Thyroid Cancer

Overview

A translocation fusing the PAX8-PPARG genes is present in follicular thyroid cancer and follicular variant of papillary thyroid carcinoma, and less frequently in follicular thyroid adenoma. In contrast in papillary thyroid cancer the RET gene is frequently involved in structural rearrangements with either PCT1, PCT3, or other genes. In the less common medullary thyroid cancer (3 to 4% of all thyroid cancers) approximately a quarter of these cases are familial - including MEN 2A (most common familial syndrome), MEN 2B, and familial non-MEN syndromes.

See also: Thyroid Cancer - clinical resources (31)

Literature Analysis

Mouse over the terms for more detail; many indicate links which you can click for dedicated pages about the topic.

Tag cloud generated 29 August, 2019 using data from PubMed, MeSH and CancerIndex

Mutated Genes and Abnormal Protein Expression (192)

How to use this data tableClicking on the Gene or Topic will take you to a separate more detailed page. Sort this list by clicking on a column heading e.g. 'Gene' or 'Topic'.

GeneLocationAliasesNotesTopicPapers
RET 10q11.21 PTC, MTC1, HSCR1, MEN2A, MEN2B, RET51, CDHF12, CDHR16, RET-ELE1 Fusion
Translocation
-RET-NTRK1 Rearangements in Papillary Thyroid Cancer
-RET-PTC1 Rearangements in Papillary Thyroid Cancer
-RET-PTC3 (RET-ELE1) Rearangements in Papillary Thyroid Cancer
-t(8,10) RET-HOOK Reaarangements in Papillary Thyroid Cancer
-RET mutations in Familial Medullary Thyroid Carcinoma
-RET mutations in Multiple Endocrine Neoplasia - type 2A
-RET mutations in Multiple Endocrine Neoplasia Type 2b
-RET Rearrangements Following Exposure to Ionizing Radiation
666
BRAF 7q34 NS7, B-raf, BRAF1, RAFB1, B-RAF1 -BRAF and Thyroid Cancer
1055
SLC5A5 19p13.11 NIS, TDH1 -SLC5A5 and Thyroid Cancer
283
CCDC6 10q21.2 H4, PTC, TPC, TST1, D10S170 Fusion
-RET-PTC1 Rearangements in Papillary Thyroid Cancer
-CCDC6 and Thyroid Cancer
239
NRAS 1p13.2 NS6, CMNS, NCMS, ALPS4, N-ras, NRAS1 -NRAS and Thyroid Cancer
216
NCOA4 10q11.22 RFG, ELE1, PTC3, ARA70 Fusion
-RET-PTC3 (RET-ELE1) Rearangements in Papillary Thyroid Cancer
164
PTEN 10q23.31 BZS, DEC, CWS1, GLM2, MHAM, TEP1, MMAC1, PTEN1, 10q23del -PTEN and Thyroid Cancer
143
CTNNB1 3p22.1 CTNNB, MRD19, armadillo -CTNNB1 and Thyroid Cancer
125
KRAS 12p12.1 NS, NS3, CFC2, RALD, KRAS1, KRAS2, RASK2, KI-RAS, C-K-RAS, K-RAS2A, K-RAS2B, K-RAS4A, K-RAS4B, c-Ki-ras2 -KRAS and Thyroid Cancer
124
NODAL 10q22.1 HTX5 -NODAL and Thyroid Cancer
104
HRAS 11p15.5 CTLO, HAMSV, HRAS1, RASH1, p21ras, C-H-RAS, H-RASIDX, C-BAS/HAS, C-HA-RAS1 -HRAS and Thyroid Cancer
87
CAMP 3p21.31 LL37, CAP18, CRAMP, HSD26, CAP-18, FALL39, FALL-39 -CAMP and Thyroid Cancer
83
APC 5q22.2 GS, DP2, DP3, BTPS2, DP2.5, PPP1R46 -APC and Thyroid Cancer (FAP Associated)
63
KIT 4q12 PBT, SCFR, C-Kit, CD117 -KIT and Thyroid Cancer
52
MEN1 11q13.1 MEAI, SCG2 -MEN1 and Thyroid Cancer
47
NTRK1 1q23.1 MTC, TRK, TRK1, TRKA, Trk-A, p140-TrkA Fusion
-RET-NTRK1 Rearangements in Papillary Thyroid Cancer
46
MTOR 1p36.22 SKS, FRAP, FRAP1, FRAP2, RAFT1, RAPT1 -MTOR and Thyroid Cancer
46
TPO 2p25 MSA, TPX, TDH2A -TPO and Thyroid Cancer
37
NKX2-1 14q13.3 BCH, BHC, NK-2, TEBP, TTF1, NKX2A, NMTC1, T/EBP, TITF1, TTF-1, NKX2.1 -NKX2-1 and Thyroid Cancer
-NKX2-1 (TITF1) and Thyroid Cancer
20
TERT 5p15.33 TP2, TRT, CMM9, EST2, TCS1, hTRT, DKCA2, DKCB4, hEST2, PFBMFT1 Prognostic
-TERT Promoter Mutations in Thyroid Cancer
33
SLC2A1 1p34.2 CSE, PED, DYT9, GLUT, DYT17, DYT18, EIG12, GLUT1, HTLVR, GLUT-1, SDCHCN, GLUT1DS -GLUT1 expression in Thyroid Cancers
31
CDKN1B 12p13.1 KIP1, MEN4, CDKN4, MEN1B, P27KIP1 -CDKN1B and Thyroid Cancer
31
BIRC5 17q25.3 API4, EPR-1 -BIRC5 and Thyroid Cancer
27
TIMP1 Xp11.3 EPA, EPO, HCI, CLGI, TIMP, TIMP-1 -TIMP1 and Thyroid Cancer
26
PAX8 2q13 Translocation
-PAX8-PPARG fusion in Folicular Thyroid Cancer
26
PPARG 3p25.2 GLM1, CIMT1, NR1C3, PPARG1, PPARG2, PPARgamma Translocation
-PAX8-PPARG fusion in Folicular Thyroid Cancer
26
GSTM1 1p13.3 MU, H-B, GST1, GTH4, GTM1, MU-1, GSTM1-1, GSTM1a-1a, GSTM1b-1b -GSTM1 and Thyroid Cancer
19
GSTT1 22q11.23 -GSTT1 and Thyroid Cancer
18
TTF1 9q34.13 TTF-1, TTF-I -TTF1 and Thyroid Cancer
17
NOTCH1 9q34.3 hN1, AOS5, TAN1, AOVD1 -NOTCH1 and Thyroid Cancer
17
SDHD 11q23.1 PGL, CBT1, CWS3, PGL1, QPs3, SDH4, cybS, CII-4 -SDHD and Thyroid Cancer
17
TFF3 21q22.3 ITF, P1B, TFI -TFF3 and Thyroid Cancer
15
RAP1A 1p13.2 RAP1, C21KG, G-22K, KREV1, KREV-1, SMGP21 -Thyroid Cancer and RAP1A
14
AKT2 19q13.2 PKBB, PRKBB, HIHGHH, PKBBETA, RAC-BETA -AKT2 and Thyroid Cancer
14
HMGA1 6p21.31 HMG-R, HMGIY, HMGA1A -HMGA1 and Thyroid Cancer
14
GSTP1 11q13.2 PI, DFN7, GST3, GSTP, FAEES3, HEL-S-22 -GSTP1 and Thyroid Cancer
14
PRKAR1A 17q24.2 CAR, CNC, CNC1, PKR1, TSE1, ADOHR, PPNAD1, PRKAR1, ACRDYS1 -PRKAR1A and Thyroid Cancer
12
GNAS 20q13.32 AHO, GSA, GSP, POH, GPSA, NESP, SCG6, SgVI, GNAS1, PITA3, C20orf45 -GNAS and Thyroid Cancer
12
TPM3 1q21.3 TM3, TM5, TRK, CFTD, NEM1, TM-5, TM30, CAPM1, TM30nm, TPM3nu, TPMsk3, hscp30, HEL-189, HEL-S-82p, OK/SW-cl.5 -TPM3 and Thyroid Cancer
11
TPR 1q31.1 -TPR and Thyroid Cancer
11
CHEK2 22q12.1 CDS1, CHK2, LFS2, RAD53, hCds1, HuCds1, PP1425 -CHEK2 and Thyroid Cancer
11
TERC 3q26.2 TR, hTR, TRC3, DKCA1, PFBMFT2, SCARNA19 -TERC and Thyroid Cancer
11
ATM 11q22.3 AT1, ATA, ATC, ATD, ATE, ATDC, TEL1, TELO1 -ATM and Thyroid Cancer
10
MTHFR 1p36.22 -MTHFR and Thyroid Cancer
10
TGFBR2 3p24.1 AAT3, FAA3, LDS2, MFS2, RIIC, LDS1B, LDS2B, TAAD2, TGFR-2, TGFbeta-RII -TGFBR2 and Thyroid Cancer
10
THRB 3p24.2 GRTH, PRTH, THR1, ERBA2, NR1A2, THRB1, THRB2, C-ERBA-2, C-ERBA-BETA -THRB and Thyroid Cancer
9
CCK 3p22.1 -CCK and Thyroid Cancer
9
CITED1 Xq13.1 MSG1 -CITED1 and Thyroid Cancer
9
TRIM27 6p22.1 RFP, RNF76 -TRIM27 and Thyroid Cancer
8
AKAP9 7q21.2 LQT11, PRKA9, AKAP-9, CG-NAP, YOTIAO, AKAP350, AKAP450, PPP1R45, HYPERION, MU-RMS-40.16A -AKAP9 and Thyroid Cancer
8
IGF1R 15q26.3 IGFR, CD221, IGFIR, JTK13 -IGF1R and Thyroid Cancer
8
MAPK1 22q11.22 ERK, p38, p40, p41, ERK2, ERT1, ERK-2, MAPK2, PRKM1, PRKM2, P42MAPK, p41mapk, p42-MAPK -MAPK1 and Thyroid Cancer
8
HIF1A 14q23.2 HIF1, MOP1, PASD8, HIF-1A, bHLHe78, HIF-1alpha, HIF1-ALPHA, HIF-1-alpha -HIF1A and Thyroid Cancer
8
AXL 19q13.2 ARK, UFO, JTK11, Tyro7 -AXL Expression in Thyroid Cancer
7
CALCA 11p15.2 CT, KC, PCT, CGRP, CALC1, CGRP1, CGRP-I -CALCA and Thyroid Cancer
7
ITGB1 10p11.22 CD29, FNRB, MDF2, VLAB, GPIIA, MSK12, VLA-BETA -ITGB1 (CD29) and Thyroid Cancer
7
GFRA1 10q25.3 GDNFR, RET1L, RETL1, TRNR1, GDNFRA, GFR-ALPHA-1 -GFRA1 and Thyroid Cancer
7
CAV1 7q31.2 CGL3, PPH3, BSCL3, LCCNS, VIP21, MSTP085 -CAV1 and Thyroid Cancer
6
ARAF Xp11.3 PKS2, A-RAF, ARAF1, RAFA1 -ARAF and Thyroid Cancer
6
DICER1 14q32.13 DCR1, MNG1, Dicer, HERNA, RMSE2, Dicer1e, K12H4.8-LIKE -DICER1 and Thyroid Cancer
6
NAT2 8p22 AAC2, PNAT, NAT-2 -NAT2 and Thyroid Cancer
6
HHEX 10q23.33 HEX, PRH, HMPH, PRHX, HOX11L-PEN -HHEX and Thyroid Cancer
5
IGF1 12q23.2 IGFI, IGF-I, IGF1A -IGF1 Expression in Thyroid Cancer
5
CD82 11p11.2 R2, 4F9, C33, IA4, ST6, GR15, KAI1, SAR2, TSPAN27 -CD82 and Thyroid Cancer
5
PTPRJ 11p11.2 DEP1, SCC1, CD148, HPTPeta, R-PTP-ETA -PTPRJ and Thyroid Cancer
5
CYP27B1 12q14.1 VDR, CP2B, CYP1, PDDR, VDD1, VDDR, VDDRI, CYP27B, P450c1, CYP1alpha -CYP27B1 and Thyroid Cancer
5
TFG 3q12.2 TF6, HMSNP, SPG57, TRKT3 -TFG and Thyroid Cancer
5
DROSHA 5p13.3 RN3, ETOHI2, RNASEN, RANSE3L, RNASE3L, HSA242976 -DROSHA and Thyroid Cancer
5
DUSP6 12q21.33 HH19, MKP3, PYST1 -DUSP6 and Thyroid Cancer
5
PDGFRA 4q12 CD140A, PDGFR2, PDGFR-2 -PDGFRA and Thyroid Cancer
5
SERPINA1 14q32.13 PI, A1A, AAT, PI1, A1AT, PRO2275, alpha1AT -SERPINA1 and Thyroid Cancer
5
CDK6 7q21.2 MCPH12, PLSTIRE -CDK6 and Thyroid Cancer
5
SLC5A8 12q23.1-q23.2 AIT, SMCT, SMCT1 -SLC5A8 and Thyroid Cancer
5
HLA-G 6p22.1 MHC-G -HLA-G and Thyroid Cancer
5
PTPRQ 12q21.2 DFNB84, DFNB84A, PTPGMC1, R-PTP-Q -PTPRQ and Thyroid Cancer
5
PIGS 17q11.2 -PIGS and Thyroid Cancer
4
MT1G 16q13 MT1, MT1K -MT1G and Thyroid Cancer
4
GPC3 Xq26.2 SGB, DGSX, MXR7, SDYS, SGBS, OCI-5, SGBS1, GTR2-2 -GPC3 and Thyroid Cancer
4
RBP3 10q11.22 IRBP, RBPI, RP66, D10S64, D10S65, D10S66 -RBP3 and Thyroid Cancer
4
FAS 10q23.31 APT1, CD95, FAS1, APO-1, FASTM, ALPS1A, TNFRSF6 -FAS and Thyroid Cancer
4
MAP2K1 15q22.31 CFC3, MEK1, MKK1, MAPKK1, PRKMK1 -MAP2K1 and Thyroid Cancer
4
NFKBIA 14q13.2 IKBA, MAD-3, NFKBI -NFKBIA and Thyroid Cancer
4
RASAL1 12q24.13 RASAL -RASAL1 and Thyroid Cancer
4
PITX2 4q25 RS, RGS, ARP1, Brx1, IDG2, IGDS, IHG2, PTX2, RIEG, ASGD4, IGDS2, IRID2, Otlx2, RIEG1 -PITX2 and Thyroid Cancer
4
CTGF 6q23.2 CCN2, NOV2, HCS24, IGFBP8 -CTGF and Thyroid Cancer
4
POLI 18q21.2 RAD30B, RAD3OB -POLI and Thyroid Cancer
3
ZNF331 19q13.42 RITA, ZNF361, ZNF463 -ZNF331 and Thyroid Cancer
3
IKBKB 8p11.21 IKK2, IKKB, IMD15, NFKBIKB, IKK-beta -IKBKB and Thyroid Cancer
3
AXIN2 17q24.1 AXIL, ODCRCS -AXIN2 and Thyroid Cancer
3
CBX7 22q13.1 -CBX7 and Thyroid Cancer
3
SSTR5 16p13.3 SS-5-R -SSTR5 and Thyroid Cancer
3
THRA 17q21.1 AR7, EAR7, ERBA, CHNG6, ERBA1, NR1A1, THRA1, THRA2, ERB-T-1, c-ERBA-1 -THRA and Thyroid Cancer
3
PDK1 2q31.1 -PDK1 and Thyroid Cancer
3
SDHAF2 11q12.2 PGL2, SDH5, C11orf79 -SDHAF2 and Thyroid Cancer
3
CA12 15q22.2 CAXII, CA-XII, T18816, HsT18816 -CA12 and Thyroid Cancer
3
BAG3 10q26.11 BIS, MFM6, BAG-3, CAIR-1 -BAG3 and Thyroid Cancer
3
AURKB 17p13.1 AIK2, AIM1, ARK2, AurB, IPL1, STK5, AIM-1, STK12, PPP1R48, aurkb-sv1, aurkb-sv2 -AURKB and Thyroid Cancer
3
PRKCA 17q24.2 AAG6, PKCA, PRKACA, PKC-alpha -PRKCA and Thyroid Cancer
3
CD63 12q13.2 MLA1, ME491, LAMP-3, OMA81H, TSPAN30 -CD63 and Thyroid Cancer
3
SLC34A2 4p15.2 NPTIIb, NAPI-3B, NAPI-IIb -SLC34A2 and Thyroid Cancer
3
BUB1 2q14 BUB1A, BUB1L, hBUB1 -BUB1 and Thyroid Cancer
3
PRC1 15q26.1 ASE1 -PRC1 and Thyroid Cancer
3
G6PD Xq28 G6PD1 -G6PD and Thyroid Cancer
2
HDAC6 Xp11.23 HD6, JM21, CPBHM, PPP1R90 -HDAC6 and Thyroid Cancer
2
PIK3CB 3q22.3 PI3K, PIK3C1, P110BETA, PI3KBETA -PIK3CB and Thyroid Cancer
2
CASP7 10q25.3 MCH3, CMH-1, LICE2, CASP-7, ICE-LAP3 -CASP7 and Thyroid Cancer
2
ATG5 6q21 ASP, APG5, APG5L, hAPG5, APG5-LIKE -ATG5 and Thyroid Cancer
2
FOXO1 13q14.11 FKH1, FKHR, FOXO1A -FOXO1 and Thyroid Cancer
2
MAP3K8 10p11.23 COT, EST, ESTF, TPL2, AURA2, MEKK8, Tpl-2, c-COT -MAP3K8 and Thyroid Cancer
2
IMP3 15q24.2 BRMS2, MRPS4, C15orf12 -IMP3 and Thyroid Cancer
2
HIPK2 7q34 PRO0593 -HIPK2 and Thyroid Cancer
2
PMS2 7p22.1 MLH4, PMSL2, HNPCC4, PMS2CL -PMS2 and Thyroid Cancer
2
PTMS 12p13 ParaT -PTMS and Thyroid Cancer
2
SPRY2 13q31.1 IGAN3, hSPRY2 -SPRY2 and Thyroid Cancer
2
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 and Thyroid Cancer
2
APEX1 14q11.2 APE, APX, APE1, APEN, APEX, HAP1, REF1 -APEX1 and Thyroid Cancer
2
CCL5 17q12 SISd, eoCP, SCYA5, RANTES, TCP228, D17S136E, SIS-delta -CCL5 and Thyroid Cancer
2
NOTCH4 6p21.32 INT3 -NOTCH4 and Thyroid Cancer
2
SSTR1 14q21.1 SS1R, SS1-R, SRIF-2, SS-1-R -SSTR1 and Thyroid Cancer
2
OLAH 10p13 SAST, AURA1, THEDC1 -OLAH and Thyroid Cancer
2
DAPK2 15q22.31 DRP1, DRP-1 -DAPK2 and Thyroid Cancer
2
RXRB 6p21.3 NR2B2, DAUDI6, RCoR-1, H-2RIIBP -RXRB and Thyroid Cancer
2
FGF7 15q21.2 KGF, HBGF-7 -FGF7 and Thyroid Cancer
2
KRT20 17q21.2 K20, CD20, CK20, CK-20, KRT21 -KRT20 and Thyroid Cancer
2
DLL4 15q15.1 AOS6, hdelta2 -DLL4 and Thyroid Cancer
2
PCM1 8p22 PTC4, RET/PCM-1 -PCM1 and Thyroid Cancer
2
XRCC6 22q13.2 ML8, KU70, TLAA, CTC75, CTCBF, G22P1 -XRCC6 and Thyroid Cancer
2
FTCDNL1 2q33.1 FONG -FONG and Thyroid Cancer
2
GPX3 5q33.1 GPx-P, GSHPx-3, GSHPx-P -GPX3 and Thyroid Cancer
2
HOOK3 8p11.21 HK3 Translocation
-t(8,10) RET-HOOK Reaarangements in Papillary Thyroid Cancer
2
RXRG 1q23.3 RXRC, NR2B3 -RXRG and Thyroid Cancer
2
IGFBP5 2q35 IBP5 -IGFBP5 and Thyroid Cancer
2
POT1 7q31.33 GLM9, CMM10, HPOT1 -POT1 and Thyroid Cancer
2
CTSL 9q21.33 MEP, CATL, CTSL1 -CTSL1 and Thyroid Cancer
2
PTGS1 9q33.2 COX1, COX3, PHS1, PCOX1, PES-1, PGHS1, PTGHS, PGG/HS, PGHS-1 -PTGS1 and Thyroid Cancer
2
HDAC4 2q37.3 HD4, AHO3, BDMR, HDACA, HA6116, HDAC-4, HDAC-A -HDAC4 and Thyroid Cancer
2
CEACAM6 19q13.2 NCA, CEAL, CD66c -CEACAM6 and Thyroid Cancer
2
GOLGA5 14q32.12 RFG5, GOLIM5, ret-II -GOLGA5 and Thyroid Cancer
2
CDKN1C 11p15.4 BWS, WBS, p57, BWCR, KIP2, p57Kip2 -CDKN1C and Thyroid Cancer
2
SSTR3 22q13.1 SS3R, SS3-R, SS-3-R, SSR-28 -SSTR3 and Thyroid Cancer
2
LTB 6p21.33 p33, TNFC, TNFSF3, TNLG1C -LTB and Thyroid Cancer
2
KLLN 10q23.31 CWS4, KILLIN -KLLN and Thyroid Cancer
2
CTSB 8p23.1 APPS, CPSB -CTSB and Thyroid Cancer
2
EPHB4 7q22.1 HTK, MYK1, HFASD, TYRO11 -EPHB4 and Thyroid Cancer
2
PTTG1 5q33.3 EAP1, PTTG, HPTTG, TUTR1 -PTTG1 and Thyroid Cancer
2
KAT5 11q13.1 TIP, ESA1, PLIP, TIP60, cPLA2, HTATIP, ZC2HC5, HTATIP1 -KAT5 and Thyroid Cancer
2
RAD52 12p13.33 -RAD52 and Thyroid Cancer
2
GRASP 12q13.13 TAMALIN -GRASP and Thyroid Cancer
2
MSH3 5q14.1 DUP, FAP4, MRP1 -MSH3 and Thyroid Cancer
2
CAV2 7q31.2 CAV -CAV2 and Thyroid Cancer
2
CEACAM7 19q13.2 CGM2 -CEACAM7 and Thyroid Cancer
1
ASAH1 8p22 AC, PHP, ASAH, PHP32, ACDase, SMAPME -ASAH1 and Thyroid Cancer
1
KTN1 14q22.3 CG1, KNT, MU-RMS-40.19 -KTN1 and Thyroid Cancer
1
ST5 11p15.4 HTS1, p126, DENND2B -ST5 and Thyroid Cancer
1
PTPRC 1q31.3-q32.1 LCA, LY5, B220, CD45, L-CA, T200, CD45R, GP180 -PTPRC and Thyroid Cancer
1
HDAC3 5q31.3 HD3, RPD3, RPD3-2 -HDAC3 and Thyroid Cancer
1
CASR 3q13 CAR, FHH, FIH, HHC, EIG8, HHC1, NSHPT, PCAR1, GPRC2A, HYPOC1 -CASR and Thyroid Cancer
1
RASSF2 20p13 CENP-34, RASFADIN Methylation
Epigenetics
-Inactivation of RASSF2 in thyroid cancer
1
IL1A 2q14 IL1, IL-1A, IL1F1, IL1-ALPHA -IL1A and Thyroid Cancer
1
PYGM 11q13.1 -PYGM and Thyroid Cancer
1
SPARC 5q33.1 ON, OI17, BM-40 -SPARC and Thyroid Cancer
1
CD24 6q21 CD24A -CD24 and Thyroid Cancer
1
SOX4 6p22.3 EVI16 -SOX4 and Thyroid Cancer
1
MAD2L1 4q27 MAD2, HSMAD2 -MAD2L1 and Thyroid Cancer
1
IDO1 8p11.21 IDO, INDO, IDO-1 -IDO1 and Thyroid Cancer
1
TSPO 22q13.2 DBI, IBP, MBR, PBR, PBS, BPBS, BZRP, PKBS, PTBR, mDRC, pk18 -TSPO and Thyroid Cancer
1
CARD11 7p22.2 PPBL, BENTA, BIMP3, IMD11, CARMA1 -CARD11 and Thyroid Cancer
1
CEACAM3 19q13.2 CEA, CGM1, W264, W282, CD66D -CEACAM3 and Thyroid Cancer
1
IL27 16p12.1-p11.2 p28, IL30, IL-27, IL27A, IL-27A, IL27p28 -IL27 and Thyroid Cancer
1
ERC1 12p13.33 ELKS, Cast2, ERC-1, RAB6IP2 -ERC1 and Thyroid Cancer
1
ITGB2 21q22.3 LAD, CD18, MF17, MFI7, LCAMB, LFA-1, MAC-1 -ITGB2 and Thyroid Cancer
1
PTHLH 12p11.22 HHM, PLP, BDE2, PTHR, PTHRP -PTHLH and Thyroid Cancer
1
CA9 9p13.3 MN, CAIX -CA9 and Thyroid Cancer
1
MYCL 1p34.2 LMYC, L-Myc, MYCL1, bHLHe38 -MYCL and Thyroid Cancer
1
LIG4 13q33.3 LIG4S -LIG4 and Thyroid Cancer
1
MYH9 22q12.3 MHA, FTNS, EPSTS, BDPLT6, DFNA17, MATINS, NMMHCA, NMHC-II-A, NMMHC-IIA -MYH9 and Thyroid Cancer
1
CEACAM4 19q13.2 NCA, CGM7, CGM7_HUMAN -CEACAM4 and Thyroid Cancer
1
IL11 19q13.42 AGIF, IL-11 -IL11 and Thyroid Cancer
1
CDK5 7q36.1 LIS7, PSSALRE -CDK5 and Thyroid Cancer
1
ABCB1 7q21.12 CLCS, MDR1, P-GP, PGY1, ABC20, CD243, GP170 -ABCB1 and Thyroid Cancer
1
MMP13 11q22.2 CLG3, MDST, MANDP1, MMP-13 -MMP13 and Thyroid Cancer
1
GAGE1 Xp11.23 CT4.1, CT4.4, GAGE4, GAGE-1, GAGE-4 -GAGE1 and Thyroid Cancer
1
FOXP1 3p13 MFH, QRF1, 12CC4, hFKH1B, HSPC215 -FOXP1 and Thyroid Cancer
1
SIRT3 11p15.5 SIR2L3 -SIRT3 and Thyroid Cancer
1
CYP2D6 22q13.2 CPD6, CYP2D, CYP2DL1, CYPIID6, P450C2D, P450DB1, CYP2D7AP, CYP2D7BP, CYP2D7P2, CYP2D8P2, P450-DB1 -CYP2D6 and Thyroid Cancer
1
GPER1 7p22.3 mER, CEPR, GPER, DRY12, FEG-1, GPR30, LERGU, LyGPR, CMKRL2, LERGU2, GPCR-Br -GPER1 and Thyroid Cancer
1
DAPK1 9q21.33 DAPK -DAPK1 and Thyroid Cancer
1
C2orf40 2q12.2 ECRG4 -C2orf40 and Thyroid Cancer
1
LIMK1 7q11.23 LIMK, LIMK-1 -LIMK1 and Thyroid Cancer
PTPRH 19q13.42 SAP1, R-PTP-H -PTPRH and Thyroid Cancer
CCKBR 11p15.4 GASR, CCK-B, CCK2R -CCKBR and Thyroid Cancer
CASP9 1p36.21 MCH6, APAF3, APAF-3, PPP1R56, ICE-LAP6 -CASP9 and Thyroid Cancer

Note: list is not exhaustive. Number of papers are based on searches of PubMed (click on topic title for arbitrary criteria used).

Recurrent Chromosome Abnormalities

Selected list of common recurrent structural abnormalities

This is a highly selective list aiming to capture structural abnormalies which are frequesnt and/or significant in relation to diagnosis, prognosis, and/or characterising specific cancers. For a much more extensive list see the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer.

Familial Thyroid Cancer (1 links)

    Nosé V
    Familial thyroid cancer: a review.
    Mod Pathol. 2011; 24 Suppl 2:S19-33 [PubMed] Related Publications
    Thyroid carcinomas can be sporadic or familial. Familial syndromes are classified into familial medullary thyroid carcinoma (FMTC), derived from calcitonin-producing C cells, and familial non-medullary thyroid carcinoma, derived from follicular cells. The familial form of medullary thyroid carcinoma (MTC) is usually a component of multiple endocrine neoplasia (MEN) IIA or IIB, or presents as pure FMTC syndrome. The histopathological features of tumors in patients with MEN syndromes are similar to those of sporadic tumors, with the exception of bilaterality and multiplicity of tumors. The genetic events in the familial C-cell-derived tumors are well known, and genotype-phenotype correlations well established. In contrast, the case for a familial predisposition of non-medullary thyroid carcinoma is only now beginning to emerge. Although, the majority of papillary and follicular thyroid carcinomas are sporadic, the familial forms are rare and can be divided into two groups. The first includes familial syndromes characterized by a predominance of non-thyroidal tumors, such as familial adenomatous polyposis and PTEN-hamartoma tumor syndrome, within others. The second group includes familial syndromes characterized by predominance of papillary thyroid carcinoma (PTC), such as pure familial PTC (fPTC), fPTC associated with papillary renal cell carcinoma, and fPTC with multinodular goiter. Some characteristic morphologic findings should alert the pathologist of a possible familial cancer syndrome, which may lead to further molecular genetics evaluation.

    Khan A, Smellie J, Nutting C, et al.
    Familial nonmedullary thyroid cancer: a review of the genetics.
    Thyroid. 2010; 20(7):795-801 [PubMed] Related Publications
    OBJECTIVE: Thyroid cancer, the commonest of endocrine malignancies, continues to increase in incidence with over 19,000 new cases diagnosed in the European Union per year. Although nonmedullary thyroid cancer (NMTC) is mostly sporadic, evidence for a familial form, which is not associated with other Mendelian cancer syndromes (e.g., familial adenomatous polyposis and Cowden's syndrome), is well documented and thought to cause more aggressive disease. Just over a decade ago, the search for a genetic susceptibility locus for familial NMTC (FNMTC) began. This review details the genetic studies conducted thus far in the search for potential genes for FNMTC.
    DESIGN: An electronic PubMed search was performed from the English literature for genetics of FNMTC and genetics of familial papillary thyroid carcinoma (subdivision of FNMTC). The references from the selected papers were reviewed to identify further studies not found in the original search criteria.
    MAIN OUTCOME: Six potential regions for harboring an FNMTC gene have been identified: MNG1 (14q32), TCO (19p13.2), fPTC/PRN (1q21), NMTC1 (2q21), FTEN (8p23.1-p22), and the telomere-telomerase complex. Important genes reported to have been excluded are RET, TRK, MET, APC, PTEN, and TSHR.
    CONCLUSION: The genetics of FNMTC is an exciting field in medical research that has the potential to permit individualized management of thyroid cancer. Studies thus far have been on small family groups using varying criteria for the diagnosis of FNMTC. Results have been contradictory and further large-scale genetic studies utilizing emerging molecular screening tests are warranted to elucidate the underlying genetic basis of FNMTC.

Latest Publications

Zhang H, Yu Y, Zhang K, et al.
Targeted inhibition of long non-coding RNA H19 blocks anaplastic thyroid carcinoma growth and metastasis.
Bioengineered. 2019; 10(1):306-315 [PubMed] Article available free on PMC after 19/07/2020 Related Publications
Long non-coding RNA H19 (H19) is highly expressed in cancers and is considered to highly correlate with the extent of malignant degree. The present study was performed to determine the expression levels of H19 in anaplastic thyroid carcinoma (ATC) tissues and the role of H19 in ATC 8505C cells

Yoo SK, Song YS, Lee EK, et al.
Integrative analysis of genomic and transcriptomic characteristics associated with progression of aggressive thyroid cancer.
Nat Commun. 2019; 10(1):2764 [PubMed] Article available free on PMC after 19/07/2020 Related Publications
Anaplastic thyroid cancer (ATC) and advanced differentiated thyroid cancers (DTCs) show fatal outcomes, unlike DTCs. Here, we demonstrate mutational landscape of 27 ATCs and 86 advanced DTCs by massively-parallel DNA sequencing, and transcriptome of 13 ATCs and 12 advanced DTCs were profiled by RNA sequencing. TERT, AKT1, PIK3CA, and EIF1AX were frequently co-mutated with driver genes (BRAF

Demin DE, Afanasyeva MA, Uvarova AN, et al.
Constitutive Expression of NRAS with Q61R Driver Mutation Activates Processes of Epithelial-Mesenchymal Transition and Leads to Substantial Transcriptome Change of Nthy-ori 3-1 Thyroid Epithelial Cells.
Biochemistry (Mosc). 2019; 84(4):416-425 [PubMed] Related Publications
The Q61R mutation of the NRAS gene is one of the most frequent driver mutations of thyroid cancer. Tumors with this mutation are characterized by invasion into blood vessels and formation of distant metastases. To study the role of this mutation in the growth of thyroid cancer, we developed a model system on the basis of thyroid epithelial cell line Nthy-ori 3-1 transduced by a lentiviral vector containing the NRAS gene with the Q61R mutation. It was found that the expression of NRAS(Q61R) in thyroid epithelial cells has a profound influence on groups of genes involved in the formation of intercellular contacts, as well as in processes of epithelial-mesenchymal transition and cell invasion. The alteration in the expression of these genes affects the phenotype of the model cells, which acquire traits of mesenchymal cells and demonstrate increased ability for survival and growth without attachment to the substrate. The key regulators of these processes are transcription factors belonging to families SNAIL, ZEB, and TWIST, and in different types of tumors the contribution of each individual factor can vary greatly. In our model system, phenotype change correlates with an increase in the expression of SNAIL2 and TWIST2 factors, which indicates their possible role in regulating invasive growth of thyroid cancer with the mutation of NRAS(Q61R).

Punda A, Bedeković V, Barić A, et al.
RET EXPRESSION AND ITS CORRELATION WITH CLINICOPATHOLOGIC DATA IN PAPILLARY THYROID CARCINOMA.
Acta Clin Croat. 2018; 57(4):646-652 [PubMed] Article available free on PMC after 19/07/2020 Related Publications
- The purpose of this study was to analyze the possible prognostic value of RET mutation in papillary thyroid carcinoma and its incidence in the past few decades in our population, due to the increasing incidence of papillary thyroid carcinoma. The present study included 180 patients operated for papillary thyroid carcinoma. The clinical and histopathologic characteristics were analyzed. Paraffin sections of the selected histologic slides were cut again and immunohistochemically stained by the Clone 3F8 P (HIER) from Novocastra (Vision Bio Systems Europe, Newcastle upon Tyne, UK) monoclonal antibody to RET oncoprotein. Univariate analysis indicated sex (p=0.01), histologic subtype (p=0.075) and capsular invasion (p=0.010) to be statistically significant predictors of lymph node metastases, whereas age (p=0.796), tumor size (p=0.556) and intraglandular dissemination (p=0.131) showed no such correlation. The presence of RET mutation (p=0.704) was not a statistically significant predictor of the tumor metastasizing potential. RET mutation (p=0.500) showed no statistically significant correlation with papillary thyroid carcinoma classifed into prognostic groups according to clinicopathologic features either. RET mutation was detected in 30% of 180 papillary thyroid carcinomas. This is the first large study demonstrating that RET mutation incidence in papillary thyroid carcinoma in Croatian population is consistent with the classic distribution of sporadic cases, despite the increased prevalence of papillary thyroid carcinoma in the past few decades.

Censi S, Barollo S, Grespan E, et al.
Prognostic significance of TERT promoter and BRAF mutations in TIR-4 and TIR-5 thyroid cytology.
Eur J Endocrinol. 2019; 181(1):1-11 [PubMed] Related Publications
Objective: Follicular-derived thyroid cancers generally have a good prognosis, but in a minority of cases, they have an aggressive behavior and develop distant metastases, with an increase in the associated mortality. None of the prognostic markers currently available prior to surgery can identify such cases.
Methods: TERT promoter and BRAF gene mutations were examined in a series of 436 consecutive TIR-4 and TIR-5 nodes referred for surgery. Follow-up (median: 59 months, range: 7-293 months) was available for 384/423 patients with malignant nodes.
Results: TERT promoter and BRAF mutations were detected in 20/436 (4.6%) and 257/434 thyroid nodules (59.2%), respectively. At the end of the follow-up, 318/384 patients (82.8%) had an excellent outcome, 48/384 (12.5%) had indeterminate response or biochemical persistence, 18/384 (4.7%) had a structural persistence or died from thyroid cancer. TERT promoter mutations correlated with older age (P < 0.0001), larger tumor size (P = 0.0002), oxyntic and aggressive PTC variants (P = 0.01), higher tumor stages (P < 0.0001), distant metastases (<0.0001) and disease outcome (P < 0.0001). At multivariate analysis, TERT promoter mutation was not an independent predictor of disease outcome. TERT promoter mutation- (OR: 40.58; 95% CI: 3.06-539.04), and N1b lymph node metastases (OR: 40.16, 95% CI: 3.48-463.04) were independent predictors of distant metastases. BRAF mutation did not predict the outcome, and it correlated with a lower incidence of distant metastases (P = 0.0201).
Conclusions: TERT promoter mutation proved an independent predictor of distant metastases, giving clinicians the chance to identify many of the patients who warranted more aggressive initial treatment and closer follow-up.

Ban Z, He J, Tang Z, et al.
LRG‑1 enhances the migration of thyroid carcinoma cells through promotion of the epithelial‑mesenchymal transition by activating MAPK/p38 signaling.
Oncol Rep. 2019; 41(6):3270-3280 [PubMed] Article available free on PMC after 19/07/2020 Related Publications
Leucine‑rich‑alpha‑2‑glycoprotein 1 (LRG‑1) has been reported to be associated with multiple malignancies. However, its participation in thyroid carcinoma progression remains unclear. In the present study, the biological function and underlying molecular mechanisms of LRG‑1 in thyroid carcinoma were investigated. It was found that LRG‑1 was overexpressed in thyroid carcinoma tissues, and high LRG‑1 expression predicted poor patient survival and late tumor stage. As shown in the mouse xenograft study, knockdown of LRG‑1 significantly attenuated thyroid cancer growth in vivo. Based on wound healing, Transwell, proliferation and apoptosis assays, it was found that the knockdown of LRG‑1, using shLRG‑1, inhibited cell migration and invasion, but did not affect proliferation and apoptosis in thyroid cancer cells. Furthermore, LRG‑1 also induced epithelial‑mesenchymal transition (EMT) in thyroid carcinoma cells. Western blot analysis revealed that this tumor‑promoting bioactivity of LRG‑1 was attributed to its selective activation of MAPK/p38 signaling. All of these findings indicate that LRG‑1 plays a deleterious role in the progression of thyroid carcinoma. LRG‑1 may serve as a promising biomarker for predicting prognosis in thyroid carcinoma patients, and LRG‑1‑based therapy may be developed into a novel strategy for the treatment of thyroid carcinoma.

Tang X, Huang X, Wang D, et al.
Identifying gene modules of thyroid cancer associated with pathological stage by weighted gene co-expression network analysis.
Gene. 2019; 704:142-148 [PubMed] Related Publications
Thyroid cancer is the most common type of endocrine tumor. The TNM classification remains a standard for treatment determination and predicting prognosis in thyroid cancer. The genes modules associated with the progression of papillary thyroid carcinoma (PTC) were not clear. We applied a weighted gene co-expression network analysis (WGCNA) and differential expression analysis to systematically identified co-expressed gene modules and hub genes associated with PTC progression based on The Cancer Genome Atlas (TCGA) PTC transcriptome sequencing data. An independent validation cohort, GSE27155, was used to evaluate the preservation of gene modules. We identified two co-expressed genes modules associated with progression of PTC. Enrichment analysis indicated that the two modules were enriched in angiogenesis and extracellular matrix organization. DCN, COL1A1, COL1A2, COL5A2 and COL3A1 were hub genes in the co-expressed network. We systematically identified co-expressed gene modules and hub genes associated with PTC progression for the first time, which provided insights into the mechanisms underlying PTC progression and some potential targets for the treatment of PTC.

Chen J, Xu Z, Yu C, et al.
MiR-758-3p regulates papillary thyroid cancer cell proliferation and migration by targeting TAB1.
Pharmazie. 2019; 74(4):235-238 [PubMed] Related Publications
Previous studied revealed that miR-758-3p was abnormally expressed in cancer patients. However, its role and underlying mechanism in papillary thyroid cancer (PTC) remains unclear. Expression of miR-758-3p in PTC cell lines was analyzed using quantitative real-time PCR. It was observed that miR-758-3p expression was significantly downregulated in PTC cell lines. Overexpression of miR-758-3p inhibited PTC cell proliferation, invasion but promoted cell apoptosis

Yang LX, Wu J, Guo ML, et al.
Suppression of long non-coding RNA TNRC6C-AS1 protects against thyroid carcinoma through DNA demethylation of STK4 via the Hippo signalling pathway.
Cell Prolif. 2019; 52(3):e12564 [PubMed] Related Publications
OBJECTIVES: Thyroid carcinoma (TC) represents a malignant neoplasm affecting the thyroid. Current treatment strategies include the removal of part of the thyroid; however, this approach is associated with a significant risk of developing hypothyroidism. In order to adequately understand the expression profiles of TNRC6C-AS1 and STK4 and their potential functions in TC, an investigation into their involvement with Hippo signalling pathway and the mechanism by which they influence TC apoptosis and autophagy were conducted.
METHODS: A microarray analysis was performed to screen differentially expressed lncRNAs associated with TC. TC cells were employed to evaluate the role of TNRC6C-AS1 by over-expression or silencing means. The interaction of TNRC6C-AS1 with methylation of STK4 promoter was evaluated to elucidate its ability to elicit autophagy, proliferation and apoptosis.
RESULTS: TNRC6C-AS1 was up-regulated while STK4 was down-regulated, where methylation level was elevated. STK4 was verified as a target gene of TNRC6C-AS1, which was enriched by methyltransferase. Methyltransferase's binding to STK4 increased expression of its promoter. Over-expressed TNRC6C-AS1 inhibited STK4 by promoting STK4 methylation and reducing the total protein levels of MST1 and LATS1/2. The phosphorylation of YAP1 phosphorylation was decreased, which resulted in the promotion of SW579 cell proliferation and tumorigenicity.
CONCLUSION: Based on our observations, we subsequently confirmed the anti-proliferative, pro-apoptotic and pro-autophagy capabilities of TNRC6C-AS1 through STK4 methylation via the Hippo signalling pathway in TC.

Pessôa-Pereira D, Medeiros MFDS, Lima VMS, et al.
Association between BRAF (V600E) mutation and clinicopathological features of papillary thyroid carcinoma: a Brazilian single-centre case series.
Arch Endocrinol Metab. 2019 Mar-Apr; 63(2):97-106 [PubMed] Related Publications
OBJECTIVES: We aimed to investigate the prevalence of the BRAF (V600E) mutation in consecutive cases of papillary thyroid carcinoma (PTC) in patients diagnosed and treated at the Hospital Sao Rafael (Salvador, BA, Brazil) and evaluate its association with clinical and pathological characteristics of PTC.
SUBJECTS AND METHODS: We retrospectively enrolled in the study a total of 43 consecutive PTC patients who underwent total thyroidectomy. We performed DNA extraction from formalin-fixed paraffin-embedded (FFPE) tumour tissue samples. Polymerase chain reaction (PCR) and direct sequencing were used to determine BRAF (V600E) mutation status. Univariate and multivariate logistic regression analyses were employed to identify independent associations.
RESULTS: The prevalence of BRAF (V600E) mutation was 65.1% (28/43). A high frequency of older patients (p value: 0.004) was observed among the BRAF-mutated PTC group and, in contrast, a low frequency of concurrent Hashimoto's thyroiditis (HT) (p value: 0.011) was noted. Multivariate analysis confirmed that older age (OR: 1.15; 95% CI: 1.00 - 1.33; p value: 0.047) and HT (OR: 0.05; 95% CI: 0.006-0.40; p value: 0.005) were independent factors associated with BRAF (V600E) mutation.
CONCLUSION: We found a high prevalence of BRAF (V600E) mutation in PTC cases. Older age and no concurrent HT were independently associated with BRAF (V600E) mutation.

Giorgenon TMV, Carrijo FT, Arruda MA, et al.
Preoperative detection of TERT promoter and BRAFV600E mutations in papillary thyroid carcinoma in high-risk thyroid nodules.
Arch Endocrinol Metab. 2019 Mar-Apr; 63(2):107-112 [PubMed] Related Publications
OBJECTIVES: This observational study analyzed telomerase reverse transcriptase (pTERT) mutations in 45 fine-needle aspiration (FNA) specimens obtained from thyroid nodules followed by postoperatively confirmation of papillary thyroid cancer (PTC) diagnosis, examining their relationship with clinicopathologic aspects and the BRAFV600E mutation.
SUBJECTS AND METHODS: Clinical information was collected from patients who presented to Ribeirao Preto University Hospital for surgical consultation regarding a thyroid nodule and who underwent molecular testing between January 2010 to October 2012. Tests included a DNA-based somatic detection of BRAFV600E and pTERT mutations.
RESULTS: We found coexistence of pTERTC228T and BRAFV600E mutations in 8.9% (4/45) of thyroid nodules. All nodules positive for pTERT mutations were BRAFV600E positives. There was a significant association between pTERTC228T/BRAFV600E with older age and advanced stage compared with the group negative for either mutation.
CONCLUSIONS: This series provides evidence that FNA is a reliable method for preoperative diagnosis of high-risk thyroid nodules. pTERTC228T/BRAFV600E mutations could be a marker of poor prognosis. Its use as a personalized molecular medicine tool to individualize treatment decisions and follow-up design needs to be further studied.

Abdullah MI, Junit SM, Ng KL, et al.
Papillary Thyroid Cancer: Genetic Alterations and Molecular Biomarker Investigations.
Int J Med Sci. 2019; 16(3):450-460 [PubMed] Article available free on PMC after 19/07/2020 Related Publications
Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all cancers of the thyroid. It is also one of the few cancers with a rapidly increasing incidence. PTC is usually contained within the thyroid gland and generally biologically indolent. Prognosis of the cancer is excellent, with less than 2% mortality at 5 years. However, more than 25% of patients with PTC developed a recurrence during a long term follow-up. The present article provides an updated condensed overview of PTC, which focuses mainly on the molecular alterations involved and recent biomarker investigations.

Ding Q, Li X, Sun Y, Zhang X
Schizandrin A inhibits proliferation, migration and invasion of thyroid cancer cell line TPC-1 by down regulation of microRNA-429.
Cancer Biomark. 2019; 24(4):497-508 [PubMed] Related Publications
OBJECTIVE: Schizandrin A (SchA) exerts anticancer potential. However, the effects of SchA on thyroid cancer (TC) have not been clear illuminated. Therefore, we investigated the effects of SchA on TC cell line TPC-1 and the underlying mechanisms.
METHODS: TPC-1 cells were treated with SchA and/or transfected with miR-429 mimic, anti-miR-429 and their corresponding negative controls (NC). Cell viability, proliferation, migration, invasion and cell apoptosis were examined by CCK-8 assay, bromodeoxyuridine, modified two-chamber migration assay, Millicell Hanging Cell Culture and flow cytometry analysis, respectively. The expression of miR-429, p16, Cyclin D1, cyclin-dependent kinases 4 (CDK4), matrix metalloprotein (MMP)-2, MMP-9 and Vimentin was detected by qRT-PCR. All protein expression was examined by western blot.
RESULTS: SchA inhibited cell proliferation, metastasis and induced cell apoptosis. Moreover, SchA negatively regulated miR-429 expression. Treatment with miR-429 mimic and SchA reversed the results led by SchA and NC. Furthermore, the phosphorylation β-catenin, mitogen-activated protein kinase (MEK) and extracellular signal-regulated kinase (ERK) were statistically down-regulated by SchA while co-treatment with miR-429 mimic and SchA led to the opposite trend. Moreover, miR-429 knockdown showed contrary results.
CONCLUSION: SchA inhibits cell proliferation, migration, invasion and inactivates Wnt/β-catenin and MEK/ERK signaling pathways by down regulating miR-429.

Zarkesh M, Zadeh-Vakili A, Akbarzadeh M, et al.
BRAF V600E mutation and microRNAs are helpful in distinguishing papillary thyroid malignant lesions: Tissues and fine needle aspiration cytology cases.
Life Sci. 2019; 223:166-173 [PubMed] Related Publications
AIMS: Mutations of BRAF oncogene are considered to contribute in the invasiveness and poor clinicopathologic features of papillary thyroid cancer (PTC). As a step towards understanding the underlying molecular mechanisms of this contribution, we aimed to examine the association of four microRNAs' (miR-222, -137, -214, -181b) levels with BRAFV600E and clinicopathological features in PTC tissues and fine needle aspiration (FNA) specimens.
METHODS: In total, 56 PTC and 27 benign with multinodular goiter tissue samples, 95 FNA samples, and B-CPAP and HEK293 cell lines were examined. BRAFV600E mutation was examined in PTC tissues and FNA samples. Expression of microRNAs was assessed by real-time quantitative reverse transcription-PCR.
KEY FINDINGS: The frequency of BRAFV600E in PTC tissues and FNA samples "suspicious for PTC" was 41.1 and 36.8%, respectively. MiR-222, -137, -214, and -181b were significantly upregulated in PTC tumors (P < 0.05) and in B-CPAP cell line (P < 0.001). In FNA, the expressions of miR-222, -181b and -214 were significantly elevated in patients suspected for PTC (P < 0.05), while there was no significant difference in miR-137. After adjustment for age and sex, miR-181b was associated with an increased risk of bearing BRAFV600E mutation (OR: 1.27; 95% CI: 1.01-1.61; P = 0.045) and risk of lymphovascular invasion (OR: 1.66; 95% CI: 1.01-2.72; P = 0.045); miR-137 was associated with the risk of larger tumor size (OR: 1.31; 95% CI: 1.04-1.65; P = 0.022); miR-222 was related to increase in extracapsular invasion (OR: 1.28; 95% CI: 1.04-1.57; P = 0.018).
SIGNIFICANCE: Upregulation of miR-222, -214 and -181b has been confirmed in PTC tumors, FNA samples and cell line. MiR-137 upregulation has been confirmed in PTC tumors and cell line, but not in FNA samples. MiR-222, -137 and -181b showed an association with the degree of malignancy in PTC tumors.

Camargo Barros-Filho M, Barreto Menezes de Lima L, Bisarro Dos Reis M, et al.
Int J Mol Sci. 2019; 20(6) [PubMed] Article available free on PMC after 19/07/2020 Related Publications
Despite the low mortality rates, well-differentiated thyroid carcinomas (WDTC) frequently relapse.

Okamoto M, Yoshioka Y, Maeda K, et al.
Mice conditionally expressing RET(C618F) mutation display C cell hyperplasia and hyperganglionosis of the enteric nervous system.
Genesis. 2019; 57(5):e23292 [PubMed] Related Publications
Medullary thyroid carcinoma (MTC) develops from hyperplasia of thyroid C cells and represents one of the major causes of thyroid cancer mortality. Mutations in the cysteine-rich domain (CRD) of the RET gene are the most prevalent genetic cause of MTC. The current consensus holds that such cysteine mutations cause ligand-independent dimerization and constitutive activation of RET. However, given the number of the CRD mutations left uncharacterized, our understanding of the pathogenetic mechanisms by which CRD mutations lead to MTC remains incomplete. We report here that RET(C618F), a mutation identified in MTC patients, displays moderately high basal activity and requires the ligand for its full activation. To assess the biological significance of RET(C618F) in organogenesis, we generated a knock-in mouse line conditionally expressing RET(C618F) cDNA by the Ret promoter. The RET(C618F) allele can be made to be Ret-null and express mCherry by Cre-loxP recombination, which allows the assessment of the biological influence of RET(C618F) in vivo. Mice expressing RET(C618F) display mild C cell hyperplasia and increased numbers of enteric neurons, indicating that RET(C618F) confers gain-of-function phenotypes. This mouse line serves as a novel biological platform for investigating pathogenetic mechanisms involved in MTC and enteric hyperganglionosis.

Lima GEDCP, Fernandes VO, Montenegro APDR, et al.
Aggressive papillary thyroid carcinoma in a child with type 2 congenital generalized lipodystrophy.
Arch Endocrinol Metab. 2019; 63(1):79-83 [PubMed] Related Publications
Thyroid carcinoma (TC) is rare in children, particularly in those aged < 10 years. Several studies have demonstrated a correlation between neoplasms and hyperinsulinemia and insulin resistance, which are often associated with a higher risk for and/or aggressiveness of the neoplasm. Congenital generalized lipodystrophy (CGL) with autosomal recessive inheritance is a rare disease and is characterized by the lack of adipose tissue, severe insulin resistance, and early metabolic disturbances. Here, we reported a rare case of a type 2 CGL in a girl who presented with a papillary TC (PTC) at the age of 7 years. She had no family history of TC or previous exposure to ionizing radiation. She had a generalized lack of subcutaneous fat, including the palmar and plantar regions, muscle hypertrophy, intense acanthosis nigricans, hepatomegaly, hypertriglyceridemia, severe insulin resistance, and hypoleptinemia. A genetic analysis revealed a mutation in the BSCL2 gene (p.Thr109Asnfs* 5). Ultrasound revealed a hypoechoic solid nodule measuring 1.8 × 1.0 × 1.0 cm, and fine needle aspiration biopsy suggested malignancy. Total thyroidectomy was performed, and a histopathological examination confirmed PTC with vascular invasion and parathyroid lymph node metastasis (pT3N1Mx stage). This is the first report to describe a case of differentiated TC in a child with CGL. Severe insulin resistance that is generally observed in patients with CGL early in life, especially in those with type 2 CGL, may be associated with this uncommon presentation of aggressive PTC during childhood.

Barros-Filho MC, Pewarchuk M, Minatel BC, et al.
Previously undescribed thyroid-specific miRNA sequences in papillary thyroid carcinoma.
J Hum Genet. 2019; 64(5):505-508 [PubMed] Related Publications
Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, wherein diagnostic limitations and lack of accurate prognostic factors are important clinical challenges. In this study, we report the discovery of 234 novel miRNAs in non-neoplastic thyroid and PTC samples, obtained from publicly available small RNA sequencing datasets (TCGA and GEO). These sequences were observed to display similar molecular features compared to currently annotated miRNAs. These potentially novel miRNAs presented tissue-specificity and largely decreased expression in PTC compared to non-neoplastic samples. We showed that the disrupted novel miRNAs have diagnostic and prognostic potential, and were associated with BRAF mutation, a frequent alteration related to more aggressive PTC. In conclusion, our results expand the miRNA repertoire in thyroid tissues and highlight the potential biological role and clinical utility of previously unannotated miRNAs.

Hummel S, Kohlmann W, Kollmeyer TM, et al.
The contribution of the rs55705857 G allele to familial cancer risk as estimated in the Utah population database.
BMC Cancer. 2019; 19(1):190 [PubMed] Article available free on PMC after 19/07/2020 Related Publications
BACKGROUND: IDH1/2 mutated glioma has been associated with a germline risk variant, the rs55705857 G allele. The Utah Population Database (UPDB), a computerized genealogy of people in Utah, is a unique resource to evaluate cancer risk in related individuals.
METHODS: One hundred and two individuals with IDH1/2 mutant or 1p/19q co-deleted glioma were genotyped and linked to the UPDB. DNA came from blood (21), tumor tissue (43), or both (38). We determined congruence between somatic and germline samples and estimated the relative risk for developing cancer to first and second-degree relatives of G and A allele carriers at rs55705857.
RESULTS: Somatic (glioma) DNA had 85.7% sensitivity (CI 57.2-98.2%) and 95.8% specificity (CI 78.9-99.89%) for germline rs55705857 G allele. Forty-one patients were linked to pedigrees in the UPDB with at least three generations of data. First-degree relatives of rs55705857 G allele carriers were at significantly increased risk for developing cancer (RR = 1.72, p = 0.045, CI 1.02-2.94), and specifically for oligodendroglioma (RR = 57.61, p = 0.017, CI 2.96-320.98) or prostate cancer (RR = 4.10, p = 0.008, CI 1.62-9.58); relatives of individuals without the G allele were not at increased risk. Second-degree relatives of G allele carriers also had significantly increased risk for developing cancer (RR = 1.50, p = 0.007, CI 1.15-2.01).
CONCLUSIONS: Tumor DNA may approximate genotype at the rs55705857 locus. We confirmed this locus confers an increased risk of all cancers and especially of oligodendroglioma. No increased cancer or brain tumor risk is seen in family members of individuals without the high-risk G allele.

Zhang J, Yang Y, Zhao J, et al.
Investigation of BRAF mutation in a series of papillary thyroid carcinoma and matched-lymph node metastasis with ARMS PCR.
Pathol Res Pract. 2019; 215(4):761-765 [PubMed] Related Publications
OBJECTIVES: To figure out that if there is a consistency relationship of the BRAF
METHODS: We collected the specimen of thyroids and matched-lymph node metastases of PTCs and tested the BRAF
RESULTS: 20 patients with PTC and metastasis lymph node were hired. In this cohort, 16 (80%) patients had the same BRAF genetic mutation status in thyroid and metastasis, and the other 4 (20%) had an inconsistent situation.
CONCLUSIONS: Within our cohort, the data suggested that wild-type BRAF

Mahmoudian-Sani MR, Jalali A, Jamshidi M, et al.
Long Non-Coding RNAs in Thyroid Cancer: Implications for Pathogenesis, Diagnosis, and Therapy.
Oncol Res Treat. 2019; 42(3):136-142 [PubMed] Related Publications
Thyroid cancer is a rare malignancy and accounts for less than 1% of malignant neoplasms in humans; however, it is the most common cancer of the endocrine system and responsible for most deaths from endocrine cancer. Long non-coding (Lnc)RNAs are defined as non-coding transcripts that are more than 200 nucleotides in length. Their expression deregulation plays an important role in the progress of cancer. These molecules are involved in physiologic cellular processes, genomic imprinting, inactivation of chromosome X, maintenance of pluripotency, and the formation of different organs via changes in chromatin, transcription, and translation. LncRNAs can act as a tumor suppressor genes or oncogenes. Several studies have shown that these molecules can interact with microRNAs and prevent their binding to messenger RNAs. Research has shown that these molecules play an important role in tumorigenicity, angiogenesis, proliferation, migration, apoptosis, and differentiation. In thyroid cancer, several lncRNAs (MALAT1, H19, BANCR, HOTAIR) have been identified as contributing factors to cancer development, and can be used as novel biomarkers for early diagnosis or even treatment. In this article, we study the newest lncRNAs and their role in thyroid cancer.

Wang H, Yan X, Zhang H, Zhan X
CircRNA circ_0067934 Overexpression Correlates with Poor Prognosis and Promotes Thyroid Carcinoma Progression.
Med Sci Monit. 2019; 25:1342-1349 [PubMed] Article available free on PMC after 19/07/2020 Related Publications
BACKGROUND Circular RNAs are important regulators in human cancers, including thyroid carcinoma. The circ_0067934 RNA is reported to participate in hepatocellular carcinoma, esophageal squamous cell carcinoma, and lung cancer. Whether it regulates thyroid carcinoma remains unclear. The purpose of this study was to research potential mechanisms of circ_0067934 in thyroid tumors to provide potential new diagnostic and treatment targets. MATERIAL AND METHODS The expression level of circ_0067934 in thyroid tumors, adjacent tissues, and cell lines was measured by qRT-PCR. The Kaplan-Meier survival curve analysis was used to explore the relationship between circ_0067934 level and survival time of patients. Circ_0067934 was knocked down to research its functional role in thyroid tumors. Cell proliferation was detected by CCK-8 (cell counting kit-8) assay. Migration and invasion were analyzed by Transwell assay. Western blot was applied to analyze the expression of epithelial-mesenchymal-transition (EMT) and PI3K/AKT related proteins. RESULTS Compared with adjacent tissue, circ_0067934 was highly expressed in thyroid tumors. Circ_0067934 expression level was highly expressed in thyroid tumor cell lines. Patients with high expression of circ_0067934 showed lower survival rates. Knockdown of circ_0067934 inhibited cell proliferation, migration, and invasion and also promoted apoptosis. In addition, circ_0067934 knockdown inhibited EMT and PI3K/AKT signaling pathways. CONCLUSIONS circ_0067934 could improve the development of thyroid carcinoma by promoting EMT and PI3K/AKT signaling pathways.

Xu Y, Chen J, Yang Z, Xu L
Identification of RNA Expression Profiles in Thyroid Cancer to Construct a Competing Endogenous RNA (ceRNA) Network of mRNAs, Long Noncoding RNAs (lncRNAs), and microRNAs (miRNAs).
Med Sci Monit. 2019; 25:1140-1154 [PubMed] Article available free on PMC after 19/07/2020 Related Publications
BACKGROUND The aims of this study were to use RNA expression profile bioinformatics data from cases of thyroid cancer from the Cancer Genome Atlas (TCGA), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Gene Ontology (GO) databases to construct a competing endogenous RNA (ceRNA) network of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs). MATERIAL AND METHODS TCGA provided RNA profiles from 515 thyroid cancer tissues and 56 normal thyroid tissues. The DESeq R package analyzed high-throughput sequencing data on differentially expressed RNAs. GO and KEGG pathway analysis used the DAVID 6.8 and the ClusterProfile R package. Kaplan-Meier survival statistics and Cox regression analysis were performed. The thyroid cancer ceRNA network was constructed based on the miRDB, miRTarBase, and TargetScan databases. RESULTS There were 1,098 mRNAs associated with thyroid cancer; 101 mRNAs were associated with overall survival (OS). Multivariate analysis developed a risk scoring system that identified seven signature mRNAs, with a discriminative value of 0.88, determined by receiver operating characteristic (ROC) curve analysis. A ceRNA network included 13 mRNAs, 31 lncRNAs, and seven miRNAs. Four out of the 31 lncRNAs and all miRNAs were down-regulated, and the remaining RNAs were upregulated. Two lncRNAs (MIR1281A2HG and OPCML-IT1) and one miRNA (miR-184) were significantly associated with OS in patients with thyroid cancer. CONCLUSIONS Differential RNA expression profiling in thyroid cancer was used to construct a ceRNA network of mRNAs, lncRNAs, and miRNAs that showed potential in evaluating prognosis.

Zhang Y, Li F, Chen J
MYC promotes the development of papillary thyroid carcinoma by inhibiting the expression of lncRNA PAX8‑AS1:28.
Oncol Rep. 2019; 41(4):2511-2517 [PubMed] Related Publications
As a common malignancy of the endocrine system, papillary thyroid carcinoma (PTC) seriously affects the quality of life of patients. lncRNA PAX8‑AS1:28, or lnc‑PSD4‑1:14 has been reported to be abnormally expressed in PTC. However, the function of PAX8‑AS1:28 in PTC is still unknown. Therefore, the present study aimed to investigate the functions of PAX8‑AS1:28 in PTC, and to explore the possible mechanisms of action. A total of 38 patients with PTC were included and the normal thyroid follicular epithelial cell line Nthy‑ori 3‑1 and PTC cell line IHH‑4 were also used. MYC and PAX8‑AS1:28 overexpression and siRNA silencing in the cell lines were carried out. Expression of PAX8‑AS1:28, PAX8 and MYC in tumor tissue, adjacent healthy tissue and different cell lines were detected by qRT‑PCR and western blot analysis. Cell proliferation was measured by CCK‑8 assay. Expression levels of PAX8‑AS1:28 and PAX8 were lower in PTC tumor tissue and PTC cells than those in healthy tissue and normal cells. In contrast, the expression level of MYC was higher in PTC cells than that in normal cells. PAX8‑AS1:28 silencing reduced the expression level of PAX8 and promoted tumor cell growth, while PAX8‑AS1:28 overexpression increased the expression level of PAX8 and inhibited tumor cell growth. MYC silencing increased expression levels of PAX8‑AS1:28 and PAX8 and inhibited tumor cell growth, while MYC overexpression decreased expression levels of PAX8‑AS1:28 and PAX8 and promoted tumor cell growth. MYC can promote PTC by inhibiting the expression of lncRNA PAX8‑AS1:28.

Mayson SE, Haugen BR
Molecular Diagnostic Evaluation of Thyroid Nodules.
Endocrinol Metab Clin North Am. 2019; 48(1):85-97 [PubMed] Related Publications
The historical management approach for many patients with indeterminate thyroid nodule fine needle aspiration cytology is a diagnostic lobectomy or thyroidectomy. However, the majority of patients undergo surgery unnecessarily, because most are proven to have benign disease on histology. Molecular testing is a diagnostic tool that can be used to help guide the clinical management of thyroid nodules with indeterminate cytology results. Testing has evolved substantially over the last decade with significant advances in testing methodology and improvements in our understanding of the genetic basis of thyroid cancer.

Valvo V, Nucera C
Coding Molecular Determinants of Thyroid Cancer Development and Progression.
Endocrinol Metab Clin North Am. 2019; 48(1):37-59 [PubMed] Article available free on PMC after 01/03/2020 Related Publications
Thyroid cancer is the most common endocrine malignancy. Its incidence and mortality rates have increased for patients with advanced-stage papillary thyroid cancer. The characterization of the molecular pathways essential in thyroid cancer initiation and progression has made huge progress, underlining the role of intracellular signaling to promote clonal evolution, dedifferentiation, metastasis, and drug resistance. The discovery of genetic alterations that include mutations (BRAF, hTERT), translocations, deletions (eg, 9p), and copy-number gain (eg, 1q) has provided new biological insights with clinical applications. Understanding how molecular pathways interplay is one of the key strategies to develop new therapeutic treatments and improve prognosis.

Xing M
Genetic-guided Risk Assessment and Management of Thyroid Cancer.
Endocrinol Metab Clin North Am. 2019; 48(1):109-124 [PubMed] Article available free on PMC after 01/03/2020 Related Publications
Controversies exist on how to optimally manage thyroid cancer because the prognosis is often uncertain based on clinical backgrounds. This can now be helped with prognostic genetic markers in thyroid cancer, exemplified by BRAF V600E and TERT promoter mutations, which have been well characterized and widely appreciated. The genetic duet of BRAF V600E/RAS and TERT promoter mutations is a most robust prognostic genetic pattern for poor prognosis of differentiated thyroid cancer. The high negative predictive values of the prognostic genetic markers are equally valuable. The best prognostic value of genetic markers in thyroid cancer is achieved through a clinical risk level-based and genotype-individualized manner.

Asa SL
The Current Histologic Classification of Thyroid Cancer.
Endocrinol Metab Clin North Am. 2019; 48(1):1-22 [PubMed] Related Publications
Thyroid cancers of follicular cell derivation provide excellent phenotype-genotype correlations. Current morphologic classifications are complex and require simplification. Benign adenomas have follicular or papillary architecture and bland cytology. Well-differentiated thyroid carcinomas exhibit follicular architecture, expansile growth, and variable cytologic atypia and invasiveness; low-risk tumors have excellent prognosis after surgical resection whereas widely-invasive and angioinvasive tumors warrant total thyroidectomy and radioablation. Papillary carcinoma is less differentiated; indolent microcarcinomas can be managed by active surveillance, whereas clinical lesions with local or distant spread require therapy. Progression gives rise to poorly differentiated and anaplastic carcinomas that are less common but far more aggressive.

Feng Z, Song Y, Qian J, et al.
Differential expression of a set of microRNA genes reveals the potential mechanism of papillary thyroid carcinoma.
Ann Endocrinol (Paris). 2019; 80(2):77-83 [PubMed] Related Publications
BACKGROUND: Our aim was to explore the potential mechanism underlying papillary thyroid carcinoma (PTC) development.
METHODS: Gene expression profile data GSE3467 and microRNA (miRNA) expression profile data E-TABM-68 were downloaded from Gene Expression Omnibus and Array Express database respectively. The differentially expressed genes (DEGs) and miRNAs between PTC patients and normal individuals were screened. Then, the significant target DEGs regulated by differentially expressed miRNAs were mapped to protein-protein interaction (PPI) network and functional modules were screened. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis for miRNA genes were performed using DAVID (the Database for Annotation, Visualization and Integration Discovery) tool.
RESULTS: Total 4307 DEGs and 23 differentially expressed miRNAs were identified. A PPI subnetwork containing 612 nodes and 713 edges was constructed. Total 5 DEGs such as SPARC (secreted protein acidic and rich in cysteine), FN1 (fibronectin 1), THBS1 (thrombospondin 1), COL1A1 (collagen, type I, alpha 1) and COL7A1 (collagen, type VII, alpha 1) were found in module M1. The up-regulated DEGs were significantly related with cell adhesion molecules (CAMs), response to wounding and immune response. The down-regulated DEGs were significantly enriched in metabolism related pathways and transcription related with GO terms.
CONCLUSIONS: ECM-receptor interaction and amino acid degradation may play key roles in the mechanism of PTC progression.

Wang Z, Lv J, Zou X, et al.
A three plasma microRNA signature for papillary thyroid carcinoma diagnosis in Chinese patients.
Gene. 2019; 693:37-45 [PubMed] Related Publications
Whether plasma miRNAs could be used as novel non-invasive biomarkers in diagnosing papillary thyroid carcinoma (PTC) remains unknown. In this study, we designed a four-phase study to identify differentially expressed plasma miRNAs in Chinese PTC patients. Exiqon panel was initially utilized to conduct plasma miRNA profile (3 PTC pools VS. 1 healthy control (HC) pool; each 10 samples were pooled as 1 sample). The dysregulated miRNAs were then analyzed in the training (30 PTC VS. 30 HCs), testing (57 PTC VS. 54 HCs) and external validation phases (33 PTC VS. 30HCs). The identified miRNAs were further affirmed in benign nodules (2 nodular goiter (NG) pool VS. 1 HC pool). We also verified the expression of identified miRNAs in 17 matched malignant and normal tissue samples, NG plasma samples (29 PTC VS. 29 NG) and plasma exosomes (25 PTC VS. 25 HCs). Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic value of the identified miRNAs. As a result, the screening phase demonstrated 30 dysregulated plasma miRNAs in PTC patients compared with HCs. After multiphase experiment processes, miR-346, miR-10a-5p and miR-34a-5p were found significantly elevated in PTC plasma samples relative to HCs. The areas under the ROC curve (AUC) of the three-miRNA panel for the training, testing and validation phases were 0.926, 0.811 and 0.816, separately. The panel could also differentiate PTC from NG with the AUC of 0.877. MiR-346 and miR-34a-5p but not miR-10a-5p were up-regulated in PTC tissues. And the three miRNAs showed consistently up-regulation in PTC plasma exosomes. In conclusion, our study established a three-miRNA panel in plasma with considerable clinical value in discriminating PTC from HC or NG.

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