br J Cancer 2013;108:941–50. 19. Birken T, Schubert J, Miekisch W, Noldge-Schomburg G. A novel visually CO2 controlled alveolar breath sampling technique. Technol Health Care 2006;14:499–506. 20. Miekisch W, Kischkel S, Sawacki A, Liebau T, Mieth M, Schubert J. Impact of sampling procedures on the results of breath analysis. J Breath Res 2008;2:026007. 21. Bajtarevic A, Ager C, Pienz M, et al. Noninvasive detection of lung cancer by analysis of exhaled breath. BMC Cancer 2009;9:348. 22. Wang C, Li P, Lian A, et al. Blood volatile compounds as biomarkers for colorectal cancer. Cancer Biol Ther 2013;15:200–6.
23. Westerhuis JA, Hoefsloot HCJ, Smit S, et al. Assessment of PLSDA cross validation. Metabolomics 2008;4:81–9. 24. Yip L, Farris C, Kabaker AS, et al. Cost impact of molecular testing for indeterminate thyroid nodule fine-needle aspiration biopsies. J Clin Endocrinol Metab 2012;97: 1905–12. 25. Filipiak W, Sponring A, Mikoviny T, et al. Release of volatile organic compounds (VOCs) from the lung cancer cell line CALU-1 in vitro. Cancer Cell Int 2008;24:17. 26. Snyder R, Hedli CC. An overview of benzene metabolism. Environ Health Perspect 1996;104(suppl 6):1165–71. 27. Ottani A, Saltini S, Bartiromo M, et al. Effect of gammahydroxybutyrate in two rat models of focal cerebral damage. Brain Res 2003;986:181–90. 28. Gobaille S, Schleef C, Hechler V, Viry S, Aunis D, Maitre M. Gamma-hydroxybutyrate increases tryptophan availability and potentiates serotonin turnover in rat brain. Life Sci 2002;70: 2101–12.
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ORIGINAL ARTICLE Exhaled breath volatile biomarker analysis for thyroid cancer Q17
LEI GUO, CHANGSONG WANG, CHUNJIE CHI, XIAOYANG WANG, SHANSHAN LIU, WEI ZHAO, CHAOFU KE, GUOWANG XU, and ENYOU LI HARBIN AND DALIAN, CHINA
Compared with other types of cancer, thyroid cancer incidence rates have increased rapidly worldwide in the past few decades. In recent years, potential thyroid cancer biomarkers have been studied, but these biomarkers have neither specificity nor good positive predictive value. Exhaled breath analysis is a recently developed convenient and noninvasive method for screening and diagnosing the disease. In this study, potential thyroid cancer biomarkers in volatile organic compounds (VOCs) were detected. Exhaled breath was collected from 64 patients with histologically confirmed cases of thyroid disease (including 39 individuals with papillary thyroid carcinoma and 25 individuals with nodular goiters) and 32 healthy volunteers. Solid-phase microextraction–gas chromatography and mass spectrometry was used to assess the exhaled VOCs of the study participants. The statistical methods of principal component analysis and partial least-squares discriminant analysis were performed to process the final data. The VOCs exhibited significant differences between nodular goiter patients and normal controls, papillary thyroid carcinoma patients and normal controls, and papillary thyroid carcinoma patients and nodular goiter patients; 7, 7, and 3 characteristic metabolites played decisive roles in sample classification, respectively. Breath analysis may provide a new, noninvasive, and directly qualitative method for the clinical diagnosis of thyroid disease. (Translational Research 2015;-:1–8)
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Abbreviations: FNAB ¼ fine-needle aspiration biopsy; GABA ¼ gamma aminobutyric acid; GC/ MS ¼ gas chromatography and mass spectrometry; GHB ¼ g-hydroxybutyric acid; IARC ¼ International Agency for Research on Cancer; PCA ¼ principal component analysis; PLS-DA ¼ partial least-squares discriminant analysis; PTC ¼ papillary thyroid carcinoma; VIP ¼ variable importance in the projection; VOCs ¼ volatile organic compounds
From the Department of Anesthesiology, The First Affiliated Hospital of Harbin Medical University, Harbin, China; Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China. Submitted for publication March 11, 2014; revision submitted December 31, 2014; accepted for publication January 15, 2015.
Reprint requests: Enyou Li, Department of Anesthesiology, The First Affiliated Hospital of Harbin Medical University, No. 23 Youzheng Street, Nangang District, Harbin, Heilongjiang 150001, China; e-mail: [email protected]. 1931-5244/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.trsl.2015.01.005
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AT A GLANCE COMMENTARY Guo L, et al. Background
Compared with other types of cancer, the growth rate of thyroid cancer is very fast. In recent years, potential thyroid cancer biomarkers have been studied, but these biomarkers have neither specificity nor good positive predictive value. Exhaled breath analysis is a recently developed convenient and noninvasive method of screening and diagnosing the disease. Translational Significance
In this study, potential thyroid cancer biomarkers in volatile organic compounds were detected. Breath analysis may provide a new, noninvasive, and directly qualitative method for the clinical diagnosis of thyroid disease.
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INTRODUCTION
The incidence of thyroid cancer has been increasing dramatically. Compared with other types of cancer in males and females, thyroid cancer incidence rates have increased rapidly worldwide in the past few decades.1 With the growing application of highresolution ultrasound in clinical practice, a growing number of thyroid nodules are being detected. Although most nodules are benign, 5%–10% of them are malignant.2 Identifying the nature of the thyroid nodules and developing subsequent treatment have become difficult issues for clinicians and patients. The most common diagnostic method is fine-needle aspiration biopsy, which has the advantage of direct characterization and the disadvantage of invasive operation. Additionally, the success of this technique is highly dependent on the strength of the technological development and the experience of the clinician. It has a specificity of less than 75%.3-5 In recent years, potential thyroid cancer biomarkers such as galectin-3, cytokeratin-19, and fibronectin-1 have been studied, but these biomarkers have neither specificity nor good positive predictive value.6,7 Accordingly, the exploration of a direct and noninvasive diagnostic method is still needed to guide clinical diagnoses and the development of treatment plans. Metabolomic approaches can identify diseasespecific metabolic patterns by measuring in vivo metabolites. This technique has been widely used in studies on a variety of disease-specific biomarkers.8-10 Yao et al
used liquid chromatography coupled to an LTQ Orbitrap XL (Thermo Fisher) hybrid mass Q3 spectrometer combined with a metabolomics analysis method to study metabolic changes in the serum of healthy subjects, patients with nodular goiter, and patients with papillary thyroid carcinoma (PTC).11 The analysis showed that the main components that changed in the serum were free fatty acids such as arachidonic acid and linolenic acid, phospholipids such as lysophospholipids, and amino acids such as tryptophan, serotonin, and lysine. However, this method is invasive because it requires collecting the patient’s blood. Exhaled breath analysis is a recently developed method of screening and diagnosing the disease. Because of its convenience, noninvasiveness, good tolerance, and easy acceptability by patients, this technique has drawn increasing attention from researchers and clinicians.12,13 Compared with normal tissue, cancerous tissue may exhibit specific metabolic patterns, and the resulting volatile metabolites will be discharged in the blood, excreted by the alveolae, and exhaled in the breath. These specific volatile metabolites can be detected and analyzed using gas chromatography and mass spectrometry (GC/MS).14 Studies have demonstrated the specificity of volatile metabolites from patients with different cancers such as lung cancer, colorectal cancer, gastric cancer, and breast cancer.15-18 In the present study, we used the multivariate data analysis method with GC/MS and metabolomics to compare volatile organic compounds in the exhaled breath from healthy subjects, patients with nodular goiter, and patients with PTC. MATERIALS AND METHODS
This study was approved by the Ethics Committee of Harbin Medical University (No. 201314). A total of 64 patients who were admitted to the First Affiliated Hospital of Harbin Medical University between April and October 2012 were included in this study. To screen and identify possible noninvasive biomarkers of malignant thyroid nodules, breath samples from 39 patients with PTC (the most common type of malignant thyroid nodule) and 25 patients with nodular goiter (benign nodule), as well as 32 age and gender-matched healthy subjects, were collected, as shown in Table I. All the participants were recruited in accordance with the strict inclusion and exclusion criteria established by our research institution. In particular, the following inclusion criteria were used: (1) an age of 25–70 years and (2) agreement to participate in the study, as indicated by a signed informed consent form. The following exclusion criteria were used: (1) pregnancy, lactation,
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Table I. Demographic characteristics of the study subjects Q14
Control group
Nodular goiters
Papillary thyroid carcinoma
Subjects (n) 32 25 39 Age (mean 6 SD) 39.8 6 13.4 51.8 6 12.1* 46.2 6 11.4* Sex Male 6 6 5 Female 26 19 34 Smokers (n) 4 6 7 Diabetes 0 2 2 *Statistically significant difference (P , 0.05).
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or the possibility of pregnancy; (2) the presence of a confirmed congenital disease; (3) a family history of mental illness; (4) the presence of a chronic inflammatory disease; (5) symptoms of an acute disease during the 2 weeks before the study enrollment; and (6) a history of infectious disease. Breath collection. Breath gas sampling was performed in conjunction with the parallel collection of ambient air within 24 hours after the subjects had fasted overnight. The alveolar breath sampling procedure was performed in accordance with previous studies.19,20 Samples were collected with carbon dioxide monitoring. Once the carbon dioxide plateau was reached, the samples were collected. Briefly, 20-mL samples of exhaled gas were drawn into gas-tight syringes (50 mL) (Agilent Inc). These samples were immediately transferred into 20mL glass vials (Supelco Inc). To remove any residual contaminants, all these vials were thoroughly cleaned by flushing with nitrogen gas (99.99% purity; Liming Gas Inc) and subsequently evacuated for breath sample collection.21 All the samples were analyzed within 3 hours of sampling. Solid-phase microextraction. A manual solid-phase microextraction holder with 75 mm thick carboxen/ polydimethylsiloxane fibers was purchased from Supelco (Bellefonte, Pennsylvania). A solid-phase microextraction fiber was inserted into each vial and exposed to the gaseous sample for 20 minutes at 40 C. Subsequently, desorption of the volatiles occurred in a hot GC injector at 200 C for 2 minutes. GC/MS analyses. The analysis was performed on a GC/MS instrument (Shimadzu GC-MS QP 2010; Shimadzu) equipped with a DB-5MS (length 30 m 3 inner diameter 0.25 mm 3 film thickness 0.25 mm; Agilent Technologies Inc) porous-layer opentubular PLOT column. The injections were performed in the splitless mode with a splitless time of 1 minute. The temperature of the injector was 200 C. The flow rate of the helium (99.99%) carrier gas was maintained at a constant rate of 2 mL/min. The column
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temperature was held at 40 C for 2 minutes to concentrate the hydrocarbons at the head of the column; subsequently, the column temperature was increased by 70 C/min to 200 C for 1 minute, ramped by 20 C/min to 230 C, and held at 230 C for 3 minutes. The MS analyses were performed in a full scan mode, with a scan range of 35–200 amu. The ion source was maintained at 200 C, and an ionization energy of 70 eV was used for each measurement. The extraction and pretreatment of raw GC/MS data. The raw GC/MS data were converted into CDF
format (NetCDF) files by the Shimadzu GCMS Postrun Analysis software package and subsequently processed using the XCMS toolbox (http://metlin.scripps.edu/ download). The default XCMS parameters were used, Q10 with the following exceptions: xcmsSet (fwhm 5 8, snthresh 5 6, max 5 200); retcor (method 5 ‘‘linear,’’ family 5 ‘‘gaussian,’’ plottype 5 ‘‘mdevden’’), a bandwidth of 8 for the first grouping command and 4 for the second grouping command.22 The data set of aligned mass ions was exported from XCMS for further processing with Microsoft Excel, which was used to normalize the data before the multivariate analyses. Statistical analyses. Each sample was normalized to the total peak area before the statistical analyses. The normalized data were subsequently exported to SIMCA-P 11.5 for principal component analysis (PCA) to detect grouping trends and outliers. Partial Q11 least-squares discriminant analysis (PLS-DA) was then performed, using the default 7-round cross-validation approach, and the values of the corresponding variable importance in the projection (VIP) in the PLS-DA model were calculated. To prevent overfitting, permutation tests with 100 iterations were performed to validate the supervised model. In addition, the nonparametric Kruskal-Wallis rank sum test was performed to determine the significance of each metabolite. Potential metabolic biomarkers were selected based on the VIP values and the nonparametric P values, with thresholds of 1.2 and 0.01, respectively. RESULTS Nodule goiters vs normal controls. A total of 149 metabolites were consistently detected in 50% of the samples from the patients with nodular goiters and the control patients. Although the 2-dimensional PCA score plot displayed a good separation trend (Fig 1, A), the PLS-DA score plot demonstrated excellent separation between the patients with nodular goiters and the controls using 4 components (R2X 5 0.656, R2Y 5 0.963, Q2 5 0.92; Fig 1, B). Moreover, all the R2 and Q2 values calculated from the permutated data
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Fig 1. (A) PCA score plot (10 principal components, R2X 5 0.878, Q2 5 0.723). (B) PLS-DA score plot (4 principal components, R2X 5 0.656, R2Y 5 0.963, Q2 5 0.92). (C) PLS-DA validation plot intercepts: R2 5 (0.0, 0.39), Q2 5 (0.0, 20.43). PCA, principal component analysis; PLS-DA, partial least-squares discriminant analysis.
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were lower than the original values in the validation plot, which confirmed the validity of the supervised model (Fig 1, C). PTCs vs normal controls. A total of 167 metabolites were consistently detected in 50% of the samples from the patients with PTC and the controls. Although the 2-dimensional PCA score plot displayed good separation (Fig 2, A), the PLS-DA score plot demonstrated excellent separation between the patients with PTC and the controls using 4 components (R2X 5 0.648, R2Y 5 0.963, Q2 5 0.916; Fig 2, B). Moreover, all the R2 and Q2 values calculated from the permutated data were lower than the original values in the validation plot, which confirmed the validity of the supervised model (Fig 2, C). Nodule goiters vs PTC. Although the results of the PCA analysis did not distinguish between the nodule goiters and PTC (Fig 3, A), a good distinction was achieved after the PLS-DA analysis (Fig 3, B). Moreover, all the R2 and Q2 values calculated from the permutated data were lower than the original values in the validation plot, which confirmed the validity of the supervised model (Fig 3, C). Standard measures of accuracy (AUC, sensitivity, specificity) were computed based on PLS-DA crossvalidation.23 Metabolic signatures for nodule goiters vs normal controls and PTCs vs normal controls re-
sulted in an AUC of 1.0000, with both sensitivity and specificity of 1.0000 at the best cutoff point. For nodular goiter vs papillary thyroid cancer, the AUC value was 0.9015, with a sensitivity of 0.9200 and a specificity of 0.8205 at the best cutoff point. Potential biomarkers. Among the significantly altered metabolites identified using the VIP values in the PLSDA model and the FDR values, 12 were annotated using the NIST 11 database with a similarity threshold of 75% (Table II). Q13 DISCUSSION
The standard diagnosis of thyroid diseases includes the patient’s history, a clinical examination, laboratory tests, scintigraphy, thyroid ultrasound, and fine-needle aspiration biopsy. Although a series of molecular markers could help to predict malignancy, molecular biomarkers are still in the research phase.24 Although exhalation research is an emerging field, it is relatively advanced for the detection of Helicobacter pylori. Applications to lung cancer, breast cancer, liver cancer, and colorectal cancer also have been reported, but the use of exhaled breath has not been examined for thyroid diseases. Thus, we conducted this study. The exhaled breath approach can be used to screen or diagnose thyroid diseases, monitor the recurrence of
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Fig 2. (A) PCA score plot (10 principal components, R2X 5 0.883, Q2 5 0.758). (B) PLS-DA score plot (4 principal components, R2X 5 0.648, R2Y 5 0.963, Q2 5 0.916). (C) PLS-DA validation plot intercepts: R2 5 (0.0, 0.28), Q2 5 (0.0, 20.42). PCA, principal component analysis; PLS-DA, partial least-squares discriminant analysis; PTC, papillary thyroid carcinoma.
thyroid disease, and assess the effect of anticancer drugs. Although the method has many deficiencies at present study, it offers a promising new way to diagnose thyroid disease. Even if it only plays a supporting role in the diagnosis at present, it is a meaningful start. Compared with the samples from healthy subjects, the samples from the patients in the nodular goiter and the PTC groups showed declines in the concentrations of many substances. This result is similar to that found for blood by Yao et al.11 It may be because of the consumption of nutrients and metabolic energy for tumor growth. Meanwhile, by culturing the cancer cell line CALU-1, Filipiak et al25 analyzed the composition in the head space and found that during cancer cell growth, the levels of volatile biomarkers such as butyl acetate and 2-methylpropanal decreased, indicating that the tumor cells had consumed them. In this study, decreases in the concentrations of the metabolites sulfurous acid, cyclohexylmethyl hexyl ester, cyclohexanone, 4-hydroxybutyric acid, 2,2-dimethyldecane, ethylhexanol, and (3-methyl-oxiran-2-yl)-methanol were detected, which could be explained by the oncology consumption mechanism. However, the experimental procedures were different from ours, and the differences in the results may have occurred because of the different sources of cancer cells.
In this study, compared with the samples from healthy controls, the concentration of ethylhexanol in the exhaled gas from the patients with PTC was significantly lower. This result likely occurred because of differences in the cytochrome P450 enzymes, which hydroxylate lipid peroxidation biomarkers to produce alcohol. This result may have occurred because of the regulation of lipid metabolism by thyroid hormone. In addition to having exogenous origins, such as contact with liquid phenol or drinking water contaminated with phenol, the phenol in the human body may also come from the metabolism of benzene. Phenol content in urine is commonly used to monitor people who have come into contact with benzene and can be used to accurately measure the duration of short-term benzene exposure. First, benzene is oxidized by cytochrome P450 monooxygenase in the liver to produce styrene, which is toxic. It can then be combined with glutathione to form phenylthio uric acid, which will continue to be metabolized to phenol, catechol, and hydroquinone and can be excreted as glucuronide or sulfate conjugates.26 In this study, the samples of exhaled gas were collected along with control samples from the background environment using the same method, so the exhaled phenol is likely to have come from the metabolism of benzene. In addition to entering the
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Fig 3. (A) PCA score plot (8 principal components, R2X 5 0.897, Q2 5 0.799). (B) PLS-DA score plot (2 principal components, R2X 5 0.577, R2Y 5 0.54, Q2 5 0.458). (C) PLS-DA validation plot intercepts: R2 5 (0.0, 0.124), Q2 5 (0.0, 20.182). PCA, principal component analysis; PLS-DA, partial least-squares discriminant analysis; PTC, papillary thyroid carcinoma.
body through direct contact, benzene can enter the body during smoking and be stored in the body before being slowly metabolized and excreted. Benzene has long been confirmed as a cancer-inducing substance by the International Agency for Research on Cancer. In the present study, compared with the healthy controls, the phenol concentrations in exhaled breath from the patients with either nodular goiter or PTC were increased. Therefore, a higher in vivo phenol level indicates a higher level of benzene and thus a greater degree and chance of harm to the human body by benzene. This hypothesis requires further investigation. 4-Hydroxybutanoic acid, also known as g-hydroxybutyric acid (GHB), is a naturally existing substance found in the human central nervous system that is involved in the exchange of information. It interacts with other neurotransmitters, such as gamma aminobutyric acid. GHB is produced by the reduction of succinic semialdehyde by succinic semialdehyde dehydrogenase. GHB exhibits neuroprotective effects and protects cells from hypoxia.27 Furthermore, it can induce the accumulation of tryptophan derivatives or tryptophan itself in the extracellular space and possibly overcome the blood-brain barrier by increasing the transport of tryptophan. An increase in GHB levels also causes increases in the levels of certain amino acids, including trypto-
phan, in blood.28 This study found that, compared with the control group, the levels of exhaled GHB from patients with nodular goiter and PTC were significantly reduced. Thus, it can be inferred that when GHB decreases, the level of tryptophan in the blood induced by GHB decreases. This inference is consistent with the findings by Yao et al. Their study showed that the levels of tryptophan and 5-hydroxytryptamine in the blood of a nodular goiter group and a PTC group were significantly lower than those of the control group. The reduction in GHB in the blood leads to reductions in tryptophan and 5-hydroxytryptamine, which lead to the weakening of the 5-hydroxytryptamine system. Because the 5-hydroxytryptamine system is involved in the regulation of sleep, mood, and anxiety, people with lower levels of 5-hydroxytryptamine are more prone to depression, impulsive behavior, alcoholism, suicide, attacks, and violence. CONCLUSIONS
This study used a gas chromatograph mass spectrometer to detect volatile organic compounds in the exhaled breath from healthy subjects, patients with nodular goiter, and patients with PTC. A statistical analysis method with a metabolomic focus was used to identify
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Table II. Significantly changed metabolites among different group Nodule goiters vs normal controls P value
Potential biomarker Q15
Sulfurous acid, cyclohexylmethyl hexyl ester Isolongifolene-5-ol 3,5-Decadien-7-yne, 6-tbutyl-2,2,9,9-tetramethyl Cyclohexanone 4-Hydroxybutyric acid Phenol 2,2-Dimethyldecane Ethylhexanol Ethyleneglycol mono vinyl ester Cyclopropane, 1-bromo1-(3-methyl-1-pentenylidene)2,2,3,3-tetramethyl (3-Methyl-oxiran-2-yl)-methanol Cyclopentane, 1,1,3-trimethyl3-(2-methyl-2-propenyl) trans-2-Dodecen-1-ol
FC
VIP
1.14 3 206
23.77
1.2055
5.96 3 208 2.30 3 210
3.22 4.76
1.2703 1.5584
2.43 3 209 1.51 3 208 2.12 3 210 1.25 3 210
23.78 23 1.89 22.94
1.4462 1.5193 1.5701 1.3183
PTC vs normal controls P value
FC
VIP
1.13 3 210 1.22 3 210 2.72 3 212 1.65 3 212 4.99 3 209 6.94 3 209 8.46 3 213
23.34 22.96 2.65 22.86 22.16 5.43 3.57
1.4577 1.5217 1.5406 1.3543 1.2644 1.2088 1.5689
PTC vs nodule goiter P value
FC
VIP
0.000314 9.97 3 209
23.27 1.28
1.6851 1.4034
1.17 3 208
1.07
1.4541
Abbreviations: PTC, papillary thyroid carcinoma; VIP, variable importance in the projection.
substances that were present at different levels between the groups. It demonstrated the effectiveness of the method and provided a new, noninvasive, and directly qualitative method for the clinical diagnosis and treatment of thyroid disease.
ACKNOWLEDGMENTS
Conflicts of Interest: All authors have read the journal’s policy on disclosure of potential conflicts of interest and have none to declare. Financial support from grants from the National Natural Science Foundation of China (No.30972839), the China Postdoctoral Science Foundation (No.2013M531069), the Foundation of Heilongjiang Educational Committee (No.12531245), the Science and Technology Planning Project of Heilongjiang Province (No.GC12C305-5), and the Doctoral Fund of the First Affiliated Hospital of Harbin Medical University (No.2012B006) is gratefully acknowledged. All authors have read the journal’s authorship agreement.
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