Distinct Metabolomic Profiles of Papillary Thyroid Carcinoma and

Jul 1, 2015 - Papillary thyroid carcinoma (PTC) and benign thyroid adenoma (BTA) are the most common head and neck tumors. However, the metabolic ...
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Distinct Metabolomic Profiles of Papillary Thyroid Carcinoma and Benign Thyroid Adenoma Yanan Xu,†,‡,∥ Xiaojiao Zheng,§ Yunping Qiu,§ Wei Jia,§ Jiadong Wang,*,‡,∥,⊥ and Shankai Yin*,†,∥,⊥ †

Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Yishan Road 600, 200233 Shanghai, China ‡ Department of Head and Neck Surgery, Renji Hospital of Shanghai Jiao Tong University School of Medicine, Shandongzhong Road 145,200001 Shanghai, China § Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Yishan Road 600, 200233 Shanghai, China ∥ Otolaryngological Institute of Shanghai Jiao Tong University, Yishan Road 600, 200233 Shanghai, China S Supporting Information *

ABSTRACT: Papillary thyroid carcinoma (PTC) and benign thyroid adenoma (BTA) are the most common head and neck tumors. However, the metabolic differences between PTC and BTA have not been characterized. The aim of this study was to identify the metabolic profiles of these two types of tumors using a metabolomics approach. Tumors and adjacent nontumor specimens collected from 57 patients with PTC and 48 patients with BTA were profiled using gas chromatography−time-of-flight mass spectrometry and ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. A panel of 46 and 44 differentially expressed metabolites were identified in the PTC and BTA specimens, respetively, and compared with nontumor tissues. Common metabolic signatures, as characterized by increased glycolysis, amino acid metabolism, one carbon metabolism and tryptophan metabolism, were found in both types of tumors. Purine and pyrimidine metabolism was significantly elevated in the PTC specimens, and taurine and hypotaurine levels were also higher in the PTC tissues. Increased fatty acid and bile acid levels were found, especially in the BTA tissues. The metabolic profiles of the PTC and BTA tissues include both similar and remarkably different metabolites, suggesting the presence of common and unique mechanistic pathways in these types of tumors during tumorigenesis. KEYWORDS: metabolomics, papillary thyroid carcinoma, benign thyroid adenoma, gas chromatography−time-of-flight mass spectrometry, ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry

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INTRODUCTION

malignant tumor in the population, as evidenced by cadaveric studies from Finland, where one-third of patients who died from non-thyroid-related conditions were found to have thyroid cancer.4 Thyroid cancer is currently the fifth most common

The incidence of thyroid nodules has dramatically increased in the past decade.1 Depending on the populations evaluated and the methods of detection used, the prevalence of thyroid nodules varies from 5% by palpation to 30−67% by ultrasound evaluation.2 Although most of these thyroid nodules are benign, 5−20% are malignant.3 As a result, thyroid cancer could be a common © 2015 American Chemical Society

Received: April 24, 2015 Published: July 1, 2015 3315

DOI: 10.1021/acs.jproteome.5b00351 J. Proteome Res. 2015, 14, 3315−3321

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Journal of Proteome Research cancer in women, accounting for 51.3% of head and neck malignancies and 3.8% of all cancers in the United States.1 Papillary thyroid carcinoma (PTC) accounts for ∼80% of all types of thyroid cancer.5 Laryngeal squamous cell carcinonma (LSCC) was the second most frequently occurring head and neck cancer.6 According to GLOBOCAN, there was an estimate of 156 877 new cases and 83 376 deaths of laryngeal cancer in the world in 2012, and the adjusted incidence and mortality rates were 2.1/100 000 and 1.1/100 000, respectively. To date, very few metabolomics studies have been conducted on head and neck cancers.7−10 Deja found that metabolic changes in thyroid cancer were related mainly to osmotic regulators (taurine and scyllo- and myo-inositol), citrate, and amino acids supplying the tricarboxylic acid (TCA) cycle.7 Yao found that the major metabolic differences between benign and malignant thyroid nodules in serum are related to lipid metabolism.8 Tripathi9 and Somashekar10 reported that LSCC tissue exhibited highly active glycolysis with increased amino acids influx (anaplerosis) into the TCA cycle, altered membrane choline phospholipid metabolism, and oxidative and osmotic defense mechanisms. These findings suggest that benign and malignant thyroid tumors have different metabolic phenotypes, and different types of head and neck cancers may also exhibit distinct metabolite profiles, resulting from varied upstream genetic regulations. In this study, metabolomic profiling was applied to PTC, BTA, and LSCC tissues using a combined gas chromatography−timeof-flight mass spectrometry (GC−TOFMS) and ultraperformance liquid chromatography−quadrupole time-of-flight mass spectrometry (UPLC−QTOFMS), which have not been reported in the field of head and neck cancer research. The use of the two mass spectrometry platforms offers high-level sensitivity and selectivity, providing remarkable metabolic information regarding the differences between PTC and BTA. LSCC specimens were used as a positive control, to ensure that the metabolic differences identified between PTC and BTA were specific to the thyroid condition.

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Table 1. Demographic and Clinical Chemistry Characteristics of the Human Subjects number of patients age (mean) male/female ratio T stage T1 T2 T3 T4 N stage N0 N1 N2 N3 a

papillary thyroid cancer

benign thyroid adenoma

57 44.5 ± 14.4 14/43

48 48.0 ± 13.2 5/43

46 9 2 0

a

33 24

Without classification.

added, and the sample was homogenized for 4 min, followed by centrifugation at 12 000 rpm for 10 min. A 150 μL aliquot of the supernatant was transferred into a new tube. The residue was extracted with 250 μL of methanol using a homogenizer, followed by centrifugation. A 150 μL aliquot of the supernatant was combined with the previous extraction sample. Pooled quality-control samples were prepared by mixing 20 μL of each sample extraction. After the addition of the internal standard (20 μL of L-2-chlorophenylalanine in water, 0.03 mg/mL), the mixture was vortexed and aliquoted into a 1.5 mL tube (100 μL) and a GC vial (100 μL) for UPLC−QTOFMS and GC−TOFMS analysis, respectively. These samples were then vacuum-dried at room temperature. For UPLC−QTOFMS analysis, 350 μL of a mixture of methanol and acetonitrile (1:9 v/v) was added to the residue, followed by vortexing for 2 min and ultrasonication for 1 min. After centrifugation, the supernatant was transferred to an UPLC−QTOFMS vial. For GC−TOFMS analysis, the residue was chemically derivatized with a two-step procedure following the protocols previously published.12 In brief, the samples were subjected to a derivatization procedure using 80 μL of methoxyamine (15 mg/mL in pyridine) for 90 min at 30 °C, followed by 80 μL of N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) (1% trimethylchlorosilane (TMCS)) for 60 min at 70 °C.

MATERIALS AND METHODS

Sample Information

Surgical specimens (n = 276) were collected from both tumor and adjacent nontumor tissues from 57 patients with PTC and 48 patients with BTA treated at Shanghai Renji Hospital and 33 patients with LSCC treated at Shanghai Sixth People’s Hospital. Tumor tissues from the center of the lesions and corresponding adjacent nontumor tissues in the same patient were obtained. All tumor tissue samples were carefully dissected to exclude surrounding normal tissue. All samples were collected within 15 min of obtaining the surgical specimens and immediately frozen at −80 °C until metabolomic analysis. The age, gender, and tumor stage of all patients are provided in Table 1. None of the patients were on neoadjuvant chemotherapy before surgical treatment. The protocol was approved by Shanghai Sixth People’s Hospital and Shanghai Renji Hospital, and all participants in this study signed informed consent before the study.

Instrumental Analysis and Metabolite Identification

Metabolomic profiling via UPLC-QTOFMS and GC-TOFMS was performed following our previous publication with minor modifications.12 The samples were run in the order of PTC − PTC adjacent tissue − BTA − BTA adjacent tissue − LSCC − LSCC adjacent tissue. One quality-control sample and one blank vial were run after each of the 10 samples. A Waters ACQUITY ultraperformance liquid chromatography (UPLC) system equipped with a binary solvent delivery manager and a sample manager (Waters Corporation, Milford, MA), coupled to a tandem quadrupole-time-of-flight (Q-TOF) mass spectrometer equipped with an electrospray interface (Waters Corporation) was used as UPLC-QTOFMS platform. An Agilent 6890N gas chromatographer coupled to a Pegasus HT time-of-flight (TOF) mass spectrometry system (Leco Corporation, St. Joseph, MI) was used as the GC-TOFMS platform. The MS data were analyzed to identify potential discriminate variables.12 For UPLC−QTOFMS, the raw data were analyzed

Sample Preparation

The metabolite extraction procedure followed that from our previous publication with minor modifications.11 Each tissue sample weighed ∼50 mg and was extracted via two steps. A 250 μL mixture of chloroform, methanol, and water (2:5:2 v/v/v) was 3316

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Figure 1. OPLS-DA scores plot for tumor tissues relative to adjacent nontumor tissues. (A) OPLS-DA scores plot for papillary thyroid cancer (PTC) samples. (B) OPLS-DA scores plot for benign thyroid adenoma (BTA) samples.

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using the MarkerLynx applications manager version 4.1 (Waters, Manchester, U.K.). The resulting matrix contained the arbitrarily assigned peak index (retention time (Rt)-m/z pairs), sample names, and peak intensity. After data processing, a list of the intensities of the peaks detected was generated for the first sample, using Rt and m/z data pairs as the identifiers for each peak. The acquired MS files from GC−TOFMS analysis were exported in the NetCDF format using ChromaTOF software (v3.30; Leco, Los Angeles, CA). CDF files were extracted using custom scripts (revised Matlab toolbox HDA) in the MATLAB 7.1 (The MathWorks, Natick, MA) for data pretreatment procedures such as baseline correction, denoising, smoothing, and alignment, time-window splitting, and peak feature extraction (based on multivariate curve resolution algorithm). The resulting 3D data set included sample information, peak retention time, and peak intensities. Compound annotation was performed using our in-house library containing ∼800 mammalian metabolite standards. For UPLC−QTOFMS generated data, identification was performed by comparison with the accurate mass and RT of the reference standards in our in-house library and with the accurate mass of the compounds obtained from web-based resources such as the Human Metabolome Database (http:// www.hmdb.ca/) and METLIN (http://metlin.scripps.edu). For GC−TOFMS generated data, identification was processed by comparison with the mass fragments and RT of the reference standards in our in-house library or with mass fragments from NIST 05 Standard mass spectral databases in NIST MS search 2.0 software (NIST, Gaithersburg, MD) with a similarity of >70%.

RESULTS

In total, 195 metabolites were detected among the study specimens. In total, 69 were detected by GC−TOFMS, 51 were detected by UPLC-QTOFMS in positive ion mode, and 75 were detected by UPLC−QTOFMS in negative ion mode. A plot of the PCA scores from the entire metabolite data set did not show separations, but instead a trend of separation between the PTC and BTA tissues compared with their adjacent nontumor tissues. The application of a supervised multivariate statistical model, the OPLS−DA model, demonstrated clear separations between each tumor and the adjacent nontumor tissue pair (Figure 1, parameters for the OPLS−DA model). Supplementary Tables 1 and 2 in the SI show two panels of 54 and 49 differentially expressed metabolites in PTC and BTA tissues compared with adjacent nontumor tissues (VIP > 1 and P < 0.05, respectively). To evaluate the impact of gender imbalance on the identified discriminant metabolites, we used a smaller, age-matched sample set (14 males vs 14 females) of adjacent nontumor tissues from PTC patients and constructed a new OPLS-DA model. Twentyone metabolites with VIP > 1 and P < 0.05 (Supplementary Table 3 in the SI) were differentially expressed in male PTC nontumor tissues compared with the female nontumor tissues. After eliminating the identified discriminating metabolites associated with gender difference, we obtained a panel of 46 and 44 differentially expressed metabolites in PTC (Supplementary Table 1 in the SI) and BTA (Supplementary Table 2 in the SI) with VIP > 1 and P < 0.05, respectively. Common metabolic signatures, as characterized by increased glycolysis, amino acid metabolism, one-carbon metabolism, and tryptophan metabolism, were found in both PTC and BTA tumor tissues. Citric acid levels were decreased in both PTC and BTA tumor tissues, while the downstream products/intermediates generated in the citric acid cycle (also known as the tricarboxylic acid cycle [TCA] cycle), including succinic acid, fumaric acid, and malic acid, were increased or unchanged in both types of tumor tissues. Figure 2 shows a heat map of the fold changes of these metabolites. The two different types of tumor tissues also showed distinct metabolite profiles. Purine and pyrimidine metabolism were significantly elevated in the PTC tumor tissues, and taurine and hypotaurine were also elevated in the PTC tumor specimens. Increased fatty acid and bile acid levels were found in BTA

Statistical Analysis

Statistical analysis was performed mainly using SPSS (SPSS, Chicago, IL) and SIMCA-P 13.0 (Umetrics, Umea, Sweden). Principal component analysis (PCA) as well as orthogonal partial least-squares discriminant analysis (OPLS−DA) were performed to visualize the metabolic differences among the PTC, BTA, and LSCC groups. Significantly altered metabolites with the variable importance in the projection (VIP) threshold (VIP > 1) in the previously mentioned OPLS-DA model, as well as the Student’s t test (P < 0.05), were selected in the three groups. 3317

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Figure 2. Heat map of the fold changes (FCs) of the common metabolites and pathways in the two types of tumor tissues compared with adjacent nontumor tissues, including glycolysis, amino acid metabolism, one-carbon metabolism, and tryptophan metabolism.

Figure 3. Heat map of the fold changes (FCs) of the differential metabolites and pathways in the two types of tumor tissues compared with adjacent nontumor tissues, including purine and pyrimidine, taurine and hypotaurine, fatty acid, and bile acid metabolism.

43 females, which is a reasonable ratio. We investigated the impact of gender using a small age-matched sample and identified a panel of 21 metabolites that were differentially expressed between PTC nontumor tissues from male and female patients. These metabolites were therefore removed from the key discriminating metabolites identified for PTC and BTA. The tumor development process requires an altered cell metabolic program to support its bioenergetics and biosynthetic needs for uncontrolled cellular proliferation.13 In this study, we found that PTC and BTA tumor tissues shared common metabolic signatures, as characterized by increased glycolytic

specimens. Figure 3 shows a heat map of the fold changes of these metabolites. A comparison of the metabolite profiles between the PTC and LSCC specimens showed that they shared common metabolic signatures. The only distinct metabolites between the PTC and LSCC tissues were taurine and hypotaurine, which were elevated in the PTC tissues but unchanged in the LSCC tissues.

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DISCUSSION The prevalence of PTC is approximately 3- to 4-fold higher in women than in men. Our PTC group comprised 14 males and 3318

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identified as anticarcinogens that characterizing and optimizing such defense systems may be an important part of a strategy of minimizing cancer and other age-related diseases.29 Taurine and its derivatives such as taurolidine and taurochloramin were found to display antineoplastic effect both in vitro and in vivo, through suppressing cell proliferation, enhancement of tumor cell apoptosis,30,31 and through an antiangiogenic effects,32 while enhancing the therapeutic index of some antitumor agent.30 Because of the multiple functions of taurine in tumor cells, its exact role in head and neck cancer is unclear. In our result, the only distinct metabolites between the PTC and LSCC tissues were taurine and hypotaurine, which were elevated in the PTC tissues but unchanged in the LSCC tissues. Yao8 reported major metabolic differences between PTC and BTA in serum with regard to lipid metabolism, where the level of 3-hydroxybutyric acid, an intermediate product of fatty acid metabolism, was much higher in PTC than in BTA. Circulating free fatty acids are important for energy supplementation, particularly when the glucose supply is insufficient. Interestingly, higher levels of six free fatty acids (docosahexaenoic acid, 9-octadecenic acid, adrenic acid, docosapentaenoic acid, palmitoleic acid, and hexadecanoic acid) were observed in BTA tissues in our study, while only eicosadienoic acid was detected at high levels in PTC tissues. These results suggest that benign tumor tissue has higher impact on fatty acid metabolism compared with malignant ones. Oxidation of fatty acids may deliver bioenergy for rapid tumor cell proliferation and growth.33 Therefore, the lower lipid levels of fatty acids in cancer tissues could be associated with increased demand of lipids for membrane biosynthesis of tumor cells, leading to a higher utilization rate of lipids. In addition to altered fatty acid levels, an increased level of bile acids was observed in BTA, a result that may be correlated with higher fatty acid metabolism in BTA. Bile acids contribute to the absorption of dietary fats and induce cellular signaling pathways that affect cell proliferation.34

activity, amino acid metabolism, one-carbon metabolism and tryptophan metabolism, resulting from increased production of metabolic substrates and macromolecular precursors to meet the increased energy and protein synthesis needs for cell proliferation. The Warburg effect is a characteristic feature of tumor metabolism and characterized by an increased rate of glycolysis, followed by lactic acid fermentation during tumor growth.14 Coinciding with the Warburg effect, we found higher levels of lactic acid and TCA cycle intermediates in both PTC and BTA. Citric acid is a TCA cycle product, which was reduced significantly in all thyroid lesions in Deja’s study.7 We also found that citric acid was reduced in PTC and BTA tissues, while the downstream products that are generated in TCA cycle were increased or unchanged in both types of tumor tissues. Reduced citric acid could be explained by an increased conversion of citric acid into acetyl-CoA and further fatty acid biosynthesis. Amino acids are critical substrates needed for mitochondrial metabolism and were usually found to be elevated in tumor tissues. Proline levels are increased in multiple types of cancer, including prostate, lymphoma, and others.15−17 Aspartate is important for the growth of human pancreatic adenocarcinoma.18 Secretion of alanine is higher in melanoma cell lines than in normal melanocytes and is also significant in human colon carcinoma.19,20 Amino acids were increased in both types of tumor tissues in the present study, which may be a substrate for protein synthesis, an essential starting point for nucleotide biosynthesis and replenish TCA cycle metabolite levels to enable their use in anabolic processes. One-carbon metabolism involves the transfer of a carbon unit from methyl donor nutrients to molecules involved in the synthesis and methylation of DNA. Cysteine, an important precursor in glutathione production, has been shown to be elevated in various types of cancers including breast, ovarian, oral, brain, and lung cancers.21 In our study, we also detected an increased level of one-carbon metabolism in both types of tumor tissues. Tryptophan, an essential amino acid, is metabolized in the local microenvironment of tumors through the kynurenine pathway, which is first catalyzed by indoleamine 2,3-dioxygenase (IDO).22 The elevated expression of IDO was suspected to be a mediator of tumor immune tolerance, which may help tumor cells avoid immune attack.23 The increased expression of IDO may result in a higher level of kynurenine, which would ultimately generate more nicotinamide adenosine dinucleotide from tryptophan to meet accelerated growth of tumor cells. Very recently kynurenine was shown in gliomas to be an endogenous ligand of the human aryl hydrocarbon receptor and to suppress antitumor immune responses and also to promote tumor cell survival and motility through the aryl hydrocarbon receptor in an autocrine/paracrine fashion.24 Significant increase in IDO gene expression was observed in the colorectal cancer11 and thyroid cancer.25 In our results, tryptophan metabolism was disrupted not only in malignant tumor (PTC) but also in benign tumor (BTA). Another important amino acid, taurine, acts as an osmoregulator, is similar to scyllo- and myo-inositol. It was reported to be increased in breast cancer,26 colon cancer,27 and head and neck cancer cells.9 This suggests that changes in osmoregulation may be a common metabolic feature for many cancers. Taurine was found elevated in PTC and unchanged in BTA and LSCC in our result. In addition to osmoregulation, taurine is also found to display antioxidant properties.28 Many antioxidants are being

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CONCLUSIONS We identified the metabolic profiles of PTC and BTA using a mass-spectrometry-based metabolomics approach, which demonstrated important similarities and differences in specific metabolic pathways. All tumor tissues showed increased glycolysis, amino acid metabolism, one-carbon metabolism, and tryptophan metabolism, suggesting that these two types of tumors share common metabolic pathways resulting from the increased need for energy, macromolecular precursors, and proteins and lipid synthesis. Purine and pyrimidine metabolism was higher particularly in the PTC specimens, and taurine and hypotaurine levels were also higher in PTC tissues. Increased fatty acid and bile acids levels were found in BTA. Taken together, the metabolic profiles of PTC and BTA tissues are composed of similar and remarkably different metabolites, suggesting the presence of common and unique mechanistic pathways in these types of tumors during tumorigenesis.

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ASSOCIATED CONTENT

S Supporting Information *

Supplementary Table 1. Identities of 54 differential metabolites between PTC and adjacent normal controls from each of the tissue samples. Supplementary Table 2. Identities of 49 differential metabolites between BTA and adjacent normal controls from each of the tissue samples. Supplementary Table 3. Identities of 21 differential metabolites between male and female 3319

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adjacent nontumor tissues from PTC samples.The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.5b00351.

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. Fax: 086-21-53882205. Tel: 086-21-53882271 (J.W.) *E-mail: [email protected]. Fax: 086-21-64834143. Tel.: 086-21-24058706 (S.Y.) Author Contributions ⊥

J.W. and S.Y. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ABBREVIATIONS PTC, papillary thyroid carcinoma; BTA, benign thyroid adenoma; LSCC, laryngeal squamous cell carcinoma; TCA, tricarboxylic acid; GC−TOFMS, gas chromatography−time-offlight mass spectrometry; UPLC−QTOFMS, ultraperformance liquid chromatography−quadrupole time-of-flight mass spectrometry; PCA, principal component analysis; OPLS−DA, orthogonal partial least-squares−discriminant analysis; VIP, variable importance in the projection

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DOI: 10.1021/acs.jproteome.5b00351 J. Proteome Res. 2015, 14, 3315−3321

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DOI: 10.1021/acs.jproteome.5b00351 J. Proteome Res. 2015, 14, 3315−3321