Cervical Cancer

Overview

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 08 August, 2015 using data from PubMed, MeSH and CancerIndex

Mutated Genes and Abnormal Protein Expression (301)

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
IL10 1q31-q32 CSIF, TGIF, GVHDS, IL-10, IL10A -Interleukin-10 and Cervical Cancer
55
FHIT 3p14.2 FRA3B, AP3Aase -FHIT and Cervical Cancer
51
HLA-DRB1 6p21.3 SS1, DRB1, DRw10, HLA-DRB, HLA-DR1B -HLA-DRB1 and Cervical Cancer
46
EGFR 7p12 ERBB, HER1, mENA, ERBB1, PIG61, NISBD2 -EGFR and Cervical Cancer
44
TERC 3q26 TR, hTR, TRC3, DKCA1, PFBMFT2, SCARNA19 -TERC and Cervical Cancer
41
MDM2 12q14.3-q15 HDMX, hdm2, ACTFS -MDM2 and Cervical Cancer
29
PTGS2 1q25.2-q25.3 COX2, COX-2, PHS-2, PGG/HS, PGHS-2, hCox-2, GRIPGHS -PTGS2 (COX2) and Cervical Cancer
28
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II -MMP2 and Cervical Cancer
28
DAPK1 9q21.33 DAPK -DAPK1 and Cervical Cancer
26
CASP8 2q33-q34 CAP4, MACH, MCH5, FLICE, ALPS2B, Casp-8 -CASP8 and Cervical Cancer
22
HLA-A 6p21.3 HLAA -HLA-A and Cervical Cancer
22
SLC2A1 1p34.2 PED, DYT9, GLUT, DYT17, DYT18, EIG12, GLUT1, HTLVR, GLUT-1, GLUT1DS -GLUT1 expression in Cervical Cancer
21
MGMT 10q26 -MGMT and Cervical Cancer
20
DAPK2 15q22.31 DRP1, DRP-1 -DAPK2 and Cervical Cancer
19
CDH1 16q22.1 UVO, CDHE, ECAD, LCAM, Arc-1, CD324 -CDH1 and Cervical Cancer
18
FAS 10q24.1 APT1, CD95, FAS1, APO-1, FASTM, ALPS1A, TNFRSF6 -FAS and Cervical Cancer
18
AKT1 14q32.32 AKT, PKB, RAC, CWS6, PRKBA, PKB-ALPHA, RAC-ALPHA -AKT1 and Cervical Cancer
17
CASP9 1p36.21 MCH6, APAF3, APAF-3, PPP1R56, ICE-LAP6 -CASP9 and Cervical Cancer
14
BCL2 18q21.3 Bcl-2, PPP1R50 -BCL2 and Cervical Cancer
14
MET 7q31 HGFR, AUTS9, RCCP2, c-Met -C-MET and Cervical Cancer
13
CYP1A1 15q24.1 AHH, AHRR, CP11, CYP1, P1-450, P450-C, P450DX -CYP1A1 and Cervical Cancer
13
NME1 17q21.3 NB, AWD, NBS, GAAD, NDKA, NM23, NDPKA, NDPK-A, NM23-H1 -NME1 and Cervical Cancer
13
TIMP1 Xp11.3-p11.23 EPA, EPO, HCI, CLGI, TIMP -TIMP1 and Cervical Cancer
11
ABCB1 7q21.12 CLCS, MDR1, P-GP, PGY1, ABC20, CD243, GP170 -ABCB1 and Cervical Cancer
11
LIPA 10q23.2-q23.3 LAL, CESD -LIPA and Cervical Cancer
11
TIMP2 17q25 DDC8, CSC-21K -TIMP2 and Cervical Cancer
10
GAPDH 12p13 G3PD, GAPD, HEL-S-162eP -GAPDH and Cervical Cancer
10
CCNB1 5q12 CCNB -CCNB1 and Cervical Cancer
10
CA9 9p13.3 MN, CAIX -CA9 and Cervical Cancer
8
CCR2 3p21.31 CKR2, CCR-2, CCR2A, CCR2B, CD192, CKR2A, CKR2B, CMKBR2, MCP-1-R, CC-CKR-2 -CCR2 and Cervical Cancer
8
MMP9 20q13.12 GELB, CLG4B, MMP-9, MANDP2 -MMP9 and Cervical Cancer
8
BECN1 17q21 ATG6, VPS30, beclin1 -BECN1 and Cervical Cancer
8
CXCL12 10q11.1 IRH, PBSF, SDF1, TLSF, TPAR1, SCYB12 -CXCL12 and Cervical Cancer
8
CD82 11p11.2 R2, 4F9, C33, IA4, ST6, GR15, KAI1, SAR2, TSPAN27 -CD82 and Cervical Cancer
8
CCL2 17q11.2-q12 HC11, MCAF, MCP1, MCP-1, SCYA2, GDCF-2, SMC-CF, HSMCR30 -CCL2 and Cervical Cancer
7
TLR9 3p21.3 CD289 -TLR9 and Cervical Cancer
7
MIR126 9q34.3 MIRN126, mir-126, miRNA126 -MicroRNA mir-126 and Cervical Cancer
7
SOX1 13q34 -SOX1 and Cervical Cancer
7
FGF2 4q26 BFGF, FGFB, FGF-2, HBGF-2 -FGF2 and Cervical Cancer
7
TGFA 2p13 TFGA -TGFA and Cervical Cancer
7
HLA-G 6p21.3 MHC-G -HLA-G and Cervical Cancer
7
ESR1 6q25.1 ER, ESR, Era, ESRA, ESTRR, NR3A1 -ESR1 and Cervical Cancer
7
FOXM1 12p13 MPP2, TGT3, HFH11, HNF-3, INS-1, MPP-2, PIG29, FKHL16, FOXM1B, HFH-11, TRIDENT, MPHOSPH2 -FOXM1 and Cervical Cancer
7
MAPK1 22q11.21 ERK, p38, p40, p41, ERK2, ERT1, ERK-2, MAPK2, PRKM1, PRKM2, P42MAPK, p41mapk, p42-MAPK -MAPK1 and Cervical Cancer
7
RARB 3p24.2 HAP, RRB2, NR1B2, MCOPS12 -RARB and Cervical Cancer
7
H2AFX 11q23.3 H2AX, H2A.X, H2A/X -H2AFX and Cervical Cancer
7
MYB 6q22-q23 efg, Cmyb, c-myb, c-myb_CDS -MYB and Cervical Cancer
7
HLA-DPB1 6p21.3 DPB1, HLA-DP, HLA-DPB, HLA-DP1B -HLA-DPB1 and Cervical Cancer
6
CDC6 17q21.3 CDC18L, HsCDC6, HsCDC18 -CDC6 and Cervical Cancer
6
HIC1 17p13.3 hic-1, ZBTB29, ZNF901 -HIC1 and Cervical Cancer
6
HLA-C 6p21.3 HLC-C, D6S204, PSORS1, HLA-JY3 -HLA-C and Cervical Cancer
6
IL1RN 2q14.2 DIRA, IRAP, IL1F3, IL1RA, MVCD4, IL-1RN, IL-1ra, IL-1ra3, ICIL-1RA -IL1RN and Cervical Cancer
6
JUNB 19p13.2 AP-1 -JUNB and Cervical Cancer
6
IFNG 12q14 IFG, IFI -IFNG and Cervical Cancer
6
CCNA1 13q12.3-q13 CT146 -CCNA1 and Cervical Cancer
5
ABCC1 16p13.1 MRP, ABCC, GS-X, MRP1, ABC29 -ABCC1 (MRP1) and Cervical Cancer
5
SMAD4 18q21.1 JIP, DPC4, MADH4, MYHRS -SMAD4 and Cervical Cancer
5
FGFR3 4p16.3 ACH, CEK2, JTK4, CD333, HSFGFR3EX -FGFR3 and Cervical Cancer
5
PDGFRA 4q12 CD140A, PDGFR2, PDGFR-2, RHEPDGFRA -PDGFRA and Cervical Cancer
5
EPB41L3 18p11.32 4.1B, DAL1, DAL-1 -EPB41L3 and Cervical Cancer
5
MAPK3 16p11.2 ERK1, ERT2, ERK-1, PRKM3, P44ERK1, P44MAPK, HS44KDAP, HUMKER1A, p44-ERK1, p44-MAPK -MAPK3 and Cervical Cancer
5
CLU 8p21-p12 CLI, AAG4, APOJ, CLU1, CLU2, KUB1, SGP2, APO-J, SGP-2, SP-40, TRPM2, TRPM-2, NA1/NA2 -Clusterin and Cervical Cancer
5
PPP2CA 5q31.1 RP-C, PP2Ac, PP2CA, PP2Calpha -PPP2CA and Cervical Cancer
5
CASP7 10q25 MCH3, CMH-1, LICE2, CASP-7, ICE-LAP3 -CASP7 and Cervical Cancer
5
S100P 4p16 MIG9 -S100P and Cervical Cancer
5
ETS1 11q23.3 p54, ETS-1, EWSR2 -ETS1 and Cervical Cancer
5
THRB 3p24.2 GRTH, PRTH, THR1, ERBA2, NR1A2, THRB1, THRB2, C-ERBA-2, C-ERBA-BETA -THRB and Cervical Cancer
4
BARD1 2q34-q35 -BARD1 and Cervical Cancer
4
PPP2R1B 11q23.2 PR65B, PP2A-Abeta -PPP2R1B and Cervical Cancer
4
ERCC2 19q13.3 EM9, TTD, XPD, COFS2, TFIIH -ERCC2 and Cervical Cancer
4
LTA 6p21.3 LT, TNFB, TNFSF1 -LTA and Cervical Cancer
4
CYP27B1 12q14.1 VDR, CP2B, CYP1, PDDR, VDD1, VDDR, VDDRI, CYP27B, P450c1, CYP1alpha -CYP27B1 and Cervical Cancer
4
RECK 9p13.3 ST15 -RECK and Cervical Cancer
4
LAMB3 1q32 AI1A, LAM5, LAMNB1, BM600-125KDA -LAMB3 and Cervical Cancer
4
PPP2CB 8p12 PP2CB, PP2Abeta -PPP2CB and Cervical Cancer
4
CDK2 12q13 CDKN2, p33(CDK2) -CDK2 and Cervical Cancer
4
HLA-DRA 6p21.3 MLRW, HLA-DRA1 -HLA-DRA and Cervical Cancer
4
SIX1 14q23.1 BOS3, TIP39, DFNA23 -SIX1 and Cervical Cancer
4
IL12A 3q25.33 P35, CLMF, NFSK, NKSF1, IL-12A -IL12A and Cervical Cancer
4
CA12 15q22 CAXII, HsT18816 -CA12 and Cervical Cancer
4
SCRIB 8q24.3 CRIB1, SCRB1, SCRIB1, Vartul -SCRIB and Cervical Cancer
4
CLDN3 7q11.23 RVP1, HRVP1, C7orf1, CPE-R2, CPETR2 -CLDN3 and Cervical Cancer
4
ICOS 2q33 AILIM, CD278, CVID1 -ICOS and Cervical Cancer
4
PAPPA 9q33.2 PAPA, DIPLA1, PAPP-A, PAPPA1, ASBABP2, IGFBP-4ase -PAPPA and Cervical Cancer
4
IL12B 5q33.3 CLMF, NKSF, CLMF2, IMD28, IMD29, NKSF2, IL-12B -IL12B and Cervical Cancer
4
H19 11p15.5 ASM, BWS, WT2, ASM1, PRO2605, D11S813E, LINC00008, NCRNA00008 -H19 and Cervical Cancer
4
SFRP1 8p11.21 FRP, FRP1, FrzA, FRP-1, SARP2 -SFRP1 and Cervical Cancer
4
MIB1 18q11.2 MIB, DIP1, ZZZ6, DIP-1, LVNC7, ZZANK2 -MIB1 and Cervical Cancer
3
TMC8 17q25.3 EV2, EVER2, EVIN2 -TMC8 and Cervical Cancer
3
HDAC1 1p34 HD1, RPD3, GON-10, RPD3L1 -HDAC1 and Cervical Cancer
3
MCM5 22q13.1 CDC46, P1-CDC46 -MCM5 and Cervical Cancer
3
DLG1 3q29 hdlg, DLGH1, SAP97, SAP-97, dJ1061C18.1.1 -DLG1 and Cervical Cancer
3
EZH2 7q35-q36 WVS, ENX1, EZH1, KMT6, WVS2, ENX-1, EZH2b, KMT6A -EZH2 and Cervical Cancer
3
MCM7 7q21.3-q22.1 MCM2, CDC47, P85MCM, P1CDC47, PNAS146, PPP1R104, P1.1-MCM3 -MCM7 and Cervical Cancer
3
APEX1 14q11.2 APE, APX, APE1, APEN, APEX, HAP1, REF1 -APEX1 and Cervical Cancer
3
RBP1 3q23 CRBP, RBPC, CRBP1, CRBPI, CRABP-I -RBP1 and Cervical Cancer
3
FBXW7 4q31.3 AGO, CDC4, FBW6, FBW7, hAgo, FBX30, FBXW6, SEL10, hCdc4, FBXO30, SEL-10 -FBXW7 mutations in Cervical Cancer
3
CAV1 7q31.1 CGL3, PPH3, BSCL3, LCCNS, VIP21, MSTP085 -CAV1 and Cervical Cancer
3
EGR1 5q31.1 TIS8, AT225, G0S30, NGFI-A, ZNF225, KROX-24, ZIF-268 -EGR1 and Cervical Cancer
3
TMC6 17q25.3 EV1, EVER1, EVIN1, LAK-4P -TMC6 and Cervical Cancer
3
TLR3 4q35 CD283, IIAE2 -TLR3 and Cervical Cancer
3
PCDH10 4q28.3 PCDH19, OL-PCDH -PCDH10 and Cervical Cancer
3
IL1B 2q14 IL-1, IL1F2, IL1-BETA -IL1B and Cervical Cancer
3
TNFRSF10B 8p22-p21 DR5, CD262, KILLER, TRICK2, TRICKB, ZTNFR9, TRAILR2, TRICK2A, TRICK2B, TRAIL-R2, KILLER/DR5 -TNFRSF10B and Cervical Cancer
3
IL1A 2q14 IL1, IL-1A, IL1F1, IL1-ALPHA -IL1A and Cervical Cancer
3
CTDSPL 3p21.3 PSR1, SCP3, HYA22, RBSP3, C3orf8 -CTDSPL and Cervical Cancer
3
TFPI 2q32 EPI, TFI, LACI, TFPI1 -TFPI and Cervical Cancer
3
NDRG1 8q24.3 GC4, RTP, DRG1, NDR1, NMSL, TDD5, CAP43, CMT4D, DRG-1, HMSNL, RIT42, TARG1, PROXY1 -NDRG1 and Cervical Cancer
3
ROBO1 3p12 SAX3, DUTT1 -ROBO1 and Cervical Cancer
3
CD55 1q32 CR, TC, DAF, CROM -CD55 and Cervical Cancer
3
ARID1A 1p35.3 ELD, B120, OSA1, P270, hELD, BM029, MRD14, hOSA1, BAF250, C1orf4, BAF250a, SMARCF1 -ARID1A and Cervical Cancer
3
UBE2C 20q13.12 UBCH10, dJ447F3.2 -UBE2C and Cervical Cancer
3
CYP19A1 15q21.1 ARO, ARO1, CPV1, CYAR, CYP19, CYPXIX, P-450AROM -CYP19A1 and Cervical Cancer
3
EPOR 19p13.3-p13.2 EPO-R -EPOR and Cervical Cancer
3
HMGA1 6p21 HMG-R, HMGIY, HMGA1A -HMGA1 and Cervical Cancer
3
MACC1 7p21.1 7A5, SH3BP4L -MACC1 and Cervical Cancer
3
TFPI2 7q22 PP5, REF1, TFPI-2 -TFPI2 and Cervical Cancer
3
CD83 6p23 BL11, HB15 -CD83 and Cervical Cancer
3
CTSB 8p22 APPS, CPSB -CTSB and Cervical Cancer
3
RECQL4 8q24.3 RECQ4 -RECQL4 and Cervical Cancer
3
PRKCD 3p21.31 MAY1, PKCD, ALPS3, CVID9, nPKC-delta -PRKCD and Cervical Cancer
2
IMP3 15q24 BRMS2, MRPS4, C15orf12 -IMP3 and Cervical Cancer
2
CKS2 9q22 CKSHS2 -CKS2 and Cervical Cancer
2
POLB 8p11.2 -POLB and Cervical Cancer
2
CD46 1q32 MCP, TLX, AHUS2, MIC10, TRA2.10 -CD46 and Cervical Cancer
2
NR3C2 4q31.1 MR, MCR, MLR, NR3C2VIT -NR3C2 and Cervical Cancer
2
MTDH 8q22.1 3D3, AEG1, AEG-1, LYRIC, LYRIC/3D3 -MTDH and Cervical Cancer
2
TWIST2 2q37.3 FFDD3, DERMO1, SETLSS, bHLHa39 -TWIST2 and Cervical Cancer
2
MSLN 16p13.3 MPF, SMRP -MSLN and Cervical Cancer
2
PDCD1 2q37.3 PD1, PD-1, CD279, SLEB2, hPD-1, hPD-l, hSLE1 -PDCD1 and Cervical Cancer
2
XRCC4 5q14.2 -XRCC4 and Cervical Cancer
2
IL17C 16q24 CX2, IL-17C -IL17C and Cervical Cancer
2
S100A9 1q21 MIF, NIF, P14, CAGB, CFAG, CGLB, L1AG, LIAG, MRP14, 60B8AG, MAC387 -S100A9 and Cervical Cancer
2
EGLN1 1q42.1 HPH2, PHD2, SM20, ECYT3, HALAH, HPH-2, HIFPH2, ZMYND6, C1orf12, HIF-PH2 -EGLN1 and Cervical Cancer
2
MMP10 11q22.3 SL-2, STMY2 -MMP10 and Cervical Cancer
2
CCR5 3p21.31 CKR5, CCR-5, CD195, CKR-5, CCCKR5, CMKBR5, IDDM22, CC-CKR-5 -CCR5 and Cervical Cancer
2
CTSL 9q21.33 MEP, CATL, CTSL1 -CTSL and Cervical Cancer
2
MTA1 14q32.3 -MTA1 and Cervical Cancer
2
HLTF 3q25.1-q26.1 ZBU1, HLTF1, RNF80, HIP116, SNF2L3, HIP116A, SMARCA3 -HLTF and Cervical Cancer
2
RBL1 20q11.2 PRB1, p107, CP107 -RBL1 and Cervical Cancer
2
MBL2 10q11.2 MBL, MBP, MBP1, MBPD, MBL2D, MBP-C, COLEC1, HSMBPC -MBL2 and Cervical Cancer
2
RRM2 2p25-p24 R2, RR2, RR2M -RRM2 and Cervical Cancer
2
ZNF350 19q13.41 ZFQR, ZBRK1 -ZNF350 and Cervical Cancer
2
BIRC2 11q22 API1, MIHB, HIAP2, RNF48, cIAP1, Hiap-2, c-IAP1 -BIRC2 and Cervical Cancer
2
EXO1 1q43 HEX1, hExoI -EXO1 and Cervical Cancer
2
MMP14 14q11.2 MMP-14, MMP-X1, MT-MMP, MT1MMP, MTMMP1, WNCHRS, MT1-MMP, MT-MMP 1 -MMP14 and Cervical Cancer
2
MIR10A 17q21.32 MIRN10A, mir-10a, miRNA10A, hsa-mir-10a -miR-10a and Cervical Cancer
2
FTCDNL1 2q33.1 FONG -FONG and Cervical Cancer
2
KRT7 12q13.13 K7, CK7, SCL, K2C7 -KRT7 and Cervical Cancer
2
AQP3 9p13 GIL, AQP-3 -AQP3 and Cervical Cancer
2
DIABLO 12q24.31 SMAC, DFNA64 -DIABLO and Cervical Cancer
2
KIAA1524 3q13.13 p90, CIP2A -KIAA1524 and Cervical Cancer
2
MYBL2 20q13.1 BMYB, B-MYB -MYBL2 and Cervical Cancer
2
IGFBP5 2q35 IBP5 -IGFBP5 and Cervical Cancer
2
PINX1 8p23 LPTL, LPTS -PINX1 and Cervical Cancer
2
MMP7 11q22.2 MMP-7, MPSL1, PUMP-1 -MMP7 and Cervical Cancer
2
TLR7 Xp22.3 TLR7-like -TLR7 and Cervical Cancer
2
MAML1 5q35 Mam1, Mam-1 -MAML1 and Cervical Cancer
2
ETS2 21q22.2 ETS2IT1 -ETS2 and Cervical Cancer
2
RALGDS 9q34.3 RGF, RGDS, RalGEF -RALGDS and Cervical Cancer
2
SLPI 20q12 ALP, MPI, ALK1, BLPI, HUSI, WAP4, WFDC4, HUSI-I -SLPI and Cervical Cancer
2
HPRT1 Xq26.1 HPRT, HGPRT -HPRT1 and Cervical Cancer
2
DHFR 5q14.1 DYR, DHFRP1 -DHFR and Cervical Cancer
2
POLI 18q21.1 RAD30B, RAD3OB -POLI and Cervical Cancer
2
CLPTM1L 5p15.33 CRR9 -CLPTM1L and Cervical Cancer
2
DUSP1 5q34 HVH1, MKP1, CL100, MKP-1, PTPN10 -DUSP1 and Cervical Cancer
2
SERPINA1 14q32.1 PI, A1A, AAT, PI1, A1AT, PRO2275, alpha1AT -SERPINA1 and Cervical Cancer
2
PBX1 1q23 -PBX1 and Cervical Cancer
2
DKK3 11p15.2 RIG, REIC -DKK3 and Cervical Cancer
2
SHBG 17p13.1 ABP, SBP, TEBG -SHBG and Cervical Cancer
2
TOP1 20q12-q13.1 TOPI -TOP1 and Cervical Cancer
2
IL17A 6p12 IL17, CTLA8, IL-17, IL-17A -IL17A and Cervical Cancer
2
MIR124-1 8p23.1 MIR124A, MIR124A1, MIRN124-1, MIRN124A1 -microRNA 124-1 and Cervical Cancer
2
S100A8 1q21 P8, MIF, NIF, CAGA, CFAG, CGLA, L1Ag, MRP8, CP-10, MA387, 60B8AG -S100A8 and Cervical Cancer
2
NAV1 1q32.3 POMFIL3, UNC53H1, STEERIN1 -NAV1 and Cervical Cancer
2
HOXB4 17q21.32 HOX2, HOX2F, HOX-2.6 -HOXB4 and Cervical Cancer
2
MTRR 5p15.31 MSR, cblE -MTRR and Cervical Cancer
2
PRIM1 12q13 p49 -PRIM1 and Cervical Cancer
1
HFE 6p21.3 HH, HFE1, HLA-H, MVCD7, TFQTL2 -HFE and Cervical Cancer
1
CHAT 10q11.2 CMS6, CMS1A, CMS1A2, CHOACTASE -CHAT and Cervical Cancer
1
HOTAIR 12q13.13 HOXAS, HOXC-AS4, HOXC11-AS1, NCRNA00072 -HOTAIR and Cervical Cancer
1
FCGR3A 1q23 CD16, FCG3, CD16A, FCGR3, IGFR3, IMD20, FCR-10, FCRIII, FCGRIII, FCRIIIA -FCGR3A and Cervical Cancer
1
PTHLH 12p12.1-p11.2 HHM, PLP, BDE2, PTHR, PTHRP -PTHLH and Cervical Cancer
1
TPR 1q25 -TPR and Cervical Cancer
1
DDR2 1q23.3 TKT, MIG20a, NTRKR3, TYRO10 -DDR2 and Cervical Cancer
1
TP53INP1 8q22 SIP, Teap, p53DINP1, TP53DINP1, TP53INP1A, TP53INP1B -TP53INP1 and Cervical Cancer
1
PTCH1 9q22.3 PTC, BCNS, HPE7, PTC1, PTCH, NBCCS, PTCH11 -PTCH1 and Cervical Cancer
1
TACC3 4p16.3 ERIC1, ERIC-1 -TACC3 and Cervical Cancer
1
CDC25B 20p13 -CDC25B and Cervical Cancer
1
FOXC2 16q24.1 LD, MFH1, MFH-1, FKHL14 -FOXC2 and Cervical Cancer
1
NDRG2 14q11.2 SYLD -NDRG2 and Cervical Cancer
1
RRM2B 8q23.1 P53R2, MTDPS8A, MTDPS8B -RRM2B and Cervical Cancer
1
EPHA2 1p36 ECK, CTPA, ARCC2, CTPP1, CTRCT6 -EPHA2 and Cervical Cancer
1
KDM5A 12p11 RBP2, RBBP2, RBBP-2 -KDM5A and Cervical Cancer
1
CDK9 9q34.1 TAK, C-2k, CTK1, CDC2L4, PITALRE -CDK9 and Cervical Cancer
1
CCNG1 5q32-q34 CCNG -CCNG1 and Cervical Cancer
1
NTRK2 9q22.1 TRKB, trk-B, GP145-TrkB -NTRK2 and Cervical Cancer
1
KRT18 12q13 K18, CYK18 -KRT18 and Cervical Cancer
1
RARRES3 11q23 RIG1, TIG3, HRSL4, HRASLS4, PLA1/2-3 -RARRES3 and Cervical Cancer
1
VIPR1 3p22 II, HVR1, RDC1, V1RG, VIPR, VIRG, VAPC1, VPAC1, VPAC1R, VIP-R-1, VPCAP1R, PACAP-R2, PACAP-R-2 -VIPR1 and Cervical Cancer
1
SLC9A1 1p36.1-p35 APNH, NHE1, LIKNS, NHE-1, PPP1R143 -SLC9A1 and Cervical Cancer
1
IL6R 1q21 IL6Q, gp80, CD126, IL6RA, IL6RQ, IL-6RA, IL-6R-1 -IL6R and Cervical Cancer
1
CDH2 18q11.2 CDHN, NCAD, CD325, CDw325 -CDH2 and Cervical Cancer
1
PPP1R13L 19q13.32 RAI, RAI4, IASPP, NKIP1 -PPP1R13L and Cervical Cancer
1
HOXA13 7p15.2 HOX1, HOX1J -HOXA13 and Cervical Cancer
1
CXCR3 Xq13 GPR9, MigR, CD182, CD183, Mig-R, CKR-L2, CMKAR3, IP10-R -CXCR3 and Cervical Cancer
1
CFTR 7q31.2 CF, MRP7, ABC35, ABCC7, CFTR/MRP, TNR-CFTR, dJ760C5.1 -CFTR and Cervical Cancer
1
CRP 1q23.2 PTX1 -CRP and Cervical Cancer
1
LAPTM4B 8q22.1 LC27, LAPTM4beta -LAPTM4B and Cervical Cancer
1
TSC22D1 13q14 Ptg-2, TSC22, TGFB1I4 -TSC22D1 and Cervical Cancer
1
LTB 6p21.3 p33, TNFC, TNFSF3 -LTB and Cervical Cancer
1
TLR1 4p14 TIL, CD281, rsc786, TIL. LPRS5 -TLR1 and Cervical Cancer
1
LINC00632 Xq27.1 -RP1-177G6.2 and Cervical Cancer
1
CST6 11q13 -CST6 and Cervical Cancer
1
TRIM27 6p22 RFP, RNF76 -TRIM27 and Cervical Cancer
1
CEBPB 20q13.1 TCF5, IL6DBP, NF-IL6, C/EBP-beta -CEBPB and Cervical Cancer
1
TFRC 3q29 T9, TR, TFR, p90, CD71, TFR1, TRFR -TFRC and Cervical Cancer
1
OPCML 11q25 OPCM, OBCAM, IGLON1 -OPCML and Cervical Cancer
1
POU2F1 1q24.2 OCT1, OTF1, oct-1B -POU2F1 and Cervical Cancer
1
CDX2 13q12.3 CDX3, CDX-3, CDX2/AS -CDX2 and Cervical Cancer
1
GUSB 7q21.11 BG, MPS7 -GUSB and Cervical Cancer
1
ISG15 1p36.33 G1P2, IP17, UCRP, IFI15, IMD38, hUCRP -ISG15 and Cervical Cancer
1
KRT14 17q21.2 K14, NFJ, CK14, EBS3, EBS4 -KRT14 and Cervical Cancer
1
AURKB 17p13.1 AIK2, AIM1, ARK2, AurB, IPL1, STK5, AIM-1, STK12, PPP1R48, aurkb-sv1, aurkb-sv2 -AURKB and Cervical Cancer
1
CMBL 5p15.2 JS-1 -CMBL and Cervical Cancer
1
SMO 7q32.3 Gx, SMOH, FZD11 -SMO and Cervical Cancer
1
SPINT2 19q13.1 PB, Kop, HAI2, DIAR3, HAI-2 -SPINT2 and Cervical Cancer
1
RASSF5 1q32.1 RAPL, Maxp1, NORE1, NORE1A, NORE1B, RASSF3 -RASSF5 and Cervical Cancer
1
ZBTB7A 19p13.3 LRF, FBI1, FBI-1, ZBTB7, ZNF857A, pokemon -ZBTB7A and Cervical Cancer
1
NBN 8q21 ATV, NBS, P95, NBS1, AT-V1, AT-V2 -NBN and Cervical Cancer
1
REV1 2q11.1-q11.2 REV1L -REV1 and Cervical Cancer
1
CCNC 6q21 CycC -CCNC and Cervical Cancer
1
MTHFD1 14q24 MTHFC, MTHFD -MTHFD1 and Cervical Cancer
1
NR4A3 9q22 CHN, TEC, CSMF, NOR1, MINOR -NR4A3 and Cervical Cancer
1
TBK1 12q14.1 NAK, T2K -TBK1 and Cervical Cancer
1
PTPRC 1q31-q32 LCA, LY5, B220, CD45, L-CA, T200, CD45R, GP180 -PTPRC and Cervical Cancer
1
YWHAZ 8q23.1 HEL4, YWHAD, KCIP-1, HEL-S-3, 14-3-3-zeta -YWHAZ and Cervical Cancer
1
WWTR1 3q23-q24 TAZ -WWTR1 and Cervical Cancer
1
CASP4 11q22.2-q22.3 TX, ICH-2, Mih1/TX, ICEREL-II, ICE(rel)II -CASP4 and Cervical Cancer
1
BDNF 11p13 ANON2, BULN2 -BDNF and Cervical Cancer
1
NR4A2 2q22-q23 NOT, RNR1, HZF-3, NURR1, TINUR -NR4A2 and Cervical Cancer
1
STIM1 11p15.5 GOK, TAM, TAM1, IMD10, STRMK, D11S4896E -STIM1 and Cervical Cancer
1
LGALS3 14q22.3 L31, GAL3, MAC2, CBP35, GALBP, GALIG, LGALS2 -LGALS3 and Cervical Cancer
1
MIR1290 1 MIRN1290, hsa-mir-1290 -miR-1290 and Cervical Cancer
1
INHA 2q35 -INHA and Cervical Cancer
1
CRTC1 19p13.11 MECT1, TORC1, TORC-1, WAMTP1 -CRTC1 and Cervical Cancer
1
PIK3CD 1p36.2 APDS, PI3K, IMD14, p110D, P110DELTA -PIK3CD and Cervical Cancer
1
KRT1 12q13.13 K1, CK1, EHK, EHK1, EPPK, KRT1A, NEPPK -KRT1 and Cervical Cancer
1
MICB 6p21.3 PERB11.2 -MICB and Cervical Cancer
1
IFITM1 11p15.5 9-27, CD225, IFI17, LEU13, DSPA2a -IFITM1 and Cervical Cancer
1
CASP1 11q23 ICE, P45, IL1BC -CASP1 and Cervical Cancer
1
LAMP1 13q34 LAMPA, CD107a, LGP120 -LAMP1 and Cervical Cancer
1
RFC1 4p14-p13 A1, RFC, PO-GA, RECC1, MHCBFB, RFC140 -RFC1 and Cervical Cancer
1
SIPA1 11q13 SPA1 -SIPA1 and Cervical Cancer
1
RASSF2 20p13 CENP-34, RASFADIN -RASSF2 and Cervical Cancer
1
PTP4A3 8q24.3 PRL3, PRL-3, PRL-R -PTP4A3 and Cervical Cancer
1
IBSP 4q21.1 BSP, BNSP, SP-II, BSP-II -IBSP and Cervical Cancer
1
CXCL11 4q21.2 IP9, H174, IP-9, b-R1, I-TAC, SCYB11, SCYB9B -CXCL11 and Cervical Cancer
1
CHI3L1 1q32.1 GP39, ASRT7, GP-39, YKL40, CGP-39, YKL-40, YYL-40, HC-gp39, HCGP-3P, hCGP-39 -CHI3L1 and Cervical Cancer
1
CAMTA1 1p36.31-p36.23 CANPMR -CAMTA1 and Cervical Cancer
1
PTCH2 1p34.1 PTC2 -PTCH2 and Cervical Cancer
1
HTRA1 10q26.3 L56, HtrA, ARMD7, ORF480, PRSS11, CARASIL -HTRA1 and Cervical Cancer
1
POLL 10q23 BETAN, POLKAPPA -POLL and Cervical Cancer
1
CREB3L1 11p11.2 OASIS -CREB3L1 and Cervical Cancer
1
GNL3 3p21.1 NS, E2IG3, NNP47, C77032 -GNL3 and Cervical Cancer
1
LRRC3B 3p24 LRP15 -LRRC3B and Cervical Cancer
1
HSPA8 11q24.1 LAP1, HSC54, HSC70, HSC71, HSP71, HSP73, LAP-1, NIP71, HEL-33, HSPA10, HEL-S-72p -HSPA8 and Cervical Cancer
1
SLC45A3 1q32.1 PRST, IPCA6, IPCA-2, IPCA-6, IPCA-8, PCANAP2, PCANAP6, PCANAP8 -SLC45A3 and Cervical Cancer
1
MEST 7q32 PEG1 -MEST and Cervical Cancer
1
PYCARD 16p11.2 ASC, TMS, TMS1, CARD5, TMS-1 -PYCARD and Cervical Cancer
1
MYCL 1p34.2 LMYC, L-Myc, MYCL1, bHLHe38 -MYCL and Cervical Cancer
1
IL16 15q26.3 LCF, NIL16, PRIL16, prIL-16 -IL16 and Cervical Cancer
1
SOX11 2p25 MRD27 -SOX11 and Cervical Cancer
1
EPHB4 7q22 HTK, MYK1, TYRO11 -EPHB4 and Cervical Cancer
1
MIR127 14q32.2 MIRN127, miRNA127 -MicroRNA miR-127 and Cervical Cancer
1
FYN 6q21 SLK, SYN, p59-FYN -FYN and Cervical Cancer
1
TNFSF13 17p13.1 APRIL, CD256, TALL2, ZTNF2, TALL-2, TRDL-1, UNQ383/PRO715 -TNFSF13 and Cervical Cancer
1
S100A2 1q21 CAN19, S100L -S100A2 and Cervical Cancer
1
DLX4 17q21.33 BP1, DLX7, DLX8, DLX9 -DLX4 and Cervical Cancer
1
CLMP 11q24.1 ACAM, ASAM, CSBM, CSBS -CLMP and Cervical Cancer
1
PPIA 7p13 CYPA, CYPH, HEL-S-69p -PPIA and Cervical Cancer
1
CASP6 4q25 MCH2 -CASP6 and Cervical Cancer
1
IRF3 19q13.3-q13.4 -IRF3 and Cervical Cancer
1
GZMB 14q11.2 HLP, CCPI, CGL1, CSPB, SECT, CGL-1, CSP-B, CTLA1, CTSGL1 -GZMB and Cervical Cancer
1
TLE1 9q21.32 ESG, ESG1, GRG1 -TLE1 and Cervical Cancer
1
HOXA7 7p15.2 ANTP, HOX1, HOX1A, HOX1.1 -HOXA7 and Cervical Cancer
1
PER1 17p13.1 PER, hPER, RIGUI -PER1 and Cervical Cancer
1
PDCD6 5p15.33 ALG2, ALG-2, PEF1B -PDCD6 and Cervical Cancer
1
NR4A1 12q13 HMR, N10, TR3, NP10, GFRP1, NAK-1, NGFIB, NUR77 -NR4A1 and Cervical Cancer
1
MCM4 8q11.2 NKCD, CDC21, CDC54, NKGCD, hCdc21, P1-CDC21 -MCM4 and Cervical Cancer
1
MEG3 14q32 GTL2, FP504, prebp1, PRO0518, PRO2160, LINC00023, NCRNA00023 -MEG3 and Cervical Cancer
1
DGCR8 22q11.2 Gy1, pasha, DGCRK6, C22orf12 -DGCR8 and Cervical Cancer
1
EPHX1 1q42.1 MEH, EPHX, EPOX, HYL1 -EPHX1 and Cervical Cancer
1
NUMB 14q24.3 S171, C14orf41, c14_5527 -NUMB and Cervical Cancer
1
MAML2 11q21 MAM2, MAM3, MAM-3, MLL-MAML2 -MAML2 and Cervical Cancer
1
KLRK1 12p13.2-p12.3 KLR, CD314, NKG2D, NKG2-D, D12S2489E -KLRK1 and Cervical Cancer
1
APOD 3q29 -APOD and Cervical Cancer
1
ETV5 3q28 ERM -ETV5 and Cervical Cancer
1
TEP1 14q11.2 TP1, TLP1, p240, TROVE1, VAULT2 -TEP1 and Cervical Cancer
1
HTRA2 2p12 OMI, PARK13, PRSS25 -HTRA2 and Cervical Cancer
1
IRAK2 3p25.3 IRAK-2 -IRAK2 and Cervical Cancer
1

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

Latest Publications

Wang S, Sun H, Jia Y, et al.
Association of 42 SNPs with genetic risk for cervical cancer: an extensive meta-analysis.
BMC Med Genet. 2015; 16:25 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: A large number of single nucleotide polymorphisms (SNPs) associated with cervical cancer have been identified through candidate gene association studies and genome-wide association studies (GWAs). However, some studies have yielded different results for the same SNP. To obtain a more comprehensive understanding, we performed a meta-analysis on previously published case-control studies involving the SNPs associated with cervical cancer.
METHODS: Electronic searches of PubMed and Embase were conducted for all publications about the association between gene polymorphisms and cervical cancer. One-hundred and sixty-seven association studies were included in our research. For each SNP, three models (the allele, dominant and recessive effect models) were adopted in the meta-analysis. For each model, the effect summary odds ratio (OR) and 95% CI were calculated. Heterogeneity between studies was evaluated by Cochran's Q test. If the p value of Q test was less than 0.01, a random effect model was used; otherwise, a fixed effect model was used.
RESULTS: The results of our meta-analysis showed that: (1) There were 8, 2 and 8 SNPs that were significantly associated with cervical cancer (P < 0.01) in the allele, dominant and recessive effect models, respectively. (2) rs1048943 (CYP1A1 A4889G) showed the strongest association with cervical cancer in the allele effect model (1.83[1.57, 2.13]); in addition, rs1048943 (CYP1A1 A4889G) had a very strong association in the dominant and recessive effect model. (3) 15, 11 and 10 SNPs had high heterogeneity (P < 0.01) in the three models, respectively. (4) There was no published bias for most of the SNPs according to Egger's test (P < 0.01) and Funnel plot analysis. For some SNPs, their association with cervical cancer was only tested in a few studies and, therefore, might have been subjected to published bias. More studies on these loci are required.
CONCLUSION: Our meta-analysis provides a comprehensive evaluation of cervical cancer association studies.

Mehta AM, Spaans VM, Mahendra NB, et al.
Differences in genetic variation in antigen-processing machinery components and association with cervical carcinoma risk in two Indonesian populations.
Immunogenetics. 2015; 67(5-6):267-75 [PubMed] Free Access to Full Article Related Publications
Genetic variation of antigen-processing machinery (APM) components has been shown to be associated with cervical carcinoma risk and outcome in a genetically homogeneous Dutch population. However, the role of APM component single nucleotide polymorphisms (SNPs) in genetically heterogeneous populations with different distributions of human papillomavirus (HPV) subtypes remains unclear. Eleven non-synonymous, coding SNPs in the TAP1, TAP2, LMP2, LMP7 and ERAP1 genes were genotyped in cervical carcinoma patients and healthy controls from two distinct Indonesian populations (Balinese and Javanese). Individual genotype and allele distributions were investigated using single-marker analysis, and combined SNP effects were assessed by haplotype construction and haplotype interaction analysis. Allele distribution patterns in Bali and Java differed in relation to cervical carcinoma risk, with four ERAP1 SNPs and one TAP2 SNP in the Javanese population showing significant association with cervical carcinoma risk, while in the Balinese population, only one TAP2 SNP showed this association. Multimarker analysis demonstrated that in the Javanese patients, one specific haplotype, consisting of the ERAP1-575 locus on chromosome 5 and the TAP2-379 and TAP2-651 loci on chromosome 6, was significantly associated with cervical carcinoma risk (global P = 0.008); no significant haplotype associations were found in the Balinese population. These data indicate not only that genetic variation in APM component genes is associated with cervical carcinoma risk in Indonesia but also that the patterns of association differ depending on background genetic composition and possibly on differences in HPV type distribution.

Wang J, Chai YL, Wang T, et al.
Genetic alterations of PIK3CA and tumor response in patients with locally advanced cervical squamous cell carcinoma treated with cisplatin-based concurrent chemoradiotherapy.
Exp Mol Pathol. 2015; 98(3):407-10 [PubMed] Related Publications
OBJECTIVE: The objective of this study was to investigate the predictive value of common genetic alterations of PI3K/AKT/mTOR and Ras/Raf/MAPK pathways in patients with locally advanced cervical squamous cell carcinoma (LACSCC) treated with cisplatin-based concurrent chemoradiotherapy (CCRT).
METHODS: Patients with LACSCC, treated at a single institution with CCRT were eligible for this retrospective study. A total of sixty pre-treatment tumor biopsies were retrieved. Somatic mutations were detected by pyrosequencing and CNV was determined by quantitative realtime PCR. The association of genetic alterations with clinicopathological characteristics and treatment response were analyzed.
RESULTS: Patients without genetic alterations (mutations or amplification) of PIK3CA had a significantly higher response rate than patients with these alterations (p=0.006). In the logistic regression analysis, PIK3CA genetic alterations retained an independent factor in predicting response to CCRT.
CONCLUSIONS: Somatic mutations and copy number amplification of PIK3CA were associated with response to CCRT in patients with cervical squamous cell carcinoma.

Wang LQ, Zhang Y, Yan H, et al.
MicroRNA-373 functions as an oncogene and targets YOD1 gene in cervical cancer.
Biochem Biophys Res Commun. 2015; 459(3):515-20 [PubMed] Related Publications
miR-373 was reported to be elevated in several tumors; however, the role of miR-373 in cervical cancer has not been investigated. In this study we aimed to investigate the role of miR-373 in tumorigenicity of cervical cancer cells in vivo and in vitro. The expression of miR-373 was investigated using real-time reverse transcription-polymerase chain reaction assay in 45 cervical specimens and cervical cancer cell lines. The role of miR-373 in tumorigenicity of cervical cancer cells was assessed by cell proliferation, colony formation in vitro as well as tumor growth assays in vivo with the overexpression of miR-373 or gene silencing. The functional target gene of miR-373 in cervical cancer cells was identified using integrated bioinformatics analysis, gene expression arrays, and luciferase assay. We founded that the expression of miR-373 is upregulated in human cervical cancer tissues and cervical carcinoma cell lines when compared to the corresponding noncancerous tissues. Ectopic overexpression of miR-373 in human cervical cancer cells promoted cell growth in vitro and tumorigenicity in vivo, whereas silencing the expression of miR-373 decreased the rate of cell growth. YOD1 was identified as a direct and functional target of miR-373 in cervical cancer cells. Expression levels of miR-373 were inversely correlated with YOD1 levels in human cervical cancer tissues. RNAi-mediated knockdown of YOD1 phenocopied the proliferation-promoting effect of miR-373. Moreover, overexpression of YOD1 abrogated miR-373-induced proliferation of cervical cancer cells. These results demonstrate that miR-373 increases proliferation by directly targeting YOD1, a new potential therapeutic target in cervical cancer.

Diao MK, Liu CY, Liu HW, et al.
Integrated HPV genomes tend to integrate in gene desert areas in the CaSki, HeLa, and SiHa cervical cancer cell lines.
Life Sci. 2015; 127:46-52 [PubMed] Related Publications
AIMS: The integration preferences of human papillomavirus (HPV) have been intensively studied and contested over recent years. To disclose the integration preferences of high-risk HPV in cervical cancer, HPV transcriptional sites and features in different cervical cancer cell lines were identified.
MAIN METHODS: In this study, three cervical cancer cell lines (CaSki, HeLa, and SiHa) were subjected for HPV genome status determination by amplification of papillomavirus oncogene transcripts (APOT) assay. The numbers of viral copies in human genomes and numbers of viral-human fusion mRNAs in three HPV-integrated cervical cancer cell lines were measured and analysed.
KEY FINDINGS: The results revealed that the gene desert region 8q24 of the HPV type 18 integrated HeLa cell line and the 13q21-22 region of the HPV type 16 integrated CaSki and SiHa cell lines were hotspots for HPV integration, and the numbers of viral copies in the human genomes of the three cell lines that we detected were not in accordance with those reported in previous studies.
SIGNIFICANCE: Integration of the HPV genome into the host cell chromosome suggests that persistent HPV infection is vital for malignant cell transformation and carcinogenesis. This study provides information to benefit health care professionals seeking more comprehensive and accurate diagnostics for HPV-related disease"? Please check, and amend as necessary.

Verhoef VM, van Kemenade FJ, Rozendaal L, et al.
Follow-up of high-risk HPV positive women by combined cytology and bi-marker CADM1/MAL methylation analysis on cervical scrapes.
Gynecol Oncol. 2015; 137(1):55-9 [PubMed] Related Publications
OBJECTIVES: Triage of HPV screen-positive women is needed to identify those with underlying cervical intraepithelial neoplasia grade 2/3 or worse (CIN2/3+). Presently, cytology on a physician-taken cervical scrape is mostly accepted as triage test, but needs follow-up testing in order not to miss severe disease. Here, we evaluated the performance of combined cytology and bi-marker CADM1/MAL-methylation analysis as triage test on physician-taken cervical scrapes of HPV positive women.
METHODS: In this post-hoc analysis, we used 364 left-over HPV positive cytology triage samples of participants of a randomized controlled trial (PROHTECT-3: n=46,001) performed in population-based cervical screening. Study endpoints were CIN2+ and CIN3+ detection. Cytology testing with and without methylation marker analysis was evaluated with regard to sensitivity, specificity, positive and negative predictive value, and referral rate.
RESULTS: Bi-marker CADM1/MAL-methylation positivity increased proportionally with severity of underlying lesions. Overall, cytology and bi-marker CADM1/MAL-methylation analysis yielded similar performances with regard to CIN3+ detection, yet in combination a significantly higher sensitivity for CIN3+ (88.7%) was obtained at a specificity of 53.6% and a colposcopy referral rate of 53.6%. The combined strategy detected all six cervical cancers, whereas triage by cytology alone failed to detect two of them.
CONCLUSIONS: Cytology and bi-marker CADM1/MAL-methylation analysis perform complementary for CIN2+/CIN3+ detection when used as triage tool on cervical scrapes of HPV positive women. This approach not only results in a higher CIN3+ sensitivity than cytology triage with an acceptable referral rate, but also seems to reduce the risk of missing cervical cancers and advanced high-grade lesions.

Zhou C, Shen L, Mao L, et al.
miR-92a is upregulated in cervical cancer and promotes cell proliferation and invasion by targeting FBXW7.
Biochem Biophys Res Commun. 2015; 458(1):63-9 [PubMed] Related Publications
MicroRNAs (miRNAs) are involved in the cervical carcinogenesis and progression. In this study, we investigated the role of miR-92a in progression and invasion of cervical cancer. MiR-92a was significantly upregulated in cervical cancer tissues and cell lines. Overexpression of miR-92a led to remarkably enhanced proliferation by promoting cell cycle transition from G1 to S phase and significantly enhanced invasion of cervical cancer cells, while its knockdown significantly reversed these cellular events. Bioinformatics analysis suggested F-box and WD repeat domain-containing 7 (FBXW7) as a novel target of miR-92a, and miR-92a suppressed the expression level of FBXW7 mRNA by direct binding to its 3'-untranslated region (3'UTR). Expression of miR-92a was negatively correlated with FBXW7 in cervical cancer tissues. Furthermore, Silencing of FBXW7 counteracted the effects of miR-92a suppression, while its overexpression reversed oncogenic effects of miR-92a. Together, these findings indicate that miR-92a acts as an onco-miRNA and may contribute to the progression and invasion of cervical cancer, suggesting miR-92a as a potential novel diagnostic and therapeutic target of cervical cancer.

Mersakova S, Visnovsky J, Holubekova V, et al.
Detection of methylation of the promoter region of the MAL and CADM1 genes by pyrosequencing in cervical carcinoma.
Neuro Endocrinol Lett. 2014; 35(7):619-23 [PubMed] Related Publications
OBJECTIVE: Cervical cancer is the second most common cancer disease affecting the female population. A key factor in development of the disease is the human papillomavirus infection (HPV). The disease is also impacted by epigenetic changes such as DNA methylation, which causes activation or exclusion of certain genes, and simultaneously the hypermethylation of cytosines in the promoters and turn-off of previously active genes occur. In this study, we focused on the introduction of pyrosequencing for the detection of DNA methylation of the selected CADM1 and MAL genes.
METHODS: DNA was isolated from cytological cervical smear of patients with different types of dysplasia [L-SIL (n=14), ASC-US (n=15), H-SIL (n=1)] and four control samples from healthy women. Prepared samples were further analyzed by bisulfite conversion and subsequent pyrosequencing (Pyromark Q96 ID, Qiagen, Germany). We examined the extent of methylation of CpG islands and as control samples of this method we used a fully methylated and unmethylated DNA. Methylation level (Met level) from each sample was quantified as the mean value [sum of all methylated CpG islands in %/total number of CpG islands (MAL n=4; CADM1 n=3)].
RESULTS: In total, 30 clinical samples and 4 control samples from healthy women were analyzed. By means of the analysis of the CADM1promoter region, the values of the Met level were obtained [fully methylated DNA (94.83 and 88); completely unmethylated DNA (0 and 0); and control samples from healthy patients (6.825 and 0.825), L-SIL (2.107 and 2.778), ASC-US (7.313 and 3.626), H-SIL (0 and 0)]. By means of the analysis of the MAL promoter region, the values of Met level were obtained [fully methylated DNA (53.25); completely unmethylated DNA (0.875); and control samples from healthy patients (2.925), L-SIL (1.517), ASC-US (2.833), and H-SIL (4)].
CONCLUSION: We introduced a pyrosequencing method for quantification of methylation of CADM1, MAL promoter regions, and detected methylations in clinical samples and also some basal methylation in healthy women.

Lv Q, Zhu D, Zhang J, et al.
Association between six genetic variants of IL-17A and IL-17F and cervical cancer risk: a case-control study.
Tumour Biol. 2015; 36(5):3979-84 [PubMed] Related Publications
We conducted a case-control study to estimate association between six common single nucleotide polymorphisms (SNPs) and risk of cervical cancer and evaluate the interaction between IL-17 gene polymorphisms and environmental factors in cervical cancer patients. This study included 264 consecutive primary cervical cancer patients and 264 age-matched controls. The genotypes of IL-17A rs2275913, rs3748067, and rs3819025 and IL-17A rs763780, rs9382084, and rs1266828 were analyzed using polymerase chain reaction-restriction fragment length of polymorphism (PCR-RFLP) assay. By logistic regression analysis, we found that individuals with AA genotype of rs2275913 were correlated with increased risk of cervical cancer when compared with GG genotype, and the odds ratio (OR) (95 % confidence interval (CI)) for AA genotype was 2.34 (1.24-4.49). By stratified analysis, individuals with AA genotype of rs2275913 were significantly associated with increased risk of cervical cancer in HPV-16- or HPV-18-infected patients when compared with GG genotype, and the OR (95 % CI) was 4.11 (1.14-22.33). In this case-control study, we suggest that rs2275913 may play an important role in the development of cervical cancer, especially in HPV-16- or HPV-18-infected patients.

Hu Z, Zhu D, Wang W, et al.
Genome-wide profiling of HPV integration in cervical cancer identifies clustered genomic hot spots and a potential microhomology-mediated integration mechanism.
Nat Genet. 2015; 47(2):158-63 [PubMed] Related Publications
Human papillomavirus (HPV) integration is a key genetic event in cervical carcinogenesis. By conducting whole-genome sequencing and high-throughput viral integration detection, we identified 3,667 HPV integration breakpoints in 26 cervical intraepithelial neoplasias, 104 cervical carcinomas and five cell lines. Beyond recalculating frequencies for the previously reported frequent integration sites POU5F1B (9.7%), FHIT (8.7%), KLF12 (7.8%), KLF5 (6.8%), LRP1B (5.8%) and LEPREL1 (4.9%), we discovered new hot spots HMGA2 (7.8%), DLG2 (4.9%) and SEMA3D (4.9%). Protein expression from FHIT and LRP1B was downregulated when HPV integrated in their introns. Protein expression from MYC and HMGA2 was elevated when HPV integrated into flanking regions. Moreover, microhomologous sequence between the human and HPV genomes was significantly enriched near integration breakpoints, indicating that fusion between viral and human DNA may have occurred by microhomology-mediated DNA repair pathways. Our data provide insights into HPV integration-driven cervical carcinogenesis.

Furtado Y, Almeida G, Silveira FA, et al.
TIMP-2 gene methylation in cervical precursor and invasive lesions.
Exp Mol Pathol. 2015; 98(1):119-23 [PubMed] Related Publications
OBJECTIVE: To analyze the presence of HPV-DNA and TIMP-2 gene methylation in cervical precursor and invasive lesions, as well as to study the associations among the latter, the presence of HPV-DNA, and the clinical evolution of such lesions.
METHODS: Cross-sectional study that includes 49 biopsy or brush smear samples from women with a normal cervix, LSIL, HSIL, microinvasive carcinoma and invasive carcinoma. The presence of HPV-DNA and specific methylation was analyzed using PCR. Thirty-eight biopsy samples for HSIL, microinvasive carcinoma and frank invasive carcinoma as well as 11 brush smear samples for LSIL and normal cervices were analyzed.
RESULTS: TIMP-2 gene methylation was detected in 86.8% (33/38) of the samples from the group with lesions and 50% (4/8) of the normal samples (p=0.03). HPV-DNA was detected in 81.6% (31/38) of the samples from the group with lesions and 25% (2/8) of the normal samples (p=0.003). HPV-DNA was more frequent in the methylated samples (50%), and the group with methylation had a higher risk of unfavorable evolution than the group without methylation; however, such observations were not statistically significant (p=0.19).
CONCLUSION: TIMP-2 gene methylation and the presence of HPV-DNA were characteristic of the group with cervical lesions. Methylation was not associated with the presence of HPV-DNA or an unfavorable clinical evolution.

Timoshenko OS, Gureeva TA, Kugaevskaia EV, Solov'eva NI
[Membrane type 1 matrix metalloproteinase (MT1-MMP) and the regulators of its activity as invasive factors in squamous cell cervical carcinomas].
Biomed Khim. 2014 Nov-Dec; 60(6):683-8 [PubMed] Related Publications
Membrane type 1 matrix metalloproteinase (MT1MMP) is one of matrix metalloproteinases (MMP), which play а key role in tumor invasion and metastasis. The aim of this study was to elucidate the peculiarities of expression of MT1MMP and endogenous regulators of its activity: the activator - furin and the inhibitor - TIMP-2, as invasive factors of squamous cell cervical carcinomas (SCC). The study was carried out using 11 specimens of SCC and 11 specimens of morphologically normal tissue adjacent to the tumor. It was shown that the increase of MT1-MMP and furin expression and low of TIMP-2 expression makes the main contribution to the destructive (invasive) potential of SCC. Moreover, substantial expression of MT1-MMP was registered in the specimens of morphologically normal adjoining to tumor tissue. This expression was found to make an additional contribution to the destructive potential of the cervical tumor.

Nallapalle SR, Daripally S, Prasad VT
Promoter polymorphism of FASL confers protection against female-specific cancers and those of FAS impact the cancers divergently.
Tumour Biol. 2015; 36(4):2709-24 [PubMed] Related Publications
We investigated risk association of FAS (-1377 G>A and -670 A>G) and FASL (-844 T>C) promoter polymorphisms with breast, ovarian, cervical, and endometrial cancers and report that the FASL -844 CC genotype was protective against breast, ovarian, cervical, and endometrial cancers (P ≤ 0.01). On the other hand, FAS -1377 GA and AA variants increased risk of breast cancer. However, the GA variant of FAS -1377 was also found to be a risk factor for cervical cancer. In contrast, FAS -670 AG variant significantly lowered risk of breast cancer. Further, we also observed that risk association of co-occurrence of FAS and/or FASL variants with the cancers varied as compared to the presence of individual polymorphisms. Although risk and protective haplotypes of FAS SNPs were observed across the cancer phenotypes, the association of the haplotypes was significant for breast cancer alone with a 3-fold enhanced risk. The protective effect of the FASL CC genotype seen in this study suggests that similar biomolecular mechanisms involving FASL might play a role in female-specific cancers.

Visnovsky J, Kudela E, Farkasova A, et al.
Amplification of TERT and TERC genes in cervical intraepithelial neoplasia and cervical cancer.
Neuro Endocrinol Lett. 2014; 35(6):518-22 [PubMed] Related Publications
OBJECTIVES: Telomerase is activated in various stages of oncogenesis. For cervical cancer, telomerase is already active in precancerous lesions. In our study we focused on the analysis of the amplification patterns of telomerase genes TERT and TERC.
DESIGN AND SETTING: We included 39 patients in our study between January 2012 and April 2013. Each patient underwent a classical gynaecological examination and a colposcopy. During the colposcopic examination we collected material for a Pap smear, HPV DNA test (HC2) and LBC (LiquiPrep™), and performed punch biopsies for histopathological evaluation. Residual cytologic sample was hybridized with the FISH probe and telomerase genes were analysed.
RESULTS: The amplification of the TERT gene showed us a very similar amplification pattern as TERC and gradually corresponded with both histolopathological (p<0.001) and cytopathological findings (p<0.001). The specificity and sensitivity of TERC gene amplification for the detection of CIN2+ lesions (cut off value 2.3) was 88.2% and 95.5% respectively (PPV 91.3%, NPV 93.8%).
CONCLUSIONS: We identified increasing amplification pattern of telomerase genes in cervical lesions. According to our results telomerase genes could help in the future to determine the malignant potential of cervical lesions and could be tested together with cytology and HPV DNA in order to obtain the highest combined sensitivity and specificity for CIN2+ lesion detection.

Zhang L, Huang H, Zhang L, et al.
URG4 overexpression is correlated with cervical cancer progression and poor prognosis in patients with early-stage cervical cancer.
BMC Cancer. 2014; 14:885 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Upregulator of cell proliferation 4 (URG4) has been implicated in the oncogenesis of certain cancers. However, the correlation between URG4 expression and clinicopathological significance in human cancer remains unclear. Therefore, this study investigated its expression and clinicopathological significance in cervical cancer patients.
METHODS: URG4 expression was examined using quantitative PCR (qPCR) and western blotting in normal cervical epithelial cells, cervical cancer cells, and eight matched pairs of cervical cancer tissues and adjacent noncancerous tissues from the same patient. In addition, immunohistochemistry (IHC) was used to examine URG4 expression in paraffin-embedded tissues from 167 cervical cancer patients (FIGO stages Ib1-IIa2). Statistical analyses were performed to evaluate associations between URG4 expression and prognostic and diagnostic factors.
RESULTS: URG4 was significantly upregulated in the cervical cancer cell lines and tissues compared with the normal cells and adjacent noncancerous cervical tissues. IHC revealed high URG4 expression in 59 out of the 167 (35.13%) cervical cancer specimens. Its expression was significantly correlated with clinical stage (P < 0.0001), tumour size (P = 0.012), T classification (P = 0.023), lymph node metastasis (P = 0.001) and vaginal involvement (P = 0.002). Patients with high URG4 expression, particularly those who received concurrent chemotherapy and radiotherapy (P < 0.0001), showed a shorter overall survival (OS) and disease-free survival (DFS) compared to those with the low expression of this protein. Multivariate analysis revealed that URG4 expression is an independent prognostic factor for cervical cancer patients.
CONCLUSIONS: Our results demonstrated that elevated URG4 protein expression is associated with a poor outcome in patients with early-stage cervical cancer. URG4 may be a novel prognostic marker and therapeutic target for the treatment of cervical cancer.

Bai LX, Wang JT, Ding L, et al.
Folate deficiency and FHIT hypermethylation and HPV 16 infection promote cervical cancerization.
Asian Pac J Cancer Prev. 2014; 15(21):9313-7 [PubMed] Related Publications
Fragile histidine triad (FHIT) is a suppressor gene related to cervical cancer through CpG island hypermethylation. Folate is a water-soluble B-vitamin and an important cofactor in one-carbon metabolism. It may play an essential role in cervical lesions through effects on DNA methylation. The purpose of this study was to observe effects of folate and FHIT methylation and HPV 16 on cervical cancer progression. In this study, DNA methylation of FHIT, serum folate level and HPV16 status were measured using methylation-specific polymerase chain reaction (MSP), radioimmunoassay (RIA) and polymerase chain reaction (PCR), respectively, in 310 women with a diagnosis of normal cervix (NC, n=109), cervical intraepithelial neoplasia (CIN, n=101) and squamous cell carcinoma of the cervix (SCC, n=101). There were significant differences in HPV16 status (χ2=36.64, P<0.001), CpG island methylation of FHIT (χ2=71.31, P<0.001) and serum folate level (F=4.57, P=0.011) across the cervical histologic groups. Interaction analysis showed that the ORs only with FHIT methylation (OR=11.47) or only with HPV 16 positive (OR=4.63) or with serum folate level lower than 3.19ng/ml (OR=1.68) in SCC group were all higher than the control status of HPV 16 negative and FHIT unmethylation and serum folate level more than 3.19ng/ml (OR=1). The ORs only with HPV 16 positive (OR=2.58) or with serum folate level lower than 3.19ng/ ml (OR=1.28) in CIN group were all higher than the control status, but the OR only with FHIT methylation (OR=0.53) in CIN group was lower than the control status. HPV 16 positivity was associated with a 7.60-fold increased risk of SCC with folate deficiency and with a 1.84-fold increased risk of CIN. The patients with FHIT methylation and folate deficiency or with FHIT methylation and HPV 16 positive were SCC or CIN, and the patients with HPV 16 positive and FHIT methylation and folate deficiency were all SCC. In conclusion, HPV 16 infection, FHIT methylation and folate deficiency might promote cervical cancer progression. This suggests that FHIT may be an effective target for prevention and treatment of cervical cancer.

Kurban S, Tursun M, Kurban G, Hasim A
Role of CXCR7 and effects on CXCL12 in SiHa cells and upregulation in cervical squamous cell carcinomas in Uighur women.
Asian Pac J Cancer Prev. 2014; 15(21):9211-6 [PubMed] Related Publications
CXCR7 is involved in tumor development and metastasis in multiple malignancies. However, the function and molecular mechanisms of action of CXCR7 in human cervical cancer are still unclear. In the present study a loss of-function approach was used to observe the effects of recombinant CXCR7 specific small interfering RNA pBSilence1.1 plasmids on biological behavior including proliferative activity and invasive potential, as indicated by MTT assays with the cervical cancer SiHa cell line in vitro. Reverse transcription polymerase chain reaction and Western blotting revealed that CXCR7 was downregulated in transfected compared with control cells, associated with inhibited cell growth, invasiveness and migration. The expression of CXCR7 and CXCL12 was also determined immunohistochemically in 152 paraffin-embedded, cervical squamous cell carcinoma (CSCC) and cervical intraepithelial neoplasia (CIN), or normal cervical epithelial to assess clinico-pathological pattern and CXCR7 status with respect to cell differentiation and lymph node metastasis in Uighur patients with CSCC. CXCR7 and CXCL12 expression was higher in cervical cancer than CIN and normal cervical mucosa, especially in those with higher stage and lymph node metastasis. CXCL12 appeared to be positively regulated by CXCR7 at the post-transcriptional level in CSCC. We propose that aberrant expression of CXCR7 plays a role in carcinogenesis, differentiation and metastasis of CSCC, implying its use as a potential target for clinical biomarkers in differentiation and lymph node metastasis.

Zhao S
Specific type epigenetic changes in cervical cancers.
Methods Mol Biol. 2015; 1238:733-49 [PubMed] Related Publications
Cancer is a genetic and epigenetic disease. Multiple genetic and epigenetic changes have been studied in cervical cancer; however, such changes are selected for during tumorigenesis and tumor aggression is not yet clear. Cervical cancer is a multistep process with accumulation of genetic and epigenetic alterations in regulatory genes, leading to activation of oncogenes and inactivation or loss of tumor suppressor genes. In cervical cancer, epigenetic alterations can affect the expression of papillomaviral as well as host genes in relation to stages representing the multistep process of carcinogenesis.

Tsai CC, Huang SC, Tai MH, et al.
Hepatoma-derived growth factor upregulation is correlated with prognostic factors of early-stage cervical adenocarcinoma.
Int J Mol Sci. 2014; 15(11):21492-504 [PubMed] Free Access to Full Article Related Publications
Hepatoma-derived growth factor (HDGF) is a unique nuclear/growth factor that plays an important role in the progression of different types of cancer. A total of 63 patients with early-stage cervical adenocarcinoma (Cx) were enrolled in this retrospective study. The expression of HDGF was significantly increased compared with adjacent non-tumor tissue samples (p < 0.001). Moreover, elevated nuclear HDGF levels were correlated with lymph-vascular space invasion (LVSI; p < 0.05), lymph node metastasis (LNM; p < 0.001), recurrence (p < 0.001) and advanced grade (AG; p < 0.001). The growth of cervical cancer cells (Hela cells) was enhanced by HDGF treatment. The HDGF mRNA and protein level were significantly higher in malignant cervical cancer cells compared with primary ones. By adenovirus gene delivery, HDGF overexpression enhanced, whereas HDGF knockdown perturbed the tumorigenic behaviors of cervical cancer cells. HDGF overexpression is common in early-stage cervical adenocarcinoma and is involved in the carcinogenesis of cervical adenocarcinoma. Cytoplasmic HDGF expression is strongly correlated with pelvic lymph node metastasis and recurrence, indicating that HDGF may serve as a novel prognostic marker for patients with Cx.

Singhal P, Kumar A, Bharadwaj S, et al.
Association of IL-10 GTC haplotype with serum level and HPV infection in the development of cervical carcinoma.
Tumour Biol. 2015; 36(4):2287-98 [PubMed] Related Publications
AIM: Cervical cancer is the most common gynecological malignancy in the developing countries like India. In addition to human papillomavirus (HPV) infection, host genetic factors play an important role in viral persistence and neoplastic growth. IL-10, a multifunctional cytokine, plays an active role to promote tumor growth in the presence of HPV. The present study aims to find out the impact of IL-10 promoter polymorphisms at -1082A/G (rs1800896), -819C/T (rs1800872), and -592C/A (rs1800871) sites along with IL-10 production and HPV infection in the progression of cervical cancer.
METHODS: We have genotyped a total of 506 subjects, 256 cases (208 cervical cancer + 48 precancer), and 250 healthy controls by using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method followed by sequencing. IL-10 serum concentration was measured by enzyme-linked immunosorbent assay.
RESULTS: The frequency of IL-10 -592 variant genotype (AA) was found significantly reduced in cases as compare to controls while -1082 variant genotype (GG) was found ~4-fold higher risk of cervical cancer (p = <0.0001, OR = 3.667, 95% CI = 2.329-5.773). On construction of haplotypes, GTC haplotype was emerged as a major risk haplotype while ACA haplotype was seemed as a marker for precancerous lesions. IL-10 serum concentration was observed higher in HPV-infected precancer and cancer cases. GTC haplotype was found to be coupled with higher serum concentration of IL-10 and HPV infection.
CONCLUSION: IL-10 polymorphisms play a role in cervical cancer development and that GTC haplotype, which is closely related to its serum concentration, maybe a useful biomarker for HPV-mediated cervical cancer.

Cao S, Liu W, Li F, et al.
Decreased expression of lncRNA GAS5 predicts a poor prognosis in cervical cancer.
Int J Clin Exp Pathol. 2014; 7(10):6776-83 [PubMed] Free Access to Full Article Related Publications
INTRODUCTION: Cervical cancer is the second leading cause of cancer morbidity and mortality for women around the world. Long non-coding RNAs (lncRNAs) have been investigated as a new class of regulators of cellular processes, such as cell growth, apoptosis, and carcinogenesis. Although downregulation of lncRNA GAS5 in several cancers has been studied, its role in cervical cancer remains unknown. The aim of this study is to investigate the expression, clinical significance and biological role in cervical cancer.
METHODS: Expression of GAS5 was analyzed in cervical cancer tissues by quantitative Real-time PCR (qRT-PCR). And its association with overall survival of patients was analyzed by statistical analysis. Small interfering RNA (siRNA) was used to suppress GAS5 expression in cervical cancer cells. In vitro assays were performed to further explore the biological functions of GAS5 in cervical cancer.
RESULTS: We found that GAS5 expression was markedly downregulated in cervical cancer tissues than in corresponding adjacent normal tissues. Decreased GAS5 expression was significantly correlated with FIGO stage, vascular invasion and lymph node metastasis. Moreover, cervical cancer patients with GAS5 lower expression have shown significantly poorer overall survival than those with higher GAS5 expression. And GAS5 expression was an independent prognostic marker of overall survival in a multivariate analysis. In vitro assays our data indicated that knockdown of GAS5 promoted cell proliferation, migration, and invasion.
CONCLUSIONS: Our study presents that lncRNA GAS5 is a novel molecule involved in cervical cancer progression, which provide a potential prognostic biomarker and therapeutic target.

Bager P, Wohlfahrt J, Sørensen E, et al.
Common filaggrin gene mutations and risk of cervical cancer.
Acta Oncol. 2015; 54(2):217-23 [PubMed] Related Publications
BACKGROUND: As carriers of filaggrin gene (FLG) mutations may have a compromised cervical mucosal barrier against human papillomavirus infection, our primary objective was to study their risk of cervical cancer.
METHODS: We genotyped 586 cervical cancer patients for the two most common FLG mutations, R501X and 2282del4, using blood from the Copenhagen Hospital Biobank, Denmark. Controls (n = 8050) were genotyped in previous population-based studies. Information on cervical cancer, mortality and emigration were obtained from national registers. Odds ratios (OR) were estimated by logistic regression with adjustment for age at blood sampling, and weighted by the genotype-specific inverse probability of death between diagnosis and sampling. Hazard ratios (HR) were estimated by Cox regression with time since diagnosis as underlying time, and with adjustment for age at diagnosis and stratification by cancer stage.
RESULTS: The primary results showed that FLG mutations were not associated with the risk of cervical cancer (6.3% of cases and 7.7% of controls were carriers; OR adjusted 0.81, 95% CI 0.57-1.14; OR adjusted+ weighted 0.96, 95% CI 0.58-1.57). Among cases, FLG mutations increased mortality due to cervical cancer (HR 4.55, 95% CI 1.70-12.2), however, the association was reduced after stratification by cancer stage (HR 2.53, 95% CI 0.84-7.59).
CONCLUSION: Carriage of FLG mutations was not associated with the risk of cervical cancer.

Lee H, Kim KR, Cho NH, et al.
MicroRNA expression profiling and Notch1 and Notch2 expression in minimal deviation adenocarcinoma of uterine cervix.
World J Surg Oncol. 2014; 12:334 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: MicroRNA (miRNA) expression is known to be deregulated in cervical carcinomas. However, no data is available about the miRNA expression pattern for the minimal deviation adenocarcinoma (MDA) of uterine cervix. We sought to detect deregulated miRNAs in MDA in an attempt to find the most dependable miRNA or their combinations to understand their tumorigenesis pathway and to identify diagnostic or prognostic biomarkers. We also investigated the association between those miRNAs and their target genes, especially Notch1 and Notch2.
METHODS: We evaluated miRNA expression profiles via miRNA microarray and validated them using.real-time PCR assays with 24 formalin-fixed, paraffin-embedded tissue blocks of MDA and 11 normal proliferative endocervical tissues as control. Expression for Notch1 and 2 was assessed by immunohistochemistry.
RESULTS: MiRNA-135a-3p, 192-5p, 194-5p, and 494 were up-regulated, whereas miR-34b-5p, 204-5p, 299-5p, 424-5p, and 136-3p were down-regulated in MDA compared with normal proliferative endocervical tissues (all P<0.05). Considering the second-order Akaike Information Criterion consisting of likelihood ratio and number of parameters, miR-34b-5p showed the best discrimination power among the nine candidate miRNAs. A combined panel of miR-34b-5p and 194-5p was the best fit model to discriminate between MDA and control, revealing 100% sensitivity and specificity. Notch1 and Notch2, respective target genes of miR-34b-5p and miR-204-5p, were more frequently expressed in MDA than in control (63% vs. 18%; 52% vs. 18%, respectively, P<0.05). MiR-34b-5p expression level was higher in Notch1-negative samples compared with Notch1-positive ones (P<0.05). Down-regulated miR-494 was associated with poor patient survival (P=0.036).
CONCLUSIONS: MDA showed distinctive expression profiles of miRNAs, Notch1, and Notch2 from normal proliferative endocervical tissues. In particular, miR-34b-5p and 194-5p might be used as diagnostic biomarkers and miR-494 as a prognostic predictor for MDA. The miR-34b-5p/Notch1 pathway as well as Notch2 might be important oncogenic contributors to MDA.

Xu LD, Muller S, Thoppe SR, et al.
Expression of the p53 target Wig-1 is associated with HPV status and patient survival in cervical carcinoma.
PLoS One. 2014; 9(11):e111125 [PubMed] Free Access to Full Article Related Publications
The p53 target gene WIG-1 (ZMAT3) is located in chromosomal region 3q26, that is frequently amplified in human tumors, including cervical cancer. We have examined the status of WIG-1 and the encoded Wig-1 protein in cervical carcinoma cell lines and tumor tissue samples. Our analysis of eight cervical cancer lines (Ca Ski, ME-180, MS751, SiHa, SW756, C-4I, C-33A, and HT-3) by spectral karyotype, comparative genomic hybridization and Southern blotting revealed WIG-1 is not the primary target for chromosome 3 gains. However, WIG-1/Wig-1 were readily expressed and WIG-1 mRNA expression was higher in the two HPV-negative cervical cell lines (C33-A, HT-3) than in HPV-positive lines. We then assessed Wig-1 expression by immunohistochemistry in 38 cervical tumor samples. We found higher nuclear Wig-1 expression levels in HPV-negative compared to HPV positive cases (p = 0.002) and in adenocarcinomas as compared to squamous cell lesions (p<0.0001). Cases with moderate nuclear Wig-1 staining and positive cytoplasmic Wig-1 staining showed longer survival than patients with strong nuclear and negative cytoplasmic staining (p = 0.042). Nuclear Wig-1 expression levels were positively associated with age at diagnosis (p = 0.023) and histologic grade (p = 0.034). These results are consistent with a growth-promoting and/or anti-cell death function of nuclear Wig-1 and suggest that Wig-1 expression can serve as a prognostic marker in cervical carcinoma.

Zidi S, Verdi H, Yilmaz-Yalcin Y, et al.
Involvement of Toll-like receptors in cervical cancer susceptibility among Tunisian women.
Bull Cancer. 2014; 101(10):E31-5 [PubMed] Related Publications
Previous studies underscored the importance of genetic factors in the pathogenesis of certain cancers, including cervical cancer. Epidemiological evidence supports an association between specific polymorphisms of Toll-like receptors (TLR) with several human pathological states, including cervical cancer. The aim of this study was to investigate the link between specific gene variants in TLR2 (-196 to -174 del), TLR3 (c.1377 C>T), TLR4 (Asp299Gly), and TLR9 (2848 G>A) and susceptibility to cervical cancer in Tunisian women. Study subjects comprised 122 women with histopathologically-confirmed cervical cancer, and 260 unrelated age- and ethnically-matched healthy females, who served as controls. TLR genotyping was done using PCR-restriction fragment length polymorphism. The C/C genotype of TLR3 (c.1377 C>T) is associated with cervical cancer susceptibility (OR: 1.71, CI: 1.08-2.70). For TLR4 (Asp299Gly), the Asp/Asp genotype and the Asp allele were associated with higher risk of developing cervical cancer (OR: 4.95, CI: 1.97-13.22) and (OR: 5.17, CI: 2.11-13.50) respectively. We demonstrated no association between the TLR2 (-196 to -174 del) and the TLR 9 (2848 G>A) polymorphisms and the susceptibility of cervical cancer among Tunisian women. However, the C/C genotype for the TLR3 (c.1377 C>T) polymorphism and the Asp/Asp genotype and the Asp allele for (Asp299Gly) TLR4 polymorphism were found to be associated with a higher risk of cervical cancer.

Kalantari M, Bernard HU
Assessment of the HPV DNA methylation status in cervical lesions.
Methods Mol Biol. 2015; 1249:267-80 [PubMed] Related Publications
The genomes of the human papillomaviruses HPV-16 and HPV-18 undergo increased CpG methylation during the progression of cervical neoplasia, possibly in response to increased recombination between viral and cellular DNA in high-grade lesions. This behavior makes HPV DNA methylation a useful biomarker of carcinogenic progression of HPV infections. The first step in detecting DNA methylation involves modification by bisulfite, which converts cytosine residues into uracil, but leaves 5-methylcytosine residues unaffected. A combination of this reaction with PCR and DNA sequencing permits to evaluate the methylation status of the sample DNA. This chapter describes the basic protocol to measure HPV-16 and HPV-18 CpG methylation by direct sequencing of the PCR products and discusses the value of modified strategies including DNA cloning, amplification with methylation-specific primers, and real-time PCR with TaqMan probes.

Peralta-Zaragoza O, De-la-O-Gómez F, Deas J, et al.
Selective silencing of gene target expression by siRNA expression plasmids in human cervical cancer cells.
Methods Mol Biol. 2015; 1249:153-71 [PubMed] Related Publications
RNA interference is a natural mechanism to silence post-transcriptional gene expression in eukaryotic cells in which microRNAs act to cleave or halt the translation of target mRNAs at specific target sequences. Mature microRNAs, 19-25 nucleotides in length, mediate their effect at the mRNA level by inhibiting translation, or inducing cleavage of the mRNA target. This process is directed by the degree of complementary nucleotides between the microRNAs and the target mRNA; perfect complementary base pairing induces cleavage of mRNA, whereas several mismatches lead to translational arrest. Biological effects of microRNAs can be manipulated through the use of small interference RNAs (siRNAs) generated by chemical synthesis, or by cloning in molecular vectors. The cloning of a DNA insert in a molecular vector that will be transcribed into the corresponding siRNAs is an approach that has been developed using siRNA expression plasmids. These vectors contain DNA inserts designed with software to generate highly efficient siRNAs which will assemble into RNA-induced silencing complexes (RISC), and silence the target mRNA. In addition, the DNA inserts may be contained in cloning cassettes, and introduced in other molecular vectors. In this chapter we describe an attractive technology platform to silence cellular gene expression using specific siRNA expression plasmids, and evaluate its biological effect on target gene expression in human cervical cancer cells.

Zeng QY, Huang Y, Zeng LJ, et al.
Sensitization of cervical carcinoma cells to paclitaxel by an IPP5 active mutant.
Asian Pac J Cancer Prev. 2014; 15(19):8337-43 [PubMed] Related Publications
Paclitaxel is one of the best anticancer agents that has been isolated from plants, but its major disadvantage is its dose-limiting toxicity. In this study, we obtained evidence that the active mutant IPP5 (8-60hIPP5m), the latest member of the inhibitory molecules for protein phosphatase 1, sensitizes human cervix carcinoma cells HeLa more efficiently to the therapeutic effects of paclitaxel. The combination of 8-60hIPP5m with paclitaxel augmented anticancer effects as compared to paclitaxel alone as evidenced by reduced DNA synthesis and increased cytotoxicity in HeLa cells. Furthermore, our results revealed that 8-60hIPP5m enhances paclitaxel- induced G2/M arrest and apoptosis, and augments paclitaxel-induced activation of caspases and release of cytochrome C. Evaluation of signaling pathways indicated that this synergism was in part related to down- regulation of NF-?B activation and serine/threonine kinase Akt pathways. We noted that 8-60hIPP5m down- regulated the paclitaxel-induced NF-?B activation, I?Bα degradation, PI3-K activity and phosphorylation of the serine/threonine kinase Akt, a survival signal which in many instances is regulated by NF-?B. Together, our observations indicate that paclitaxel in combination with 8-60hIPP5m may provide a therapeutic advantage for the treatment of human cervical carcinoma.

Liu F, Zhang R, Wang ZY, et al.
Malignant perivascular epithelioid cell tumor (PEComa) of cervix with TFE3 gene rearrangement: a case report.
Int J Clin Exp Pathol. 2014; 7(9):6409-14 [PubMed] Free Access to Full Article Related Publications
In this study, we reported the first PEComa arising within the cervix with TFE3 gene rearrangement and aggressive biological behavior. Morphologically, the tumor showed infiltrative growth into the surrounding parenchyma. The majority of tumor cells were arrayed in sheets, alveolar structures, or nests separated by delicate fibrovascular septa. There was marked intratumoral hemorrhage, necrosis, and stromal calcifications. The tumor cells had abundant clear cytoplasm, focally containing finely granular dark brown pigment, morphologically considered to be melanin. Immunohistochemically, the tumor cells demonstrated moderately (2+) or strongly (3+) positive staining for TFE3, HMB45, and Melan A but negative for CKpan, SMA, S100, PAX8, and PAX2. The presence of Ki-67 protein demonstrated a moderate proliferation rate, with a few Ki-67-positive nuclei. Using a recently developed TFE3 split FISH assay, the presence of TFE3 rearrangement was demonstrated. All these clinicopathologic features are suggestive of TFE3-rearranged PEComas of the cervix. Our results both expand the known characteristics of primary cervix PEComas and add to the data regarding TFE3 rearrangement-associated PEComas.

Hu JM, Sun Q, Li L, et al.
Human leukocyte antigen-DRB1*1501 and DQB1*0602 alleles are cervical cancer protective factors among Uighur and Han people in Xinjiang, China.
Int J Clin Exp Pathol. 2014; 7(9):6165-71 [PubMed] Free Access to Full Article Related Publications
Human papillomavirus (HPV) infection is a major risk factor for cervical cancer. However, only some high risk human papillomavirus (HR-HPV)-infected women progress to cervical cancer, host immunogenetic factors human leukocyte antigen (HLA) may account for viral antigens presenting individually or together in the progression to cervical cancer. This study examined the association between the development of invasive cervical cancer (ICC) and the determinant factors including HLA-DRB1*1501 and DQB1*0602, HR-HPV infection among Chinese Uighur and Han populations. Blood samples, cervical swabs and biopsies were obtained from 287 patients with ICC (192 Uighurs and 95 Hans) and 312 healthy controls (218 Uighurs and 94 Hans). HPV DNA was detected by PCR and HLA-DRB1*1501 and DQB1*0602 alleles were performed using PCR-SSP method. HPV16 infection rates was significantly higher among Uighur and Han with ICC as compared to healthy controls (OR = 58.317; 95% CI: 39.663-85.744; OR = 33.778; 95% CI: 12.581-90.691; P < 0.05 for all). HLA-DRB1*1501 (OR = 0.305; 95% CI: 0.115-0.813; P < 0.05) and HLA-DRB1*1501-DQB1*0602 haplotype frequencies (OR = 0.274; 95% CI: 0.086-0.874; P < 0.05) were significantly reduced in Han ICC. The HLA-DQB1*0602 frequency significantly decreased among Uighur women with ICC (OR = 0.482; 95% CI: 0.325-0.716; P < 0.05). Similar tendencies were observed for DQB1*0602 with HPV16-positive ICC (OR = 0.550; 95% CI: 0.362-0.837; P < 0.05). This study suggests that HLA-DRB1*1501 and DQB1*0602 alleles may influence the immune response to HPV16 infection and decrease the risk of ICC among Uighurs and Hans in Xinjiang, China.

Disclaimer: This site is for educational purposes only; it can not be used in diagnosis or treatment.

Cite this page: Cotterill SJ. Cervical Cancer, Cancer Genetics Web: http://www.cancer-genetics.org/X1002.htm Accessed:

Creative Commons License
This page in Cancer Genetics Web by Simon Cotterill is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Note: content of abstracts copyright of respective publishers - seek permission where appropriate.

 [Home]    Page last revised: 08 August, 2015     Cancer Genetics Web, Established 1999