Liver Cancer

Overview

Literature Analysis

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

Mutated Genes and Abnormal Protein Expression (363)

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, LSDMCA1 -HCCS and Hepatocellular Carcinoma
846
TP53 17p13.1 P53, BCC7, LFS1, TRP53 -TP53 and Liver Cancer
690
CTNNB1 3p22.1 CTNNB, MRD19, armadillo -CTNNB1 and Liver Cancer
512
MET 7q31 HGFR, AUTS9, RCCP2, c-Met Prognostic
-C-MET and Liver Cancer
-C-MET and Hepatocellular Carcinoma
205
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
BAX 19q13.33 BCL2L4 -BAX and Liver Cancer
148
TNF 6p21.3 DIF, TNFA, TNFSF2, TNF-alpha -TNF and Liver Cancer
147
IGF2 11p15.5 GRDF, IGF-II, PP9974, C11orf43 -IGF2 and Hepatoblastoma
-IGF2 Expression in Hepatocarcinoma
76
MYC 8q24.21 MRTL, MYCC, c-Myc, bHLHe39 -MYC protein, human and Liver Cancer
98
STAT3 17q21.31 APRF, HIES, ADMIO -STAT3 and Liver Cancer
94
CDKN1A 6p21.2 P21, CIP1, SDI1, WAF1, CAP20, CDKN1, MDA-6, p21CIP1 -CDKN1A and Hepatocellular Carcinoma
91
PCNA 20pter-p12 ATLD2 -PCNA and Liver Cancer
86
HIF1A 14q23.2 HIF1, MOP1, PASD8, HIF-1A, bHLHe78, HIF-1alpha, HIF1-ALPHA -HIF1A and Liver Cancer
83
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II -MMP2 and Liver Cancer
83
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
67
FOS 14q24.3 p55, AP-1, C-FOS -FOS and Liver Cancer
66
TGFB1 19q13.1 CED, LAP, DPD1, TGFB, TGFbeta -TGFB1 and Liver Cancer
64
IFNA17 9p22 IFNA, INFA, LEIF2C1, IFN-alphaI -IFNA17 and Hepatocellular Carcinoma
61
IFNA2 9p22 IFNA, INFA2, IFNA2B, IFN-alphaA -IFNA2 and Hepatocellular Carcinoma
61
IFNA7 9p22 IFNA-J, IFN-alphaJ -IFNA7 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
CDH1 16q22.1 UVO, CDHE, ECAD, LCAM, Arc-1, CD324 -CDH1 and Liver Cancer
50
SMAD3 15q22.33 LDS3, LDS1C, MADH3, JV15-2, HSPC193, HsT17436 -SMAD3 and Liver Cancer
48
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
37
FOXM1 12p13 MPP2, TGT3, HFH11, HNF-3, INS-1, MPP-2, PIG29, FKHL16, FOXM1B, HFH-11, TRIDENT, MPHOSPH2 -FOXM1 and Liver Cancer
34
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
33
GSTT1 22q11.23 -GSTT1 and Liver Cancer
32
CCNB1 5q12 CCNB -CCNB1 and Hepatocellular Carcinoma
32
RHOA 3p21.3 ARHA, ARH12, RHO12, RHOH12 -RHOA and Liver Cancer
32
M6PR 12p13 SMPR, MPR46, CD-MPR, MPR 46, MPR-46 -M6PR and Liver Cancer
29
GAPDH 12p13 G3PD, GAPD, HEL-S-162eP -GAPDH and Liver Cancer
29
BAD 11q13.1 BBC2, BCL2L8 -BAD and Liver Cancer
28
ACHE 7q22 YT, ACEE, ARACHE, N-ACHE -ACHE and Liver Cancer
28
IL6 7p21 HGF, HSF, BSF2, IL-6, IFNB2 -IL6 and Hepatocellular Carcinoma
27
JUNB 19p13.2 AP-1 -JUNB and Hepatocellular Carcinoma
27
DNMT1 19p13.2 AIM, DNMT, MCMT, CXXC9, HSN1E, ADCADN -DNMT1 and Liver Cancer
27
MIR21 17q23.1 MIRN21, miR-21, miRNA21, hsa-mir-21 -MicroRNA miR-21 and Liver Cancer
27
SOCS1 16p13.13 JAB, CIS1, SSI1, TIP3, CISH1, SSI-1, SOCS-1 -SOCS1 and Liver Cancer
26
YAP1 11q22.1 YAP, YKI, COB1, YAP2, YAP65 -YAP1 and Liver Cancer
25
IFNL3 19q13.13 IL28B, IL28C, IL-28B -IL28B and Liver Cancer
25
RHOC 1p13.1 H9, ARH9, ARHC, RHOH9 -RHOC and Liver Cancer
24
KLF6 10p15 GBF, ZF9, BCD1, CBA1, CPBP, PAC1, ST12, COPEB -KLF6 and Liver Cancer
24
SERPINE1 7q22.1 PAI, PAI1, PAI-1, PLANH1 -SERPINE1 and Liver Cancer
24
XIAP Xq25 API3, ILP1, MIHA, XLP2, BIRC4, IAP-3, hIAP3, hIAP-3 -XIAP and Liver Cancer
24
AXIN1 16p13.3 AXIN, PPP1R49 -AXIN1 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
IGFBP3 7p12.3 IBP3, BP-53 -IGFBP3 and Liver Cancer
23
ANXA8 10q11.22 ANX8, CH17-360D5.2 -ANXA8 and Liver Cancer
23
CCK 3p22.1 -CCK and Liver Cancer
23
FTCDNL1 2q33.1 FONG -FONG and Liver Cancer
22
HNF4A 20q13.12 TCF, HNF4, MODY, FRTS4, MODY1, NR2A1, TCF14, HNF4a7, HNF4a8, HNF4a9, NR2A21, HNF4alpha -HNF4A and Liver Cancer
22
DLC1 8p22 HP, ARHGAP7, STARD12, p122-RhoGAP Deletion
-DLC1 and Hepatocellular Carcinoma
22
RUNX3 1p36 AML2, CBFA3, PEBP2aC -RUNX3 and Liver Cancer
22
ANGPT2 8p23.1 ANG2, AGPT2 -ANGPT2 and Liver Cancer
21
PTK2 8q24.3 FAK, FADK, FAK1, FRNK, PPP1R71, p125FAK, pp125FAK -PTK2 and Liver Cancer
21
MICA 6p21.33 MIC-A, PERB11.1 -MICA and Liver Cancer
20
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 and Hepatocellular Carcinoma
19
MAT1A 10q22 MAT, SAMS, MATA1, SAMS1 -MAT1A and Liver Cancer
18
SIRT1 10q21.3 SIR2, hSIR2, SIR2L1 -SIRT1 and Liver Cancer
18
DNMT3B 20q11.2 ICF, ICF1, M.HsaIIIB -DNMT3B and Liver Cancer
18
VIP 6q25 PHM27 -VIP and Liver Cancer
17
MTDH 8q22.1 3D3, AEG1, AEG-1, LYRIC, LYRIC/3D3 -MTDH and Liver Cancer
17
APOB 2p24-p23 FLDB, LDLCQ4 -APOB and Liver Cancer
17
TCF7L2 10q25.3 TCF4, TCF-4 -TCF7L2 and Liver Cancer
16
BECN1 17q21 ATG6, VPS30, beclin1 -BECN1 and Liver Cancer
16
SFRP1 8p11.21 FRP, FRP1, FrzA, FRP-1, SARP2 -SFRP1 and Hepatocellular Carcinoma
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
MALAT1 11q13.1 HCN, NEAT2, PRO2853, LINC00047, NCRNA00047 -MALAT1 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
IL6ST 5q11.2 CD130, GP130, CDW130, IL-6RB -IL6ST and Liver Cancer
14
HEBP1 12p13.1 HBP, HEBP -HEBP1 and Liver Cancer
14
GNMT 6p12 -GNMT and Liver Cancer
14
FGF19 11q13.3 -FGF19 and Hepatocellular Carcinoma
13
PEG10 7q21 EDR, HB-1, Mar2, MEF3L, Mart2, RGAG3 -PEG10 and Hepatocellular Carcinoma
13
SREBF1 17p11.2 SREBP1, bHLHd1, SREBP-1c -SREBF1 and Liver Cancer
13
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Hepatocellular Carcinoma
13
HMGB1 13q12 HMG1, HMG3, SBP-1 -HMGB1 and Liver Cancer
13
TIMP3 22q12.3 SFD, K222, K222TA2, HSMRK222 -TIMP3 and Liver Cancer
13
PECAM1 17q23.3 CD31, PECA1, GPIIA', PECAM-1, endoCAM, CD31/EndoCAM -PECAM1 and Liver Cancer
13
PLK1 16p12.2 PLK, STPK13 -PLK1 and Liver Cancer
13
CDC42 1p36.1 G25K, CDC42Hs -CDC42 and Liver Cancer
13
HES1 3q28-q29 HHL, HRY, HES-1, bHLHb39 -HES1 and Liver Cancer
12
ZEB2 2q22.3 SIP1, SIP-1, ZFHX1B, HSPC082, SMADIP1 -ZEB2 and Liver Cancer
12
CCNE1 19q12 CCNE, pCCNE1 -CCNE1 and Liver Cancer
12
FOXA2 20p11 HNF3B, TCF3B -FOXA2 and Liver Cancer
11
DKK1 10q11.2 SK, DKK-1 -DKK1 and Hepatocellular Carcinoma
11
PDCD4 10q24 H731 -PDCD4 and Liver Cancer
11
AXIN2 17q24.1 AXIL, ODCRCS -AXIN2 and Liver Cancer
10
CCR7 17q12-q21.2 BLR2, EBI1, CCR-7, CD197, CDw197, CMKBR7, CC-CKR-7 -CCR7 and Liver Cancer
10
ANXA2 15q22.2 P36, ANX2, LIP2, LPC2, CAL1H, LPC2D, ANX2L4, PAP-IV, HEL-S-270 -ANXA2 and Hepatocellular Carcinoma
10
PSMD10 Xq22.3 p28, p28(GANK), dJ889N15.2 -PSMD10 and Liver Cancer
10
ING1 13q34 p33, p47, p33ING1, p24ING1c, p33ING1b, p47ING1a -ING1 and Hepatocellular Carcinoma
10
IGFBP1 7p12.3 AFBP, IBP1, PP12, IGF-BP25, hIGFBP-1 -IGFBP1 and Liver Cancer
10
APOE 19q13.2 AD2, LPG, APO-E, LDLCQ5 -APOE and Hepatocellular Carcinoma
10
DLK1 14q32 DLK, FA1, ZOG, pG2, DLK-1, PREF1, Delta1, Pref-1 -DLK1 and Liver Cancer
9
KIF1B 1p36.2 KLP, CMT2, CMT2A, CMT2A1, HMSNII, NBLST1 -KIF1B and Liver Cancer
9
STMN1 1p36.11 Lag, SMN, OP18, PP17, PP19, PR22, LAP18, C1orf215 -STMN1 and Liver Cancer
9
AKR1B10 7q33 HIS, HSI, ARL1, ARL-1, ALDRLn, AKR1B11, AKR1B12 -AKR1B10 and Liver Cancer
9
ACTB 7p22 BRWS1, PS1TP5BP1 -ACTB and Hepatocellular Carcinoma
9
VIM 10p13 HEL113, CTRCT30 -VIM and Liver Cancer
9
AREG 4q13.3 AR, SDGF, AREGB, CRDGF -AREG and Liver Cancer
9
LGR5 12q22-q23 FEX, HG38, GPR49, GPR67, GRP49 -LGR5 and Liver Cancer
8
LIN28B 6q21 CSDD2 -LIN28B and Liver Cancer
8
ADAM10 15q22 RAK, kuz, AD10, AD18, MADM, CD156c, HsT18717 -ADAM10 and Liver Cancer
8
MIRLET7C 21q21.1 LET7C, let-7c, MIRNLET7C, hsa-let-7c -MicroRNA let-7c and Liver Cancer
8
NDRG1 8q24.3 GC4, RTP, DRG1, NDR1, NMSL, TDD5, CAP43, CMT4D, DRG-1, HMSNL, RIT42, TARG1, PROXY1 -NDRG1 and Hepatocellular Carcinoma
8
CXCL10 4q21 C7, IFI10, INP10, IP-10, crg-2, mob-1, SCYB10, gIP-10 -CXCL10 and Liver Cancer
8
CDH2 18q11.2 CDHN, NCAD, CD325, CDw325 -CDH2 and Liver Cancer
8
ANXA5 4q27 PP4, ANX5, ENX2, RPRGL3, HEL-S-7 -ANXA5 and Liver Cancer
8
HOTAIR 12q13.13 HOXAS, HOXC-AS4, HOXC11-AS1, NCRNA00072 -HOTAIR and Liver Cancer
8
TIMP2 17q25 DDC8, CSC-21K -TIMP2 and Liver Cancer
8
DDX3X Xp11.3-p11.23 DBX, DDX3, HLP2, DDX14, CAP-Rf -DDX3X and Liver Cancer
8
GPX1 3p21.3 GPXD, GSHPX1 -GPX1 and Liver Cancer
8
PTP4A3 8q24.3 PRL3, PRL-3, PRL-R -PTP4A3 and Liver Cancer
8
TLR2 4q32 TIL4, CD282 -TLR2 and Liver Cancer
7
FASN 17q25 FAS, OA-519, SDR27X1 -FASN and Liver Cancer
7
CD81 11p15.5 S5.7, CVID6, TAPA1, TSPAN28 -CD81 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
TLR3 4q35 CD283, IIAE2 -TLR3 and Liver Cancer
7
PGK1 Xq13.3 PGKA, MIG10, HEL-S-68p -PGK1 and Liver Cancer
7
CCNG1 5q32-q34 CCNG -CCNG1 and Liver Cancer
7
FYN 6q21 SLK, SYN, p59-FYN -FYN and Liver Cancer
7
PRDM2 1p36.21 RIZ, KMT8, RIZ1, RIZ2, MTB-ZF, HUMHOXY1 -PRDM2 and Liver Cancer
7
LAPTM4B 8q22.1 LC27, LAPTM4beta -LAPTM4B and Liver Cancer
7
SULF2 20q13.12 HSULF-2 -SULF2 and Liver Cancer
7
YES1 18p11.31-p11.21 Yes, c-yes, HsT441, P61-YES -Proto-Oncogene Proteins c-yes and Liver Cancer
7
CYP2C19 10q24 CPCJ, CYP2C, P450C2C, CYPIIC17, CYPIIC19, P450IIC19 -CYP2C19 and Liver Cancer
7
ID2 2p25 GIG8, ID2A, ID2H, bHLHb26 -ID2 Expression in hepatocellular carcinoma
7
MACC1 7p21.1 7A5, SH3BP4L -MACC1 and Liver Cancer
7
KRT19 17q21.2 K19, CK19, K1CS -KRT19 and Liver Cancer
7
ATG5 6q21 ASP, APG5, APG5L, hAPG5, APG5-LIKE -ATG5 and Hepatocellular Carcinoma
7
IRF2 4q34.1-q35.1 IRF-2 -IRF2 and Hepatocellular Carcinoma
6
HDGF 1q23.1 HMG1L2 -HDGF 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
CXCR2 2q35 CD182, IL8R2, IL8RA, IL8RB, CMKAR2, CDw128b -CXCR2 and Liver Cancer
6
SPRY2 13q31.1 hSPRY2 -SPRY2 and Liver Cancer
6
HTATIP2 11p15.1 CC3, TIP30, SDR44U1 -HTATIP2 and Liver Cancer
6
LCN2 9q34 24p3, MSFI, NGAL -LCN2 and Liver Cancer
6
SULF1 8q13.2 SULF-1, HSULF-1 -SULF1 and Liver Cancer
6
ARID2 12q12 p200, BAF200 -ARID2 and Liver Cancer
6
SPINT2 19q13.1 PB, Kop, HAI2, DIAR3, HAI-2 -SPINT2 and Liver Cancer
6
MIRLET7G 3p21.1 LET7G, let-7g, MIRNLET7G, hsa-let-7g -MicroRNA let-7g and Liver Cancer
6
ADAM17 2p25 CSVP, TACE, NISBD, ADAM18, CD156B, NISBD1 -ADAM17 and Liver Cancer
6
SERPINA1 14q32.1 PI, A1A, AAT, PI1, A1AT, PRO2275, alpha1AT -SERPINA1 and Liver Cancer
6
XAF1 17p13.1 BIRC4BP, XIAPAF1, HSXIAPAF1 -XAF1 and Liver Cancer
6
IGFBP7 4q12 AGM, PSF, TAF, FSTL2, IBP-7, MAC25, IGFBP-7, RAMSVPS, IGFBP-7v, IGFBPRP1 -IGFBP7 and Liver Cancer
6
SNAI1 20q13.2 SNA, SNAH, SNAIL, SLUGH2, SNAIL1, dJ710H13.1 -SNAI1 and Liver Cancer
6
MTSS1 8p22 MIM, MIMA, MIMB -MTSS1 and Liver Cancer
6
GDF15 19p13.11 PDF, MIC1, PLAB, MIC-1, NAG-1, PTGFB, GDF-15 -GDF15 and Liver Cancer
6
LRP6 12p13.2 ADCAD2 -LRP6 and Liver Cancer
6
GADD45B 19p13.3 MYD118, GADD45BETA -GADD45B and Hepatocellular Carcinoma
6
PTPN6 12p13 HCP, HCPH, SHP1, SHP-1, HPTP1C, PTP-1C, SHP-1L, SH-PTP1 -PTPN6 and Liver Cancer
6
S100A6 1q21 2A9, PRA, 5B10, CABP, CACY -S100A6 and Liver Cancer
6
BCL2L2 14q11.2-q12 BCLW, BCL-W, PPP1R51, BCL2-L-2 -BCL2L2 and Liver Cancer
6
MICB 6p21.3 PERB11.2 -MICB and Liver Cancer
6
PINX1 8p23 LPTL, LPTS -PINX1 and Hepatocellular Carcinoma
6
SMYD3 1q44 KMT3E, ZMYND1, ZNFN3A1, bA74P14.1 -SMYD3 and Liver Cancer
6
ATF2 2q32 HB16, CREB2, TREB7, CREB-2, CRE-BP1 -ATF2 and Hepatocellular Carcinoma
5
MCM7 7q21.3-q22.1 MCM2, CDC47, P85MCM, P1CDC47, PNAS146, PPP1R104, P1.1-MCM3 -MCM7 and Liver Cancer
5
GNA11 19p13.3 FBH, FBH2, FHH2, HHC2, GNA-11, HYPOC2 -GNA11 and Liver Cancer
5
MAGEA4 Xq28 CT1.4, MAGE4, MAGE4A, MAGE4B, MAGE-41, MAGE-X2 -MAGEA4 and Liver Cancer
5
ABCA1 9q31.1 TGD, ABC1, CERP, ABC-1, HDLDT1 -ABCA1 and Liver Cancer
5
SATB1 3p23 -SATB1 and Hepatocellular Carcinoma
5
JAG1 20p12.1-p11.23 AGS, AHD, AWS, HJ1, CD339, JAGL1 -JAG1 and Liver Cancer
5
TCF3 19p13.3 E2A, E47, ITF1, VDIR, TCF-3, bHLHb21 -TCF3 and Liver Cancer
5
GAGE1 Xp11.23 CT4.1, GAGE-1 -GAGE1 and Liver Cancer
5
RALGDS 9q34.3 RGF, RGDS, RalGEF -RALGDS and Liver Cancer
5
CXCL5 4q13.3 SCYB5, ENA-78 -CXCL5 and Liver Cancer
5
MIR122 18q21.31 MIR122A, MIRN122, mir-122, MIRN122A, miRNA122, miRNA122A, hsa-mir-122 -MIR122 and Liver Cancer
5
NDRG2 14q11.2 SYLD -NDRG2 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
USF1 1q22-q23 UEF, FCHL, MLTF, FCHL1, MLTFI, HYPLIP1, bHLHb11 -USF1 and Liver Cancer
5
CRY2 11p11.2 HCRY2, PHLL2 -CRY2 and Liver Cancer
5
RPS6 9p21 S6 -RPS6 and Liver Cancer
5
AGO2 8q24 Q10, EIF2C2 -EIF2C2 and Hepatocellular Carcinoma
5
LYVE1 11p15.4 HAR, XLKD1, LYVE-1, CRSBP-1 -LYVE1 and Liver Cancer
5
LAMC2 1q25-q31 B2T, CSF, EBR2, BM600, EBR2A, LAMB2T, LAMNB2 -LAMC2 and Liver Cancer
5
RASSF5 1q32.1 RAPL, Maxp1, NORE1, NORE1A, NORE1B, RASSF3 -RASSF5 and Liver Cancer
5
IQGAP1 15q26.1 SAR1, p195, HUMORFA01 -IQGAP1 and Liver Cancer
5
SFRP5 10q24.1 SARP3 -SFRP5 and Liver Cancer
5
FOSB 19q13.32 AP-1, G0S3, GOS3, GOSB -FOSB 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
ANGPT1 8q23.1 AGP1, AGPT, ANG1 -ANGPT1 and Liver Cancer
5
ATG7 3p25.3 GSA7, APG7L, APG7-LIKE -ATG7 and Liver Cancer
5
PAK4 19q13.2 -PAK4 and Liver Cancer
5
PER1 17p13.1 PER, hPER, RIGUI -PER1 and Liver Cancer
4
PIM2 Xp11.23 -PIM2 and Liver Cancer
4
TGFB2 1q41 LDS4, TGF-beta2 -TGFB2 and Liver Cancer
4
XBP1 22q12.1 XBP2, TREB5, XBP-1, TREB-5 -XBP1 and Liver Cancer
4
KIAA1524 3q13.13 p90, CIP2A -KIAA1524 and Hepatocellular Carcinoma
4
NOX4 11q14.3 KOX, KOX-1, RENOX -NOX4 and Liver Cancer
4
MIR1301 2 MIRN1301, mir-1301, hsa-mir-1301 -MicroRNA miR-1301and Liver Cancer
4
MT1G 16q13 MT1, MT1K -MT1G and Liver Cancer
4
CCNC 6q21 CycC -CCNC and Liver Cancer
4
FZD7 2q33 FzE3 -FZD7 and Liver Cancer
4
B2M 15q21.1 -B2M and Liver Cancer
4
ATP7B 13q14.3 WD, PWD, WC1, WND -ATP7B and Liver Cancer
4
EREG 4q13.3 ER, Ep, EPR -EREG and Liver Cancer
4
TP53INP1 8q22 SIP, Teap, p53DINP1, TP53DINP1, TP53INP1A, TP53INP1B -TP53INP1 and Liver Cancer
4
IL12A 3q25.33 P35, CLMF, NFSK, NKSF1, IL-12A -IL12A and Liver Cancer
4
EFEMP1 2p16 DHRD, DRAD, FBNL, MLVT, MTLV, S1-5, FBLN3, FIBL-3 -EFEMP1 and Liver Cancer
4
LDHA 11p15.1 LDHM, GSD11, PIG19, HEL-S-133P -LDHA and Liver Cancer
4
ADARB1 21q22.3 RED1, ADAR2, DRABA2, DRADA2 -ADARB1 and Liver Cancer
4
NFKBIA 14q13 IKBA, MAD-3, NFKBI -NFKBIA and Liver Cancer
4
HLA-E 6p21.3 MHC, QA1, EA1.2, EA2.1, HLA-6.2 -HLA-E and Liver Cancer
4
HSPA1B 6p21.3 HSP70-2, HSP70.2, HSP70-1B -HSPA1B and Hepatocellular Carcinoma
4
AIFM1 Xq26.1 AIF, CMT2D, CMTX4, COWCK, NADMR, NAMSD, PDCD8, COXPD6 -AIFM1 and Liver Cancer
4
BTG2 1q32 PC3, TIS21 -BTG2 and Liver Cancer
4
CYP2B6 19q13.2 CPB6, EFVM, IIB1, P450, CYP2B, CYP2B7, CYP2B7P, CYPIIB6 -CYP2B6 and Hepatocellular Carcinoma
4
CD151 11p15.5 GP27, MER2, RAPH, SFA1, PETA-3, TSPAN24 -CD151 and Liver Cancer
4
CKS2 9q22 CKSHS2 -CKS2 and Liver Cancer
4
SPINK1 5q32 TCP, PCTT, PSTI, TATI, Spink3 -SPINK1 and Liver Cancer
4
ATF6 1q23.3 ATF6A -ATF6 and Liver Cancer
4
CDC6 17q21.3 CDC18L, HsCDC6, HsCDC18 -CDC6 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
LDLR 19p13.2 FH, FHC, LDLCQ2 -LDLR and Liver Cancer
4
CDH17 8q22.1 HPT1, CDH16, HPT-1 -CDH17 and Liver Cancer
4
CD46 1q32 MCP, TLX, AHUS2, MIC10, TRA2.10 -CD46 and Liver Cancer
4
ADAR 1q21.3 DSH, AGS6, G1P1, IFI4, P136, ADAR1, DRADA, DSRAD, IFI-4, K88DSRBP -ADAR and Liver Cancer
4
COL1A2 7q22.1 OI4 -COL1A2 and Liver Cancer
4
HPSE 4q21.3 HPA, HPA1, HPR1, HSE1, HPSE1 -HPSE and Liver Cancer
3
HSP90AA1 14q32.33 EL52, HSPN, LAP2, HSP86, HSPC1, HSPCA, Hsp89, Hsp90, LAP-2, HSP89A, HSP90A, HSP90N, HSPCAL1, HSPCAL4 -HSP90AA1 and Liver Cancer
3
BTRC 10q24.32 FWD1, FBW1A, FBXW1, bTrCP, FBXW1A, bTrCP1, betaTrCP, BETA-TRCP -BTRC and Liver Cancer
3
STARD13 13q13.1 DLC2, GT650, ARHGAP37, LINC00464 -STARD13 and Liver Cancer
3
SALL4 20q13.2 DRRS, HSAL4, ZNF797, dJ1112F19.1 -SALL4 and Liver Cancer
3
HINT1 5q31.2 HINT, NMAN, PKCI-1, PRKCNH1 -HINT1 and Liver Cancer
3
CD40 20q12-q13.2 p50, Bp50, CDW40, TNFRSF5 -CD40 and Liver Cancer
3
RXRA 9q34.3 NR2B1 -RXRA and Liver Cancer
3
LOXL2 8p21.3 LOR2, WS9-14 -LOXL2 and Liver Cancer
3
MEG3 14q32 GTL2, FP504, prebp1, PRO0518, PRO2160, LINC00023, NCRNA00023, onco-lncRNA-83 -MEG3 and Liver Cancer
3
PPARGC1A 4p15.1 LEM6, PGC1, PGC1A, PGC-1v, PPARGC1, PGC-1(alpha) -PPARGC1A and Liver Cancer
3
HHIP 4q28-q32 HIP -HHIP and Liver Cancer
3
ZBTB7A 19p13.3 LRF, FBI1, FBI-1, ZBTB7, ZNF857A, pokemon -ZBTB7A and Liver Cancer
3
TBX3 12q24.21 UMS, XHL, TBX3-ISO -TBX3 and Liver Cancer
3
ZNF331 19q13.42 RITA, ZNF361, ZNF463 -ZNF331 and Liver Cancer
3
YWHAZ 8q23.1 HEL4, YWHAD, KCIP-1, HEL-S-3, 14-3-3-zeta -YWHAZ and Liver Cancer
3
FEN1 11q12.2 MF1, RAD2, FEN-1 -FEN1 and Liver Cancer
3
CRY1 12q23-q24.1 PHLL1 -CRY1 and Liver Cancer
3
SOX1 13q34 -SOX1 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
IFT88 13q12.1 DAF19, TG737, TTC10, hTg737, D13S1056E -IFT88 and Hepatocellular Carcinoma
3
LGALS4 19q13.2 GAL4, L36LBP -LGALS4 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
DGCR8 22q11.2 Gy1, pasha, DGCRK6, C22orf12 -DGCR8 and Liver Cancer
3
RAD23B 9q31.2 P58, HR23B, HHR23B -RAD23B and Hepatocellular Carcinoma
3
IL23R 1p31.3 -IL23R and Liver Cancer
3
ADAMTS1 21q21.2 C3-C5, METH1 -ADAMTS1 and Liver Cancer
3
COPS5 8q13.1 CSN5, JAB1, SGN5, MOV-34 -COPS5 and Hepatocellular Carcinoma
3
OCLN 5q13.1 BLCPMG, PPP1R115 -OCLN and Liver Cancer
3
IRF9 14q11.2 p48, IRF-9, ISGF3, ISGF3G -IRF9 and Hepatocellular Carcinoma
3
UCP2 11q13.4 UCPH, BMIQ4, SLC25A8 -UCP2 and Liver Cancer
3
LEPR 1p31 OBR, OB-R, CD295, LEP-R, LEPRD -LEPR and Liver Cancer
3
TNFRSF10C 8p22-p21 LIT, DCR1, TRID, CD263, TRAILR3, TRAIL-R3, DCR1-TNFR -TNFRSF10C and Liver Cancer
3
CD276 15q23-q24 B7H3, B7-H3, B7RP-2, 4Ig-B7-H3 -CD276 and Liver Cancer
3
DLEC1 3p21.3 F56, DLC1, CFAP81 -DLEC1 and Liver Cancer
3
STC1 8p21.2 STC -STC1 and Liver Cancer
3
MAGEB2 Xp21.3 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Liver Cancer
3
EBAG9 8q23 EB9, PDAF -EBAG9 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
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
NR0B2 1p36.1 SHP, SHP1 -NR0B2 and Liver Cancer
3
CXCL14 5q31 KEC, KS1, BMAC, BRAK, NJAC, MIP2G, MIP-2g, SCYB14 -CXCL14 and Liver Cancer
3
TXNIP 1q21.1 THIF, VDUP1, HHCPA78, EST01027 -TXNIP and Liver Cancer
3
TRIO 5p15.2 tgat, ARHGEF23 -TRIO and Hepatocellular Carcinoma
3
HOXA13 7p15.2 HOX1, HOX1J -HOXA13 and Liver Cancer
3
GSTO1 10q25.1 P28, SPG-R, GSTO 1-1, GSTTLp28, HEL-S-21 -GSTO1 and Liver Cancer
3
ING2 4q35.1 ING1L, p33ING2 -ING2 and Liver Cancer
3
DDR1 6p21.3 CAK, DDR, NEP, HGK2, PTK3, RTK6, TRKE, CD167, EDDR1, MCK10, NTRK4, PTK3A -DDR1 and Liver Cancer
3
MIR127 14q32.2 MIRN127, mir-127, miRNA127 -MIRN127 microRNA, human and Liver Cancer
3
SLC9A1 1p36.1-p35 APNH, NHE1, LIKNS, NHE-1, PPP1R143 -SLC9A1 and Liver Cancer
3
SPRY1 4q28.1 hSPRY1 -SPRY1 and Hepatocellular Carcinoma
3
TRIM24 7q32-q34 PTC6, TF1A, TIF1, RNF82, TIF1A, hTIF1, TIF1ALPHA -TRIM24 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
TP53BP2 1q41 BBP, 53BP2, ASPP2, P53BP2, PPP1R13A -TP53BP2 and Liver Cancer
3
SLCO1B3 12p12 LST3, HBLRR, LST-2, OATP8, OATP-8, OATP1B3, SLC21A8, LST-3TM13 -SLCO1B3 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
IL12B 5q33.3 CLMF, NKSF, CLMF2, IMD28, IMD29, NKSF2, IL-12B -IL12B and Liver Cancer
3
BAGE 21p11.1 not on ref BAGE1, CT2.1 -BAGE 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
PTMS 12p13 ParaT -PTMS and Liver Cancer
3
CEBPD 8p11.2-p11.1 CELF, CRP3, C/EBP-delta, NF-IL6-beta -CEBPD and Hepatocellular Carcinoma
3
RAC2 22q13.1 Gx, EN-7, HSPC022, p21-Rac2 -RAC2 and Liver Cancer
2
PTPRT 20q12-q13 RPTPrho -PTPRT and Hepatocellular Carcinoma
2
MERTK 2q14.1 MER, RP38, c-Eyk, c-mer, Tyro12 -MERTK and Liver Cancer
2
MIR124-1 8p23.1 MIR124A, MIR124A1, MIRN124-1, MIRN124A1, mir-124-1 -microRNA 124-1 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
ANXA7 10q22.2 SNX, ANX7, SYNEXIN -ANXA7 and Liver Cancer
2
FTL 19q13.33 LFTD, NBIA3 -FTL and Liver Cancer
2
ZFP36 19q13.1 TTP, G0S24, GOS24, TIS11, NUP475, zfp-36, RNF162A -ZFP36 and Liver Cancer
2
CXCL16 17p13 SRPSOX, CXCLG16, SR-PSOX -CXCL16 and Liver Cancer
2
ITGA6 2q31.1 CD49f, VLA-6, ITGA6B -ITGA6 and Liver Cancer
2
GSTO2 10q25.1 GSTO 2-2, bA127L20.1 -GSTO2 and Liver Cancer
2
MIR34A 1p36.22 mir-34, MIRN34A, mir-34a, miRNA34A -MIR34A and Liver Cancer
2
CA12 15q22 CAXII, HsT18816 -CA12 and Liver Cancer
2
EPHA5 4q13.1 EK7, CEK7, EHK1, HEK7, EHK-1, TYRO4 -EPHA5 and Liver Cancer
2
PRSS1 7q34 TRP1, TRY1, TRY4, TRYP1 -PRSS1 and Liver Cancer
2
FABP5 8q21.13 EFABP, KFABP, E-FABP, PAFABP, PA-FABP -FABP5 and Liver Cancer
2
IGF2-AS 11p15.5 PEG8, IGF2AS, IGF2-AS1 -IGF2-AS and Liver Cancer
2
THBS2 6q27 TSP2 -THBS2 and Hepatocellular Carcinoma
2
SOX6 11p15.2 SOXD, HSSOX6 -SOX6 and Liver Cancer
2
HTRA2 2p12 OMI, PARK13, PRSS25 -HTRA2 and Liver Cancer
2
PPP1R3A 7q31.1 GM, PP1G, PPP1R3 -PPP1R3A and Liver Cancer
2
XRCC6 22q13.2 ML8, KU70, TLAA, CTC75, CTCBF, G22P1 -XRCC6 and Liver Cancer
2
RASAL1 12q23-q24 RASAL -RASAL1 and Liver Cancer
2
MUC7 4q13.3 MG2 -MUC7 and Hepatocellular Carcinoma
2
SLC22A18 11p15.4 HET, ITM, BWR1A, IMPT1, TSSC5, ORCTL2, BWSCR1A, SLC22A1L, p45-BWR1A -SLC22A18 and Liver Cancer
2
DDIT4 10q22.1 Dig2, REDD1, REDD-1 -DDIT4 and Liver Cancer
2
SRSF3 6p21 SFRS3, SRp20 -SRSF3 and Liver Cancer
2
LIPA 10q23.2-q23.3 LAL, CESD -LIPA and Liver Cancer
2
EPHA1 7q34 EPH, EPHT, EPHT1 -EPHA1 and Liver Cancer
2
SAT2 17p13.1 SSAT2 -SAT2 and Liver Cancer
2
CSE1L 20q13 CAS, CSE1, XPO2 -CSE1L and Liver Cancer
2
GRASP 12q13.13 TAMALIN -GRASP and Liver Cancer
2
PPIA 7p13 CYPA, CYPH, HEL-S-69p -PPIA and Liver Cancer
2
VCAM1 1p32-p31 CD106, INCAM-100 -VCAM1 and Liver Cancer
2
DMPK 19q13.3 DM, DM1, DMK, MDPK, DM1PK, MT-PK -DMPK and Liver Cancer
2
PDCD5 19q13.11 TFAR19 -PDCD5 and Liver Cancer
2
KRT8 12q13 K8, KO, CK8, CK-8, CYK8, K2C8, CARD2 -KRT8 and Liver Cancer
2
KRT18 12q13 K18, CK-18, CYK18 -KRT18 and Liver Cancer
2
DNAJB4 1p31.1 DjB4, HLJ1, DNAJW -DNAJB4 and Liver Cancer
2
MIR1271 5q35 MIRN1271, hsa-mir-1271 -MicroRNA miR-1271 and Liver Cancer
2
CCR3 3p21.3 CKR3, CD193, CMKBR3, CC-CKR-3 -CCR3 and Liver Cancer
2
YWHAE 17p13.3 MDS, HEL2, MDCR, KCIP-1, 14-3-3E -YWHAE and Liver Cancer
2
HAVCR2 5q33.3 TIM3, CD366, KIM-3, TIMD3, Tim-3, TIMD-3, HAVcr-2 -HAVCR2 and Liver Cancer
2
MKL1 22q13 MAL, BSAC, MRTF-A -MKL1 and Liver Cancer
2
ING3 7q31 Eaf4, ING2, MEAF4, p47ING3 -ING3 and Liver Cancer
2
CCKBR 11p15.4 GASR, CCK-B, CCK2R -CCKBR and Liver Cancer
1
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
MIR125A 19q13.41 MIRN125A, miRNA125A -MIR125A and Liver Cancer
1
ERC1 12p13.3 ELKS, Cast2, ERC-1, RAB6IP2 -ERC1 and Liver Cancer
1
NOV 8q24.1 CCN3, NOVh, IBP-9, IGFBP9, IGFBP-9 -NOV 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

Zhou L, Liu S, Han M, et al.
MicroRNA-185 induces potent autophagy via AKT signaling in hepatocellular carcinoma.
Tumour Biol. 2017; 39(2):1010428317694313 [PubMed] Related Publications
Studies have demonstrated that microRNA 185 may be a promising therapeutic target in liver cancer. However, its role in hepatocellular carcinoma is largely unknown. In this study, the proliferation of human HepG2 cells was inhibited by transfection of microRNA 185 mimics. Cell-cycle analysis revealed arrest at the G0/G1 phase. Transfection of HepG2 cells with microRNA 185 mimics significantly induced apoptosis. These data confirmed microRNA 185 as a potent cancer suppressor. We demonstrated that microRNA 185 was a compelling inducer of autophagy, for the first time. When cell autophagy was inhibited by chloroquine or 3-methyladenine, microRNA 185 induced more cell apoptosis. MicroRNA 185 acted as a cancer suppressor by regulating AKT1 expression and phosphorylation. Dual-luciferase reporter assays indicated that microRNA 185 suppressed the expression of target genes including RHEB, RICTOR, and AKT1 by directly interacting with their 3'-untranslated regions. Binding site mutations eliminated microRNA 185 responsiveness. Our findings demonstrate a new role of microRNA 185 as a key regulator of hepatocellular carcinoma via autophagy by dysregulation of AKT1 pathway.

Zeng H, Wu HC, Wang Q, et al.
Telomere Length and Risk of Hepatocellular Carcinoma: A Nested Case-control Study in Taiwan Cancer Screening Program Cohort.
Anticancer Res. 2017; 37(2):637-644 [PubMed] Related Publications
BACKGROUND: Telomere length (TL) measured in peripheral blood leucocytes (PBL) might be a useful biomarker to identify elevated cancer risk.
PATIENTS AND METHODS: A case-control study which included 268 newly-diagnosed HCC cases and 536 matched controls, was conducted. Absolute TL in PBL was analyzed by quantitative real-time PCR.
RESULTS: The overall median length of TL was not statistically shorter in HCC cases compared to healthy controls. However, we found a significant synergistic effect of longer TL and HCV infection to increase HCC risk with a relative excess risk of 6.86 (95% CI: 2.14-11.58). Among HCC cases, significant shorter TLs were observed for <5 years (OR=3.93, 95% CI: 2.00-7.72); 5-10 years (OR=2.16, 95% CI: 1.10-4.24) compared to >10 years prior to diagnosis.
CONCLUSION: Shorter PBL TL alone was not significantly associated with increased HCC risk. Among HCC cases, significant shorter TLs were observed for <5 years prior to diagnosis.

Lee HW, Park TI, Jang SY, et al.
Clinicopathological characteristics of TERT promoter mutation and telomere length in hepatocellular carcinoma.
Medicine (Baltimore). 2017; 96(5):e5766 [PubMed] Free Access to Full Article Related Publications
Promoter mutations in telomerase reverse transcriptase (TERT) and telomere length have been studied in various tumors. In the present study, the frequency and clinical characteristics of TERT promoter mutation and telomere length were studied in hepatocellular carcinoma (HCC). TERT promoter mutation and telomere length were analyzed in 162 tumor samples of the patients with HCC by sequencing and real-time PCR, respectively. The TERT promoter mutation rate was 28.8% (46/160) in HCC and was associated with males (P = 0.027). The telomere length was not significantly different in the presence of a TERT promoter mutation but was shorter in high-grade tumor stages (P = 0.048). Survival analyses showed that poor overall survival was associated with longer telomere length (P = 0.013). However, the TERT promoter mutation did not have a prognostic value for HCC. Multivariate survival analyses demonstrated that the telomere length was an independent prognostic marker for poor overall survival (hazard ratio = 1.75, 95% confidence interval: 1.046-2.913, P = 0.033). These data demonstrated that TERT promoter mutation is a frequent event in HCC; however, telomere length, but not the presence of a TERT promoter mutation, might have potential value as a prognostic indicator of HCC.

Kgatle MM, Setshedi M, Hairwadzi HN
Hepatoepigenetic Alterations in Viral and Nonviral-Induced Hepatocellular Carcinoma.
Biomed Res Int. 2016; 2016:3956485 [PubMed] Free Access to Full Article Related Publications
Hepatocellular carcinoma (HCC) is a major public health concern and one of the leading causes of tumour-related deaths worldwide. Extensive evidence endorses that HCC is a multifactorial disease characterised by hepatic cirrhosis mostly associated with chronic inflammation and hepatitis B/C viral infections. Interaction of viral products with the host cell machinery may lead to increased frequency of genetic and epigenetic aberrations that cause harmful alterations in gene transcription. This may provide a progressive selective advantage for neoplastic transformation of hepatocytes associated with phenotypic heterogeneity of intratumour HCC cells, thus posing even more challenges in HCC treatment development. Epigenetic aberrations involving DNA methylation, histone modifications, and noncoding miRNA dysregulation have been shown to be intimately linked with and play a critical role in tumour initiation, progression, and metastases. The current review focuses on the aberrant hepatoepigenetics events that play important roles in hepatocarcinogenesis and their utilities in the development of HCC therapy.

Pan Y, Qin T, Yin S, et al.
Long non-coding RNA UC001kfo promotes hepatocellular carcinoma proliferation and metastasis by targeting α-SMA.
Biomed Pharmacother. 2017; 87:669-677 [PubMed] Related Publications
Several long non-coding RNAs (lncRNAs) have been investigated and found to be correlated with the behaviours and prognosis of hepatocellular carcinoma (HCC); Specifically, we revealed that the lncRNA UC001kfo was differentially expressed in HCC tissues compared with normal liver tissues using lncRNA microarrays, but its functional role in cancers, including HCC, has not yet been elucidated. The present study found that the expression of UC001kfo was upregulated in HCC tissues and cell lines in comparison with tumour-adjacent tissues and normal hepatocytes, respectively. In addition, a high UC001kfo level was determined to be correlated with macro-vascular invasion and TNM stage of HCC. Specifically, patients with high UC001kfo expression displayed a significantly lower overall survival rate and progression-free survival rate. Moreover, both univariate and multivariate COX regression analyses identified TNM stage and high UC001kfo expression as risk factors for poor prognosis in HCC patients. In addition, UC001kfo was verified to promote the proliferation, metastasis and epithelial-mesenchymal transition (EMT) in HCC cells in both in vitro and in vivo assays. Mechanistically, α-SMA was indicated as a potential target gene of UC001kfo in mediating HCC metastasis. In conclusion, UC001kfo promotes HCC proliferation and metastasis by targeting α-SMA, and UC001kfo may potentially serve as a prognostic marker and a therapeutic target for treatment of HCC.

Wang F, Wang R, Li Q, et al.
A transcriptome profile in hepatocellular carcinomas based on integrated analysis of microarray studies.
Diagn Pathol. 2017; 12(1):4 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Despite new treatment options for hepatocellular carcinomas (HCC) recently, 5-year survival remains poor, ranging from 50 to 70%, which may attribute to the lack of early diagnostic biomarkers. Thus, developing new biomarkers for early diagnosis of HCC, is extremely urgent, aiming to decrease HCC-related deaths.
METHODS: In the study, we conducted a comprehensive characterization of gene expression data of HCC based on a bioinformatics method. The results were confirmed by real time polymerase chain reaction (RT-PCR) and TCGA database to prove the credibility of this integrated analysis.
RESULTS: After integrating analysis of seven HCC gene expression datasets, 1167 differential expressed genes (DEGs) were identified. These genes mainly participated in the process of cell cycle, oocyte meiosis, and oocyte maturation mediated by progesterone. The results of experiments and TCGA database validation in 10 genes was in full accordance with findings in integrated analysis, indicating the high credibility of our integrated analysis of different gene expression datasets. ASPM, CCT3, and NEK2 was showed to be significantly associated with overall survival of HCC patients in TCGA database.
CONCLUSION: This method of integrated analysis may be a useful tool to minish the heterogeneity of individual microarray, hopefully outputs more accurate HCC transcriptome profiles based on large sample size, and explores some potential biomarkers and therapy targets for HCC.

Gai L, Liu H, Cui JH, et al.
The allele combinations of three loci based on, liver, stomach cancers, hematencephalon, COPD and normal population: A preliminary study.
Gene. 2017; 605:123-130 [PubMed] Related Publications
The purpose of this study was to examine the specific allele combinations of three loci connected with the liver cancers, stomach cancers, hematencephalon and patients with chronic obstructive pulmonary disease (COPD) and to explore the feasibility of the research methods. We explored different mathematical methods for statistical analyses to assess the association between the genotype and phenotype. At the same time we still analyses the statistical results of allele combinations of three loci by difference value method and ratio method. All the DNA blood samples were collected from patients with 50 liver cancers, 75 stomach cancers, 50 hematencephalon, 72 COPD and 200 normal populations. All the samples were from Chinese. Alleles from short tandem repeat (STR) loci were determined using the STR Profiler plus PCR amplification kit (15 STR loci). Previous research was based on combinations of single-locus alleles, and combinations of cross-loci (two loci) alleles. Allele combinations of three loci were obtained by computer counting and stronger genetic signal was obtained. The methods of allele combinations of three loci can help to identify the statistically significant differences of allele combinations between liver cancers, stomach cancers, patients with hematencephalon, COPD and the normal population. The probability of illness followed different rules and had apparent specificity. This method can be extended to other diseases and provide reference for early clinical diagnosis.

Sonohara F, Inokawa Y, Kanda M, et al.
Association of Inflammasome Components in Background Liver with Poor Prognosis After Curatively-resected Hepatocellular Carcinoma.
Anticancer Res. 2017; 37(1):293-300 [PubMed] Related Publications
BACKGROUND/AIM: Inflammasomes are multiprotein complexes that evoke key inflammatory cascades. The present study evaluated the influence of inflammasome component expression in non-tumorous tissue on postsurgical hepatocellular carcinoma (HCC) prognosis.
MATERIALS AND METHODS: The expressions of candidate genes were investigated using real-time quantitative reverse-transcription polymerase chain reaction in resected HCC cases. In order to identify potential prognostic factors, statistical analyses were performed for each gene.
RESULTS: The expression of nod-like receptor family, pyrin domain containing 3 (NLRP3), nod-like receptor family, CARD domain containing 4 (NLRC4), and absent in melanoma 2 (AIM2) was significantly higher in corresponding normal tissue (CN) compared to those in HCC. High expression of NLRP3, NLRC4, and caspase 1 (CASP1) in CN was significantly correlated with worse overall survival. Furthermore, multivariate analysis revealed that NLRP3 expression in CN greater than the median was an independent prognostic factor for poorer overall survival.
CONCLUSION: High expression of NLRP3, NLRC4, and CASP1 in background non-tumorous liver is significantly correlated with poor prognosis of patients after resection of HCC.

Xiao Q, Fu B, Chen P, et al.
Three polymorphisms of tumor necrosis factor-alpha and hepatitis B virus related hepatocellular carcinoma: A meta-analysis.
Medicine (Baltimore). 2016; 95(50):e5609 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: To assess the association between tumor necrosis factor-alpha (TNF-α) G308A, G238A and C863T polymorphisms and hepatitis B virus related hepatocellular carcinoma (HBV-HCC) susceptibility.
METHODS: We interrogated the databases of Pubmed, Sciencedirect and Viley online library up to March 8, 2016. Odds ratios (ORs) and corresponding 95% confidence intervals (95%CIs) were calculated in a fixed-effects model or a random-effects model when appropriate.
RESULTS: In total, 12 case-control studies which containing 1580 HBV-HCC cases, 2033 HBV carrier controls, 395 HBV spontaneously recovered (SR) controls and 1116 healthy controls were included. Compared with GG genotype, the genotypes GA/AA of G308A were associated with a significantly increased HBV-HCC risk when the controls were all healthy individuals (AA vs. GG, OR 2.483, 95%CI 1.243 to 4.959; GA vs. GG, OR 1.383, 95%CI 1.028 to 1.860; GA/AA vs. GG, OR 1.381, 95%CI 1.048 to 1.820). Meanwhile, only the AA vs. GG model of G238A and HBV-HCC showed a statistic significance when the controls were healthy individuals (OR 4.776, 95%CI 1.280 to 17.819). CT genotype of TNF-α C863T could increase HBV-HCC risk whenever the controls were healthy individuals, HBV carriers or HBV recovers.
CONCLUSION: This meta-analysis shows that AA genotype in TNF-α G308A and TNF-α G238A and CT genotype in TNF-α C863T may increase HBV-HCC risk. Therefore, HBV infection seemed to be a more important factor for tumorigenesis of HCC than genetic predisposition in G308A of TNF-α, and interaction between TNF-α C863T polymorphisms and HBV infection might be associated with increased HCC risk.

Khoshnam N, Robinson H, Clay MR, et al.
Calcifying nested stromal-epithelial tumor (CNSET) of the liver in Beckwith-Wiedemann syndrome.
Eur J Med Genet. 2017; 60(2):136-139 [PubMed] Related Publications
Calcifying nested stromal-epithelial tumor (CNSET) is a rare neoplasm. In the 31 reported cases, CNSET is predominantly found in young girls and women. Beckwith-Wiedemann syndrome (BWS) (OMIM #130650) is an overgrowth syndrome with an increased risk to develop cancer. Associations have been seen between BWS and embryonal tumors, especially Wilms tumor, hepatoblastoma, and adrenocortical carcinoma. Here we report on a female patient with BWS who presented with CNSET. Two other cases with the same association have been reported, with our case representing the third such reported in the literature. Although we recognize a potential reporting bias we speculate that CNSET may represent an unrecognized addition to the spectrum of BWS tumorigenesis.

Zhang L, Li Z, Sun F, et al.
Effect of inserted spacer in hepatic cell-penetrating multifunctional peptide component on the DNA intracellular delivery of quaternary complexes based on modular design.
Int J Nanomedicine. 2016; 11:6283-6295 [PubMed] Free Access to Full Article Related Publications
A safe and efficient quaternary gene delivery system (named Q-complexes) was constructed based on self-assembly of molecules through noncovalent bonds. This system was formulated through the cooperation and competing interactions of cationic liposomes, multifunctional peptides, and DNA, followed by coating hyaluronic acid on the surface of the ternary complexes. The multifunctional peptide was composed of two functional domains: penetrating hepatic tumor-targeted cell moiety (KRPTMRFRYTWNPMK) and a wrapping gene sequence (polyarginine 16). The effect of spacer insertion between the two domains of multifunctional peptide on the intracellular transfection of Q-complexes was further studied. Experimental results showed that the formulations assembled with various peptides in the spacer elements possessed different intercellular pathways and transfection efficiencies. The Q-complexes containing peptide in the absence of spacer element (Pa) showed the highest gene expression among all samples. The Q-complexes containing peptides with a noncleavable spacer GA (Pc) had no ability of intracellular nucleic acid delivery, whereas those with a cleavable spacer RVRR (Pd) showed moderate transfection activity. These results demonstrated that the different spacers inserted in the multifunctional peptide played an important role in in vitro DNA transfection efficiency. Atomic force microscopy images showed that the morphologies of ternary complexes (LPcD) and Q-complexes (HLcPD) were crystal lamellas, whereas those of other nanocomplexes were spheres. Circular dichroism showed the changed configuration of peptide with spacer GA in nanocomplexes compared with that of its free state, whereas the Pa configuration without spacer in nanocomplexes was consistent with that of its free state. The present study contributed to the structural understanding of Q-complexes, and further effective modification is in progress.

Zhang L, Jia G, Shi B, et al.
PRSS8 is Downregulated and Suppresses Tumour Growth and Metastases in Hepatocellular Carcinoma.
Cell Physiol Biochem. 2016; 40(3-4):757-769 [PubMed] Related Publications
BACKGROUND: Protease serine 8 (PRSS8), a trypsin-like serine peptidase, has been shown to function as a tumour suppressor in various malignancies. The present study aimed to investigate the expression pattern, prognostic value and the biological role of PRSS8 in human hepatocellular carcinoma (HCC).
METHODS: PRSS8 expression in 106 HCC surgical specimens was examined by Real-time polymerase chain reaction (PCR) and immunohistochemistry, and its clinical significance was analysed. The role of PRSS8 in cell proliferation, apoptosis and invasion were examined in vitro and in vivo.
RESULTS: PRSS8 mRNA and protein expression were decreased in most HCC tumours from that in matched adjacent non-tumour tissues. Low intratumoral PRSS8 expression was significantly correlated with poor overall survival (OS) in patients with HCC (P = 0.001). PRSS8 expression was an independent prognostic factor for OS (hazard ratio [HR] = 1.704, P = 0.009). Furthermore, restoring PRSS8 expression in high metastatic HCCLM3 cells significantly inhibited cell proliferation and invasion. In contrast, silencing PRSS8 expression in non-metastatic HepG2 cells significantly enhanced cell growth and invasion. Moreover, our in vivo data revealed that attenuated PRSS8 expression in HepG2 cells greatly promoted tumour growth, while overexpression of PRSS8 remarkably inhibited tumour growth in an HCCLM3 xenograft model. Enhanced cell growth and invasion ability mediated by the loss of PRSS8 expression was associated with downregulation of PTEN, Bax and E-cadherin and an upregulation in Bcl-2, MMP9 and N-cadherin.
CONCLUSIONS: Our data demonstrate that PRSS8 may serve as a tumour suppressor in HCC progression, and represent a valuable prognostic marker and potential therapeutic target for HCC.

Huang R, Wang X, Zhang W, et al.
Down-Regulation of LncRNA DGCR5 Correlates with Poor Prognosis in Hepatocellular Carcinoma.
Cell Physiol Biochem. 2016; 40(3-4):707-715 [PubMed] Related Publications
BACKGROUND/AIMS: Long non-coding RNAs (lncRNAs) have been reported to play pivotal roles in multiple tumors and can act as tumor biomarkers. In this study, we explored the association of the expression of an lncRNA, DGCR5 with clinicopathological features and prognosis in HCC.
METHODS: Expression levels of DGCR5 were detected by quantitative real-time PCR (qRT-PCR) and the clinical data was obtained, including basic information, data of clinicopathology and cancer specific survival rate. Receiver operating characteristic (ROC) curve, Kaplan-Meier methods and multivariable Cox regression models were used to analyze predictive efficiency, long-term survival outcomes and risk factors.
RESULTS: DGCR5 was found down-regulated in HCC tissues (P<0.001) and serum (P = 0.0035) and low expression of DGCR5 was correlated with a poor cancer specific survival (CSS) (P = 0.0019), as the overall 5-year CSS rates were 10.3% (low expression group) and 36.6% (high expression group), respectively. A stratified analysis demonstrated that low DGCR5 expression was an independent negative prognostic factor for HCC. In addition, the area under the ROC curve was 0.782 with a sensitivity of 0.633 and a specificity of 0.833.
CONCLUSIONS: Our results suggest that DGCR5 may be a participator in HCC and can serve as potential biomarker for the diagnosis and prognosis in HCC.

Allen JC, Nault JC, Zhu G, et al.
The transcriptomic G1-G6 signature of hepatocellular carcinoma in an Asian population: Association of G3 with microvascular invasion.
Medicine (Baltimore). 2016; 95(47):e5263 [PubMed] Free Access to Full Article Related Publications
In this study, a transcriptomic group classification based on a European population is tested on a Singapore cohort. The results highlight the genotype/phenotype correlation in a Southeast Asian population. The G1-G6 transcriptomic classification derived from hepatocellular carcinoma (HCC) resected from European patients, robustly reflected group-specific clinical/pathological features. We investigated the application of this molecular classification in Southeast Asian HCC patients.Gene expression analysis was carried out on HCC surgically resected in Singapore patients who were grouped into G1-G6 transcriptomic categories according to expression of 16 predictor genes (illustrated in Supplementary Table 1, http://links.lww.com/MD/B413 and Supplementary Fig. 1, http://links.lww.com/MD/B413) using quantitative reverse transcription polymerase chain reaction (RT-PCR). Univariate and multivariate polytomous logistic regression was used to investigate association between clinical variables and pooled transcriptomic classes G12, G3, and G456.HCC from Singapore (n = 82) were distributed (%) into G1 (13.4), G2 (24.4), G3 (15.9), G4 (24.4), G5 (14.6), and G6 (7.3) subgroups. Compared to the European data, the Singapore samples were relatively enriched in G1-G3 versus G4-G6 tumors (53.7% vs 46.3%) reflecting the higher proportion of hepatitis B virus (HBV) patients in Singapore versus Europe samples (43% vs 30%). Pooled classes were defined as G12, G3, and G456. G12 was associated with higher alpha-fetoprotein (AFP) concentrations (OR = 1.69, 95% CI: 1.30-2.20; P < 0.0001) and G3 with microvascular invasion (OR = 4.91, 95% CI: 1.06-24.8; P = 0.047).The European and Singapore cohorts were generally similar relative to associations between transcriptomic groups and clinical features. This lends credence to the G1-G6 transcriptomic classifications being applicable regardless of the ethnic origin of HCC patients. The G3 group was associated with microvascular invasion and holds potential for investigation into the underlying mechanisms and selection for therapeutic clinical trials.

Tian YW, Shen Q, Jiang QF, et al.
Decreased levels of miR-34a and miR-217 act as predictor biomarkers of aggressive progression and poor prognosis in hepatocellular carcinoma.
Minerva Med. 2017; 108(2):108-113 [PubMed] Related Publications
BACKGROUND: MicroRNAs (miRNAs) play key roles in tumor development and progression. The aim of this study was to explore the expression levels of miR-34a and miR-217 in hepatocellular carcinoma (HCC) and to further investigate the clinicopathological and prognostic value of miR-34a and miR-217.
METHODS: The expression levels of miR-34a and miR-217 were evaluated using quantitative real-time PCR (qRT-PCR). Associations between these miRNAs expression and clinicopathological features were analyzed. Survival rate was determined with Kaplan-Meier and statistically analyzed with the log-rank method between groups.
RESULTS: We found that miR-34a expression was significantly downregulated in HCC tissues (P<0.05). Reduced expression of miR-34a was associated with vascular invasion, and advanced TNM stage (P<0.05). Kaplan-Meier revealed that reduced expression of miR-34a was associated with poor overall survival (log-rank test, P<0.05). We found that miR-217 was downregulated in HCC tissues. Decreased expression of miR-217 was remarkably correlated vascular invasion, and advanced TNM stage (P<0.05). Kaplan-Meier analysis and log-rank test showed that HCC patients with low expression of miR-217 was associated with shorter overall survival than patients with high expression (log-rank test, P<0.05).
CONCLUSIONS: Our data showed that downregulation of miR-34a and miR-217 was associated with HCC progression and both of them may act as tumor suppressor in HCC.

Li Y, Ou C, Shu H, et al.
The ERCC1-4533/8092, TNF-α 238/308 polymorphisms and the risk of hepatocellular carcinoma in Guangxi Zhuang populations of China: Case-control study.
Medicine (Baltimore). 2016; 95(44):e5217 [PubMed] Related Publications
OBJECTIVE: To investigate the relationship between excision repair cross-complementing group 1 (ERCC1)-4533/8092, tumor necrosis factor-alpha (TNF-α)-238/308 polymorphisms, and the risk of hepatocellular carcinoma (HCC) in Guangxi Zhuang population of China.
METHODS: Polymerase chain reaction-restriction fragment length polymorphism method was used to detect the ERCC1-4533/8092 and TNF-α-238/308 polymorphisms in 88 cases with HCC and 82 cases of normal control.
RESULTS: There were no differences in the frequency distribution of ERCC1-4533 and TNF-α-238 polymorphisms in the HCC group and the control group (P > 0.05). The genotype frequency distributions of the ERCC1-8092 and TNF-α-308 in the HCC group and the control group were different (P < 0.05). Compared with ERCC1-8092 CC genotype, ERCC1-C8092 CA/AA genotype had higher risk of HCC (CA/AA vs CC; odds ratio 3.51, 95% confidence interval 1.03-12.016). Compared with TNF-α-308 GG genotype, TNF-α-308 GA/AA genotype was significantly associated with an increased risk of HCC (GA/AA vs GG; odds ratio 3.84, 95% confidence interval 1.011-14.57).
CONCLUSION: The genetic polymorphisms of ERCC1-8092 and TNF-α-308 are associated with the risk of HCC in Guangxi Zhuang population of China.

Han C, Yu L, Liu X, et al.
ATXN7 Gene Variants and Expression Predict Post-Operative Clinical Outcomes in Hepatitis B Virus-Related Hepatocellular Carcinoma.
Cell Physiol Biochem. 2016; 39(6):2427-2438 [PubMed] Related Publications
BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) is a lethal disease with nearly equal morbidity and mortality. Thus, the discovery and application of more useful predictive biomarkers for improving therapeutic effects and prediction of clinical outcomes is of crucial significance.
METHODS: A total of 475 HBV-related HCC patients were enrolled. Ataxin 7 (ATXN7) single nucleotide polymorphisms (SNPs) were genotyped by Sanger DNA sequencing after PCR amplification. The associations between ATXN7 SNPs and mRNA expression with the prognosis of HBV-related HCC were analyzed.
RESULTS: In all, rs3774729 was significantly associated with overall survival (OS) of HBV-related HCC (P = 0.013, HR = 0.66, 95% CI: 0.48-0.94). And patients with the AA genotype and a high level of serum alpha fetoprotein (AFP) had significantly worse OS when compared to patients with AG/GG genotypes and a low level of AFP (adjusted P = 0.007, adjusted HR = 1.83, 95% CI = 1.18-2.82). Furthermore, low expression of ATXN7 was significantly associated with poor recurrence-free survival (RFS) and OS (P = 0.007, HR = 2.38, 95% CI = 1.27-4.45 and P = 0.025, HR = 1.75, 95% CI = 1.18-2.62).
CONCLUSION: ATXN7 may be a potential predictor of post-operative prognosis of HBV-related HCC.

Wang F, Yang H, Deng Z, et al.
HOX Antisense lincRNA HOXA-AS2 Promotes Tumorigenesis of Hepatocellular Carcinoma.
Cell Physiol Biochem. 2016; 40(1-2):287-296 [PubMed] Related Publications
BACKGROUND: Recent studies reveal that long non-coding RNAs (LncRNAs) play critical roles in the proliferation and migration of human cancer. Previous report has shown that LncRNA HOXA-AS2 was involved in carcinoma processes. However, the expression and biological function of HOXA-AS2 in hepatocellular carcinoma (HCC) are poorly understood.
METHODS: Quantitative real-time PCR (qRT-PCR) was performed to detect the expression of HOXA-AS2 in HCC tissues and cell lines. The relation between lncRNA HOXA-AS2 expression and clinicopathological characteristics was assessed by chi-square test. The prognosis was analyzed using Kaplan-Meier method, and compared differences between the two groups by log-rank test. The biological function of HOXA-AS2 on HCC cells were determined both in vitro and in vivo.
RESULTS: In the present study, we found that HOXA-AS2 expression was increased in HCC tissues and adjacent normal tissues and high HOXA-AS2 expression was associated with bigger tumor size, advanced tumor stage, and shorter survival time. Knockdown of HOXA-AS2 significantly inhibited HCC cell proliferation and invasion and resulted in an increase of apoptosis. Furthermore, inhibition of HOXA-AS2 in HCC cells significantly repressed tumorigenicity in nude mice.
CONCLUSION: Our results indicated that the inhibition of HOXA-AS2 in HCC cells significantly inhibited cell proliferation in vitro and in vivo, which might provide a potential possibility for targeted therapy of HCC.

Zhang H, Wang F, Hu Y
STARD13 promotes hepatocellular carcinoma apoptosis by acting as a ceRNA for Fas.
Biotechnol Lett. 2017; 39(2):207-217 [PubMed] Related Publications
OBJECTIVES: To study the roles of STARD13 in cellular apoptosis of hepatocellular carcinoma (HCC).
RESULTS: Quantitative real-time PCR and immunohistochemistry analyses showed that the expression levels of STARD13 and Fas were lower in clinical HCC tissues than in normal tissues and were positively correlated, which is consistent with the results analyzed by The Cancer Genome Atlas (TCGA) data. Patients with higher STARD13 or Fas expression levels had longer overall survival. Additionally, STARD13 3'-UTR enhanced cellular apoptosis and the 3'-UTRs of STARD13 and Fas were predicted to harbor nine similar miRNA binding sites. And STARD13 3'-UTR promoted Fas expression in a 3'-UTR- and miRNA-dependent way and increased the sensitivity of HCC cells to chemotherapy. Importantly, the coding sequence of STARD13 did not increase Fas expression.
CONCLUSIONS: STARD13 3'-UTR promotes HCC apoptosis through acting as a ceRNA for Fas.

Ren K, Li T, Zhang W, et al.
miR-199a-3p inhibits cell proliferation and induces apoptosis by targeting YAP1, suppressing Jagged1-Notch signaling in human hepatocellular carcinoma.
J Biomed Sci. 2016; 23(1):79 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: miR-199a-3p was significantly downregulated in the majority of human hepatocellular carcinoma (HCC) tissues and HCC cell lines. Yes associated protein 1 (YAP1) was overexpressed in human HCC, which promoted HCC development and progression by upregulating Jagged1 and activating the Notch pathway. We searched potential targets of miR-199a-3p with DIANA, TargetScan and PicTar tools, and found that YAP1 is one of the potential targets. Based on these findings, we speculated that miR-199a-3p might suppress HCC growth by targeting YAP1, downregulating Jagged1 and suppressing the Notch pathway.
RESULTS: We determined the expression of miR-199a-3p and YAP1 by quantitative Real-Time PCR (qRT-PCR) and western blot assays, respectively, and found downregulation of miR-199a-3p and upregulation of YAP1 in HCC cell lines. Cell proliferation and apoptosis assays showed that miR-199a-3p suppresses HCC cell proliferation and promotes apoptosis, and knockdown of YAP1 has similar role. Furthermore, we verified that miR-199a-3p can directly target YAP1. We further investigated and confirmed that miR-199a-3p and YAP1 regulate HCC cell proliferation and apoptosis through Jagged1-Notch signaling.
CONCLUSION: miR-199a-3p targets YAP1, downregulates Jagged1 and suppresses the Notch signaling to inhibit HCC cell proliferation and promote apoptosis. These findings provide new insights into the mechanism by which miR-199a-3p suppresses HCC cell proliferation and induces apoptosis.

Kazan H
Modeling Gene Regulation in Liver Hepatocellular Carcinoma with Random Forests.
Biomed Res Int. 2016; 2016:1035945 [PubMed] Free Access to Full Article Related Publications
Liver hepatocellular carcinoma (HCC) remains a leading cause of cancer-related death. Poor understanding of the mechanisms underlying HCC prevents early detection and leads to high mortality. We developed a random forest model that incorporates copy-number variation, DNA methylation, transcription factor, and microRNA binding information as features to predict gene expression in HCC. Our model achieved a highly significant correlation between predicted and measured expression of held-out genes. Furthermore, we identified potential regulators of gene expression in HCC. Many of these regulators have been previously found to be associated with cancer and are differentially expressed in HCC. We also evaluated our predicted target sets for these regulators by making comparison with experimental results. Lastly, we found that the transcription factor E2F6, one of the candidate regulators inferred by our model, is predictive of survival rate in HCC. Results of this study will provide directions for future prospective studies in HCC.

Liu L, Guo W, Zhang J
Association of HLA-DRB1 gene polymorphisms with hepatocellular carcinoma risk: a meta-analysis.
Minerva Med. 2017; 108(2):176-184 [PubMed] Related Publications
INTRODUCTION: The study aimed to assess the association between human leukocyte antigen (HLA)-DRB1 allele polymorphisms and hepatocellular carcinoma (HCC) susceptibility.
EVIDENCE ACQUISITION: Relevant case-control studies on HLA-DRB1 allele correlation with HCC risk published between 2000 and 2015 were searched and retrieved in literature database. The odds ratio (OR) with its 95% confidence interval (CI) were employed to calculate the strength of association. Total 16 articles including 2208 HCC patients and 3028 relevant controls were finally screened out.
EVIDENCE SYNTHESIS: A total of 12 case-control studies including 2030 HCC patients and 2817 relevant controls were screened out. Thirteen alleles (HLA-DRB1 *01, *03, *04, *07, *08, *09, *10, *11, *12, *13, *14, *15, and *16) were reported. Overall, we found that HLA-DRB1 *1 and *11 allele polymorphisms were significantly associated with decreased the HCC risk (*1: OR=0.53, 95% CI: 0.29-0.96, P=0.04; *11: OR=0.58, 95% CI: 0.38-0.88, P=0.010); while *12 and *14 allele polymorphisms were significantly associated with increased the HCC risk (*12: OR=1.49, 95% CI: 1.08-2.07, P=0.02; *14: OR=1.89, 95% CI: 1.27-2.82, P=0.002) in a fixed-effect model. However, other HLA-DRB1 allele polymorphisms were not associated with HCC susceptibility (P>0.05).
CONCLUSIONS: HLA-DRB1 *1 and *11 allele polymorphisms were protective factors, *12 and *14 allele polymorphisms were risk factors for HCC development. Future large-scale studies with more ethnicities are still needed.

Ding J, Lu SC
Low metallothionein 1M expression association with poor hepatocellular carcinoma prognosis after curative resection.
Genet Mol Res. 2016; 15(4) [PubMed] Related Publications
According to the typical clinical characteristics of hepatocellular carcinoma (HCC), recurrence and prognosis can differ dramatically between patients. Using RNA sequencing, we identified differential expression of the gene metallothionein 1M (MT1M) by comparing early-recurrence HCC (N = 11), no-recurrence HCC (N = 10), and normal liver tissues (N = 5). Reverse transcription-polymerase chain reaction was employed to test MT1M expression levels in 92 HCC tissue samples from a cohort of patients with whom contact was established for post-operative follow-up. Low MT1M expression correlated with high alpha-fetoprotein levels (P = 0.017) and tumor recurrence within 24 months after surgery (P = 0.029). Recurrence rates in high- and low-MT1M groups were significantly different (MT1M cutoff point = 0.066; P = 0.008). Moreover, the disease-free survival time of patients in the former was longer than that of those in the latter (median of 20.39 vs 14.35 months, respectively; P = 0.002). Among early-stage HCC patients (Barcelona Clinic Liver Cancer stage 0/A), those with reduced MT1M expression exhibited higher recurrence rates (37.5 vs 12.1%; P = 0.023). Low MT1M expression is associated with poor HCC prognosis following curative resection, and this also applies to the early stage of this disease.

Chen LL, Shen Y, Zhang JB, et al.
Association between polymorphisms in the promoter region of pri-miR-34b/c and risk of hepatocellular carcinoma.
Genet Mol Res. 2016; 15(4) [PubMed] Related Publications
Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. MicroRNA-34 (miR-34) gene plays a key role in altering the apoptotic cycle and pathways of downstream cells, and therefore influences carcinogenesis. In this case-control study, we assessed the role of the pri-miR-34b/c rs4938723 polymorphism in HCC risk. The pri-miR-34b/c polymorphic genotype was determined in 286 patients with HCC and 572 controls using polymerase chain reaction-restriction fragment length polymorphism. The male gender (X(2) = 12.95, P < 0.001), regular alcohol consumption (X(2) = 16.81, P < 0.001), and a family history of cancer (X(2) = 11.88, P = 0.001) were associated with HCC risk. However, the age (t = 1.19, P = 0.12) and tobacco smoking habit (X(2) = 0.64, P = 0.42) of HCC patients were comparable to those of the controls. The TC (adjusted OR = 1.46, 95%CI = 1.06-2.01) and CC (adjusted OR = 3.07, 95%CI = 1.77-5.34) genotypes of pri-miR-34b/c rs4938723 were correlated with a higher risk of HCC compared to the TT genotype. Moreover, the TC+CC genotype was correlated with an increased risk of HCC compared to the TT genotype (adjusted OR = 1.64, 95%CI = 1.21-2.22). In the recessive model, the CC genotype of pri-miR-34b/c rs4938723 was significantly correlated with an elevated risk of HCC compared to the TT+TC genotype (adjusted OR = 2.50, 95%CI = 1.49-4.22). Further large-scale and multi-center studies are required to confirm these results.

Gupta MK, Behara SK, Vadde R
In silico analysis of differential gene expressions in biliary stricture and hepatic carcinoma.
Gene. 2017; 597:49-58 [PubMed] Related Publications
In-silico attempt was made to identify the key hub genes which get differentially expressed in biliary stricture and hepatic carcinoma. Gene expression data, GSE34166, was downloaded from the GEO database, which contains 10 biliary stricture samples (4 benign control and 6 malignant carcinoma), for screening of key hub genes associated with the disease. R packages scripts were identified 85 differentially expressed genes. Further these genes were uploaded in WebGestalt database and identified nine key genes. Using STRING database and Gephi software, the protein-protein interaction networks were constructed and also studied gene ontology through WebGestalt. Finally, we identified four key genes (CXCR4, ADH1C, ABCB1 and ADH1A) are associated with liver carcinoma and further cross-validated with Liverome, Protein Atlas database and bibliography. In addition, transcription factors and their binding sites also studied. These identified hub genes and their transcription factors are the probable potential targets for possible future drug design.

Hiwatashi K, Ueno S, Sakoda M, et al.
Expression of Maternal Embryonic Leucine Zipper Kinase (MELK) Correlates to Malignant Potentials in Hepatocellular Carcinoma.
Anticancer Res. 2016; 36(10):5183-5188 [PubMed] Related Publications
BACKGROUND/AIM: Maternal embryonic leucine zipper kinase (MELK) is categorized as a member of AMP-activated protein kinase families. Various MELK-associated cellular and biological processes affect multiple stages of tumorigenesis. The aim of the present study was to clarify the relationship between MELK expression and hepatocellular carcinoma (HCC) clinicopathological features.
MATERIALS AND METHODS: In thirty conserved frozen primary HCC and non-HCC samples MELK mRNA expression was examined by quantitative real-time polymerase chain reaction (PCR).
RESULTS: HCC tissues exhibited significantly higher expression levels compared to non-cancerous tissues. MELK expression had a statistically parallel correlation between tumor diameter and protein induced by vitamin K absence or antagonist II (PIVKA-II). The overall survival (OS) and recurrence-free survival (RFS) of the low MELK mRNA expression group was significantly longer than that of the high MELK mRNA expression group.
CONCLUSION: MELK expression in HCC is extremely intense compared to its expression reported in other types of cancer. MELK could be a promising effective tumor marker of HCC and further consideration is needed.

Hass HG, Vogel U, Scheurlen M, Jobst J
Gene-expression Analysis Identifies Specific Patterns of Dysregulated Molecular Pathways and Genetic Subgroups of Human Hepatocellular Carcinoma.
Anticancer Res. 2016; 36(10):5087-5095 [PubMed] Related Publications
BACKGROUND: Hepatocellular carcinoma comprises of a group of heterogeneous tumors of different etiologies. The multistep process of liver carcinogenesis involves various genetic and phenotypic alterations. The molecular pathways and driver mutations involved are still under investigation.
MATERIALS AND METHODS: DNA micorarray technology was used to identify differentially expressed genes between human hepatocarcinoma and non-tumorous liver tissues to establish a unique specific gene-expression profile independent of the underlying liver disease. The validity of this global gene-expression profile was tested for its robustness against biopsies from other liver entities (cirrhotic and non-cirrhotic liver) by diagnosing HCC in blinded samples.
RESULTS: Most of the consistently and strongly overexpressed genes were related to cell-cycle regulation and DNA replication [27 genes, e.g. cyclin B1, karyopherin alpha 2 (KPNA2), cyclin-dependent kinase 2 (CDC2)], G-protein depending signaling [e.g. Rac GTPase activating protein 1 (RACGAP1), Rab GTPase YPT1 homolog (RAB1), and ADP-ribosylation factor-like 2 (ARL2)] and extracellular matrix re-modelling or cytoskeleton structure [22 genes, e.g. serine proteinase inhibitor 1 kazal-type (SPINK1), osteopontin (OPN), secreted protein acidic and rich in cysteine (SPARC), collagen type 1 alpha2 (COL1A2), integrin alpha6 (ITGA6), and metalloproteinase 12 (MMP12)]. Furthermore, significantly differentially expressed genes (e.g. calcium-binding proteins, G-proteins, oncofetal proteins) in relation to tumor differentiation were detected using gene-expression analysis.
CONCLUSION: It is suggested that these significantly dysregulated genes are highly specific and potentially utilizable as prognostic markers and may lead to a better understanding of human hepatocarcinogenesis.

Wang L, Cai Y, Shi Y, et al.
[Role of inhibition of nuclear factor-kappa B gene transcription by specific miRNA in reversing multi-drug resistance of liver cancer].
Zhonghua Gan Zang Bing Za Zhi. 2016; 24(7):493-499 [PubMed] Related Publications
Objective: To investigate the reversal effect of inhibition of nuclear factor-kappa B (NF-κB) gene transcription by specific miRNA on multi-drug resistance (MDR)of liver cancer. Methods: The expression of P-glycoprotein (P-gp) and NF-κB in hepatoma cells, drug-resistant HepG2/ADM cells, and liver cells (LO2 cells) was analyzed. Specific NF-κB miRNA plasmids were constructed, screened, and transfected into HepG2 or HepG2/ADM cells. Western blot was used to measure the concentrations of P-gp and NF-κB, and FQ-PCR was used to measure gene expression; Cell Counting Kit-8 assay was used to measure cell proliferation and the influence of drugs on cell proliferation; flow cytometry and Annexin-V-PE/7-ADD double staining were used to observe cell cycle and apoptosis. The t-test was used to compare means between groups, and a one-way analysis of variance was used to compare means between multiple groups. Results: After being treated by adriamycin, hepatoma cells showed increased expression of P-gp and an increased level of NF-κB phosphorylation. At 24, 48, and 72 hours, the resistance index of the HepG2/ADM cells (IC50 = 4.166, 1.522, and 1.380 μmol/L) was 8.519, 6.874, and 6.166 times that of the HepG2 cells (IC50 = 0.489, 0.221, and 0.224 μmol/L). The HepG2/ADM cells showed significantly higher relative mRNA expression (∆ct value) of mdr1 and NF-κB than the HepG2 cells (3.310±0.154/2.580±0.040 vs 0.084±0.038/0.6067±0.032, both P < 0.01). After being transfected with miRNA1, the HepG2/ADM cells showed significantly lower mRNA expression of mdr1 than the cells in the miRNA-negative group (2(-∆∆ct) = 0.326±0.011 vs 0.804±0.057, t = 14.262, P < 0.01), as well as significant reductions in the expression of intracellular t-p65, nuclear p-p65, and P-gp compared with the cells in the miRNA-negative group (P < 0.01), with inhibited cell proliferation, G1 phase arrest, and increased apoptosis. Conclusion: Abnormal expression of MDR1/P-gp is closely associated with MDR, and inhibition of NF-κB activation by specific miRNA can significantly inhibit MDR1/P-gp gene transcription and reverse MDR of liver cancer.

Likhitrattanapisal S, Tipanee J, Janvilisri T
Meta-analysis of gene expression profiles identifies differential biomarkers for hepatocellular carcinoma and cholangiocarcinoma.
Tumour Biol. 2016; 37(9):12755-12766 [PubMed] Related Publications
Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) are the members of hepatobiliary diseases. Both types of cancer often exert high levels of similarity in terms of phenotypic characteristics, thus leading to difficulties in HCC and CCA differential diagnoses. In this study, a transcriptome meta-analysis was performed on HCC and CCA microarray data to identify differential transcriptome networks and potential biomarkers for HCC and CCA. Raw data from nine gene expression profiling datasets, consisting of 1,185 samples in total, were methodologically compiled and analyzed. To evaluate differentially expressed (DE) genes in HCC and CCA, the levels of gene expression were compared between cancer and its normal counterparts (i.e., HCC versus normal liver and CCA versus normal bile duct) using t test (P < 0.05) and k-fold validation. A total of 226 DE genes were specific to HCC, 249 DE genes specific to CCA, and 41 DE genes in both HCC and CCA. Gene ontology and pathway enrichment analyses revealed different patterns between functional transcriptome networks of HCC and CCA. Cell cycle and glycolysis/gluconeogenesis pathways were exclusively dysregulated in HCC whereas complement and coagulation cascades as well as glycine, serine, and threonine metabolism were prodominantly differentially expressed in CCA. Our meta-analysis revealed distinct dysregulation in transcriptome networks between HCC and CCA. Certain genes in these networks were discussed in the context of HCC and CCA transition, unique characteristics of HCC and CCA, and their potentials as HCC and CCA differential biomarkers.

Zekri AN, Youssef AS, El-Desouky ED, et al.
Serum microRNA panels as potential biomarkers for early detection of hepatocellular carcinoma on top of HCV infection.
Tumour Biol. 2016; 37(9):12273-12286 [PubMed] Related Publications
The identification of new high-sensitivity and high-specificity markers for hepatocellular carcinoma (HCC) is essential. We aimed at identifying serum microRNAs (miRNAs) as potential biomarkers for early detection of HCC on top hepatitis C virus (HCV) infection. We investigated serum expression of 13 miRNAs in 384 patients with HCV-related chronic liver disease (192 with HCC, 96 with liver cirrhosis (LC), and 96 with chronic hepatitis C (CHC)) in addition to 96 healthy participants enrolled as a control group. The miRNA expression was performed using real-time quantitative PCR-based SYBR Green custom miRNA arrays. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic performance of miRNA panels for early detection of HCC. Using miRNA panel of miR-122, miR-885-5p, and miR-29b with alpha fetoprotein (AFP) provided high diagnostic accuracy (AUC = 1) for early detection of HCC in normal population while using miRNA panel of miR-122, miR-885-5p, miR-221, and miR-22 with AFP provided high diagnostic accuracy (AUC = 0.982) for early detection of HCC in LC patients. However, using miRNA panel of miR-22 and miR-199a-3p with AFP provided high diagnostic accuracy (AUC = 0.988) for early detection of HCC in CHC patients. We identified serum miRNA panels that could have a considerable clinical use in early detection of HCC in both normal population and high-risk patients.

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Cite this page: Cotterill SJ. Liver Cancer, Cancer Genetics Web: http://www.cancer-genetics.org/X070601.htm Accessed:

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