sign in sign up
<<
Home , Cytochrome b5 reductase, Dual oxidase 1, Dual oxidase 2, Eosinophil peroxidase, ETFDH, GLRX2

Endocrine Journal 2015, 62 (10), 857-877

Original Genomic evidence of reactive species elevation in papillary carcinoma with Hashimoto thyroiditis

Jin Wook Yi1), 2), Ji Yeon Park1), Ji-Youn Sung1), 3), Sang Hyuk Kwak1), 4), Jihan Yu1), 5), Ji Hyun Chang1), 6), Jo-Heon Kim1), 7), Sang Yun Ha1), 8), Eun Kyung Paik1), 9), Woo Seung Lee1), Su-Jin Kim2), Kyu Eun Lee2)* and Ju Han Kim1)*

1) Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea 2) Department of Surgery, Seoul National University Hospital and College of Medicine, Seoul, Korea 3) Department of Pathology, Kyung Hee University Hospital, Kyung Hee University School of Medicine, Seoul, Korea 4) Kwak Clinic, Okcheon-gun, Chungbuk, Korea 5) Department of Internal Medicine, Uijeongbu St. Mary’s Hospital, Uijeongbu, Korea 6) Department of Radiation Oncology, Seoul St. Mary’s Hospital, Seoul, Korea 7) Department of Pathology, Chonnam National University Hospital, Kwang-Ju, Korea 8) Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 9) Department of Radiation Oncology, Korea Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul, Korea

Abstract. Elevated levels of (ROS) have been proposed as a risk factor for the development of papillary thyroid carcinoma (PTC) in patients with Hashimoto thyroiditis (HT). However, it has yet to be proven that the total levels of ROS are sufficiently increased to contribute to . We hypothesized that if the ROS levels were increased in HT, ROS-related would also be differently expressed in PTC with HT. To find differentially expressed genes (DEGs) we analyzed data from the Cancer Genomic Atlas, expression data from RNA sequencing: 33 from normal thyroid tissue, 232 from PTC without HT, and 60 from PTC with HT. We prepared 402 ROS-related genes from three gene sets by genomic database searching. We also analyzed a public microarray data to validate our results. Thirty- three ROS related genes were up-regulated in PTC with HT, whereas there were only nine genes in PTC without HT (Chi- square p-value < 0.001). Mean log2 fold changes of up-regulated genes was 0.562 in HT group and 0.252 in PTC without HT group (t-test p-value = 0.001). In microarray data analysis, 12 of 32 ROS-related genes showed the same differential expression pattern with statistical significance. In analysis, up-regulated ROS-related genes were related with ROS and . Immune function-related and carcinogenesis-related gene sets were enriched only in HT group in Gene Set Enrichment Analysis. Our results suggested that ROS levels may be increased in PTC with HT. Increased levels of ROS may contribute to PTC development in patients with HT.

Key words: Reactive oxygen species, Thyroid cancer, Hashimoto thyroiditis, , Bioinformatics

THE CLINICAL association between Hashimoto thy- illary thyroid carcinoma (PTC) has been extensively roiditis (HT; chronic lymphocytic thyroiditis) and pap- studied, but the precise relationship between these two diseases is still being debated. Several analyses of sur- Submitted Apr. 21, 2015; Accepted Jun. 20, 2015 as EJ15-0234 gical resection specimens have shown that PTC is fre- Released online in J-STAGE as advance publication Jul. 23, 2015 quently accompanied by HT. Interestingly, PTC with Correspondence to: Ju Han Kim, M.D., Ph.D., Division of Biomedical Informatics, Systems Biomedical Informatics Research HT is associated with a lower rate of neck lymph node Centre, Seoul National University College of Medicine, 101 and has a better prognosis than PTC with- Daehak-ro, Jongno-gu, Seoul 110-744, Korea. out thyroiditis in spite of increased incidence corre- E-mail: [email protected] lation [1-10]. In contrast, other studies conducted by Correspondence to: Kyu Eun Lee, M.D., Ph.D., Department of fine-needle aspiration biopsy specimens and ultraso- Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul nographic findings have found no correlation between 110-744, Korea. E-mail: [email protected] the incidences of these two diseases, with researchers * These two authors contributed equally to this article. insisting that HT does not increase the risk of thyroid ©The Japan Endocrine Society 858 Yi et al. cancer [11-13]. To date these studies have consisted only of clinical retrospective reviews. To resolve the controversial relationship between these two diseases that occur in the same organ, additional biological and/ or genomic evidence is needed. One possible biological mechanism for a connection between these two diseases is the elevated levels of reactive oxygen species (ROS), expelled from the lym- phocytes during the progression of HT [14, 15]. HT is a chronic autoimmune disease accompanied by exten- sive infiltration of . Lymphocytes produce ROS as a byproduct of cell-mediated immune pro- cesses. Although ROS production from the lympho- cytes increases in HT, we cannot safely conclude that the total amount of intra-thyroidal ROS level also rises enough to induce the development of cancer because thyroid cells normally produce huge amounts of hydro- gen peroxide (H2O2), a typical form of ROS, during thyroid hormone biosynthesis. With the progression of HT, thyroid cells are destroyed by the lymphocytic invasion. Thyroid hormone production ceases and byproduct ROS production is also stopped as thyroid cell destruction proceeds [16]. In summary, ROS pro- Fig. 1 Flow chart for grouping TCGA RNA-sequencing results according to clinical information. duction from the lymphocytes was increased but ROS The final groups used for our analyses were: (1) Normal, from normal thyroid cells was decreased. Therefore, it (2) PTC, and (3) HT-PTC. is difficult to estimate the changes in total intra-thyroi- dal ROS levels in thyroid gland with HT. To circumvent these challenges, we took a bioinfor- groups. TCGA RNA sequencing data included 558 matics approach to reveal the ROS level elevation in results: 58 from normal thyroid tissue, 492 from pri- PTC with HT. We hypothesized that if the total intra- mary PTC tissue, and 8 from metastatic tissue. Each thyroidal ROS level increases in HT due to the accu- sample was reported as 20,531 rows (genes) and one mulation of lymphocytic ROS resulting in disruption column (read counts, in units of RSEM [RNA-Seq. by of the balance, ROS-related genes and pathways Expectation Maximization]), with upper-quartile nor- would be differently expressed from the PTC with/ malization performed by the TCGA team. We inte- without HT. To test this hypothesis, we analyzed gene grated 58 normal thyroid tissue and 492 primary PTC expression data from RNA-sequencing in the Cancer results into a meta-table that comprised 20,531 rows Genome Atlas (TCGA, The Cancer Genome Atlas (genes) and 550 columns (patients). Eight results from research network: http://cancergenome.nih.gov) and metastatic tissue were excluded. Next, we examined microarray data in Gene Expression Omnibus (GEO, all clinical records and excluded samples with lack of http://www.ncbi.nlm.nih.gov/geo/). pathologic information. Patients with a history of exter- nal radiation exposure were also excluded because the Materials and Methods radiation could have degraded ROS-related systems and thus acted as a critical confounding factor in this TCGA data processing and making subgroups ROS-related study. As of May 2014, TCGA group has released multi- We split the PTC samples into two groups accord- genomic analyses results from 502 PTC patients with ing to pathologic status, i.e., PTC without HT and PTC partially open access. We downloaded clinical infor- with HT. Although the TCGA data had already been mation and normalized RNA-sequencing mRNA read normalized, we verified the density plot to flag outlier count results. Fig. 1 outlines the steps for creating sub- samples ourselves and identified two outliers that we ROS-related gene expression in thyroid cancer 859 excluded from our analysis (Supplemental Fig. 1). The final analysis included the following three groups: (1) Normal thyroid (n = 33, hereafter referred to as “Normal group”, (2) PTC without HT (n = 232, “PTC group”), and (3) PTC with HT (n = 60, “HT-PTC group”).

Generating ROS-related gene sets We created three ROS-related gene sets to use in our analysis. Gene Set 1 was prepared by searching the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) profile data- base (http://www.ncbi.nlm.nih.gov/geoprofiles) and the AmiGO2 gene ontology database (http://amigo. geneontology.org/amigo) [17]. As of May 2014, the NCBI GEO profile database provided 182,114- per tinent gene records and the AmiGO2 search engine returned 358 genes, in response to our query terms: “reactive oxygen species, homo sapiens”. From these numerous records, we extracted unique gene names and IDs, including 364 unique genes that are known to be associated with the ROS-related system in the Fig. 2 Steps in the statistical analysis used in this study. From the full set of 20,531 genes, we identified 42 DEGs human system. between the PTC and HT-PTC groups. The information for Gene Sets 2 and 3 was obtained from the Gene Ontology Consortium (http://geneontol- ogy.org/), specifically, the gene ontology terms related background correction, normalization, and extract to “reactive oxygen species”. Gene Set 2 includes 146 gene expressions (RMA, Robust Multi-array Average), genes annotated with Gene Ontology (GO):0000302, we used “affy” package in Bioconductor (http://www. “response to ROS” and Gene Set 3 contains 133 genes bioconductor.org/) [18]. annotated with GO:0072593, “ROS metabolic pro- cess”. As a result we compiled a total of 402 ROS- Statistical analysis related genes when the three gene sets are combined. All statistical analyses were performed in R.3.1.2 [19], with graphs generated in the same program. Fig. 2 Validation Study using public microarray data from shows the steps of our statistical analysis. To find dif- GEO database ferentially expressed genes (DEGs) among the three To validate our results from TCGA data, we ana- groups, we performed one way ANOVA test with all lyzed a public microarray repository from the Gene 20,531 genes. To correct false positive rates from mul- Expression Omnibus (GEO): “GSE29315, compila- tiple comparisons, the False Discovery Rate method tion of expression profiles in distinct thyroid neopla- was used, with adjusted q-values < 0.05 considered as sias”, conducted by Tomas G et al. (http://www.ncbi. statistically significant. To find specific DEGs between nlm.nih.gov/geo/query/acc.cgi?acc=GSE29315). The the PTC and HT-PTC groups that represented differ- microarray platform used was the Affymetrix Human ential expression according to just HT, and not from Genome U95 Version 2 Array. GSE29315 data con- the normal versus cancer, we applied Tukey’s hon- tain 12,625 gene expression counts from various types estly significant difference (HSD) test on the signifi- of thyroid tissues: 17 follicular adenomas, 9 follicu- cant genes obtained from initial ANOVA test. We fil- lar thyroid carcinomas, 13 follicular variant PTCs, 9 tered out 2,111 significant genes that show differential PTCs, 6 HT (HT-PTCs), 8 thyroid hyperplasias, and 9 expression pattern in PTC versus HT-PTC group only Hurthle cell adenomas. We downloaded the microar- using Tukey’s HSD test. From these filtered genes, we ray results served as “*.CEL” files from six HT-PTC selected ROS-related DEGs that intersected with the and nine PTC results. To analyze microarray data with 402 ROS-related genes that we had prepared above. 860 Yi et al.

The fold-change of each DEG was calculated by the group, five genes were expressed in immune cells and following equation: log2 (mean expression value in three genes were in thyroid cells. No ROS-producing HT-PTC/mean expression value in PTC). gene and only one ROS-eliminating gene was found in To validate ROS-related DEGs in microarray data, PTC group. Detailed information of statistical anal- unpaired t-test was applied on gene expression counts ysis of the 402 ROS-related genes are described in (RMA) between six HT-PTC and nine PTC groups. A Supplemental Table 1. p-value < 0.05 was considered as a statistically signif- Fold changes of 42 ROS-related DEGs are shown icant differential expression between the two groups. in Fig. 3. Positive fold changes indicate that the genes had higher expression in the HT-PTC whereas negative Gene ontology (GO) and gene set enrichment analysis fold changes indicate that the genes had higher expres- (GSEA) sion in the PTC group. The proportions of up-regu- After obtaining the ROS-related DEGs, we used the lated genes were significantly different between the two DAVID (Database for Annotation, Visualization and groups according to the Chi-square test (p < 0.001). Integrated Discovery Bioinformatic Resources 6.7) Mean log2 fold change was 0.562 in the HT-PTC group program to check each DEG’s dominantly-expressed and 0.252 in the PTC group. These values were also tissue and its ROS-related biologic function such as significantly different (t-test p = 0.001). In the HT-PTC ROS-producing or ROS-eliminating (scavenging) from group, 13 DEGs had log2 fold-change values greater microenvironment [20]. We also checked gene ontol- than 0.5. However, no DEGs in the PTC group had ogy enrichment using up-regulated genes in HT-PTC or log2 fold-change values greater than 0.5. PTC group with DAVID gene ontology analyzing tool. After obtaining DEGs from TCGA RNA sequenc- The Gene Set Enrichment Analysis (GSEA) program ing data, we validated our results with another dataset was used for pathway analysis [21]. We initially input- from the microarray experiment. Among the 42 ROS- ted all 20,531 gene expression counts from the RNA related DEGs, 32 genes were represented in the micro- sequencing RSEM results to find which pathways were array gene list. Twelve genes showed significant differ- differently enriched between HT-PTC and PTC group. ential expression pattern under the t-test p-value < 0.05 The Kyoto Encyclopedia of Genes and Genomes between HT-PTC and PTC groups. Eleven genes were (KEGG) database was used to reference pathway data- up-regulated in HT-PTC and one gene was up-regulated base in GSEA program [22]. Additionally, we used our in PTC (Fig. 4). The differential expression patterns of three ROS-related gene sets as another metric to deter- these 12 genes were the same as the TCGA fold change mine whether these specific ROS-related gene sets results. Detailed analysis result for 32 ROS-related were enriched or not in the HT-PTC group. In GSEA, genes are described in Supplemental Table 2. an FDR q-value < 0.25 was considered to indicate sta- To determine possible biased effects from clinical tistically significant enrichment. variables such as age, sex, or disease stage that may influence gene expression, we reclassified the PTC and Results HT-PTC groups according to clinical variables and again performed the t-test to compare the different clin- DEG analysis ical subgroups. No significant variables were identi- Table 1 shows the gene name, their description, fied that could have confounded our DEG results. dominantly-expressed tissue, and ROS-related func- tion of 42 ROS-related DEGs between HT-PTC Gene ontology (GO) and gene set enrichment analysis and PTC. Thirty-three genes were up-regulated in Table 2 shows the significantly enriched GOs in bio- HT-PTC group and nine genes were up-regulated in logic process using up-regulated ROS-related DEGs PTC group. Among the 33 genes that were up-regu- as input to DAVID analysis. Total 24 GOs were sig- lated in HT-PTC, 22 genes were dominantly expressed nificantly enriched. Twelve GOs were related to ROS in immune function-related cells, such as lymphocytes, metabolism and . Six GOs contained , lymph nodes, and so on, and 11 genes the term “homeostasis” and three GOs were related were thyroid cells. Five genes were related to ROS to cell apoptosis. There was no significantly enriched production whereas 15 genes were correlated with the pathway using up-regulated DEGs in PTC group. removal or elimination of ROS from tissue. In the PTC In the GSEA with all 20,531 genes, 53 pathways ROS-related gene expression in thyroid cancer 861

Table 1 42 DEGs between HT-PTC versus PTC group. Dominantly expressed tissue and ROS-related function of DEGs are indicated by check sign. Dominant Tissue ROS-related Function Gene Name Description Immune cell* Thyroid Production Elimination SFTPC Surfactant C CD38 CD38 molecule TPO Thyroid IPCEF1 Interaction protein for cytohesin exchange factors 1 LCK LCK proto-oncogene, Src family kinase NCF1 cytosolic factor 1 BTK Bruton agammaglobulinemia tyrosine kinase PPARGC1A proliferator-activated receptor gamma, coactivator 1 alpha TRPM2 Transient receptor potential cation channel, subfamily M, member 2 CYBB Cytochrome b-245, beta polypeptide BCO2 Beta-carotene oxygenase 2 RAC2 Ras-related C3 botulinum toxin 2 (rho family, small GTP binding protein Rac2) PPIF Peptidylprolyl F PDK1 Pyruvate kinase, isozyme 1 SLC8A1 Solute carrier family 8 (sodium/ exchanger), member 1 IL18BP Interleukin 18 binding protein NCF2 Neutrophil cytosolic factor 2 SFTPD Surfactant protein D MICB MHC class I polypeptide-related sequence B GPX3 peroxidase 3 (plasma) SOD2 dismutase 2, mitochondrial PTGS1 Prostaglandin-endoperoxide synthase 1 (prostaglandin G/H synthase and cyclooxygenase) BAK1 BCL2-antagonist/killer 1 JAK2 Janus kinase 2 GCLC Glutamate- , catalytic subunit PREX1 Phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 1 MSRB2 sulfoxide B2 PRDX1 1 ETFDH Electron-transferring- dehydrogenase C12orf5 12 open reading frame 5 NDUFS1 NADH dehydrogenase (ubiquinone) Fe-S protein 1, 75kDa (NADH-coenzyme Q reductase) GRB2 Growth factor receptor-bound protein 2 PRDX3 Peroxiredoxin 3 ERCC3 Excision repair cross-complementation group 3 HDAC6 Histone deacetylase 6 LIG1 Ligase I, DNA, ATP-dependent PON2 Paraoxonase 2 RBM11 RNA binding motif protein 11 OXR1 Oxidation resistance 1 ANXA1 Annexin A1 PLK3 Polo-like kinase 3 KLF4 Kruppel-like factor 4 (gut) * Lymphocytes, neutrophil and lymph nodes. 862 Yi et al.

Fig. 3 Log2 fold changes of ROS-related DEGs ordered by fold-change values.

were significantly enriched in HT-PTC group but no [23]. As discussed in a recent review article, many stud- pathway was enriched in PTC group (Table 3). Ten ies have shown that the loss of ROS balance is related to pathways were related to immune process, and only cancer, such as cancer of the breast, prostate, , , one pathway, the chemokine signaling pathway, was pancreas and so on. Furthermore, known cancer-related correlated with carcinogenesis. The enrichment plots pathways, such as chemokine, c-myc, EGF, DNA repair from GSEA using our three ROS-related gene sets are mechanism, JAK/STAT, JNK, MAPK, mTOR, PI3K/ shown in Fig. 5. Gene Sets 1 and 3 were significantly Akt, , and PTEN, are linked with altered ROS sta- enriched toward the HT-PTC group, using FDR q-value tus [24]. A recent clinical experimental study with radi- < 0.25. Gene Set 2 was also enriched toward HT-PTC cal prostatectomy tissue has shown that Nox-5 derived side but had no significant q-value. This result sug- ROS exerts a carcinogenic effect by depletion of protein gests that the ROS-related gene sets are highly acti- kinase C zeta and c-Jun N-terminal kinase (JNK) [25]. vated in the HT-PTC group. In the same context, oxidative stress and elevated ROS levels have been proposed to be a risk factor for Discussion the development of thyroid cancer [26-28]. Various experimental methods have been developed to detect Increased levels of ROS and oxidative stress are the levels of ROS in specific tissues. However, these strongly associated with age-dependent disease, chronic techniques tend to be extremely demanding for a vari- , and increased risk of cancer development ety of reasons. For instance, ROS have extremely ROS-related gene expression in thyroid cancer 863

Fig. 4 Twelve genes that have differential expression pattern under the t-test p-value < 0.05 between HT-PTC and PTC groups. Eleven genes were up-regulated in HT-PTC and one gene was up-regulated in PTC. Y-axis means the gene expression value, unit is RMA (Robust Multi-array Average).

Table 2 Significantly enriched (FDR q-value < 0.05) gene ontologies (Biologic Process) in the HT-PTC group. GO Number Term Count Fold enrichment FDR GO:0006979 Response to oxidative stress 14 36.088 <0.001 GO:0042592 Homeostatic process 13 7.318 <0.001 GO:0055114 Oxidation reduction 12 7.939 <0.001 GO:0006800 Oxygen and reactive oxygen species metabolic process 11 69.407 <0.001 GO:0019725 Cellular homeostasis 11 9.979 <0.001 GO:0048878 Chemical homeostasis 11 9.083 <0.001 GO:0042981 Regulation of apoptosis 11 5.784 0.011 GO:0043067 Regulation of programmed cell death 11 5.727 0.012 GO:0010941 Regulation of cell death 11 5.706 0.013 GO:0055082 Cellular chemical homeostasis 10 11.125 0.000 GO:0006873 Cellular ion homeostasis 9 10.173 0.002 GO:0050801 Ion homeostasis 9 9.303 0.004 GO:0010035 Response to inorganic substance 7 14.435 0.009 GO:0006801 Superoxide metabolic process 6 101.460 0.000 GO:0042743 metabolic process 6 101.460 0.000 GO:0042542 Response to hydrogen peroxide 6 45.295 0.000 GO:0000302 Response to reactive oxygen species 6 33.820 0.001 GO:0034614 Cellular response to reactive oxygen species 5 70.458 0.001 GO:0034599 Cellular response to oxidative stress 5 49.157 0.004 GO:0033194 Response to hydroperoxide 4 281.833 0.000 GO:0051881 Regulation of mitochondrial 4 153.727 0.003 GO:0042554 Superoxide anion generation 4 130.077 0.005 GO:0042744 Hydrogen peroxide catabolic process 4 99.471 0.011 GO:0070301 Cellular response to hydrogen peroxide 4 93.944 0.013 864 Yi et al.

Table 3 Significantly enriched pathways in HT-PTC group from the GSEA analysis. Pathways in KEGG database FDR q-value Antigen processing and presentation * 0.012 Autoimmune thyroid disease * 0.026 Natural killer cell mediated cytotoxicity * 0.050 Type I diabetes mellitus 0.052 Intestinal immune network for IgA production 0.052 Systemic lupus erythematosus 0.058 Hematopoietic cell lineage 0.061 Asthma 0.062 T cell receptor signaling pathway * 0.063 Glycolysis gluconeogenesis 0.065 Graft versus host disease 0.068 Allograft rejection 0.069 Primary immunodeficiency 0.074 Ascorbate and aldarate metabolism 0.075 Pyruvate metabolism 0.076 Beta metabolism 0.077 Chemokine signaling pathway * 0.079 Leishmania infection 0.080 metabolism 0.080 Butanoate metabolism 0.080 B cell receptor signaling pathway * 0.082 Propanoate metabolism 0.082 and degradation 0.084 Proteasome 0.085 Viral myocarditis 0.092 metabolism 0.101 Terpenoid backbone biosynthesis 0.111 Porphyrin and chlorophyll metabolism 0.116 Pantothenate and coa biosynthesis 0.118 Citrate cycle TCA cycle 0.119 Peroxisome 0.119 Cell adhesion molecules cams * 0.121 Oxidative phosphorylation 0.122 Cytokine cytokine receptor interaction * 0.123 Cytosolic DNA sensing pathway 0.124 Toll like receptor signaling pathway * 0.126 Fc gamma R mediated * 0.145 Huntingtons disease 0.163 Parkinson’s disease 0.166 Fructose and mannose metabolism 0.168 Nicotinate and nicotinamide metabolism 0.192 Fc epsilon RI signaling pathway * 0.197 Alzheimers disease 0.200 Amino sugar and nucleotide sugar metabolism 0.202 degradation 0.208 Pentose and glucuronate interconversions 0.209 metabolism 0.211 Protein export 0.216 RNA degradation 0.216 and metabolism 0.246 Fig. 5 Enrichment plots of three ROS-related gene sets. Vibrio cholerae infection 0.246 (A) Gene Set 1, FDR q-value = 0.155. (B) Gene Set 2, Snare interactions in vesicular transport 0.247 FDR q-value = 0.252. (C) Gene Set 3, FDR q-value = Glutathione metabolism 0.249 0.093. FDR q-value < 0.25 was considered to indicate * Immune process and cancer related pathways statistical significance. ROS-related gene expression in thyroid cancer 865 short half-lives, generally on the order of nanosec- this result, we can infer that ROS from immune cells onds to seconds. Moreover, their steady-state concen- are increased, and that the protective mechanism from trations are in the picomolar to very low nanomolar thyroid cells was enhanced against the increased ROS range. ROS detection techniques also have demand- from immune cells in the HT-PTC microenvironment. ing experimental requirements, such as fresh blood or In the validation study from microarray data, part tissue, expensive detection kits, and specialized tech- of the ROS-related DEGs from TCGA had the same niques [29]. However, estimating the ROS level in thy- expression pattern between the two PTC groups. roid or thyroid cancer tissue is more challenging than Moreover, genes that showed higher fold change value in other organs because thyroid cells themselves nor- over 1.0 in TCGA analysis, such as TPO, CD38 and mally produce substantial amounts of hydrogen perox- IPCEF1, have similarly large differential expressions ide. To protect itself from the harmful effects of hydro- between the two groups. Although some genes show a gen peroxide production, the thyroid gland contains reverse expression direction in microarray dataset, they many anti-ROS defense mechanisms, such as antiox- have no statistical significance in t-test (Supplemental idants and free -scavengers [16]. This active Table 2). With this validation analysis, we can assert ROS-production and elimination system in normal thy- that up-regulated genes in HT-PTC from RNA sequenc- roid tissue makes it difficult to conduct ROS-related ing have similar expression patterns in different analy- experimental study in thyroid cancer or HT. sis platforms such as microarray chip analysis. Several groups have developed promising methods Our study proposed to obtain genomic evidence for accurately quantifying the levels of ROS in thyroid of ROS elevation in PTC with HT by RNA sequenc- tissue. Although these methods were shown to effec- ing and microarray, without resorting to difficult ROS tively estimate ROS levels in the thyroid, a biological detection experiments. Increased ROS from immune or genomic relationship has not been clearly demon- cells in HT status can destroy the normal thyroid tissue strated between ROS and PTC using these methods by its cytotoxic effect which can then lead to thyroid [29]. Only a limited number of genomic studies focus- cancer development. In the GSEA result in Table 3, the ing on ROS-related genes in thyroid cancer or HT HT-PTC group was more enriched than the PTC group have been performed. Moreover, the genomic stud- in immune function-related pathways and one cancer- ies that have been performed have focused on spe- related pathway, the “chemokine signaling pathway”, cific genes, includingDUOX1/2, NOX, TPO, TG, GSH that contains cancer-related sub-pathways, such as the and GPX [30-34]. In 2013, Martinez et al. performed Jak-STAT signaling pathway, MAPK signaling path- a purely bioinformatics-based analysis of TCGA data way, and PI3K/Akt pathway. Moreover, ROS-related and found that the oxidative response pathway is fre- Gene Sets 1, 2, and 3 were enriched toward the HT-PTC quently down-regulated in PTC via genomic and epi- group. Increased enrichment of immune function- genetic mechanisms [35]. However, only one protein related pathways may be derived from chronic inflam- complex, the KEAP1/CUL3/RBX1 E3-ubiquitin ligase mation under HT. However, chemokine signaling complex, was considered in the activation of the path- pathway enrichment could provide a molecular back- way. Furthermore, samples were not grouped accord- ground for the carcinogenic effect of elevated ROS lev- ing to their clinical information such as thyroiditis. els in PTC with HT. To date, there is yet no study that According to our TCGA RNA-sequencing data provides clear evidence of biologic mechanisms for analysis, a number of ROS-related genes were signifi- prognostic advantage and low metastatic potential in cantly up-regulated in the HT-PTC group. Compared PTC with HT patients [36]. Enhanced immune func- with the PTC group, more genes were up-regulated in tion may be able to suppress the metastatic potential of the HT-PTC group including those that showed higher thyroid cancer, leading to more favorable clinical prog- fold changes. Among the up-regulated genes, NCF1, noses in patients with PTC with HT. However, this CYBB, SOD2 and NCF2 are dominantly expressed in hypothesis requires solid verification because there is immune cells and its role in the ROS system is corre- no published report of any particular immune response lated with ROS production. In contrast, genes that are pathway that contributes to either progression or sup- dominantly expressed in thyroid tissue, TPO, IPCEF1, pression of carcinogenesis. GPX3, SOD2, PRDX1, ETFDH, NDUFS1 and PRDX3, This study has another meaning for clinical practitio- are correlated with ROS elimination (Table 1). From ners. Several clinical studies have reported that antioxi- 866 Yi et al. dant therapy provides protective effects against various higher proportions of normal tissue would presumably thyroid diseases, including improvements in chronic exhibit different gene expression patterns compared thyroiditis, amelioration of thyroid disease in pregnant with samples with higher proportions of cancer tissue. women, and relief of ophthalmopathic symptoms in Ideally, such variations in pathologic factors would be Graves’ disease [37-39]. Our genomic background for eliminated before beginning the analysis. However, ROS elevation in HT-PTC could support the potential the TCGA pathologic reports did not contain sufficient benefits of therapy in HT patients, not only information for this purpose. Thus, it would be advan- in treating thyroiditis symptoms, but also in delaying or tageous for future RNA-sequencing studies to exclu- preventing the development of thyroid cancer. sively use quality-controlled PTC samples. Our study has some limitations. Although we could In summary, we obtained several lines of evidence estimate the ROS level elevation in HT-PTC group that ROS levels are altered in HT-PTC using recent from the gene expression study using RNA sequenc- public next-generation sequencing and microarray ing and microarray data, classic experimental valida- data. First, more DEGs were observed in the HT-PTC tion such as quantitative RT-PTC is still necessary to group compared with the PTC group (33 versus 9), and provide biological confirmation of our findings. Also, the functions of the up-regulated genes in the HT-PTC there is no experimental or published evidence that the group were associated with ROS production in immune differential expression of ROS-related genes is signifi- cell-related genes and ROS elimination in thyroid cell- cantly correlated with actual ROS levels in thyroid tis- related genes. Second, part of the ROS-related DEGs sue. To address these issues, future studies will need showed a similar expression pattern in HT in microar- to focus on ROS level estimation as well as the expres- ray data analysis. Third, the fold changes were higher sion levels of ROS-related genes. in the HT-PTC group, as demonstrated by the differ- From the oncologic standpoint, TCGA clinical data ence in mean log fold change. Thirteen genes in the do not provide a clear description of the obligate order HT-PTC group had log fold changes greater than 0.5, of PTC and HT. Therefore, we cannot safely conclude whereas only one gene in the PTC group exhibited this that the increased ROS levels in patients with long- degree of change. Fourth, immune function-related standing HT contribute to the development of thyroid pathways and one cancer-related pathway were signif- cancer because the obligate order in which these dis- icantly associated with HT-PTC group only. Finally, eases develop in each patient has yet to be established. the ROS-related gene sets were enriched only in the Prospective studies should be performed in patients HT-PTC group. Taken together, ROS levels may be with HT with sequencing or microarray before and elevated to sufficiently high levels that it contributes to after they are diagnosed with PTC. Such studies would the progression of carcinogenesis in HT-PTC. enable a more meaningful evaluation of the real carci- nogenic effect of longstanding HT. Acknowledgements The final limitation is the shortage of pathologic description in TCGA, especially for the tissue prepa- This study was performed as a team project for rations used in the RNA-sequencing analyses. In the the educational course of Certified Physicians in TCGA pathologic reports, which were provided with Biomedical Informatics (CPBMI) supported by the *.pdf files, the amounts of infiltration and Korean Society of Medical Informatics. The authors the proportions of normal tissue in the PTC samples wish to offer their deepest gratitude to their CPBMI were not described. If some of the HT-PTC samples instructors. This work was supported by a National contained extremely large numbers of lymphocytes or Research Foundation grant from the Korea govern- only small amounts of cancer tissue, the expression of ment (MSIP) (2012-0000994) and by a grant from the ROS-related genes could have been profoundly influ- Korean Health Technology, R&D Project, Ministry of enced by the presence of the lymphocytes, which them- Health and Welfare (HI13C2164). selves express high levels of ROS-related genes. This phenomenon could have significantly biased the RNA- Disclosure sequencing results. Similarly, all the PTC samples, from patients both with and without HT, contained The authors declare they have nothing to disclose. remnants of normal thyroid tissue. Samples with ROS-related gene expression in thyroid cancer 867

Supplemental Fig. 1 Density plot of the 234 initial PTC samples. Two outliers, drawn in red and sky blue, were excluded from the analysis.

Supplemental Table 1 402 ROS-related genes and their statistical analysis results among the normal, PTC and HT-PTC groups. Genes and their affiliated gene set are marked by check sign. p-value from Log Gene 2 ANOVA FDR Tukey’s Gene Gene Gene ID Description Name Fold p-value q-value HSD, PTC: Set 1 Set 2 Set 3 Change* HT-PTC 10394 PRG3 proteoglycan 3 3.542 0.057 0.096 NA 6440 SFTPC surfactant protein C 1.766 0.000 0.000 0.000 3240 HP haptoglobin 1.747 0.078 0.125 NA 952 CD38 CD38 molecule 1.598 0.000 0.000 0.000 338324 S100A7A S100 calcium binding protein A7A 1.420 0.355 0.445 NA 6363 CCL19 chemokine (C-C motif) ligand 19 1.367 0.000 0.000 0.419 2877 GPX2 2 (gastrointestinal) 1.315 0.186 0.261 NA 337 APOA4 apolipoprotein A-IV 1.271 0.434 0.526 NA 2253 FGF8 fibroblast growth factor 8 (androgen-induced) 1.217 0.256 0.340 NA 10249 GLYAT -N-acyltransferase 1.194 0.028 0.052 NA 8644 AKR1C3 aldo-keto reductase family 1, member C3 1.182 0.143 0.209 NA 7173 TPO 1.172 0.000 0.000 0.004 6295 SAG S-antigen; retina and pineal gland (arrestin) 1.085 0.204 0.282 NA 26034 IPCEF1 interaction protein for cytohesin exchange factors 1 1.059 0.000 0.000 0.016 3932 LCK LCK proto-oncogene, Src family tyrosine kinase 0.981 0.000 0.000 0.005 653361 NCF1 neutrophil cytosolic factor 1 0.941 0.000 0.000 0.037 316 AOX1 aldehyde oxidase 1 0.920 0.000 0.000 0.064 6278 S100A7 S100 calcium binding protein A7 0.881 0.572 0.654 NA 7020 TFAP2A factor AP-2 alpha (activating enhancer binding 0.855 0.157 0.226 NA protein 2 alpha) 695 BTK Bruton agammaglobulinemia tyrosine kinase 0.843 0.000 0.001 0.003 10891 PPARGC1A peroxisome proliferator-activated receptor gamma, 0.819 0.000 0.000 0.003 coactivator 1 alpha 1236 CCR7 chemokine (C-C motif) receptor 7 0.816 0.000 0.000 0.474 3848 KRT1 keratin 1 0.772 0.048 0.082 NA 7498 XDH 0.754 0.089 0.140 NA 7226 TRPM2 transient receptor potential cation channel, subfamily M, 0.727 0.000 0.000 0.000 member 2 1536 CYBB cytochrome b-245, beta polypeptide 0.692 0.000 0.001 0.000 868 Yi et al.

Supplemental Table 1 Cont. p-value from Log Gene 2 ANOVA FDR Tukey’s Gene Gene Gene ID Description Name Fold p-value q-value HSD, PTC: Set 1 Set 2 Set 3 Change* HT-PTC 383 ARG1 arginase 1 0.644 0.000 0.001 0.617 83875 BCO2 beta-carotene oxygenase 2 0.640 0.000 0.000 0.022 948 CD36 CD36 molecule (thrombospondin receptor) 0.619 0.020 0.039 0.076 139189 DGKK diacylglycerol kinase, kappa 0.580 0.337 0.427 NA 5880 RAC2 ras-related C3 botulinum toxin substrate 2 (rho family, 0.565 0.023 0.043 0.033 small GTP binding protein Rac2) 185 AGTR1 angiotensin II receptor, type 1 0.558 0.000 0.000 0.309 79400 NOX5 NADPH oxidase, EF-hand calcium binding domain 5 0.548 0.055 0.094 NA 10105 PPIF peptidylprolyl isomerase F 0.547 0.014 0.029 0.022 22925 PLA2R1 phospholipase A2 receptor 1, 180kDa 0.536 0.000 0.000 0.234 29949 IL19 interleukin 19 0.527 0.392 0.484 NA 5163 PDK1 pyruvate dehydrogenase kinase, isozyme 1 0.495 0.000 0.000 0.000 6546 SLC8A1 solute carrier family 8 (sodium/calcium exchanger), 0.488 0.001 0.002 0.001 member 1 10068 IL18BP interleukin 18 binding protein 0.484 0.000 0.001 0.000 6649 SOD3 3, extracellular 0.479 0.000 0.000 0.317 3082 HGF hepatocyte growth factor (hepapoietin A; scatter factor) 0.465 0.000 0.000 0.071 137902 PXDNL peroxidasin homolog (Drosophila)-like 0.420 0.263 0.347 NA 4688 NCF2 neutrophil cytosolic factor 2 0.411 0.003 0.008 0.046 330 BIRC3 baculoviral IAP repeat containing 3 0.396 0.110 0.168 NA 8876 VNN1 vanin 1 0.394 0.162 0.232 NA 2628 GATM glycine amidinotransferase (L-arginine:glycine 0.389 0.000 0.000 0.090 amidinotransferase) 8288 EPX peroxidase 0.380 0.109 0.167 NA 6441 SFTPD surfactant protein D 0.378 0.002 0.004 0.018 4277 MICB MHC class I polypeptide-related sequence B 0.378 0.005 0.011 0.012 2878 GPX3 glutathione peroxidase 3 (plasma) 0.372 0.000 0.000 0.035 6648 SOD2 superoxide dismutase 2, mitochondrial 0.349 0.018 0.035 0.013 4780 NFE2L2 nuclear factor, erythroid 2-like 2 0.347 0.013 0.027 0.115 5742 PTGS1 prostaglandin-endoperoxide synthase 1 (prostaglandin G/H 0.336 0.011 0.024 0.008 synthase and cyclooxygenase) 8989 TRPA1 transient receptor potential cation channel, subfamily A, 0.310 0.705 0.767 NA member 1 578 BAK1 BCL2-antagonist/killer 1 0.309 0.000 0.000 0.000 133522 PPARGC1B peroxisome proliferator-activated receptor gamma, 0.292 0.033 0.061 NA coactivator 1 beta 3717 JAK2 Janus kinase 2 0.283 0.000 0.000 0.002 7128 TNFAIP3 , alpha-induced protein 3 0.280 0.000 0.000 0.426 4973 OLR1 oxidized low density lipoprotein (lectin-like) receptor 1 0.268 0.122 0.183 NA 2729 GCLC glutamate-cysteine ligase, catalytic subunit 0.257 0.000 0.000 0.026 57580 PREX1 phosphatidylinositol-3,4,5-trisphosphate-dependent Rac 0.257 0.000 0.000 0.002 exchange factor 1 22921 MSRB2 methionine sulfoxide reductase B2 0.257 0.000 0.000 0.044 5052 PRDX1 0.248 0.000 0.000 0.012 220042 C11orf82 DNA damage-induced apoptosis suppressor 0.242 0.090 0.143 NA 596 BCL2 B-cell CLL/lymphoma 2 0.239 0.000 0.000 0.237 100 ADA adenosine deaminase 0.239 0.170 0.242 NA 5027 P2RX7 purinergic receptor P2X, ligand-gated ion channel, 7 0.235 0.163 0.234 NA 6772 STAT1 signal transducer and activator of transcription 1, 91kDa 0.235 0.004 0.010 0.270 59272 ACE2 angiotensin I converting 2 0.233 0.023 0.044 0.114 23530 NNT nicotinamide nucleotide transhydrogenase 0.231 0.038 0.068 NA 7356 SCGB1A1 secretoglobin, family 1A, member 1 (uteroglobin) 0.230 0.346 0.436 NA ROS-related gene expression in thyroid cancer 869

Supplemental Table 1 Cont. p-value from Log Gene 2 ANOVA FDR Tukey’s Gene Gene Gene ID Description Name Fold p-value q-value HSD, PTC: Set 1 Set 2 Set 3 Change* HT-PTC 2110 ETFDH electron-transferring-flavoprotein dehydrogenase 0.224 0.000 0.000 0.044 57103 C12orf5 chromosome 12 open reading frame 5 0.222 0.000 0.000 0.025 1718 DHCR24 24-dehydrocholesterol reductase 0.221 0.144 0.210 NA 9263 STK17A / kinase 17a 0.216 0.003 0.008 0.272 1137 CHRNA4 cholinergic receptor, nicotinic, alpha 4 (neuronal) 0.211 0.000 0.000 0.877 83667 SESN2 sestrin 2 0.209 0.001 0.002 0.232 375387 LRRC33 negative regulator of reactive oxygen species 0.208 0.000 0.000 0.062 183 AGT angiotensinogen (serpin peptidase inhibitor, clade A, 0.205 0.022 0.043 0.688 member 8) 4700 NDUFA6 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 6, 0.194 0.025 0.047 0.070 14kDa 4728 NDUFS8 NADH dehydrogenase (ubiquinone) Fe-S protein 8, 23kDa 0.193 0.005 0.012 0.175 (NADH-coenzyme Q reductase) 4720 NDUFS2 NADH dehydrogenase (ubiquinone) Fe-S protein 2, 49kDa 0.186 0.027 0.051 NA (NADH-coenzyme Q reductase) 94241 TP53INP1 tumor protein p53 inducible nuclear protein 1 0.181 0.000 0.000 0.058 4719 NDUFS1 NADH dehydrogenase (ubiquinone) Fe-S protein 1, 75kDa 0.180 0.007 0.015 0.039 (NADH-coenzyme Q reductase) 4722 NDUFS3 NADH dehydrogenase (ubiquinone) Fe-S protein 3, 30kDa 0.174 0.066 0.109 NA (NADH-coenzyme Q reductase) 1728 NQO1 NAD(P)H dehydrogenase, quinone 1 0.174 0.808 0.849 NA 58515 SELK selenoprotein K 0.174 0.000 0.000 0.052 983 CDK1 cyclin-dependent kinase 1 0.169 0.215 0.293 NA 1622 DBI binding inhibitor (GABA receptor modulator, 0.167 0.001 0.002 0.246 acyl-CoA binding protein) 51167 CYB5R4 reductase 4 0.166 0.046 0.079 NA 5831 PYCR1 pyrroline-5-carboxylate reductase 1 0.164 0.168 0.239 NA 6770 STAR steroidogenic acute regulatory protein 0.159 0.003 0.007 0.674 405753 DUOXA2 dual oxidase maturation factor 2 0.157 0.501 0.589 NA 6382 SDC1 syndecan 1 0.156 0.000 0.000 0.310 10636 RGS14 regulator of G-protein signaling 14 0.156 0.000 0.000 0.347 3373 HYAL1 hyaluronoglucosaminidase 1 0.155 0.168 0.239 NA 27089 UQCRQ ubiquinol- reductase, complex III subunit VII, 0.154 0.128 0.190 NA 9.5kDa 55967 NDUFA12 NADH dehydrogenase (ubiquinone) 1 alpha 0.153 0.101 0.157 NA subcomplex, 12 7048 TGFBR2 transforming growth factor, beta receptor II (70/80kDa) 0.152 0.040 0.070 NA 347 APOD apolipoprotein D 0.150 0.000 0.000 0.920 25828 TXN2 2 0.146 0.001 0.002 0.063 51022 GLRX2 2 0.145 0.175 0.247 NA 10549 PRDX4 peroxiredoxin 4 0.143 0.035 0.063 NA 7225 TRPC6 transient receptor potential cation channel, subfamily C, 0.141 0.000 0.001 0.674 member 6 4193 MDM2 proto-oncogene, E3 ubiquitin protein ligase 0.141 0.000 0.000 0.147 5071 PARK2 parkin RBR E3 ubiquitin protein ligase 0.138 0.000 0.000 0.338 11315 PARK7 parkinson protein 7 0.137 0.061 0.102 NA 5034 P4HB prolyl 4-hydroxylase, beta polypeptide 0.134 0.062 0.103 NA 2885 GRB2 growth factor receptor-bound protein 2 0.133 0.000 0.000 0.000 54 ACP5 acid phosphatase 5, tartrate resistant 0.133 0.011 0.023 0.738 4710 NDUFB4 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 4, 0.133 0.056 0.094 NA 15kDa 870 Yi et al.

Supplemental Table 1 Cont. p-value from Log Gene 2 ANOVA FDR Tukey’s Gene Gene Gene ID Description Name Fold p-value q-value HSD, PTC: Set 1 Set 2 Set 3 Change* HT-PTC 3163 HMOX2 oxygenase (decycling) 2 0.123 0.026 0.049 0.163 25824 PRDX5 peroxiredoxin 5 0.121 0.433 0.525 NA 10935 PRDX3 peroxiredoxin 3 0.120 0.000 0.001 0.021 27068 PPA2 pyrophosphatase (inorganic) 2 0.120 0.005 0.012 0.055 114757 CYGB cytoglobin 0.120 0.750 0.803 NA 2113 ETS1 v-ets avian erythroblastosis virus E26 oncogene 0.119 0.496 0.585 NA homolog 1 10587 TXNRD2 2 0.118 0.014 0.028 0.262 1410 CRYAB crystallin, alpha B 0.117 0.000 0.000 0.925 6850 SYK spleen tyrosine kinase 0.114 0.000 0.000 0.175 93100 NAPRT1 nicotinate phosphoribosyltransferase 0.112 0.000 0.001 0.601 1544 CYP1A2 cytochrome P450, family 1, subfamily A, polypeptide 2 0.112 0.104 0.160 NA 2730 GCLM glutamate-cysteine ligase, modifier subunit 0.112 0.062 0.103 NA 55829 SELS VCP-interacting membrane protein 0.110 0.000 0.000 0.071 655 BMP7 bone morphogenetic protein 7 0.107 0.000 0.000 0.492 475 ATOX1 antioxidant 1 copper chaperone 0.106 0.010 0.022 0.656 493869 GPX8 glutathione peroxidase 8 (putative) 0.106 0.224 0.304 NA 4613 MYCN v-myc avian myelocytomatosis viral oncogene 0.104 0.000 0.000 0.898 neuroblastoma derived homolog 2395 FXN 0.099 0.000 0.000 0.129 5604 MAP2K1 mitogen-activated protein kinase kinase 1 0.095 0.013 0.027 0.298 9616 RNF7 ring finger protein 7 0.093 0.001 0.002 0.138 10365 KLF2 Kruppel-like factor 2 0.092 0.000 0.000 0.957 1161 ERCC8 excision repair cross-complementation group 8 0.090 0.000 0.000 0.172 5371 PML promyelocytic leukemia 0.087 0.000 0.000 0.206 143686 SESN3 sestrin 3 0.083 0.000 0.000 0.899 1147 CHUK conserved helix-loop-helix ubiquitous kinase 0.081 0.000 0.000 0.285 378 ARF4 ADP-ribosylation factor 4 0.081 0.000 0.000 0.292 6927 HNF1A HNF1 homeobox A 0.080 0.000 0.000 0.655 8870 IER3 immediate early response 3 0.078 0.433 0.525 NA 7001 PRDX2 0.078 0.034 0.061 NA 54840 APTX aprataxin 0.076 0.154 0.222 NA 93974 ATPIF1 ATPase inhibitory factor 1 0.073 0.607 0.686 NA 51079 NDUFA13 NADH dehydrogenase (ubiquinone) 1 alpha 0.072 0.437 0.529 NA subcomplex, 13 54583 EGLN1 egl-9 family hypoxia-inducible factor 1 0.072 0.212 0.290 NA 4724 NDUFS4 NADH dehydrogenase (ubiquinone) Fe-S protein 4, 18kDa 0.071 0.436 0.528 NA (NADH-coenzyme Q reductase) 1535 CYBA cytochrome b-245, alpha polypeptide 0.070 0.174 0.246 NA 8455 ATRN attractin 0.068 0.038 0.068 NA 5693 PSMB5 proteasome (prosome, macropain) subunit, beta type, 5 0.068 0.130 0.193 NA 9361 LONP1 lon peptidase 1, mitochondrial 0.067 0.001 0.002 0.719 2937 GSS 0.067 0.000 0.000 0.347 6414 SEPP1 selenoprotein P, plasma, 1 0.067 0.000 0.000 0.791 9833 MELK maternal embryonic leucine zipper kinase 0.065 0.085 0.135 NA 8737 RIPK1 receptor (TNFRSF)-interacting serine-threonine kinase 1 0.063 0.000 0.000 0.153 11334 TUSC2 tumor suppressor candidate 2 0.062 0.343 0.433 NA 2876 GPX1 glutathione peroxidase 1 0.061 0.000 0.000 0.796 7707 ZNF148 zinc finger protein 148 0.059 0.000 0.000 0.632 836 CASP3 3, apoptosis-related cysteine peptidase 0.059 0.000 0.000 0.458 23593 HEBP2 heme binding protein 2 0.059 0.042 0.073 NA ROS-related gene expression in thyroid cancer 871

Supplemental Table 1 Cont. p-value from Log Gene 2 ANOVA FDR Tukey’s Gene Gene Gene ID Description Name Fold p-value q-value HSD, PTC: Set 1 Set 2 Set 3 Change* HT-PTC 10229 COQ7 coenzyme Q7 homolog, ubiquinone (yeast) 0.056 0.000 0.000 0.350 23408 SIRT5 sirtuin 5 0.055 0.287 0.374 NA 4358 MPV17 MpV17 mitochondrial inner membrane protein 0.054 0.000 0.000 0.668 7157 TP53 tumor protein p53 0.053 0.000 0.000 0.487 50484 RRM2B M2 B (TP53 inducible) 0.053 0.003 0.007 0.711 572 BAD BCL2-associated agonist of cell death 0.050 0.718 0.778 NA 6403 SELP selectin P ( membrane protein 140kDa, 0.048 0.201 0.277 NA antigen CD62) 7507 XPA xeroderma pigmentosum, complementation group A 0.045 0.000 0.000 0.775 2775 GNAO1 guanine nucleotide binding protein (G protein), alpha 0.045 0.011 0.023 0.961 activating activity polypeptide O 25942 SIN3A SIN3 transcription regulator family member A 0.044 0.000 0.000 0.585 4255 MGMT O-6-methylguanine-DNA methyltransferase 0.044 0.839 0.868 NA 10211 FLOT1 flotillin 1 0.043 0.011 0.023 0.767 140809 SRXN1 sulfiredoxin 1 0.041 0.000 0.000 0.881 9588 PRDX6 peroxiredoxin 6 0.039 0.716 0.776 NA 59338 PLEKHA1 pleckstrin homology domain containing, family A 0.039 0.000 0.000 0.755 (phosphoinositide binding specific) member 1 9955 HS3ST3A1 heparan sulfate (glucosamine) 3-O-sulfotransferase 3A1 0.038 0.394 0.486 NA 2176 FANCC Fanconi anemia, complementation group C 0.036 0.239 0.321 NA 2539 G6PD glucose-6-phosphate dehydrogenase 0.036 0.641 0.714 NA 4520 MTF1 metal-regulatory transcription factor 1 0.035 0.000 0.000 0.828 10494 STK25 serine/threonine kinase 25 0.034 0.000 0.000 0.853 2305 FOXM1 forkhead box M1 0.034 0.160 0.229 NA 5580 PRKCD protein kinase C, delta 0.033 0.000 0.000 0.762 5879 RAC1 ras-related C3 botulinum toxin substrate 1 (rho family, 0.032 0.000 0.000 0.672 small GTP binding protein Rac1) 5155 PDGFB platelet-derived growth factor beta polypeptide 0.030 0.000 0.000 0.957 6869 TACR1 tachykinin receptor 1 0.028 0.957 0.964 NA 5165 PDK3 pyruvate dehydrogenase kinase, isozyme 3 0.028 0.018 0.036 0.951 8614 STC2 stanniocalcin 2 0.025 0.027 0.050 0.988 201191 SAMD14 sterile alpha motif domain containing 14 0.025 0.000 0.000 0.979 664 BNIP3 BCL2/adenovirus E1B 19kDa interacting protein 3 0.024 0.253 0.337 NA 1471 CST3 cystatin C 0.024 0.399 0.491 NA 2068 ERCC2 excision repair cross-complementation group 2 0.024 0.158 0.228 NA 55022 PID1 phosphotyrosine interaction domain containing 1 0.023 0.000 0.000 0.997 405 ARNT aryl hydrocarbon receptor nuclear translocator 0.022 0.002 0.005 0.868 27244 SESN1 sestrin 1 0.021 0.536 0.621 NA 2034 EPAS1 endothelial PAS domain protein 1 0.016 0.049 0.083 NA 9943 OXSR1 oxidative stress responsive 1 0.014 0.000 0.000 0.944 56616 DIABLO diablo, IAP-binding mitochondrial protein 0.012 0.008 0.018 0.969 2950 GSTP1 glutathione S- pi 1 0.011 0.962 0.969 NA 26574 AATF apoptosis antagonizing transcription factor 0.007 0.126 0.188 NA 51157 ZNF580 zinc finger protein 580 0.007 0.283 0.369 NA 5663 PSEN1 presenilin 1 0.003 0.014 0.028 0.992 3630 INS 0.000 0.541 0.626 NA 50508 NOX3 NADPH oxidase 3 0.000 0.819 0.853 NA 257202 GPX6 glutathione peroxidase 6 (olfactory) 0.000 0.345 0.435 NA 6647 SOD1 superoxide dismutase 1, soluble -0.001 0.444 0.536 NA 8575 PRKRA protein kinase, interferon-inducible double stranded RNA -0.001 0.128 0.191 NA dependent activator 872 Yi et al.

Supplemental Table 1 Cont. p-value from Log Gene 2 ANOVA FDR Tukey’s Gene Gene Gene ID Description Name Fold p-value q-value HSD, PTC: Set 1 Set 2 Set 3 Change* HT-PTC 558 AXL AXL receptor tyrosine kinase -0.002 0.004 0.009 1.000 1894 ECT2 epithelial cell transforming 2 -0.003 0.079 0.127 NA 3162 HMOX1 heme oxygenase (decycling) 1 -0.004 0.086 0.136 NA 1861 TOR1A torsin family 1, member A (torsin A) -0.005 0.939 0.951 NA 9973 CCS copper chaperone for superoxide dismutase -0.007 0.192 0.267 NA 64094 SMOC2 SPARC related modular calcium binding 2 -0.008 0.000 0.000 1.000 4790 NFKB1 nuclear factor of kappa light polypeptide gene enhancer in -0.009 0.005 0.012 0.982 B-cells 1 4482 MSRA methionine sulfoxide reductase A -0.010 0.000 0.001 0.984 9962 SLC23A2 solute carrier family 23 (ascorbic acid transporter), member 2 -0.011 0.000 0.000 0.987 7755 ZNF205 zinc finger protein 205 -0.014 0.002 0.004 0.984 358 AQP1 aquaporin 1 (Colton blood group) -0.015 0.000 0.000 0.990 538 ATP7A ATPase, Cu++ transporting, alpha polypeptide -0.016 0.576 0.659 NA 706 TSPO translocator protein (18kDa) -0.016 0.003 0.007 0.987 23657 SLC7A11 solute carrier family 7 (anionic transporter light -0.017 0.736 0.792 NA chain, xc- system), member 11 4521 NUDT1 nudix (nucleoside diphosphate linked moiety X)-type motif 1 -0.017 0.559 0.642 NA 53905 DUOX1 -0.017 0.714 0.775 NA 8428 STK24 serine/threonine kinase 24 -0.018 0.000 0.001 0.894 3315 HSPB1 heat shock 27kDa protein 1 -0.018 0.991 0.993 NA 2882 GPX7 glutathione peroxidase 7 -0.019 0.926 0.939 NA 8754 ADAM9 ADAM metallopeptidase domain 9 -0.023 0.625 0.701 NA 140823 ROMO1 reactive oxygen species modulator 1 -0.024 0.219 0.298 NA 5562 PRKAA1 protein kinase, AMP-activated, alpha 1 catalytic subunit -0.027 0.000 0.000 0.863 10193 RNF41 ring finger protein 41, E3 ubiquitin protein ligase -0.029 0.098 0.152 NA 7444 VRK2 vaccinia related kinase 2 -0.031 0.019 0.037 0.720 11168 PSIP1 PC4 and SFRS1 interacting protein 1 -0.032 0.001 0.004 0.750 6009 RHEB Ras homolog enriched in brain -0.032 0.298 0.385 NA 55075 UACA uveal autoantigen with coiled-coil domains and -0.033 0.012 0.024 0.966 ankyrin repeats 29957 SLC25A24 solute carrier family 25 (; phosphate -0.035 0.505 0.593 NA carrier), member 24 5516 PPP2CB protein phosphatase 2, catalytic subunit, beta isozyme -0.036 0.000 0.000 0.706 11019 LIAS lipoic acid synthetase -0.037 0.005 0.011 0.824 1432 MAPK14 mitogen-activated protein kinase 14 -0.037 0.049 0.085 NA 54929 TMEM161A transmembrane protein 161A -0.037 0.276 0.362 NA 255027 MPV17L MPV17 mitochondrial membrane protein-like -0.037 0.426 0.518 NA 5970 RELA v-rel avian reticuloendotheliosis viral oncogene -0.038 0.000 0.000 0.509 homolog A 22933 SIRT2 sirtuin 2 -0.038 0.192 0.267 NA 2053 EPHX2 epoxide 2, cytoplasmic -0.039 0.000 0.000 0.805 207 AKT1 v-akt murine thymoma viral oncogene homolog 1 -0.041 0.000 0.001 0.723 2067 ERCC1 excision repair cross-complementation group 1 -0.041 0.010 0.021 0.800 4323 MMP14 matrix metallopeptidase 14 (membrane-inserted) -0.042 0.001 0.002 0.960 6778 STAT6 signal transducer and activator of transcription 6, -0.042 0.000 0.000 0.555 interleukin-4 induced 4891 SLC11A2 solute carrier family 11 (proton-coupled divalent metal ion -0.042 0.265 0.349 NA transporter), member 2 7168 TPM1 tropomyosin 1 (alpha) -0.043 0.640 0.713 NA 5164 PDK2 pyruvate dehydrogenase kinase, isozyme 2 -0.044 0.000 0.001 0.733 5587 PRKD1 protein kinase D1 -0.045 0.000 0.000 0.847 ROS-related gene expression in thyroid cancer 873

Supplemental Table 1 Cont. p-value from Log Gene 2 ANOVA FDR Tukey’s Gene Gene Gene ID Description Name Fold p-value q-value HSD, PTC: Set 1 Set 2 Set 3 Change* HT-PTC 23334 KIAA0467 seizure threshold 2 homolog (mouse) -0.045 0.001 0.003 0.727 3417 IDH1 1 (NADP+), soluble -0.047 0.000 0.000 0.778 7352 UCP3 3 (mitochondrial, proton carrier) -0.047 0.003 0.007 0.925 2308 FOXO1 forkhead box O1 -0.049 0.000 0.000 0.876 328 APEX1 APEX nuclease (multifunctional DNA repair enzyme) 1 -0.050 0.000 0.000 0.361 8382 NME5 NME/NM23 family member 5 -0.051 0.006 0.013 0.753 440503 PLIN5 perilipin 5 -0.053 0.214 0.293 NA 5159 PDGFRB platelet-derived growth factor receptor, beta polypeptide -0.054 0.106 0.163 NA 90441 ZNF622 zinc finger protein 622 -0.054 0.030 0.056 NA 84919 PPP1R15B protein phosphatase 1, regulatory subunit 15B -0.057 0.000 0.000 0.711 7006 TEC tec protein tyrosine kinase -0.060 0.000 0.000 0.706 116988 AGAP3 ArfGAP with GTPase domain, ankyrin repeat and PH -0.060 0.039 0.069 NA domain 3 4257 MGST1 microsomal glutathione S-transferase 1 -0.060 0.000 0.000 0.712 57679 ALS2 amyotrophic lateral sclerosis 2 (juvenile) -0.062 0.000 0.000 0.376 6788 STK3 serine/threonine kinase 3 -0.062 0.008 0.017 0.391 23411 SIRT1 sirtuin 1 -0.062 0.000 0.000 0.509 3091 HIF1A hypoxia inducible factor 1, alpha subunit (basic helix-loop- -0.064 0.015 0.030 0.756 helix transcription factor) 2879 GPX4 glutathione peroxidase 4 -0.069 0.000 0.000 0.756 11284 PNKP polynucleotide kinase 3'-phosphatase -0.072 0.000 0.000 0.550 2281 FKBP1B FK506 binding protein 1B, 12.6 kDa -0.072 0.574 0.656 NA 5829 PXN paxillin -0.072 0.000 0.000 0.154 7514 XPO1 exportin 1 -0.073 0.025 0.047 0.184 2074 ERCC6 excision repair cross-complementation group 6 -0.075 0.454 0.545 NA 22850 ADNP2 ADNP homeobox 2 -0.076 0.000 0.000 0.390 4803 NGF nerve growth factor (beta polypeptide) -0.079 0.529 0.615 NA 2033 EP300 E1A binding protein p300 -0.081 0.000 0.000 0.503 5598 MAPK7 mitogen-activated protein kinase 7 -0.082 0.000 0.000 0.116 9963 SLC23A1 solute carrier family 23 (ascorbic acid transporter), member 1 -0.084 0.004 0.010 0.893 10516 FBLN5 fibulin 5 -0.085 0.000 0.000 0.821 2056 EPO erythropoietin -0.087 0.501 0.590 NA 2549 GAB1 GRB2-associated binding protein 1 -0.087 0.022 0.042 0.379 51526 C20orf111 oxidative stress responsive serine-rich 1 -0.090 0.000 0.000 0.074 25865 PRKD2 protein kinase D2 -0.090 0.000 0.000 0.277 2119 ETV5 ets variant 5 -0.093 0.000 0.000 0.450 7486 WRN Werner syndrome, RecQ helicase-like -0.094 0.000 0.000 0.445 10628 TXNIP thioredoxin interacting protein -0.096 0.046 0.079 NA 2071 ERCC3 excision repair cross-complementation group 3 -0.096 0.010 0.021 0.007 24142 NAT6 N-acetyltransferase 6 (GCN5-related) -0.099 0.003 0.007 0.214 9644 SH3PXD2A SH3 and PX domains 2A -0.101 0.613 0.690 NA 10013 HDAC6 histone deacetylase 6 -0.104 0.000 0.000 0.036 1017 CDK2 cyclin-dependent kinase 2 -0.105 0.002 0.004 0.092 87178 PNPT1 polyribonucleotide nucleotidyltransferase 1 -0.105 0.000 0.000 0.064 7351 UCP2 uncoupling protein 2 (mitochondrial, proton carrier) -0.108 0.000 0.000 0.576 5621 PRNP prion protein -0.108 0.007 0.016 0.242 2195 FAT1 FAT atypical cadherin 1 -0.108 0.034 0.061 NA 4355 MPP2 membrane protein, palmitoylated 2 (MAGUK p55 -0.108 0.001 0.003 0.708 subfamily member 2) 2185 PTK2B protein tyrosine kinase 2 beta -0.109 0.000 0.000 0.495 7837 PXDN peroxidasin homolog (Drosophila) -0.111 0.000 0.000 0.828 874 Yi et al.

Supplemental Table 1 Cont. p-value from Log Gene 2 ANOVA FDR Tukey’s Gene Gene Gene ID Description Name Fold p-value q-value HSD, PTC: Set 1 Set 2 Set 3 Change* HT-PTC 4968 OGG1 8-oxoguanine DNA glycosylase -0.116 0.000 0.000 0.137 388 RHOB ras homolog family member B -0.116 0.000 0.000 0.744 351 APP amyloid beta (A4) precursor protein -0.116 0.010 0.021 0.278 285590 SH3PXD2B SH3 and PX domains 2B -0.120 0.003 0.007 0.594 847 CAT -0.123 0.240 0.322 NA 5796 PTPRK protein tyrosine phosphatase, receptor type, K -0.125 0.000 0.000 0.267 286148 DPY19L4 dpy-19-like 4 (C. elegans) -0.127 0.066 0.109 NA 130497 OSR1 odd-skipped related transciption factor 1 -0.130 0.000 0.000 0.953 10276 NET1 neuroepithelial cell transforming 1 -0.132 0.460 0.550 NA 4153 MBL2 mannose-binding lectin (protein C) 2, soluble -0.132 0.984 0.987 NA 2055 CLN8 ceroid-lipofuscinosis, neuronal 8 (epilepsy, progressive -0.133 0.049 0.084 NA with mental retardation) 132864 CPEB2 cytoplasmic element binding protein 2 -0.134 0.122 0.183 NA 246777 SPESP1 sperm equatorial segment protein 1 -0.135 0.815 0.853 NA 27165 GLS2 2 (liver, mitochondrial) -0.136 0.000 0.000 0.335 4353 MPO -0.138 0.448 0.539 NA 124056 NOXO1 NADPH oxidase organizer 1 -0.140 0.144 0.210 NA 5310 PKD1 polycystic kidney disease 1 (autosomal dominant) -0.141 0.000 0.000 0.088 1244 ABCC2 ATP-binding cassette, sub-family C (CFTR/MRP), member 2 -0.142 0.001 0.002 0.728 5311 PKD2 polycystic kidney disease 2 (autosomal dominant) -0.143 0.000 0.000 0.125 4843 NOS2 nitric oxide synthase 2, inducible -0.144 0.025 0.047 0.820 8692 HYAL2 hyaluronoglucosaminidase 2 -0.147 0.000 0.000 0.060 50507 NOX4 NADPH oxidase 4 -0.151 0.000 0.000 0.590 3934 LCN2 lipocalin 2 -0.157 0.001 0.002 0.857 5366 PMAIP1 phorbol-12-myristate-13-acetate-induced protein 1 -0.157 0.008 0.018 0.738 3978 LIG1 ligase I, DNA, ATP-dependent -0.159 0.001 0.002 0.021 4233 MET MET proto-oncogene, receptor tyrosine kinase -0.166 0.000 0.000 0.363 4217 MAP3K5 mitogen-activated protein kinase kinase kinase 5 -0.171 0.000 0.000 0.278 2241 FER fer (fps/fes related) tyrosine kinase -0.178 0.000 0.000 0.245 2150 F2RL1 coagulation factor II (thrombin) receptor-like 1 -0.181 0.086 0.137 NA 1026 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) -0.184 0.001 0.003 0.329 5445 PON2 paraoxonase 2 -0.186 0.000 0.000 0.034 1956 EGFR epidermal growth factor receptor -0.190 0.296 0.383 NA 875 CBS cystathionine-beta-synthase -0.191 0.097 0.151 NA 51435 SCARA3 scavenger receptor class A, member 3 -0.191 0.003 0.008 0.497 7040 TGFB1 transforming growth factor, beta 1 -0.193 0.000 0.000 0.070 6622 SNCA synuclein, alpha (non A4 component of amyloid precursor) -0.198 0.000 0.000 0.461 1800 DPEP1 dipeptidase 1 (renal) -0.207 0.759 0.810 NA 54033 RBM11 RNA binding motif protein 11 -0.211 0.000 0.000 0.031 2353 FOS FBJ murine osteosarcoma viral oncogene homolog -0.211 0.000 0.000 0.670 54541 DDIT4 DNA-damage-inducible transcript 4 -0.213 0.000 0.000 0.264 55074 OXR1 oxidation resistance 1 -0.221 0.010 0.021 0.016 79661 NEIL1 nei endonuclease VIII-like 1 (E. coli) -0.222 0.159 0.228 NA 90527 DUOXA1 dual oxidase maturation factor 1 -0.229 0.032 0.058 NA 7057 THBS1 thrombospondin 1 -0.233 0.742 0.797 NA 221 ALDH3B1 aldehyde dehydrogenase 3 family, member B1 -0.234 0.000 0.000 0.121 5625 PRODH proline dehydrogenase (oxidase) 1 -0.244 0.159 0.229 NA 7349 UCN urocortin -0.253 0.011 0.022 0.679 3725 JUN jun proto-oncogene -0.261 0.000 0.000 0.515 1647 GADD45A growth arrest and DNA-damage-inducible, alpha -0.262 0.000 0.000 0.393 3159 HMGA1 high mobility group AT-hook 1 -0.264 0.000 0.000 0.065 9124 PDLIM1 PDZ and LIM domain 1 -0.279 0.000 0.000 0.125 1906 EDN1 endothelin 1 -0.281 0.216 0.296 NA ROS-related gene expression in thyroid cancer 875

Supplemental Table 1 Cont. p-value from Log Gene 2 ANOVA FDR Tukey’s Gene Gene Gene ID Description Name Fold p-value q-value HSD, PTC: Set 1 Set 2 Set 3 Change* HT-PTC 120892 LRRK2 leucine-rich repeat kinase 2 -0.290 0.000 0.000 0.249 50506 DUOX2 -0.292 0.208 0.286 NA 8877 SPHK1 sphingosine kinase 1 -0.292 0.000 0.000 0.061 301 ANXA1 annexin A1 -0.299 0.000 0.000 0.028 10811 NOXA1 NADPH oxidase activator 1 -0.300 0.033 0.059 NA 239 ALOX12 arachidonate 12-lipoxygenase -0.303 0.667 0.736 NA 1583 CYP11A1 cytochrome P450, family 11, subfamily A, polypeptide 1 -0.303 0.652 0.723 NA 5063 PAK3 p21 protein (Cdc42/Rac)-activated kinase 3 -0.309 0.000 0.000 0.820 1571 CYP2E1 cytochrome P450, family 2, subfamily E, polypeptide 1 -0.323 0.038 0.068 NA 1843 DUSP1 dual specificity phosphatase 1 -0.340 0.000 0.000 0.206 253827 MSRB3 methionine sulfoxide reductase B3 -0.359 0.253 0.337 NA 27035 NOX1 NADPH oxidase 1 -0.361 0.009 0.019 0.577 80149 ZC3H12A zinc finger CCCH-type containing 12A -0.361 0.004 0.010 0.162 1263 PLK3 polo-like kinase 3 -0.371 0.000 0.000 0.010 5743 PTGS2 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H -0.380 0.338 0.429 NA synthase and cyclooxygenase) 348 APOE apolipoprotein E -0.388 0.001 0.002 0.325 3491 CYR61 cysteine-rich, angiogenic inducer, 61 -0.420 0.000 0.000 0.396 10110 SGK2 serum/glucocorticoid regulated kinase 2 -0.421 0.014 0.028 0.307 374887 YJEFN3 YjeF N-terminal domain containing 3 -0.446 0.000 0.000 0.140 1545 CYP1B1 cytochrome P450, family 1, subfamily B, polypeptide 1 -0.447 0.000 0.000 0.136 1490 CTGF connective tissue growth factor -0.493 0.000 0.000 0.305 51384 WNT16 wingless-type MMTV integration site family, member 16 -0.507 0.716 0.776 NA 3040 HBA2 , alpha 2 -0.510 0.000 0.000 0.687 8061 FOSL1 FOS-like antigen 1 -0.524 0.085 0.136 NA 6898 TAT tyrosine aminotransferase -0.558 0.747 0.801 NA 343521 TCTEX1D4 Tctex1 domain containing 4 -0.561 0.000 0.000 0.072 54363 HAO1 hydroxyacid oxidase (glycolate oxidase) 1 -0.563 0.890 0.910 NA 2168 FABP1 fatty acid binding protein 1, liver -0.570 0.887 0.907 NA 761 CA3 carbonic anhydrase III, muscle specific -0.590 0.260 0.344 NA 9314 KLF4 Kruppel-like factor 4 (gut) -0.623 0.000 0.000 0.023 3039 HBA1 hemoglobin, alpha 1 -0.645 0.000 0.000 0.838 8013 NR4A3 nuclear receptor subfamily 4, group A, member 3 -0.646 0.000 0.000 0.656 3043 HBB hemoglobin, beta -0.656 0.000 0.000 0.694 1816 DRD5 dopamine receptor D5 -0.728 0.170 0.242 NA 5166 PDK4 pyruvate dehydrogenase kinase, isozyme 4 -0.752 0.058 0.098 NA 5076 PAX2 paired box 2 -0.780 0.811 0.851 NA 3569 IL6 interleukin 6 -0.782 0.000 0.000 0.568 2880 GPX5 glutathione peroxidase 5 -0.784 0.844 0.872 NA 2147 F2 coagulation factor II (thrombin) -0.785 0.826 0.857 NA 4929 NR4A2 nuclear receptor subfamily 4, group A, member 2 -0.804 0.001 0.002 0.122 1409 CRYAA crystallin, alpha A -0.834 0.209 0.287 NA 4025 LPO -0.847 0.838 0.867 NA 4504 MT3 metallothionein 3 -0.850 0.853 0.879 NA 1277 COL1A1 collagen, type I, alpha 1 -0.986 0.053 0.090 NA 4151 MB myoglobin -1.267 0.740 0.796 NA 5798 PTPRN protein tyrosine phosphatase, receptor type, N -1.419 0.636 0.709 NA 1543 CYP1A1 cytochrome P450, family 1, subfamily A, polypeptide 1 -1.471 0.011 0.023 0.713 9370 ADIPOQ adiponectin, C1Q and collagen domain containing -1.802 0.076 0.123 NA 10218 ANGPTL7 angiopoietin-like 7 -1.877 0.265 0.350 NA 1421 CRYGD crystallin, gamma D -2.079 0.731 0.788 NA 6677 SPAM1 sperm adhesion molecule 1 (PH-20 hyaluronidase, zona -2.114 0.706 0.768 NA pellucida binding) 189 AGXT alanine-glyoxylate aminotransferase -3.446 0.577 0.659 NA 10202 DHRS2 dehydrogenase/reductase (SDR family) member 2 -3.798 0.810 0.851 NA 4632 MYL1 myosin, light chain 1, alkali; skeletal, fast -9.395 0.693 0.757 NA 876 Yi et al.

Supplemental Table 2 RMA counts, t-test results, fold change value and their correlation with TCGA results, analyzed from microarray data; GSE39315. HT-PTC Sample PTC Sample Log Correlation t-test 2 Fold with Genes GSM724489. GSM724490. GSM724491. GSM724492. GSM724493. GSM724494. GSM724512. GSM724513. GSM724514. GSM724515. GSM724516. GSM724517. GSM724518. GSM724519. GSM724520. p-value CEL CEL CEL CEL CEL CEL CEL CEL CEL CEL CEL CEL CEL CEL CEL Change TCGA PRDX1 10.42549021 10.58902481 10.82780005 10.65578824 11.49137455 10.25909074 11.1462045 10.74177243 10.64113376 10.74904014 10.7255548 9.998812698 10.74074336 8.111803003 10.82060929 0.410 0.041 TRUE GPX3 8.809176801 10.86988854 10.58944679 10.61945627 11.39305487 8.615940546 11.05197327 8.733707317 10.73258068 11.0211076 10.48078889 10.29284006 10.46962311 7.931250325 9.359341023 0.817 0.020 TRUE CD38 5.901045867 6.091775703 5.709526271 4.856772867 3.912584684 5.355582184 3.741463129 3.967038699 3.750474812 3.568600817 3.965214284 3.566171092 3.894041104 4.295019056 4.588948028 0.007 0.434 TRUE JAK2 5.334904354 4.774570353 4.362675591 4.005806106 4.015585877 5.178674333 3.790567345 4.646410118 3.881264661 3.770445602 3.990647604 3.745739486 4.234416512 3.694144978 3.834560991 0.038 0.222 TRUE NCF2 4.628221629 4.977994134 4.672199507 4.968227164 3.849842103 4.880309693 4.229190363 4.52262888 4.44151703 4.58652751 4.567397237 3.727654476 4.522476979 4.585061142 5.070033533 0.387 0.060 TRUE IPCEF1 5.52209451 5.123557701 5.146357543 5.746818603 6.469614342 5.83126597 5.139600099 5.202886892 4.51465225 4.684893985 4.510958809 4.654566147 4.480265885 4.687629318 4.188675108 0.003 0.271 TRUE PDK1 5.265674495 5.593971775 5.281032756 5.282843588 4.886387071 5.251097954 4.311401307 4.399166967 4.566892318 4.802535085 4.729807699 4.378235201 4.762274236 5.60558694 5.10131588 0.008 0.150 TRUE ETFDH 5.210659933 4.957753745 4.954535631 5.372575816 5.663662537 4.745137507 4.60915428 5.356486642 4.852568874 4.879410117 4.69916475 4.993015324 5.201221096 4.910096776 4.679080504 0.164 0.069 TRUE BTK 6.86431894 5.982709545 5.504550076 5.239724209 4.774648675 6.221965016 4.87420296 5.490246663 4.863559913 4.690048255 4.790343347 4.706155763 4.898086963 5.060226542 4.971764778 0.040 0.226 TRUE BAK1 5.175897532 5.364867051 5.342571403 5.573763921 5.294285147 5.427164251 4.999582239 4.792178221 4.965066531 4.822827136 5.094781139 5.06823324 4.842763471 5.837381954 4.911315069 0.019 0.090 TRUE LCK 5.389087789 5.371635963 5.469988063 5.19903363 5.088041385 5.547714648 5.33111595 5.566679829 5.237950141 6.841579461 5.245518045 5.357379855 4.991605102 6.451166185 5.345273386 0.276 -0.067 FALSE MICB 7.807796505 7.328821728 6.532408995 6.623189714 5.040610015 7.889676058 5.380387139 6.124591135 5.364451153 4.697104226 6.649788916 6.224586087 6.8002112 5.37381242 4.935254577 0.051 0.262 TRUE NDUFS1 5.4922734 5.419828956 5.501903963 5.755843373 6.297666869 5.647639891 5.141187753 5.366433343 5.770714687 5.536501854 6.131358134 5.62455274 5.828071856 4.062335836 5.615956046 0.345 0.060 TRUE GRB2 6.760926954 6.70218412 6.391627057 6.580059281 6.114859411 6.45156904 5.803545652 5.784249259 6.042358526 6.050220957 6.266839743 6.321557224 6.329816722 6.392125959 5.947934507 0.009 0.091 TRUE PTGS1 6.089273092 6.204960844 6.075768934 6.1095817 6.395302118 6.542371352 6.355662118 6.145448925 6.151035001 6.147745412 6.261539733 6.146027385 6.16140636 6.80290451 6.533565656 0.568 -0.015 FALSE GCLC 5.978022037 6.047163898 7.751882885 6.593882037 7.179366702 5.902982318 7.094576855 6.734246301 6.331226845 5.726090145 6.382073082 6.416591803 6.810743085 5.297422498 6.037511552 0.487 0.058 TRUE SFTPD 6.326165306 7.128931557 6.697076452 6.375291113 6.395496284 6.570894688 6.694615583 6.340480207 6.512639297 6.669414001 6.514591569 6.485612946 6.630354832 6.983321837 6.574289979 0.897 -0.004 FALSE CYBB 7.767930723 8.359233503 7.868676634 8.273522737 6.237321308 6.696418097 5.604398364 6.831508752 6.843988686 5.745570019 7.650568342 5.729248577 6.326563671 6.225731948 6.545523452 0.023 0.238 TRUE TPO 8.245335635 10.15768659 10.62975149 12.63206044 12.31947857 8.229076327 9.167622442 5.503827073 6.998159396 6.619643056 6.541223146 8.35562195 6.838531131 6.213662455 5.732878385 0.004 0.591 TRUE RAC2 10.24500851 9.218239101 8.283421703 8.008231246 6.73557314 9.987763073 6.690889334 8.119914165 6.998624896 6.996272928 7.537109508 7.043741393 7.513610421 6.832456215 8.1857999 0.046 0.256 TRUE TRPM2 7.066748584 7.049855341 7.073828973 7.031249935 6.818813641 7.217079456 6.872635229 6.898362343 7.026199048 7.388124253 6.940447068 6.974759709 6.772132762 7.872544335 7.063619396 0.714 -0.010 FALSE SLC8A1 6.834251446 6.893894145 6.848710307 6.850908934 6.944659167 7.152691539 6.895806157 6.730276483 7.090405493 7.10496007 6.869223295 6.874243435 6.698771615 7.526974699 7.041664718 0.544 -0.013 FALSE PPIF 7.565528483 7.998652226 8.202846347 9.150637173 8.556909957 7.595993573 7.238730292 7.270653093 7.371607092 7.287034867 7.474293826 7.544659435 7.197649241 7.981868439 7.784093116 0.033 0.132 TRUE SFTPC 7.235032827 7.433931544 7.704466489 8.315218926 7.643304266 7.133251774 7.310619452 7.169467029 7.379055068 7.317460165 7.506404457 7.600021008 7.520394132 8.374494717 7.596855353 0.826 0.009 TRUE PRDX3 6.449319303 6.822561001 6.615054174 7.196966711 8.256206551 6.89236497 6.814295604 7.292587712 7.511709126 6.078928438 7.489102013 7.198715675 7.722854438 4.362926133 7.102338764 0.660 0.041 TRUE KLF4 5.469078242 5.876078996 6.006383254 5.330049014 4.841285306 5.034332993 5.012366565 5.713637226 5.278070797 5.109022204 4.911142438 4.892027535 4.496322269 5.332656401 5.210598143 0.178 0.088 FALSE HDAC6 6.861164694 6.833569106 6.460591025 6.844696248 6.772387265 6.668939247 6.969456387 6.629923535 6.597712568 6.827193468 6.542719435 6.577723518 6.646951442 7.55132343 6.701186831 0.736 -0.009 TRUE PON2 4.554962193 4.91822902 5.021221448 5.691585353 6.09550999 4.791580601 6.327009903 6.729016008 6.898241234 6.250024601 6.261049813 6.383825215 6.619341432 5.446106514 6.406008771 0.002 -0.298 TRUE LIG1 7.327036773 6.867586902 6.971782116 6.768369515 6.768815699 7.242049405 7.021310983 7.090635662 6.918772976 7.120083461 6.931147712 7.176467524 6.964706292 7.387558459 6.938778647 0.545 -0.014 TRUE ERCC3 7.165590629 6.910203478 7.044296162 6.797594926 6.991488832 7.176157999 6.98336973 7.500973295 6.937420775 6.777176296 6.891210323 7.10493682 7.016506614 7.335563878 6.857803033 0.760 -0.006 TRUE ANXA11 7.926015566 7.608148328 7.58552573 7.773643423 7.557066697 7.626366563 7.506462141 7.433091 7.671321472 7.0273177 7.60948704 7.582139288 7.869519026 7.373775987 7.026794798 0.065 0.043 FALSE PLK3 7.862148271 7.928309396 8.276718599 7.904876747 7.635864492 7.717864463 8.105019878 8.110917671 7.919654714 8.162573865 7.925001403 7.88147317 7.843278445 8.088120283 8.215548444 0.206 -0.025 TRUE

References 1. Lee JH, Kim Y, Choi JW, Kim YS (2013) The associa- high prevalence of Hashimoto’s disease. BMC Cancer tion between papillary thyroid carcinoma and histolog- 12: 610. ically proven Hashimoto’s thyroiditis: a meta-analysis. 5. Yoon YH, Kim HJ, Lee JW, Kim JM, Koo BS (2012) Eur J Endocrinol 168: 343-349. The clinicopathologic differences in papillary thyroid 2. Jankovic B, Le KT, Hershman JM (2013) Clinical carcinoma with or without co-existing chronic lympho- Review: Hashimoto’s thyroiditis and papillary thyroid cytic thyroiditis. Eur Arch Otorhinolaryngol 269: 1013- carcinoma: is there a correlation? J Clin Endocrinol 1017. Metab 98: 474-482. 6. Paulson LM, Shindo ML, Schuff KG (2012) Role of 3. Dvorkin S, Robenshtok E, Hirsch D, Strenov Y, Shimon chronic lymphocytic thyroiditis in central node metas- I, et al. (2013) Differentiated thyroid cancer is associ- tasis of papillary thyroid carcinoma. Otolaryngol Head ated with less aggressive disease and better outcome in Neck Surg 147: 444-449. patients with coexisting Hashimotos thyroiditis. J Clin 7. Jeong JS, Kim HK, Lee CR, Park S, Park JH, et al. Endocrinol Metab 98: 2409-2414. (2012) Coexistence of chronic lymphocytic thyroiditis 4. Zhang L, Li H, Ji QH, Zhu YX, Wang ZY, et al. (2012) with papillary thyroid carcinoma: clinical manifestation The clinical features of papillary thyroid cancer in and prognostic outcome. J Korean Med Sci 27: 883-889. Hashimoto’s thyroiditis patients from an area with a 8. Kim KW, Park YJ, Kim EH, Park SY, Park do J, et ROS-related gene expression in thyroid cancer 877

al. (2011) Elevated risk of papillary thyroid cancer in 25. Holl M, Koziel R, Schafer G, Pircher H, Pauck A, et Korean patients with Hashimoto’s thyroiditis. Head al. (2015) ROS signaling by NADPH oxidase 5 modu- Neck 33: 691-695. lates the proliferation and survival of prostate carcinoma 9. Ahn D, Heo SJ, Park JH, Kim JH, Sohn JH, et al. (2011) cells. Mol Carcinog doi: 10.1002/mc.22255. (Epub) Clinical relationship between Hashimoto’s thyroiditis 26. Xing M (2012) Oxidative stress: a new risk factor for and papillary thyroid cancer. Acta Oncol 50: 1228-1234. thyroid cancer. Endocr Relat Cancer 19: C7-11. 10. Repplinger D, Bargren A, Zhang YW, Adler JT, Haymart 27. Wang D, Feng JF, Zeng P, Yang YH, Luo J, et al. (2011) M, et al. (2008) Is Hashimoto’s thyroiditis a risk factor Total oxidant/antioxidant status in sera of patients with for papillary thyroid cancer? J Surg Res 150: 49-52. thyroid . Endocr Relat Cancer 18: 773-782. 11. Grani G, Calvanese A, Carbotta G, D’Alessandri M, 28. Metere A, Chiesa C, Di Cosimo C, Fierro G, Giacomelli Nesca A, et al. (2015) Thyroid autoimmunity and risk of L, et al. (2012) A novel approach to study oxidative malignancy in thyroid nodules submitted to fine-needle stress in thyroid diseases: a preliminary study. Eur Rev aspiration cytology. Head Neck 37: 260-264. Med Pharmacol Sci 16: 646-652. 12. Anil C, Goksel S, Gursoy A (2010) Hashimoto’s thy- 29. Dikalov SI, Harrison DG (2014) Methods for detection roiditis is not associated with increased risk of thyroid of mitochondrial and cellular reactive oxygen species. cancer in patients with thyroid nodules: a single-center Antioxid Redox Signal 20: 372-382. prospective study. Thyroid 20: 601-606. 30. Weyemi U, Caillou B, Talbot M, Ameziane-El-Hassani 13. Del Rio P, Cataldo S, Sommaruga L, Concione L, Arcuri R, Lacroix L, et al. (2010) Intracellular expression of MF, et al. (2008) The association between papillary car- reactive oxygen species-generating NADPH oxidase cinoma and chronic lymphocytic thyroiditis: does it NOX4 in normal and cancer thyroid tissues. Endocr modify the prognosis of cancer? Minerva Endocrinol Relat Cancer 17: 27-37. 33: 1-5. 31. Song Y, Ruf J, Lothaire P, Dequanter D, Andry G, et 14. Akinci M, Kosova F, Cetin B, Sepici A, Altan N, et al. al. (2010) Association of duoxes with thyroid peroxi- (2008) Oxidant/antioxidant balance in patients with thy- dase and its regulation in thyrocytes. J Clin Endocrinol roid cancer. Acta Cir Bras 23: 551-554. Metab 95: 375-382. 15. Sadani GR, Nadkarni GD (1996) Role of tissue antioxidant 32. Ohye H, Sugawara M (2010) Dual oxidase, hydrogen defence in thyroid cancers. Cancer Lett 109: 231-235. peroxide and thyroid diseases. Exp Biol Med (Maywood) 16. Karbownik-Lewinska M, Kokoszko-Bilska A (2012) 235: 424-433. Oxidative damage to macromolecules in the thyroid - 33. Rigutto S, Hoste C, Grasberger H, Milenkovic M, Communi experimental evidence. Thyroid Res 5: 25. D, et al. (2009) Activation of dual oxidases Duox1 and 17. Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, et Duox2: differential regulation mediated by camp-depen- al. (2009) AmiGO: online access to ontology and anno- dent protein kinase and protein kinase C-dependent phos- tation data. Bioinformatics 25: 288-289. phorylation. J Biol Chem 284: 6725-6734. 18. Gautier L, Cope L, Bolstad BM, Irizarry RA (2004) 34. Rostami R, Aghasi MR, Mohammadi A, Nourooz-Zadeh affy--analysis of Affymetrix GeneChip data at the probe J (2013) Enhanced oxidative stress in Hashimoto’s thy- level. Bioinformatics 20: 307-315. roiditis: inter-relationships to biomarkers of thyroid 19. Team RC (2013) R: A Language and Environment for function. Clin Biochem 46: 308-312. Statistical Computing. R Foundation for Statistical 35. Martinez VD, Vucic EA, Pikor LA, Thu KL, Hubaux Computing, Vienna, Austria. http://www.R-project.org/. R, et al. (2013) Frequent concerted genetic mechanisms 20. Huang da W, Sherman BT, Lempicki RA (2009) Systematic disrupt multiple components of the NRF2 inhibitor and integrative analysis of large gene lists using DAVID KEAP1/CUL3/RBX1 E3-ubiquitin ligase complex in bioinformatics resources. Nat Protoc 4: 44-57. thyroid cancer. Mol Cancer 12: 124. 21. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, 36. Lun Y, Wu X, Xia Q, Han Y, Zhang X, et al. (2013) Ebert BL, et al. (2005) Gene set enrichment analysis: Hashimoto’s thyroiditis as a risk factor of papillary thy- a knowledge-based approach for interpreting genome- roid cancer may improve cancer prognosis. Otolaryngol wide expression profiles.Proc Natl Acad Sci U S A 102: Head Neck Surg 148: 396-402. 15545-15550. 37. Dharmasena A (2014) Selenium supplementation in 22. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia thyroid associated ophthalmopathy: an update. Int J of genes and genomes. Nucleic Acids Res 28: 27-30. Ophthalmol 7: 365-375. 23. Brieger K, Schiavone S, Miller FJ, Jr., Krause KH 38. Drutel A, Archambeaud F, Caron P (2013) Selenium and (2012) Reactive oxygen species: from health to disease. the thyroid gland: more good news for clinicians. Clin Swiss Med Wkly 142: w13659. Endocrinol (Oxf) 78: 155-164. 24. Reuter S, Gupta SC, Chaturvedi MM, Aggarwal BB 39. Kohrle J, Gartner R (2009) Selenium and thyroid. Best (2010) Oxidative stress, inflammation, and cancer: how Pract Res Clin Endocrinol Metab 23: 815-827. are they linked? Free Radic Biol Med 49: 1603-1616.