Investigating the Mechanisms of Papillary Thyroid Carcinoma Using Transcriptome Analysis

5954 MOLECULAR MEDICINE REPORTS 16: 5954-5964, 2017

Investigating the mechanisms of papillary thyroid carcinoma using transcriptome analysis

JIE QIU1*, WENWEI ZHANG2*, QINGSHENG XIA3, FUXUE LIU4, SHUWEI ZHAO5, KAILING ZHANG1, MIN CHEN1, CHUANSHAN ZANG1, RUIFENG GE1, DAPENG LIANG1 and YAN SUN1

1Otolaryngology Head and Neck Surgery; 2Radiology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266003; 3Otolaryngology Head and Neck Surgery, Qingdao Municipal Hospital, Qingdao, Shandong 266071; 4Otolaryngology Head and Neck Surgery, Shaoxing Municipal Hospital, Shaoxing, Zhejiang 312000; 5Otolaryngology Head and Neck Surgery, Shanghai Chang Zheng Hospital, Shanghai 200003, P.R. China

Received October 13, 2016; Accepted June 12, 2017

DOI: 10.3892/mmr.2017.7346

Abstract. As the predominant thyroid cancer, papillary thyroid results indicated that TGFβR1 and the NKX2‑1‑SFTA3 gene cancer (PTC) accounts for 75‑85% of thyroid cancer cases. fusion may be involved in PTC. Furthermore, FN1 and TNC This research aimed to investigate transcriptomic changes containing the non‑synonymous risk SNP loci might serve a and key genes in PTC. Using RNA‑sequencing technology, role in PTC by interacting with each other. the transcriptional profiles of 5 thyroid tumor tissues and 5 adjacent normal tissues were obtained. The single nucleo- Introduction tide polymorphisms (SNPs) were identified by SAMtools software and then annotated by ANNOVAR software. After Thyroid cancer originates from parafollicular or follicular thyroid differentially expressed genes (DEGs) were selected by edgR cells, and includes papillary thyroid cancer (PTC), anaplastic software, they were further investigated by enrichment anal- thyroid cancer, medullary thyroid cancer, poorly differentiated ysis, protein domain analysis, and protein‑protein interaction thyroid cancer and follicular thyroid cancer (1). PTC accounts (PPI) network analysis. Additionally, the potential gene fusion for 75‑85% of all thyroid cancer cases and thus is the predomi- events were predicted using FusionMap software. A total of nant thyroid cancer (2). It often occurs in young females, and is 70,172 SNPs and 2,686 DEGs in the tumor tissues, as well the most common thyroid cancer in children and patients who as 83,869 SNPs in the normal tissues were identified. In the have undergone radiation therapy to the head and neck (3). PPI network, fibronectin 1 FN1( ; degree=31) and transforming Research has focused on the pathogenesis of PTC. For growth factor β receptor 1 (TGFβR1; degree=22) had higher instance, point mutation of serine‑threonine protein kinase degrees. A total of 7 PPI pairs containing the non‑synonymous B‑RAF (BRAF) occurs in approximately one‑third to one‑half risk SNP loci in the interaction domains were identified. of PTC cases, and BRAF can result in the activation of the Particularly, the interaction domains involved in the interac- carcinogenic mitogen‑activated protein kinase (MAPK)/extra- tions of FN1 and 5 other proteins (such as FN1‑tenascin C, cellular signal‑regulated kinase signaling pathway (4). The TNC) had non‑synonymous risk SNP loci. Furthermore, 11 BRAF mutation in PTC patients is associated with poorer and 4 gene fusion events were identified in all of the tumor clinicopathological outcomes and can be used to predict tissues and normal tissues, respectively. Additionally, the NK2 recurrence independently; therefore, the BRAF mutation may homeobox 1‑surfactant associated 3 (NKX2‑1‑SFTA3) gene serve as a promising marker for risk assessment of PTC (5‑7). fusion was identified in both tumor and normal tissues. These Stephens et al (8) investigated the loss of heterozygosity for three single nucleotide polymorphisms (SNPs; G691S, S904S, and L769L) of ret proto‑oncogene (RET) in thyroid tumor and normal tissues, and demonstrated that RET SNPs may function in sporadic PTC. Salvatore et al (9) demonstrated that Correspondence to: Dr Qingsheng Xia, Otolaryngology Head both RET/PTC rearrangements and the BRAF mutation are and Neck Surgery, Qingdao Municipal Hospital, 5 Donghai Road, markers of PTC and can be utilized for fine‑needle aspiration Qingdao, Shandong 266071, P.R. China in adjunction with traditional cytology. Vascular endothelial E‑mail: [email protected] growth factor regulates cancer‑associated neo‑angiogenesis *Contributed equally and progression, and its expression and polymorphisms may indicate the aggressiveness behavior of PTC (10). The A‑kinase Key words: papillary thyroid cancer, transcriptome analysis, anchoring protein 9‑BRAF gene fusion, which is induced by differentially expressed gene, gene fusion, single nucleotide the BRAF rearrangement through paracentric inversion of polymorphism chromosome 7q, serves a role in activating the MAPK pathway in thyroid cancer (11). However, the molecular mechanisms of PTC have not yet been fully investigated. QIU et al: MECHANISMS OF PTC 5955

RNA‑sequencing (RNA‑seq), which is a useful tool for cDNA library construction and Illumina RNA‑seq. Library transcriptome analysis, can be applied to reveal genomic preparation and RNA‑seq were performed by Beijing structural variations, gene fusion events, novel genes and BerryGenomics Co., Ltd. (Beijing, China). Briefly, oligo(dT) transcripts (12,13). By RNA‑seq, Costa et al (14) identified magnetic beads were used to enrich mRNAs from total RNA, new missense mutations in Casitas B‑cell lymphoma gene; and then fragmentation buffer was applied to fragment the NOTCH1; phosphoinositide‑3‑ kinase regulatory subunit 4 and enriched mRNAs. Subsequently, the first cDNA strand was SW/SNF‑related, matrix‑associated, actin‑dependent regulator amplified using the mRNA templates and random hexamer of chromatin, subfamily a, member 4 genes; somatic mutations primers. Subsequently, the second strand of cDNA was in dicer 1, ribonuclease type III; met proto‑oncogene (MET); synthesized by adding DNA polymerase I (New England and von Hippel‑Lindau genes; and a new chimeric transcript BioLabs, Inc., Ipswich, MA, USA), dNTPs (New England induced by the WNK lysine deficient protein kinase 1‑β1, BioLabs, Inc.), RNase H (New England BioLabs, Inc.) and 4‑N‑acetylgalactosaminyltransferase‑3 gene fusion in PTC buffer. The double‑stranded cDNA fragments were puri- patients. Smallridge et al (15) used RNA‑seq to analyze differ- fied using QIAquick PCR Purification kit (Qiagen GmbH, entially expressed genes (DEGs) between BRAF wild‑type and Hilden, Germany) and eluted with elution buffer, followed by BRAF V600E mutation PTCs, demonstrating that the BRAF end repair and the ligation of sequencing adaptors. After the V600E mutation inhibits expression of immune/inflammatory fragments with suitable size were selected by agarose gel elec- response genes but promotes expression of chemokine (C‑X‑C trophoresis, polymerase chain reaction (PCR) amplification motif) ligand 14 and human leukocyte antigen‑G. However, the was performed. Finally, the cDNA libraries were constructed above studies did not perform comprehensive bioinformatics and sequenced on an Illumina HiSeq 2500 platform according analysis. In the present study, the transcriptional profiles the manufacturer's protocol. of thyroid tumor tissues and adjacent normal tissues were obtained by RNA‑seq. Thereafter, SNPs were identified and Sequencing data processing. After removing low‑quality reads functionally annotated. In addition, the DEGs were screened (exceeding 50 bp and with >50% of bases with Q‑value ≤3) and further investigated by enrichment analysis, protein and those reads with >3% (N bases) of adaptors or unknown domain analysis and protein‑protein interaction (PPI) network nucleotides, the clean reads were obtained from the raw reads. analysis. Furthermore, the potential gene fusion events were separately predicted for the tumor tissues and normal tissues. SNP identification and DEG screening. The clean reads The flow chart of the bioinformatics analysis is presented in were mapped to the hg19 human reference genome by Fig. 1. the Burrows‑Wheeler Aligner (bio‑bwa.sourceforge. net/bwa.shtml) software (16), and then SNPs were identified Materials and methods by the SAMtools software (samtools.sourceforge.net) (17). Using ANNOVAR software (www.openbioinformatics Sample source and RNA sample preparation. Thyroid tumor .org/annovar/) (18), functional annotation was performed for tissues and adjacent normal tissues were collected from 5 PTC the identified SNPs to investigate their genomic locations and patients [2 women and 3 men; mean age, 37.4 years; 3 patients variation information. in stage I (T1N1aM0) and 2 patients in stage III (T1N1aM0)] The DEGs in the thyroid tumor tissues compared with the from The Affiliated Hospital of Qingdao University (Qingdao, adjacent normal tissues were screened using edgR software China). All patients were treated by thyroidectomy and lymph- (bioconductor.org/packages/2.4/bioc/html/edgeR.html) (19). adenectomy; ultrasonography confirmed that the patients had Based on the negative binomial model (20), the gene no obvious abnormality after surgery. Total RNA was isolated significance levels between the two groups were calculated. from the tumor and normal tissues, using a TRIzol total RNA Following this, the adjusted P‑value (the false discovery rate, extraction kit (Invitrogen; Thermo Fisher Scientific, Inc., FDR) of each gene was calculated by the Benjamini‑Hochberg

Waltham, MA, USA). The concentration of RNA was tested method (21). FDR<0.05 and |log2 fold-change (FC)|≥1 were with Qubit® RNA Assay kit in Qubit 2.0 Fluorometer (Thermo considered thresholds. Fisher Scientific, Inc.). Next, the RNA integrity was assessed by spectrophotometry using NanoDrop™ 2000 (Thermo Gene fusion analysis and enrichment analysis. The poten- Fisher Scientific, Inc., Wilmington, DE, USA) at 260 and tial gene fusion events were predicted using FusionMap 280 nm and 1% (w/v) agarose gel electrophoresis at 37˚C for software (www.omicsoft.com/fusionmap/) (22), and then 15 min with 95˚C preheat for 5 min. The results were stained visualized using Circos software (www.circos.ca/) (23). with ethidium bromide (0.50 µg/ml; Sigma‑Aldrich; Merck The Gene Ontology (GO; www.geneontology.org/) database KGaA, Darmstadt, Germany), followed by visualization using describes cellular component (CC), molecular function (MF), the Agilent 2100 Bioanalyzer system (Agilent Technologies, and biological process (BP) of gene products (24). The Kyoto Inc., Santa Clara, CA, USA). The following primers were used Encyclopedia of Genes and Genomes (KEGG; www.genome in this study: P5 (forward, AATGATACGGCGACCACCGAG .ad.jp/kegg/) database, which consists of known genes and A and reverse, CAAGCAGAAGACGGCATACGAG); P7 their functions, can be used for pathway mapping (25). Using (forward, ATCTCG TAT GCC GTC TTC TGC TTG and reverse, the Database for Annotation, Visualization and Integrated CAAGCAGAAGACGGCATACGAGAT). The Research Discovery tool (DAVID; version 6.8; david.abcc.ncifcrf.gov/) Ethics Committee at The Affiliated Hospital of Qingdao software (26), GO functional and KEGG pathway enrichment University gave the approval for this study, and all participants analyses were conducted for the identified DEGs. P<0.05 was wrote informed consent. considered as the cut‑off criterion. 5956 MOLECULAR MEDICINE REPORTS 16: 5954-5964, 2017

Figure 1. Flow chart of the bioinformatics analysis. DEGs, differentially expressed genes; SNP, single nucleotide polymorphism; PPI, protein‑protein interaction.

Results

SNP analysis. Upon mapping the clean reads to human reference genome hg19, 70,172 and 83,869 SNPs separately were identified in all of the thyroid tumor tissues and all of the adjacent normal tissues. A Venn diagram demonstrated that 18,795 SNPs were specific in the thyroid tumor tissues and were considered risk SNPs for PTC (Fig. 2). Subsequently, functional annotation was performed for these risk SNPs. Among the 18,795 SNPs, 739 risk SNPs were located in the exon regions (Fig. 3A). The variation information of the risk Figure 2. Venn diagram of the single nucleotide polymorphisms in the SNPs located in the exon regions is presented in Fig. 3B. thyroid tumor tissues and adjacent normal tissues. Integrated analysis of SNPs and DEGs. With the thresholds

of FDR<0.05 and|log2 FC|≥1, a total of 2,686 DEGs were PPI network analysis and protein domain analysis. The screened in the thyroid tumor tissues compared with adjacent interactions among the proteins encoded by the DEGs were normal tissues, including 1,361 upregulated genes and 1,325 predicted using the Human Protein Reference Database (www downregulated genes. The integrated analysis of SNPs and .hprd.org) (27), and then PPI network was visualized using DEGs demonstrated that 12,528 risk SNPs were located in Cytoscape software (version 3.2.0; www.cytoscape.org/) (28). 4,317 genes (157 upregulated genes and 519 downregulated Using pfam_scan tool (www.ebi.ac.uk/Tools/pfa/pfam- genes). scan/) (29), the domains in the proteins containing risk SNP Using the pfam_scan tool, the domains in the proteins loci were predicted, and the domains in the proteins containing containing risk SNP loci or non‑synonymous risk SNP loci non‑synonymous risk SNP loci were selected. In addi- were predicted. The results demonstrated that the risk SNPs tion, the non‑synonymous risk SNP loci in the interaction had no significant effects on protein domains, whereas the domains involved in the PPI pairs were identified using the non‑synonymous risk SNPs may affect protein domains. The Database of Protein Domain Interactions (domine.utdallas. non‑synonymous risk SNPs in the protein domains are listed edu/cgi‑bin/Domine) (30). in Table I. QIU et al: MECHANISMS OF PTC 5957

Table I. Non‑synonymous risk SNPs in the protein domains.

Gene AA Change SNP id Domain start Domain end Hmm acc Hmm name

CSGALNACT1 S193N rs7017776 168 507 PF05679.13 CHGN IYD C265R rs612421 98 267 PF00881.21 Nitroreductase ITGA7 R655H rs1800974 515 1,006 PF08441.9 Integrin_alpha2 TMEM171 R86G rs637450 2 319 PF15471.3 TMEM171 TMEM171 N139K rs636926 2 319 PF15471.3 TMEM171 AGT M268T rs699 114 481 PF00079.17 Serpin ATP11A M317V rs368865 100 377 PF00122.17 E1‑E2_ATPase CDT1 C234R rs507329 186 350 PF08839.8 CDT1 CDT1 T262A rs480727 186 350 PF08839.8 CDT1 CENPF N3106K rs7289 3,061 3,109 PF10490.6 CENP‑F_C_Rb_bdg COL1A1 T1075A rs1800215 1,019 1,077 PF01391.15 Collagen EBI3 V201I rs4740 130 209 PF00041.18 fn3 FN1 T817P rs2577301 812 889 PF00041.18 fn3 HES4 R44S rs2298214 35 91 PF00010.23 HLH LCP1 K533E rs4941543 520 621 PF00307.28 CH LOXL2 M570L rs1063582 548 719 PF01186.14 Lysyl_oxidase MCM4 L650M rs762679 447 769 PF00493.20 MCM PLXND1 H894R rs2625962 891 976 PF01833.21 TIG SLC17A9 N228S rs2427463 30 386 PF07690.13 MFS_1 SPHK1 A34T rs346803 16 137 PF00781.21 DAGK_cat SPTBN2 S825G rs4930388 749 849 PF00435.18 Spectrin SYNC R274Q rs360042 187 456 PF00038.18 Filament TNC Q539R rs1757095 532 558 PF07974.10 EGF_2

SNP, single nucleotide polymorphism; Hmm acc, accession number of hmmer.

Figure 3. Location statistics of the risk SNPs. (A) Genomic locations of all the risk SNPs. (B) Variation information of the risk SNPs located in the exon regions. UTR, untranslated region; SNV, single nucleotide variant; SNP, single nucleotide polymorphism.

Gene fusion analysis. The potential gene fusion events were channel, non‑voltage gated 1 α‑tumor necrosis factor receptor predicted and are presented in Fig. 4. A total of 11 and 4 superfamily 1A gene fusion was predicted not in thyroid tumor gene fusion events were identified in all of the thyroid tumor tissues but in normal tissues. Furthermore, no gene fusion event tissues and all of the normal tissues, respectively. A total of was predicted in thyroid tumor tissues but not in normal tissues. three gene fusion events were predicted in both thyroid tumor tissues and normal tissues, including RNA binding motif Enrichment analysis of the DEGs containing risk SNPs. The protein (RBM)14‑RBM4, NK2 homeobox 1‑surfactant asso- DEGs containing risk SNPs were investigated by functional ciated 3 (NKX2‑1‑SFTA3), and chromosome 1 open reading and pathway enrichment analyses. The upregulated genes frame 86 (C1orf86)‑LOC100128003 gene fusions. The sodium containing risk SNPs were significantly enriched in urogenital 5958 MOLECULAR MEDICINE REPORTS 16: 5954-5964, 2017

Figure 4. Potential gene fusion events in the 5 matched samples. Red represents gene fusion events predicted only in thyroid tumor tissues. Grey indicates gene fusion events predicted only in adjacent normal tissues. Green represents gene fusion events predicted in both thyroid tumor tissues and adjacent normal tissues. system development (GO_BP, P=1.38x103), extracellular were identified in all of the thyroid tumor tissues and all of the region part (GO_CC, P=5.34x103), hormone binding (GO_MF, normal tissues, respectively. A Venn diagram demonstrated P=7.23x103), and pathways in cancer (pathway, P=1.28x102) that 18,795 risk SNPs were specific in the thyroid tumor (Table IIA). Meanwhile, the significant terms enriched for the tissues. There were more SNPs in the normal tissues than downregulated genes containing risk SNPs included regula- in the thyroid tumor tissues; however, only the 18,795 SNPs tion of cell morphogenesis (GO_BP, P=4.41x109), plasma specific in the thyroid tumor tissues were potential risk SNPs membrane part (GO_CC, P‑value = 2.22x1011), calcium ion for PTC. Total 2,686 DEGs were screened in the thyroid tumor binding (GO_MF, P=6.18x107) and cell adhesion molecules tissues, including 1,361 upregulated genes and 1,325 down- (pathway, P=1.16x106; Table IIB). regulated genes. The integrated analysis of SNPs and DEGs demonstrated that 12,528 risk SNPs were located in 4,317 PPI network analysis. The PPI network constructed for the genes (157 upregulated and 519 downregulated genes). DEGs consisted of 716 nodes (273 upregulated genes and A previous study demonstrated that the activity of the 443 downregulated genes) and 1,011 interactions (Fig. 5). TGFβ/mothers against decapentaplegic (Smad)‑dependent Importantly, fibronectin 1 FN1( , degree=31) and transforming signaling pathway is associated with nodal metastasis, local growth factor β (TGFβ) receptor 1 (TGFβR1, degree=22) had invasion and BRAF‑mutated PTCs (31). TGFβ1 has been iden- higher degrees in the PPI network. Thereafter, the non‑synon- tified as a key factor in PTC cells that affects the activation ymous risk SNP loci in the interaction domains involved in of stromal fibroblasts in a paracrine manner. Furthermore, the the PPI pairs were identified. A total of 7 PPI pairs containing activation of the TGFβ/Smad3 and Notch signaling pathways the non‑synonymous risk SNP loci in the interaction domains can impact tumor growth (32). Choe et al (33) investigated the were identified (Table III). Particularly, the interaction domains association between SNPs (‑1444C/G, Asn389Asn, ‑834 G/A) involved in the interactions of FN1 and 5 other proteins (such of TGFβR2 and PTC development, and the SNPs and clini- as tenascin C, TNC) contained non‑synonymous risk SNP loci. copathological characteristics of PTC (including lymph node metastasis, location, size, number and extrathyroidal invasion), Discussion and demonstrated that TGFβR2 may serve a role in PTC progression in the Korean population. These studies suggested There were some limitations in the present study (low case that TGFβR had a correlation with the pathogenesis of PTC. In number, no own in vivo or in vitro experiments), thus the find- the present study, FN1 (degree=31) and TGFβR1 (degree=22) ings in five PTC cases had the characteristics of an advanced had higher degrees in the PPI network. Thus, TGFβR1 may case report. In this study, a total of 70,172 and 83,869 SNPs serve an important role in the progression of PTC. QIU et al: MECHANISMS OF PTC 5959 P ‑ value P ‑ value 1.38E ‑ 03 2.83E ‑ 03 3.56E ‑ 03 5.75E ‑ 03 5.91E ‑ 03 5.34E ‑ 03 1.14E ‑ 02 1.35E ‑ 02 1.53E ‑ 02 1.65E ‑ 02 7.23E ‑ 03 8.86E ‑ 03 1.06E ‑ 02 1.44E ‑ 02 1.57E ‑ 02 1.28E ‑ 02 2.56E ‑ 08 4.41E ‑ 09

Gene symbol Gene symbol 2, NRCAM, FYN, TIAM1,MYH14, CDC42EP3, β ARAP1, RASA1, MYH10, CDC42EP5, FN1 NRCAM, PCDH1, WISP1, CD44 ALDH6A1, SORD, FOXRED2, OGDHL, AASS, CYP20A1, ALDH6A1, SORD, FOXRED2, OGDHL, ALDH1A1, ERO1LB, DIO2, BCO2, WWOX SOD3, IYD, ADIPOR2, GHR LIPG, MAP1LC3A, PPARG, BMP2, GATM, WFS1, ZBTB16 ACE, BMP2, PGF, FOXO1, GHR PPARG, AR, EGR2, GATM, MMP15, BMP2, PODN, SORD, PGF, TG, MATN2, ACE, FBLN2, EMID1, CCL28, SOD3, TNFSF10, GHR ANGPTL1, MFAP4, NUCB2, LIPG, ACACB, MAN1A1, NCS1, TG, SLC39A14, RAP1GAP, AP1S3, SYNE1, EMID1, SOD3, CSGALNACT1, NUCB2, NDRG2, GRB14, WWOX, SMPD3 LPP TNS3, PGM5, PRUNE, LAYN, LPP TNS3, PGM5, PRUNE, LAYN, CSGALNACT1, NUCB2, SMPD3 ALDH1A1, AR, ADIPOR2, GHR TBC1D4, RAP1GAP, AGAP11, ALDH1A1, OPHN1, RGS16, STARD13 AR, MAP1LC3A, SDPR, ALDH6A1, ALDH1A1, CYTH3, PLEKHA2 PPARG, OSBPL1A, FAAH, CYTH3, PLEKHA2 MAP1LC3A, SDPR, OSBPL1A, FAAH, ALDH6A1, ERO1LB, SORD, OGDHL, FOXRED2, WWOX FOXO1, ZBTB16, TCF7L1, DAPK1 PPARG, AR, BMP2, PGF, LIMK1, PLXNB2, CDH2, CDH4, LST1, LZTS1, PTPRF, PALM, MPZL3, NRP2, CLDN4, POSTN, CD151, CDSN, CTNNB1, ACE, AR, BMP2, PGF, WFS1, ZBTB16 AR, BMP2, PGF, ACE, TTL, TGF

18 16 5 5 3 4 7 10 6 6 8 13 7 5 6 20 48 6 Gene number number Term Term

GO:0044421~extracellular region part GO:0005794~Golgi apparatus GO:0005925~focal adhesion GO:0005924~cell ‑ substrate adherens junction GO:0031985~Golgi cisterna GO:0042562~hormone binding GO:0005096~GTPase activator activity GO:0008289~lipid binding GO:0005543~phospholipid binding GO:0050662~coenzyme binding hsa05200: Pathways in cancer morphogenesis GO:0055114~oxidation reduction GO:0055114~oxidation GO:0031667~response to nutrient levels GO:0001822~kidney development GO:0043434~response to peptide hormone stimulus GO:0022604~regulation of cell GO:0007155~cell adhesion GO:0001655~urogenital system development

GO_BP GO_BP GO_BP GO_BP GO_CC GO_CC GO_CC GO_CC GO_CC GO_MF GO_MF GO_MF GO_MF GO_MF Pathway B, Significant GO and KEGG terms enriched for the downregulated genes containing risk SNPs Category Gene GO_BP GO_BP Table II. Significant GO and KEGG terms separately enriched for the upregulated downregulated genes containing risk SNPs. Table A, Significant GO and KEGG terms enriched for the upregulated genes containing risk SNPs Category GO_BP 5960 MOLECULAR MEDICINE REPORTS 16: 5954-5964, 2017 P ‑ value 2.63E ‑ 08 1.63E ‑ 07 2.51E ‑ 06 2.83E ‑ 06 4.39E ‑ 06 2.76E ‑ 05 7.90E ‑ 05 6.18E ‑ 07

2, CCL22, β Gene symbol R1, DOCK9, DOCK3, FLNA, DVL1, β R1, POSTN, CDH2, COL5A2, CTNNB1, ANXA2, R1, POSTN, CDH2, COL5A2, CTNNB1, AGRN, COL1A2, AGT, 2, DVL1, NRCAM, β β RAPGEF5, RAP1GAP2, DOCK10, RASA1, RHOBTB3, NGEF, MPZL3, NRP2, CLDN4, POSTN, CD151, CDSN, CTNNB1, NRCAM, PCDH1, WISP1, CD44 ADAMTS14, LGALS3, SPOCK2, TNC, MPZL3, MYO6, COL1A1, ADAMTS2 DCBLD2, TNC, CLU, TLR2, CDH3, TNFRSF4, TGF HRH1, NOD1, CD44, MAP3K1, SERPINE1, ITGB6, CFI, BLNK, OLR1, CFB, IL1RN, NFKBIZ, LYN, RAB27A, FN1, NOX4, CIITA, PROS1, CD55, BAX, VCAN, CTSB, PARP4, ANXA1, CHST2, NFAM1, ACVR1, MYH10 ARSJ, EFHD2, PCDH1, GALNTL6, S100A5, LDLR, PXDN, LTBP1, ACTN1, NPTXR, CAPN12 PADI2, TBC1D9, RASGEF1A, IQGAP1, PLEKHG2, RINL, TIAM1, RAPGEF5, RAP1GAP2, DOCK10, RASA1, RHOBTB3, NGEF, ARHGAP23, ABR, TNIK, PSD3, DOCK9, SIPA1L2, ARHGEF2, ANXA2, MAP4K4, CDC42BPG, RASGRF1, RGS4, DOCK3, ARAP1 RIN1, SYTL2, SH3BP1, LRRK2, SYTL1, TBC1D9, RASGEF1A, IQGAP1, PLEKHG2, RINL, TIAM1, ARHGAP23, ABR, TNIK, PSD3, DOCK9, SIPA1L2, ARHGEF2, ANXA2, MAP4K4, CDC42BPG, RASGRF1, RGS4, DOCK3, ARAP1 RIN1, SYTL2, SH3BP1, LRRK2, SYTL1, CDH2, IQGAP1, CTNNB1, MAP3K1, SERPINE1, CDK5RAP2, DOCK10, INSR, RASA1, CDC42EP5, RHOBTB3, FMNL2, FAS, TGF ARHGEF2, LYN, ANXA2, NRIP1, RFC5, MICALCL, CCND1, MAST2, CCND3, ADAMTS4 CACNA1C, SYTL1, CCND2, IGF2R, SYTL2, PARP4, SHROOM4, FXYD5, SDC4, KLHL2, NRCAM, FRMD3, CORO2A, FRMD5, MACF1, CDK5RAP2, MSN, CDC42EP3, RAB27A, MYO1E, MYO1D, MYO1G, ARHGEF2, MYO6, HTT, FMNL2, ARPC5L, SPTBN2, FLII, MYH14, ANXA2, FYN, ACTN1, FLNA, FHOD1, LCP1, MYH10 DST, TGF

TGF 20 36 55 31 31 34 48 32 number Term

GO:0043062~extracellular structure organization to wounding GO:0009611~response GO:0005509~calcium ion binding GO:0030695~GTPase regulator activity GO:0060589~nucleoside ‑ triphosphatase regulator activity GO:0019899~enzyme binding GO:0008092~cytoskeletal protein binding GO:0022610~biological adhesion

GO_BP GO_BP GO_MF GO_MF GO_MF GO_MF GO_MF Table II. Continued. Table B, Significant GO and KEGG terms enriched for the downregulated genes containing risk SNPs Category GO_BP Gene QIU et al: MECHANISMS OF PTC 5961 P ‑ value 3.48E ‑ 09 7.79E ‑ 09 1.16E ‑ 06 1.68E ‑ 06 1.17E ‑ 05 8.78E ‑ 05 1.61E ‑ 04 2.00E ‑ 08 2.22E ‑ 11 2.42E ‑ 10

Gene symbol 2, TIMP1, LAMB3, CD44, FBN3, AGRN, FBN2, 2, TIMP1, LAMB3, CD44, FBN3, β NRCAM, ST3GAL5, CD44, KCNK5 CTNNB1, NRCAM, MCOLN3 POSTN, TGF SPARC, FGF1, LOXL1, FN1, LGALS3, COL13A1, PAPLN, COL1A2, ADAMTS9, BGN, NAV2, ANXA2, EMILIN2, COL5A2, ADAMTS4 ADAMTS2, LAMC2, VCAN, COL1A1, DST, AGRN, FBN2, FGF1, LOXL1, POSTN, TIMP1, LAMB3, FBN3, EMILIN2, COL5A2, SPARC, FN1, LGALS3, COL13A1, PAPLN, COL1A2, LAMC2, VCAN, ADAMTS9, BGN, NAV2, ANXA2, ADAMTS2, ADAMTS4 COL1A1, DST, TLR2, GABBR2, CD151, SDC4 SDC4, CDH4, NRCAM, ITGA9, ITGB8, CD58, CLDN1, VCAN, CNTNAP1, HLA‑DOA, HLA‑DRA HLA‑DPA1, AGRN, COL1A1, FN1 ITGB8, ITGB6, COL1A2, LAMC2, ITGA9, LAMB3, CCND1, CCND3, CCND2, ITGB8, RASGRF1, FYN, ITGB6, COL1A2, LAMC2, COL1A1, SHC3, FN1 PERP BAX, SERPINE1, DDB2, FAS, MICB, SRCIN1, PTGS2, ATP1B3, GABRB2, TLR2, CTNNB1, ATP1B3, MICB, SRCIN1, PTGS2, PTGS2, SRCIN1, GABRB2, TLR2, IQGAP1, ATP1B3, MICB, ADAMTSL5, ADAMTS14, SPOCK2, TNC, ADAMTS17, LTBP1, ADAMTSL5, ADAMTS14, SPOCK2, TNC, ADAMTS17, LTBP1, CORIN, GABRB2, ATP1B3, MICB, LDLR, CLDN4, SLC20A1, CDH2, HLA‑B, CDH3, CLDN16, ICAM1, CLDN4, PTPRF, ITGA3, SDC4, COL5A2, ITGA9, LAMB3, CD44, TNC, ITGA11, ACTN1, ITGA3, COL5A2, FLNA, CTNNB1, ITGA11, TNC, MET, CACNB1, ITGA9, DSG2, PKP2, ITGB8, ITGB6, ITGA11, CCNE2, CCND1, TNFRSF10B, CCND3, CCND2, ZMAT3, ACTN1, ITGA3, CDH2, CACNA1C, CTNNB1

119 11 171 34 32 71 19 15 22 12 number

Term GO:0044459~plasma membrane part GO:0005886~plasma membrane GO:0031012~extracellular matrix GO:0005578~proteinaceous extracellular matrix GO:0005887~integral to plasma membrane hsa04514: Cell adhesion molecules (CAMs) hsa04512: ECM ‑ receptor interaction hsa04510: Focal adhesion Arrhythmogenic right ventricular hsa05412: p53 signaling pathway hsa04115: cardiomyopathy (ARVC) Table II. Continued. Table B, Significant GO and KEGG terms enriched for the downregulated genes containing risk SNPs Category GO_CC Gene GO_CC GO_CC GO_CC GO_CC Pathway Pathway Pathway Pathway Pathway molecular function. biological process; CC, cellular component; MF, single nucleotides polymorphism; BP, GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; SNP, 5962 MOLECULAR MEDICINE REPORTS 16: 5954-5964, 2017

Figure 5. Protein‑protein interaction network constructed for the differentially expressed genes.

A total of 7 PPI pairs containing the non‑synonymous cells. Using reverse transcription‑quantitative polymerase risk SNP loci in the interaction domains were identified. chain reaction, da Silveira Mitteldorf et al (35) demonstrated Particularly, the interaction domains involved in the interac- that FN1, MET, glutaminyl‑peptide cyclotransferase, and tions of FN1 and 5 other proteins (such as TNC) contained UDP‑galactose‑4‑epimerase were significantly upregulated non‑synonymous risk SNP loci. Prasad et al (34) hypothesized in patients with PTC. TNC re‑expression can be detected by that an immunohistochemical panel containing FN1, Hector immunohistochemistry in papillary and medullary thyroid Battifora mesothelial cell 1 and galectin‑3 may contribute carcinomas supplemented by the analysis of two TNC mRNA to the diagnosis of thyroid tumors derived from follicular splice variants; thus TNC may be synthesized by tumor QIU et al: MECHANISMS OF PTC 5963

Table III. Protein‑protein interaction pairs containing the Acknowledgements non‑synonymous risk single nucleotide polymorphism loci in the interaction domains. The present study was supported by the Qingdao Municipal Science and Technology Bureau (grant no. 13‑1‑4‑166‑jch). Protein‑1 Protein‑2 Domain‑1 Domain‑2 References TNC FN1 PF00041 PF00041 FN1 COL1A1 PF00041 PF01391 1. Carling T and Udelsman R: Thyroid cancer. Annu Rev Med 65: FN1 COL1A2 PF00041 PF01391 125‑137, 2014. 2. Vecchia CL, Malvezzi M, Bosetti C, Garavello W, Bertuccio P, COL1A1 COL7A1 PF01391 PF01391 Levi F and Negri E: Thyroid cancer mortality and incidence: A FN1 COL7A1 PF00041 PF01391 global overview. Int J Cancer 136: 2187‑2195, 2015. 3. Dinets A, Hulchiy M, Sofiadis A, Ghaderi M, Höög A, Larsson C FN1 COL13A1 PF00041 PF01391 and Zedenius J: Clinical, genetic, and immunohistochemical MCM4 MCM2 PF00493 PF00493 characterization of 70 Ukrainian adult cases with post‑Chornobyl papillary thyroid carcinoma. Eur J Endocrinol 166: 1049‑1060, TNC, tenascin C; FN1, fibronectin 1; MCM, minichromosome main- 2012. 4. Mitchell RS, Kumar V, Abbas AK and Fausto N (eds): Robbins tenance; COL, collagen type. Basic Pathology. 8 edition. Philadelphia, Saunders 8: pp345‑355, 2007. 5. Xing M, Alzahrani AS, Carson KA, Shong YK, Kim TY, Viola D, Elisei R, Bendlová B, Yip L, Mian C, et al: Association between BRAF V600E mutation and recurrence of papillary cells (36). The above studies indicated that FN1 is involved in thyroid cancer. J Clin Oncol 33: 42‑50, 2015. PTC, and TNC is also associated with tumor. Therefore, FN1 6. Xing M, Liu R, Liu X, Murugan AK, Zhu G, Zeiger MA, Pai S and Bishop J: BRAF V600E and TERT promoter mutations and TNC containing the non‑synonymous risk SNP loci might cooperatively identify the most aggressive papillary thyroid serve a role in PTC by interacting with each other. cancer with highest recurrence. J Clin Oncol 32: 2718‑2726, 2014. Furthermore, 11 and 4 gene fusion events were identified 7. Lesueur F, Corbex M, McKay JD, Lima J, Soares P, Griseri P, Burgess J, Ceccherini I, Landolfi S, Papotti M, et al: Specific in all of the thyroid tumor tissues and all of the normal tissues, haplotypes of the RET proto‑oncogene are over‑represented respectively. A total of three gene fusion events were predicted in patients with sporadic papillary thyroid carcinoma. J Med in both normal tissues and thyroid tumor tissues, including Genet 39: 260‑265, 2002. 8. Stephens LA, Powell NG, Grubb J, Jeremiah SJ, Bethel JA, RBM14‑RBM4, NKX2‑1‑SFTA3 and C1orf86‑LOC100128003 Demidchik EP, Bogdanova TI, Tronko MD and Thomas GA: gene fusions. Through NKX2‑1 and forkhead box E1 (FOXE1) Investigation of loss of heterozygosity and SNP frequencies genotyping, Matsuse et al (37) revealed that both NKX2‑1 in the RET gene in papillary thyroid carcinoma. Thyroid 15: 100‑104, 2005. and FOXE1 can increase the risk of Japanese sporadic PTC. 9. Salvatore G, Giannini R, Faviana P, Caleo A, Migliaccio I, The missense mutation (1016C>T) in homeobox transcription Fagin JA, Nikiforov YE, Troncone G, Palombini L, Basolo F factor TTF1 (TITF‑1)/NKX2‑1 is the first germline mutation and Santoro M: Analysis of BRAF point mutation and RET/PTC rearrangement refines the fine‑needle aspiration detected in multinodular goiter (MNG)/PTC patients, which diagnosis of papillary thyroid carcinoma. J Clin Endocrinol increases the susceptibility for PTC and/or MNG and contrib- Metab 89: 5175‑5180, 2004. utes to the development of PTC (38,39). Impaired paired box 10. Salajegheh A, Smith RA, Kasem K, Gopalan V, Nassiri MR, William R and Lam AK: Single nucleotide polymorphisms 8 and TITF‑1/NKX2‑action may be correlated with the expres- and mRNA expression of VEGF‑A in papillary thyroid carci- sion changes of type 1 and type 2 iodothyronine 5' deiodinases noma: Potential markers for aggressive phenotypes. Eur J Surg in PTC (40). NKX2‑1 is important for thyroid organogenesis Oncol 37: 93‑99, 2011. 11. Ciampi R, Knauf JA, Kerler R, Gandhi M, Zhu Z, Nikiforova MN, and controls thyroid functions, and its inactivation is impli- Rabes HM, Fagin JA and Nikiforov YE: Oncogenic cated in epigenetics in thyroid carcinomas; thus, TTF‑1 may AKAP9‑BRAF fusion is a novel mechanism of MAPK pathway serve as a therapeutic target via epigenetic modification (41). activation in thyroid cancer. J Clin Invest 115: 94‑101, 2005. 12. Wang X and Clark AG: Using next‑generation RNA sequencing These previous studies demonstrated that NKX2‑1 also acted to identify imprinted genes. Heredity (Edinb) 113: 156‑166, in the mechanisms of PTC. Therefore, the NKX2‑1‑SFTA3 2014. gene fusion might be involved in PTC. However, small sample 13. de Klerk E, den Dunnen JT and 't Hoen PA: RNA sequencing: From tag‑based profiling to resolving complete transcript struc- size, as well as the lack of experimental validation and the ture. Cell Mol Life Sci 71: 3537‑3551, 2014. basic molecular analysis of RAS and BRAF status were the 14. Costa V, Esposito R, Ziviello C, Sepe R, Bim LV, Cacciola NA, limitations of the present study. Furthermore, the definitive Decaussin‑Petrucci M, Pallante P, Fusco A and Ciccodicola A: New somatic mutations and WNK1‑B4GALNT3 gene fusion in pathophysiological role and underlying mechanism of the papillary thyroid carcinoma. Oncotarget 6: 11242‑11251, 2015. TGFβR1/NKX2‑1‑SFTA3 gene fusion as well as non‑synony- 15. Smallridge RC, Chindris AM, Asmann YW, Casler JD, Serie DJ, mous risk SNP loci of FN1 and TNC in PTC remain unclear. Reddi HV, Cradic KW, Rivera M, Grebe SK, Necela BM, et al: RNA sequencing identifies multiple fusion transcripts, differ- Thus, further confirmation of these results is required. entially expressed genes, and reduced expression of immune In conclusion, the current study compared the transcrip- function genes in BRAF (V600E) mutant vs BRAF wild‑type tional profiles of thyroid tumor tissues and normal tissues. papillary thyroid carcinoma. J Clin Endocrinol Metab 99: E338‑E347, 2014. A total of 18795 risk SNPs and 2686 DEGs were identified 16. Abuín JM, Pichel JC, Pena TF and Amigo J: BigBWA: in the thyroid tumor tissues. Therefore, TGFβR1 and the Approaching the Burrows‑Wheeler aligner to Big Data technolo- NKX2‑1‑SFTA3 gene fusion might be implicated in PTC. gies. Bioinformatics 31: 4003‑4005, 2015. FN1 TNC 17. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Additionally, and containing the non‑synonymous Marth G, Abecasis G, Durbin R; 1000 Genome Project Data risk SNP loci might serve a role in PTC by interacting with Processing Subgroup: The Sequence alignment/map (SAM) each other. format and SAMtools. Bioinformatics 25: 2078-2079, 2009. 5964 MOLECULAR MEDICINE REPORTS 16: 5954-5964, 2017

18. Yang H and Wang K: Genomic variant annotation and priori- 32. Zhang J, Wang Y, Li D and Jing S: Notch and TGF‑β/Smad3 tization with ANNOVAR and wANNOVAR. Nat Protoc 10: pathways are involved in the interaction between cancer cells 1556‑1566, 2015. and cancer‑associated fibroblasts in papillary thyroid carcinoma. 19. Robinson MD, McCarthy DJ and Smyth GK: edgeR: A Tumor Biol 35: 379‑385, 2014. Bioconductor package for differential expression analysis of 33. Choe BK, Kim SK, Park HJ, Park HK, Kwon KH, Lim SH and digital gene expression data. Bioinformatics 26: 139‑140, 2010. Yim SV: Polymorphisms of TGFBR2 contribute to the progres- 20. Park BJ, Lord D and Hart J: Bias properties of Bayesian statistics sion of papillary thyroid carcinoma. Mol Cellular Toxicol 8: 1‑8, in finite mixture of negative binomial regression models in crash 2012. data analysis. Accid Anal Prev 42: 741‑749, 2010. 34. Prasad ML, Pellegata NS, Huang Y, Nagaraja HN, de la 21. Haynes W: Benjamini‑Hochberg Method. In: Encyclopedia of Chapelle A and Kloos RT: Galectin‑3, fibronectin‑1, CITED‑1, Systems Biology, Springer, pp78‑78, 2013. HBME1 and cytokeratin‑19 immunohistochemistry is useful 22. Ge H, Liu K, Juan T, Fang F, Newman M and Hoeck W: for the differential diagnosis of thyroid tumors. Mod Pathol 18: FusionMap: Detecting fusion genes from next‑generation 48‑57, 2005. sequencing data at base‑pair resolution. Bioinformatics 27: 35. da Silveira Mitteldorf CA, de Sousa‑Canavez JM, Leite KR, 1922‑1928, 2011. Massumoto C and Camara‑Lopes LH: FN1, GALE, MET and 23. An J, Lai J, Sajjanhar A, Batra J, Wang C and Nelson CC: QPCT overexpression in papillary thyroid carcinoma: Molecular J‑Circos: An interactive Circos plotter. Bioinformatics 31: analysis using frozen tissue and routine fine‑needle aspiration 1463‑1465, 2015. biopsy samples. Diagn Cytopathol 39: 556‑561, 2011. 24. Tweedie S, Ashburner M, Falls K, Leyland P, McQuilton P, 36. Tseleni‑Balafouta S, Gakiopoulou H, Fanourakis G, Voutsinas G, Marygold S, Millburn G, Osumi‑Sutherland D, Schroeder A, Balafoutas D and Patsouris E: Tenascin‑C protein expression and Seal R, et al: FlyBase: Enhancing Drosophila gene ontology mRNA splice variants in thyroid carcinoma. Exp Mol Pathol 80: annotations. Nucleic Acids Res 37 (Database issue): D555‑D559, 177‑182, 2006. 2009. 37. Matsuse M, Takahashi M, Mitsutake N, Nishihara E, 25. Kotera M, Moriya Y, Tokimatsu T, Kanehisa M and Goto S: Hirokawa M, Kawaguchi T, Rogounovitch T, Saenko V, KEGG and GenomeNet, New Developments, Metagenomic Bychkov A, Suzuki K, et al: The FOXE1 and NKX2‑1 loci are Analysis. In: Encyclopedia of Metagenomics. Nelson KE (ed). associated with susceptibility to papillary thyroid carcinoma in Springer, New York, NY, pp329-339, 2015. the Japanese population. J Med Genet 48: 645‑648, 2011. 26. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC 38. Ngan ES, Lang BH, Liu T, Shum CK, So MT, Lau DK, Leon TY, and Lempicki RA: DAVID: Database for annotation, visualiza- Cherny SS, Tsai SY, Lo CY, et al: A germline mutation (A339V) tion, and integrated discovery. Genome Biol 4: P3, 2003. in thyroid transcription factor‑1 (TITF‑1/NKX2. 1) in patients 27. Goel R, Harsha HC, Pandey A and Prasad TSK: Human Protein with multinodular goiter and papillary thyroid carcinoma. J Natl Reference Database and Human Proteinpedia as resources for Cancer Inst 101: 162‑175, 2009. phosphoproteome analysis. Mol Biosyst 8: 453‑463, 2012. 39. Gilbert‑Sirieix M, Makoukji J, Kimura S, Talbot M, Caillou B, 28. Kohl M, Wiese S and Warscheid B: Cytoscape: Software for Massaad C and Massaad‑Massade L: Wnt/β‑catenin signaling visualization and analysis of biological networks. Methods Mol pathway is a direct enhancer of thyroid transcription factor‑1 in Biol 696: 291‑303, 2011. human papillary thyroid carcinoma cells. PLoS One 6: e22280, 29. Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, 2011. Potter SC, Punta M, Qureshi M, Sangrador‑Vegas A, et al: The 40. Ambroziak M, Pachucki J, Stachlewska‑Nasfeter E, Nauman J Pfam protein families database: Towards a more sustainable and Nauman A: Disturbed expression of type 1 and type 2 future. Nucleic Acids Res 44: D279‑D285, 2016. iodothyronine deiodinase as well as titf1/nkx2‑1 and pax‑8 tran- 30. Yellaboina S, Tasneem A, Zaykin DV, Raghavachari B and scription factor genes in papillary thyroid cancer. Thyroid 15: Jothi R: DOMINE: A comprehensive collection of known and 1137‑1146, 2005. predicted domain‑domain interactions. Nucleic Acids Res 39 41. Kondo T, Nakazawa T, Ma D, Niu D, Mochizuki K, Kawasaki T, (Database issue): D730‑D735, 2011. Nakamura N, Yamane T, Kobayashi M and Katoh R: Epigenetic 31. Ma S, Wang Q, Ma X, Wu L, Guo F, Ji H, Liu F, Zhao Y and silencing of TTF‑1/NKX2‑1 through DNA hypermethylation Qin G: FoxP3 in papillary thyroid carcinoma induces NIS repres- and histone H3 modulation in thyroid carcinomas. Lab Invest 89: sion through activation of the TGF‑β1/Smad signaling pathway. 791‑799, 2009. Tumor Biol 120: 34‑35, 2016.