Ovarian Cancer

Overview

Approximately 10% of all epithelial ovarian carcinomas are associated with autosomal dominant genetic predisposition, primarily by inherited mutations in the BRCA1 or BRCA2 tumour supressor genes (Boyd 1998). Mutations of these genes are also seen in some sporadic ovarian cancers. Other genetic features tend to relate to specific types of ovarian cancer;

Invasive serous and undifferentiated ovarian carcinomas are characterized by TP53 mutations and TP53 protein accumulation. Loss of genetic material from chromosome 17 is also common.

Overexpression of BCL2 is seen in most endometrioid carcinomas (90% of cases). These tumours can also show microsatellite instability.

KRAS mutations are characteristic for mucinous carcinomas (40-50% of cases). In mucinous tumors with low malignant potential (LMP) KRAS mutations are less frequent ( about 30% of cases).

See also: Ovarian Cancer - clinical resources (31)

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 (307)

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
BRCA1 17q21.31 IRIS, PSCP, BRCAI, BRCC1, FANCS, PNCA4, RNF53, BROVCA1, PPP1R53 -BRCA1 and Ovarian Cancer
3000
BRCA2 13q13.1 FAD, FACD, FAD1, GLM3, BRCC2, FANCD, PNCA2, FANCD1, XRCC11, BROVCA2 -BRCA2 and Ovarian Cancer
2435
TP53 17p13.1 P53, BCC7, LFS1, TRP53 -TP53 and Ovarian Cancer
404
CTNNB1 3p22.1 CTNNB, MRD19, armadillo -CTNNB1 and Ovarian Cancer
333
ERBB2 17q12 NEU, NGL, HER2, TKR1, CD340, HER-2, MLN 19, HER-2/neu -HER2 and Ovarian Cancer
137
MSH2 2p21 FCC1, COCA1, HNPCC, LCFS2, HNPCC1 -MSH2 and Ovarian Cancer
120
PIK3CA 3q26.3 MCM, CWS5, MCAP, PI3K, CLOVE, MCMTC, p110-alpha -PIK3CA and Ovarian Cancer
101
CDKN1A 6p21.2 P21, CIP1, SDI1, WAF1, CAP20, CDKN1, MDA-6, p21CIP1 Prognostic
-CDKN1A Expression in Ovarian Cancer
84
MDM2 12q14.3-q15 HDMX, hdm2, ACTFS -MDM2 and Ovarian Cancer
81
RAD51 15q15.1 RECA, BRCC5, FANCR, MRMV2, HRAD51, RAD51A, HsRad51, HsT16930 -RAD51 and Ovarian Cancer
62
ERCC1 19q13.32 UV20, COFS4, RAD10 -ERCC1 and Ovarian Cancer
59
AKT2 19q13.1-q13.2 PKBB, PRKBB, HIHGHH, PKBBETA, RAC-BETA -AKT2 and Ovarian Cancer
59
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II -MMP2 and Ovarian Cancer
57
CHEK2 22q12.1 CDS1, CHK2, LFS2, RAD53, hCds1, HuCds1, PP1425 -CHEK2 and Ovarian Cancer
53
ARID1A 1p35.3 ELD, B120, OSA1, P270, hELD, BM029, MRD14, hOSA1, BAF250, C1orf4, BAF250a, SMARCF1 -ARID1A and Ovarian Cancer
50
ACHE 7q22 YT, ACEE, ARACHE, N-ACHE -ACHE and Ovarian Cancer
45
BLID 11q24.1 BRCC2 -BLID and Ovarian Cancer
44
MSH6 2p16 GTBP, HSAP, p160, GTMBP, HNPCC5 -MSH6 and Ovarian Cancer
44
PMS2 7p22.1 MLH4, PMSL2, HNPCC4, PMS2CL -PMS2 and Ovarian Cancer
44
FOXL2 3q23 BPES, PFRK, POF3, BPES1, PINTO -FOXL2 and Ovarian Cancer
43
WT1 11p13 GUD, AWT1, WAGR, WT33, NPHS4, WIT-2, EWS-WT1 -WT1 expression in Ovarian Cancer
43
BCL2L1 20q11.21 BCLX, BCL2L, BCLXL, BCLXS, Bcl-X, bcl-xL, bcl-xS, PPP1R52, BCL-XL/S -BCL2L1 and Ovarian Cancer
40
BARD1 2q35 -BARD1 and Ovarian Cancer
40
RAD51C 17q22 FANCO, R51H3, BROVCA3, RAD51L2 -RAD51C and Ovarian Cancer
39
CYP19A1 15q21.1 ARO, ARO1, CPV1, CYAR, CYP19, CYPXIX, P-450AROM -CYP19A1 and Ovarian Cancer
31
FGF2 4q26 BFGF, FGFB, FGF-2, HBGF-2 -FGF2 and Ovarian Cancer
30
KLK3 19q13.41 APS, PSA, hK3, KLK2A1 -PSA expression in Ovarian Cancere
29
PARP1 1q41-q42 PARP, PPOL, ADPRT, ARTD1, ADPRT1, PARP-1, ADPRT 1, pADPRT-1 -PARP1 and Ovarian Cancer
28
OLAH 10p13 SAST, AURA1, THEDC1 -OLAH and Ovarian Cancer
26
MUC16 19p13.2 CA125 -MUC16 and Ovarian Cancer
26
DICER1 14q32.13 DCR1, MNG1, Dicer, HERNA, RMSE2, Dicer1e, K12H4.8-LIKE -DICER1 and Ovarian Cancer
25
ABCC1 16p13.1 MRP, ABCC, GS-X, MRP1, ABC29 -ABCC1 (MRP1) and Ovarian Cancer
25
BRAP 12q24 IMP, BRAP2, RNF52 -BRAP and Ovarian Cancer
24
XIAP Xq25 API3, ILP1, MIHA, XLP2, BIRC4, IAP-3, hIAP3, hIAP-3 -XIAP and Ovarian Cancer
23
SERPINB5 18q21.33 PI5, maspin -SERPIN-B5 and Ovarian Cancer
23
HMGA2 12q15 BABL, LIPO, HMGIC, HMGI-C, STQTL9 -HMGA2 and Ovarian Cancer
23
CXCL1 4q21 FSP, GRO1, GROa, MGSA, NAP-3, SCYB1, MGSA-a -CXCL1 and Ovarian Cancer
22
ZNF217 20q13.2 ZABC1 -ZNF217 and Ovarian Cancer
21
DIRAS3 1p31 ARHI, NOEY2 -DIRAS3 and Ovarian Cancer
21
SMARCA4 19p13.2 BRG1, CSS4, SNF2, SWI2, MRD16, RTPS2, BAF190, SNF2L4, SNF2LB, hSNF2b, BAF190A -SMARCA4 and Ovarian Cancer
20
JUN 1p32-p31 AP1, AP-1, c-Jun -c-Jun and Ovarian Cancer
19
FHIT 3p14.2 FRA3B, AP3Aase -FHIT and Ovarian Cancer
17
KLK6 19q13.3 hK6, Bssp, Klk7, SP59, PRSS9, PRSS18 -KLK6 and Ovarian Cancer
15
CLDN4 7q11.23 CPER, CPE-R, CPETR, CPETR1, WBSCR8, hCPE-R -CLDN4 and Ovarian Cancer
15
EPHA2 1p36 ECK, CTPA, ARCC2, CTPP1, CTRCT6 -EPHA2 and Ovarian Cancer
15
NBN 8q21 ATV, NBS, P95, NBS1, AT-V1, AT-V2 -NBN and Ovarian Cancer
15
PPP2R1A 19q13.41 MRD36, PR65A, PP2AAALPHA, PP2A-Aalpha -PPP2R1A and Ovarian Cancer
15
CLDN3 7q11.23 RVP1, HRVP1, C7orf1, CPE-R2, CPETR2 -CLDN3 and Ovarian Cancer
15
RAD51D 17q11 TRAD, R51H3, BROVCA4, RAD51L3 -RAD51D and Ovarian Cancer
14
FH 1q42.1 MCL, FMRD, LRCC, HLRCC, MCUL1 -FH and Ovarian Cancer
14
OPCML 11q25 OPCM, OBCAM, IGLON1 -OPCML and Ovarian Cancer
14
E2F3 6p22 E2F-3 -E2F3 and Ovarian Cancer
14
PTER 10p12 HPHRP, RPR-1 -PTER and Ovarian Cancer
14
L1CAM Xq28 S10, HSAS, MASA, MIC5, SPG1, CAML1, CD171, HSAS1, N-CAML1, NCAM-L1, N-CAM-L1 -L1CAM and Ovarian Cancer
13
DAB2 5p13.1 DOC2, DOC-2 -DAB2 and Ovarian Cancer
13
XRCC2 7q36.1 -XRCC2 and Ovarian Cancer
13
COL18A1 21q22.3 KS, KNO, KNO1 -COL18A1 and Ovarian Cancer
12
GATA4 8p23.1-p22 TOF, ASD2, VSD1, TACHD -GATA4 and Ovarian Cancer
12
XRCC3 14q32.3 CMM6 -XRCC3 and Ovarian Cancer
12
HSD17B2 16q24.1-q24.2 HSD17, SDR9C2, EDH17B2 -HSD17B2 and Ovarian Cancer
12
EPCAM 2p21 ESA, KSA, M4S1, MK-1, DIAR5, EGP-2, EGP40, KS1/4, MIC18, TROP1, EGP314, HNPCC8, TACSTD1 -EPCAM and Ovarian Cancer
12
KLK10 19q13 NES1, PRSSL1 -KLK10 and Ovarian Cancer
11
FSHR 2p21-p16 LGR1, ODG1, FSHRO -FSHR and Ovarian Cancer
11
CHEK1 11q24.2 CHK1 -CHEK1 and Ovarian Cancer
11
MRE11 11q21 ATLD, HNGS1, MRE11A, MRE11B -MRE11A and Ovarian Cancer
11
NOTCH3 19p13.12 IMF2, LMNS, CASIL, CADASIL, CADASIL1 -NOTCH3 and Ovarian Cancer
11
PDCD4 10q24 H731 -PDCD4 and Ovarian Cancer
11
FOLR1 11q13.4 FBP, FOLR -FOLR1 and Ovarian Cancer
11
STAR 8p11.2 STARD1 -STAR and Ovarian Cancer
10
CDH13 16q23.3 CDHH, P105 -CDH13 and Ovarian Cancer
10
E2F2 1p36 E2F-2 -E2F2 and Ovarian Cancer
10
HNF1B 17q12 FJHN, HNF2, LFB3, TCF2, HPC11, LF-B3, MODY5, TCF-2, VHNF1, HNF-1B, HNF1beta, HNF-1-beta -HNF1B and Ovarian Cancer
9
KLK5 19q13.33 SCTE, KLKL2, KLK-L2 -KLK5 and Ovarian Cancer
9
TUBB3 16q24.3 CDCBM, FEOM3, TUBB4, CDCBM1, CFEOM3, beta-4, CFEOM3A -TUBB3 and Ovarian Cancer
9
RHOC 1p13.1 H9, ARH9, ARHC, RHOH9 -RHOC and Ovarian Cancer
9
PAK1 11q13.5-q14.1 PAKalpha -PAK1 and Ovarian Cancer
9
CLMP 11q24.1 ACAM, ASAM, CSBM, CSBS -CLMP and Ovarian Cancer
9
TRPM2 21q22.3 KNP3, EREG1, TRPC7, LTRPC2, NUDT9H, NUDT9L1 -TRPM2 and Ovarian Cancer
9
MIRLET7B 22q13.31 LET7B, let-7b, MIRNLET7B, hsa-let-7b -MicroRNA let-7b and Ovarian Cancer
9
MAP2K4 17p12 JNKK, MEK4, MKK4, SEK1, SKK1, JNKK1, SERK1, MAPKK4, PRKMK4, SAPKK1, SAPKK-1 -MAP2K4 and Ovarian Cancer
9
AKT3 1q44 MPPH, PKBG, MPPH2, PRKBG, STK-2, PKB-GAMMA, RAC-gamma, RAC-PK-gamma -AKT3 and Ovarian Cancer
9
HOXA10 7p15.2 PL, HOX1, HOX1H, HOX1.8 -HOXA10 and Ovarian Cancer
8
GALT 9p13 -GALT and Ovarian Cancer
8
POSTN 13q13.3 PN, OSF2, OSF-2, PDLPOSTN, periostin -POSTN and Ovarian Cancer
8
ATP7B 13q14.3 WD, PWD, WC1, WND -ATP7B and Ovarian Cancer
8
SCGB2A2 11q12.3 MGB1, UGB2 -SCGB2A2 and Ovarian Cancer
8
TSG101 11p15.1 TSG10, VPS23 -TSG101 and Ovarian Cancer
8
CLU 8p21-p12 CLI, AAG4, APOJ, CLU1, CLU2, KUB1, SGP2, APO-J, SGP-2, SP-40, TRPM2, TRPM-2, NA1/NA2 -CLU and Ovarian Cancer
8
HBEGF 5q23 DTR, DTS, DTSF, HEGFL -HBEGF and Ovarian Cancer
8
IL18 11q23.1 IGIF, IL-18, IL-1g, IL1F4 -IL18 and Ovarian Cancer
8
SNAI1 20q13.2 SNA, SNAH, SNAIL, SLUGH2, SNAIL1, dJ710H13.1 -SNAI1 and Ovarian Cancer
8
DNMT3A 2p23 TBRS, DNMT3A2, M.HsaIIIA -DNMT3A and Ovarian Cancer
8
STIP1 11q13.1 HOP, P60, STI1, STI1L, HEL-S-94n, IEF-SSP-3521 Prognostic
-STIP1 Expression in Ovarian Cancer
7
TP53BP1 15q15-q21 p202, 53BP1 -TP53BP1 and Ovarian Cancer
7
SPINT2 19q13.1 PB, Kop, HAI2, DIAR3, HAI-2 -SPINT2 and Ovarian Cancer
7
BACH1 21q22.11 BACH-1, BTBD24 -BACH1 and Ovarian Cancer
7
RBBP8 18q11.2 RIM, COM1, CTIP, JWDS, SAE2, SCKL2 -RBBP8 and Ovarian Cancer
7
PPM1D 17q23.2 WIP1, PP2C-DELTA -PPM1D and Ovarian Cancer
7
EEF1A2 20q13.3 HS1, STN, EF1A, STNL, EEF1AL, EF-1-alpha-2 -EEF1A2 and Ovarian Cancer
7
BMP4 14q22-q23 ZYME, BMP2B, OFC11, BMP2B1, MCOPS6 -BMP4 and Ovarian Cancer
7
KLK4 19q13.41 ARM1, EMSP, PSTS, AI2A1, EMSP1, KLK-L1, PRSS17, kallikrein -KLK4 and Ovarian Cancer
7
SNAI2 8q11 SLUG, WS2D, SLUGH1, SNAIL2 -SNAI2 and Ovarian Cancer
7
EP300 22q13.2 p300, KAT3B, RSTS2 -EP300 and Ovarian Cancer
7
GNRHR 4q21.2 HH7, GRHR, LRHR, LHRHR, GNRHR1 -GNRHR and Ovarian Cancer
7
TACSTD2 1p32 EGP1, GP50, M1S1, EGP-1, TROP2, GA7331, GA733-1 -TACSTD2 and Ovarian Cancer
7
IGFBP2 2q35 IBP2, IGF-BP53 -IGFBP2 and Ovarian Cancer
7
CTCFL 20q13.31 CT27, BORIS, CTCF-T, HMGB1L1, dJ579F20.2 -CTCFL and Ovarian Cancer
7
ABCC2 10q24 DJS, MRP2, cMRP, ABC30, CMOAT -ABCC2 and Ovarian Cancer
7
LIN28B 6q21 CSDD2 -LIN28B and Ovarian Cancer
7
RAB25 1q22 CATX-8, RAB11C -RAB25 and Ovarian Cancer
6
SFRP5 10q24.1 SARP3 -SFRP5 and Ovarian Cancer
6
SULF1 8q13.2 SULF-1, HSULF-1 -SULF1 and Ovarian Cancer
6
SKIL 3q26 SNO, SnoA, SnoI, SnoN -SKIL and Ovarian Cancer
6
CCNB2 15q22.2 HsT17299 -CCNB2 and Ovarian Cancer
6
OSCAR 19q13.42 PIGR3, PIgR-3 -OSCAR and Ovarian Cancer
6
TGFBI 5q31 CSD, CDB1, CDG2, CSD1, CSD2, CSD3, EBMD, LCD1, BIGH3, CDGG1 -TGFBI and Ovarian Cancer
6
SLPI 20q12 ALP, MPI, ALK1, BLPI, HUSI, WAP4, WFDC4, HUSI-I -SLPI and Ovarian Cancer
6
PPIA 7p13 CYPA, CYPH, HEL-S-69p -PPIA and Ovarian Cancer
6
CCL5 17q12 SISd, eoCP, SCYA5, RANTES, TCP228, D17S136E, SIS-delta -CCL5 and Ovarian Cancer
6
MIRLET7I 12q14.1 LET7I, let-7i, MIRNLET7I, hsa-let-7i -MicroRNA let-7i and Ovarian Cancer
6
CDK12 17q12 CRK7, CRKR, CRKRS -CDK12 and Ovarian Cancer
6
RAD52 12p13-p12.2 -RAD52 and Ovarian Cancer
6
ATP7A Xq21.1 MK, MNK, DSMAX, SMAX3 -ATP7A and Ovarian Cancer
6
ARL11 13q14.2 ARLTS1 -ARL11 and Ovarian Cancer
5
CYP2C8 10q23.33 CPC8, CYPIIC8, MP-12/MP-20 -CYP2C8 and Ovarian Cancer
5
MTDH 8q22.1 3D3, AEG1, AEG-1, LYRIC, LYRIC/3D3 -MTDH and Ovarian Cancer
5
KRT7 12q13.13 K7, CK7, SCL, K2C7 -KRT7 and Ovarian Cancer
5
CCR1 3p21 CKR1, CD191, CKR-1, HM145, CMKBR1, MIP1aR, SCYAR1 -CCR1 and Ovarian Cancer
5
TOPBP1 3q22.1 TOP2BP1 -TOPBP1 and Ovarian Cancer
5
FOSB 19q13.32 AP-1, G0S3, GOS3, GOSB -FOSB and Ovarian Cancer
5
GATA6 18q11.1-q11.2 -GATA6 and Ovarian Cancer
5
GSTA1 6p12.1 GST2, GTH1, GSTA1-1 -GSTA1 and Ovarian Cancer
5
GPER1 7p22.3 mER, CEPR, GPER, DRY12, FEG-1, GPR30, LERGU, LyGPR, CMKRL2, LERGU2, GPCR-Br -GPER and Ovarian Cancer
5
AIDA 1q41 C1orf80 -AIDA and Ovarian Cancer
5
CD46 1q32 MCP, TLX, AHUS2, MIC10, TRA2.10 -CD46 and Ovarian Cancer
5
ETV5 3q28 ERM -ETV5 and Ovarian Cancer
5
IGFBP1 7p12.3 AFBP, IBP1, PP12, IGF-BP25, hIGFBP-1 -IGFBP1 and Ovarian Cancer
5
ACTB 7p22 BRWS1, PS1TP5BP1 -ACTB and Ovarian Cancer
5
ESR2 14q23.2 Erb, ESRB, ESTRB, NR3A2, ER-BETA, ESR-BETA -ESR2 and Ovarian Cancer
5
RASSF2 20p13 CENP-34, RASFADIN -RASSF2 and Ovarian Cancer
5
XIST Xq13.2 SXI1, swd66, DXS1089, DXS399E, LINC00001, NCRNA00001 -XIST and Ovarian Cancer
4
GUSB 7q21.11 BG, MPS7 -GUSB and Ovarian Cancer
4
BAGE 21p11.1 not on ref BAGE1, CT2.1 -BAGE and Ovarian Cancer
4
WT1-AS 11p13 WIT1, WIT-1, WT1AS, WT1-AS1 -WT1-AS and Ovarian Cancer
4
ITGB3 17q21.32 GT, CD61, GP3A, BDPLT2, GPIIIa, BDPLT16 -ITGB3 and Ovarian Cancer
4
PAEP 9q34 GD, GdA, GdF, GdS, PEP, PAEG, PP14 -PAEP and Ovarian Cancer
4
MSLN 16p13.3 MPF, SMRP -MSLN and Ovarian Cancer
4
GAB2 11q14.1 -GAB2 and Ovarian Cancer
4
B2M 15q21.1 -B2M and Ovarian Cancer
4
SMAD5 5q31 DWFC, JV5-1, MADH5 -SMAD5 and Ovarian Cancer
4
CDKN2D 19p13 p19, INK4D, p19-INK4D -CDKN2D and Ovarian Cancer
4
NBR1 17q21.31 IAI3B, M17S2, MIG19, 1A1-3B -NBR1 and Ovarian Cancer
4
PYCARD 16p11.2 ASC, TMS, TMS1, CARD5, TMS-1 -PYCARD and Ovarian Cancer
4
ZMYND10 3p21.3 BLU, FLU, CILD22 -ZMYND10 and Ovarian Cancer
4
ZNF350 19q13.41 ZFQR, ZBRK1 -ZNF350 and Ovarian Cancer
4
PTPN1 20q13.1-q13.2 PTP1B -PTPN1 and Ovarian Cancer
4
SNCG 10q23.2-q23.3 SR, BCSG1 -SNCG and Ovarian Cancer
4
DPH1 17p13.3 DPH2L, OVCA1, DPH2L1 -DPH1 and Ovarian Cancer
4
VCAN 5q14.3 WGN, ERVR, GHAP, PG-M, WGN1, CSPG2 -VCAN and Ovarian Cancer
4
CA12 15q22 CAXII, HsT18816 -CA12 and Ovarian Cancer
4
HOXA7 7p15.2 ANTP, HOX1, HOX1A, HOX1.1 -HOXA7 and Ovarian Cancer
4
GAST 17q21 GAS -GAST and Ovarian Cancer
4
PEG3 19q13.4 PW1, ZNF904, ZSCAN24, ZKSCAN22 -PEG3 and Ovarian Cancer
4
HTRA1 10q26.3 L56, HtrA, ARMD7, ORF480, PRSS11, CARASIL -HTRA1 and Ovarian Cancer
4
SAT2 17p13.1 SSAT2 -SAT2 and Ovarian Cancer
4
SNRPN 15q11.2 SMN, PWCR, SM-D, sm-N, RT-LI, HCERN3, SNRNP-N, SNURF-SNRPN -SNRPN and Ovarian Cancer
4
PITX2 4q25 RS, RGS, ARP1, Brx1, IDG2, IGDS, IHG2, PTX2, RIEG, IGDS2, IRID2, Otlx2, RIEG1 -PITX2 and Ovarian Cancer
4
PLAGL1 6q24-q25 ZAC, LOT1, ZAC1 -PLAGL1 and Ovarian Cancer
4
HDAC4 2q37.3 HD4, AHO3, BDMR, HDACA, HA6116, HDAC-4, HDAC-A -HDAC4 and Ovarian Cancer
4
FGF9 13q11-q12 GAF, FGF-9, SYNS3, HBFG-9, HBGF-9 -FGF9 and Ovarian Cancer
4
MLH3 14q24.3 HNPCC7 -MLH3 and Ovarian Cancer
4
FMR1 Xq27.3 POF, FMRP, POF1, FRAXA -FMR1 and Ovarian Cancer
4
KL 13q12 -KL and Ovarian Cancer
4
ARHGDIB 12p12.3 D4, GDIA2, GDID4, LYGDI, Ly-GDI, RAP1GN1, RhoGDI2 -ARHGDIB and Ovarian Cancer
3
HLA-DRA 6p21.3 MLRW, HLA-DRA1 -HLA-DRA and Ovarian Cancer
3
LHCGR 2p21 HHG, LHR, LCGR, LGR2, ULG5, LHRHR, LSH-R, LH/CGR, LH/CG-R -LHCGR and Ovarian Cancer
3
E2F5 8q21.2 E2F-5 -E2F5 and Ovarian Cancer
3
NR5A1 9q33 ELP, SF1, FTZ1, POF7, SF-1, AD4BP, FTZF1, SPGF8, SRXY3 -NR5A1 and Ovarian Cancer
3
CCL19 9p13 ELC, CKb11, MIP3B, MIP-3b, SCYA19 -CCL19 and Ovarian Cancer
3
MARS 12q13.3 MRS, ILLD, CMT2U, ILFS2, METRS, MTRNS, SPG70 -MARS and Ovarian Cancer
3
LIG4 13q33-q34 LIG4S -LIG4 and Ovarian Cancer
3
SRSF3 6p21 SFRS3, SRp20 -SRSF3 and Ovarian Cancer
3
PLK2 5q12.1-q13.2 SNK, hSNK, hPlk2 -PLK2 and Ovarian Cancer
3
CARS 11p15.4 CARS1, CYSRS, MGC:11246 -CARS and Ovarian Cancer
3
LZTS1 8p22 F37, FEZ1 -LZTS1 and Ovarian Cancer
3
HAS3 16q22.1 -HAS3 and Ovarian Cancer
3
KISS1R 19p13.3 HH8, CPPB1, GPR54, AXOR12, KISS-1R, HOT7T175 -KISS1R and Ovarian Cancer
3
MIRLET7D 9q22.32 LET7D, let-7d, MIRNLET7D, hsa-let-7d -MicroRNA let-d and Ovarian Cancer
3
IL1A 2q14 IL1, IL-1A, IL1F1, IL1-ALPHA -IL1A and Ovarian Cancer
3
HAS2 8q24.12 -HAS2 and Ovarian Cancer
3
ADRM1 20q13.33 ARM1, ARM-1, GP110 -ADRM1 and Ovarian Cancer
3
TCEAL7 Xq22.1 WEX5, MPMGp800C04260Q003 -TCEAL7 and Ovarian Cancer
3
SPRY1 4q28.1 hSPRY1 -SPRY1 and Ovarian Cancer
3
POLL 10q23 BETAN, POLKAPPA -POLL and Ovarian Cancer
3
GNAI2 3p21.31 GIP, GNAI2B, H_LUCA15.1, H_LUCA16.1 -GNAI2 and Ovarian Cancer
3
CRABP1 15q24 RBP5, CRABP, CRABPI, CRABP-I -CRABP1 and Ovarian Cancer
3
MUC5B 11p15.5 MG1, MUC5, MUC9, MUC-5B -MUC5B and Ovarian Cancer
3
SLC7A5 16q24.3 E16, CD98, LAT1, 4F2LC, MPE16, hLAT1, D16S469E -SLC7A5 and Ovarian Cancer
3
RUNX2 6p21 CCD, AML3, CCD1, CLCD, OSF2, CBFA1, OSF-2, PEA2aA, PEBP2aA, CBF-alpha-1 -RUNX2 and Ovarian Cancer
3
THBS2 6q27 TSP2 -THBS2 and Ovarian Cancer
3
BAG3 10q25.2-q26.2 BIS, MFM6, BAG-3, CAIR-1 -BAG3 and Ovarian Cancer
3
MIRLET7E 19q13.41 LET7E, let-7e, MIRNLET7E, hsa-let-7e -MicroRNA let-7e and Ovarian Cancer
3
CUL3 2q36.2 CUL-3, PHA2E -CUL3 and Ovarian Cancer
3
KLK14 19q13.3-q13.4 KLK-L6 -KLK14 and Ovarian Cancer
3
KLK2 19q13.41 hK2, hGK-1, KLK2A2 -KLK2 and Ovarian Cancer
3
AQP1 7p14 CO, CHIP28, AQP-CHIP -AQP1 and Ovarian Cancer
3
GATA5 20q13.33 GATAS, bB379O24.1 -GATA5 and Ovarian Cancer
3
CASP4 11q22.3 TX, Mih1, ICH-2, Mih1/TX, ICEREL-II, ICE(rel)II -CASP4 and Ovarian Cancer
3
CCNE2 8q22.1 CYCE2 -CCNE2 and Ovarian Cancer
3
GAS6 13q34 AXSF, AXLLG -GAS6 and Ovarian Cancer
3
SMAD6 15q22.31 AOVD2, MADH6, MADH7, HsT17432 -SMAD6 and Ovarian Cancer
3
BTG1 12q22 -BTG1 and Ovarian Cancer
3
CALCA 11p15.2 CT, KC, PCT, CGRP, CALC1, CGRP1, CGRP-I -CALCA and Ovarian Cancer
3
SNRPF 12q23.1 SMF, Sm-F, snRNP-F -SNRPF and Ovarian Cancer
3
ACVR1 2q23-q24 FOP, ALK2, SKR1, TSRI, ACTRI, ACVR1A, ACVRLK2 -ACVR1 and Ovarian Cancer
3
IL21 4q26-q27 Za11, IL-21, CVID11 -IL21 and Ovarian Cancer
3
LMNA 1q22 FPL, IDC, LFP, CDDC, EMD2, FPLD, HGPS, LDP1, LMN1, LMNC, PRO1, CDCD1, CMD1A, FPLD2, LMNL1, CMT2B1, LGMD1B -LMNA and Ovarian Cancer
3
GAGE1 Xp11.23 CT4.1, GAGE-1 -GAGE1 and Ovarian Cancer
3
LSP1 11p15.5 WP34, pp52 -LSP1 and Ovarian Cancer
3
EBAG9 8q23 EB9, PDAF -EBAG9 and Ovarian Cancer
3
HYAL1 3p21.31 MPS9, NAT6, LUCA1, HYAL-1 -HYAL1 and Ovarian Cancer
3
MTHFD1 14q24 MTHFC, MTHFD -MTHFD1 and Ovarian Cancer
3
SNRPE 1q32 SME, Sm-E, B-raf, HYPT11 -SNRPE and Ovarian Cancer
3
IFITM1 11p15.5 9-27, CD225, IFI17, LEU13, DSPA2a -IFITM1 and Ovarian Cancer
3
EFEMP1 2p16 DHRD, DRAD, FBNL, MLVT, MTLV, S1-5, FBLN3, FIBL-3 -EFEMP1 and Ovarian Cancer
3
TWIST2 2q37.3 FFDD3, DERMO1, SETLSS, bHLHa39 -TWIST2 and Ovarian Cancer
3
SPRY4 5q31.3 HH17 -SPRY4 and Ovarian Cancer
3
PPARGC1A 4p15.1 LEM6, PGC1, PGC1A, PGC-1v, PPARGC1, PGC-1(alpha) -PPARGC1A and Ovarian Cancer
3
HOXA11 7p15.2 HOX1, HOX1I -HOXA11 and Ovarian Cancer
3
PRDX6 1q25.1 PRX, p29, AOP2, 1-Cys, NSGPx, aiPLA2, HEL-S-128m -PRDX6 and Ovarian Cancer
3
PIK3R2 19q13.2-q13.4 p85, MPPH, P85B, MPPH1, p85-BETA -PIK3R2 and Ovarian Cancer
2
PTK6 20q13.3 BRK -PTK6 and Ovarian Cancer
2
FOXC2 16q24.1 LD, MFH1, MFH-1, FKHL14 -FOXC2 and Ovarian Cancer
2
TRIO 5p15.2 tgat, ARHGEF23 -TRIO and Ovarian Cancer
2
CASP2 7q34-q35 ICH1, NEDD2, CASP-2, NEDD-2, PPP1R57 -CASP2 and Ovarian Cancer
2
IRF3 19q13.3-q13.4 -IRF3 and Ovarian Cancer
2
PCM1 8p22-p21.3 PTC4, RET/PCM-1 -PCM1 and Ovarian Cancer
2
PEA15 1q21.1 PED, MAT1, HMAT1, MAT1H, PEA-15, HUMMAT1H -PEA15 and Ovarian Cancer
2
MTA2 11q12.3 PID, MTA1L1 -MTA2 and Ovarian Cancer
2
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 and Ovarian Cancer
2
GSTO2 10q25.1 GSTO 2-2, bA127L20.1 -GSTO2 and Ovarian Cancer
2
WNT4 1p36.23-p35.1 WNT-4, SERKAL -WNT4 and Ovarian Cancer
2
CCR3 3p21.3 CKR3, CD193, CMKBR3, CC-CKR-3 -CCR3 and Ovarian Cancer
2
IL2 4q26-q27 IL-2, TCGF, lymphokine -IL2 and Ovarian Cancer
2
FRAT1 10q24.1 -FRAT1 and Ovarian Cancer
2
HAS1 19q13.4 HAS -HAS1 and Ovarian Cancer
2
HRK 12q24.22 DP5, HARAKIRI -HRK and Ovarian Cancer
2
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Ovarian Cancer
2
SALL4 20q13.2 DRRS, HSAL4, ZNF797, dJ1112F19.1 -SALL4 and Ovarian Cancer
2
HTRA2 2p12 OMI, PARK13, PRSS25 -HTRA2 and Ovarian Cancer
2
HSD3B2 1p13.1 HSDB, HSD3B, SDR11E2 -HSD3B2 and Ovarian Cancer
2
LRP1 12q13.3 APR, LRP, A2MR, CD91, APOER, LRP1A, TGFBR5, IGFBP3R -LRP1 and Ovarian Cancer
2
IL27 16p11 p28, IL30, IL-27, IL27A, IL-27A, IL27p28 -IL27 and Ovarian Cancer
2
STIM1 11p15.4 GOK, TAM, TAM1, IMD10, STRMK, D11S4896E -STIM1 and Ovarian Cancer
2
PAPPA 9q33.2 PAPA, DIPLA1, PAPP-A, PAPPA1, ASBABP2, IGFBP-4ase -PAPPA and Ovarian Cancer
2
PAK4 19q13.2 -PAK4 and Ovarian Cancer
2
FALEC 1 FAL1, ncRNA-a1, LINC00568 -FALEC and Ovarian Cancer
2
LAMP1 13q34 LAMPA, CD107a, LGP120 -LAMP1 and Ovarian Cancer
2
CSE1L 20q13 CAS, CSE1, XPO2 -CSE1L and Ovarian Cancer
2
IGF2-AS 11p15.5 PEG8, IGF2AS, IGF2-AS1 -IGF2-AS and Ovarian Cancer
2
WNT7A 3p25 -WNT7A and Ovarian Cancer
2
HLA-DQA1 6p21.3 CD, GSE, DQ-A1, CELIAC1, HLA-DQA -HLA-DQA1 and Ovarian Cancer
2
RAD54L 1p32 HR54, hHR54, RAD54A, hRAD54 -RAD54L and Ovarian Cancer
2
MMP10 11q22.2 SL-2, STMY2 -MMP10 and Ovarian Cancer
2
CTSD 11p15.5 CPSD, CLN10, HEL-S-130P -CTSD and Ovarian Cancer
2
RAC2 22q13.1 Gx, EN-7, HSPC022, p21-Rac2 -RAC2 and Ovarian Cancer
2
RAG1 11p12 RAG-1, RNF74 -RAG1 and Ovarian Cancer
2
XRCC6 22q13.2 ML8, KU70, TLAA, CTC75, CTCBF, G22P1 -XRCC6 and Ovarian Cancer
2
ST14 11q24.3 HAI, MTSP1, SNC19, ARCI11, MT-SP1, PRSS14, TADG15, TMPRSS14 -ST14 and Ovarian Cancer
2
CX3CL1 16q13 NTN, NTT, CXC3, CXC3C, SCYD1, ABCD-3, C3Xkine, fractalkine, neurotactin -CX3CL1 and Ovarian Cancer
2
BMPR1B 4q22-q24 ALK6, ALK-6, CDw293 -BMPR1B and Ovarian Cancer
2
MAGEA4 Xq28 CT1.4, MAGE4, MAGE4A, MAGE4B, MAGE-41, MAGE-X2 -MAGEA4 and Ovarian Cancer
2
CTSL 9q21.33 MEP, CATL, CTSL1 -CTSL and Ovarian Cancer
2
TNKS 8p23.1 TIN1, ARTD5, PARPL, TINF1, TNKS1, pART5, PARP5A, PARP-5a -TNKS and Ovarian Cancer
2
SLC34A2 4p15.2 NPTIIb, NAPI-3B, NAPI-IIb -SLC34A2 and Ovarian Cancer
2
KRT8 12q13 K8, KO, CK8, CK-8, CYK8, K2C8, CARD2 -KRT8 and Ovarian Cancer
2
GREB1 2p25.1 -GREB1 and Ovarian Cancer
2
NQO2 6p25.2 QR2, DHQV, DIA6, NMOR2 -NQO2 and Ovarian Cancer
2
PELP1 17p13.2 MNAR, P160 -PELP1 and Ovarian Cancer
2
HOXD11 2q31.1 HOX4, HOX4F -HOXD11 and Ovarian Cancer
1
HSP90AA1 14q32.33 EL52, HSPN, LAP2, HSP86, HSPC1, HSPCA, Hsp89, Hsp90, LAP-2, HSP89A, HSP90A, HSP90N, HSPCAL1, HSPCAL4 -HSP90AA1 and Ovarian Cancer
1
ST7 7q31.2 HELG, RAY1, SEN4, TSG7, ETS7q, FAM4A, FAM4A1 -ST7 and Ovarian Cancer
1
PDGFRL 8p22-p21.3 PDGRL, PRLTS -PDGFRL and Ovarian Cancer
1
CD276 15q23-q24 B7H3, B7-H3, B7RP-2, 4Ig-B7-H3 -CD276 and Ovarian Cancer
1
REST 4q12 XBR, NRSF -REST and Ovarian Cancer
1
RHBDF2 17q25.1 TEC, TOC, TOCG, RHBDL5, RHBDL6, iRhom2 -RHBDF2 and Ovarian Cancer
1
ARF1 1q42 -ARF1 and Ovarian Cancer
1
DNM2 19p13.2 DYN2, CMT2M, DYNII, LCCS5, CMTDI1, CMTDIB, DI-CMTB -DNM2 and Ovarian Cancer
1
APOD 3q29 -APOD and Ovarian Cancer
1
IDO1 8p12-p11 IDO, INDO, IDO-1 -IDO1 and Ovarian Cancer
1
PDCD6 5p15.33 ALG2, ALG-2, PEF1B -PDCD6 and Ovarian Cancer
1
RNF217-AS1 6q22.33 STL -STL and Ovarian Cancer
1
ARID2 12q12 p200, BAF200 -ARID2 and Ovarian Cancer
1
PATZ1 22q12.2 ZSG, MAZR, PATZ, RIAZ, ZBTB19, ZNF278, dJ400N23 -PATZ1 and Ovarian Cancer
1
GOPC 6q21 CAL, FIG, PIST, GOPC1, dJ94G16.2 -GOPC and Ovarian Cancer
1
TAL2 9q32 -TAL2 and Ovarian Cancer
1
PLA2G16 11q12.3-q13.1 AdPLA, HRSL3, HRASLS3, HREV107, HREV107-1, HREV107-3, H-REV107-1 -PLA2G16 and Ovarian Cancer
1
TPD52L1 6q22-q23 D53, hD53 -TPD52L1 and Ovarian Cancer
1
CXCL13 4q21 BLC, BCA1, ANGIE, BCA-1, BLR1L, ANGIE2, SCYB13 -CXCL13 and Ovarian Cancer
1
REV1 2q11.1-q11.2 REV1L -REV1 and Ovarian Cancer
1
SACS 13q12 SPAX6, ARSACS, DNAJC29, PPP1R138 -SACS and Ovarian Cancer
1
HSP90AB1 6p12 HSP84, HSPC2, HSPCB, D6S182, HSP90B -HSP90AB1 and Ovarian Cancer
1
MAGEB2 Xp21.3 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Ovarian Cancer
1
PPP1R3A 7q31.1 GM, PP1G, PPP1R3 -PPP1R3A and Ovarian Cancer
1
ST2 11p14.3-p12 -ST2 and Ovarian Cancer
1
ST8 6q25-q27 OVC, OVCS LOH
-LOH in 6q27 in Serous Ovarian Carcinoma
TUBE1 6q21 TUBE, dJ142L7.2 -TUBE1 and Ovarian 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

Zhang LQ, Yang SQ, Wang Y, et al.
Long Noncoding RNA MIR4697HG Promotes Cell Growth and Metastasis in Human Ovarian Cancer.
Anal Cell Pathol (Amst). 2017; 2017:8267863 [PubMed] Free Access to Full Article Related Publications
Ovarian cancer is one of the three most common gynecological malignant tumors worldwide. The prognosis of patients suffering from this malignancy remains poor because of limited therapeutic strategies. Herein, we investigated the role of a long noncoding RNA named MIR4697 host gene (MIR4697HG) in the cell growth and metastasis of ovarian cancer. Results showed that the transcriptional level of MIR4697HG in cancerous tissues increased twofold compared with that in adjacent noncancerous tissues. MIR4697HG was differentially expressed in ovarian cancer cell lines, with the highest levels in OVCAR3 and SKOV3 cells. MIR4697HG knockdown by specific shRNA significantly inhibited cell proliferation and colony formation in both OVCAR3 and SKOC3 cells. Consistently, in a xenograft model of ovarian cancer, MIR4697HG depletion also significantly restricted tumor volumes and weights. Furthermore, MIR4697HG knockdown inhibited cell migration and invasion capacities. Invasion ability was inhibited by 58% in SKOV3 cells and 40% in OVCAR3 cells, and migration ability was inhibited by 73% in SKOV3 cells and 62% in OVCAR3 cells after MIR4697HG knockdown. MIR4697HG knockdown also caused a decrease in matrix metalloprotease-9, phosphorylated ERK, and phosphorylated AKT. These data suggested that MIR4697HG promoted ovarian cancer growth and metastasis. The aggressive role of MIR4697HG in ovarian cancer may be related to the ERK and AKT signaling pathways.

Zhang M, Zhuang G, Sun X, et al.
TP53 mutation-mediated genomic instability induces the evolution of chemoresistance and recurrence in epithelial ovarian cancer.
Diagn Pathol. 2017; 12(1):16 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Genomic instability caused by mutation of the checkpoint molecule TP53 may endow cancer cells with the ability to undergo genomic evolution to survive stress and treatment. We attempted to gain insight into the potential contribution of ovarian cancer genomic instability resulted from TP53 mutation to the aberrant expression of multidrug resistance gene MDR1.
METHODS: TP53 mutation status was assessed by performing nucleotide sequencing and immunohistochemistry. Ovarian cancer cell DNA ploidy was determined using Feulgen-stained smears or flow cytometry. DNA copy number was analyzed by performing fluorescence in situ hybridization (FISH).
RESULTS: In addition to performing nucleotide sequencing for 5 cases of ovarian cancer, TP53 mutations were analyzed via immunohistochemical staining for P53. Both intensive P53 immunohistochemical staining and complete absence of signal were associated with the occurrence of TP53 mutations. HE staining and the quantification of DNA content indicated a significantly higher proportion of polyploidy and aneuploidy cells in the TP53 mutant group than in the wild-type group (p < 0.05). Moreover, in 161 epithelial ovarian cancer patients, multivariate logistic analysis identified late FIGO (International Federation of Gynecology and Obstetrics) stage, serous histotype, G3 grade and TP53 mutation as independent risk factors for ovarian cancer recurrence. In relapse patients, the proportion of chemoresistant cases in the TP53 wild-type group was significantly lower than in the mutant group (63.6% vs. 91.8%, p < 0.05). FISH results revealed a higher percentage of cells with >6 MDR1 copies and chromosome 7 amplication in the TP53 mutant group than in the wild-type group [11.7 ± 2.3% vs. 3.0 ± 0.7% and 2.1 ± 0.7% vs. 0.3 ± 0.05%, (p < 0.05), respectively]. And we observed a specific increase of MDR1 and chromosome 7 copy numbers in the TP53 mutant group upon disease regression (p < 0.01).
CONCLUSIONS: TP53 mutation-associated genomic instability may promote chromosome 7 accumulation and MDR1 amplification during ovarian cancer chemoresistance and recurrence. Our findings lay the foundation for the development of promising chemotherapeutic approaches to treat aggressive and recurrent ovarian cancer.

Chang C, Liu T, Huang Y, et al.
MicroRNA-134-3p is a novel potential inhibitor of human ovarian cancer stem cells by targeting RAB27A.
Gene. 2017; 605:99-107 [PubMed] Related Publications
The cluster of microRNAs (miRNAs) in the DLK1-DIO3 genomic imprinted region contains several miRNAs that have a significant regulatory role in tumor proliferation and invasion. One of these miRNAs is miR-134-3p, and its expression changes significantly in human ovarian cancer stem cells (OCSCs) and in CD44-/CD133- ovarian cancer. The results of a luciferase assay showed that miR-134-3p silenced RAB27A by binding to the 3'-UTR of RAB27A mRNA. Overexpression of miR-134-3p in human OCSCs can not only inhibit the expression of RAB27A but also can effectively downregulate the expression of some tumor proliferation and invasion genes. Overexpression of miR-134-3p can not only inhibit the in vitro proliferation and cell cycle progression of human OCSCs but also can decrease the tumorigenicity in nude mice.

Cui Y, She K, Tian D, et al.
miR-146a Inhibits Proliferation and Enhances Chemosensitivity in Epithelial Ovarian Cancer via Reduction of SOD2.
Oncol Res. 2016; 23(6):275-282 [PubMed] Related Publications
Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy, accounting for 90% of all ovarian cancer. Dysregulation of miRNAs is associated with several types of EOC. In the current research, we aimed to study the role of abnormal expression of miR-146a in the development of EOC and to elucidate the possible molecular mechanisms. Compared with control samples, mRNA expression of miR-146a was significantly decreased in EOC tissues and cell lines. Overexpression of miR-146a prohibited cell proliferation, enhanced apoptosis, and increased sensitivity to chemotherapy drugs in EOC cells. In contrast, downregulation of miR-146a promoted cell proliferation, suppressed apoptosis, and decreased sensitivity to chemotherapy drugs in EOC cells. Overexpression of miR-146a increased the reactive oxygen species (ROS) level and decreased SOD2 mRNA and protein expression. Downregulation of miR-146a increased SOD2 mRNA and protein expression. Overexpression of SOD2 significantly inhibited miR-146a mimics-induced suppression of cell proliferation and the increase of apoptosis and chemosensitivity. In conclusion, we identify miR-146a as a potential tumor suppressor in patients with EOC. miR-146a downregulates the expression of SOD2 and enhances ROS generation, leading to increased apoptosis, inhibition of proliferation, and enhanced sensitivity to chemotherapy. The data demonstrate that the miR-146a/SOD2/ROS pathway may serve as a novel therapeutic target and prognostic marker in patients with EOC.

Matsumura N, Yamaguchi K, Murakami R, et al.
[Perspectives of Individualized Treatment by Genome-Wide Analyses in Ovarian Cancer].
Gan To Kagaku Ryoho. 2016; 43(11):1316-1320 [PubMed] Related Publications
Genome-wide analyses have recently been reported for ovarian cancer. High-grade serous ovarian carcinoma(HGSOC) almost exclusively harbor TP53 mutations and prominent copy number aberrations. Approximately 20% of HGSOCs harbor BRCA mutations, in which case PARP inhibitors may be effective. HGSOCs are classified into 4 molecular subtypes with distinct histopathological features by transcriptional profiling. These subtypes differ in prognosis and drug sensitivity. Additionally, a whole-genome analysis for HGSOC has revealed various factors that can induce resistance to chemotherapy. On the other hand, ovarian clear cell carcinoma(OCCC), a chemoresistant subtype, develops through oxidative stress conditions in an endometriotic cyst. OCCC specific genes include HNF1B and its downstream genes and genes related to oxidative stress. HNF1B mediates resistance to oxidative stress and platinum in OCCC cells. In the future, development of new therapeutic strategies based on these OCCC specific features is expected.

Cao F, Chen L, Liu M, et al.
Expression of preoperative KISS1 gene in tumor tissue with epithelial ovarian cancer and its prognostic value.
Medicine (Baltimore). 2016; 95(46):e5296 [PubMed] Free Access to Full Article Related Publications
Our study aimed to elucidate the role of Kisspeptin (KISS1) in tumor tissues of patients with epithelial ovarian cancer (EOC) and investigate the prognostic value of this biomarker.Forty EOC patients and 20 uterine fibroids female patients with healthy ovaries undergoing cytoreductive surgery between January 2010 and January 2014 in our hospital were enrolled in this study. KISS1 expression in tumor and normal tissues was detected. Correlations between clinic-pathologic variables and KISS1 expression in EOC tissues and the prognostic value of KISS1 for overall survival were evaluated.During the follow-up of 11.2 to 62.1 months, the overall survival rate and mean survival time were 28.9% (11/38) and 38.35 ± 2.84 months. Preoperative KISS1 mRNA was higher in tumor tissue than in normal tissue (P <0.001), and it was associated with histologic grade of tumor, surgical FIGO stage, metastasis, and residual tumor size (all P <0.05). Multivariate survival analysis indicated significant influence of residual tumor size (HR = 2.357, P = 0.039) and preoperative KISS1 mRNA (HR = 0.0001, P <0.001) on mean survival time. Patients with low KISS1 mRNA expression had shorter survival time than those with high expression (P = 0.001).Preoperative KISS1 mRNA was a potential prognostic biomarker for EOC, and high preoperative KISS1 expression indicated a favorable prognosis.

Kang SH, Hwang IH, Son E, et al.
Allergen-Removed Rhus verniciflua Extract Induces Ovarian Cancer Cell Death via JNK Activation.
Am J Chin Med. 2016; 44(8):1719-1735 [PubMed] Related Publications
Nuclear factor-[Formula: see text]B (NF-[Formula: see text]B)/Rel transcription factors are best known for their central roles in promoting cell survival in cancer. NF-[Formula: see text]B antagonizes tumor necrosis factor (TNF)-[Formula: see text]-induced apoptosis through a process involving attenuation of the c-Jun-N-terminal kinase (JNK). However, the role of JNK activation in apoptosis induced by negative regulation of NF-[Formula: see text]B is not completely understood. We found that allergen-removed Rhus verniciflua Stokes (aRVS) extract-mediated NF-[Formula: see text]B inhibition induces apoptosis in SKOV-3 ovarian cancer cells via the serial activation of caspases and SKOV-3 cells are most specifically suppressed by aRVS. Here, we show that in addition to activating caspases, aRVS extract negatively modulates the TNF-[Formula: see text]-mediated I[Formula: see text]B/NF-[Formula: see text]B pathway to promote JNK activation, which results in apoptosis. When the cytokine TNF-[Formula: see text] binds to the TNF receptor, I[Formula: see text]B dissociates from NF-[Formula: see text]B. As a result, the active NF-[Formula: see text]B translocates to the nucleus. aRVS extract (0.5[Formula: see text]mg/ml) clearly prevented NF-[Formula: see text]B from mobilizing to the nucleus, resulting in the upregulation of JNK phosphorylation. This subsequently increased Bax activation, leading to marked aRVS-induced apoptosis, whereas the JNK inhibitor SP600125 in aRVS extract treated SKOV-3 cells strongly inhibited Bax. Bax subfamily proteins induced apoptosis through caspase-3. Thus, these results indicate that aRVS extract contains components that inhibit NF-[Formula: see text]B signaling to upregulate JNK activation in ovarian cancer cells and support the potential of aRVS as a therapeutic agent for ovarian cancer.

Liu Z, Li G
Efficient Regularized Regression with L0 Penalty for Variable Selection and Network Construction.
Comput Math Methods Med. 2016; 2016:3456153 [PubMed] Free Access to Full Article Related Publications
Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology. One appealing approach is the L0 regularized regression which penalizes the number of nonzero features in the model directly. However, it is well known that L0 optimization is NP-hard and computationally challenging. In this paper, we propose efficient EM (L0EM) and dual L0EM (DL0EM) algorithms that directly approximate the L0 optimization problem. While L0EM is efficient with large sample size, DL0EM is efficient with high-dimensional (n ≪ m) data. They also provide a natural solution to all Lp   p ∈ [0,2] problems, including lasso with p = 1 and elastic net with p ∈ [1,2]. The regularized parameter λ can be determined through cross validation or AIC and BIC. We demonstrate our methods through simulation and high-dimensional genomic data. The results indicate that L0 has better performance than lasso, SCAD, and MC+, and L0 with AIC or BIC has similar performance as computationally intensive cross validation. The proposed algorithms are efficient in identifying the nonzero variables with less bias and constructing biologically important networks with high-dimensional big data.

Teng Y, Zuo X, Hou M, et al.
A Double-Negative Feedback Interaction between MicroRNA-29b and DNMT3A/3B Contributes to Ovarian Cancer Progression.
Cell Physiol Biochem. 2016; 39(6):2341-2352 [PubMed] Related Publications
BACKGROUND: Epigenetic abnormalities are increasingly observed in multiple malignancies, including epithelial ovarian cancer (EOC), and their effects can be significantly counteracted by tumor-suppressor microRNAs, namely epi-miRNAs. Here, we investigated the role of miR-29b, a well-established epi-miRNA, in the DNA methylation regulation of EOC cells.
METHODS: The correlation between miR-29b and DNMT3A/3B expression was evaluated by RT-qPCR, western blotting and immunohistochemical analysis. The functional roles of miR-29b and DNMT3A/3B were tested by anti-miRs and microRNA precursors. A luciferase reporter assay was employed to detect the direct binding of miR-29b to DNMT3A/3B 3' UTRs. Co-IP was utilized for investigating Id-1 binding activity.
RESULTS: miR-29b was negatively correlated with DNMT3A/3B expression at the cellular/histological levels. miR-29b silencing was correlated with increased DNMT3A/3B levels, whereasmiR-29b over-expression caused DNMT3A/3B down-regulation. Luciferase reporter assays confirmed that the miR-29b-mediated downregulation of DNMT3A/3Boccurred through the direct targeting of theirmRNAs'3'-UTRs,whereasBGS assays found that DNMT3A/3B knockdown increased miR-29b expression via CpG island promoter hypomethylation, thus suggesting a crucial crosstalk betweenmiR-29b and DNMT3A/3B via a double-negative feedback loop. Co-IP assay confirmed direct binding between DNMT3A and Id-1.
CONCLUSION: Taken together, our study sheds light on a novel epigenetic circuitry regulating EOC progression and may provide novel options for miR-29b-based epi-therapeutic approaches for future EOC treatment.

Meurgey A, Descotes F, Mery-Lamarche E, Devouassoux-Shisheboran M
Lack of mutation of DICER1 and FOXL2 genes in microcystic stromal tumor of the ovary.
Virchows Arch. 2017; 470(2):225-229 [PubMed] Related Publications
Microcystic stromal tumors (MCST), first described in 2009 by Irving et al., are rare ovarian neoplasms. The entity was introduced into the 2014 WHO classification of tumors of female reproductive organs in the group of sex cord-stromal tumors, which is rather heterogeneous. We studied three cases of ovarian tumor with the characteristic morphological features and immunohistochemical marker profiles of MCST. The three tumors showed micro, and macrocystic patterns with solid areas, and were composed of small round to spindle-shaped cells, without atypia. The tumors diffusely and strongly expressed CD10, FOXL2, and nuclear β-catenin, but without immmunoreactivity for hormone receptors, calretinin, or inhibin. Genome analyses showed no somatic mutation of exon 1 of the FOXL2 gene and of exons 24 and 25 of DICER1 gene, the latter not having been reported previously. The patients are well, without evidence of tumor progression 1 to 10 years after diagnosis.The absence of FOXL2 and DICER1 gene mutation, along with strong FOXL2 immunoreactivity provides additional evidence to place MCST within pure gonadal stromal rather than sex cord ovarian tumors.

Cimpean AM, Cobec IM, Ceaușu RA, et al.
Platelet Derived Growth Factor BB: A "Must-have" Therapeutic Target "Redivivus" in Ovarian Cancer.
Cancer Genomics Proteomics. 2016 11-12; 13(6):511-517 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: We aimed to validate PDGF-BB protein expression by RNAscope, a sensitive method for PDGF-BB mRNA evaluation on paraffin embedded (FFPE) specimens of ovarian tumors.
MATERIALS AND METHODS: Seventy-five FFPE ovarian cancer biopsies were assessed by immunohistochemistry followed by PDGF-BB mRNA RNAscope validation.
RESULTS AND CONCLUSION: Dual PDGF-BB expression in tumor and stromal cells have been observed, being highly suggestive for PDGF-BB mediated stromal-tumor cells reciprocal interaction in ovarian cancer (p=0.008). It seems that the nuclear expression of the PDGF-BB represents a negative prognostic factor in ovarian tumors. Being a controversial issue in the literature, PDGF-BB nuclear expression detected by immunohistochemistry was validated by RNAscope in situ hybridization. More than 65% of cases had PDGF-BB mRNA amplification, confirming immunohistochemical results. We herein validated PDGF-BB as a potential therapeutic and prognostic tool of ovarian cancer aggressiveness.

Weidle UH, Birzele F, Kollmorgen G, Rueger R
Mechanisms and Targets Involved in Dissemination of Ovarian Cancer.
Cancer Genomics Proteomics. 2016 11-12; 13(6):407-423 [PubMed] Free Access to Full Article Related Publications
Ovarian carcinoma is associated with the highest death rate of all gynecological tumors. On one hand, its aggressiveness is based on the rapid dissemination of ovarian cancer cells to the peritoneum, the omentum, and organs located in the peritoneal cavity, and on the other hand, on the rapid development of resistance to chemotherapeutic agents. In this review, we focus on the metastatic process of ovarian cancer, which involves dissemination of, homing to and growth of tumor cells in distant organs, and describe promising molecular targets for possible therapeutic intervention. We provide an outline of the interaction of ovarian cancer cells with the microenvironment such as mesothelial cells, adipocytes, fibroblasts, endothelial cells, and other stromal components in the context of approaches for therapeutic interference with dissemination. The targets described in this review are discussed with respect to their validity as drivers of metastasis and to the availability of suitable efficient agents for their blockage, such as small molecules, monoclonal antibodies or antibody conjugates as emerging tools to manage this disease.

Chen Y, Wang X, Duan C, et al.
Loss of TAB3 expression by shRNA exhibits suppressive bioactivity and increased chemical sensitivity of ovarian cancer cell lines via the NF-κB pathway.
Cell Prolif. 2016; 49(6):657-668 [PubMed] Related Publications
Ovarian cancer is a leading cause of death among gynaecologic malignancies. Despite many years of research, it still remains sparing in reliable diagnostic markers and methods for early detection and screening. Transforming growth factor β-activated protein kinase 1 (TAK1)-binding protein 3 (TAB3) was initially characterized as an adapter protein essential for TAK1 activation in response to IL-1β or TNFα, however, the physiological role of TAB3 in ovarian cancer tumorigenesis is still not fully understood. In this study, we evaluated the effects of TAB3 on ovarian cancer cell lines. Expressions of TAB3 and PCNA (proliferating cell nuclear antigen) were found to be gradually increased in EOC tissues and cell lines, by western blot analysis and qRT-PCR. Distribution of TAB3 was further analysed by immunohistochemistry. In vitro, knockdown of TAB3 expression in HO8910 or SKOV3 ovarian cancer cells significantly inhibited bioactivity of ovarian cancer cells, including proliferation and cell-cycle distribution, and promoted chemical sensitivity to cisplatin and paclitaxel treatment via inhibiting NF-κB pathways. In conclusion, our study strongly suggests a novel function of TAB3 as an oncogene that could be used as a biomarker for ovarian cancer. It provides a new insight into the potential mechanism for therapeutic targeting, in chemotherapy resistance, common in ovarian cancer.

Wu M, Lou J, Zhang S, et al.
Gene expression profiling of CD8(+) T cells induced by ovarian cancer cells suggests a possible mechanism for CD8(+) Treg cell production.
Cell Prolif. 2016; 49(6):669-677 [PubMed] Related Publications
OBJECTIVES: The aim of this study was to investigate a possible mechanism of CD8(+) regulatory T-cell (Treg) production in an ovarian cancer (OC) microenvironment.
MATERIALS AND METHODS: Agilent microarray was used to detect changes in gene expression between CD8(+) T cells cultured with and without the SKOV3 ovarian adenocarcinoma cell line. QRT-PCR was performed to determine glycolysis gene expression in CD8(+) T cells from a transwell culturing system and OC patients. We also detected protein levels of glycolysis-related genes using Western blot analysis.
RESULTS: Comparing gene expression profiles revealed significant differences in expression levels of 1420 genes, of which 246 were up-regulated and 1174 were down-regulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that biological processes altered in CD8(+) Treg are particularly associated with energy metabolism. CD8(+) Treg cells induced by co-culture with SKOV3 had lower glycolysis gene expression compared to CD8(+) T cells cultured alone. Glycolysis gene expression was also decreased in the CD8(+) T cells of OC patients.
CONCLUSIONS: These findings provide a comprehensive bioinformatics analysis of DEGs in CD8(+) T cells cultured with and without SKOV3 and suggests that metabolic processes may be a possible mechanism for CD8(+) Treg induction.

Wen X, Lu R, Xie S, et al.
APE1 overexpression promotes the progression of ovarian cancer and serves as a potential therapeutic target.
Cancer Biomark. 2016; 17(3):313-322 [PubMed] Related Publications
BACKGROUND: Apurinic/apyrimidinic endonuclease 1 (APE1) is a multifunctional enzyme that is involved in DNA repair and the redox regulation of transcription factors. Blocking these functions leads to cell-growth inhibition, apoptosis and other effects. Previous studies have demonstrated that high expression levels of the APE1 protein are associated with the progression and chemoresistance of cancers. We hypothesized that APE1 silencing in ovarian cancer cells might have anticancer effects mediated by cell-growth inhibition and an increase in drug-sensitivity.
OBJECTIVE: In this study, we explored the consequences of APE1 silencing in ovarian cancer cells.
METHODS: Immunohistochemistry (IHC) was used to detect the APE1 protein levels in tissue samples from twelve ovarian cancer (OC) patients and eleven non-OC patients. APE1 knockdown was achieved via the stable transfection of SKOV3 and A2780 cells with a construct encoding a short hairpin DNA directed against the APE1 gene. Then, cell proliferation, colony formation, cell cycle and apoptosis assays were performed to reveal the consequences of APE1 silencing in ovarian cancer cells. Additionally, the SKOV3 and A2780 cells were subjected to the treatment with camptothecin (CPT) and ultraviolet rays (UV) to assess the possible link between the APE1 protein and drug-resistance.
RESULTS: Our results revealed that the APE1 protein was overexpressed in OC tissues. APE1 knockdown in A2780 and SKOV3 cells reduced cell proliferation, arrested cell cycle progression, repressed colony formation and weakly promoted cell apoptosis through the BAX and BCL-2 apoptotic pathways. Additionally, the down-regulation of APE1 significantly enhanced the sensitivity of ovarian cancer cells to the CPT/UV treatment.
CONCLUSION: Our study suggested that the APE1 protein is important for the proliferation and growth of ovarian cancer cells. APE1 silencing might enhance drug-sensitivity, and thus APE1 might serve as a novel anti-OC therapeutic target.

Egloff H, Jatoi A
Do Ovarian Cancer Patients with a Family History of Cancer (Suspected BRCA1 or BRCA2 Mutation) Suffer Greater Chemotherapy Toxicity?
Cancer Invest. 2016; 34(10):531-535 [PubMed] Related Publications
OBJECTIVE: Few studies have examined toxicity from potentially curative chemotherapy in ovarian cancer patients at risk for breast cancer susceptibility (BRCA) mutation.
METHODS/RESULTS: Ninety-four of the 482 patients appeared at risk for a mutation based on family history and 23 had a confirmed mutation. Hospitalization or emergency department visits were not increased based on family history with odds ratios (95% confidence intervals) of 0.88 (0.52, 1.45) (p =.62) and 0.90 (0.49, 1.58) (p =.71), respectively; similar findings were observed with confirmed mutations. Trends favored improved survival.
CONCLUSIONS: Concern for a BRCA mutation should not preclude full dose chemotherapy in ovarian cancer patients treated with curative intent.

Kitade S, Onoyama I, Kobayashi H, et al.
FBXW7 is involved in the acquisition of the malignant phenotype in epithelial ovarian tumors.
Cancer Sci. 2016; 107(10):1399-1405 [PubMed] Free Access to Full Article Related Publications
FBXW7 is a ubiquitin ligase that mediates ubiquitylation of oncoproteins, such as c-Myc, cyclin E, Notch and c-Jun. FBXW7 is a known tumor-suppressor gene, and mutations in FBXW7 have been reported in various human malignancies. In this study, we examined the sequences of the FBXW7 and p53 genes in 57 ovarian cancer clinical samples. Interestingly, we found no FBXW7 mutations associated with amino acid changes. We also investigated FBXW7 expression levels in 126 epithelial ovarian tumors. FBXW7 expression was negatively correlated with the malignant potential of ovarian tumors. That is to say, FBXW7 expression levels in ovarian cancer samples were significantly lower than those in borderline and benign tumors (P < 0.01). FBXW7 expression levels in serous carcinoma samples were the lowest among four major histological subtypes. In addition, p53-mutated ovarian cancer samples showed significantly lower levels of FBXW7 expression compared with p53 wild-type cancer samples (P < 0.001). DNA methylation arrays and bisulfite PCR sequencing experiments revealed that 5'-upstream regions of FBXW7 gene in p53-mutated samples were significantly higher methylated compared with those in p53 wild-type samples (P < 0.01). This data indicates that p53 mutations might suppress FBXW7 expression through DNA hypermethylation of FBXW7 5'-upstream regions. Thus, FBXW7 expression was downregulated in ovarian cancers, and was associated with p53 mutations and the DNA methylation status of the 5'-upstream regions of FBXW7.

Camerin GR, Brito AB, Vassallo J, et al.
VEGF gene polymorphisms and outcome of epithelial ovarian cancer patients.
Future Oncol. 2017; 13(5):409-414 [PubMed] Related Publications
AIM: Since VEGF polymorphisms were associated with variable protein production, we analyzed herein their roles in outcome of epithelial ovarian cancer (EOC) patients.
METHODS: Genotypes of 85 patients with primary EOC were identified in DNA by real-time PCR. Progression-free survival and overall survival were analyzed using Kaplan-Meier method, univariate Cox model and bootstrap resampling study.
RESULTS: At 60 months of follow-up, progression-free survival was shorter in patients with VEGF c.-2578 CC genotype compared with others (52.7 vs 82.2%; p = 0.04). Those patients had 2.15 more chance of presenting disease progression than others (p = 0.04); bootstrap study validated the result (p = 0.03).
CONCLUSION: Our data suggest that VEGF c.-2578C>A polymorphism acts as a prognostic factor in EOC.

Yan Q, Wang F, Miao Y, et al.
Sex-determining region Y-box3 (SOX3) functions as an oncogene in promoting epithelial ovarian cancer by targeting Src kinase.
Tumour Biol. 2016; 37(9):12263-12271 [PubMed] Related Publications
Ovarian cancer is one of the most common cancers which cause female mortality. The knowledge of ovarian cancer initiation and progression is critical to develop new therapeutic strategies to treat and prevent it. Recently, SOX3 has been reported to play a pivotal role in tumor progression. However, the clinical significance of SOX3 in human ovarian cancer remains elusive, and the identity of SOX3 in ovarian cancer initiation, progression, and the related underlying mechanism is unknown. In this study, we showed that SOX3 expression increased from benign and borderline to malignant ovarian tumors. Subsequently, we found that overexpression of SOX3 in EOC cells promoted proliferation, migration, and invasion, while restrained apoptosis and adhesion of ovarian cancer cells. In contrast, silencing of SOX3 gained the opposite results. Finally, we discovered SOX3 targeted Src kinase in EOC cells. These data imply that SOX3, acting as an oncogene in EOC, is not only a crucial factor in the carcinogenesis but also a promising therapeutic target for EOC.

Gao S, Zhu L, Feng H, et al.
Gene expression profile analysis in response to α1,2-fucosyl transferase (FUT1) gene transfection in epithelial ovarian carcinoma cells.
Tumour Biol. 2016; 37(9):12251-12262 [PubMed] Related Publications
The aim of this study was to identify differentially expressed genes (DEGs) in response to α1,2-fucosyl transferase (FUT1) gene transfection in epithelial ovarian cancer cells. Human whole-genome oligonucleotide microarrays were used to determine whether gene expression profile may differentiate the epithelial ovarian cell line Caov-3 transfected with FUT1 from the empty plasmid-transfected cells. Quantitative real-time PCR and immunohistochemical staining validated the microarray results. Gene expression profile identified 215 DEGs according to the selection criteria, in which 122 genes were upregulated and 93 genes were downregulated. Gene Ontology (GO) and canonical pathway enrichment analysis were applied, and we found that these DEGs are involved in BioCarta mammalian target of rapamycin (mTOR) pathway, BioCarta eukaryotic translation initiation factor 4 (EIF4) pathway, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in cancer. Interaction network analysis predicted genes participating in the regulatory connection. Highly differential expression of TRIM46, PCF11, BCL6, PTEN, and FUT1 genes was validated by quantitative real-time PCR in two cell line samples. Finally, BCL6 and Lewis Y antigen were validated at the protein level by immunohistochemistry in 103 paraffin-embedded ovarian cancer tissues. The identification of genes in response to FUT1 may provide a theoretical basis for the investigations of the molecular mechanism of ovarian cancer.

Färkkilä A, Zauli G, Haltia UM, et al.
Circulating levels of TNF-related apoptosis inducing-ligand are decreased in patients with large adult-type granulosa cell tumors-implications for therapeutic potential.
Tumour Biol. 2016; 37(9):11909-11916 [PubMed] Related Publications
Targeted treatments are needed for advanced adult-type granulosa cell tumors (AGCTs). We set out to assess tumor tissue and circulating levels of TNF-related apoptosis-inducing ligand (TRAIL), a promising anti-cancer cytokine, in patients affected by AGCT. We analyzed tissue expression of TRAIL in 127 AGCTs using immunohistochemistry or RT-PCR. Soluble TRAIL was measured by means of ELISA from 141 AGCT patient serum samples, as well as the conditioned media of 15 AGCT patient-derived primary cell cultures, and the KGN cell line. Tissue and serum TRAIL levels were analyzed in relationship with clinical parameters, and serum estradiol, FSH, and LH levels. We found that AGCT samples expressed TRAIL mRNA and protein at levels comparable to normal granulosa cells. AGCT cells did not release soluble TRAIL. TRAIL protein levels were decreased in tumors over 10 cm in diameter (p = 0.04). Consistently, circulating TRAIL levels correlated negatively to tumor dimension (p = 0.01). Circulating TRAIL levels negatively associated with serum estradiol levels. In multiple regression analysis, tumor size was an independent factor contributing to the decreased levels of soluble TRAIL in AGCT patients. AGCTs associate with significantly decreased tumor tissue and serum TRAIL levels in patients with a large tumor mass. These findings encourage further study of agonistic TRAIL treatments in patients with advanced or recurrent AGCT.

Liu J, Dou Y, Sheng M
Inhibition of microRNA-383 has tumor suppressive effect in human epithelial ovarian cancer through the action on caspase-2 gene.
Biomed Pharmacother. 2016; 83:1286-1294 [PubMed] Related Publications
BACKGROUND: MicroRNAs are important cancer regulators. In this work, we examined the expression pattern and mechanistic implications of microRNA-383 (miR-383) in human epithelial ovarian cancer (EOC).
METHODS: Gene expression level of miR-383 was compared by qRT-PCR between EOC cell lines and normal ovarian epithelial cell line, and between clinical EOC tumors and adjacent non-tumor ovarian epithelial tissues. Endogenous miR-383 was downregulated through lentiviral infection. Its effects on regulating EOC proliferation, cell cycle, invasion and in vivo explant development were assessed. Possible downstream target of miR-383 in EOC, human caspase-2 gene (CASP2), was evaluated by luciferase assay and qRT-PCR. CASP2 was then genetically knocked down by siRNA to assess its functional relationship with miR-383 in regulating EOC development both in vitro and in vivo.
RESULTS: MiR-383 was overexpressed in both immortal EOC cell lines and human EOC tumors. In stably miR-383-downregulated EOC cell lines, cancer proliferation, cell cycle progression, invasion and in vivo explant development were significantly suppressed. CASP2 was confirmed to be downstream of miR-383 in EOC. SiRNA-mediated CASP2 downregulation had reverse relationship with miR-383 downregulation in regulating EOC development both in vitro and in vivo.
CONCLUSION: Inhibition of miR-383 has profound tumor suppressing effect on EOC development. And the functional regulation of miR-383 in EOC is very likely inversely associated with CASP2 gene.

Gong L, Wang C, Gao Y, Wang J
Decreased expression of microRNA-148a predicts poor prognosis in ovarian cancer and associates with tumor growth and metastasis.
Biomed Pharmacother. 2016; 83:58-63 [PubMed] Related Publications
OBJECTIVE: MicroRNA-148a (MiR-148a) had been reported to take part in some cancer progresses, but its clinical significance in ovarian cancer had been rarely reported. The purpose of this study was to evaluate the prognostic value of miR-148a as well as its roles in ovarian cancer progression.
METHODS: Relative expression of miR-148a in the plasma specimens of ovarian cancer patients was detected by qRT-PCR. Chi-square test was used to analyze the relationship between miR-148a expression and clinical characteristics. The overall survival was analyzed by Kaplan-Meier method and Cox regression analysis was used to evaluate the prognostic value of miR-148a. In addition, the ovarian cancer cell line SKOV-3 was separately transfected with pcDNA3-microRNA-148a over-expression vector and pcDNA3 empty vector to detect the functional roles of miR-148a in ovarian cancer progression.
RESULTS: Decreased level of plasma miR-148a was observed in ovarian cancer patients compared with healthy controls. The expression level was associated with histopathologic grade, TNM stage and lymph node metastasis (P<0.05 for all). Besides, patients with high level of miR-148a had a longer survival time than those with low level (40.3 months vs 31.6 months, log rank test, P=0.002). Cox regression analysis indicated that miR-148a might be a potential biomarker for ovarian cancer prognosis (HR=1.699, 95%CI=1.175-2.456, P=0.005). Moreover, cell experiments confirmed that miR-148a could inhibit proliferation, migration and invasion of ovarian cancer cells.
CONCLUSION: MiR-148a may be a potential prognostic factor for ovarian cancer and it can suppress tumor progression.

Tomar T, de Jong S, Alkema NG, et al.
Genome-wide methylation profiling of ovarian cancer patient-derived xenografts treated with the demethylating agent decitabine identifies novel epigenetically regulated genes and pathways.
Genome Med. 2016; 8(1):107 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: In high-grade serous ovarian cancer (HGSOC), intrinsic and/or acquired resistance against platinum-containing chemotherapy is a major obstacle for successful treatment. A low frequency of somatic mutations but frequent epigenetic alterations, including DNA methylation in HGSOC tumors, presents the cancer epigenome as a relevant target for innovative therapy. Patient-derived xenografts (PDXs) supposedly are good preclinical models for identifying novel drug targets. However, the representativeness of global methylation status of HGSOC PDXs compared to their original tumors has not been evaluated so far. Aims of this study were to explore how representative HGSOC PDXs are of their corresponding patient tumor methylome and to evaluate the effect of epigenetic therapy and cisplatin on putative epigenetically regulated genes and their related pathways in PDXs.
METHODS: Genome-wide analysis of the DNA methylome of HGSOC patients with their corresponding PDXs, from different generations, was performed using Infinium 450 K methylation arrays. Furthermore, we analyzed global methylome changes after treatment of HGSOC PDXs with the FDA approved demethylating agent decitabine and cisplatin. Findings were validated by bisulfite pyrosequencing with subsequent pathway analysis. Publicly available datasets comprising HGSOC patients were used to analyze the prognostic value of the identified genes.
RESULTS: Only 0.6-1.0 % of all analyzed CpGs (388,696 CpGs) changed significantly (p < 0.01) during propagation, showing that HGSOC PDXs were epigenetically stable. Treatment of F3 PDXs with decitabine caused a significant reduction in methylation in 10.6 % of CpG sites in comparison to untreated PDXs (p < 0.01, false discovery rate <10 %). Cisplatin treatment had a marginal effect on the PDX methylome. Pathway analysis of decitabine-treated PDX tumors revealed several putative epigenetically regulated pathways (e.g., the Src family kinase pathway). In particular, the C-terminal Src kinase (CSK) gene was successfully validated for epigenetic regulation in different PDX models and ovarian cancer cell lines. Low CSK methylation and high CSK expression were both significantly associated (p < 0.05) with improved progression-free survival and overall survival in HGSOC patients.
CONCLUSIONS: HGSOC PDXs resemble the global epigenome of patients over many generations and can be modulated by epigenetic drugs. Novel epigenetically regulated genes such as CSK and related pathways were identified in HGSOC. Our observations encourage future application of PDXs for cancer epigenome studies.

Sun XC, Zhang AC, Tong LL, et al.
miR-146a and miR-196a2 polymorphisms in ovarian cancer risk.
Genet Mol Res. 2016; 15(3) [PubMed] Related Publications
We investigated the relationship between miR-146a and miR-196a2 genetic polymorphisms and development of ovarian cancer in a Chinese population. A total of 134 patients and 227 control subjects were involved in our study between January 2012 and October 2014 from China-Japan Union Hospital of Jilin University. Genotyping of miR-146a and miR-196a2 was accomplished by polymerase chain reaction coupled with restriction fragment length polymorphism analysis. Unconditional multiple-logistic regression analysis indicated that the GG genotype of miR-146a was associated with an increased risk of ovarian cancer when compared to the CC genotype, and the adjusted OR (95%CI) was 3.73 (1.79-7.80). Moreover, the CG+GG genotype of miR-146a was associated with an increased risk of ovarian cancer compared with the CC genotype (OR = 1.68, 95%CI = 1.06-2.66), and the GG genotype had a higher risk of ovarian cancer than the CC+CG genotype (OR = 3.02, 95%CI = 1.55-5.98). In conclusion, our study suggests that the miR-146a polymorphism is associated with increased risk of ovarian cancer and could be used as a biomarker for ovarian cancer susceptibility.

Mahdian-Shakib A, Dorostkar R, Tat M, et al.
Differential role of microRNAs in prognosis, diagnosis, and therapy of ovarian cancer.
Biomed Pharmacother. 2016; 84:592-600 [PubMed] Related Publications
Ovarian cancer (OC) is the most lethal of malignant gynecological cancers, and has a very poor prognosis, frequently, attributable to late diagnosis and responsiveness to chemotherapy. In spite of the technological and medical approaches over the past four decades, involving the progression of several biological markers (mRNA and proteins biomarkers), the mortality rate of OC remains a challenge due to its late diagnosis, which is expressly ascribed to low specificities and sensitivities. Consequently, there is a crucial need for novel diagnostic and prognostic markers that can advance and initiate more individualized treatment, finally increasing survival of the patients. MiRNAs are non-coding RNAs that control target genes post transcriptionally. They are included in tumorigenesis, apoptosis, proliferation, invasion, metastasis, and chemoresistance. Several studies have within the last decade demonstrated that miRNAs are dysregulated in OC and have possibilities as diagnostic and prognostic biomarkers for OC. Additionally; recent studies have also focused on miRNAs as predictors of chemotherapy sensitivities and their potential as therapeutic targets. In this review, we discuss the current data involving the accumulating evidence of the altered expression of miRNAs in OC, their role in diagnosis, prognosis, and forecast of response to therapy. Given the heterogeneity of this disease, it is likely that advances in long-term survival might be also attained by translating the recent insights of miRNAs participation in OC into new targeted therapies that will have a crucial effect on the management of ovarian cancer.

Qiu H, Wang X, Guo R, et al.
HOTAIR rs920778 polymorphism is associated with ovarian cancer susceptibility and poor prognosis in a Chinese population.
Future Oncol. 2017; 13(4):347-355 [PubMed] Related Publications
AIM: The aim of this study was to determine if HOTAIR rs920778 polymorphism is associated with ovarian cancer susceptibility and prognosis.
MATERIALS & METHODS: The data were obtained from two independent groups including 329 ovarian cancer patients and 680 cancer-free, age-matched women. Blood samples were collected and genomic DNA was extracted for genotyping.
RESULTS: TT genotype and T allele of HOTAIR rs920778 were significantly associated with a decreased ovarian cancer risk (p = 0.0004 and p < 0.0001, respectively), which associated with advanced tumor stage, lymph node metastasis and poor prognosis. Moreover, TT and TC carriers obtained a much shorter survival (p = 0.026).
CONCLUSION: These findings propose that HOTAIR rs920778 polymorphism influences ovarian cancer susceptibility and prognosis, and further studies are warranted in other populations.

Vlad C, Kubelac P, Onisim A, et al.
Expression of CDCP1 and ADAM12 in the ovarian cancer microenvironment.
J BUON. 2016 Jul-Aug; 21(4):973-978 [PubMed] Related Publications
PURPOSE: The tumor microenvironment in ovarian cancer (OC) seems to play an important role, and besides tumor cells, biomarkers can derive from endothelial cells. We investigated CDCP1 and ADAM12 expression in relation with other clinical and pathological characteristics in OC patients.
METHODS: We retrospectively evaluated patient files between 2006-2011. A histochemical score was developed to evaluate tumor staining, the microvessel density (MVD), and stromal expression patterns for both ADAM12 and CDCP1. A CD34 antibody was used to assess tumor MVD.
RESULTS: 102 patients were selected and 83% had FIGO stage III/IV. A high CDCP1 tumor score correlated significantly with a shorter overall survival (OS) (p<0.01). Cases with positive CDCP1 had an elevated CD34 MVD (p<0.01). An absent/low ADAM12 tumor score correlated with significantly improved OS (p<0.01). Mean CD34 MVD was higher in cases with positive ADAM12 MVD (p=0.012).
CONCLUSIONS: Our results indicate that both tumor markers are negative prognostic factors for overall survival and additional studies are required to validate their future potential.

Li Z, Wei D, Yang C, et al.
Overexpression of long noncoding RNA, NEAT1 promotes cell proliferation, invasion and migration in endometrial endometrioid adenocarcinoma.
Biomed Pharmacother. 2016; 84:244-251 [PubMed] Related Publications
Long noncoding RNAs (lncRNAs) are emerging as important modulators in the biological processes and tumorigenesis. However, whether lncRNAs are involved in endometrial endometrioid adenocarcinoma (EEC) remains unclear. In the present study, we explored the expression pattern, clinical significance and biological function of nuclear enriched abundant transcript 1 (NEAT1) in EEC. The expression levels of NEAT1 were elevated in EEC tissues and cell lines, and higher expression levels of NEAT1 were positively correlated with FIGO stage and lymph node metastasis. Overexpression of NEAT1 in HEC-59 cells transfected with pGCMV-NEAT1 promotes cell growth, colony formation ability as well as invasive and migratory ability; while knock-down of NEAT1 in HEC-59 cells by siNEAT1 transfection exhibited the opposite effects. Flow cytometry analysis showed that overexpression of NEAT1 led to an increase in S-phase cells and attenuated cell apoptosis, and knock-down of NEAT1 induced G0/G1 arrest and also induced cell apoptosis in HEC-59 cells. Tumor metastasis real-time PRC array showed that six metastasis-related genes (c-myc, insulin like growth factor 1(IGF1), matrix metallopeptidase 2 (MMP-2) and matrix metallopeptidase 7(MMP-7) were up-regulated, and Cadherin 1 and TIMP metallopeptidase inhibitor 2 were down-regulated) in NEAT1-overexpressing HEC-59 cells. Further qRT-PCR and western blot results confirmed that c-myc, IFG1, MMP-2 and MMP-7 were dys-regulated by NEAT1. Together, our data underscore the significance of NEAT1 in EEC development, and NEAT1 may a potential therapeutic target for EEC.

Xiaohong Z, Lichun F, Na X, et al.
MiR-203 promotes the growth and migration of ovarian cancer cells by enhancing glycolytic pathway.
Tumour Biol. 2016; 37(11):14989-14997 [PubMed] Related Publications
MicroRNAs (miRNAs) play an important role in the tumorigenesis of ovarian cancer. Previously, we have reported the dysregulation of miR-203 in the ovarian cancer tissues. However, the biological functions and molecular mechanisms of miR-203 in ovarian cancer remain unknown. Here, we showed that the expression of miR-203 was increased in ovarian cancer tissues compared with the adjacent non-cancerous tissues and the transcription of miR-203 was inhibited by P53. Forced expression of miR-203 in ovarian cancer promoted cell growth and migration, while depletion of miR-203 inhibited the growth and migration of ovarian cancer cells. In addition, miR-203 promoted the metastasis of ovarian cancer cells in vivo and shorted the survival of the nude mice. Mechanically, miR-203 targeted the 3'-UTR of pyruvate dehydrogenase B (PDHB) and increased the consumption of glucose and the production of lactate. Overexpression of PDHB abolished the oncogenic effects of miR-203 on the growth of ovarian cancer cells. Together, our data suggested the oncogenic roles of miR-203 in ovarian cancer by promoting glycolysis, and miR-203 might be a therapeutic target for ovarian cancer.

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