Cervical Cancer

Overview

Literature Analysis

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Tag cloud generated 10 March, 2017 using data from PubMed, MeSH and CancerIndex

Mutated Genes and Abnormal Protein Expression (311)

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
59
FHIT 3p14.2 FRA3B, AP3Aase -FHIT and Cervical Cancer
53
HLA-DRB1 6p21.3 SS1, DRB1, DRw10, HLA-DRB, HLA-DR1B -HLA-DRB1 and Cervical Cancer
47
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
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II -MMP2 and Cervical Cancer
28
PTGS2 1q25.2-q25.3 COX2, COX-2, PHS-2, PGG/HS, PGHS-2, hCox-2, GRIPGHS -PTGS2 (COX2) and Cervical Cancer
28
DAPK1 9q21.33 DAPK -DAPK1 and Cervical Cancer
26
HLA-A 6p21.3 HLAA -HLA-A and Cervical Cancer
22
CASP8 2q33-q34 CAP4, MACH, MCH5, FLICE, ALPS2B, Casp-8 -CASP8 and Cervical Cancer
22
SLC2A1 1p34.2 PED, DYT9, GLUT, DYT17, DYT18, EIG12, GLUT1, HTLVR, GLUT-1, GLUT1DS -GLUT1 expression in Cervical Cancer
21
CDH1 16q22.1 UVO, CDHE, ECAD, LCAM, Arc-1, CD324 -CDH1 and Cervical Cancer
20
MGMT 10q26 -MGMT and Cervical Cancer
20
DAPK2 15q22.31 DRP1, DRP-1 -DAPK2 and Cervical Cancer
20
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
CYP1A1 15q24.1 AHH, AHRR, CP11, CYP1, P1-450, P450-C, P450DX -CYP1A1 and Cervical Cancer
15
BCL2 18q21.3 Bcl-2, PPP1R50 -BCL2 and Cervical Cancer
15
CASP9 1p36.21 MCH6, APAF3, APAF-3, PPP1R56, ICE-LAP6 -CASP9 and Cervical Cancer
14
MET 7q31 HGFR, AUTS9, RCCP2, c-Met -C-MET and Cervical Cancer
13
NME1 17q21.3 NB, AWD, NBS, GAAD, NDKA, NM23, NDPKA, NDPK-A, NM23-H1 -NME1 and Cervical Cancer
13
MTOR 1p36.2 FRAP, FRAP1, FRAP2, RAFT1, RAPT1 -MTOR and Cervical Cancer
12
ABCB1 7q21.12 CLCS, MDR1, P-GP, PGY1, ABC20, CD243, GP170 -ABCB1 and Cervical Cancer
11
TIMP1 Xp11.3-p11.23 EPA, EPO, HCI, CLGI, TIMP -TIMP1 and Cervical Cancer
11
LIPA 10q23.2-q23.3 LAL, CESD -LIPA and Cervical Cancer
11
CD82 11p11.2 R2, 4F9, C33, IA4, ST6, GR15, KAI1, SAR2, TSPAN27 -CD82 and Cervical Cancer
10
GAPDH 12p13 G3PD, GAPD, HEL-S-162eP -GAPDH and Cervical Cancer
10
TIMP2 17q25 DDC8, CSC-21K -TIMP2 and Cervical Cancer
10
CCNB1 5q12 CCNB -CCNB1 and Cervical Cancer
10
BECN1 17q21 ATG6, VPS30, beclin1 -BECN1 and Cervical Cancer
8
CXCL12 10q11.1 IRH, PBSF, SDF1, TLSF, TPAR1, SCYB12 -CXCL12 and Cervical Cancer
8
MMP9 20q13.12 GELB, CLG4B, MMP-9, MANDP2 -MMP9 and Cervical Cancer
8
CHEK1 11q24.2 CHK1 -CHEK1 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
CA9 9p13.3 MN, CAIX -CA9 and Cervical Cancer
8
H2AFX 11q23.3 H2AX, H2A.X, H2A/X -H2AFX and Cervical Cancer
8
RARB 3p24.2 HAP, RRB2, NR1B2, MCOPS12 -RARB 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
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
CCNA1 13q12.3-q13 CT146 -CCNA1 and Cervical Cancer
7
HLA-C 6p21.3 HLC-C, D6S204, PSORS1, HLA-JY3 -HLA-C 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
FGF2 4q26 BFGF, FGFB, FGF-2, HBGF-2 -FGF2 and Cervical Cancer
7
HLA-G 6p21.3 MHC-G -HLA-G and Cervical Cancer
7
TGFA 2p13 TFGA -TGFA and Cervical Cancer
7
ESR1 6q25.1 ER, ESR, Era, ESRA, ESTRR, NR3A1 -ESR1 and Cervical Cancer
7
MYB 6q22-q23 efg, Cmyb, c-myb, c-myb_CDS -MYB 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
IFNG 12q14 IFG, IFI -IFNG and Cervical Cancer
6
JUNB 19p13.2 AP-1 -JUNB 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
CDC6 17q21.3 CDC18L, HsCDC6, HsCDC18 -CDC6 and Cervical Cancer
6
EPB41L3 18p11.32 4.1B, DAL1, DAL-1 -EPB41L3 and Cervical Cancer
6
HIC1 17p13.3 hic-1, ZBTB29, ZNF901 -HIC1 and Cervical Cancer
6
HLA-DPB1 6p21.3 DPB1, HLA-DP, HLA-DPB, HLA-DP1B -HLA-DPB1 and Cervical Cancer
6
SMAD4 18q21.1 JIP, DPC4, MADH4, MYHRS -SMAD4 and Cervical Cancer
5
S100P 4p16 MIG9 -S100P 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
SIX1 14q23.1 BOS3, TIP39, DFNA23 -SIX1 and Cervical Cancer
5
FGFR3 4p16.3 ACH, CEK2, JTK4, CD333, HSFGFR3EX -FGFR3 and Cervical Cancer
5
ETS1 11q24.3 p54, ETS-1, EWSR2, c-ets-1 -ETS1 and Cervical Cancer
5
PPP2CB 8p12 PP2CB, PP2Abeta -PPP2CB 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
ABCC1 16p13.1 MRP, ABCC, GS-X, MRP1, ABC29 -ABCC1 (MRP1) 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
MMP14 14q11.2 MMP-14, MMP-X1, MT-MMP, MT1MMP, MTMMP1, WNCHRS, MT1-MMP, MT-MMP 1 -MMP14 and Cervical Cancer
4
EZH2 7q35-q36 WVS, ENX1, EZH1, KMT6, WVS2, ENX-1, EZH2b, KMT6A -EZH2 and Cervical Cancer
4
IL12B 5q33.3 CLMF, NKSF, CLMF2, IMD28, IMD29, NKSF2, IL-12B -IL12B and Cervical Cancer
4
THRB 3p24.2 GRTH, PRTH, THR1, ERBA2, NR1A2, THRB1, THRB2, C-ERBA-2, C-ERBA-BETA -THRB and Cervical Cancer
4
ERCC2 19q13.3 EM9, TTD, XPD, TTD1, COFS2, TFIIH -ERCC2 and Cervical Cancer
4
CA12 15q22 CAXII, HsT18816 -CA12 and Cervical Cancer
4
BARD1 2q35 -BARD1 and Cervical Cancer
4
LAMB3 1q32 AI1A, LAM5, LAMNB1, BM600-125KDA -LAMB3 and Cervical Cancer
4
RECK 9p13.3 ST15 -RECK and Cervical Cancer
4
CLDN3 7q11.23 RVP1, HRVP1, C7orf1, CPE-R2, CPETR2 -CLDN3 and Cervical Cancer
4
LTA 6p21.3 LT, TNFB, TNFSF1 -LTA and Cervical Cancer
4
H19 11p15.5 ASM, BWS, WT2, ASM1, D11S813E, LINC00008, NCRNA00008 -H19 and Cervical Cancer
4
CDK2 12q13 CDKN2, p33(CDK2) -CDK2 and Cervical Cancer
4
ICOS 2q33 AILIM, CD278, CVID1 -ICOS and Cervical Cancer
4
SFRP1 8p11.21 FRP, FRP1, FrzA, FRP-1, SARP2 -SFRP1 and Cervical Cancer
4
PAPPA 9q33.2 PAPA, DIPLA1, PAPP-A, PAPPA1, ASBABP2, IGFBP-4ase -PAPPA and Cervical Cancer
4
TLR3 4q35 CD283, IIAE2 -TLR3 and Cervical Cancer
4
PDGFRA 4q12 CD140A, PDGFR2, PDGFR-2, RHEPDGFRA -PDGFRA and Cervical Cancer
4
CYP27B1 12q14.1 VDR, CP2B, CYP1, PDDR, VDD1, VDDR, VDDRI, CYP27B, P450c1, CYP1alpha -CYP27B1 and Cervical Cancer
4
SCRIB 8q24.3 CRIB1, SCRB1, SCRIB1, Vartul -SCRIB and Cervical Cancer
4
PPP2R1B 11q23.1 PR65B, PP2A-Abeta -PPP2R1B and Cervical Cancer
4
IL12A 3q25.33 P35, CLMF, NFSK, NKSF1, IL-12A -IL12A and Cervical Cancer
4
HLA-DRA 6p21.3 MLRW, HLA-DRA1 -HLA-DRA and Cervical Cancer
4
CAV1 7q31.1 CGL3, PPH3, BSCL3, LCCNS, VIP21, MSTP085 -CAV1 and Cervical Cancer
4
IL17A 6p12 IL17, CTLA8, IL-17, IL-17A -IL17A and Cervical Cancer
3
MCM5 22q13.1 CDC46, P1-CDC46 -MCM5 and Cervical Cancer
3
TMC6 17q25.3 EV1, EVER1, EVIN1, LAK-4P -TMC6 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
PCDH10 4q28.3 PCDH19, OL-PCDH -PCDH10 and Cervical Cancer
3
MIB1 18q11.2 MIB, DIP1, ZZZ6, DIP-1, LVNC7, ZZANK2 -MIB1 and Cervical Cancer
3
HDAC1 1p34 HD1, RPD3, GON-10, RPD3L1 -HDAC1 and Cervical Cancer
3
DLG1 3q29 hdlg, DLGH1, SAP97, SAP-97, dJ1061C18.1.1 -DLG1 and Cervical Cancer
3
RECQL4 8q24.3 RECQ4 -RECQL4 and Cervical Cancer
3
SMARCA4 19p13.2 BRG1, CSS4, SNF2, SWI2, MRD16, RTPS2, BAF190, SNF2L4, SNF2LB, hSNF2b, BAF190A -SMARCA4 and Cervical Cancer
3
CYP19A1 15q21.1 ARO, ARO1, CPV1, CYAR, CYP19, CYPXIX, P-450AROM -CYP19A1 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
IL17C 16q24 CX2, IL-17C -IL17C and Cervical Cancer
3
MMP7 11q22.2 MMP-7, MPSL1, PUMP-1 -MMP7 and Cervical Cancer
3
APEX1 14q11.2 APE, APX, APE1, APEN, APEX, HAP1, REF1 -APEX1 and Cervical Cancer
3
CTSB 8p22 APPS, CPSB -CTSB and Cervical Cancer
3
MIR10A 17q21.32 MIRN10A, mir-10a, miRNA10A, hsa-mir-10a -miR-10a and Cervical Cancer
3
HOTAIR 12q13.13 HOXAS, HOXC-AS4, HOXC11-AS1, NCRNA00072 -HOTAIR and Cervical Cancer
3
EGR1 5q31.1 TIS8, AT225, G0S30, NGFI-A, ZNF225, KROX-24, ZIF-268 -EGR1 and Cervical Cancer
3
MCM7 7q21.3-q22.1 MCM2, CDC47, P85MCM, P1CDC47, PNAS146, PPP1R104, P1.1-MCM3 -MCM7 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
IL1B 2q14 IL-1, IL1F2, IL1-BETA -IL1B and Cervical Cancer
3
CD55 1q32 CR, TC, DAF, CROM -CD55 and Cervical Cancer
3
CTDSPL 3p21.3 PSR1, SCP3, HYA22, RBSP3, C3orf8 -CTDSPL and Cervical Cancer
3
MACC1 7p21.1 7A5, SH3BP4L -MACC1 and Cervical Cancer
3
ARID1A 1p35.3 ELD, B120, OSA1, P270, hELD, BM029, MRD14, hOSA1, BAF250, C1orf4, BAF250a, SMARCF1 -ARID1A and Cervical Cancer
3
HMGA1 6p21 HMG-R, HMGIY, HMGA1A -HMGA1 and Cervical Cancer
3
CD83 6p23 BL11, HB15 -CD83 and Cervical Cancer
3
IL1A 2q14 IL1, IL-1A, IL1F1, IL1-ALPHA -IL1A and Cervical Cancer
3
TFPI 2q32 EPI, TFI, LACI, TFPI1 -TFPI and Cervical Cancer
3
TMC8 17q25.3 EV2, EVER2, EVIN2 -TMC8 and Cervical Cancer
3
ROBO1 3p12 SAX3, DUTT1 -ROBO1 and Cervical Cancer
3
UBE2C 20q13.12 UBCH10, dJ447F3.2 -UBE2C and Cervical Cancer
3
EPOR 19p13.3-p13.2 EPO-R -EPOR and Cervical Cancer
3
RBP1 3q23 CRBP, RBPC, CRBP1, CRBPI, CRABP-I -RBP1 and Cervical Cancer
3
TFPI2 7q22 PP5, REF1, TFPI-2 -TFPI2 and Cervical Cancer
3
TWIST2 2q37.3 FFDD3, DERMO1, SETLSS, bHLHa39 -TWIST2 and Cervical Cancer
2
POLI 18q21.1 RAD30B, RAD3OB -POLI and Cervical Cancer
2
NR3C2 4q31.1 MR, MCR, MLR, NR3C2VIT -NR3C2 and Cervical Cancer
2
PBX1 1q23 -PBX1 and Cervical Cancer
2
MSLN 16p13.3 MPF, SMRP -MSLN and Cervical Cancer
2
MTRR 5p15.31 MSR, cblE -MTRR and Cervical Cancer
2
TOP1 20q12-q13.1 TOPI -TOP1 and Cervical Cancer
2
DUSP1 5q34 HVH1, MKP1, CL100, MKP-1, PTPN10 -DUSP1 and Cervical Cancer
2
PRKCD 3p21.31 MAY1, PKCD, ALPS3, CVID9, nPKC-delta -PRKCD and Cervical Cancer
2
S100A8 1q21 P8, MIF, NIF, CAGA, CFAG, CGLA, L1Ag, MRP8, CP-10, MA387, 60B8AG -S100A8 and Cervical Cancer
2
MTDH 8q22.1 3D3, AEG1, AEG-1, LYRIC, LYRIC/3D3 -MTDH and Cervical Cancer
2
KIAA1524 3q13.13 p90, CIP2A -KIAA1524 and Cervical Cancer
2
CYBA 16q24 p22-PHOX -CYBA and Cervical Cancer
2
BIRC2 11q22.2 API1, MIHB, HIAP2, RNF48, cIAP1, Hiap-2, c-IAP1 -BIRC2 and Cervical Cancer
2
ZNF350 19q13.41 ZFQR, ZBRK1 -ZNF350 and Cervical Cancer
2
CCR5 3p21.31 CKR5, CCR-5, CD195, CKR-5, CCCKR5, CMKBR5, IDDM22, CC-CKR-5 -CCR5 and Cervical Cancer
2
RASSF2 20p13 CENP-34, RASFADIN -RASSF2 and Cervical Cancer
2
MTA1 14q32.3 -MTA1 and Cervical Cancer
2
XRCC4 5q14.2 -XRCC4 and Cervical Cancer
2
TLR7 Xp22.3 TLR7-like -TLR7 and Cervical Cancer
2
S100A9 1q21 MIF, NIF, P14, CAGB, CFAG, CGLB, L1AG, LIAG, MRP14, 60B8AG, MAC387 -S100A9 and Cervical Cancer
2
CD46 1q32 MCP, TLX, AHUS2, MIC10, TRA2.10 -CD46 and Cervical Cancer
2
AQP3 9p13 GIL, AQP-3 -AQP3 and Cervical Cancer
2
CLPTM1L 5p15.33 CRR9 -CLPTM1L and Cervical Cancer
2
MAML1 5q35 Mam1, Mam-1 -MAML1 and Cervical Cancer
2
SLPI 20q12 ALP, MPI, ALK1, BLPI, HUSI, WAP4, WFDC4, HUSI-I -SLPI and Cervical Cancer
2
RBL1 20q11.2 PRB1, p107, CP107 -RBL1 and Cervical Cancer
2
FTCDNL1 2q33.1 FONG -FONG and Cervical Cancer
2
EGLN1 1q42.1 HPH2, PHD2, SM20, ECYT3, HALAH, HPH-2, HIFPH2, ZMYND6, C1orf12, HIF-PH2 -EGLN1 and Cervical Cancer
2
DIABLO 12q24.31 SMAC, DFNA64 -DIABLO and Cervical Cancer
2
RRM2 2p25-p24 R2, RR2, RR2M -RRM2 and Cervical Cancer
2
MYBL2 20q13.1 BMYB, B-MYB -MYBL2 and Cervical Cancer
2
EXO1 1q43 HEX1, hExoI -EXO1 and Cervical Cancer
2
KRT7 12q13.13 K7, CK7, SCL, K2C7 -KRT7 and Cervical Cancer
2
HOXB4 17q21.32 HOX2, HOX2F, HOX-2.6 -HOXB4 and Cervical Cancer
2
CASP1 11q22.3 ICE, P45, IL1BC -CASP1 and Cervical Cancer
2
PDCD1 2q37.3 PD1, PD-1, CD279, SLEB2, hPD-1, hPD-l, hSLE1 -PDCD1 and Cervical Cancer
2
IMP3 15q24 BRMS2, MRPS4, C15orf12 -IMP3 and Cervical Cancer
2
CKS2 9q22 CKSHS2 -CKS2 and Cervical Cancer
2
HPRT1 Xq26.1 HPRT, HGPRT -HPRT1 and Cervical Cancer
2
SERPINA1 14q32.1 PI, A1A, AAT, PI1, A1AT, PRO2275, alpha1AT -SERPINA1 and Cervical Cancer
2
IGFBP5 2q35 IBP5 -IGFBP5 and Cervical Cancer
2
POLB 8p11.2 -POLB and Cervical Cancer
2
DHFR 5q14.1 DYR, DHFRP1 -DHFR and Cervical Cancer
2
ETS2 21q22.2 ETS2IT1 -ETS2 and Cervical Cancer
2
RALGDS 9q34.3 RGF, RGDS, RalGEF -RALGDS and Cervical Cancer
2
PINX1 8p23 LPTL, LPTS -PINX1 and Cervical Cancer
2
MIR124-1 8p23.1 MIR124A, MIR124A1, MIRN124-1, MIRN124A1, mir-124-1 -microRNA 124-1 and Cervical Cancer
2
NAV1 1q32.3 POMFIL3, UNC53H1, STEERIN1 -NAV1 and Cervical Cancer
2
MMP10 11q22.2 SL-2, STMY2 -MMP10 and Cervical Cancer
2
DKK3 11p15.3 RIG, REIC -DKK3 and Cervical Cancer
2
CTSL 9q21.33 MEP, CATL, CTSL1 -CTSL and Cervical Cancer
2
SHBG 17p13.1 ABP, SBP, TEBG -SHBG and Cervical Cancer
2
HLTF 3q25.1-q26.1 ZBU1, HLTF1, RNF80, HIP116, SNF2L3, HIP116A, SMARCA3 -HLTF and Cervical Cancer
2
MBL2 10q11.2 MBL, MBP, MBP1, MBPD, MBL2D, MBP-C, COLEC1, HSMBPC -MBL2 and Cervical Cancer
2
SLC9A1 1p36.1-p35 APNH, NHE1, LIKNS, NHE-1, PPP1R143 -SLC9A1 and Cervical Cancer
1
FYN 6q21 SLK, SYN, p59-FYN -FYN 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
HTRA1 10q26.3 L56, HtrA, ARMD7, ORF480, PRSS11, CARASIL -HTRA1 and Cervical Cancer
1
S100A2 1q21 CAN19, S100L -S100A2 and Cervical Cancer
1
PTP4A3 8q24.3 PRL3, PRL-3, PRL-R -PTP4A3 and Cervical Cancer
1
EPHX1 1q42.1 MEH, EPHX, EPOX, HYL1 -EPHX1 and Cervical Cancer
1
LAPTM4B 8q22.1 LC27, LAPTM4beta -LAPTM4B and Cervical Cancer
1
PYCARD 16p11.2 ASC, TMS, TMS1, CARD5, TMS-1 -PYCARD and Cervical Cancer
1
MUC5AC 11p15.5 TBM, leB, MUC5, mucin -MUC5AC and Cervical Cancer
1
RASSF5 1q32.1 RAPL, Maxp1, NORE1, NORE1A, NORE1B, RASSF3 -RASSF5 and Cervical Cancer
1
PER1 17p13.1 PER, hPER, RIGUI -PER1 and Cervical Cancer
1
CEBPB 20q13.1 TCF5, IL6DBP, NF-IL6, C/EBP-beta -CEBPB and Cervical Cancer
1
CDK9 9q34.1 TAK, C-2k, CTK1, CDC2L4, PITALRE -CDK9 and Cervical Cancer
1
POU2F1 1q24.2 OCT1, OTF1, oct-1B -POU2F1 and Cervical Cancer
1
CLMP 11q24.1 ACAM, ASAM, CSBM, CSBS -CLMP and Cervical Cancer
1
KRT14 17q21.2 K14, NFJ, CK14, EBS3, EBS4 -KRT14 and Cervical Cancer
1
CASP6 4q25 MCH2 -CASP6 and Cervical Cancer
1
KRT18 12q13 K18, CK-18, CYK18 -KRT18 and Cervical Cancer
1
KDM5A 12p11 RBP2, RBBP2, RBBP-2 -KDM5A and Cervical Cancer
1
CMBL 5p15.2 JS-1 -CMBL and Cervical Cancer
1
TP53INP1 8q22 SIP, Teap, p53DINP1, TP53DINP1, TP53INP1A, TP53INP1B -TP53INP1 and Cervical Cancer
1
GJA1 6q22.31 HSS, CMDR, CX43, EKVP, GJAL, ODDD, AVSD3, HLHS1 -GJA1 and Cervical Cancer
1
HOXA7 7p15.2 ANTP, HOX1, HOX1A, HOX1.1 -HOXA7 and Cervical Cancer
1
SPINT2 19q13.1 PB, Kop, HAI2, DIAR3, HAI-2 -SPINT2 and Cervical Cancer
1
ZBTB7A 19p13.3 LRF, FBI1, FBI-1, ZBTB7, ZNF857A, pokemon -ZBTB7A and Cervical Cancer
1
HFE 6p21.3 HH, HFE1, HLA-H, MVCD7, TFQTL2 -HFE and Cervical Cancer
1
CD59 11p13 1F5, EJ16, EJ30, EL32, G344, MIN1, MIN2, MIN3, MIRL, HRF20, MACIF, MEM43, MIC11, MSK21, 16.3A5, HRF-20, MAC-IP, p18-20 -CD59 and Cervical Cancer
1
MEG3 14q32 GTL2, FP504, prebp1, PRO0518, PRO2160, LINC00023, NCRNA00023, onco-lncRNA-83 -MEG3 and Cervical Cancer
1
IL6R 1q21 IL6Q, gp80, CD126, IL6RA, IL6RQ, IL-6RA, IL-6R-1 -IL6R and Cervical Cancer
1
NR4A1 12q13 HMR, N10, TR3, NP10, GFRP1, NAK-1, NGFIB, NUR77 -NR4A1 and Cervical Cancer
1
CCNC 6q21 CycC -CCNC and Cervical Cancer
1
EPHA2 1p36 ECK, CTPA, ARCC2, CTPP1, CTRCT6 -EPHA2 and Cervical Cancer
1
NUMB 14q24.3 S171, C14orf41, c14_5527 -NUMB and Cervical Cancer
1
KLRK1 12p13.2-p12.3 KLR, CD314, NKG2D, NKG2-D, D12S2489E -KLRK1 and Cervical Cancer
1
YWHAZ 8q23.1 HEL4, YWHAD, KCIP-1, HEL-S-3, 14-3-3-zeta -YWHAZ and Cervical Cancer
1
TPR 1q25 -TPR and Cervical Cancer
1
TLR1 4p14 TIL, CD281, rsc786, TIL. LPRS5 -TLR1 and Cervical Cancer
1
HOXA13 7p15.2 HOX1, HOX1J -HOXA13 and Cervical Cancer
1
NR4A2 2q22-q23 NOT, RNR1, HZF-3, NURR1, TINUR -NR4A2 and Cervical Cancer
1
RARRES3 11q12.3 RIG1, TIG3, HRSL4, HRASLS4, PLA1/2-3 -RARRES3 and Cervical Cancer
1
STIM1 11p15.4 GOK, TAM, TAM1, IMD10, STRMK, D11S4896E -STIM1 and Cervical Cancer
1
PTCH1 9q22.3 PTC, BCNS, HPE7, PTC1, PTCH, NBCCS, PTCH11 -PTCH1 and Cervical Cancer
1
ISG15 1p36.33 G1P2, IP17, UCRP, IFI15, IMD38, hUCRP -ISG15 and Cervical Cancer
1
GUSB 7q21.11 BG, MPS7 -GUSB and Cervical Cancer
1
FCGR3A 1q23 CD16, FCG3, CD16A, FCGR3, IGFR3, IMD20, FCR-10, FCRIII, FCGRIII, FCRIIIA -FCGR3A and Cervical Cancer
1
CDX2 13q12.3 CDX3, CDX-3, CDX2/AS -CDX2 and Cervical Cancer
1
CFTR 7q31.2 CF, MRP7, ABC35, ABCC7, CFTR/MRP, TNR-CFTR, dJ760C5.1 -CFTR and Cervical Cancer
1
LAMP1 13q34 LAMPA, CD107a, LGP120 -LAMP1 and Cervical Cancer
1
TSC22D1 13q14 Ptg-2, TSC22, TGFB1I4 -TSC22D1 and Cervical Cancer
1
EPHB4 7q22 HTK, MYK1, TYRO11 -EPHB4 and Cervical Cancer
1
CXCL11 4q21.2 IP9, H174, IP-9, b-R1, I-TAC, SCYB11, SCYB9B -CXCL11 and Cervical Cancer
1
NDRG2 14q11.2 SYLD -NDRG2 and Cervical Cancer
1
REV1 2q11.1-q11.2 REV1L -REV1 and Cervical Cancer
1
CDC25B 20p13 -CDC25B and Cervical Cancer
1
POLL 10q23 BETAN, POLKAPPA -POLL 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
TBK1 12q14.1 NAK, T2K -TBK1 and Cervical Cancer
1
NBN 8q21 ATV, NBS, P95, NBS1, AT-V1, AT-V2 -NBN and Cervical Cancer
1
MEST 7q32 PEG1 -MEST 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
CRP 1q23.2 PTX1 -CRP and Cervical Cancer
1
CCNG1 5q32-q34 CCNG -CCNG1 and Cervical Cancer
1
CASP4 11q22.3 TX, Mih1, ICH-2, Mih1/TX, ICEREL-II, ICE(rel)II -CASP4 and Cervical Cancer
1
NR4A3 9q22 CHN, TEC, CSMF, NOR1, MINOR -NR4A3 and Cervical Cancer
1
MIR1290 1 MIRN1290, hsa-mir-1290 -miR-1290 and Cervical Cancer
1
PDCD6 5p15.33 ALG2, ALG-2, PEF1B -PDCD6 and Cervical Cancer
1
TNFSF13 17p13.1 APRIL, CD256, TALL2, ZTNF2, TALL-2, TRDL-1, UNQ383/PRO715 -TNFSF13 and Cervical Cancer
1
LTB 6p21.3 p33, TNFC, TNFSF3 -LTB and Cervical Cancer
1
KRT1 12q13.13 K1, CK1, EHK, EHK1, EPPK, KRT1A, NEPPK -KRT1 and Cervical Cancer
1
DLX4 17q21.33 BP1, DLX7, DLX8, DLX9 -DLX4 and Cervical Cancer
1
TRIM27 6p22 RFP, RNF76 -TRIM27 and Cervical Cancer
1
PIK3CD 1p36.2 APDS, PI3K, IMD14, p110D, P110DELTA -PIK3CD and Cervical Cancer
1
CDH2 18q11.2 CDHN, NCAD, CD325, CDw325 -CDH2 and Cervical Cancer
1
PPIA 7p13 CYPA, CYPH, HEL-S-69p -PPIA and Cervical Cancer
1
TFRC 3q29 T9, TR, TFR, p90, CD71, TFR1, TRFR -TFRC and Cervical Cancer
1
PPP1R13L 19q13.32 RAI, RAI4, IASPP, NKIP1 -PPP1R13L and Cervical Cancer
1
IFITM1 11p15.5 9-27, CD225, IFI17, LEU13, DSPA2a -IFITM1 and Cervical Cancer
1
CRTC1 19p13.11 MECT1, TORC1, TORC-1, WAMTP1 -CRTC1 and Cervical Cancer
1
TLE1 9q21.32 ESG, ESG1, GRG1 -TLE1 and Cervical Cancer
1
SIPA1 11q13.1 SPA1 -SIPA1 and Cervical Cancer
1
BDNF 11p14.1 ANON2, BULN2 -BDNF and Cervical Cancer
1
TEP1 14q11.2 TP1, TLP1, p240, TROVE1, VAULT2 -TEP1 and Cervical Cancer
1
MCM4 8q11.2 NKCD, CDC21, CDC54, NKGCD, hCdc21, P1-CDC21 -MCM4 and Cervical Cancer
1
LGALS3 14q22.3 L31, GAL3, MAC2, CBP35, GALBP, GALIG, LGALS2 -LGALS3 and Cervical Cancer
1
SMO 7q32.3 Gx, SMOH, FZD11 -SMO 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
OPCML 11q25 OPCM, OBCAM, IGLON1 -OPCML and Cervical Cancer
1
PTCH2 1p34.1 PTC2 -PTCH2 and Cervical Cancer
1
DGCR8 22q11.2 Gy1, pasha, DGCRK6, C22orf12 -DGCR8 and Cervical Cancer
1
GNL3 3p21.1 NS, E2IG3, NNP47, C77032 -GNL3 and Cervical Cancer
1
CAMTA1 1p36.31-p36.23 CANPMR -CAMTA1 and Cervical Cancer
1
IL16 15q26.3 LCF, NIL16, PRIL16, prIL-16 -IL16 and Cervical Cancer
1
CREB3L1 11p11.2 OASIS -CREB3L1 and Cervical Cancer
1
PTHLH 12p12.1-p11.2 HHM, PLP, BDE2, PTHR, PTHRP -PTHLH and Cervical Cancer
1
IRAK2 3p25.3 IRAK-2 -IRAK2 and Cervical Cancer
1
MIR127 14q32.2 MIRN127, mir-127, miRNA127 -MicroRNA miR-127 and Cervical Cancer
1
MYCL 1p34.2 LMYC, L-Myc, MYCL1, bHLHe38 -MYCL and Cervical Cancer
1
WWTR1 3q23-q24 TAZ -WWTR1 and Cervical Cancer
1
CHAT 10q11.2 CMS6, CMS1A, CMS1A2, CHOACTASE -CHAT and Cervical Cancer
1
MTHFD1 14q24 MTHFC, MTHFD -MTHFD1 and Cervical Cancer
1
IRF3 19q13.3-q13.4 -IRF3 and Cervical Cancer
1
LRRC3B 3p24 LRP15 -LRRC3B and Cervical Cancer
1
DDR2 1q23.3 TKT, MIG20a, NTRKR3, TYRO10 -DDR2 and Cervical Cancer
1
SOX11 2p25 MRD27 -SOX11 and Cervical Cancer
1
NTRK2 9q22.1 TRKB, trk-B, GP145-TrkB -NTRK2 and Cervical Cancer
1
TACC3 4p16.3 ERIC1, ERIC-1 -TACC3 and Cervical Cancer
1
FOXC2 16q24.1 LD, MFH1, MFH-1, FKHL14 -FOXC2 and Cervical Cancer
1
APOD 3q29 -APOD and Cervical Cancer
1
RRM2B 8q23.1 P53R2, MTDPS8A, MTDPS8B -RRM2B and Cervical Cancer
1
IBSP 4q21.1 BSP, BNSP, SP-II, BSP-II -IBSP and Cervical Cancer
1
INHA 2q35 -INHA and Cervical Cancer
1
LRIG3 12q14.1 LIG3 -LRIG3 and Cervical Cancer
1
SLC45A3 1q32.1 PRST, IPCA6, IPCA-2, IPCA-6, IPCA-8, PCANAP2, PCANAP6, PCANAP8 -SLC45A3 and Cervical Cancer
1
MICB 6p21.3 PERB11.2 -MICB and Cervical Cancer
1
HTRA2 2p12 OMI, PARK13, PRSS25 -HTRA2 and Cervical Cancer
1
MAML2 11q21 MAM2, MAM3, MAM-3, MLL-MAML2 -MAML2 and Cervical Cancer
1
GZMB 14q11.2 HLP, CCPI, CGL1, CSPB, SECT, CGL-1, CSP-B, CTLA1, CTSGL1 -GZMB and Cervical Cancer
1
CXCR3 Xq13 GPR9, MigR, CD182, CD183, Mig-R, CKR-L2, CMKAR3, IP10-R -CXCR3 and Cervical Cancer
1
PRIM1 12q13 p49 -PRIM1 and Cervical Cancer
1
RFC1 4p14-p13 A1, RFC, PO-GA, RECC1, MHCBFB, RFC140 -RFC1 and Cervical Cancer
1
CST6 11q13.1 -CST6 and Cervical Cancer
1
ETV5 3q28 ERM -ETV5 and Cervical Cancer
1
SRSF3 6p21 SFRS3, SRp20 -SRSF3 and Cervical Cancer
1
LINC00632 Xq27.1 -RP1-177G6.2 and Cervical Cancer
PTPRC 1q31-q32 LCA, LY5, B220, CD45, L-CA, T200, CD45R, GP180 -PTPRC and Cervical Cancer
FOLR1 11q13.4 FBP, FOLR -FOLR1 and Cervical Cancer

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

Latest Publications

Kim TS, Lim MS, Hong YJ, et al.
Significance of "Not Detected but Amplified" Results by Real-Time PCR Method for HPV DNA Detection.
Biomed Res Int. 2016; 2016:5170419 [PubMed] Free Access to Full Article Related Publications
Human papillomavirus (HPV) infection is an important etiologic factor in cervical carcinogenesis. Various HPV DNA detection methods have been evaluated for clinicopathological level. For the specimens with normal cytological finding, discrepancies among the detection methods were frequently found and adequate interpretation can be difficult. 6,322 clinical specimens were submitted and evaluated for real-time PCR and Hybrid Capture 2 (HC2). 573 positive or "Not Detected but Amplified" (NDBA) specimens by real-time PCR were additionally tested using genetic analyzer. For the reliability of real-time PCR, 325 retests were performed. Optimal cut-off cycle threshold (CT ) value was evaluated also. 78.7% of submitted specimens showed normal or nonspecific cytological finding. The distributions of HPV types by real-time PCR were not different between positive and NDBA cases. For positive cases by fragment analysis, concordance rates with real-time PCR and HC2 were 94.2% and 84.2%. In NDBA cases, fragment analysis and real-time PCR showed identical results in 77.0% and HC2 revealed 27.6% of concordance with fragment analysis. Optimal cut-off CT value was different for HPV types. NDBA results in real-time PCR should be regarded as equivocal, not negative. The adjustment of cut-off CT value for HPV types will be helpful for the appropriate result interpretation.

Dang YZ, Zhang Y, Li JP, et al.
High VEGFR1/2 expression levels are predictors of poor survival in patients with cervical cancer.
Medicine (Baltimore). 2017; 96(1):e5772 [PubMed] Free Access to Full Article Related Publications
The aim of the study to evaluate the prognostic significance of vascular endothelial growth factor receptor 1 and 2 (VEGFR1/2) expression levels and to correlate these levels with clinicopathological parameters in patients with cervical cancer.Forty-two patients with International Federation of Gynecology and Obstetrics Stage IIB-IVB cervical cancer were analyzed between January 2011 and December 2012. RNA expression levels of VEGFR1/2 were assessed by branched DNA-liquidchip technology and immunohistochemistry. Associations between RNA expression levels, important clinicopathological parameters, and patient survival were statistically evaluated.Higher VEGFR1/2 expression levels were predictive of poor overall survival (P = 0.009 and P = 0.024, respectively). Patients with higher VEGFR1 expression levels were associated with poorer progression-free survival than those with lower VEGFR1 expression levels (P = 0.043). In addition, patients with higher VEGFR1 expression levels were more likely to develop distant metastases than those with lower VEGFR1 expression levels (P = 0.049). Higher VEGFR2 expression levels were associated with larger tumor size (P = 0.037).VEGFR1/2 expression levels were prognostic factors for patients with cervical cancer. Higher VEGFR1/2 expression levels were also predictive of poor overall survival.

Zhang D, Sun G, Zhang H, et al.
Long non-coding RNA ANRIL indicates a poor prognosis of cervical cancer and promotes carcinogenesis via PI3K/Akt pathways.
Biomed Pharmacother. 2017; 85:511-516 [PubMed] Related Publications
Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are playing critical roles in tumorgenesis. LncRNA ANRIL has been reported to promote tumor progression in types of cancers. However, the expression and function of ANRIL in cervical cancer are still largely unclear. We measured the expression of ANRIL in cervical cancer tissues and cell lines and analyzed its association with clinicopathological features and prognosis. Loss-of-function experiments were used to identify the biological function of ANRIL. Our results showed that the expression of lncRNA ANRIL was significantly increased both in cervical cancer tissues and cell lines. Patients with high ANRIL expression had advanced FIGO stage, lymph node metastasis and poor overall survival than those with low ANRIL expression. Multivariable Cox proportional hazards regression analysis suggested that high ANRIL expression was an independent prognostic factor of prognosis. Loss-of-function experiments showed that decreased expression of ANRIL inhibited cell proliferation, migration and invasion of cervical cancer. Finally, western blot indicated that the PI3K/Akt pathway was found to be inactivated in cervical cancer cells after ANRIL inhibition. These results indicated that lncRNA ANRIL might play an important role in cervical cancer progression and could serve as a novel prognostic biomarker and therapeutic target in cervical cancer.

Lai XJ, Cheng XY, Hu LD
microRNA 421 induces apoptosis of c-33a cervical cancer cells via down-regulation of Bcl-xL.
Genet Mol Res. 2016; 15(4) [PubMed] Related Publications
Cervical cancer is a life-threatening condition. MicroRNAs (miRNAs) can promote or inhibit cell death and proliferation. The present study investigated the effect of miRNA 421 on the growth and apoptosis of cervical cancer cells. miRNA 421 and control miRNA were synthesized and transfected into c-33a cervical cancer cells. A thiazolyl blue tetrazolium bromide assay, caspase-3 activity, and flow cytometry were used to study the effects of miRNA 421 on c-33a cell growth, and apoptosis. Small interfering RNA targeting Bcl-xL was synthesized and transfected into c-33a cells along with miRNA 421. Bcl-xL expression and cell apoptosis were then measured by western blot and flow cytometry, respectively. Transfection of miRNA 421 into c-33a cells reduced their growth, promoted their apoptosis (measured by increased phosphatidylserine eversion), activated caspase-3, and decreased Bcl-xL expression. Silencing and overexpression of Bcl-xL enhanced and inhibited miRNA 421-induced apoptosis of c-33a cells, respectively. miRNA 421 induces c-33a cell apoptosis via down-regulation of Bcl-xL, suggesting that this latter might be used as a potential clinical target.

Rotar IC, Dumitras DE, Popp RA, et al.
VEGF +936 C/T Genetic Polymorphism in Patients with Cervical Dysplasia.
Anal Cell Pathol (Amst). 2016; 2016:6074275 [PubMed] Free Access to Full Article Related Publications
Aim. The present study aims to analyze the potential role of VEGF +936 C/T polymorphism in cervical intraepithelial neoplasia. Material and Method. One hundred and eighty-six patients were included in the study: 75 cases (patients diagnosed with CIN) and 111 controls (negative for both HPV testing and cytology). For each patient a single visit was scheduled when colposcopy was performed. From cervical specimen, cytology and HPV testing were performed and from peripheral blood VEGF +936 genotyping was determined. For statistical analysis purposes OR and chi-square were used at a level of significance of <0.05. Results. No link has been found in the detection of CT genotype in cases versus controls, OR = 0.8295, [0.42, 1.62]. An inverse correlation has been found between T allele and HSIL, OR = 0.2121, [0.0473, 0.9517], p = 0.0866. Conclusion. No link has been found between VEGF +936 C/T and cervical intraepithelial neoplasia.

Molano M, Moreno-Acosta P, Morales N, et al.
Association Between Type-specific HPV Infections and hTERT DNA Methylation in Patients with Invasive Cervical Cancer.
Cancer Genomics Proteomics. 2016 11-12; 13(6):483-491 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: There exists limited information on the role of hTERT methylation, and its association with type-specific HPV infections in cervical cancer.
MATERIALS AND METHODS: Eighty-seven frozen samples were analyzed for type-specific HPV infection using a GP5(+)/GP6(+) PCR-RLB assay (RLB). hTERT DNA methylation analysis was performed using a newly developed PCR-RLB-hTERT.
RESULTS: Ninety-three percent of samples were HPV-positive and fifteen different types were detected. hTERT methylation analysis of region 1 revealed no methylation in 78.8% of the samples and partial methylation in 21.2%. In region two, 68.2% showed no methylation and 31.8% showed a pattern of partial methylation. An association between the alpha 9 and alpha 7 species with a pattern of no methylation of hTERT in the region 1 was established (p=0.02 and p=0.03, respectively).
CONCLUSION: Differences in patterns of methylation of the hTERT core promoter [region 1 (nt -208 to -1) and region 2 (nt +1 to +104) relative to first ATG] are related to the HPV species present.

Lorincz AT
Virtues and Weaknesses of DNA Methylation as a Test for Cervical Cancer Prevention.
Acta Cytol. 2016; 60(6):501-512 [PubMed] Related Publications
Epigenetics is the study of heritable and non-heritable genetic coding that is additive to information contained within classical DNA base pair sequences. Differential methylation has a fundamental role in the development and outcome of malignancies, chronic and degenerative diseases and aging. DNA methylation can be measured accurately and easily via various molecular methods and has become a key technology for research and healthcare delivery, with immediate roles in the elucidation of disease natural history, diagnostics and drug discovery. This review focuses on cancers of the lower genital tract, for which the most epigenetic information exists. DNA methylation has been proposed as a triage for women infected with human papillomavirus (HPV) and may eventually directly complement or replace HPV screening as a one-step molecular diagnostic and prognostic test. Methylation of human genes is strongly associated with cervical intraepithelial neoplasia (CIN) and cancer. Of the more than 100 human methylation biomarker genes tested so far in cervical tissue, close to 20 have been reported in different studies, and approximately 10 have been repeatedly shown to have elevated methylation in cervical cancers and high-grade CIN (CIN2 and CIN3), most prominently CADM1, EPB41L3, FAM19A4, MAL, miR-124, PAX1 and SOX1. Obtaining consistent performance data from the literature is quite difficult because most methylation studies used a variety of different assay methodologies and had incomplete and/or biased clinical specimen sets, varying assay thresholds and disparate target gene regions. There have been relatively few validation studies of DNA methylation biomarkers in large population-based screening studies, but an encouraging development more recently is the execution of well-designed studies to test the true performance of the markers in real-world settings. Methylation of HPV genes, especially HPV16, HPV18, HPV31, HPV33 and HPV45, in disease progression has been a major focus of research. Elevated methylation of the HPV16 L1 and L2 open reading frames, in particular, is associated with CIN2, CIN3 and invasive cancer. Essentially all cancers have high levels of methylation for human genes and for driver HPV types, which suggests that quantitative methylation tests may have utility in predicting CIN2 and CIN3 that are likely to progress. It is still early in the process of development of methylation biomarkers, but already they are showing strong promise as a universal and systematic approach to molecular triage, applicable to all cancers, not just cancer of the cervix. DNA methylation testing is better than HPV genotyping triage and is competitive with or complementary to other approaches such as cytology and p16 staining. Genome-wide studies are underway to systematically expand methylation classifier panels and find the best combinations of biomarkers. Methylation testing is likely to show big improvements in performance in the next 5 years.

Gui H, Guo XR, Fang J, et al.
The Tumor-promoting Effects of FAM92A1-289 in Cervical Carcinoma Cells.
Anticancer Res. 2016; 36(10):5197-5204 [PubMed] Related Publications
BACKGROUND/AIM: FAM92A1-289 is recognized as one of the newly-discovered putative oncogenes. This study was performed to reveal its oncogenic functions in human cervical carcinoma cells.
MATERIALS AND METHODS: The FAM92A1-289(+) cell line was established with knock-in technique and selected by puromycin-resistance screening. Scratch assay, methylthiazol tetrazolium assay, colony forming assay and xenograft test were used to examine cell migration, cell proliferation, cell viability and tumor formation, respectively.
RESULTS: FAM92A1-289(+) cells showed higher migration rate (p<0.05), higher cell viability (p<0.01), higher colony formation and tumor growth. The FAM92A1-289 protein was pulled-down by antibodies against proliferating cell nuclear antigen (PCNA) in the co-immunoprecipitation assay.
CONCLUSION: The up-regulated expression of FAM92A1-289 could facilitate cell migration, boost cell proliferation and promote colony formation in vitro and tumor growth in vivo. The interaction between FAM92A1-289 and PCNA was verified by co-immunoprecipitation. This study provided functional evidence for FAM92A1-289 to be developed as a therapeutic target for cancer treatment.

Huang J, Liou YL, Kang YN, et al.
Real-time colorimetric detection of DNA methylation of the PAX1 gene in cervical scrapings for cervical cancer screening with thiol-labeled PCR primers and gold nanoparticles.
Int J Nanomedicine. 2016; 11:5335-5347 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: DNA methylation can induce carcinogenesis by silencing key tumor suppressor genes. Analysis of aberrant methylation of tumor suppressor genes can be used as a prognostic and predictive biomarker for cancer. In this study, we propose a colorimetric method for the detection of DNA methylation of the paired box gene 1 (PAX1) gene in cervical scrapings obtained from 42 patients who underwent cervical colposcopic biopsy.
METHODS: A thiolated methylation-specific polymerase chain reaction (MSP) primer was used to generate MSP products labeled with the thiol group at one end. After bisulfite conversion and MSP amplification, the unmodified gold nanoparticles (AuNPs) were placed in a reaction tube and NaCl was added to induce aggregation of bare AuNPs without generating polymerase chain reaction products. After salt addition, the color of AuNPs remained red in the methylated PAX1 gene samples because of binding to the MSP-amplified products. By contrast, the color of the AuNP colloid solution changed from red to blue in the non-methylated PAX1 gene samples because of aggregation of AuNPs in the absence of the MSP-amplified products. Furthermore, PAX1 methylation was quantitatively detected in cervical scrapings of patients with varied pathological degrees of cervical cancer. Conventional quantitative MSP (qMSP) was also performed for comparison.
RESULTS: The two methods showed a significant correlation of the methylation frequency of the PAX1 gene in cervical scrapings with severity of cervical cancer (n=42, P<0.05). The results of the proposed method showed that the areas under the receiver operating characteristic curve (AUCs) of PAX1 were 0.833, 0.742, and 0.739 for the detection of cervical intraepithelial neoplasms grade 2 and worse lesions (CIN2+), cervical intraepithelial neoplasms grade 3 and worse lesions (CIN3+), and squamous cell carcinoma, respectively. The sensitivity and specificity for detecting CIN2+ lesions were 0.941 and 0.600, respectively, with a cutoff value of 31.27%. The proposed method also showed superior sensitivity over qMSP methods for the detection of CIN2+ and CIN3+ (0.941 vs 0.824 and 1.000 vs 0.800, respectively). Furthermore, the novel method exhibited higher AUC (0.833) for the detection of CIN2+ than qMSP (0.807).
CONCLUSION: The results of thiol-labeled AuNP method were clearly observed by the naked eyes without requiring any expensive equipment. Therefore, the thiol-labeled AuNP method could be a simple but efficient strategy for cervical cancer screening.

Revathidevi S, Manikandan M, Rao AK, et al.
Analysis of APOBEC3A/3B germline deletion polymorphism in breast, cervical and oral cancers from South India and its impact on miRNA regulation.
Tumour Biol. 2016; 37(9):11983-11990 [PubMed] Related Publications
Breast cancer and cervical cancer are the leading causes of death in women worldwide as well as in India, whilst oral cancer is the top most common cancer among Asian especially in Indian men in terms of both incidence and mortality rate. Genetic factors determining the predisposition to cancer are being explored to identify the signature genetic variations associated with these cancers. Recently, a germline deletion polymorphism in APOBEC3 gene cluster which completely deletes APOBEC3B coding region has been studied for its association with cancer risk. We screened the germline deletion polymorphism in 409 cancer patients (224 breast cancer, 88 cervical cancer and 97 oral cancer samples), 478 controls and 239 cervical cancer tissue DNAs of South Indian origin. The results suggest that the APOBEC3A/3B deletion polymorphism is not significantly associated with cancer risk in our study population (OR 0.739, 95 % CI, p value 0.91457). Considering the viral restriction property of APOBEC3s, we also screened cervical cancer tissue DNAs for the human papilloma virus infection. We observed a gradual increase in the frequency of HPV16 infection from AA/BB cases (66.86 %) to AA/-- cases (71.43) which signifies the impact of this deletion polymorphism in HPV infection. In addition, we performed in silico analysis to understand the effect of this polymorphism on miRNA regulation of the APOBEC3A/3B fusion transcript. Only 8 APOBEC3B targeting miRNAs were observed to regulate the fusion transcript of which miR-34b-3p and miR-138-5p were found to be frequently downregulated in cancers suggesting miRNA-mediated deregulation of APOBEC3A expression in cancer patients harbouring this particular deletion polymorphism.

Wei H, Wang N, Zhang Y, et al.
Wnt-11 overexpression promoting the invasion of cervical cancer cells.
Tumour Biol. 2016; 37(9):11789-11798 [PubMed] Related Publications
Wnt-11 is a positive regulator of the Wnt signaling pathway, which plays a crucial role in carcinogenesis. However, Wnt-11 expression in cervical cancer has not been well investigated. The aim of this study was to investigate the role of Wnt-11 in cervical tumor proliferation and invasion. This study examined 24 normal cervical squamous epithelia, 29 cervical intraepithelial neoplasia (CIN), and 78 cervical cancer samples. The expression of Wnt-11 was investigated by immunohistochemistry and quantitative reverse transcription-polymerase chain reaction analysis. The expression of the high-risk human papilloma virus (HR-HPV) E6 oncoprotein was also investigated by immunohistochemistry. In addition, the expression of Wnt-11, HR-HPV E6, JNK-1, phosphorylated JNK-1(P-JNK1), and β-catenin was examined by western blot analysis following Wnt-11 knockdown or overexpression in HeLa or SiHa cells, respectively. The promotion of cervical cancer cell proliferation and invasion was investigated using the cell counting kit-8 and Matrigel invasion assay, respectively. Wnt-11 and HR-HPV E6 expression increased in a manner that corresponded with the progression of cervical cancer and was significantly correlated with the International Federation of Gynecology and Obstetrics cancer stage, lymph node metastasis, tumor size, and HPV infection. Wnt-11 protein expression was positively associated with HR-HPV E6 protein expression in all 78 cervical cancer samples (P < 0.001). Furthermore, Wnt-11 was positively associated with P-JNK1 expression and promoted cervical cancer cell proliferation and invasion. These observations suggest that the increased Wnt-11 expression observed in cervical cancer cells may lead to the phosphorylation and activation of JNK-1 and significantly promote tumor cell proliferation and cell migration/invasion through activation of the Wnt/JNK pathway. Consequently, Wnt-11 may serve as a novel target for cervical cancer therapy.

Mendes de Oliveira C, Levi JE
The Biological Impact of Genomic Diversity in Cervical Cancer Development.
Acta Cytol. 2016; 60(6):513-517 [PubMed] Related Publications
Human papillomaviruses (HPVs) are the etiologic agents of cervical cancer, the unique human neoplasia that has one single necessary cause. The diversity of HPVs is well described, with 200 HPV types existing as distinct taxonomic units and each receiving an Arabic number. On a clinical basis, they are usually grouped by their site of occurrence and disease associations. Those types inhabiting the anogenital mucosa are more intensively studied and further divided into cancer-associated HPVs, which are termed 'high risk', while those linked to benign proliferative lesions are assigned as 'low risk'. HPV16 is responsible for approximately 50% of all ICC cases, and paradoxically is one of the most prevalent types among healthy women. Longitudinal studies have shown that when an incidental HPV16 infection becomes persistent it will result in an enhanced risk for the development of high-grade lesions. However, it is unknown why some persistent, HPV16 infections (or infections by other HR-HPV types) progress to CIN3+ while most clear spontaneously. Several epidemiological investigations have focused on cofactors, from the most obvious such as cigarette and other carcinogenic exposures, to coinfections by other STDs such as chlamydia, with no significant findings. Thus, the current focus is on genomic variation from both virus and host. Such studies have been potentialized by the enormous technical advances in nucleic acid sequencing, allowing this relationship to be broadly interrogated. Corroborating subgenomic data from decades ago, an association between HPV16 lineages and carcinogenesis is being revealed. However, this effect does not seem to apply across female populations from different continents/ethnicities, again highlighting a role played by HPV16 adaptation and evasion from the host over time.

Huang P, Xi J, Liu S
MiR-139-3p induces cell apoptosis and inhibits metastasis of cervical cancer by targeting NOB1.
Biomed Pharmacother. 2016; 83:850-856 [PubMed] Related Publications
MicroRNAs (miRNAs) play an important role in the development of various cancers, including cervical cancer (CC). The dysregulation of miRNA expression is associated with oncogenic transformation and miRNA often act as tumor suppressors. In this study, we aimed to analyze the effect on and mechanism of miR-139-3p in the progression of CC. The result of real-time PCR showed that miR-139-3p was down-regulated in CC tissues and cell lines. Overexpression of miR-139-3p significantly suppressed HeLa cell proliferation, migration and invasion and induced cell apoptosis. Bioinformatics analysis and luciferase reporter gene assay confirmed that NOB1 was targeted by miR-139-3p at the 3'-untranslated region (3'UTR) of its mRNA sequence. Furthermore, overexpression of NOB1 counteracted the effects of miR-139-3p suppression. Our results suggest that miR-139-3p may act as a tumor suppressor that can inhibit CC cell proliferation, migration and invasion and induce cell apoptosis through down-regulation of NOB1 expression. Taken together, this study provides a novel potential therapeutic strategy for the treatment of CC.

Fang H, Shuang D, Yi Z, et al.
Up-regulated microRNA-155 expression is associated with poor prognosis in cervical cancer patients.
Biomed Pharmacother. 2016; 83:64-69 [PubMed] Related Publications
BACKGROUND: MicroRNAs (miRNAs) play important roles in tumor development and progression. The purposes of the study was to investigate the role of miR-155 in cervical cancer.
METHODS: Quantitative real-time RT-PCR (qRT-PCR) was performed to examine miR-155 expression in cervical cancer tissues and adjacent non-cancerous tissues. The association with overall survival of patients was analyzed by Kaplan-Meier survival analysis. Small interfering RNA (siRNA) was used to suppress miR-155 expression in cervical cancer cells. In vitro assays were performed to further explore the biological functions of miR-155 in cervical cancer.
RESULTS: We found that miR-155 expression was markedly up-regulated in cervical cancer tissues and correlated with FIGO stage, lymph nodes metastasis, vascular invasion and HPV. Patients with high miR-155 expression level had poorer overall survival than those with low miR-155 expression. Furthermore, multivariate Cox regression analysis suggested that increased miR-155 was an independent prognostic indicator for cervical cancer (P=0.007; HR=2.320; 95%CI: 1.259-4.276). Moreover, knockdown of miR-155 was demonstrated to inhibit cell proliferation, migration, and invasion in vitro.
CONCLUSION: Our study presents that miR-155 is a novel molecule involved in cervical cancer progression, which provide a potential prognostic biomarker and therapeutic target.

Drozd E, Krzysztoń-Russjan J, Marczewska J, et al.
Up-regulation of glutathione-related genes, enzyme activities and transport proteins in human cervical cancer cells treated with doxorubicin.
Biomed Pharmacother. 2016; 83:397-406 [PubMed] Related Publications
Doxorubicin (DOX), one of the most effective anticancer drugs, acts in a variety of ways including DNA damage, enzyme inhibition and generation of reactive oxygen species. Glutathione (GSH) and glutathione-related enzymes including: glutathione peroxidase (GPX), glutathione reductase (GSR) and glutathione S-transferases (GST) may play a role in adaptive detoxification processes in response to the oxidative stress, thus contributing to drug resistance phenotype. In this study, we investigated effects of DOX treatment on expression and activity of GSH-related enzymes and multidrug resistance-associated proteins in cultured human cervical cancer cells displaying different resistance against this drug (HeLa and KB-V1). Determination of expression level of genes encoding GST isoforms and MRP proteins (GCS, GPX, GSR, GSTA1-3, GSTM1, GSTP1, ABCC1-3, MGST1-3) was performed using StellARray™ Technology. Enzymatic activities of GPX and GSR were measured using biochemical methods. Expression of MRP1 was examined by immunofluorescence microscopy. This study showed that native expression levels of GSTM1 and GSTA3 were markedly higher in KB-V1 cells (2000-fold and 200-fold) compared to HeLa cells. Resistant cells have also shown significantly elevated expression of GSTA1 and GSTA2 genes (200-fold and 50-fold) as a result of DOX treatment. In HeLa cells, exposure to DOX increased expression of all genes: GSTM1 (7-fold) and GSTA1-3 (550-fold, 150-fold and 300-fold). Exposure to DOX led to the slight increase of GCS expression as well as GPX activity in KB-V1 cells, while in HeLa cells it did not. Expression of ABCC1 (MRP1) was not increased in any of the tested cell lines. Our results indicate that expression of GSTM1 and GSTA1-3 genes is up-regulated by DOX treatment and suggest that activity of these genes may be associated with drug resistance of the tested cells. At the same time, involvement of MRP1 in DOX resistance in the given experimental conditions is unlikely.

Song J, Li Y
miR-25-3p reverses epithelial-mesenchymal transition via targeting Sema4C in cisplatin-resistance cervical cancer cells.
Cancer Sci. 2017; 108(1):23-31 [PubMed] Free Access to Full Article Related Publications
Acquisition of epithelial-mesenchymal transition (EMT) has recently been proposed as an important contributor of drug resistance in cervical cancer cells. However, the underlying mechanisms are still unclear. MicroRNAs play a crucial role in regulating EMT. The aim of this study was to explore the potential role of miR-25-3p in regulating EMT in cisplatin-resistant (CR) cervical cancer cells. To this end, we established stable CR cervical cancer cells, HeLa-CR and CaSki-CR, and investigated the function of miR-25-3p in regulating EMT. It is found that CR cervical cancer cells possessed more EMT characteristics and demonstrated higher migratory abilities and invasiveness. miR-25-3p downregulation was also seen in HeLa-CR and CaSki-CR cells. Of note, ectopic expression of miR-25-3p reversed the EMT phenotype and sensitized CR cells to cisplatin via targeting Sema4C. Furthermore, stable overexpression of miR-25-3p in HeLa-CR cells suppressed tumor growth in mice, downregulated Sema4C and Snail, and upregulated E-cadherin compared with the control group. These results suggest that miR-25-3p is an important regulator of cervical cancer EMT and chemoresistance. Thus, upregulation of miR-25-3p could be a novel approach to treat cervical cancers that are resistant to chemotherapy.

Wang S, Cao X, Ding B, et al.
The rs767649 polymorphism in the promoter of miR-155 contributes to the decreased risk for cervical cancer in a Chinese population.
Gene. 2016; 595(1):109-114 [PubMed] Related Publications
Genetic variants in miRNAs have attracted more and more attention these years because they are capable of altering miRNA function and/or expression, consequently affecting downstream biological pathways and disease risk. The rs767649 polymorphism, locating in the promoter of miR-155, was recently reported to be able to alter transcriptional activity of miR-155 and relate to lung cancer risk. In this study, we aimed to assess the relationship between rs767649 and cervical cancer (CC) risk. We investigated the association of rs767649 with CC risk in a two-stage case-control study with 1157 cases and 1280 controls. Genotyping was determined with TaqMan allelic discrimination method. The results showed that the rs767649 TT genotype was associated with a significantly reduced risk of CC in both test (549 cases and 603 controls), validation (608 cases and 677 controls) and combined sets [adjusted odds ratio (OR)=0.67, 95% confidence interval (CI)=0.51-0.87 for the combined set] compared with the AA/AT genotypes. Moreover, the association was more prominent among patients of age>49years and postmenopausal status (OR=0.56, 95% CI=0.38-0.83, and 0.60, 0.40-0.89, respectively) and patients with clinical stage I and II CC (OR=0.67, 95% CI=0.50-0.91, and 0.60, 0.40-0.92, respectively). Further analyses showed that miR-155 was overexpressed in the CC tissues as compared with normal tissues, suggesting an oncogenic role in CC. Luciferase assay indicated that the transition of A to T allele might lead to miR-155 downregulation at the transcriptional level. In conclusion, rs767649 might be a causal variant for CC susceptibility.

Yin XZ, Zhao DM, Zhang GX, Liu L
Effect of miRNA-203 on cervical cancer cells and its underlying mechanism.
Genet Mol Res. 2016; 15(3) [PubMed] Related Publications
miRNA-203 is involved in the development and progression of various types of cancer. However, its role in cervical cancer remains unclear. The aim of this study was to investigate the effect of miRNA-203 on the proliferation and migration of HeLa cervical cancer cells, as well as survivin expression in these cells. A miRNA-203 primer probe was designed according to a sequence obtained from NCBI. The expression of miRNA-203 in cervical epithelial cells and cervical cancer cells was detected by quantitative reverse transcriptase-polymerase chain reaction. The miRNA-203 expression pattern was compared between these two cell lines. The cervical cancer cells were transfected with miRNA-203 mimic or inhibitor to determine their effects on proliferation and migration. The expression of the miRNA-203 target protein (survivin) was analyzed by western blot. Cervical cancer cells showed reduced miRNA-203 expression compared to cervical epithelial cells. Transfection of miRNA-203 mimic upregulated the expression of miRNA-203, suppressed cell proliferation and migration, and downregulated survivin expression (P < 0.05). However, downregulation of miRNA-203 expression did not affect proliferation, migration, and survivin expression in cervical cancer cells (P > 0.05). In conclusion, upregulation of miRNA-203 in cervical cancer cells inhibits the proliferative and migratory capacities of these cells by downregulating the expression of survivin.

Sharma Saha S, Roy Chowdhury R, Mondal NR, et al.
Identification of genetic variation in the lncRNA HOTAIR associated with HPV16-related cervical cancer pathogenesis.
Cell Oncol (Dordr). 2016; 39(6):559-572 [PubMed] Related Publications
PURPOSE: Previously, over-expression of the long noncoding RNA (lncRNA) HOTAIR has been found to be associated with the invasive and metastatic capacities of several epithelial cancers, including cervical cancer (CaCx). Here, we aimed at identifying functionally relevant genetic variants that may be employed to differentiate between clinically distinct CaCx subtypes, i.e., those exhibiting high HOTAIR levels and molecular signatures of metastasis and those lacking such signatures in the presence of low HOTAIR expression levels.
METHODS: Genomic DNA isolated from various cervical tissue samples (characterized by histopathology and HPV status) was used for HOTAIR amplicon sequencing, followed by validation of the findings by Sanger sequencing. The impact of the genetic variants found on the secondary structure of HOTAIR and the concomitant alterations in miRNA binding sites were determined through in silico analysis, followed by miRNA expression analysis by quantitative real-time PCR and confirmation of miRNA binding using a luciferase reporter assay.
RESULTS: We found that rs2366152C was over-represented [ORage-adjusted = 2.58 (1.23-5.57); p = 0.014] in low HOTAIR expressing HPV positive CaCx cases compared to HPV negative controls. This genetic variant showed the propensity of a secondary structure alteration and gain of a miR-22 binding site in HOTAIR, which was found to be concordant with miR-22 over-expression in low HOTAIR CaCx cases compared to controls. We found that miR-22 expression negatively correlated with HOTAIR and E7 expression in HPV16 positive cases and in an E7 transfected HPV negative CaCx-derived cell line (C33A), but was not altered in high HOTAIR cases compared to controls. Reduced luciferase activity of a HOTAIR rs2366152C expression plasmid in C33A cells through miR-22 co-transfection confirmed the ability of miR-22 to specifically target rs2366152C-harbouring HOTAIR lncRNA in CaCx cells, ultimately leading to its down-regulation.
CONCLUSIONS: Our data indicate that rs2366152C not only has the potential to serve as a marker for singling out CaCx cases lacking metastatic molecular signatures, but also to explain the functional inactivation of HOTAIR in these cases, including the mechanism of its down-regulation.

Gupta A, Ahmad MK, Mahndi AA, et al.
Promoter Methylation and Relative mRNA Expression of the p16 Gene in Cervical Cancer in North Indians.
Asian Pac J Cancer Prev. 2016; 17(8):4149-54 [PubMed] Related Publications
BACKGROUND: Cervical carcinoma is one of the main causes of mortality in women worldwide as well as in India. It occurs as a result of various molecular events that develop from the combined influences of an individual's genetic predisposition and external agents such as smoking and menstrual hygiene, for example. However, infection with human papillomavirus (HPV) is the established major risk factor. The aim of the current study was to investigate p16 CpG island methylation and establish any correlation with mRNA expression in a north Indian population.
MATERIALS AND METHODS: We analyzed 196 woman volunteers out of which 98 were cases and 98 healthy controls. For the analysis of methylation pattern, DNA extracted from blood samples was modified with a bisulfate kit and used as template for methylation specific PCR (MSP). Quantitative real-time PCR (QRT-PCR) was performed to check mRNA expression.
RESULTS: Correlation between methylation status of p16 gene and poor menstrual hygiene was significant (p=0.006), high parity cases showed methylation of p16 gene (p=0.031) with increased risk up to 1.86 times for cervical cancer and smoking was a strong risk factor associated with cervical cancer. We analyzed methylation pattern and found 60.3% methylation in cases with low mRNA expression level (0.014) as compared to controls (1.24). It was also observed that promoter methylation of p16 gene was significantly greater in FIGO stage III.
CONCLUSIONS: We conclude that p16 methylation plays an important role in cervical cancer in the north Indian population and its methylation decreases mRNA expression. It can be used as an important and consistent blood biomarker in cervical cancer patients.

Wang Y, Wang S, Shen J, et al.
Genotype Distribution of Human Papillomavirus among Women with Cervical Cytological Abnormalities or Invasive Squamous Cell Carcinoma in a High-Incidence Area of Esophageal Carcinoma in China.
Biomed Res Int. 2016; 2016:1256384 [PubMed] Free Access to Full Article Related Publications
Data of HPV genotype including 16 high-risk HPV (HR-HPV) and 4 low-risk HPV from 38,397 women with normal cytology, 1341 women with cervical cytology abnormalities, and 223 women with ISCC were retrospectively evaluated by a hospital-based study. The prevalence of high-risk HPV (HR-HPV) was 6.51%, 41.83%, and 96.86% in women with normal cytology, cervical cytology abnormalities, and ISCC, respectively. The three most common HPV types were HPV-52 (1.76%), HPV-16 (1.28%), and HPV-58 (0.97%) in women with normal cytology, whereas the most prevalent HPV type was HPV-16 (16.85%), followed by HPV-52 (9.55%) and HPV-58 (7.83%) in women with cervical cytology abnormalities. Specifically, HPV-16 had the highest frequency in ASC-H (24.16%, 36/149) and HSIL (35.71%, 110/308), while HPV-52 was the most common type in ASC-US (8.28%, 53/640) and LSIL (16.80%, 41/244). HPV-16 (75.78%), HPV18 (10.31%), and HPV58 (9.87%) were the most common types in women with ISCC. These data might contribute to increasing the knowledge of HPV epidemiology and providing the guide for vaccine selection for women in Shantou.

Poomipark N, Flatley JE, Hill MH, et al.
Methyl Donor Status Influences DNMT Expression and Global DNA Methylation in Cervical Cancer Cells.
Asian Pac J Cancer Prev. 2016; 17(7):3213-22 [PubMed] Related Publications
BACKGROUND: Methyl donor status influences DNA stability and DNA methylation although little is known about effects on DNA methyltransferases. The aim of this study was to determine whether methyldonor status influences DNA methyltransferase (Dnmt) gene expression in cervical cancer cells, and if so, whether there are associated effects on global DNA methylation.
MATERIALS AND METHODS: The human cervical cancer cell line, C4 II, was grown in complete medium and medium depleted of folate (FM+) and folate and methionine (FM). Growth rate, intracellular folate, intracellular methionine and homocysteine in the extracellular medium were measured to validate the cancer cell model of methyl donor depletion. Dnmt expression was measured by qRT PCR using relative quantification and global DNA methylation was measured using a flow cytometric method.
RESULTS: Intracellular folate and methionine concentrations were significantly reduced after growth in depleted media. Growth rate was also reduced in response to methyl donor depletion. Extracellular homocysteine was raised compared with controls, indicating disturbance to the methyl cycle. Combined folate and methionine depletion led to a significant downregulation of Dnmt3a and Dnmt3b; this was associated with an 18% reduction in global DNA methylation compared with controls. Effects of folate and methionine depletion on Dnmt3a and 3b expression were reversed by transferring depleted cells to complete medium.
CONCLUSIONS: Methyl donor status can evidently influence expression of Dnmts in cervical cancer cells, which is associated with DNA global hypomethylation. Effects on Dnmt expression are reversible, suggesting reversible modulating effects of dietary methyl donor intake on gene expression, which may be relevant for cancer progression.

Coimbra EC, DA Conceição Gomes Leitão M, Júnior MR, et al.
Expression Profile of MicroRNA-203 and its ΔNp63 Target in Cervical Carcinogenesis: Prospects for Cervical Cancer Screening.
Anticancer Res. 2016; 36(8):3939-46 [PubMed] Related Publications
BACKGROUND/AIM: Host molecules disturbed by human papillomavirus (HPV) oncoproteins have been shown to be potential biomarkers of cervical carcinogenesis and represent an alternative or supplementary aid to cytological testing and HPV detection. The miR-203 and one of its targets, ΔNp63, are known to be host molecules that interact with each other to control the proliferation and differentiation of keratinocytes; both have been found to be dysregulated in many cancers. As the role of p63 and miR-203 in cervical carcinogenesis is not yet well-understood, we have, thus, decided to evaluate the changes of expression of both in cervical carcinogenesis.
MATERIALS AND METHODS: This study was carried out by obtaining quantitative polymerase chain reaction (qPCR) data from cervical biopsies.
RESULTS: miR-203 and ΔNp63 displayed a similar expression pattern across cervical tissues and both targets showed statistically significant differences between low-grade squamous intraepithelial lesion (LSIL) x high-grade squamous intraepithelial lesion (HSIL); HSIL x Cancer. Additionally, we did not observe an inverse correlation between ΔNp63 mRNA and miR-203 levels as expected but, rather, a positive correlation between cervical tissues.
CONCLUSION: Although preliminary, the expression levels of ΔNp63 mRNA and miR-203 seem to be promising for cervical cancer screening. In addition, positive correlation between miR-203 and ΔNp63 expression suggests the possible existence of some indirect pathways. However, further studies are needed to clarify the role of ΔNp63 and miR-203 in cervical carcinogenesis and, thus, determine how they can be applied in new strategies for diagnosis.

Stamenković M, Knežević A, Knežević I, et al.
High-risk human papilloma virus genotypes in cervical carcinoma of Serbian women: Distribution and association with pathohistological findings.
Biologicals. 2016; 44(5):412-6 [PubMed] Related Publications
A significant role of high-risk Human papilloma viruses (HR HPV) in the development of cervical carcinoma is well known. HR HPV 16 and 18 account for approximately 70% of all cases of cervical cancer worldwide. The incidence of cervical cancer in Serbia, is one of the highest in Europe. The aim of our study was to investigate the distribution of HR HPV types in cervical carcinoma of Serbian women, as well as association between the HPV types and pathohistological findings. The study included 80 archival cervical cancer tissues from the same number of patients. The presence of HPV DNA was determined using MY09/MY11 primers for L1 gene and GP1/GP2 primers for E1 gene. HPV was detected in 78.75% tissues. HR HPV genotypes found in the decreasing order of frequency were: HPV16 (80.39%), HPV33 (7.84%), HPV58 (5.88%), HPV18 (1.96%), HPV45 (1.96%) and HPV53 (1.96%). The examined tissues were 91.25% squamous cell carcinomas and 8.75% adenocarcinoma. The high frequency of HPV 16 was observed in both types of carcinoma (80.8% and 75%, respectively) while the prevalence of HPV18 was low. These results may contribute to the implementation of cervical carcinoma prevention program in Serbia, including the selection of the most appropriate vaccine and immunization program.

Chandrasekaran KS, Sathyanarayanan A, Karunagaran D
Downregulation of HMGB1 by miR-34a is sufficient to suppress proliferation, migration and invasion of human cervical and colorectal cancer cells.
Tumour Biol. 2016; 37(10):13155-13166 [PubMed] Related Publications
High mobility group box 1 (HMGB1) is a ubiquitous nuclear protein known to be highly expressed in human cervical (CaCx) and colorectal (CRC) cancers, and sustained high levels of HMGB1 contribute to tumourigenesis and metastasis. HMGB1-targeted cancer therapy is of recent interest, and there are not many studies on miRNA-mediated HMGB1 regulation in these cancers. Since miRNA-based therapeutics for cancer is gaining importance in recent years, it was of interest to predict miRNAs targeting HMGB1. Based on the identification of a potential miR-34a response element in HMGB1-3' untranslated region (3'UTR) and an inverse correlation between HMGB1 and miR-34a expression levels in CaCx and CRC tissues, from a subset of the local population as well as a large sampling from TCGA database, experiments were performed to validate HMGB1 as a direct target of miR-34a in CaCx and CRC cells. Ectopic expression of miR-34a decreased the wild-type HMGB1-3'UTR luciferase activity but not that of its mutant in 3'UTR luciferase assays. While forced expression of miR-34a in CaCx and CRC cells inhibited HMGB1 mRNA and protein levels, proliferation, migration and invasion, inhibition of endogenous miR-34a enhanced these tumourigenic properties. siRNA-mediated HMGB1 suppression imitated miR-34a expression in reducing proliferation and metastasis-related events. Combined with the disparity in expression of miR-34a and HMGB1 in clinical specimens, the current findings would help in not only understanding the complexity of miRNA-target regulatory mechanisms but also in designing novel therapeutic interventions in CaCx and CRC.

Evans W, Filippova M, Filippov V, et al.
Overexpression of HPV16 E6* Alters β-Integrin and Mitochondrial Dysfunction Pathways in Cervical Cancer Cells.
Cancer Genomics Proteomics. 2016 Jul-Aug; 13(4):259-73 [PubMed] Related Publications
BACKGROUND: High-risk human papillomaviruses (HPV) cause nearly all cases of cervical cancer, as well as many types of oral and anogenital cancer. Alternative splicing increases the capacity of the HPV genome to encode the proteins necessary for successful completion of its infectious life cycle. However, the roles of these splice variants, including E6*, the smaller splice isoform of the E6 oncogene, in carcinogenesis are not clear.
MATERIALS AND METHODS: SiHa (HPV16(+)) and C33A (HPV(-)) cells were transfected with the E6* plasmid, and tandem mass tag-labeled protein levels were quantified by mass spectrometry. Proteomic analyses identified pathways affected by E6* in both HPV(+) and HPV(-) cells, and pathways were validated using in vitro methods.
RESULTS: A total of 4,300 proteins were identified and quantified in lysates of SiHa and C33A cells with and without HPV16 E6* expression. SiHa and C33A cells expressing E6* underwent changes in protein expression affecting integrin signaling and mitochondrial dysfunction pathways, respectively. Subsequent experiments were performed to validate selected E6*-mediated alterations in protein levels.
CONCLUSION: E6* modifies the expression of proteins involved in mitochondrial dysfunction and oxidative phosphorylation in C33A cells, and β-integrin signaling in SiHa cells.

Nogueira A, Assis J, Faustino I, et al.
Base excision repair pathway: PARP1 genotypes as modulators of therapy response in cervical cancer patients.
Biomarkers. 2017; 22(1):70-76 [PubMed] Related Publications
CONTEXT: Genetic polymorphisms in genes of the base excision repair (BER) pathway appear to modulate the therapy response of cancer patients. PARP1 protein recognizes the DNA strand damage and facilitates the subsequent recruitment of BER proteins. Few studies have reported an association between PARP1 Val762Ala polymorphism (rs1136410) and cancer therapy response.
OBJECTIVE: The purpose of our study was to determine whether PARP1 Val762Ala polymorphism have prognostic value in patients with cervical cancer.
MATERIALS AND METHODS: Two hundred and sixty adult patients, with histologically confirmed cervical cancer, at FIGO-stages IB2-IVA, primarily treated with concurrent chemotherapy (cisplatin) and radiotherapy. Overall survival (OS) and disease-free survival (DFS) were the primary end points of the analysis. The PARP1 Val762Ala genetic variants were analyzed by allelic discrimination by real-time PCR.
RESULTS: We observed that peri- and postmenopausal women carrying the C-allele present a statistically significant lower OS and DFS (log-rank test, p = 0.008 and p = 0.006, respectively) among those with early stage cervical cancer. Cox regression analysis confirmed these results, after adjustment for other prognostic factors (for OS: HR, 3.70; 95%CI, 1.32-10.38; p = 0.013 and for DFS: HR, 3.97; 95%CI, 1.59-9.93; p = 0.003).
CONCLUSIONS: This is the first study evaluating the effect of PARP1 Val762Ala polymorphism in treatment response in cervical cancer patients. PARP1 genotypes may contribute as an independent prognostic factor in cervical cancer, being useful in predicting the clinical outcome.

Freier CP, Stiasny A, Kuhn C, et al.
Immunohistochemical Evaluation of the Role of p53 Mutation in Cervical Cancer: Ser-20 p53-Mutant Correlates with Better Prognosis.
Anticancer Res. 2016; 36(6):3131-7 [PubMed] Related Publications
BACKGROUND: Cervical cancer is driven by human papillomavirus virus-specific oncoprotein E6. E6 interacts with E3 ubiquitin-protein ligase, resulting in the proteolysis of p53 protein. The aim of this study was to analyze one TP53 mutation in patients with cervical cancer and to correlate it to prognosis.
MATERIALS AND METHODS: A total of 248 paraffin-embedded tumor samples were stained for mutated p53 protein. The distribution and intensity of staining both in the nucleus and cytoplasm were evaluated with a semi-quantitative immunohistochemical score.
RESULTS: A total of 66% of studied cervical carcinomas expressed the mutated p53 protein. The overall survival was better for patients expressing the mutated p53 protein in the nucleus.
CONCLUSION: Interestingly, we found a very high mutation rate of TP53 in a cancer type where p53 is initially inactivated via E6 during the development of cervical cancer. An unexpected finding is the correlation of this mutation with better survival, possibly due to better response to therapy.

Rathika C, Murali V, Dhivakar M, et al.
Susceptible and Protective Associations of HLA Alleles and Haplotypes with Cervical Cancer in South India.
Asian Pac J Cancer Prev. 2016; 17(5):2491-7 [PubMed] Related Publications
BACKGROUND: Human leukocyte antigen (HLA) genes have been implicated in cervical cancer in several populations.
OBJECTIVES: To study the predispositions of HLA alleles/haplotypes with cervical cancer.
MATERIALS AND METHODS: Clinically diagnosed and PAP smear confirmed cervical cancer patients (n 48) and age matched controls (n 47) were genotyped for HLA-A,-B,-DRB1* and DQB1* alleles by PCR-SSP methods.
RESULTS: The frequencies of alleles DRB1*04 (OR=2.57), DRB1*15 (OR=2.04), DQB1*0301 (OR=4.91), DQB1*0601 (OR=2.21), B*15 (OR=13.03) and B*07 (OR=6.23) were higher in cervical cancer patients than in the controls. The frequencies of alleles DRB1*10 (OR=0.22) and B*35 (OR=0.19) were decreased. Strong disease associations were observed for haplotypes DRB1*15-DQB1*0601 (OR=6.56; < 3.5.10-4), DRB1*14-DQB1*0501 (OR=6.51; <0.039) and A*11-B*07 (OR=3.95; <0.005). The reduced frequencies of haplotypes DRB1*10-DQB1*0501 (OR=0.45), A*03-B*35 (OR=0.25) and A*11-B*35 (OR= 0.06) among patients suggested a protective association. HLA-C* typing of 8 patients who possessed a unique three locus haplotype 'A*11-B*07-DRB1*04' (8/48; 16.66%; OR=6.51; <0.039) revealed the presence of a four locus haplotype 'A*11-B*07-C*01-DRB1*04' in patients (4/8; 50%). Amino acid variation analysis of susceptible allele DQB1*0601 suggested 'tyrosine' at positions β9 and β37 and tyrosine-non-tyrosine genotype combination increased the risk of cervical cancer.
CONCLUSIONS: Strong susceptible associations were documented for HLA alleles B*15, B*07, DRB1*04, DRB1*15, DQB1*0301, DQB1*0601 and haplotypes DRB1*15-DQB1*0601 and DRB1*14-DQB1*0501. Further, protective associations were evidenced for alleles B*35 and DRB1*10 and haplotypes A*11-B*35 and DRB1*10-DQB1*0501 with cervical cancer in South India.

Chen XF, Liu Y
MicroRNA-744 inhibited cervical cancer growth and progression through apoptosis induction by regulating Bcl-2.
Biomed Pharmacother. 2016; 81:379-87 [PubMed] Related Publications
Growing evidence suggests that microRNA plays an essential role in the development and metastasis of many tumor progressions, including cervical cancer. Aberrant miR-744 expression has been indicated in many growth of tumor, the mechanism of miR-744 inhibits both the proliferation and metastatic ability for cervical cancer remains unclear. Accumulating evidences reported that Bcl-2 signal pathway plays an important role in the cellular process, such as apoptosis, cell growth and proliferation. The goal of this study was to identify miR-744 that could inhibit the growth, migration, invasion, proliferation and metastasis of gastric cancer through targeting Bcl-2 expression. Real-time PCR (RT-qPCR) was used to quantify miR-744 expression in vitro and vivo experiments. The biological functions of miR-744 were determined via cell proliferation. Our study indicated that miR-744 targeted on Bcl-2, which leads to the inactivation of apoptosis signaling and the cell proliferation of cervical cancer cells, ameliorating cervical cancer growth and progression. In addition, both up-regulation of miR-744 and down-regulation of Bcl-2 could stimulate Caspase-3 expression, promoting apoptosis of cervical cancer cells. Therefore, our research revealed the mechanistic links between miR-744 and Bcl-2 in the pathogenesis of cervical cancer through modulation of Caspase-3, leading to the inhibition of cervical cancer cell growth. And targeting miR-744 could be served as a novel strategy for future cervical cancer therapy clinically.

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

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