Liver Cancer

Overview

Literature Analysis

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Tag cloud generated 08 August, 2015 using data from PubMed, MeSH and CancerIndex

Mutated Genes and Abnormal Protein Expression (339)

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
HCCS Xp22.3 MLS, CCHL, MCOPS7 -HCCS and Hepatocellular Carcinoma
819
TP53 17p13.1 P53, BCC7, LFS1, TRP53 -TP53 and Liver Cancer
641
MET 7q31 HGFR, AUTS9, RCCP2, c-Met Prognostic
-C-MET and Liver Cancer
-C-MET and Hepatocellular Carcinoma
205
CTNNB1 3p21 CTNNB, MRD19, armadillo -CTNNB1 and Liver Cancer
371
AFP 4q13.3 AFPD, FETA, HPAFP -AFP and Hepatocellular Carcinoma
319
GPC3 Xq26.1 SGB, DGSX, MXR7, SDYS, SGBS, OCI-5, SGBS1, GTR2-2 -GPC3 and Liver Cancer
257
TNF 6p21.3 DIF, TNFA, TNFSF2, TNF-alpha -TNF and Liver Cancer
147
BAX 19q13.3-q13.4 BCL2L4 -BAX and Liver Cancer
98
CDKN1A 6p21.2 P21, CIP1, SDI1, WAF1, CAP20, CDKN1, MDA-6, p21CIP1 -CDKN1A and Hepatocellular Carcinoma
91
MYC 8q24.21 MRTL, MYCC, c-Myc, bHLHe39 -MYC protein, human and Liver Cancer
89
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II -MMP2 and Liver Cancer
83
PCNA 20pter-p12 ATLD2 -PCNA and Liver Cancer
82
IGF2 11p15.5 IGF-II, PP9974, C11orf43 -IGF2 and Hepatoblastoma
-IGF2 Expression in Hepatocarcinoma
59
STAT3 17q21.31 APRF, HIES, ADMIO -STAT3 and Liver Cancer
78
IGF2R 6q26 MPR1, MPRI, CD222, CIMPR, M6P-R -IGF2R and Liver Cancer
67
HGF 7q21.1 SF, HGFB, HPTA, F-TCF, DFNB39 -HGF and Liver Cancer
66
FOS 14q24.3 p55, AP-1, C-FOS -FOS and Liver Cancer
65
TGFB1 19q13.1 CED, LAP, DPD1, TGFB, TGFbeta -TGFB1 and Liver Cancer
64
IFNA2 9p22 IFNA, INFA2, IFNA2B, IFN-alphaA -IFNA2 and Hepatocellular Carcinoma
61
IFNA7 9p22 IFNA-J, IFN-alphaJ -IFNA7 and Hepatocellular Carcinoma
61
IFNA17 9p22 IFNA, INFA, LEIF2C1, IFN-alphaI -IFNA17 and Hepatocellular Carcinoma
61
HNF1A 12q24.2 HNF1, LFB1, TCF1, MODY3, TCF-1, HNF-1A, IDDM20 -HNF1A and Liver Cancer
59
TERT 5p15.33 TP2, TRT, CMM9, EST2, TCS1, hTRT, DKCA2, DKCB4, hEST2, PFBMFT1 -TERT and Liver Cancer
58
VEGFA 6p12 VPF, VEGF, MVCD1 -VEGFA and Liver Cancer
56
ABCC1 16p13.1 MRP, ABCC, GS-X, MRP1, ABC29 -ABCC1 (MRP1) and Hepatocellular Carcinoma
52
SMAD3 15q22.33 LDS3, LDS1C, MADH3, JV15-2, HSPC193, HsT17436 -SMAD3 and Liver Cancer
48
CDH1 16q22.1 UVO, CDHE, ECAD, LCAM, Arc-1, CD324 -CDH1 and Liver Cancer
47
TGFA 2p13 TFGA -TGFA and Liver Cancer
42
RASSF1 3p21.3 123F2, RDA32, NORE2A, RASSF1A, REH3P21 -RASSF1 and Liver Cancer
41
E2F1 20q11.2 RBP3, E2F-1, RBAP1, RBBP3 -E2F1 and Liver Cancer
41
HFE 6p21.3 HH, HFE1, HLA-H, MVCD7, TFQTL2 -HFE and Liver Cancer
41
SMAD4 18q21.1 JIP, DPC4, MADH4, MYHRS -SMAD4 and Liver Cancer
38
XRCC1 19q13.2 RCC -XRCC1 and Liver Cancer
37
HLF 17q22 -HLF and Hepatocellular Carcinoma
36
FOXM1 12p13 MPP2, TGT3, HFH11, HNF-3, INS-1, MPP-2, PIG29, FKHL16, FOXM1B, HFH-11, TRIDENT, MPHOSPH2 -FOXM1 and Liver Cancer
34
CCNB1 5q12 CCNB -CCNB1 and Hepatocellular Carcinoma
32
TCF4 18q21.1 E2-2, ITF2, PTHS, SEF2, ITF-2, SEF-2, TCF-4, SEF2-1, SEF2-1A, SEF2-1B, SEF2-1D, bHLHb19 -TCF4 and Liver Cancer
31
GSTT1 22q11.23 -GSTT1 and Liver Cancer
29
M6PR 12p13 SMPR, MPR46, CD-MPR, MPR 46, MPR-46 -M6PR and Liver Cancer
29
RHOA 3p21.3 ARHA, ARH12, RHO12, RHOH12 -RHOA and Liver Cancer
29
GAPDH 12p13 G3PD, GAPD, HEL-S-162eP -GAPDH and Liver Cancer
28
DNMT1 19p13.2 AIM, DNMT, MCMT, CXXC9, HSN1E, ADCADN -DNMT1 and Liver Cancer
27
JUNB 19p13.2 AP-1 -JUNB and Hepatocellular Carcinoma
27
MIR21 17q23.1 MIRN21, miR-21, miRNA21, hsa-mir-21 -MicroRNA miR-21 and Liver Cancer
27
ACHE 7q22 YT, ACEE, ARACHE, N-ACHE -ACHE and Liver Cancer
26
SOCS1 16p13.13 JAB, CIS1, SSI1, TIP3, CISH1, SSI-1, SOCS-1 -SOCS1 and Liver Cancer
26
IL6 7p21 HGF, HSF, BSF2, IL-6, IFNB2 -IL6 and Hepatocellular Carcinoma
25
IFNL3 19q13.13 IL28B, IL28C, IL-28B -IL28B and Liver Cancer
25
KLF6 10p15 GBF, ZF9, BCD1, CBA1, CPBP, PAC1, ST12, COPEB -KLF6 and Liver Cancer
24
SPP1 4q22.1 OPN, BNSP, BSPI, ETA-1 -SPP1 and Liver Cancer
24
EPCAM 2p21 ESA, KSA, M4S1, MK-1, DIAR5, EGP-2, EGP40, KS1/4, MIC18, TROP1, EGP314, HNPCC8, TACSTD1 -EPCAM and Liver Cancer
24
RHOC 1p13.1 H9, ARH9, ARHC, RHOH9 -RHOC and Liver Cancer
24
ANXA8 10q11.22 ANX8, CH17-360D5.2 -ANXA8 and Liver Cancer
23
SERPINE1 7q22.1 PAI, PAI1, PAI-1, PLANH1 -SERPINE1 and Liver Cancer
23
CCK 3p22.1 -CCK and Liver Cancer
23
IGFBP3 7p12.3 IBP3, BP-53 -IGFBP3 and Liver Cancer
23
DLC1 8p22 HP, ARHGAP7, STARD12, p122-RhoGAP Deletion
-DLC1 and Hepatocellular Carcinoma
22
RUNX3 1p36 AML2, CBFA3, PEBP2aC -RUNX3 and Liver Cancer
22
FTCDNL1 2q33.1 FONG -FONG and Liver Cancer
22
XIAP Xq25 API3, ILP1, MIHA, XLP2, BIRC4, IAP-3, hIAP3, hIAP-3 -XIAP and Liver Cancer
22
ANGPT2 8p23.1 ANG2, AGPT2 -ANGPT2 and Liver Cancer
21
AXIN1 16p13.3 AXIN, PPP1R49 -AXIN1 and Liver Cancer
21
PTK2 8q24.3 FAK, FADK, FAK1, FRNK, PPP1R71, p125FAK, pp125FAK -PTK2 and Liver Cancer
20
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 and Hepatocellular Carcinoma
19
HNF4A 20q13.12 TCF, HNF4, MODY, FRTS4, MODY1, NR2A1, TCF14, HNF4a7, HNF4a8, HNF4a9, NR2A21, HNF4alpha -HNF4A and Liver Cancer
18
MAT1A 10q22 MAT, SAMS, MATA1, SAMS1 -MAT1A and Liver Cancer
18
DNMT3B 20q11.2 ICF, ICF1, M.HsaIIIB -DNMT3B and Liver Cancer
18
SIRT1 10q21.3 SIR2, hSIR2, SIR2L1 -SIRT1 and Liver Cancer
18
APOB 2p24-p23 FLDB, LDLCQ4 -APOB and Liver Cancer
17
MTDH 8q22.1 3D3, AEG1, AEG-1, LYRIC, LYRIC/3D3 -MTDH and Liver Cancer
16
SFRP1 8p11.21 FRP, FRP1, FrzA, FRP-1, SARP2 -SFRP1 and Hepatocellular Carcinoma
16
TCF7L2 10q25.3 TCF4, TCF-4 -TCF7L2 and Liver Cancer
16
HNF1B 17q12 FJHN, HNF2, LFB3, TCF2, HPC11, LF-B3, MODY5, TCF-2, VHNF1, HNF-1B, HNF1beta, HNF-1-beta -HNF1B and Liver Cancer
16
MICA 6p21.33 MIC-A, PERB11.1 -MICA and Liver Cancer
16
VIP 6q25 PHM27 -VIP and Liver Cancer
15
CCL2 17q11.2-q12 HC11, MCAF, MCP1, MCP-1, SCYA2, GDCF-2, SMC-CF, HSMCR30 -CCL2 and Hepatocellular Carcinoma
15
NFE2L2 2q31 NRF2 -NFE2L2 and Liver Cancer
14
YY1AP1 1q22 YAP, HCCA1, HCCA2, YY1AP -YY1AP1 and Liver Cancer
14
TNFRSF1A 12p13.2 FPF, MS5, p55, p60, TBP1, TNF-R, TNFAR, TNFR1, p55-R, CD120a, TNFR55, TNFR60, TNF-R-I, TNF-R55, TNFR1-d2 -TNFRSF1A and Hepatocellular Carcinoma
14
GNMT 6p12 -GNMT and Liver Cancer
14
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Hepatocellular Carcinoma
13
SREBF1 17p11.2 SREBP1, bHLHd1, SREBP-1c -SREBF1 and Liver Cancer
13
PEG10 7q21 EDR, HB-1, Mar2, MEF3L, Mart2, RGAG3 -PEG10 and Hepatocellular Carcinoma
13
YAP1 11q13 YAP, YKI, COB1, YAP2, YAP65 -YAP1 and Liver Cancer
13
IL6ST 5q11.2 CD130, GP130, CDW130, IL-6RB -IL6ST and Liver Cancer
13
PECAM1 17q23.3 CD31, PECA1, GPIIA', PECAM-1, endoCAM, CD31/EndoCAM -PECAM1 and Liver Cancer
12
PLK1 16p12.2 PLK, STPK13 -PLK1 and Liver Cancer
12
HES1 3q28-q29 HHL, HRY, HES-1, bHLHb39 -HES1 and Liver Cancer
12
BECN1 17q21 ATG6, VPS30, beclin1 -BECN1 and Liver Cancer
12
ZEB2 2q22.3 SIP1, SIP-1, ZFHX1B, HSPC082, SMADIP1 -ZEB2 and Liver Cancer
12
HMGB1 13q12 HMG1, HMG3, SBP-1 -HMGB1 and Liver Cancer
12
DKK1 10q11.2 SK, DKK-1 -DKK1 and Hepatocellular Carcinoma
11
PDCD4 10q24 H731 -PDCD4 and Liver Cancer
11
APOE 19q13.2 AD2, LPG, APO-E, LDLCQ5 -APOE and Hepatocellular Carcinoma
10
PSMD10 Xq22.3 p28, p28(GANK), dJ889N15.2 -PSMD10 and Liver Cancer
10
CCNE1 19q12 CCNE -CCNE1 and Liver Cancer
10
ING1 13q34 p33, p47, p33ING1, p24ING1c, p33ING1b, p47ING1a -ING1 and Hepatocellular Carcinoma
10
ANXA2 15q22.2 P36, ANX2, LIP2, LPC2, CAL1H, LPC2D, ANX2L4, PAP-IV, HEL-S-270 -ANXA2 and Hepatocellular Carcinoma
10
MALAT1 11q13.1 HCN, NEAT2, PRO2853, mascRNA, LINC00047, NCRNA00047 -MALAT1 and Liver Cancer
10
AXIN2 17q24.1 AXIL, ODCRCS -AXIN2 and Liver Cancer
9
CCR7 17q12-q21.2 BLR2, EBI1, CCR-7, CD197, CDw197, CMKBR7, CC-CKR-7 -CCR7 and Liver Cancer
9
IGFBP1 7p12.3 AFBP, IBP1, PP12, IGF-BP25, hIGFBP-1 -IGFBP1 and Liver Cancer
9
ACTB 7p22 BRWS1, PS1TP5BP1 -ACTB and Hepatocellular Carcinoma
9
AREG 4q13.3 AR, SDGF, AREGB, CRDGF -AREG and Liver Cancer
9
VIM 10p13 HEL113, CTRCT30 -VIM and Liver Cancer
9
DLK1 14q32 DLK, FA1, ZOG, pG2, DLK-1, PREF1, Delta1, Pref-1 -DLK1 and Liver Cancer
9
FGF19 11q13.1 -FGF19 and Hepatocellular Carcinoma
9
KIF1B 1p36.2 KLP, CMT2, CMT2A, CMT2A1, HMSNII, NBLST1 -KIF1B and Liver Cancer
9
ANXA5 4q27 PP4, ANX5, ENX2, RPRGL3, HEL-S-7 -ANXA5 and Liver Cancer
8
LIN28B 6q21 CSDD2 -LIN28B and Liver Cancer
8
MIRLET7C 21q21.1 LET7C, let-7c, MIRNLET7C, hsa-let-7c -MicroRNA let-7c and Liver Cancer
8
GPX1 3p21.3 GPXD, GSHPX1 -GPX1 and Liver Cancer
8
TIMP2 17q25 DDC8, CSC-21K -TIMP2 and Liver Cancer
8
STMN1 1p36.11 Lag, SMN, OP18, PP17, PP19, PR22, LAP18, C1orf215 -STMN1 and Liver Cancer
8
DDX3X Xp11.3-p11.23 DBX, DDX3, HLP2, DDX14, CAP-Rf -DDX3X and Liver Cancer
8
PTP4A3 8q24.3 PRL3, PRL-3, PRL-R -PTP4A3 and Liver Cancer
8
LGR5 12q22-q23 FEX, HG38, GPR49, GPR67, GRP49 -LGR5 and Liver Cancer
8
AKR1B10 7q33 HIS, HSI, ARL1, ARL-1, ALDRLn, AKR1B11, AKR1B12 -AKR1B10 and Liver Cancer
8
CXCL10 4q21 C7, IFI10, INP10, IP-10, crg-2, mob-1, SCYB10, gIP-10 -CXCL10 and Liver Cancer
8
FOXA2 20p11 HNF3B, TCF3B -FOXA2 and Liver Cancer
8
TLR3 4q35 CD283, IIAE2 -TLR3 and Liver Cancer
7
PGK1 Xq13.3 PGKA, MIG10, HEL-S-68p -PGK1 and Liver Cancer
7
PRDM2 1p36.21 RIZ, KMT8, RIZ1, RIZ2, MTB-ZF, HUMHOXY1 -PRDM2 and Liver Cancer
7
CCNG1 5q32-q34 CCNG -CCNG1 and Liver Cancer
7
CYP2C19 10q24 CPCJ, CYP2C, P450C2C, CYPIIC17, CYPIIC19, P450IIC19 -CYP2C19 and Liver Cancer
7
HOTAIR 12q13.13 HOXAS, HOXC-AS4, HOXC11-AS1, NCRNA00072 -HOTAIR and Liver Cancer
7
TLR2 4q32 TIL4, CD282 -TLR2 and Liver Cancer
7
SULF2 20q13.12 HSULF-2 -SULF2 and Liver Cancer
7
ID2 2p25 GIG8, ID2A, ID2H, bHLHb26 -ID2 Expression in hepatocellular carcinoma
7
YES1 18p11.31-p11.21 Yes, c-yes, HsT441, P61-YES -Proto-Oncogene Proteins c-yes and Liver Cancer
7
PTTG1 5q35.1 EAP1, PTTG, HPTTG, TUTR1 -PTTG1 and Liver Cancer
7
CTAG1B Xq28 CTAG, ESO1, CT6.1, CTAG1, LAGE-2, LAGE2B, NY-ESO-1 -CTAG1B and Liver Cancer
7
ADAM10 15q22 RAK, kuz, AD10, AD18, MADM, CD156c, HsT18717 -ADAM10 and Liver Cancer
7
IGFBP7 4q12 AGM, PSF, TAF, FSTL2, IBP-7, MAC25, IGFBP-7, RAMSVPS, IGFBP-7v, IGFBPRP1 -IGFBP7 and Liver Cancer
6
CCL4 17q12 ACT2, G-26, HC21, LAG1, LAG-1, MIP1B, SCYA2, SCYA4, MIP1B1, AT744.1, MIP-1-beta -CCL4 and Hepatocellular Carcinoma
6
GDF15 19p13.11 PDF, MIC1, PLAB, MIC-1, NAG-1, PTGFB, GDF-15 -GDF15 and Liver Cancer
6
SPINT2 19q13.1 PB, Kop, HAI2, DIAR3, HAI-2 -SPINT2 and Liver Cancer
6
CXCR2 2q35 CD182, IL8R2, IL8RA, IL8RB, CMKAR2, CDw128b -CXCR2 and Liver Cancer
6
SPRY2 13q31.1 hSPRY2 -SPRY2 and Liver Cancer
6
LCN2 9q34 24p3, MSFI, NGAL -LCN2 and Liver Cancer
6
FYN 6q21 SLK, SYN, p59-FYN -FYN and Liver Cancer
6
MTSS1 8p22 MIM, MIMA, MIMB -MTSS1 and Liver Cancer
6
NDRG1 8q24.3 GC4, RTP, DRG1, NDR1, NMSL, TDD5, CAP43, CMT4D, DRG-1, HMSNL, RIT42, TARG1, PROXY1 -NDRG1 and Hepatocellular Carcinoma
6
ADAM17 2p25 CSVP, TACE, NISBD, ADAM18, CD156B, NISBD1 -ADAM17 and Liver Cancer
6
PINX1 8p23 LPTL, LPTS -PINX1 and Hepatocellular Carcinoma
6
SNAI1 20q13.2 SNA, SNAH, SNAIL, SLUGH2, SNAIL1, dJ710H13.1 -SNAI1 and Liver Cancer
6
ATG5 6q21 ASP, APG5, APG5L, hAPG5, APG5-LIKE -ATG5 and Hepatocellular Carcinoma
6
LAPTM4B 8q22.1 LC27, LAPTM4beta -LAPTM4B and Liver Cancer
6
GADD45B 19p13.3 MYD118, GADD45BETA -GADD45B and Hepatocellular Carcinoma
6
MIRLET7G 3p21.1 LET7G, let-7g, MIRNLET7G, hsa-let-7g -MicroRNA let-7g and Liver Cancer
6
MACC1 7p21.1 7A5, SH3BP4L -MACC1 and Liver Cancer
6
S100A6 1q21 2A9, PRA, 5B10, CABP, CACY -S100A6 and Liver Cancer
6
SERPINA1 14q32.1 PI, A1A, AAT, PI1, A1AT, PRO2275, alpha1AT -SERPINA1 and Liver Cancer
6
KRT19 17q21.2 K19, CK19, K1CS -KRT19 and Liver Cancer
6
SMYD3 1q44 KMT3E, ZMYND1, ZNFN3A1, bA74P14.1 -SMYD3 and Liver Cancer
6
FASN 17q25 FAS, OA-519, SDR27X1 -FASN and Liver Cancer
6
HDGF 1q23.1 HMG1L2 -HDGF and Liver Cancer
6
CD81 11p15.5 S5.7, CVID6, TAPA1, TSPAN28 -CD81 and Liver Cancer
6
SATB1 3p23 -SATB1 and Hepatocellular Carcinoma
5
ABCA1 9q31.1 TGD, ABC1, CERP, ABC-1, HDLDT1 -ABCA1 and Liver Cancer
5
MAGEA4 Xq28 CT1.4, MAGE4, MAGE4A, MAGE4B, MAGE-41, MAGE-X2 -MAGEA4 and Liver Cancer
5
TCF3 19p13.3 E2A, E47, ITF1, VDIR, TCF-3, bHLHb21 -TCF3 and Liver Cancer
5
RALGDS 9q34.3 RGF, RGDS, RalGEF -RALGDS and Liver Cancer
5
USF1 1q22-q23 UEF, FCHL, MLTF, FCHL1, MLTFI, HYPLIP1, bHLHb11 -USF1 and Liver Cancer
5
SULF1 8q13.2 SULF-1, HSULF-1 -SULF1 and Liver Cancer
5
GAGE1 Xp11.23 CT4.1, GAGE-1 -GAGE1 and Liver Cancer
5
WNT3 17q21 INT4, TETAMS -WNT3 and Liver Cancer
5
MST1 3p21 MSP, HGFL, NF15S2, D3F15S2, DNF15S2 -MST1 and Liver Cancer
5
PROX1 1q41 -PROX1 and Liver Cancer
5
LRP6 12p13.2 ADCAD2 -LRP6 and Liver Cancer
5
ANGPT1 8q23.1 AGP1, AGPT, ANG1 -ANGPT1 and Liver Cancer
5
ARID2 12q12 p200, BAF200 -ARID2 and Liver Cancer
5
HTATIP2 11p15.1 CC3, TIP30, SDR44U1 -HTATIP2 and Liver Cancer
5
AGO2 8q24 Q10, EIF2C2 -EIF2C2 and Hepatocellular Carcinoma
5
LAMC2 1q25-q31 B2T, CSF, EBR2, BM600, EBR2A, LAMB2T, LAMNB2 -LAMC2 and Liver Cancer
5
RPS6 9p21 S6 -RPS6 and Liver Cancer
5
MIR122 18q21.31 MIR122A, MIRN122, MIRN122A, miRNA122, miRNA122A, hsa-mir-122 -MIR122 and Liver Cancer
5
IQGAP1 15q26.1 SAR1, p195, HUMORFA01 -IQGAP1 and Liver Cancer
5
SFRP5 10q24.1 SARP3 -SFRP5 and Liver Cancer
5
MICB 6p21.3 PERB11.2 -MICB and Liver Cancer
5
RASSF5 1q32.1 RAPL, Maxp1, NORE1, NORE1A, NORE1B, RASSF3 -RASSF5 and Liver Cancer
5
BCL2L2 14q11.2-q12 BCLW, BCL-W, PPP1R51, BCL2-L-2 -BCL2L2 and Liver Cancer
5
CHUK 10q24-q25 IKK1, IKKA, IKBKA, TCF16, NFKBIKA, IKK-alpha -CHUK and Hepatocellular Carcinoma
5
PER3 1p36.23 GIG13 -PER3 and Liver Cancer
5
ATG7 3p25.3 GSA7, APG7L, APG7-LIKE -ATG7 and Liver Cancer
5
PAK4 19q13.2 -PAK4 and Liver Cancer
5
LYVE1 11p15 HAR, XLKD1, LYVE-1, CRSBP-1 -LYVE1 and Liver Cancer
5
IRF2 4q34.1-q35.1 IRF-2 -IRF2 and Hepatocellular Carcinoma
5
ATF2 2q32 HB16, CREB2, TREB7, CREB-2, CRE-BP1 -ATF2 and Hepatocellular Carcinoma
5
FZD7 2q33 FzE3 -FZD7 and Liver Cancer
4
CCNC 6q21 CycC -CCNC and Liver Cancer
4
IL12A 3q25.33 P35, CLMF, NFSK, NKSF1, IL-12A -IL12A and Liver Cancer
4
ATP7B 13q14.3 WD, PWD, WC1, WND -ATP7B and Liver Cancer
4
ADAR 1q21.3 DSH, AGS6, G1P1, IFI4, P136, ADAR1, DRADA, DSRAD, IFI-4, K88DSRBP -ADAR and Liver Cancer
4
MIR1301 2 MIRN1301, mir-1301, hsa-mir-1301 -MicroRNA miR-1301and Liver Cancer
4
B2M 15q21.1 -B2M and Liver Cancer
4
EREG 4q13.3 ER -EREG and Liver Cancer
4
AIFM1 Xq26.1 AIF, CMT2D, CMTX4, COWCK, NADMR, NAMSD, PDCD8, COXPD6 -AIFM1 and Liver Cancer
4
CD151 11p15.5 GP27, MER2, RAPH, SFA1, PETA-3, TSPAN24 -CD151 and Liver Cancer
4
SPINK1 5q32 TCP, PCTT, PSTI, TATI, Spink3 -SPINK1 and Liver Cancer
4
CKS2 9q22 CKSHS2 -CKS2 and Liver Cancer
4
EFEMP1 2p16 DHRD, DRAD, FBNL, MLVT, MTLV, S1-5, FBLN3, FIBL-3 -EFEMP1 and Liver Cancer
4
CYP2B6 19q13.2 CPB6, EFVM, IIB1, P450, CYP2B, CYP2B7, CYP2B7P, CYPIIB6 -CYP2B6 and Hepatocellular Carcinoma
4
CDC6 17q21.3 CDC18L, HsCDC6, HsCDC18 -CDC6 and Liver Cancer
4
ATF6 1q23.3 ATF6A -ATF6 and Liver Cancer
4
ADARB1 21q22.3 RED1, ADAR2, DRABA2, DRADA2 -ADARB1 and Liver Cancer
4
TDGF1 3p21.31 CR, CRGF, CRIPTO -TDGF1 and Liver Cancer
4
STAT4 2q32.2-q32.3 SLEB11 -STAT4 and Liver Cancer
4
CCR6 6q27 BN-1, DCR2, DRY6, CCR-6, CD196, CKRL3, GPR29, CKR-L3, CMKBR6, GPRCY4, STRL22, CC-CKR-6, C-C CKR-6 -CCR6 and Liver Cancer
4
CDH17 8q22.1 HPT1, CDH16, HPT-1 -CDH17 and Liver Cancer
4
LDLR 19p13.2 FH, FHC, LDLCQ2 -LDLR and Liver Cancer
4
KIAA1524 3q13.13 p90, CIP2A -KIAA1524 and Hepatocellular Carcinoma
4
CD46 1q32 MCP, TLX, AHUS2, MIC10, TRA2.10 -CD46 and Liver Cancer
4
COL1A2 7q22.1 OI4 -COL1A2 and Liver Cancer
4
FOSB 19q13.32 AP-1, G0S3, GOS3, GOSB -FOSB and Liver Cancer
4
PIM2 Xp11.23 -PIM2 and Liver Cancer
4
NDRG2 14q11.2 SYLD -NDRG2 and Liver Cancer
4
XBP1 22q12.1 XBP2, TREB5, XBP-1, TREB-5 -XBP1 and Liver Cancer
4
BTG2 1q32 PC3, TIS21 -BTG2 and Liver Cancer
4
XAF1 17p13.1 BIRC4BP, XIAPAF1, HSXIAPAF1 -XAF1 and Liver Cancer
4
TGFB2 1q41 LDS4, TGF-beta2 -TGFB2 and Liver Cancer
4
LOXL2 8p21.3 LOR2, WS9-14 -LOXL2 and Liver Cancer
3
RXRA 9q34.3 NR2B1 -RXRA and Liver Cancer
3
HHIP 4q28-q32 HIP -HHIP and Liver Cancer
3
TBX3 12q24.21 UMS, XHL, TBX3-ISO -TBX3 and Liver Cancer
3
YWHAZ 8q23.1 HEL4, YWHAD, KCIP-1, HEL-S-3, 14-3-3-zeta -YWHAZ and Liver Cancer
3
FEN1 11q12 MF1, RAD2, FEN-1 -FEN1 and Liver Cancer
3
ZBTB7A 19p13.3 LRF, FBI1, FBI-1, ZBTB7, ZNF857A, pokemon -ZBTB7A and Liver Cancer
3
NR0B2 1p36.1 SHP, SHP1 -NR0B2 and Liver Cancer
3
SMAD6 15q22.31 AOVD2, MADH6, MADH7, HsT17432 -SMAD6 and Liver Cancer
3
TLR7 Xp22.3 TLR7-like -TLR7 and Liver Cancer
3
PTMS 12p13 ParaT -PTMS and Liver Cancer
3
CCR1 3p21 CKR1, CD191, CKR-1, HM145, CMKBR1, MIP1aR, SCYAR1 -CCR1 and Hepatocellular Carcinoma
3
TNFRSF6B 20q13.3 M68, TR6, DCR3, M68E, DJ583P15.1.1 -TNFRSF6B expression in Hepatocellular Carcinoma (HCC)
3
RAD23B 9q31.2 P58, HR23B, HHR23B -RAD23B and Hepatocellular Carcinoma
3
IL23R 1p31.3 -IL23R and Liver Cancer
3
ING2 4q35.1 ING1L, p33ING2 -ING2 and Liver Cancer
3
DGCR8 22q11.2 Gy1, pasha, DGCRK6, C22orf12 -DGCR8 and Liver Cancer
3
ADAMTS1 21q21.2 C3-C5, METH1 -ADAMTS1 and Liver Cancer
3
OCLN 5q13.1 BLCPMG, PPP1R115 -OCLN and Liver Cancer
3
COPS5 8q13.1 CSN5, JAB1, SGN5, MOV-34 -COPS5 and Hepatocellular Carcinoma
3
BTRC 10q24.32 FWD1, FBW1A, FBXW1, bTrCP, FBXW1A, bTrCP1, betaTrCP, BETA-TRCP -BTRC and Liver Cancer
3
HLA-E 6p21.3 MHC, QA1, EA1.2, EA2.1, HLA-6.2 -HLA-E and Liver Cancer
3
LDHA 11p15.4 LDH1, LDHM, GSD11, PIG19, HEL-S-133P -LDHA and Liver Cancer
3
IRF9 14q11.2 p48, IRF-9, ISGF3, ISGF3G -IRF9 and Hepatocellular Carcinoma
3
DLEC1 3p21.3 F56, DLC1, CFAP81 -DLEC1 and Liver Cancer
3
LEPR 1p31 OBR, OB-R, CD295, LEP-R, LEPRD -LEPR and Liver Cancer
3
LGALS4 19q13.2 GAL4, L36LBP -LGALS4 and Liver Cancer
3
TNFRSF10C 8p22-p21 LIT, DCR1, TRID, CD263, TRAILR3, TRAIL-R3, DCR1-TNFR -TNFRSF10C and Liver Cancer
3
MAGEB2 Xp21.3 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Liver Cancer
3
CRY2 11p11.2 HCRY2, PHLL2 -CRY2 and Liver Cancer
3
CD276 15q23-q24 B7H3, B7-H3, B7RP-2, 4Ig-B7-H3 -CD276 and Liver Cancer
3
HINT1 5q31.2 HINT, NMAN, PKCI-1, PRKCNH1 -HINT1 and Liver Cancer
3
DDR1 6p21.3 CAK, DDR, NEP, HGK2, PTK3, RTK6, TRKE, CD167, EDDR1, MCK10, NTRK4, PTK3A -DDR1 and Liver Cancer
3
TCF7 5q31.1 TCF-1 -TCF7 and Liver Cancer
3
MBL2 10q11.2 MBL, MBP, MBP1, MBPD, MBL2D, MBP-C, COLEC1, HSMBPC -MBL2 and Hepatocellular Carcinoma
3
EBAG9 8q23 EB9, PDAF -EBAG9 and Liver Cancer
3
BNIP3L 8p21 NIX, BNIP3a -BNIP3L and Hepatocellular Carcinoma
3
PRDX1 1p34.1 PAG, PAGA, PAGB, PRX1, PRXI, MSP23, NKEFA, TDPX2, NKEF-A -PRDX1 and Liver Cancer
3
SOX1 13q34 -SOX1 and Liver Cancer
3
CXCL14 5q31 KEC, KS1, BMAC, BRAK, NJAC, MIP2G, MIP-2g, SCYB14 -CXCL14 and Liver Cancer
3
STARD13 13q13.1 DLC2, GT650, ARHGAP37, LINC00464 -STARD13 and Liver Cancer
3
TRIO 5p15.2 tgat, ARHGEF23 -TRIO and Hepatocellular Carcinoma
3
BAGE 21p11.1 not on ref BAGE1, CT2.1 -BAGE and Liver Cancer
3
IFT88 13q12.1 DAF19, TG737, TTC10, hTg737, D13S1056E -IFT88 and Hepatocellular Carcinoma
3
CEBPD 8p11.2-p11.1 CELF, CRP3, C/EBP-delta, NF-IL6-beta -CEBPD and Hepatocellular Carcinoma
3
TXNIP 1q21.1 THIF, VDUP1, HHCPA78, EST01027 -TXNIP and Liver Cancer
3
SLC9A1 1p36.1-p35 APNH, NHE1, LIKNS, NHE-1, PPP1R143 -SLC9A1 and Liver Cancer
3
UGT2B7 4q13 UGT2B9, UDPGTH2, UDPGT2B7, UDPGT 2B9 -UGT2B7 and Liver Cancer
3
CCL19 9p13 ELC, CKb11, MIP3B, MIP-3b, SCYA19 -CCL19 and Liver Cancer
3
MEG3 14q32 GTL2, FP504, prebp1, PRO0518, PRO2160, LINC00023, NCRNA00023 -MEG3 and Liver Cancer
3
IL12B 5q33.3 CLMF, NKSF, CLMF2, IMD28, IMD29, NKSF2, IL-12B -IL12B and Liver Cancer
3
NR5A2 1q32.1 B1F, CPF, FTF, B1F2, LRH1, LRH-1, FTZ-F1, hB1F-2, FTZ-F1beta -NR5A2 and Liver Cancer
3
TP53INP1 8q22 SIP, Teap, p53DINP1, TP53DINP1, TP53INP1A, TP53INP1B -TP53INP1 and Liver Cancer
3
HOXA13 7p15.2 HOX1, HOX1J -HOXA13 and Liver Cancer
3
ROCK2 2p24 ROCK-II -ROCK2 and Liver Cancer
3
S100A11 1q21 MLN70, S100C, HEL-S-43 -S100A11 and Liver Cancer
3
PPARGC1A 4p15.1 LEM6, PGC1, PGC1A, PGC-1v, PPARGC1, PGC-1(alpha) -PPARGC1A and Liver Cancer
3
CD40 20q12-q13.2 p50, Bp50, CDW40, TNFRSF5 -CD40 and Liver Cancer
3
SALL4 20q13.2 DRRS, HSAL4, ZNF797, dJ1112F19.1 -SALL4 and Liver Cancer
3
NOX4 11q14.2-q21 KOX, KOX-1, RENOX -NOX4 and Liver Cancer
3
GSTO1 10q25.1 P28, SPG-R, GSTO 1-1, GSTTLp28, HEL-S-21 -GSTO1 and Liver Cancer
3
MT1G 16q13 MT1, MT1K -MT1G and Liver Cancer
3
SPRY1 4q28.1 hSPRY1 -SPRY1 and Hepatocellular Carcinoma
3
FTL 19q13.33 LFTD, NBIA3 -FTL and Liver Cancer
2
MIR124-1 8p23.1 MIR124A, MIR124A1, MIRN124-1, MIRN124A1 -microRNA 124-1 and Liver Cancer
2
DMPK 19q13.3 DM, DM1, DMK, MDPK, DM1PK, MT-PK -DMPK and Liver Cancer
2
KRT18 12q13 K18, CYK18 -KRT18 and Liver Cancer
2
HSP90AA1 14q32.33 EL52, HSPN, LAP2, HSP86, HSPC1, HSPCA, Hsp89, Hsp90, LAP-2, HSP89A, HSP90A, HSP90N, HSPCAL1, HSPCAL4 -HSP90AA1 and Liver Cancer
2
CA12 15q22 CAXII, HsT18816 -CA12 and Liver Cancer
2
HAVCR2 5q33.3 TIM3, CD366, KIM-3, TIMD3, Tim-3, TIMD-3, HAVcr-2 -HAVCR2 and Liver Cancer
2
FABP5 8q21.13 EFABP, KFABP, E-FABP, PAFABP, PA-FABP -FABP5 and Liver Cancer
2
DNAJB4 1p31.1 DjB4, HLJ1, DNAJW -DNAJB4 and Liver Cancer
2
EPHA5 4q13.1 EK7, CEK7, EHK1, HEK7, EHK-1, TYRO4 -EPHA5 and Liver Cancer
2
RAC2 22q13.1 Gx, EN-7, HSPC022, p21-Rac2 -RAC2 and Liver Cancer
2
IGF2-AS 11p15.5 PEG8, IGF2AS, IGF2-AS1 -IGF2-AS and Liver Cancer
2
RASAL1 12q23-q24 RASAL -RASAL1 and Liver Cancer
2
MUC7 4q13.3 MG2 -MUC7 and Hepatocellular Carcinoma
2
THBS2 6q27 TSP2 -THBS2 and Hepatocellular Carcinoma
2
SOX6 11p15.3 SOXD, HSSOX6 -SOX6 and Liver Cancer
2
CXCL16 17p13 SRPSOX, CXCLG16, SR-PSOX -CXCL16 and Liver Cancer
2
XRCC6 22q13.2 ML8, KU70, TLAA, CTC75, CTCBF, G22P1 -XRCC6 and Liver Cancer
2
GRASP 12q13.13 TAMALIN -GRASP and Liver Cancer
2
ITGA6 2q31.1 CD49f, VLA-6, ITGA6B -ITGA6 and Liver Cancer
2
PPP1R3A 7q31.1 GM, PP1G, PPP1R3 -PPP1R3A and Liver Cancer
2
GSTO2 10q25.1 GSTO 2-2, bA127L20.1 -GSTO2 and Liver Cancer
2
DDIT4 10q22.1 Dig2, REDD1, REDD-1 -DDIT4 and Liver Cancer
2
ING3 7q31 Eaf4, ING2, MEAF4, p47ING3 -ING3 and Liver Cancer
2
SLC22A18 11p15.5 HET, ITM, BWR1A, IMPT1, TSSC5, ORCTL2, BWSCR1A, SLC22A1L, p45-BWR1A -SLC22A18 and Liver Cancer
2
SAT2 17p13.1 SSAT2 -SAT2 and Liver Cancer
2
EPHA1 7q34 EPH, EPHT, EPHT1 -EPHA1 and Liver Cancer
2
VCAM1 1p32-p31 CD106, INCAM-100 -VCAM1 and Liver Cancer
2
TRIM24 7q32-q34 PTC6, TF1A, TIF1, RNF82, TIF1A, hTIF1, TIF1ALPHA -TRIM24 and Liver Cancer
2
PPIA 7p13 CYPA, CYPH, HEL-S-69p -PPIA and Liver Cancer
2
TP53BP2 1q41 BBP, 53BP2, ASPP2, P53BP2, PPP1R13A -TP53BP2 and Liver Cancer
2
HTRA2 2p12 OMI, PARK13, PRSS25 -HTRA2 and Liver Cancer
2
YWHAE 17p13.3 MDS, HEL2, MDCR, KCIP-1, 14-3-3E -YWHAE and Liver Cancer
2
KRT8 12q13 K8, KO, CK8, CK-8, CYK8, K2C8, CARD2 -KRT8 and Liver Cancer
2
HSPA1B 6p21.3 HSP70-2, HSP70-1B -HSPA1B and Hepatocellular Carcinoma
2
PDCD5 19q13.11 TFAR19 -PDCD5 and Liver Cancer
2
LIPA 10q23.2-q23.3 LAL, CESD -LIPA and Liver Cancer
2
CCR3 3p21.3 CKR3, CD193, CMKBR3, CC-CKR-3 -CCR3 and Liver Cancer
2
LTBR 12p13 CD18, TNFCR, TNFR3, D12S370, TNFR-RP, TNFRSF3, TNFR2-RP, LT-BETA-R, TNF-R-III -LTBR and Liver Cancer
2
GMNN 6p22.3 Gem -GMNN and Liver Cancer
2
CSE1L 20q13 CAS, CSE1, XPO2 -CSE1L and Liver Cancer
2
MERTK 2q14.1 MER, RP38, c-Eyk, c-mer, Tyro12 -MERTK and Liver Cancer
2
MIR1271 5q35 MIRN1271, hsa-mir-1271 -MicroRNA miR-1271 and Liver Cancer
2
PRSS1 7q34 TRP1, TRY1, TRY4, TRYP1 -PRSS1 and Liver Cancer
2
ANXA7 10q22.2 SNX, ANX7, SYNEXIN -ANXA7 and Liver Cancer
2
MKL1 22q13 MAL, BSAC, MRTF-A -MKL1 and Liver Cancer
2
ZFP36 19q13.1 TTP, G0S24, GOS24, TIS11, NUP475, zfp-36, RNF162A -ZFP36 and Liver Cancer
2
IL24 1q32 C49A, FISP, MDA7, MOB5, ST16, IL10B -IL24 and Liver Cancer
1
KTN1 14q22.1 CG1, KNT, MU-RMS-40.19 -KTN1 and Liver Cancer
1
NOV 8q24.1 CCN3, NOVh, IBP-9, IGFBP9, IGFBP-9 -NOV and Liver Cancer
1
ERC1 12p13.3 ELKS, Cast2, ERC-1, RAB6IP2 -ERC1 and Liver Cancer
1
MIR125A 19q13.41 MIRN125A, miRNA125A -MIR125A and Liver Cancer
1
GOLGA5 14q32.12 RFG5, GOLIM5, ret-II -GOLGA5 and Liver Cancer
1
SEPP1 5q31 SeP, SELP, SEPP -SEPP1 and Hepatocellular Carcinoma
MYBL2 20q13.1 BMYB, B-MYB -MYBL2 and HCC

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

Latest Publications

Wang D, Zhai JX, Zhang LM, et al.
[EPHX1 Tyr113His polymorphism contributes to hepatocellular carcinoma risk: evidfnce from a meta-analysis].
Mol Biol (Mosk). 2015 Mar-Apr; 49(2):351-61 [PubMed] Related Publications
To clarify the association between microsomal epoxide hydrolase gene (EPHX1) Tyr113His polymorphism and hepatocellular carcinoma (HCC) risk, a meta-analysis was performed. Overall, EPHX1 Tyr113His polymorphism was associated with increased risk of HCC. Subgroup analyses by status of Hardy-Weinberg equilibrium (HWE) in controls further confirmed this association. Through a literature search, 119 relevant records were identified, and 17 individual case-control studies from 13 publications were finally included, involving a total of 1,480 HCC cases and 2,564 controls. In subgroup analyses, increased associations were found in Asians, Caucasians, hepatitis B virus (HBV)- dominant areas, hepatitis C virus (HCV)-dominant areas, high-rate areas of HCC, and medium-rate areas of HCC, but not in Africans and low-rate areas of HCC, respectively. This meta-analysis suggests that EPHX1 Tyr113His polymorphism contributes to HCC risk.

Muresan M, Zaharie F, Bojan A, et al.
MicroRNAs in liver malignancies. Basic science applied in surgery.
J BUON. 2015 Mar-Apr; 20(2):361-75 [PubMed] Related Publications
Liver malignancies represent one of the major public health problems worldwide because of late diagnosis and failure of current treatments to offer a curative option for many of the patients. MicroRNAs (miRs) are small non-coding RNA molecules that are known to regulate the gene expression at a post-transcriptional level through complementary base pairing with thousands of messenger (m)RNAs. Recent data has shown the involvement of miRs in the pathogenesis of many human cancers, including those of the liver, with huge possible impact in the clinic, mainly due to the identification of non-coding RNAs as biomarkers that can often be detected in the systemic circulation. In the current review, we present the importance of miRs in liver cancers by discussing their role in the pathobiology of these diseases, apart from their role as diagnostic and prognostic markers for liver malignancies.

Wang D, Tan J, Xu Y, et al.
Identification of MicroRNAs and target genes involvement in hepatocellular carcinoma with microarray data.
Hepatogastroenterology. 2015 Mar-Apr; 62(138):378-82 [PubMed] Related Publications
The aim of the study is to identify the differentially expressed microRNAs (miRNAs) between hepatocellular carcinoma (HCC) samples and controls and provide new diagnostic potential miRNAs for HCC. The miRNAs expression profile data GSE20077 included 7 HCC samples, 1 HeLa sample and 3 controls. Differentially expressed miRNAs (DE-miRNAs) were identified by t-test and wilcox test. The miRNA with significantly differential expression was chosen for further analysis. Target genes for this miRNA were selected using TargetScan and miRbase database. STRING software was applied to construct the target genes interaction network and topology analysis was carried out to identify the hub gene in the network. And we identified the mechanism for affecting miRNA function. A total of 54 differentially expressed miRNAs were identified, in which there were 13 miRNAs published to be related to HCC. The differentially expressed hsa-miR-106b was chosen for further analysis and PTPRT (Receptor-type tyrosine-protein phosphatase T) was its potential target gene. The target genes interaction network was constructed among 33 genes, in which PTPRT was the hub gene. We got the conclusion that the differentially expressed hsa-miR-106b may play an important role in the development of HCC by regulating the expression of its potential target gene PT-PRT.

Hu B, Jiang D, Chen Y, et al.
High CHMP4B expression is associated with accelerated cell proliferation and resistance to doxorubicin in hepatocellular carcinoma.
Tumour Biol. 2015; 36(4):2569-81 [PubMed] Related Publications
Charged multivesicular body protein 4B (CHMP4B), a subunit of the endosomal sorting complex required for transport (ESCRT)-III complex, plays an important part in cytokinetic membrane abscission and the late stage of mitotic cell division. In this study, we explored the prognostic significance of CHMP4B in human hepatocellular carcinoma (HCC) and its impact on the physiology of HCC cells. Western blot and immunohistochemistrical analyses showed that CHMP4B was significantly upregulated in HCC tissues, compared with adjacent non-tumorous tissues. Meanwhile, clinicopathological analysis revealed that high CHMP4B expression was correlated with multiple clinicopathological variables, including AFP, cirrhosis, AJCC stage, Ki-67 expression, and poor prognosis. More importantly, univariate and multivariate survival analyses demonstrated that CHMP4B served as an independent prognostic factor for survival of HCC patients. Using HCC cell cultures, we found that the expression of CHMP4B was progressively upregulated after the release from serum starvation. To verify whether CHMP4B could regulate the proliferation of HCC cells, CHMP4B was knocked down through the transfection of CHMP4B-siRNA oligos. Flow cytometry and CCK-8 assays indicated that interference of CHMP4B led to cell cycle arrest and proliferative impairment of HCC cells. Additionally, depletion of CHMP4B expression could increase the sensitivity to doxorubicin in HepG2 and Huh7 cells. Taken together, our results implied that CHMP4B could be a promising prognostic biomarker as well as a potential therapeutic target of HCC.

Liu Y, Xie L, Zhao J, et al.
Association between catalase gene polymorphisms and risk of chronic hepatitis B, hepatitis B virus-related liver cirrhosis and hepatocellular carcinoma in Guangxi population: a case-control study.
Medicine (Baltimore). 2015; 94(13):e702 [PubMed] Related Publications
Reactive oxygen species (ROS) play critical roles in hepatocarcinogenesis. The catalase (CAT) enzyme is involved in the repair of ROS. Therefore, we investigate the association between CAT gene polymorphisms and the risk of hepatocellular carcinoma (HCC). A total of 715 subjects were divided into 4 groups: 111 chronic hepatitis B (CHB) patients, 90 hepatitis B virus (HBV)-related liver cirrhosis (LC) patients, 266 HBV-HCC patients, and 248 healthy controls. The polymerase chain reaction-restriction fragment length polymorphism strategy was used to detect CAT gene rs1001179, rs769217, and rs7943316 polymorphisms. Binary logistic regression analyses adjusting for sex, age, ethnicity, smoking and alcohol consumption, and body mass index suggested that subjects carrying the rs769217 T allele were at marginally increased risk of CHB, LC, and HCC, with adjusted odds ratios (ORs) of 1.51 (95% confidence interval [CI] = 1.04-2.20, P = 0.029), 1.48 (95% CI = 1.03-2.14, P = 0.035), and 1.51 (95% CI = 1.14-1.98, P = 0.004), respectively. Similarly, those individuals carrying the rs769217 TT genotype had a moderately increased risk of CHB, LC, and HCC, with adjusted ORs of 2.11 (95% CI = 1.05-4.22, P = 0.035), 2.00 (95% CI, 1.01-3.95, P = 0.047), and 1.93 (95% CI = 1.14-3.28, P = 0.015), respectively. Moreover, subjects carrying the rs769217 CT genotype and at least 1 copy of the T allele (dominant model) were 1.78 times and 1.83 times more likely to develop HCC, respectively (OR = 1.78, 95% CI = 1.16-2.73, P = 0.009 and OR = 1.83, 95% CI = 1.23-2.71, P = 0.003). This association between CAT rs769217 T alleles and HCC risk is significantly strengthened among men, nonsmokers, nondrinkers, and among individuals <50 years of age. Furthermore, we found 1 high-risk haplotype GTA for CHB (OR = 1.45, 95% CI = 1.05-2.01) and 1 protective haplotype GCA for HCC risk (OR = 0.67, 95% CI = 0.52-0.87). We did not found any significant difference in CAT rs1001179 and rs7943316 polymorphisms between controls and cases. Our findings suggest that the CAT rs769217 T allele is associated with increased risk of CHB, HBV-LC, and HBV-HCC in Guangxi population.

Schulze K, Imbeaud S, Letouzé E, et al.
Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets.
Nat Genet. 2015; 47(5):505-11 [PubMed] Related Publications
Genomic analyses promise to improve tumor characterization to optimize personalized treatment for patients with hepatocellular carcinoma (HCC). Exome sequencing analysis of 243 liver tumors identified mutational signatures associated with specific risk factors, mainly combined alcohol and tobacco consumption and exposure to aflatoxin B1. We identified 161 putative driver genes associated with 11 recurrently altered pathways. Associations of mutations defined 3 groups of genes related to risk factors and centered on CTNNB1 (alcohol), TP53 (hepatitis B virus, HBV) and AXIN1. Analyses according to tumor stage progression identified TERT promoter mutation as an early event, whereas FGF3, FGF4, FGF19 or CCND1 amplification and TP53 and CDKN2A alterations appeared at more advanced stages in aggressive tumors. In 28% of the tumors, we identified genetic alterations potentially targetable by US Food and Drug Administration (FDA)-approved drugs. In conclusion, we identified risk factor-specific mutational signatures and defined the extensive landscape of altered genes and pathways in HCC, which will be useful to design clinical trials for targeted therapy.

Liu T, Zhang X, Sha K, et al.
miR-709 up-regulated in hepatocellular carcinoma, promotes proliferation and invasion by targeting GPC5.
Cell Prolif. 2015; 48(3):330-7 [PubMed] Related Publications
OBJECTIVES: Hepatocellular carcinoma (HCC) is one of the most common cancers and is a significant leading cause of cancer-related deaths worldwide. Emerging evidence has shown that microRNAs (miRNAs) are associated with cancer development and progression. However, up to now little has been known concerning the role of miR-709 in HCC.
MATERIALS AND METHODS: Real-time RT-PCR was performed to detect expression of miR-709 in HCC cell lines and tissues. To further understand its role in HCC, we restored its expression in HepG2 cell line through transfection with miR-709 mimics or inhibitors. CCK-8 proliferation assay, migration assay and invasion assay were used to detect functional roles of miR-709. Luciferase assay and western blotting were performed to detect the target gene of miR-709.
RESULTS: We found that miR-709 was highly expressed in HCC tissues and in HCC cell lines by qRT-PCR. Re-expression of miR-709 in HCC cells remarkably promoted cell migration and invasiveness in vitro. Subsequent investigation revealed that glypican-5 (GPC5) was a direct and functional target of miR-709 in HCC cells where overexpression of miR-709 impaired GPC5-induced inhibition of proliferation and invasion. Finally, analysis of miR-709 and GPC5 levels in human HCC tissues revealed that miR-709 inversely correlated with GPC5 expression.
CONCLUSIONS: These results suggest that miR-709 may positively regulate invasion and metastasis of HCC through targeting GPC5.

Song Y, Wang F, Huang Q, et al.
MicroRNAs Contribute to Hepatocellular Carcinoma.
Mini Rev Med Chem. 2015; 15(6):459-66 [PubMed] Related Publications
Hepatocellular carcinoma is a leading unnatural death worldwide, and it causes second most common cancer related death. Hepatocellular carcinoma development is distinct from other types of cancer, which is usually based on hepatic cirrhosis resulted from various etiologies including viral hepatitis, non-alcoholic liver diseases and alcohol abuse. MicroRNAs (miRNAs) are a group of small, non-coding sequences with approximate 20~ bp in length, which post-transcriptionally regulates target genes to control multiple biological activities. Recent studies have indicated that miRNAs contribute to hepatocellular carcinoma, indicating that targeting miRNAs might be a novel therapeutic strategy for the management of hepatocellular carcinoma. In this review, we summarized recent advances in the role of miRNAs in hepatocellular carcinoma, and also discussed the potential therapeutic and prognostic values of miRNAs in hepatocellular carcinoma.

Liang J, Lv J, Liu Z
Identification of dysfunctional biological pathways and their synergistic mechanism in hepatocellular carcinoma process.
Exp Mol Pathol. 2015; 98(3):540-5 [PubMed] Related Publications
BACKGROUND: Hepatocellular carcinoma (HCC) is a lethal and prevalent cancer worldwide. This study was conducted to investigate dysfunctional pathways and their synergistic mechanism in the HCC process.
METHODS: We downloaded transcriptome profiling data (GSE25097) from the Gene Expression Omnibus (GEO) database, including 6 healthy liver samples, 40 cirrhosis samples, 243 adjacent non-tumor samples, and 268 HCC samples. Robust Multi-Array (RMA) in R software was employed to preprocess the downloaded dataset, and Student's t-test (FDR less than 0.001) was performed to identify the differentially expressed genes (DEGs) between 4 sample groups. Then, pathway enrichment analysis (FDR less than 0.05) based on iSubpathwayMiner was performed. Furthermore, we performed collaborative analysis on these pathways through calculating the Jaccard index, and crosstalk networks were constructed and visualized by Cytoscape.
RESULTS: Totally, 4617, 9517, and 12,479 DEGs were identified between healthy liver and cirrhosis samples, cirrhosis and adjacent non-tumor samples, and adjacent non-tumor and HCC samples, respectively. Furthermore, a total of 26 crosstalks involving 13 pathways, 78 crosstalks involving 54 pathways, and 86 crosstalks involving 52 pathways were identified through the DEGs between healthy liver and cirrhosis samples, cirrhosis and adjacent non-tumor samples, and adjacent non-tumor and HCC samples, respectively. Moreover, 5 dysfunctional pathways were found to co-exist in the three processes of HCC. Among them, 3 dysfunctional pathways have collaborative relationship, including Staphylococcus aureus infection, leishmaniasis, and Chagas disease.
CONCLUSIONS: In this study, dysfunctional pathways in the HCC process and crosstalks between these pathways were investigated for the first time, providing new insight into the potential mechanisms of HCC.

Liu F, Luo LM, Wei YG, et al.
Polymorphisms of the CYP1B1 gene and hepatocellular carcinoma risk in a Chinese population.
Gene. 2015; 564(1):14-20 [PubMed] Related Publications
BACKGROUND: CYP1B1 is a P450 enzyme which is involved in the activation of pro-carcinogens to carcinogens as well as estrogen metabolism. We hypothesized that genetic variants in CYP1B1 may modify individual susceptibility to hepatocellular carcinoma (HCC).
METHODS: To test this hypothesis, we evaluated the associations of three CYP1B1 single nucleotide polymorphisms (SNPs) and HCC risk in a case-control study of 468 HCC cases and 515 cancer-free controls in a Chinese population. The matrix-assisted laser desorption ionization time-of-flight mass spectrometry method and direct DNA sequencing were performed to detect these polymorphisms.
RESULTS: In overall analysis, we found that only the variant G allele of rs1056836 was associated with a significantly increased risk of HCC among the three SNPs (rs10012, rs1056836 and rs1800440). Moreover, we found that the variant genotypes containing the G allele of rs1056836 were associated with a significantly increased risk of HCC among HbsAg-positive individuals (adjusted OR=2.13, 95% CI=1.18, 3.86), but not among HbsAg-negative individuals. When stratifying by smoking status, we found that the variant GG genotype increased a 13.97-fold (95% CI=1.28, 152.94) risk of HCC among smokers. Furthermore, high risk for liver cirrhosis-positive clinical status was exhibited in HCC patients with rs1056836 CG and GG genotypes as compared with CC homozygotes. For the other two SNPs, we did not find any significant evidence of association with HCC risk in any subgroup.
CONCLUSION: This study suggests that CYP1B1 rs1056836 polymorphism may be an important factor contributing to increased susceptibility and pathological development of HCC in Chinese population.

Sun H, Deng Q, Pan Y, et al.
Association between estrogen receptor 1 (ESR1) genetic variations and cancer risk: a meta-analysis.
J BUON. 2015 Jan-Feb; 20(1):296-308 [PubMed] Related Publications
PURPOSE: Emerging published reports on the association between estrogen receptor 1 (ESR1) genetic variation and cancer susceptibility are inconsistent. This review and meta- analysis was performed to achieve a more precise evaluation of this relationship.
METHODS: A literature search of PubMed database was conducted from the inception of this study through April 1st 2014. Crude odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated to assess the association.
RESULTS: 87 studies were enrolled in this meta-analysis. The results indicated that PvuII (T>C) polymorphism was associated with an increased risk of hepatocellular carcinoma (HCC) and prostate cancer, in contrast with the decreased risk of gallbladder cancer. No significant association was found in Asian and Caucasian populations. Furthermore, XbaI (A>G) genetic variation was only associated with an increased risk of prostate cancer, but was not related with race. In addition, T594T (G>A) polymorphisms were significantly associated with an increased risk of cancer, especially in Asian populations.
CONCLUSIONS: This meta-analysis indicated that PvuII (T>C) genetic variation may be risk factor for HCC, prostate cancer and gallbladder cancer. Meanwhile, XbaI (A>G) polymorphism may be potential prognostic factor for prostate cancer. Furthermore, T594T (G>A) was closely related with cancer susceptibility, especially in Asian populations.

Siyar Ekinci A, Demirci U, Cakmak Oksuzoglu B, et al.
KRAS discordance between primary and metastatic tumor in patients with metastatic colorectal carcinoma.
J BUON. 2015 Jan-Feb; 20(1):128-35 [PubMed] Related Publications
PURPOSE: Adding targeted therapies to chemotherapy in metastatic colorectal cancer (CRC) improves response rates and survival. KRAS is a predictive indicator for anti-epidermal growth factor receptor (EGFR) treatments. The most important reasons for KRAS discordance are intratumoral heterogeneity and incorrect mutation analysis. Evaluating the status of KRAS in primary and metastatic lesions becomes even more crucial to ensure efficient usage of anti-EGFR treatments.
METHODS: Patients with metastatic CRC, whose primary disease and liver and/or lung metastases were operated, were retrospectively evaluated, and KRAS assessment was performed on 31 patients who were suitable for DNA analysis. Pyrosequencing with polymerase chain reaction (PCR) was used for KRAS analysis.
RESULTS: The median age of 31 patients diagnosed with rectal cancer (N=13) and colon cancer (N=18) was 63 years (range 33-73). Metastasectomy locations included the liver (N=27), lung (N=3), and both lung and liver (N=1). KRAS discordance was detected in 22% (7/31) of the patients. While 3 patients with detected discordance had mutated KRAS in the primary material, wild type KRAS was detected in their liver or lung lesions. On the other hand, while 4 patients had wild type KRAS in the primary material, mutated KRAS was determined in their liver or lung lesions. The McNemar test revealed no significant discordance between primary and metastatic disease (p=1.00). No progression free survival (PFS) difference was detected between patients with determined discordance and patients with undetermined discordance (10.6 vs 14.7 months, p=0.719).
CONCLUSION: This is the first study to evaluate KRAS discordance between primary and metastasis in CRC patients, who underwent metastasectomy, together with survival data. In the literature and recent studies with large patient numbers in which modern KRAS tests were used, the KRAS discordance rate varies between 3-12%. In our study, a higher KRAS discordance (22%) was detected, and no survival difference was determined between patients with or without discordance. In recent years, the rising interest in borderline resectable disease may bring forward discussions related to which material the KRAS status should be analyzed.

Pilat N, Grünberger T, Längle F, et al.
Assessing the TP53 marker type in patients treated with or without neoadjuvant chemotherapy for resectable colorectal liver metastases: a p53 Research Group study.
Eur J Surg Oncol. 2015; 41(5):683-9 [PubMed] Related Publications
The type of a biomarker - whether it is prognostic or predictive - is frequently not known, although such information is crucial for assessing the clinical value of a marker. In order to evaluate the type of marker TP53 is, we identified a cohort of 76 patients with colorectal liver metastases (CLM), homogeneously staged as resectable, who had been treated either with or without fluorouracil-based neoadjuvant chemotherapy. The TP53 genotype was assessed retrospectively from paraffin-embedded, diagnostic tumour biopsies using a standardised, p53 gene-specific sequencing protocol (mark53(®) kit). The overall median survival was 44.2 months, and the overall TP53 mutation frequency was 55%. A significant interaction was observed between chemotherapy and TP53 status (P = 0.045). To illustrate this effect, the 51 patients with and the 25 patients without neoadjuvant chemotherapy were described separately. In patients with neoadjuvant chemotherapy, mutated TP53 was significantly associated with poor survival (P = 0.0025), resulting in five-year survival rates of 22%, compared to 60% in patients with normal TP53. The hazard ratio was 3.12 (95% confidence intervals (CI): 1.46-6.95) to the disadvantage of TP53-mutated patients and 5.49 (P = 0.0001; 95% CI: 2.28-13.24) after adjustment for known prognostic factors. In patients treated with surgery alone, a mutated TP53 did not have a negative effect on survival (P = 0.54). A mutated TP53 status independently predicted survival disadvantage in CLM patients in the presence, but not in the absence, of neoadjuvant chemotherapy. Our data suggest that TP53 might be a pure predictive marker.

Huo X, Zhang S, Li Z, et al.
Analysis of the relationship between microsatellite instability and thymic lymphoma induced by N-methyl-N-nitrosourea in C57BL/6J mice.
Mutat Res. 2015; 771:21-8 [PubMed] Related Publications
Microsatellite instability (MSI) has been found to be closely associated with many types of human tumors and often shows strong correlations with specific tumor features. However, the relationship between MSI and tumors are still unclear. The aim of the present study is to explore the relationships between MSI, tumor formation under the mutagenic effects of N-methyl-N-nitrosourea (MNU). Mice were administered with either MNU (90 mg/kg) or PBS and DMSO (control) at the beginning of the 1st week of the experiment. Of the 31 mice that survived the entire experimental time course, 19 (61.3%) mice developed thymic lymphomas. In addition, 52.6% (10/19) of the tumors had metastasized to the liver. We detected MSI in MNU-treated mice using a panel of 42 mutation-sensitive loci. Nineteen loci (45.2%) in six organs showed 70 MSI events. Locus D8Mit14 showed enhanced MSI compared with the other examined loci. MSI frequency in thymus was higher than in other organs. Interestingly, there was no significant difference observed between the metastatic and non-metastatic livers. The MSI frequency (4.6%, 23/(42×12)) in the MNU-treated thymus that had never developed tumor was significantly higher than this in the thymus that had developed lymphoma (0.5%, 4/(42×19)) (p<0.0001). These results indicate that, although thymic tumorigenesis is associated with MSI, it occurs with higher frequency in these that have not developed tumors upon the MNU-treatment. Our study provides additional insights into the relationship between MSI occurrence and tumorigenesis.

Liu F, Li H, Chang H, et al.
Identification of hepatocellular carcinoma-associated hub genes and pathways by integrated microarray analysis.
Tumori. 2015 Mar-Apr; 101(2):206-14 [PubMed] Related Publications
AIMS AND BACKGROUND: Hepatocellular carcinoma (HCC) is a dismal malignancy associated with multiple molecular changes. The purpose of this study was to identify the differentially expressed genes and analyze the biological processes related to HCC.
METHODS AND STUDY DESIGN: Datasets of HCC were obtained from the NCBI Gene Expression Omnibus. Integrated analysis of differentially expressed genes was performed using the INMEX program. Then Gene Ontology enrichment analyses and pathway analysis were performed based on the Gene Ontology website and Kyoto Encyclopedia of Genes and Genomes. A protein-protein interaction network was constructed using the Cytoscape software; the netwerk served to find hub genes for HCC. Real-time RT-PCR was used to validate the microarray data for hub genes.
RESULTS: We identified 273 genes that were differentially expressed in HCC. Gene Ontology enrichment analyses revealed response to cadmium ion, cellular response to cadmium ion, and cellular response to zinc ion for these genes. Pathway analysis showed that significant pathways included fatty acid metabolism, butanoate metabolism, and PPAR signaling pathway. The protein-protein interaction network indicated that CDH1, ECHS1, ACAA1, MT2A, and MYC were important genes which participated in many interactions. Experimental validation of the role of four upregulated genes (ECHS1, ACAA1, MT2A and MYC) in the progression of HCC was carried out.
CONCLUSIONS: Our study displayed genes that were consistently differentially expressed in HCC. The biological pathways and protein-protein interaction networks associated with those genes were also identified. We predicted that CDH1, ECHS1, ACAA1, MT2A, and MYC might be target genes for diagnosing HCC.

Kato N, Muroyama R, Goto K
[Hepatitis C virus induced hepatocellular carcinoma associated genes].
Nihon Rinsho. 2015; 73(2):333-8 [PubMed] Related Publications
Hepatitis C virus (HCV) infection is a major risk factor for developing hepatocellular carcinoma (HCC). The host genetic factors involved in the development of HCC in patients with HCV infection were investigated. To identify the genetic susceptibility factors for HCV-induced HCC, genome wide association studies (GWAS) were conducted in HCV-induced HCC cases and controls of Japanese origin. Single nucleotide polymorphisms (SNPs) which showed possible association in the GWAS were further genotyped using different cohorts. By these analyses, MICA and DEPDC5 SNPs were found to be strongly associated with HCV-induced HCC. These results highlight the importance of MICA and DEPDC5 genetic variations not only as predictive biomarkers for HCV-induced HCC but also as therapeutic targets against hepatocarcinogenesis or HCC.

Yamada HY, Zhang Y, Reddy A, et al.
Tumor-promoting/progressing role of additional chromosome instability in hepatic carcinogenesis in Sgo1 (Shugoshin 1) haploinsufficient mice.
Carcinogenesis. 2015; 36(4):429-40 [PubMed] Article available free on PMC after 01/04/2016 Related Publications
A major etiological risk factor for hepatocellular carcinoma (HCC) is infection by Hepatitis viruses, especially hepatitis B virus and hepatitis C virus. Hepatitis B virus and hepatitis C virus do not cause aggressive activation of an oncogenic pathway, but they transactivate a broad array of genes, cause chronic inflammation, and, through interference with mitotic processes, lead to mitotic error-induced chromosome instability (ME-CIN). However, how ME-CIN is involved in the development of HCC remains unclear. Delineating the effect of ME-CIN on HCC development should help in identifying measures to combat HCC. In this study, we used ME-CIN model mice haploinsufficient in Shugoshin 1 (Sgo1(-/+)) to assess the role of ME-CIN in HCC development. Treatment with the carcinogen azoxymethane caused Sgo1(-/+) ME-CIN model mice to develop HCCs within 6 months, whereas control mice developed no HCC (P < 0.003). The HCC development was associated with expression of early HCC markers (glutamine synthetase, glypican 3, heat shock protein 70, and the serum marker alpha fetoprotein), although without fibrosis. ME-CIN preceded the expression of HCC markers, suggesting that ME-CIN is an important early event in HCC development. In 12-month-old untreated Sgo1 mice, persistent DNA damage, altered gene expression, and spontaneous HCCs were observed. Sgo1 protein accumulated in response to DNA damage in vitro. Overall, Sgo1(-/+)-mediated ME-CIN strongly promoted/progressed development of HCC in the presence of an initiator carcinogen, and it had a mild initiator effect by itself. Use of the ME-CIN model mice should help in identifying drugs to counteract the effects of ME-CIN and should accelerate anti-HCC drug development.

Yang X, Xie X, Xiao YF, et al.
The emergence of long non-coding RNAs in the tumorigenesis of hepatocellular carcinoma.
Cancer Lett. 2015; 360(2):119-24 [PubMed] Related Publications
Hepatocellular carcinoma (HCC) is the third cause of cancer-related death worldwide. However, the treatments for HCC are limited, and most of them are only available to the early stage. In the later stages, traditional chemotherapy has only marginal effects and may include toxicity. Thus, the identification of new predictive markers is urgently needed. New targets for non-conventional treatments will help to accelerate research on the molecular pathogenesis of HCC. A new class of transcripts, long non-coding RNAs (lncRNAs), has recently been found to be pervasively transcribed in the human genome. Aberrant expression of several lncRNAs was found to be involved in the tumorigenesis of HCC. In this review, we describe the possible molecular mechanisms that underlie lncRNA expression changes in HCC, as well as potential future applications of lncRNA research in the diagnosis and treatment of HCC.

Zhang S, Li J, Yin ZY, et al.
Expression pattern and clinicopathologic significance of NKD1 in human primary hepatocellular carcinoma.
APMIS. 2015; 123(4):315-20 [PubMed] Related Publications
It has been reported that NKD1 was an antagonist of the canonical Wnt/β-catenin pathway. While there is little information regarding NKD1 expression pattern in human hepatocellular carcinoma (HCC). The aim of this study was to investigate the clinicopathologic significance and expression pattern of NKD1 in HCC. NKD1 protein expressions in 69 paired HCC cancer/adjacent non-cancerous tissues were detected by Western blot. Immunohistochemical studies were performed on 58 cases of HCC with integrated clinical information. NKD1 protein expression was divided into normal and low expression group and correlations between NKD1 protein expression and clinicopathologic factors were then evaluated. Western blot results showed that NKD1 protein levels were significantly lower in cancerous tissues compared with corresponding normal tissue (p < 0.05). In addition, we found that the level of NKD1 protein expression in HCC was significantly associated with tumor size (p = 0.011), intra or extra-hepatic metastasis (p = 0.010) and differentiation (p = 0.003). This is to our knowledge the first report investigating NKD1 protein expression pattern in HCC. Our data show that decreased NKD1 protein expression is associated with clinicopathologic factors, and suggest that NKD1 may play an important role in the development of HCC and could serve as a novel biomarker for HCC after further investigation.

Ye G, Qin Y, Lu X, et al.
The association of renin-angiotensin system genes with the progression of hepatocellular carcinoma.
Biochem Biophys Res Commun. 2015; 459(1):18-23 [PubMed] Related Publications
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Angiogenesis is reported to play a pivotal role in the occurrence, development and metastasis of HCC. The renin-angiotensin system (RAS) is involved in the regulation of angiogenesis. Here, based on the analysis of HCC datasets from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA), we found that there was a negative correlation between the mRNA levels of angiotensin converting enzyme 2 (ACE2) and CD34. To explore the association of RAS with the progression from fibrosis to cirrhosis to HCC, liver specimens and serum samples were collected from patients with hepatic fibrosis, cirrhosis and HCC. Relative hepatic mRNA levels of CD34 and ACE2 were determined by real-time PCR, and the serum concentrations of Angiotensin II (Ang II), Ang (1-7) and vascular endothelial growth factor (VEGF) were detected by ELISA. We found that ACE2 mRNA was gradually decreased, while CD34 mRNA was progressively increased with the increasing grade of disease severity. Concentrations of Ang II, Ang (1-7) and VEGF were higher in the sera of patients than in that of healthy volunteers. These proteins' concentrations were also progressively increased with the increasing grade of disease severity. Moreover, a positive correlation was found between VEGF and Ang II or Ang (1-7), while negative correlation was observed between mRNA levels of CD34 and ACE2. More importantly, patients with higher level of ACE2 expression had longer survival time than those with lower level of ACE2 expression. Taken together, our data suggests that the low expression of ACE2 may be a useful indicator of poor prognosis in HCC. The RAS may have a role in the progression of HCC.

Kikuchi A, Monga SP
PDGFRα in liver pathophysiology: emerging roles in development, regeneration, fibrosis, and cancer.
Gene Expr. 2015; 16(3):109-27 [PubMed] Article available free on PMC after 01/04/2016 Related Publications
Platelet-derived growth factor receptor α (PDGFRα) is an isoform of the PDGFR family of tyrosine kinase receptors involved in cell proliferation, survival, differentiation, and growth. In this review, we highlight the role of PDGFRα and the current evidence of its expression and activities in liver development, regeneration, and pathology-including fibrosis, cirrhosis, and liver cancer. Studies elucidating PDGFRα signaling in processes ranging from profibrotic signaling, angiogenesis, and oxidative stress to epithelial-to-mesenchymal transition point toward PDGFRα as a potential therapeutic target in various hepatic pathologies, including hepatic fibrosis and liver cancer. Furthermore, PDGFRα localization and modulation during liver development and regeneration may lend insight into its potential roles in various pathologic states. We will also briefly discuss some of the current targeted treatments for PDGFRα, including multi receptor tyrosine kinase inhibitors and PDGFRα-specific inhibitors.

Zheng F, Liao YJ, Cai MY, et al.
Systemic delivery of microRNA-101 potently inhibits hepatocellular carcinoma in vivo by repressing multiple targets.
PLoS Genet. 2015; 11(2):e1004873 [PubMed] Article available free on PMC after 01/04/2016 Related Publications
Targeted therapy based on adjustment of microRNA (miRNA)s activity takes great promise due to the ability of these small RNAs to modulate cellular behavior. However, the efficacy of miR-101 replacement therapy to hepatocellular carcinoma (HCC) remains unclear. In the current study, we first observed that plasma levels of miR-101 were significantly lower in distant metastatic HCC patients than in HCCs without distant metastasis, and down-regulation of plasma miR-101 predicted a worse disease-free survival (DFS, P<0.05). In an animal model of HCC, we demonstrated that systemic delivery of lentivirus-mediated miR-101 abrogated HCC growth in the liver, intrahepatic metastasis and distant metastasis to the lung and to the mediastinum, resulting in a dramatic suppression of HCC development and metastasis in mice without toxicity and extending life expectancy. Furthermore, enforced overexpression of miR-101 in HCC cells not only decreased EZH2, COX2 and STMN1, but also directly down-regulated a novel target ROCK2, inhibited Rho/Rac GTPase activation, and blocked HCC cells epithelial-mesenchymal transition (EMT) and angiogenesis, inducing a strong abrogation of HCC tumorigenesis and aggressiveness both in vitro and in vivo. These results provide proof-of-concept support for systemic delivery of lentivirus-mediated miR-101 as a powerful anti-HCC therapeutic modality by repressing multiple molecular targets.

Gao K, Xu C, Jin X, et al.
HDGF-related protein-2 (HRP-2) acts as an oncogene to promote cell growth in hepatocellular carcinoma.
Biochem Biophys Res Commun. 2015; 458(4):849-55 [PubMed] Related Publications
HDGFRP2 (HRP-2) belongs to the Hepatoma-derived growth factor (HDGF)-related proteins (HRPs) family, which are characterized by a conserved HATH/PWWP domain at a well-conserved region of the N-terminus. However, the cellular function of HRP-2 remains unknown. In this study, we showed for the first time that HRP-2 is frequently overexpressed in human HCC tissues at mRNA and protein levels. We further showed that HRP-2 can promote HCC cells growth in vitro and xenograft tumors in vivo. Using protein affinity purification methods, we searched for functional partners of HRP-2, and found that HRP-2 interacts with various proteins known to be involved in transcription elongation and processing. Furthermore, we demonstrate HRP-2 interacts and co-localizes with RNA processing regulator IWS1, and positively regulated the mRNA level of Cyclin D1. Together, our study suggests HRP-2 may act as an mRNA processing co-factor to promote cells growth by regulating the mRNA of key oncogenes, which can be explored further for cancer treatment.

Labib HA, Ahmed HS, Shalaby SM, et al.
Genetic polymorphism of IL-23R influences susceptibility to HCV-related hepatocellular carcinoma.
Cell Immunol. 2015; 294(1):21-4 [PubMed] Related Publications
BACKGROUND: Genetic variations may play an important role in the development of HCC in HCV patients. Variants of IL23R gene were investigated for association with many diseases like chronic inflammatory disorders, RA, inflammatory bowel diseases and the susceptibility to the development of gastric cancer but no data are available concerning the association of IL23R gene (rs11209026) polymorphism with HCC development in HCV patients. Therefore the current study aimed to analyze this polymorphism within the gene to evaluate its contribution to chronic HCV susceptibility and/or HCC development in Egyptian patients.
SUBJECTS AND METHODS: One hundred and ninety-two patients with chronic HCV infection were included in this study (92 of them without HCC and 100 of them with HCC). One hundred healthy control subjects with no history of previous liver disease (HBV and HCV infection were negative) were included in the study. The IL23R polymorphism (rs11209026 G>A) were genotyped by real time PCR.
RESULTS: We found a significant lower incidence of GA and AA genotype in HCV patients with HCC compared to those without HCC (p=0.026 and 0.040 respectively) and compared to control group (p=0.008 and 0.007 respectively). While, no significant difference between control and HCV patients without HCC groups was found.
CONCLUSIONS: Our study suggests that wild type IL-23R GG serves as a risk factor for HCC and supports for the protective role of the rare variant rs11209026 (Arg381Gln) against HCV-related HCC in Egyptian patients.

Chen W, Wang M, Zhang Z, et al.
Replication the association of 2q32.2-q32.3 and 14q32.11 with hepatocellular carcinoma.
Gene. 2015; 561(1):63-7 [PubMed] Related Publications
Hepatocellular carcinoma (HCC) is a malignant tumor. The morbidity and mortality of HCC tend to ascend and become a serious threat to the population health. Genetic studies of HCC have identified several susceptibility loci of HCC. In this study, we aim to replicate the association of these loci in our samples from Chinese population and further investigate the genetic interaction. We selected 16 SNPs within 1p36.22, 2q32.2-q32.3, 3p21.33, 8p12, 14q32.11 and 21q21.3 and genotyped in 507 HCC patients and 3014 controls by using Sequenom MassARRAY system. Association analyses were performed by using PLINK 1.07. We observed that the STAT4 (2q32.2-q32.3) at rs7574865 (P=1.17×10(-3), OR=0.79) and EFCAB11 (14q32.11) at rs8013403 (P=1.54×10(-3), OR=0.80) were significantly associated with HCC in this study. In 3p21.33, genetic variant rs6795737 within GLB1 was also observed with suggestive evidence (P=9.98×10(-3), OR=0.84). In the interaction analysis, the pair of associated SNPs (rs7574865 within STAT4, rs8013403 within EFCAB11) generated evidence for interaction (P=4.10×10(-3)). In summary, our work first reported the association of 14q32.11 (EFCAB11) with HCC in Chinese Han population and revealed the genetic interaction between STAT4 (2q32.2-q32.3) and EFCAB11 (14q32.11) in HCC.

Zhang X, He H, Zhang X, et al.
RUNX3 Promoter Methylation Is Associated with Hepatocellular Carcinoma Risk: A Meta-Analysis.
Cancer Invest. 2015; 33(4):121-5 [PubMed] Related Publications
Runt-related transcription factor 3 (RUNX3) has been reported to be a tumor-suppressing gene in hepatocellular carcinoma. Association between hepatocellular carcinoma and RUNX3 promoter methylation has been investigated in studies with specific ethnic backgrounds and small sample sizes. In this study, a meta-analysis was adopted to combine the data from 11 studies of association between RUNX3 promoter methylation and hepatocellular carcinoma. Pooled odds ratio for RUNX3 promoter methylation status in hepatocellular carcinoma versus control liver tissue was 24.37 (95%CI: 12.14, 48.92), p < .00001, indicates a strong association between methylation of the RUNX3 promoter and hepatocellular carcinoma.

Liu Z, Song T, Dou C, et al.
[Detection of Bcl-2 in human hepatocellular carcinoma and down-regulation with siRNA].
Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 2015; 31(2):221-5, 230 [PubMed] Related Publications
OBJECTIVE: To investigate the relationship between the level of Bcl-2 in human hepatocellular carcinoma (HCC) tissues and the clinicopathological characteristics and prognosis of patients, and to detect the proliferation of SMMC-7721 cells after Bcl-2 is down-regulated with siRNA.
METHODS: The level of Bcl-2 in 68 HCC tissues was detected by reverse transcription PCR, SP immunohistochemistry and Western blotting. The 68 HCC tissues were divided into two groups based on the level of Bcl-2. The clinicopathological characteristics and prognosis of the patients were compared between groups. The proliferation of SMMC-7721 cells was evaluated by MTT assay after Bcl-2 was inhibited with siRNA.
RESULTS: Compared with corresponding noncancerous liver tissues, the level of Bcl-2 was significantly elevated in HCC tissues. The level of Bcl-2 was correlated with clinicopathological characteristics, and the patient with higher expression level of Bcl-2 had relatively poorer prognosis. Down-regulation of Bcl-2 expression by siRNA significantly inhibited the proliferation of SMMC-7721 cells.
CONCLUSION: The level of Bcl-2 is abnormally elevated in HCC tissues and can serve as a predictor of the prognosis of patients with HCC. The down-regulated of Bcl-2 in SMMC-7721 cells can inhibit the proliferation of cancer cells.

Li K, Xyu Q, Liu X, et al.
[Growth inhibition of human hepatocellular carcinoma by miRNA-204 via down-regulation of Bcl-2 and Sirt1 expression].
Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 2015; 31(2):168-72 [PubMed] Related Publications
OBJECTIVE: To investigate the level of microRNA-204 (miR-204) in human hepatocellular carcinoma (HCC) and its effect on the potential mechanism of tumorgenesis.
METHODS: Real-time quantitative PCR (qRT-PCR) was applied to detect the expression of miR-204 in HCC (n=60) and the corresponding tumor-adjacent normal tissues. The expressions of Bcl-2 and Sirt1 were measured by immunohistochemistry (IHC). The artificial miR-204 was transiently transfected into human SMMC-7721 cells in vitro. The proliferation and apoptosis of the cells were detected by MTT assay and flow cytometry, respectively. The expression levels of Bcl-2 and Sirt1 mRNA and protein were determined by qRT-PCR and Western blotting, respectively.
RESULTS: The expression level of miR-204 in HCC tissues was significantly lower than that in the adjacent normal tissues, and it was associated with tumor size, number of tumors and advanced TNM stage. The expressions of Bcl-2 and Sirt1 in the lower miR-204 level group were both higher than those in the higher miR-204 level group. Correlation analysis showed that miR-204 expression was negatively correlated with Bcl-2 and Sirt1 protein expression levels. Over-expressed miR-204 in SMMC-7721 cells suppressed cell proliferation and promoted cell apoptosis, and down-regulated mRNA and protein expressions of both Bcl-2 and Sirt1.
CONCLUSION: The expression of miR-204 in HCC tissues was significantly lower than that in tumor-adjacent normal tissues. miR-204 could inhibit HCC cell proliferation and induce apoptosis by down-regulating the expressions of Bcl-2 and Sirt1.

Lu XJ, Shi Y, Chen JL, Ma S
Krüppel-like factors in hepatocellular carcinoma.
Tumour Biol. 2015; 36(2):533-41 [PubMed] Related Publications
Hepatocellular carcinoma (HCC) is a disease with a high incidence and mortality rate worldwide. However, the mechanisms underlying its pathogenesis are still elusive. In recent years, studies on functions of Krüppel-like factors (KLFs) in HCC have shed new light on this field. To date, five members (KLF4, KLF6, KLF8, KLF9, and KLF17) in the KLF family have been reported to function in the pathogenesis of HCC in multiple ways, which hold the potential of deepening and widening our understanding in the initiation and progression of HCC. In this review, we focus on the functions, roles, and regulatory networks of these five KLFs in HCC, summarize key pathways, and propose areas for further investigation, with the hope that this review will provide a reliable and concise reference for readers interested in this area.

Liu X, Fang Z, Pan Z, et al.
Pituitary transcriptome profile of liver cancer mice with different syndromes reveals the relevance of pituitary to the cancer and syndromes.
J Tradit Chin Med. 2014; 34(6):691-8 [PubMed] Related Publications
OBJECTIVE: To investigate the relevance of the pituitary to liver syndromes and cancer by studying the pituitary transcriptome profile in liver cancer mice with different syndromes.
METHODS: The quantitative four diagnosis and syndrome differentiation methods were used to screen normal control mice without syndromes (NC), liver cancer mice with poisonous pathogenic factors syndrome (PPFS), and Qi deficiency syndrome mice (QDS). An Affymetrix GeneChip Mouse Exon 1.0 ST Array was performed to detect the gene expression of different groups. Gene clustering was applied to analyze the gene expression patterns of the PPFS and QDS groups compared with the NC group. The transcriptional networks analysis tool, FunNet, was used to enrich the biological categories of differentially expressed genes in the PPFS and QDS groups.
RESULTS: Biological categories of differentially expressed genes showed that excessive metabolism and extracellular matrix interaction, insufficient communication of cells (especially nerve cells), and the bidirectional regulation of genetic information processing were enriched in both syndromes. However, the degree of excessive metabolism in the PPFS group was higher than that in the QDS group. The hyperfunction of cancer and infection, and the hypofunction of the nervous and endocrine systems were obvious in the QDS group.
CONCLUSION: The pituitary plays an important role in the development of liver cancer and syndromes. This study further studied the role of the pituitary in the combination of disease and syndromes.

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