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 29 August, 2019 using data from PubMed, MeSH and CancerIndex

Mutated Genes and Abnormal Protein Expression (343)

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
335
KRAS 12p12.1 NS, NS3, CFC2, RALD, KRAS1, KRAS2, RASK2, KI-RAS, C-K-RAS, K-RAS2A, K-RAS2B, K-RAS4A, K-RAS4B, c-Ki-ras2 -KRAS and Ovarian Cancer
205
ERBB2 17q12 NEU, NGL, HER2, TKR1, CD340, HER-2, MLN 19, HER-2/neu -HER2 and Ovarian Cancer
137
PIK3CA 3q26.32 MCM, CWS5, MCAP, PI3K, CLOVE, MCMTC, PI3K-alpha, p110-alpha -PIK3CA and Ovarian Cancer
125
MSH2 2p21 FCC1, COCA1, HNPCC, LCFS2, HNPCC1 -MSH2 and Ovarian Cancer
120
ARID1A 1p36.11 ELD, B120, CSS2, OSA1, P270, hELD, BM029, MRD14, hOSA1, BAF250, C1orf4, BAF250a, SMARCF1 -ARID1A and Ovarian Cancer
110
CDKN1A 6p21.2 P21, CIP1, SDI1, WAF1, CAP20, CDKN1, MDA-6, p21CIP1 Prognostic
-CDKN1A Expression in Ovarian Cancer
100
MDM2 12q15 HDMX, hdm2, ACTFS -MDM2 and Ovarian Cancer
90
CHEK2 22q12.1 CDS1, CHK2, LFS2, RAD53, hCds1, HuCds1, PP1425 -CHEK2 and Ovarian Cancer
85
RAD51 15q15.1 RECA, BRCC5, FANCR, MRMV2, HRAD51, RAD51A, HsRad51, HsT16930 -RAD51 and Ovarian Cancer
77
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II -MMP2 and Ovarian Cancer
74
ERCC1 19q13.32 UV20, COFS4, RAD10 -ERCC1 and Ovarian Cancer
65
MMP9 20q13.12 GELB, CLG4B, MMP-9, MANDP2 -MMP9 and Ovarian Cancer
64
AKT2 19q13.2 PKBB, PRKBB, HIHGHH, PKBBETA, RAC-BETA -AKT2 and Ovarian Cancer
64
PARP1 1q42.12 PARP, PPOL, ADPRT, ARTD1, ADPRT1, PARP-1, ADPRT 1, pADPRT-1 -PARP1 and Ovarian Cancer
61
FOXL2 3q22.3 BPES, PFRK, POF3, BPES1, PINTO -FOXL2 and Ovarian Cancer
54
RAD51C 17q22 FANCO, R51H3, BROVCA3, RAD51L2 -RAD51C and Ovarian Cancer
53
ACHE 7q22.1 YT, ACEE, ARACHE, N-ACHE -ACHE and Ovarian Cancer
52
BCL2L1 20q11.21 BCLX, BCL2L, Bcl-X, PPP1R52, BCL-XL/S -BCL2L1 and Ovarian Cancer
51
SNAI1 20q13.13 SNA, SNAH, SNAIL, SLUGH2, SNAIL1, dJ710H13.1 -SNAI1 and Ovarian Cancer
50
PMS2 7p22.1 MLH4, PMSL2, HNPCC4, PMS2CL -PMS2 and Ovarian Cancer
44
MSH6 2p16 GTBP, HSAP, p160, GTMBP, HNPCC5 -MSH6 and Ovarian Cancer
44
BLID 11q24.1 BRCC2 -BLID and Ovarian Cancer
44
WT1 11p13 GUD, AWT1, WAGR, WT33, NPHS4, WIT-2, EWS-WT1 -WT1 expression in Ovarian Cancer
43
BARD1 2q35 -BARD1 and Ovarian Cancer
40
MUC16 19p13.2 CA125 -MUC16 and Ovarian Cancer
37
CDH1 16q22.1 UVO, CDHE, ECAD, LCAM, Arc-1, CD324 -CDH1 and Ovarian Cancer
37
DROSHA 5p13.3 RN3, ETOHI2, RNASEN, RANSE3L, RNASE3L, HSA242976 -DROSHA and Ovarian Cancer
36
DICER1 14q32.13 DCR1, MNG1, Dicer, HERNA, RMSE2, Dicer1e, K12H4.8-LIKE -DICER1 and Ovarian Cancer
35
XIAP Xq25 API3, ILP1, MIHA, XLP2, BIRC4, IAP-3, hIAP3, hIAP-3 -XIAP and Ovarian Cancer
34
CYP19A1 15q21.2 ARO, ARO1, CPV1, CYAR, CYP19, CYPXIX, P-450AROM -CYP19A1 and Ovarian Cancer
34
FGF2 4q28.1 BFGF, FGFB, FGF-2, HBGF-2 -FGF2 and Ovarian Cancer
32
ABCC1 16p13.11 MRP, ABCC, GS-X, MRP1, ABC29 -ABCC1 (MRP1) and Ovarian Cancer
31
KLK3 19q13.33 APS, PSA, hK3, KLK2A1 -PSA expression in Ovarian Cancere
30
OLAH 10p13 SAST, AURA1, THEDC1 -OLAH and Ovarian Cancer
28
CXCL1 4q13.3 FSP, GRO1, GROa, MGSA, NAP-3, SCYB1, MGSA-a -CXCL1 and Ovarian Cancer
27
CXCL12 10q11.21 IRH, PBSF, SDF1, TLSF, TPAR1, SCYB12 -CXCL12 and Ovarian Cancer
27
ZNF217 20q13.2 ZABC1 -ZNF217 and Ovarian Cancer
26
DIRAS3 1p31.3 ARHI, NOEY2 -DIRAS3 and Ovarian Cancer
25
HMGA2 12q14.3 BABL, LIPO, HMGIC, HMGI-C, STQTL9 -HMGA2 and Ovarian Cancer
25
SERPINB5 18q21.33 PI5, maspin -SERPIN-B5 and Ovarian Cancer
25
BRAP 12q24.12 IMP, BRAP2, RNF52 -BRAP and Ovarian Cancer
24
JUN 1p32.1 AP1, p39, AP-1, cJUN, c-Jun -c-Jun and Ovarian Cancer
23
FH 1q43 MCL, FMRD, HsFH, LRCC, HLRCC, MCUL1 -FH and Ovarian Cancer
22
RHOA 3p21.31 ARHA, ARH12, RHO12, RHOH12 -RHOA and Ovarian Cancer
22
RAD51D 17q12 TRAD, R51H3, BROVCA4, RAD51L3 -RAD51D and Ovarian Cancer
22
NBN 8q21.3 ATV, NBS, P95, NBS1, AT-V1, AT-V2 -NBN and Ovarian Cancer
21
L1CAM Xq28 S10, HSAS, MASA, MIC5, SPG1, CAML1, CD171, HSAS1, N-CAML1, NCAM-L1, N-CAM-L1 -L1CAM and Ovarian Cancer
20
SMARCA4 19p13.2 BRG1, CSS4, SNF2, SWI2, MRD16, RTPS2, BAF190, SNF2L4, SNF2LB, hSNF2b, BAF190A -SMARCA4 and Ovarian Cancer
20
RHOC 1p13.2 H9, ARH9, ARHC, RHOH9 -RHOC and Ovarian Cancer
19
EPHA2 1p36.13 ECK, CTPA, ARCC2, CTPP1, CTRCT6 -EPHA2 and Ovarian Cancer
18
XRCC2 7q36.1 FANCU -XRCC2 and Ovarian Cancer
17
RAD50 5q31.1 NBSLD, RAD502, hRad50 -RAD50 and Ovarian Cancer
17
FHIT 3p14.2 FRA3B, AP3Aase -FHIT and Ovarian Cancer
17
PPP2R1A 19q13.41 MRD36, PP2AA, PR65A, PP2AAALPHA, PP2A-Aalpha -PPP2R1A and Ovarian Cancer
16
E2F3 6p22.3 E2F-3 -E2F3 and Ovarian Cancer
15
KLK6 19q13.41 hK6, Bssp, Klk7, SP59, PRSS9, PRSS18 -KLK6 and Ovarian Cancer
15
CLDN3 7q11.23 RVP1, HRVP1, C7orf1, CPE-R2, CPETR2 -CLDN3 and Ovarian Cancer
15
CLDN4 7q11.23 CPER, CPE-R, CPETR, CPETR1, WBSCR8, hCPE-R -CLDN4 and Ovarian Cancer
15
NOTCH3 19p13.12 IMF2, LMNS, CASIL, CADASIL, CADASIL1 -NOTCH3 and Ovarian Cancer
15
PTER 10p13 HPHRP, RPR-1 -PTER and Ovarian Cancer
14
AKT3 1q43-q44 MPPH, PKBG, MPPH2, PRKBG, STK-2, PKB-GAMMA, RAC-gamma, RAC-PK-gamma -AKT3 and Ovarian Cancer
14
GATA4 8p23.1 TOF, ASD2, VSD1, TACHD -GATA4 and Ovarian Cancer
14
XRCC3 14q32.33 CMM6 -XRCC3 and Ovarian Cancer
14
MIRLET7B 22q13.31 LET7B, let-7b, MIRNLET7B, hsa-let-7b -MicroRNA let-7b and Ovarian Cancer
14
OPCML 11q25 OPCM, OBCAM, IGLON1 -OPCML and Ovarian Cancer
14
DAB2 5p13.1 DOC2, DOC-2 -DAB2 and Ovarian Cancer
13
HNF1B 17q12 FJHN, HNF2, LFB3, TCF2, HPC11, LF-B3, MODY5, TCF-2, VHNF1, HNF-1B, HNF1beta, HNF-1-beta -HNF1B and Ovarian Cancer
13
PDCD4 10q25.2 H731 -PDCD4 and Ovarian Cancer
13
COL18A1 21q22.3 KS, KNO, KNO1 -COL18A1 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
HSD17B2 16q23.3 HSD17, SDR9C2, EDH17B2 -HSD17B2 and Ovarian Cancer
12
FSHR 2p21-p16 LGR1, ODG1, FSHRO -FSHR and Ovarian Cancer
11
CHEK1 11q24.2 CHK1 -CHEK1 and Ovarian Cancer
11
FOLR1 11q13.4 FBP, FOLR -FOLR1 and Ovarian Cancer
11
MRE11 11q21 ATLD, HNGS1, MRE11A, MRE11B -MRE11A and Ovarian Cancer
11
LIN28B 6q16.3-q21 CSDD2 -LIN28B and Ovarian Cancer
11
KLK10 19q13.41 NES1, PRSSL1 -KLK10 and Ovarian Cancer
11
STAR 8p11.23 STARD1 -STAR and Ovarian Cancer
11
ATP7A Xq21.1 MK, MNK, DSMAX, SMAX3 -ATP7A and Ovarian Cancer
11
SNAI2 8q11.21 SLUG, WS2D, SLUGH, SLUGH1, SNAIL2 -SNAI2 and Ovarian Cancer
11
E2F2 1p36.12 E2F-2 -E2F2 and Ovarian Cancer
11
CCL5 17q12 SISd, eoCP, SCYA5, RANTES, TCP228, D17S136E, SIS-delta -CCL5 and Ovarian Cancer
10
CDH13 16q23.3 CDHH, P105 -CDH13 and Ovarian Cancer
10
HOXA10 7p15.2 PL, HOX1, HOX1H, HOX1.8 -HOXA10 and Ovarian Cancer
10
AXIN2 17q24.1 AXIL, ODCRCS -AXIN2 and Ovarian Cancer
10
EP300 22q13.2 p300, KAT3B, MKHK2, RSTS2 -EP300 and Ovarian Cancer
10
TRPM2 21q22.3 KNP3, EREG1, TRPC7, LTRPC2, NUDT9H, LTrpC-2, NUDT9L1 -TRPM2 and Ovarian Cancer
10
ABCC2 10q24.2 DJS, MRP2, cMRP, ABC30, CMOAT -ABCC2 and Ovarian Cancer
9
KLK5 19q13.41 SCTE, KLKL2, KLK-L2 -KLK5 and Ovarian Cancer
9
CLU 8p21.1 CLI, AAG4, APOJ, CLU1, CLU2, KUB1, SGP2, APO-J, SGP-2, SP-40, TRPM2, TRPM-2, NA1/NA2 -CLU and Ovarian Cancer
9
TACSTD2 1p32.1 EGP1, GP50, M1S1, EGP-1, TROP2, GA7331, GA733-1 -TACSTD2 and Ovarian Cancer
9
ATP7B 13q14.3 WD, PWD, WC1, WND -ATP7B and Ovarian Cancer
9
PPM1D 17q23.2 WIP1, PP2C-DELTA -PPM1D and Ovarian Cancer
9
TUBB3 16q24.3 CDCBM, FEOM3, TUBB4, CDCBM1, CFEOM3, beta-4, CFEOM3A -TUBB3 and Ovarian Cancer
9
POSTN 13q13.3 PN, OSF2, OSF-2, PDLPOSTN -POSTN and Ovarian Cancer
9
CLMP 11q24.1 ACAM, ASAM, CSBM, CSBS -CLMP and Ovarian Cancer
9
TP53BP1 15q15.3 TP53, p202, 53BP1, TDRD30, p53BP1 -TP53BP1 and Ovarian Cancer
9
PAK1 11q13.5-q14.1 PAKalpha -PAK1 and Ovarian Cancer
9
FOXA2 20p11.21 HNF3B, TCF3B -FOXA2 and Ovarian Cancer
9
BMP4 14q22.2 ZYME, BMP2B, OFC11, BMP2B1, MCOPS6 -BMP4 and Ovarian Cancer
9
MAP2K4 17p12 JNKK, MEK4, MKK4, SEK1, SKK1, JNKK1, SERK1, MAPKK4, PRKMK4, SAPKK1, SAPKK-1 -MAP2K4 and Ovarian Cancer
9
TSG101 11p15.1 TSG10, VPS23 -TSG101 and Ovarian Cancer
8
IL18 11q23.1 IGIF, IL-18, IL-1g, IL1F4 -IL18 and Ovarian Cancer
8
CTCFL 20q13.31 CT27, BORIS, CTCF-T, HMGB1L1, dJ579F20.2 -CTCFL and Ovarian Cancer
8
OSCAR 19q13.42 PIGR3, PIgR-3 -OSCAR and Ovarian Cancer
8
RAB25 1q22 CATX-8, RAB11C -RAB25 and Ovarian Cancer
8
EEF1A2 20q13.33 HS1, STN, EF1A, STNL, MRD38, EEF1AL, EIEE33, EF-1-alpha-2 -EEF1A2 and Ovarian Cancer
8
GALT 9p13.3 -GALT and Ovarian Cancer
8
SCGB2A2 11q12.3 MGB1, UGB2 -SCGB2A2 and Ovarian Cancer
8
DNMT3A 2p23 TBRS, DNMT3A2, M.HsaIIIA -DNMT3A and Ovarian Cancer
8
SPINT2 19q13.2 PB, Kop, HAI2, DIAR3, HAI-2 -SPINT2 and Ovarian Cancer
7
RAD52 12p13.33 -RAD52 and Ovarian Cancer
7
KLK4 19q13.41 ARM1, EMSP, PSTS, AI2A1, EMSP1, KLK-L1, PRSS17, kallikrein -KLK4 and Ovarian Cancer
7
SLPI 20q13.12 ALP, MPI, ALK1, BLPI, HUSI, WAP4, WFDC4, HUSI-I -SLPI and Ovarian Cancer
7
PPIA 7p13 CYPA, CYPH, HEL-S-69p -PPIA and Ovarian Cancer
7
CYP2C8 10q23.33 CPC8, CYPIIC8, MP-12/MP-20 -CYP2C8 and Ovarian Cancer
7
GNRHR 4q13.2 HH7, GRHR, LRHR, LHRHR, GNRHR1 -GNRHR and Ovarian Cancer
7
BACH1 21q21.3 BACH-1, BTBD24 -BACH1 and Ovarian Cancer
7
STIP1 11q13.1 HOP, P60, STI1, STI1L, HEL-S-94n, IEF-SSP-3521 Prognostic
-STIP1 Expression in Ovarian Cancer
7
MTDH 8q22.1 3D3, AEG1, AEG-1, LYRIC, LYRIC/3D3 -MTDH and Ovarian Cancer
7
MIRLET7I 12q14.1 LET7I, let-7i, MIRNLET7I, hsa-let-7i -MicroRNA let-7i and Ovarian Cancer
7
TGFBI 5q31.1 CSD, CDB1, CDG2, CSD1, CSD2, CSD3, EBMD, LCD1, BIGH3, CDGG1 -TGFBI and Ovarian Cancer
7
RBBP8 18q11.2 RIM, COM1, CTIP, JWDS, SAE2, SCKL2 -RBBP8 and Ovarian Cancer
7
CDK12 17q12 CRK7, CRKR, CRKRS -CDK12 and Ovarian Cancer
7
SULF1 8q13.2-q13.3 SULF-1 -SULF1 and Ovarian Cancer
7
IGFBP2 2q35 IBP2, IGF-BP53 -IGFBP2 and Ovarian Cancer
7
SKIL 3q26 SNO, SnoA, SnoI, SnoN -SKIL and Ovarian Cancer
6
ITGB3 17q21.32 GT, CD61, GP3A, BDPLT2, GPIIIa, BDPLT16 -ITGB3 and Ovarian Cancer
6
SFRP5 10q24.2 SARP3 -SFRP5 and Ovarian Cancer
6
XIST Xq13.2 SXI1, swd66, DXS1089, DXS399E, LINC00001, NCRNA00001 -XIST and Ovarian Cancer
6
ESR2 14q23.2-q23.3 Erb, ESRB, ESTRB, NR3A2, ER-BETA, ESR-BETA -ESR2 and Ovarian Cancer
6
SALL4 20q13.2 DRRS, HSAL4, ZNF797 -SALL4 and Ovarian Cancer
6
RASSF2 20p13 CENP-34, RASFADIN -RASSF2 and Ovarian Cancer
6
CCNB2 15q22.2 HsT17299 -CCNB2 and Ovarian Cancer
6
HBEGF 5q31.3 DTR, DTS, DTSF, HEGFL -HBEGF and Ovarian Cancer
6
AIDA 1q41 C1orf80 -AIDA and Ovarian Cancer
6
MSN Xq12 HEL70, IMD50 -MSN and Ovarian Cancer
6
ELAVL1 19p13.2 HUR, Hua, MelG, ELAV1 -ELAVL1 and Ovarian Cancer
6
IGFBP1 7p12.3 AFBP, IBP1, PP12, IGF-BP25, hIGFBP-1 -IGFBP1 and Ovarian Cancer
6
WNT4 1p36.12 WNT-4, SERKAL -WNT4 and Ovarian Cancer
6
POLL 10q24.32 BETAN, POLKAPPA -POLL and Ovarian Cancer
5
PITX2 4q25 RS, RGS, ARP1, Brx1, IDG2, IGDS, IHG2, PTX2, RIEG, ASGD4, IGDS2, IRID2, Otlx2, RIEG1 -PITX2 and Ovarian Cancer
5
TOPBP1 3q22.1 TOP2BP1 -TOPBP1 and Ovarian Cancer
5
BAG3 10q26.11 BIS, MFM6, BAG-3, CAIR-1 -BAG3 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
ARL11 13q14.2 ARLTS1 -ARL11 and Ovarian Cancer
5
GATA6 18q11.2 -GATA6 and Ovarian Cancer
5
GSTA1 6p12.2 GST2, GTH1, GSTA1-1, GST-epsilon -GSTA1 and Ovarian Cancer
5
FASN 17q25.3 FAS, OA-519, SDR27X1 -FASN and Ovarian Cancer
5
SPRY4 5q31.3 HH17 -SPRY4 and Ovarian Cancer
5
SMAD5 5q31.1 DWFC, JV5-1, MADH5 -SMAD5 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
ETV5 3q27.2 ERM -ETV5 and Ovarian Cancer
5
FOSB 19q13.32 AP-1, G0S3, GOS3, GOSB -FOSB and Ovarian Cancer
5
PTPN1 20q13.13 PTP1B -PTPN1 and Ovarian Cancer
5
CD46 1q32.2 MCP, TLX, AHUS2, MIC10, TRA2.10 -CD46 and Ovarian Cancer
5
ACTB 7p22.1 BRWS1, PS1TP5BP1 -ACTB and Ovarian Cancer
5
HOXA11 7p15.2 HOX1, HOX1I, RUSAT1 -HOXA11 and Ovarian Cancer
5
DPH1 17p13.3 DPH2L, OVCA1, DEDSSH, DPH2L1 -DPH1 and Ovarian Cancer
4
PAEP 9q34.3 GD, GdA, GdF, GdS, PEP, PAEG, PP14 -PAEP and Ovarian Cancer
4
HDAC4 2q37.3 HD4, AHO3, BDMR, HDACA, HA6116, HDAC-4, HDAC-A -HDAC4 and Ovarian Cancer
4
GAB2 11q14.1 -GAB2 and Ovarian Cancer
4
MLH3 14q24.3 HNPCC7 -MLH3 and Ovarian Cancer
4
MSLN 16p13.3 MPF, SMRP -MSLN and Ovarian Cancer
4
PTPRC 1q31.3-q32.1 LCA, LY5, B220, CD45, L-CA, T200, CD45R, GP180 -PTPRC and Ovarian Cancer
4
FGF9 13q12.11 GAF, FGF-9, SYNS3, HBFG-9, HBGF-9 -FGF9 and Ovarian Cancer
4
PRDX6 1q25.1 PRX, p29, AOP2, 1-Cys, NSGPx, aiPLA2, HEL-S-128m -PRDX6 and Ovarian Cancer
4
S100A10 1q21.3 42C, P11, p10, GP11, ANX2L, CAL1L, CLP11, Ca[1], ANX2LG -S100A10 and Ovarian Cancer
4
AQP1 7p14.3 CO, CHIP28, AQP-CHIP -AQP1 and Ovarian Cancer
4
AGR2 7p21.1 AG2, AG-2, HPC8, GOB-4, HAG-2, XAG-2, PDIA17, HEL-S-116 -AGR2 and Ovarian Cancer
4
HTRA1 10q26.13 L56, HtrA, ARMD7, ORF480, PRSS11, CARASIL, CADASIL2 -HTRA1 and Ovarian Cancer
4
ZNF350 19q13.41 ZFQR, ZBRK1 -ZNF350 and Ovarian Cancer
4
CA12 15q22.2 CAXII, CA-XII, T18816, HsT18816 -CA12 and Ovarian Cancer
4
WT1-AS 11p13 WIT1, WIT-1, WT1AS, WT1-AS1 -WT1-AS and Ovarian Cancer
4
PLAGL1 6q24.2 ZAC, LOT1, ZAC1 -PLAGL1 and Ovarian Cancer
4
LMNA 1q22 FPL, IDC, LFP, CDDC, EMD2, FPLD, HGPS, LDP1, LMN1, LMNC, MADA, PRO1, CDCD1, CMD1A, FPLD2, LMNL1, CMT2B1, LGMD1B -LMNA and Ovarian Cancer
4
KL 13q13.1 -KL and Ovarian Cancer
4
SAT2 17p13.1 SSAT2 -SAT2 and Ovarian Cancer
4
XRCC6 22q13.2 ML8, KU70, TLAA, CTC75, CTCBF, G22P1 -XRCC6 and Ovarian Cancer
4
CHI3L1 1q32.1 GP39, ASRT7, GP-39, YK-40, YKL40, CGP-39, YKL-40, YYL-40, HC-gp39, HCGP-3P, hCGP-39 -CHI3L1 and Ovarian Cancer
4
PEG3 19q13.43 PW1, ZNF904, ZSCAN24, ZKSCAN22 -PEG3 and Ovarian Cancer
4
PYCARD 16p11.2 ASC, TMS, TMS1, CARD5, TMS-1 -PYCARD and Ovarian Cancer
4
RUNX2 6p21.1 CCD, AML3, CCD1, CLCD, OSF2, CBFA1, OSF-2, PEA2aA, PEBP2aA, CBF-alpha-1 -RUNX2 and Ovarian Cancer
4
NFATC1 18q23 NFAT2, NFATc, NF-ATC, NF-ATc1.2 -NFATC1 and Ovarian Cancer
4
VCAN 5q14.2-q14.3 WGN, ERVR, GHAP, PG-M, WGN1, CSPG2 -VCAN and Ovarian Cancer
4
GAST 17q21.2 GAS -GAST and Ovarian Cancer
4
HOXA7 7p15.2 ANTP, HOX1, HOX1A, HOX1.1 -HOXA7 and Ovarian Cancer
4
GUSB 7q11.21 BG, MPS7 -GUSB and Ovarian Cancer
4
IL21 4q27 Za11, IL-21, CVID11 -IL21 and Ovarian Cancer
4
SRSF3 6p21.31-p21.2 SFRS3, SRp20 -SRSF3 and Ovarian Cancer
4
CDKN2D 19p13.2 p19, INK4D, p19-INK4D -CDKN2D and Ovarian Cancer
4
B2M 15q21.1 IMD43 -B2M and Ovarian Cancer
4
FMR1 Xq27.3 POF, FMRP, POF1, FRAXA -FMR1 and Ovarian Cancer
4
NBR1 17q21.31 IAI3B, M17S2, MIG19, 1A1-3B -NBR1 and Ovarian Cancer
4
SNCG 10q23.2 SR, BCSG1 -SNCG and Ovarian Cancer
4
GAS6 13q34 AXSF, AXLLG -GAS6 and Ovarian Cancer
4
ADRM1 20q13.33 ARM1, ARM-1, GP110 -ADRM1 and Ovarian Cancer
4
HAS2 8q24.13 -HAS2 and Ovarian Cancer
4
BAGE 21p11.1 BAGE1, CT2.1 -BAGE and Ovarian Cancer
4
ZMYND10 3p21.31 BLU, FLU, CILD22 -ZMYND10 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
MIRLET7D 9q22.32 LET7D, let-7d, MIRNLET7D, hsa-let-7d -MicroRNA let-d and Ovarian Cancer
3
CX3CL1 16q21 NTN, NTT, CXC3, CXC3C, SCYD1, ABCD-3, C3Xkine, fractalkine, neurotactin -CX3CL1 and Ovarian Cancer
3
HAS3 16q22.1 -HAS3 and Ovarian Cancer
3
SLC7A5 16q24.2 E16, CD98, LAT1, 4F2LC, MPE16, D16S469E -SLC7A5 and Ovarian Cancer
3
SPRY1 4q28.1 hSPRY1 -SPRY1 and Ovarian Cancer
3
GNAI2 3p21.31 GIP, GNAI2B, H_LUCA15.1, H_LUCA16.1 -GNAI2 and Ovarian Cancer
3
CARS 11p15.4 CARS1, CYSRS, MGC:11246 -CARS and Ovarian Cancer
3
CUL3 2q36.2 CUL-3, PHA2E -CUL3 and Ovarian Cancer
3
MAP3K8 10p11.23 COT, EST, ESTF, TPL2, AURA2, MEKK8, Tpl-2, c-COT -MAP3K8 and Ovarian Cancer
3
MIRLET7E 19q13.41 LET7E, let-7e, MIRNLET7E, hsa-let-7e -MicroRNA let-7e and Ovarian Cancer
3
HLA-DRA 6p21.32 HLA-DRA1 -HLA-DRA and Ovarian Cancer
3
KLK14 19q13.41 KLK-L6 -KLK14 and Ovarian Cancer
3
MAGEA4 Xq28 CT1.4, MAGE4, MAGE4A, MAGE4B, MAGE-41, MAGE-X2 -MAGEA4 and Ovarian Cancer
3
MARS 12q13.3 MRS, ILLD, CMT2U, ILFS2, METRS, MTRNS, SPG70 -MARS and Ovarian Cancer
3
PAK4 19q13.2 -PAK4 and Ovarian Cancer
3
CALCA 11p15.2 CT, KC, PCT, CGRP, CALC1, CGRP1, CGRP-I -CALCA and Ovarian Cancer
3
IL2 4q27 IL-2, TCGF, lymphokine -IL2 and Ovarian Cancer
3
CASP4 11q22.3 TX, Mih1, ICH-2, Mih1/TX, ICEREL-II, ICE(rel)II -CASP4 and Ovarian Cancer
3
S100A3 1q21.3 S100E -S100A3 and Ovarian Cancer
3
LIG4 13q33.3 LIG4S -LIG4 and Ovarian Cancer
3
IL23R 1p31.3 -IL23R and Ovarian Cancer
3
GATA5 20q13.33 CHTD5, GATAS, bB379O24.1 -GATA5 and Ovarian Cancer
3
IL1A 2q14 IL1, IL-1A, IL1F1, IL1-ALPHA -IL1A and Ovarian Cancer
3
GAGE1 Xp11.23 CT4.1, CT4.4, GAGE4, GAGE-1, GAGE-4 -GAGE1 and Ovarian Cancer
3
EBAG9 8q23.2 EB9, PDAF -EBAG9 and Ovarian Cancer
3
ACVR1 2q23-q24 FOP, ALK2, SKR1, TSRI, ACTRI, ACVR1A, ACVRLK2 -ACVR1 and Ovarian Cancer
3
SMAD6 15q22.31 AOVD2, MADH6, MADH7, HsT17432 -SMAD6 and Ovarian Cancer
3
KLK2 19q13.33 hK2, hGK-1, KLK2A2 -KLK2 and Ovarian Cancer
3
SNRPF 12q23.1 SMF, Sm-F, snRNP-F -SNRPF and Ovarian Cancer
3
CRABP1 15q25.1 RBP5, CRABP, CRABPI, CRABP-I -CRABP1 and Ovarian Cancer
3
HYAL1 3p21.31 MPS9, NAT6, LUCA1, HYAL-1 -HYAL1 and Ovarian Cancer
3
BTG1 12q21.33 APRO2 -BTG1 and Ovarian Cancer
3
SLC34A2 4p15.2 NPTIIb, NAPI-3B, NAPI-IIb -SLC34A2 and Ovarian Cancer
3
PPARGC1A 4p15.2 LEM6, PGC1, PGC1A, PGC-1v, PPARGC1, PGC-1alpha, PGC-1(alpha) -PPARGC1A 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
LZTS1 8p21.3 F37, FEZ1 -LZTS1 and Ovarian Cancer
3
CCNE2 8q22.1 CYCE2 -CCNE2 and Ovarian Cancer
3
FOXC2 16q24.1 LD, MFH1, MFH-1, FKHL14 -FOXC2 and Ovarian Cancer
3
TRIO 5p15.2 tgat, MEBAS, MRD44, ARHGEF23 -TRIO and Ovarian Cancer
3
CASP2 7q34 ICH1, NEDD2, CASP-2, NEDD-2, PPP1R57 -CASP2 and Ovarian Cancer
3
ITGAM 16p11.2 CR3A, MO1A, CD11B, MAC-1, MAC1A, SLEB6 -ITGAM and Ovarian Cancer
3
TWIST2 2q37.3 FFDD3, DERMO1, SETLSS, bHLHa39 -TWIST2 and Ovarian Cancer
3
TCEAL7 Xq22.2 WEX5 -TCEAL7 and Ovarian Cancer
3
NR5A1 9q33.3 ELP, SF1, FTZ1, POF7, SF-1, AD4BP, FTZF1, SPGF8, SRXY3, hSF-1 -NR5A1 and Ovarian Cancer
3
MUC5B 11p15.5 MG1, MUC5, MUC9, MUC-5B -MUC5B and Ovarian Cancer
3
ARHGDIB 12p12.3 D4, GDIA2, GDID4, LYGDI, Ly-GDI, RAP1GN1, RhoGDI2 -ARHGDIB and Ovarian Cancer
3
THBS2 6q27 TSP2 -THBS2 and Ovarian Cancer
3
CCL19 9p13.3 ELC, CKb11, MIP3B, MIP-3b, SCYA19 -CCL19 and Ovarian Cancer
3
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 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
LSP1 11p15.5 WP34, pp52 -LSP1 and Ovarian Cancer
3
KISS1R 19p13.3 HH8, CPPB1, GPR54, AXOR12, KISS-1R, HOT7T175 -KISS1R and Ovarian Cancer
3
PEA15 1q23.2 PED, MAT1, HMAT1, MAT1H, PEA-15, HUMMAT1H, PED-PEA15, PED/PEA15 -PEA15 and Ovarian Cancer
3
PLK2 5q11.2 SNK, hSNK, hPlk2 -PLK2 and Ovarian Cancer
3
MTHFD1 14q23.3 MTHFC, MTHFD -MTHFD1 and Ovarian Cancer
3
HAS1 19q13.41 HAS -HAS1 and Ovarian Cancer
2
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Ovarian Cancer
2
IGF2-AS 11p15.5 PEG8, IGF2AS, IGF2-AS1 -IGF2-AS and Ovarian Cancer
2
CTSD 11p15.5 CPSD, CLN10, HEL-S-130P -CTSD and Ovarian Cancer
2
HRK 12q24.22 DP5, HARAKIRI -HRK and Ovarian Cancer
2
PATZ1 22q12.2 ZSG, MAZR, PATZ, RIAZ, ZBTB19, ZNF278, dJ400N23 -PATZ1 and Ovarian Cancer
2
LRP1 12q13.3 APR, LRP, A2MR, CD91, APOER, LRP1A, TGFBR5, IGFBP3R -LRP1 and Ovarian Cancer
2
MIR1271 5q35.2 MIRN1271, hsa-mir-1271 -MIRN1271 microRNA, human and Ovarian Cancer
2
HTRA2 2p12 OMI, PARK13, PRSS25 -HTRA2 and Ovarian Cancer
2
IL27 16p12.1-p11.2 p28, IL30, IL-27, IL27A, IL-27A, IL27p28 -IL27 and Ovarian Cancer
2
HSD3B2 1p12 HSDB, HSD3B, SDR11E2 -HSD3B2 and Ovarian Cancer
2
PSIP1 9p22.3 p52, p75, PAIP, DFS70, LEDGF, PSIP2 -PSIP1 and Ovarian Cancer
2
STIM1 11p15.4 GOK, TAM, TAM1, IMD10, STRMK, D11S4896E -STIM1 and Ovarian Cancer
2
WNT7A 3p25 -WNT7A and Ovarian Cancer
2
FALEC 1q21.2 FAL1, ncRNA-a1, LINC00568 -FALEC and Ovarian Cancer
2
RAD54L 1p34.1 HR54, hHR54, RAD54A, hRAD54 -RAD54L and Ovarian Cancer
2
FLNC 7q32.1 ABPA, ABPL, FLN2, MFM5, MPD4, RCM5, CMH26, ABP-280, ABP280A -FLNC and Ovarian Cancer
2
CSE1L 20q13.13 CAS, CSE1, XPO2 -CSE1L and Ovarian Cancer
2
PAPPA 9q33.1 PAPA, DIPLA1, PAPP-A, PAPPA1, ASBABP2, IGFBP-4ase -PAPPA and Ovarian Cancer
2
TRIM27 6p22.1 RFP, RNF76 -TRIM27 and Ovarian Cancer
2
IMP3 15q24.2 BRMS2, MRPS4, C15orf12 -IMP3 and Ovarian Cancer
2
LAMP1 13q34 LAMPA, CD107a, LGP120 -LAMP1 and Ovarian Cancer
2
RAC2 22q13.1 Gx, EN-7, HSPC022, p21-Rac2 -RAC2 and Ovarian Cancer
2
MTA2 11q12.3 PID, MTA1L1 -MTA2 and Ovarian Cancer
2
MMP10 11q22.2 SL-2, STMY2 -MMP10 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
NQO2 6p25.2 QR2, DHQV, DIA6, NMOR2 -NQO2 and Ovarian Cancer
2
BMPR1B 4q22.3 ALK6, AMDD, BDA2, ALK-6, BDA1D, CDw293 -BMPR1B and Ovarian Cancer
2
FRAT1 10q24.1 -FRAT1 and Ovarian Cancer
2
TYRO3 15q15.1 BYK, Dtk, RSE, Rek, Sky, Tif, Etk-2 -TYRO3 and Ovarian Cancer
2
ST14 11q24.3 HAI, MTSP1, SNC19, ARCI11, MT-SP1, PRSS14, TADG15, TMPRSS14 -ST14 and Ovarian Cancer
2
GREB1 2p25.1 -GREB1 and Ovarian Cancer
2
PELP1 17p13.2 MNAR, P160 -PELP1 and Ovarian Cancer
2
PIK3R2 19p13.11 p85, MPPH, P85B, MPPH1, p85-BETA -PIK3R2 and Ovarian Cancer
2
PCM1 8p22 PTC4, RET/PCM-1 -PCM1 and Ovarian Cancer
2
KRT8 12q13 K8, KO, CK8, CK-8, CYK8, K2C8, CARD2 -KRT8 and Ovarian Cancer
2
HLA-DQA1 6p21.32 DQ-A1, CELIAC1, HLA-DQA -HLA-DQA1 and Ovarian Cancer
2
IRF3 19q13.33 IIAE7 -IRF3 and Ovarian Cancer
2
PTK6 20q13.33 BRK -PTK6 and Ovarian Cancer
2
RAG1 11p12 RAG-1, RNF74 -RAG1 and Ovarian Cancer
2
PDCD5 19q13.11 TFAR19 -PDCD5 and Ovarian Cancer
2
CCR3 3p21.3 CKR3, CD193, CMKBR3, CC-CKR-3 -CCR3 and Ovarian Cancer
2
INHBA 7p14.1 EDF, FRP -INHBA and Ovarian Cancer
2
GSTO2 10q25.1 GSTO 2-2, bA127L20.1 -GSTO2 and Ovarian Cancer
2
HOXD11 2q31.1 HOX4, HOX4F -HOXD11 and Ovarian Cancer
1
SNX29 16p13.13-p13.12 RUNDC2A, A-388D4.1 -SNX29 and Ovarian Cancer
1
CD276 15q24.1 B7H3, B7-H3, B7RP-2, 4Ig-B7-H3 -CD276 and Ovarian Cancer
1
HSP90AA1 14q32.31 EL52, HSPN, LAP2, HSP86, HSPC1, HSPCA, Hsp89, Hsp90, LAP-2, HSP89A, HSP90A, HSP90N, Hsp103, HSPCAL1, HSPCAL4, HEL-S-65p -HSP90AA1 and Ovarian Cancer
1
TPD52L1 6q22.31 D53 -TPD52L1 and Ovarian Cancer
1
IDO1 8p11.21 IDO, INDO, IDO-1 -IDO1 and Ovarian Cancer
1
RNF217-AS1 6q22.31 STL -STL 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
APOD 3q29 -APOD and Ovarian Cancer
1
DNM2 19p13.2 DYN2, CMT2M, DYNII, LCCS5, CMTDI1, CMTDIB, DI-CMTB -DNM2 and Ovarian Cancer
1
PPP1R3A 7q31.1 GM, PP1G, PPP1R3 -PPP1R3A and Ovarian Cancer
1
TAL2 9q31.2 -TAL2 and Ovarian Cancer
1
ST2 11p14.3-p12 -ST2 and Ovarian Cancer
1
FRS2 12q15 SNT, SNT1, FRS1A, FRS2A, SNT-1, FRS2alpha -FRS2 and Ovarian Cancer
1
ARID2 12q12 p200, BAF200 -ARID2 and Ovarian Cancer
1
REST 4q12 WT6, XBR, NRSF -REST and Ovarian Cancer
1
REV1 2q11.1-q11.2 REV1L -REV1 and Ovarian Cancer
1
PDGFRL 8p22 PDGRL, PRLTS -PDGFRL and Ovarian Cancer
1
GOPC 6q22.1 CAL, FIG, PIST, GOPC1, dJ94G16.2 -GOPC and Ovarian Cancer
1
SACS 13q12.12 SPAX6, ARSACS, DNAJC29, PPP1R138 -SACS and Ovarian Cancer
1
MIR106A Xq26.2 mir-106, MIRN106A, mir-106a -MIR106A and Ovarian Cancer
1
ST7 7q31.2 HELG, RAY1, SEN4, TSG7, ETS7q, FAM4A, FAM4A1 -ST7 and Ovarian Cancer
1
RHBDF2 17q25.1 TEC, TOC, TOCG, RHBDL5, RHBDL6, iRhom2 -RHBDF2 and Ovarian Cancer
1
ARF1 1q42.13 PVNH8 -ARF1 and Ovarian Cancer
1
HSP90AB1 6p21.1 HSP84, HSPC2, HSPCB, D6S182, HSP90B -HSP90AB1 and Ovarian Cancer
1
KLLN 10q23.31 CWS4, KILLIN -killin protein, human and Ovarian Cancer
1
PDCD6 5p15.33 ALG2, ALG-2, PEF1B -PDCD6 and Ovarian Cancer
1
CXCL13 4q21.1 BLC, BCA1, ANGIE, BCA-1, BLR1L, ANGIE2, SCYB13 -CXCL13 and Ovarian Cancer
1
PDE4DIP 1q21.2 MMGL, CMYA2 -PDE4DIP and Ovarian Cancer
1
MAGEB2 Xp21.2 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Ovarian Cancer
1
SPRR1A 1q21.3 SPRK -SPRR1A and Ovarian Cancer
1
PTPRH 19q13.42 SAP1, R-PTP-H -PTPRH and Ovarian Cancer
1
TUBE1 6q21 TUBE, dJ142L7.2 -TUBE1 and Ovarian Cancer
ST8 6q25-q27 OVC, OVCS LOH
-LOH in 6q27 in Serous Ovarian Carcinoma
SNRPE 1q32.1 SME, Sm-E, HYPT11, snRNP-E -SNRPE 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

An HJ, Song DH
Displacement of Vitamin D Receptor Is Related to Lower Histological Grade of Endometrioid Carcinoma.
Anticancer Res. 2019; 39(8):4143-4147 [PubMed] Related Publications
BACKGROUND/AIM: Vitamin D analogs have a protective effect on carcinogenesis in humans. Since vitamin D receptor (VDR) is detected in many histotypes of cancer, this study evaluated the role of VDR expression in endometrioid carcinoma.
MATERIALS AND METHODS: Tumor samples were collected from 60 patients who had undergone surgery, and the pattern of VDR expression assessed in tissue microarray (TMA) blocks of tumor samples. When VDR expression in the cytoplasm was higher than that in the nucleus, this was noted as 'displacement'. Using statistical analysis, the relationship between VDR expression and clinicopathological factors was evaluated.
RESULTS: Immunohistochemical staining of nuclear VDR was as follows: Negative: 32 (53.3%); mild: 13 (21.7%); moderate: 14 (23.3%); strong: 1 (1.7%). For cytoplasmic VDR expression: Negative: 2 (3.3%); mild: 19 (31.7%); moderate: 31 (51.7%); strong: 7 (11.7%). VDR displacement was found in 42 (70%) cores. VDR displacement was significantly positively correlated with endometrioid carcinoma having lower histological grade (1, p=0.03).
CONCLUSION: Displacement of VDR was significantly correlated with lower histological grade. Clinicians might be able to predict prognosis and decide therapies related to vitamin D analogs using this remarkable biomarker for endometrial carcinoma.

Feng Y, Peng Z, Liu W, et al.
Evaluation of the epidemiological and prognosis significance of ESR2 rs3020450 polymorphism in ovarian cancer.
Gene. 2019; 710:316-323 [PubMed] Related Publications
AIM: To investigate the correlation between the polymorphism of estrogen receptor β gene (ESR2) rs3020450 and cancer susceptibility, and explore the epidemiological significance and the effect of ESR2 expression levels on the prognosis of ovarian cancer.
METHODS: Based on meta-analysis the association between ESR2 rs3020450 polymorphism and cancer susceptibility was estimated and a case-control design was used to verify this result in ovarian cancer. The epidemiological effect of ESR2 rs3020450 polymorphism was assessed by attributable risk percentage (ARP) and population attributable risk percentage (PARP). Kaplan Meier plotters were used to evaluate overall survival (OS) and progression-free survival (PFS) in ovarian cancer patients and GEPIA for the differential expression of ESR2 levels in ovarian cancer and adjacent normal tissues.
RESULTS: The pooled analysis indicated no significant correlation between the ESR2 rs3020450 polymorphism and the cancer susceptibility. In the stratified analysis by cancer types, significantly decreased risk was found in ovarian cancer (AG vs GG: OR = 0.73, 95%CI: 0.53-0.97, P = 0.03). Unconditional logistic regression results of case-control study in ovarian cancer observed significant differences in all comparisons (AG vs GG: OR = 0.81, 95%CI: 0.62-0.98, P = 0.04; AA vs GG: OR = 0.63, 95%CI: 0.42-0.92, P = 0.01 and AG + AA vs GG: OR = 0.73, 95%CI: 0.53-0.96, P < 0.001). Based on meta-analysis and case-control pooled results, ARP and PARP were evaluated respectively in allele (21.95% and7.97%), heterozygote (36.99% and 12.11%) and dominant model (36.84% and 12.97%) of rs3020450 polymorphism in ovarian cancer. The expression levels of ESR2 in normal tissues was significantly higher than that in cancer tissues (OV, Median, 4.7:0.21), and significant correlations were observed between high ESR2 expression levels and long OS (HR = 0.80, 95%CI: 0.70-0.92, P = 0.002) and PFS (HR = 0.767, 95%Cl: 0.67-0.88, P < 0.001).
CONCLUSION: Our results indicated that ESR2 rs3020450 polymorphism was associated with ovarian cancer risk from epidemiological perspective, and high ESR2 expression levels was associated with long survival in patients with ovarian cancer.

Shen F, Feng L, Zhou J, et al.
Overexpression of CASC11 in ovarian squamous cell carcinoma mediates the development of cancer cell resistance to chemotherapy.
Gene. 2019; 710:363-366 [PubMed] Related Publications
LncRNA CASC11 promotes gastric cancer and colon cancer. Our study analyzed the role of CASC11 in ovarian squamous cell carcinoma (OSCC). In the present study we showed that plasma CASC11 was upregulated in OSCC, and the upregulation of CASC11 distinguished OSCC patients from control group. Plasma levels of CASC11 were further increased after chemotherapy. Treatment with oxaliplatin, tetraplatin, cisplatin, and carboplatin mediated the upregulation of CASC11 in cells of OSCC cell line. In addition, overexpression of CASC11 led to increased cancer cell viability under oxaliplatin, tetraplatin, cisplatin, and carboplatin treatment, while CASC11 siRNA silencing played an opposite role. Therefore, overexpression of CASC11 in OSCC mediated the development of cancer cell resistance to chemotherapy.

Stasenko M, Cybulska P, Feit N, et al.
Brain metastasis in epithelial ovarian cancer by BRCA1/2 mutation status.
Gynecol Oncol. 2019; 154(1):144-149 [PubMed] Article available free on PMC after 01/07/2020 Related Publications
OBJECTIVE: To evaluate clinical outcomes of patients with BRCA-associated ovarian cancer who developed brain metastases (BM).
METHODS: Patients with epithelial ovarian, fallopian tube, and primary peritoneal cancer (EOC) and BM, treated at a single institution from 1/1/2008-7/1/2018, were identified from two institutional databases. Charts and medical records were retrospectively reviewed for clinical characteristics and germline BRCA mutation status. Appropriate statistics were used.
RESULTS: Of 3649 patients with EOC, 91 had BM (2.5%). Germline mutation status was available for 63 (69%) cases; 21 (35%) of these harbored a BRCA1/2 mutation (15 BRCA1, 6 BRCA2). Clinical characteristics were similar between groups. BM were diagnosed at a median of 31 months (95% CI, 22.6-39.4) in BRCA-mutated (mBRCA) and 32 months (95% CI, 23.7-40.3) in wild-type BRCA (wtBRCA) (p = 0.78) patients. Brain metastases were the only evidence of disease at time of BM diagnoses in 48% (n = 10) mBRCA and 19% (n = 8) wtBRCA (p = 0.02) patients. There was no difference in treatment of BM by mutation status (p = 0.84). Survival from time of BM diagnosis was 29 months (95%CI, 15.5-42.5) in mBRCA and 9 months (95% CI, 5.5-12.5) in wtBRCA patients, with an adjusted hazard ratio (HR) of 0.53, p = 0.09; 95% CI, 0.25-1.11. HR was adjusted for presence of systemic disease at time of BM diagnosis.
CONCLUSION: This is the largest study to date comparing outcomes in patients with EOC and BM by mutation status. mBRCA patients were more likely to have isolated BM, which may be a factor in their long survival. This supports the pursuit of aggressive treatment for mBRCA EOC patients with BM. Additional studies examining the correlation of BRCA mutational status with BM are warranted.

Zhang GH, Chen MM, Kai JY, et al.
Molecular profiling of mucinous epithelial ovarian cancer by weighted gene co-expression network analysis.
Gene. 2019; 709:56-64 [PubMed] Related Publications
PURPOSE: In order to identify the molecular characteristics and improve the efficacy of early diagnosis of mucinous epithelial ovarian cancer (mEOC), here, the transcriptome profiling by weighted gene co-expression network analysis (WGCNA) has been proposed as an effective method.
METHODS: The gene expression dataset GSE26193 was reanalyzed with a systematical approach, WGCNA. mEOC-related gene co-expression modules were detected and the functional enrichments of these modules were performed at GO and KEGG terms. Ten hub genes in the mEOC-related modules were validated using two independent datasets GSE44104 and GSE30274.
RESULTS: 11 co-expressed gene modules were identified by WGCNA based on 4917 genes and 99 epithelial ovarian cancer samples. The turquoise module was found to be significantly associated with the subtype of mEOC. KEGG pathway enrichment analysis showed genes in the turquoise module significantly enriched in metabolism of xenobiotics by cytochrome P450 and steroid hormone biosynthesis. Ten hub genes (LIPH, BCAS1, FUT3, ZG16B, PTPRH, SLC4A4, MUC13, TFF1, HNF4G and TFF2) in the turquoise module were validated to be highly expressed in mEOC using two independent gene expression datasets GSE44104 and GSE30274.
CONCLUSION: Our work proposed an applicable framework of molecular characteristics for patients with mEOC, which may help us to obtain a precise and comprehensive understanding on the molecular complexities of mEOC. The hub genes identified in our study, as potential specific biomarkers of mEOC, may be applied in the early diagnosis of mEOC in the future.

Reijnen C, Küsters-Vandevelde HVN, Prinsen CF, et al.
Mismatch repair deficiency as a predictive marker for response to adjuvant radiotherapy in endometrial cancer.
Gynecol Oncol. 2019; 154(1):124-130 [PubMed] Related Publications
BACKGROUND: Mismatch repair (MMR) deficiency is found in 20 to 40% of endometrial cancers (ECs) and was recently identified as a discerning feature of one of the four prognostic subgroups identified by The Cancer Genome Atlas. There is accumulating evidence that MMR proteins are involved in the DNA repair processes following radiotherapy. We investigated the predictive value of MMR status for response to adjuvant radiotherapy in patients with stage IB/II, grade 3 endometrioid endometrial cancer (EEC).
METHODS: A retrospective multicenter cohort study was performed to compare patients with histopathologically confirmed stage IB/II grade 3 EEC with and without adjuvant radiotherapy. Patients were classified according to the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) identifying ECs as either MMR-deficient, POLE, p53abn or p53wt. Multivariable Cox regression analysis explored associations between adjuvant treatment and outcome.
RESULTS: A total of 128 patients were analyzed, including 57 patients (43.0%) with MMR-deficient EECs. Baseline characteristics were comparable, except a higher proportion of MMR-deficient EECs were stage II (36.8% vs. 15.5%, p = 0.006). Eighty-two patients (64.1%) received adjuvant radiotherapy (external beam [n = 55], vaginal brachytherapy [n = 27]). In multivariable analysis, adjuvant radiotherapy was associated with improved disease-specific survival in patients with MMR-deficient EECs (hazard ratio 0.19, 95%-CI 0.05-0.77), but not in patients with MMR-proficient EECs (hazard ratio 0.92, 95%-CI 0.37-2.31).
CONCLUSION: Adjuvant radiotherapy improved survival in patients with MMR-deficient EECs. MMR status could be used as a predictive biomarker to select patients that benefit most from adjuvant radiotherapy.

Christensen MV, Høgdall C, Jensen SG, et al.
Annexin A2 and S100A10 as Candidate Prognostic Markers in Epithelial Ovarian Cancer.
Anticancer Res. 2019; 39(5):2475-2482 [PubMed] Related Publications
BACKGROUND/AIM: Ovarian cancer (OC) is the 5th most common cancer among European women. Approximately 70-80% of OC is diagnosed at advanced stage resulting in an elevated mortality rate. The aim of this study was to examine whether Annexin A2 and S100A10 expression can be used as prognostic markers for epithelial ovarian cancer (EOC).
MATERIALS AND METHODS: Expression of Annexin A2 and S100A10 was evaluated in EOC tissue samples (n=303) by immunohistochemistry. The staining of the membrane, cytoplasmic and stroma was assessed according to intensity.
RESULTS: The expression of both markers correlated to histological subtype, histological grading, International Federation of Gynecology and Obstetrics (FIGO) stage, and macro-radical surgery. Univariate Cox regression analysis showed that Annexin A2 and S100A10 in stromal tissue correlated with shorter overall survival (OS). Multivariate Cox regression analysis demonstrated no independent prognostic significance of stromal Annexin A2 expression.
CONCLUSION: High expression of Annexin A2 and S100A10 in stromal tissue from EOC patients was associated with reduced OS; however, no independent prognostic value was found for any of the markers.

Kim JY, Kim SH, Kim HS
Promoter Methylation Down-regulates Osteoprotegerin Expression in Ovarian Carcinoma.
Anticancer Res. 2019; 39(5):2361-2367 [PubMed] Related Publications
BACKGROUND/AIM: Previous studies have documented that osteoprotegerin (OPG) is involved in the development and progression of several human malignancies. However, OPG has also been shown to act as a tumor suppressor. The aim of this study was to examine the expression status of OPG in ovarian carcinoma cells and investigate the underlying mechanism responsible for alterations in OPG expression.
MATERIALS AND METHODS: The expression levels of OPG mRNA and protein were assessed in human ovarian carcinoma cell lines. The methylation status of the OPG promoter region was determined using the bisulfite pyrosequencing technique. The effects of demethylation on OPG expression were also analyzed.
RESULTS: The human ovarian carcinoma cell lines, SW 626, OVCAR-3, ES-2, TOV-112D, and TOV-21G, expressed significantly lower levels of OPG mRNA and protein than the normal human ovarian epithelial cell line, HS823.Tc. Moreover, three CpG sites in the OPG promoter region were highly methylated in the SW 626, OVCAR-3, ES-2, and TOV-112D ovarian carcinoma cell lines compared to normal control cells. Furthermore, in all the examined ovarian carcinoma cell lines, treatment with the demethylating agent, 5-aza-2-deoxycytidine, resulted in significantly increased expression levels of OPG mRNA and protein compared to the respective pre-treatment levels.
CONCLUSION: OPG expression was down-regulated in the studied ovarian carcinoma cells compared to the normal control cells, while demethylation significantly restored OPG expression in the OPG-down-regulated cell lines. Our results suggest that OPG down-regulation in ovarian carcinoma occurs, at least partly, through epigenetic repression, suggesting its involvement in ovarian carcinogenesis.

Nikolopoulou A, Galli-Vareia I, Stravodimou A, et al.
[New therapeutic strategies in advanced stage breast and tubo-ovarian cancers].
Rev Med Suisse. 2019; 15(651):1027-1031 [PubMed] Related Publications
New targeted therapies modify therapeutic strategies for advanced stage breast and tubo-ovarian cancers. Chemotherapy and endocrine therapy remain the cornerstones of breast cancer treatment. Inhibitors of CDK4/6, mTOR and PI3K are associated with endocrine therapy to increase its effectiveness. PARP inhibitors outperform chemotherapy in BRCA1/2 mutation carriers. Immunotherapy integrates into the treatment of triple-negative cancers with very promising results. For tubo-ovarian cancers, the concept of « platinum-sensitive » has been tempered since the arrival of antiangiogenic treatment and PARP inhibitors that prolong the disease control not only in patients with BRCA1/2 mutation, but also in others.

Toss A, Molinaro E, Sammarini M, et al.
Hereditary ovarian cancers: state of the art.
Minerva Med. 2019; 110(4):301-319 [PubMed] Related Publications
The identification of a mutation in ovarian cancer (OC) predisposition genes plays a crucial role in the management of cancer prevention, diagnosis, and treatment. In healthy carriers, the detection of a specific mutation might justify more intensive and personalised surveillance programmes, chemopreventive measures, and prophylactic surgeries. Moreover, the identification of a mutation in affected OC patients might provide fundamental knowledge of the tumour pathogenesis, thus guiding treatment choices. This is a comprehensive review of the molecular pathways involved in the pathogenesis of hereditary ovarian cancers, the clinical-pathological features of these tumours, and the potential implications for their prevention and clinical management.

Gusev A, Lawrenson K, Lin X, et al.
A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants.
Nat Genet. 2019; 51(5):815-823 [PubMed] Article available free on PMC after 01/11/2019 Related Publications
We sought to identify susceptibility genes for high-grade serous ovarian cancer (HGSOC) by performing a transcriptome-wide association study of gene expression and splice junction usage in HGSOC-relevant tissue types (N = 2,169) and the largest genome-wide association study available for HGSOC (N = 13,037 cases and 40,941 controls). We identified 25 transcriptome-wide association study significant genes, 7 at the junction level only, including LRRC46 at 19q21.32, (P = 1 × 10

Zakrzewski F, Gieldon L, Rump A, et al.
Targeted capture-based NGS is superior to multiplex PCR-based NGS for hereditary BRCA1 and BRCA2 gene analysis in FFPE tumor samples.
BMC Cancer. 2019; 19(1):396 [PubMed] Article available free on PMC after 01/11/2019 Related Publications
BACKGROUND: With the introduction of Olaparib treatment for BRCA-deficient recurrent ovarian cancer, testing for somatic and/or germline mutations in BRCA1/2 genes in tumor tissues became essential for treatment decisions. In most cases only formalin-fixed paraffin-embedded (FFPE) samples, containing fragmented and chemically modified DNA of minor quality, are available. Thus, multiplex PCR-based sequencing is most commonly applied in routine molecular testing, which is predominantly focused on the identification of known hot spot mutations in oncogenes.
METHODS: We compared the overall performance of an adjusted targeted capture-based enrichment protocol and a multiplex PCR-based approach for calling of pathogenic SNVs and InDels using DNA extracted from 13 FFPE tissue samples. We further applied both strategies to seven blood samples and five matched FFPE tumor tissues of patients with known germline exon-spanning deletions and gene-wide duplications in BRCA1/2 to evaluate CNV detection based solely on panel NGS data. Finally, we analyzed DNA from FFPE tissues of 11 index patients from families suspected of having hereditary breast and ovarian cancer, of whom no blood samples were available for testing, in order to identify underlying pathogenic germline BRCA1/2 mutations.
RESULTS: The multiplex PCR-based protocol produced inhomogeneous coverage among targets of each sample and between samples as well as sporadic amplicon drop out, leading to insufficiently or non-covered nucleotides, which subsequently hindered variant detection. This protocol further led to detection of PCR-artifacts that could easily have been misinterpreted as pathogenic mutations. No such limitations were observed by application of an adjusted targeted capture-based protocol, which allowed for CNV calling with 86% sensitivity and 100% specificity. All pathogenic CNVs were confirmed in the five matched FFPE tumor samples from patients carrying known pathogenic germline mutations and we additionally identified somatic loss of the second allele in BRCA1/2. Furthermore we detected pathogenic BRCA1/2 variants in four the eleven FFPE samples from patients of whom no blood was available for analysis.
CONCLUSIONS: We demonstrate that an adjusted targeted capture-based enrichment protocol is superior to commonly applied multiplex PCR-based protocols for reliable BRCA1/2 variant detection, including CNV-detection, using FFPE tumor samples.

Świerczewska M, Sterzyńska K, Wojtowicz K, et al.
PTPRK Expression Is Downregulated in Drug Resistant Ovarian Cancer Cell Lines, and Especially in ALDH1A1 Positive CSCs-Like Populations.
Int J Mol Sci. 2019; 20(8) [PubMed] Article available free on PMC after 01/11/2019 Related Publications

Salem M, Shan Y, Bernaudo S, Peng C
miR-590-3p Targets Cyclin G2 and FOXO3 to Promote Ovarian Cancer Cell Proliferation, Invasion, and Spheroid Formation.
Int J Mol Sci. 2019; 20(8) [PubMed] Article available free on PMC after 01/11/2019 Related Publications
Ovarian cancer is the leading cause of death from gynecological cancers. MicroRNAs (miRNAs) are small, non-coding RNAs that interact with the 3' untranslated region (3' UTR) of target genes to repress their expression. We have previously reported that miR-590-3p promoted ovarian cancer growth and metastasis, in part by targeting Forkhead box A (FOXA2). In this study, we further investigated the mechanisms by which miR-590-3p promotes ovarian cancer development. Using luciferase reporter assays, real-time PCR, and Western blot analyses, we demonstrated that miR-590-3p targets cyclin G2 (CCNG2) and Forkhead box class O3 (FOXO3) at their 3' UTRs. Silencing of CCNG2 or FOXO3 mimicked, while the overexpression of CCNG2 or FOXO3 reversed, the stimulatory effect of miR-590-3p on cell proliferation and invasion. In hanging drop cultures, the overexpression of mir-590 or the transient transfection of miR-590-3p mimics induced the formation of compact spheroids. Transfection of the CCNG2 or FOXO3 plasmid into the mir-590 cells resulted in the partial disruption of the compact spheroid formation. Since we have shown that CCNG2 suppressed β-catenin signaling, we investigated if miR-590-3p regulated β-catenin activity. In the TOPFlash luciferase reporter assays, mir-590 increased β-catenin/TCF transcriptional activity and the nuclear accumulation of β-catenin. Silencing of β-catenin attenuated the effect of mir-590 on the compact spheroid formation. Taken together, these results suggest that miR-590-3p promotes ovarian cancer development, in part by directly targeting CCNG2 and FOXO3.

Wu D, Yu X, Wang J, et al.
Ovarian Cancer Stem Cells with High ROR1 Expression Serve as a New Prophylactic Vaccine for Ovarian Cancer.
J Immunol Res. 2019; 2019:9394615 [PubMed] Article available free on PMC after 01/11/2019 Related Publications
Tumor vaccines offer a number of advantages for cancer treatment. In the study, the vaccination with cancer stem cells (CSCs) with high expression of the type I receptor tyrosine kinase-like orphan receptor (ROR1) was evaluated in a murine model for the vaccine's immunogenicity and protective efficacy against epithelial ovarian carcinoma (EOC). CD117

Dong J, Xu M
A 19‑miRNA Support Vector Machine classifier and a 6‑miRNA risk score system designed for ovarian cancer patients.
Oncol Rep. 2019; 41(6):3233-3243 [PubMed] Article available free on PMC after 01/11/2019 Related Publications
Ovarian cancer (OC) is the most common gynecologic malignancy with high incidence and mortality. The present study aimed to develop approaches for determining the recurrence type and identify potential miRNA markers for OC prognosis. The miRNA expression profile of OC (the training set, including 390 samples with recurrence information) was downloaded from The Cancer Genome Atlas database. The validation sets GSE25204 and GSE27290 were obtained from the Gene Expression Omnibus database. Prescreening of clinical factors was conducted using the survival package, and the differentially expressed miRNAs (DE‑miRNAs) were identified using the limma package. Using the Caret package, the optimal miRNA set was selected to build a Support Vector Machine (SVM) classifier. The miRNAs and clinical factors independently related to prognosis were analyzed using the survival package, and the risk score system was constructed. Finally, the miRNA‑target regulatory network was built by Cytoscape software, and enrichment analysis was performed. There were 46 DE‑miRNAs between the recurrent and non‑recurrent samples. After the optimal 19‑miRNA set was selected for constructing the SVM classifier, 6 DE‑miRNAs (miR‑193b, miR‑211, miR‑218, miR‑505, miR‑508 and miR‑514) independently related to prognosis were further extracted to build the risk score system. The neoplasm cancer status was independently correlated with the prognosis and conducted with stratified analysis. Additionally, the target genes in the regulatory network were enriched in the regulation of actin cytoskeleton and the TGF‑β signaling pathway. The 6‑miRNA signature may serve as a potential biomarker for OC prognosis, particularlyfor recurrence.

Zhu H, Gan X, Jiang X, et al.
ALKBH5 inhibited autophagy of epithelial ovarian cancer through miR-7 and BCL-2.
J Exp Clin Cancer Res. 2019; 38(1):163 [PubMed] Article available free on PMC after 01/11/2019 Related Publications
BACKGROUND: ALKBH5 regulated the malignant behavior of breast cancer and glioblastoma. However, the expression and function of ALKBH5 in epithelial ovarian cancer have not yet been determined. In the present study, we investigated the expression and function of ALKBH5 in epithelial ovarian cancer with respect to its potential role in the tumorigenesis of the disease as well as an early diagnostic marker.
METHODS: Immunohistochemistry and western blot were used to detect protein expression. Gene silencing and over-expression experiment were used to study gene function. Cell proliferation assay and Matrigel invasion assays were used to detect cell proliferation and invasion, respectively. The nude mouse tumor formation experiment was used to evaluate the growth of cells in vivo.
RESULTS: The expression of ALKBH5 was found to be increased in epithelial ovarian cancer tissue as compared to the normal ovarian tissues. The silencing of ALKBH5 in SKOV3 cells enhanced the autophagy and inhibited the proliferation and invasion in vitro and in vivo, whereas the ectopic expression of ALKBH5 in A2780 cells exerted an opposite effect. Mechanical study revealed that ALKBH5 physically interacted with HuR. ALKBH5 activated EGFR-PIK3CA-AKT-mTOR signaling pathway. Also, ALKBH5 enhanced the stability of BCL-2 mRNA and promoted the interaction between Bcl-2 and Beclin1.
CONCLUSION: Overall, the present study identified ALKBH5 as a candidate oncogene in epithelial ovarian cancer and a potential target for ovarian cancer therapy.

Zajda K, Rak A, Ptak A, Gregoraszczuk EL
Compounds of PAH mixtures dependent interaction between multiple signaling pathways in granulosa tumour cells.
Toxicol Lett. 2019; 310:14-22 [PubMed] Related Publications
Mechanism of PAH mixtures, using granulosa tumour cells, was investigated. Cells were exposed to a mixture of all 16 priority PAHs (M1) or a mixture of five PAHs not classified as human carcinogens (M2). The effect of siAHR, siAHRR and siNFKB2 on the expression of CYP1A1, CYP1B1, GSTM1, ERα, AR and cell proliferation was described. M1 decreased AhR and CYP1A1, while increased AhRR and ARNT expression. M2 also decreased AhR and CYP1A1 but had no effect on AhRR expression. siAHRR reversed the inhibitory effect of M1 on AhR and CYP1A1,while inhibitory effect of M2 was still observed. siNFKB2 reversed inhibitory effect of both mixtures on AhR and CYP1A1 expression and stimulatory effect of M1 on AhRR expression. siAHR reversed stimulatory effect of both mixtures on ERα expression. Stimulatory effect of M1 on cell proliferation was not observed in siAHR, was still observed in siESR1 cells. M2 had no effect on cell proliferation, however stimulatory effect was appeared in siAHR and siESR1cells. In conclusion: M1 by activation of AhRR and NFkB p52, but M2 only by activation of NFκB attenuated AhR signalling and ligand-induced CYP1A1 expression. Interaction between AhR and ER following M1 and M2 exposure is primarily initiated through AhR.

Ouh YT, Cho HW, Lee JK, et al.
CXC chemokine ligand 1 mediates adiponectin-induced angiogenesis in ovarian cancer.
Tumour Biol. 2019; 42(4):1010428319842699 [PubMed] Related Publications
OBJECTIVES: Adiponectin is a cytokine secreted from adipose tissue that regulates energy homeostasis, inflammation, and cell proliferation. Obesity is associated with increased risk of various cancers, including ovarian cancer. Adipokines, including adiponectin, have been implicated as a factor linking obesity and carcinogenesis. The oncogenic role of adiponectin is not known with regard to various cancer types. We sought to determine the role of adiponectin in angiogenesis in ovarian cancer in vitro.
METHODS: We transfected SKOV3 cells with vascular endothelial growth factor small interfering RNA in order to identify the independent angiogenic role of adiponectin in ovarian cancer. The vascular endothelial growth factor knockdown SKOV3 cell lines were treated with adiponectin for 48 h. The cytokines involved in adiponectin-mediated angiogenesis were explored using the human angiogenesis cytokine array and were verified with the enzyme-linked immunosorbent assay. The angiogenic effect of adiponectin was evaluated using the human umbilical vein endothelial cell tube formation assay. We also investigated the effects of adiponectin treatment on the migration and invasion of SKOV3 cells.
RESULTS: The number of tubes formed by human umbilical vein endothelial cell decreased significantly after knockdown of vascular endothelial growth factor (via transfection of vascular endothelial growth factor small interfering RNA into SKOV3 cells). When these vascular endothelial growth factor knockdown SKOV3 cells were treated with adiponectin, there was an increase in the number of tubes in a tube formation assay. Following adiponectin treatment, the CXC chemokine ligand 1 secretion increased in a cytokine array. This was confirmed by both enzyme-linked immunosorbent assay and Western blot. The increased secretion of CXC chemokine ligand 1 by adiponectin occurred regardless of vascular endothelial growth factor knockdown. In addition, the induction of migration and invasion of SKOV3 cells were significantly stronger with adiponectin treatment than they were without.
CONCLUSION: Adiponectin treatment of ovarian cancer cells induces angiogenesis via CXC chemokine ligand 1 independently of vascular endothelial growth factor. These findings suggest that adiponectin may serve as a novel therapeutic target for ovarian cancer.

Wu J, Cai Q, Wang J, Liao Y
Identifying mutated driver pathways in cancer by integrating multi-omics data.
Comput Biol Chem. 2019; 80:159-167 [PubMed] Related Publications
Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.

Márton É, Lukács J, Penyige A, et al.
Circulating epithelial-mesenchymal transition-associated miRNAs are promising biomarkers in ovarian cancer.
J Biotechnol. 2019; 297:58-65 [PubMed] Related Publications
Ovarian cancer is the fifth most common cause of cancer death among women that is mostly due to the difficulty of early diagnosis. Circulating miRNAs proved to be reliable biomarkers in various cancers. We screened 9 miRNAs, which are involved in epithelial-mesenchymal transition, in the plasma samples of patients with malignant (n = 28) or non-malignant (n = 12) ovarian tumors and disease-free healthy volunteers (n = 60) by qRT-PCR. The expression levels of miR200a, miR200b, miR200c, miR141, miR429, miR203a, miR34b (p < 0.001) and miR34a (p < 0.01) were significantly higher in the malignant samples than in healthy controls. MiR203a, miR141 (p < 0.01), miR200a and miR429 (p < 0.05) levels were also higher in malignant compared to non-malignant samples. ROC-AUC was the highest in the case of miR200c: 0.861 (95%CI = 0.776-0.947). Spearman's rank correlation analysis revealed positive correlation between the plasma levels of the studied miRNAs that was the highest between miR200b and miR200c (r

Jeleniewicz W, Cybulski M, Nowakowski A, et al.
Anticancer Res. 2019; 39(4):1821-1827 [PubMed] Related Publications
BACKGROUND/AIM: Ovarian cancer is the most frequent cause of death in women among gynecological cancers in Poland. MMP-2 and MMP-9 are frequently dysregulated in cancers and they are considered as potential biomarkers. Our goal was to assess the associations between MMP-2 and MMP-9 mRNA expression, clinicopathological parameters and patients' response to chemotherapy.
MATERIALS AND METHODS: We evaluated MMP-2 and MMP-9 mRNA expression in epithelial ovarian cancer (EOC) tissues from 44 untreated patients, four ovarian cancer cell lines, and human skin fibroblasts (HSF). The expression of both MMPs was estimated using qPCR.
RESULTS: MMP-2 expression was significantly higher (p=0.020) in EOCs sensitive to chemotherapy compared to resistant and refractory tumors. The highest MMP-2 expression was found in HSF and MMP-9 expression was the highest in EOCs (p<0.001). The expression of neither MMP was significantly associated with patients' overall survival (OS).
CONCLUSION: MMP-2 may be engaged in early stages of ovarian carcinogenesis. MMP-2 expression in EOCs may discriminate patients with a favorable response to first line chemotherapy.

Liu J, Liu T, Liang L, et al.
Clinical relationships between the rs2212020 and rs189897 polymorphisms of the
J Genet. 2019; 98 [PubMed] Related Publications
To better understand the role of integrin subunit alpha 9 (

Zhang M, Xia B, Xu Y, et al.
Circular RNA (hsa_circ_0051240) promotes cell proliferation, migration and invasion in ovarian cancer through miR-637/KLK4 axis.
Artif Cells Nanomed Biotechnol. 2019; 47(1):1224-1233 [PubMed] Related Publications
In this study, we identified hsa_circ_0051240 was significantly increased in ovarian cancer (OC) tissues. Our results indicated that silencing of hsa_circ_0051240 inhibited OC cell proliferation, migration and invasion in vitro, and also prevented OC tumour formation in vivo. In addition, the inhibitory effects by blockage of hsa_circ_0051240 could be attenuated by the miR-637 inhibitor. Furthermore, we also identified that hsa_circ_0051240 act as a sponge of miR-637, and miR-637 directly targeted KLK4 mRNA in OC cells. Altogether, hsa_circ_0051240 promotes OC cell proliferation, migration and invasion through inhibiting the miR-637/KLK4 axis. Therefore, these results demonstrated that the hsa_circ_0051240/miR-637/KLK4 axis might serve as a therapeutic target for OC treatment.

Globus T, Moskaluk C, Pramoonjago P, et al.
Sub-terahertz vibrational spectroscopy of ovarian cancer and normal control tissue for molecular diagnostic technology.
Cancer Biomark. 2019; 24(4):405-419 [PubMed] Related Publications
We introduce here recently developed highly resolved Sub-Terahertz resonance spectroscopy of biological molecules and cells combined with molecular dynamics (MD) computational analysis as a new approach for optical visualization and quantification of the presence of microRNAs, particularly the mir-200 family, as potential biomarkers in samples from tissue of epithelial ovarian cancers for disease early detection, analysis, prognosis and treatment.METHOD: A set of samples for this study was prepared from anonymized archival formalin-fixed, paraffin-embedded ovarian epithelial tissue containing regions of invasive neoplastic cells from cases of high-histologic grade serous papillary ovarian carcinoma. Control samples were normal mucosa from fallopian tubes of patients with no known malignancy. Spectroscopic characterization of tissue samples in this study was performed using a continuous wave, frequency domain automated spectrometer operating at room temperature in the spectral region of 310-500 GHz. The spectral results were compared with molecular dynamics simulations and absorption coefficient calculations utilized to predict the absorption spectra.RESULTS: The characteristic spectroscopic features in absorption spectra, particularly the presence of absorption peaks near 13 cm-1 have been identified as cancer indicators. Tissue samples heterogeneity, reflected by diverse spectral signatures, provides additional, very specific information that may be used for identification of cancer subtypes, clinical behavior or sensitivity to specific therapies. Further work is warranted to determine if this signature can be detected in bio-fluids from ovarian cancer patients. If strongly correlated with cancer burden, it may then be investigated as a potential new biomarker for disease monitoring, and also perhaps as a biomarker for cancer screening.

Zhou JW, Tang JJ, Sun W, Wang H
PGK1 facilities cisplatin chemoresistance by triggering HSP90/ERK pathway mediated DNA repair and methylation in endometrial endometrioid adenocarcinoma.
Mol Med. 2019; 25(1):11 [PubMed] Article available free on PMC after 01/11/2019 Related Publications
BACKGROUND: Endometrial carcinoma represents one of the most common cancer types of the female reproductive tract. If diagnosed at an early stage, the 5-year survival rate is promising. However, recurrence and chemoresistance remain problematic for at least 15% of the patients. In the present study, we aim to reveal the mechanism by which PGK1 regulates chemoresistance in endometrial carcinoma.
METHODS: qPCR was performed to detect expression of PGK1 in clinical tissue samples of endometrial carcinoma. Specific shRNAs were employed to knockdown PGK1 expression in endometrial cancer cell lines. MTT assay was used to evaluate cell viability and cisplatin sensitivity of endometrial carcinoma cell lines. Western blot was performed to assess the effects of PGK1 knockdown on the expression levels of HSP90, DNA repair-associated proteins (c-JUN, FOSL1, and POLD1), and DNA methylation-related enzymes (DNMT1, DNMT3A and DNMT3B). Immunoprecipitation was performed to verify direct binding between PGK1 and HSP90.
RESULTS: We first showed that PGK1 expression is elevated in tumor tissues of endometrial cancer, and high PGK1 levels are associated with clinical stages and metastasis. Knockdown of PGK1 inhibits proliferation of endometrial cancer cells, and enhances the inhibitory effect of cisplatin on cell viability. In addition, knockdown of PGK1 down-regulates the expression of DNA repair-related proteins, methylation-related enzymes, and total cellular methylation level. PGK1 was next shown to interact directly with HSP90 and exhibit pro-tumor effects by modulating the ATPase activity of HSP90.
CONCLUSIONS: We propose that PGK1 mediates DNA repair and methylation through the HSP90/ERK pathway, and eventually enhances the chemoresistance to cisplatin. The results provide new insights on functions of PGK1 and HSP90, which might make them as promising targets for endometrial cancer chemotherapy.

Lheureux S, Gourley C, Vergote I, Oza AM
Epithelial ovarian cancer.
Lancet. 2019; 393(10177):1240-1253 [PubMed] Related Publications
Epithelial ovarian cancer generally presents at an advanced stage and is the most common cause of gynaecological cancer death. Treatment requires expert multidisciplinary care. Population-based screening has been ineffective, but new approaches for early diagnosis and prevention that leverage molecular genomics are in development. Initial therapy includes surgery and adjuvant therapy. Epithelial ovarian cancer is composed of distinct histological subtypes with unique genomic characteristics, which are improving the precision and effectiveness of therapy, allowing discovery of predictors of response such as mutations in breast cancer susceptibility genes BRCA1 and BRCA2, and homologous recombination deficiency for DNA damage response pathway inhibitors or resistance (cyclin E1). Rapidly evolving techniques to measure genomic changes in tumour and blood allow for assessment of sensitivity and emergence of resistance to therapy, and might be accurate indicators of residual disease. Recurrence is usually incurable, and patient symptom control and quality of life are key considerations at this stage. Treatments for recurrence have to be designed from a patient's perspective and incorporate meaningful measures of benefit. Urgent progress is needed to develop evidence and consensus-based treatment guidelines for each subgroup, and requires close international cooperation in conducting clinical trials through academic research groups such as the Gynecologic Cancer Intergroup.

Moroney MR, Davies KD, Wilberger AC, et al.
Molecular markers in recurrent stage I, grade 1 endometrioid endometrial cancers.
Gynecol Oncol. 2019; 153(3):517-520 [PubMed] Related Publications
OBJECTIVES: Stage I, grade 1 endometrial cancers have low recurrence rates and often do not receive adjuvant therapy. We compared recurrent cases to matched non-recurrent controls to evaluate for molecular markers associated with higher risk of recurrence.
METHODS: A case-control study including all cases of recurrent stage I, grade 1 endometrioid endometrial cancer at one institution in a ten-year period. Cases were matched to controls by age, BMI, weight and stage. Molecular testing and immunohistochemistry were performed on archival tumor specimens: microsatellite instability (MSI-H), mismatch repair status, POLE mutational status, and next-generation sequencing.
RESULTS: 15 stage I, grade 1 endometrial cancer cases with recurrent disease and available tumor specimens were identified. CTNNB1 and MSI-H were present at significantly higher rates in cases than controls (CTNNB1 60% vs. 28%, OR 3.9, 95%CI 1.1-14.7, p = 0.04 and MSI-H 53% vs. 21%, OR 4.4, 95%CI 1.1-17.0, p = 0.03). POLE mutations were found in 0% of cases vs. 7% of controls (p = 0.54). Among specimens demonstrating microsatellite stability (MSS), 100% of cases vs. 26% of controls had CTNNB1 mutations (p < 0.001). CTNNB1 wild type tumors were MSI-H in 100% of cases vs. 19% of controls (p < 0.001).
CONCLUSIONS: Compared to controls, CTNNB1 mutation is present at significantly higher rates in recurrent stage I, grade 1 endometrial cancers and is found most commonly in MSS tumors. MSI-H is also present at significantly higher rates in recurrent cases. These markers may be useful for prognostic risk stratification and adjuvant therapy decision-making in this otherwise low-risk population.

Zeng S, Liu S, Feng J, et al.
Upregulation of lncRNA AB073614 functions as a predictor of epithelial ovarian cancer prognosis and promotes tumor growth in vitro and in vivo.
Cancer Biomark. 2019; 24(4):421-428 [PubMed] Related Publications
BACKGROUNDS: Upregulation of lncRNA AB073614 is found in some cancer types and involved in tumor development and progression including ovarian cancer. However, the clinical value and functional role of lncRNA AB073614 in epithelial ovarian cancer (EOC) still needed to be investigated.
METHODS: We examined lncRNA AB073614 expression using quantitative real time polymerase chain reaction (qRT-PCR) in 75 paired of EOC tissue samples and adjacent normal tissues. Association of lncRNA AB073614 expression with overall survival (OS) was evaluated using Kaplan-Meier analysis. Univariate and multivariate analysis of factors associated with OS were assessed in EOC patients. After lncRNA AB073614 knockdown using siRNAs, the cell viability and cell colony forming assays were performed. Western blot analysis was used to assess relative protein expression.
RESULTS: In present study, we demonstrated that lncRNA AB073614 was significantly upregulated in ovarian cancer tissues compared to adjacent normal tissues in patients. Higher lncRNA AB073614 expression significantly associated with tumor size, lymph node invasion, FIGO stage, and shorter OS rate of EOC patients. Furthermore, multivariate Cox regression analysis results showed that higher lncRNA AB0736141 was identified as an independent risk factor of OS in EOC patients. Moreover, we demonstrated that lncRNA AB0736141 knockdown suppressed EOC cell proliferation ability and cell colony formation in vitro. In vivo, we showed that AB0736141 knockdown suppressed tumor growth. We also revealed that lncRNA AB0736141 knockdown inhibited the PTEN/PI3K/AKT signaling pathway in EOC.
CONCLUSIONS: Thus, these results indicated that LncRNA AB073614 may serve as a prognostic biomarker and potential target of treatment for EOC.

Lukács J, Soltész B, Penyige A, et al.
Identification of miR-146a and miR-196a-2 single nucleotide polymorphisms at patients with high-grade serous ovarian cancer.
J Biotechnol. 2019; 297:54-57 [PubMed] Related Publications
MicroRNAs play an essential role in the regulation of gene expression and tumor development. Single nucleotide polymorphism (SNP) can be observed in miRNAs and could influence gene expression. We aimed to identify miR-146a rs2910164 and miR-196a-2 rs11614913 polymorphisms in ovarian cancer patients and controls. 75 patients and 75 controls were involved. DNA was isolated from blood samples. MiR-146a rs2910164 and miR-196a-2 rs11614913 were determined by LightSnip kit. We used melting curve analysis for allele classification. Network analysis was made to find common target genes. We detected 72.67% G allele frequency of miR-146a rs2910164 in controls and 82.00% in patients group (p = 0,053). GG, GC and CC genotypes occurred with 53.33%, 38.67% and 8.00% among controls, with 65.33%, 33.33% and 1.33% among patients, (p = 0.0917). Allele C of miR-196a-2 rs11614913 occurred in 59.33% of controls and in 67.33% of patients (p = 0.15). CC, CT and TT genotypes occurred with 37.33%, 44.00%, and 18.67% frequency in controls, with 46.67%; 41.33% and 12.00% in patients (p = 0.3815). Network analysis found ATG9A, LBR, MBD4 and RUFY2 genes to be targets for both miRNAs. SNPs of miR-146a and miR-196a-2 showed no significant differences between patients and controls. More investigations are required to clarify the exact role of these SNPs in ovarian cancer.

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Cite this page: Cotterill SJ. Ovarian Cancer, Cancer Genetics Web: http://www.cancer-genetics.org/X1003.htm Accessed:

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