Gastric 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 (409)

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
CTNNB1 3p22.1 CTNNB, MRD19, armadillo -CTNNB1 mutations in Gastric Cancer
460
CDH1 16q22.1 UVO, CDHE, ECAD, LCAM, Arc-1, CD324 -CDH1 and Stomach Cancer
298
TNF 6p21.3 DIF, TNFA, TNFSF2, TNF-alpha -TNF and Stomach Cancer
237
MET 7q31 HGFR, AUTS9, RCCP2, c-Met Prognostic
-C-MET and Stomach Cancer
219
BAX 19q13.33 BCL2L4 -BAX and Stomach Cancer
170
APC 5q21-q22 GS, DP2, DP3, BTPS2, DP2.5, PPP1R46 -APC and Stomach Cancer
168
PTGS2 1q25.2-q25.3 COX2, COX-2, PHS-2, PGG/HS, PGHS-2, hCox-2, GRIPGHS -PTGS2 (COX2) and Stomach Cancer
122
CEACAM5 19q13.2 CEA, CD66e -CEACAM5 and Stomach Cancer
107
CD44 11p13 IN, LHR, MC56, MDU2, MDU3, MIC4, Pgp1, CDW44, CSPG8, HCELL, HUTCH-I, ECMR-III -CD44 and Stomach Cancer
98
KRAS 12p12.1 NS, NS3, CFC2, KRAS1, KRAS2, RASK2, KI-RAS, C-K-RAS, K-RAS2A, K-RAS2B, K-RAS4A, K-RAS4B -KRAS and Stomach Cancer
96
IL10 1q31-q32 CSIF, TGIF, GVHDS, IL-10, IL10A -Interleukin-10 and Stomach Cancer
93
PTEN 10q23.31 BZS, DEC, CWS1, GLM2, MHAM, TEP1, MMAC1, PTEN1, 10q23del -PTEN and Stomach Cancer
91
CASP3 4q34 CPP32, SCA-1, CPP32B -CASP3 and Stomach Cancer
86
RUNX3 1p36 AML2, CBFA3, PEBP2aC -RUNX3 and Stomach Cancer
79
MTHFR 1p36.22 -MTHFR and Stomach Cancer
79
ABCB1 7q21.12 CLCS, MDR1, P-GP, PGY1, ABC20, CD243, GP170 -ABCB1 and Stomach Cancer
73
PCNA 20pter-p12 ATLD2 -PCNA and Stomach Cancer
68
MUC1 1q21 EMA, MCD, PEM, PUM, KL-6, MAM6, MCKD, PEMT, CD227, H23AG, MCKD1, MUC-1, ADMCKD, ADMCKD1, CA 15-3, MUC-1/X, MUC1/ZD, MUC-1/SEC -MUC1 and Gastric Cancer
-MUC1 polymorphisms and cancer suseptability?
59
MUC2 11p15.5 MLP, SMUC, MUC-2 -MUC2 and Stomach Cancer
65
KITLG 12q22 SF, MGF, SCF, FPH2, FPHH, KL-1, Kitl, SHEP7 -KITLG and Stomach Cancer
64
MSH2 2p21 FCC1, COCA1, HNPCC, LCFS2, HNPCC1 -MSH2 and Stomach Cancer
62
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II Prognostic
-MMP2 and Stomach Cancer
62
CDKN1B 12p13.1-p12 KIP1, MEN4, CDKN4, MEN1B, P27KIP1 Prognostic
-CDKN1B and Gastric Cancer
54
MYC 8q24.21 MRTL, MYCC, c-Myc, bHLHe39 -MYC protein, human and Stomach Cancer
52
DCC 18q21.3 CRC18, CRCR1, MRMV1, IGDCC1, NTN1R1 -DCC and Stomach Cancer
51
MALT1 18q21 MLT, MLT1, IMD12 -MALT1 and Stomach Cancer
50
MGMT 10q26 -MGMT and Stomach Cancer
49
TGFBR2 3p22 AAT3, FAA3, LDS2, MFS2, RIIC, LDS1B, LDS2B, TAAD2, TGFR-2, TGFbeta-RII -TGFBR2 and Stomach Cancer
48
MUC5AC 11p15.5 TBM, leB, MUC5, mucin -MUC5AC and Stomach Cancer
46
VEGFA 6p12 VPF, VEGF, MVCD1 -VEGFA and Stomach Cancer
45
PIK3CA 3q26.3 MCM, CWS5, MCAP, PI3K, CLOVE, MCMTC, p110-alpha -PIK3CA and Stomach Cancer
43
PSCA 8q24.2 PRO232 -MUC1 polymorphisms and cancer suseptability?
-PSCA and Stomach Cancer
34
TFF1 21q22.3 pS2, BCEI, HPS2, HP1.A, pNR-2, D21S21 -TFF1 and Stomach Cancer
42
FGFR2 10q26 BEK, JWS, BBDS, CEK3, CFD1, ECT1, KGFR, TK14, TK25, BFR-1, CD332, K-SAM -FGFR2 and Stomach Cancer
42
PDGFRA 4q12 CD140A, PDGFR2, PDGFR-2, RHEPDGFRA -PDGFRA and Stomach Cancer
41
TLR4 9q33.1 TOLL, CD284, TLR-4, ARMD10 -TLR4 and Stomach Cancer
41
ERCC1 19q13.32 UV20, COFS4, RAD10 -ERCC1 and Stomach Cancer
39
SMAD4 18q21.1 JIP, DPC4, MADH4, MYHRS -SMAD4 and Stomach Cancer
38
FHIT 3p14.2 FRA3B, AP3Aase -FHIT and Stomach Cancer
37
MUC6 11p15.5 MUC-6 -MUC6 and Stomach Cancer
37
PPARG 3p25 GLM1, CIMT1, NR1C3, PPARG1, PPARG2, PPARgamma -PPARG and Stomach Cancer
36
IL1RN 2q14.2 DIRA, IRAP, IL1F3, IL1RA, MVCD4, IL-1RN, IL-1ra, IL-1ra3, ICIL-1RA -IL1RN and Stomach Cancer
35
MTOR 1p36.2 FRAP, FRAP1, FRAP2, RAFT1, RAPT1 -MTOR and Stomach Cancer
33
FOS 14q24.3 p55, AP-1, C-FOS -FOS and Stomach Cancer
31
MDM2 12q14.3-q15 HDMX, hdm2, ACTFS -MDM2 and Stomach Cancer
29
CYP2E1 10q26.3 CPE1, CYP2E, P450-J, P450C2E -CYP2E1 and Stomach Cancer
28
MMP7 11q22.2 MMP-7, MPSL1, PUMP-1 -MMP7 and Stomach Cancer
28
DAPK1 9q21.33 DAPK -DAPK1 and Stomach Cancer
25
GAPDH 12p13 G3PD, GAPD, HEL-S-162eP -GAPDH and Stomach Cancer
24
H19 11p15.5 ASM, BWS, WT2, ASM1, D11S813E, LINC00008, NCRNA00008 -H19 and Stomach Cancer
23
BCL10 1p22 CLAP, mE10, CIPER, IMD37, c-E10, CARMEN -BCL10 and Stomach Cancer
22
HLA-A 6p21.3 HLAA -HLA-A and Stomach Cancer
22
VEGFC 4q34.3 VRP, Flt4-L, LMPH1D -VEGFC and Stomach Cancer
22
DAPK2 15q22.31 DRP1, DRP-1 -DAPK2 and Stomach Cancer
22
CD82 11p11.2 R2, 4F9, C33, IA4, ST6, GR15, KAI1, SAR2, TSPAN27 -CD82 and Stomach Cancer
21
IL17A 6p12 IL17, CTLA8, IL-17, IL-17A -IL17A and Stomach Cancer
20
MAPK1 22q11.21 ERK, p38, p40, p41, ERK2, ERT1, ERK-2, MAPK2, PRKM1, PRKM2, P42MAPK, p41mapk, p42-MAPK -MAPK1 and Stomach Cancer
20
ALDH2 12q24.2 ALDM, ALDHI, ALDH-E2 -ALDH2 and Stomach Cancer
20
CHFR 12q24.33 RNF116, RNF196 -CHFR and Stomach Cancer
19
FAS 10q24.1 APT1, CD95, FAS1, APO-1, FASTM, ALPS1A, TNFRSF6 -FAS and Stomach Cancer
19
TFF2 21q22.3 SP, SML1 -TFF2 and Stomach Cancer
19
JAK2 9p24.1 JTK10, THCYT3 -JAK2 and Stomach Cancer
19
IL17C 16q24 CX2, IL-17C -IL17C and Stomach Cancer
17
CDK6 7q21-q22 MCPH12, PLSTIRE -CDK6 and Stomach Cancer
17
CBL 11q23.3 CBL2, NSLL, C-CBL, RNF55, FRA11B -Proto-Oncogene Proteins c-cbl and Stomach Cancer
17
TIMP3 22q12.3 SFD, K222, K222TA2, HSMRK222 -TIMP3 and Stomach Cancer
16
TLR2 4q32 TIL4, CD282 -TLR2 and Stomach Cancer
16
MCC 5q21 MCC1 -MCC and Stomach Cancer
14
CDH17 8q22.1 HPT1, CDH16, HPT-1 -CDH17 and Stomach Cancer
14
IGF2 11p15.5 GRDF, IGF-II, PP9974, C11orf43 -IGF2 and Stomach Cancer
13
CDX1 5q32 -CDX1 and Stomach Cancer
13
PTP4A3 8q24.3 PRL3, PRL-3, PRL-R -PTP4A3 and Stomach Cancer
12
OLFM4 13q14.3 GC1, OLM4, OlfD, GW112, hGC-1, hOLfD, UNQ362, bA209J19.1 -OLFM4 and Stomach Cancer
12
MMP1 11q22.2 CLG, CLGN -MMP1 and Stomach Cancer
12
CISH 3p21.3 CIS, G18, SOCS, CIS-1, BACTS2 -CISH and Stomach Cancer
12
FASLG 1q23 APTL, FASL, CD178, CD95L, ALPS1B, CD95-L, TNFSF6, APT1LG1 -FASLG and Stomach Cancer
12
WNT2 7q31.2 IRP, INT1L1 -WNT2 and Stomach Cancer
12
GLI1 12q13.2-q13.3 GLI -GLI1 and Stomach Cancer
12
SMAD7 18q21.1 CRCS3, MADH7, MADH8 -SMAD7 and Stomach Cancer
12
REG4 1p13.1-p12 GISP, RELP, REG-IV -REG4 and Stomach Cancer
11
PTCH1 9q22.3 PTC, BCNS, HPE7, PTC1, PTCH, NBCCS, PTCH11 -PTCH1 and Stomach Cancer
11
PTPN11 12q24 CFC, NS1, SHP2, BPTP3, PTP2C, PTP-1D, SH-PTP2, SH-PTP3 -PTPN11 and Stomach Cancer
11
MMP3 11q22.2 SL-1, STMY, STR1, CHDS6, MMP-3, STMY1 -MMP3 and Stomach Cancer
11
KRT20 17q21.2 K20, CD20, CK20, CK-20, KRT21 -KRT20 and Stomach Cancer
11
EZH2 7q35-q36 WVS, ENX1, EZH1, KMT6, WVS2, ENX-1, EZH2b, KMT6A -EZH2 and Stomach Cancer
11
GRB7 17q12 -GRB7 and Stomach Cancer
11
UGT1A1 2q37 GNT1, UGT1, UDPGT, UGT1A, HUG-BR1, BILIQTL1, UDPGT 1-1 -UGT1A1 and Stomach Cancer
11
MMP14 14q11.2 MMP-14, MMP-X1, MT-MMP, MT1MMP, MTMMP1, WNCHRS, MT1-MMP, MT-MMP 1 -MMP14 and Stomach Cancer
10
WNT5A 3p21-p14 hWNT5A -WNT5A and Stomach Cancer
10
CCND2 12p13 MPPH3, KIAK0002 -CCND2 and Stomach Cancer
10
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Stomach Cancer
10
BUB1 2q14 BUB1A, BUB1L, hBUB1 -BUB1 and Stomach Cancer
10
S100A4 1q21 42A, 18A2, CAPL, FSP1, MTS1, P9KA, PEL98 -S100A4 and Stomach Cancer
10
BMI1 10p11.23 PCGF4, RNF51, FLVI2/BMI1 -BMI1 and Stomach Cancer
10
NOS2 17q11.2 NOS, INOS, NOS2A, HEP-NOS -NOS2 and Stomach Cancer
10
IL4 5q31.1 BSF1, IL-4, BCGF1, BSF-1, BCGF-1 -IL4 and Stomach Cancer
9
XAF1 17p13.1 BIRC4BP, XIAPAF1, HSXIAPAF1 -XAF1 and Stomach Cancer
9
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 and Stomach Cancer
9
TOP2A 17q21-q22 TOP2, TP2A -TOP2A and Stomach Cancer
9
MIR107 10q23.31 MIRN107, miR-107 -MicroRNA mir-107 and Stomach Cancer
9
ODC1 2p25 ODC -ODC1 and Stomach Cancer
8
KLF4 9q31 EZF, GKLF -KLF4 and Stomach Cancer
8
KLF6 10p15 GBF, ZF9, BCD1, CBA1, CPBP, PAC1, ST12, COPEB -KLF6 and Stomach Cancer
8
LGR5 12q22-q23 FEX, HG38, GPR49, GPR67, GRP49 -LGR5 and Stomach Cancer
8
SIRT1 10q21.3 SIR2, hSIR2, SIR2L1 -SIRT1 and Stomach Cancer
8
ZEB2 2q22.3 SIP1, SIP-1, ZFHX1B, HSPC082, SMADIP1 -ZEB2 and Stomach Cancer
8
MUC4 3q29 ASGP, MUC-4, HSA276359 -MUC4 and Stomach Cancer
8
EPCAM 2p21 ESA, KSA, M4S1, MK-1, DIAR5, EGP-2, EGP40, KS1/4, MIC18, TROP1, EGP314, HNPCC8, TACSTD1 -EPCAM and Stomach Cancer
8
HLA-B 6p21.3 AS, HLAB, SPDA1 -HLA-B and Stomach Cancer
8
STAR 8p11.2 STARD1 -STAR and Stomach Cancer
8
MIF 22q11.23 GIF, GLIF, MMIF -MIF and Stomach Cancer
8
AURKA 20q13 AIK, ARK1, AURA, BTAK, STK6, STK7, STK15, AURORA2, PPP1R47 -AURKA and Stomach Cancer
8
HLA-DRB1 6p21.3 SS1, DRB1, DRw10, HLA-DRB, HLA-DR1B -HLA-DRB1 and Stomach Cancer
8
LGALS3 14q22.3 L31, GAL3, MAC2, CBP35, GALBP, GALIG, LGALS2 -LGALS3 and Stomach Cancer
8
FADD 11q13.3 GIG3, MORT1 -FADD and Stomach Cancer
8
MOS 8q11 MSV -MOS and Stomach Cancer
8
SSTR2 17q24 -SSTR2 and Stomach Cancer
8
HMGB1 13q12 HMG1, HMG3, SBP-1 -HMGB1 and Stomach Cancer
8
WNT3A 1q42 -WNT3A and Stomach Cancer
8
PAK1 11q13.5-q14.1 PAKalpha -PAK1 and Stomach Cancer
8
SFRP2 4q31.3 FRP-2, SARP1, SDF-5 -SFRP2 and Stomach Cancer
8
ICAM1 19p13.3-p13.2 BB2, CD54, P3.58 -ICAM1 and Stomach Cancer
8
VIP 6q25 PHM27 -VIP and Stomach Cancer
8
DPYD 1p22 DHP, DPD, DHPDHASE -DPYD and Stomach Cancer
8
HLTF 3q25.1-q26.1 ZBU1, HLTF1, RNF80, HIP116, SNF2L3, HIP116A, SMARCA3 -HLTF and Stomach Cancer
7
GATA4 8p23.1-p22 TOF, ASD2, VSD1, TACHD -GATA4 and Stomach Cancer
7
PTER 10p12 HPHRP, RPR-1 -PTER and Stomach Cancer
7
ADH1B 4q23 ADH2, HEL-S-117 -ADH1B and Stomach Cancer
7
AICDA 12p13 AID, ARP2, CDA2, HIGM2, HEL-S-284 -AICDA and Stomach Cancer
7
GPX3 5q33.1 GPx-P, GSHPx-3, GSHPx-P -GPX3 and Stomach Cancer
7
ZNF217 20q13.2 ZABC1 -ZNF217 and Stomach Cancer
7
SMO 7q32.3 Gx, SMOH, FZD11 -SMO and Stomach Cancer
7
TPR 1q25 -TPR and Stomach Cancer
7
MALAT1 11q13.1 HCN, NEAT2, PRO2853, LINC00047, NCRNA00047 -MALAT1 and Stomach Cancer
7
MAD2L1 4q27 MAD2, HSMAD2 -MAD2L1 and Stomach Cancer
6
DKK3 11p15.3 RIG, REIC -DKK3 and Stomach Cancer
6
DROSHA 5p13.3 RN3, ETOHI2, RNASEN, RANSE3L, RNASE3L, HSA242976 -DROSHA and Stomach Cancer
6
S100A6 1q21 2A9, PRA, 5B10, CABP, CACY -S100A6 and Stomach Cancer
6
JAK1 1p32.3-p31.3 JTK3, JAK1A, JAK1B -JAK1 and Stomach Cancer
6
DICER1 14q32.13 DCR1, MNG1, Dicer, HERNA, RMSE2, Dicer1e, K12H4.8-LIKE -DICER1 and Stomach Cancer
6
SCFV 14 -SCFV and Stomach Cancer
6
ANO1 11q13.3 DOG1, TAOS2, ORAOV2, TMEM16A -ANO1 and Stomach Cancer
6
ROCK1 18q11.1 ROCK-I, P160ROCK -ROCK1 and Stomach Cancer
6
SFRP5 10q24.1 SARP3 -SFRP5 and Stomach Cancer
6
CLDN3 7q11.23 RVP1, HRVP1, C7orf1, CPE-R2, CPETR2 -CLDN3 and Stomach Cancer
6
TFPI2 7q22 PP5, REF1, TFPI-2 -TFPI2 and Stomach Cancer
6
PTK2 8q24.3 FAK, FADK, FAK1, FRNK, PPP1R71, p125FAK, pp125FAK -PTK2 and Stomach Cancer
6
GNAS 20q13.3 AHO, GSA, GSP, POH, GPSA, NESP, SCG6, SgVI, GNAS1, C20orf45 -GNAS and Stomach Cancer
6
IL11 19q13.3-q13.4 AGIF, IL-11 -IL11 and Stomach Cancer
6
FZD7 2q33 FzE3 -FZD7 and Stomach Cancer
6
TYMS 18p11.32 TS, TMS, HST422 -TYMS and Stomach Cancer
6
REG1A 2p12 P19, PSP, PTP, REG, ICRF, PSPS, PSPS1 -REG1A and Stomach Cancer
6
S100P 4p16 MIG9 -S100P and Stomach Cancer
6
GATA6 18q11.1-q11.2 -GATA6 and Stomach Cancer
6
APEX1 14q11.2 APE, APX, APE1, APEN, APEX, HAP1, REF1 -APEX1 and Stomach Cancer
6
FSCN1 7p22 HSN, SNL, p55, FAN1 -FSCN1 and Stomach Cancer
5
FYN 6q21 SLK, SYN, p59-FYN -FYN and Stomach Cancer
5
NOTO 2p13.2 -NOTO and Stomach Cancer
5
BCL2L12 19q13.3 -BCL2L12 and Stomach Cancer
5
POT1 7q31.33 CMM10, HPOT1 -POT1 and Stomach Cancer
5
ST7 7q31.2 HELG, RAY1, SEN4, TSG7, ETS7q, FAM4A, FAM4A1 -ST7 and Stomach Cancer
5
SPHK1 17q25.2 SPHK -SPHK1 and Stomach Cancer
5
CXCR3 Xq13 GPR9, MigR, CD182, CD183, Mig-R, CKR-L2, CMKAR3, IP10-R -CXCR3 and Stomach Cancer
5
PLA2G2A 1p35 MOM1, PLA2, PLA2B, PLA2L, PLA2S, PLAS1, sPLA2 -PLA2G2A and Stomach Cancer
5
RASSF2 20p13 CENP-34, RASFADIN -RASSF2 and Stomach Cancer
5
IL1A 2q14 IL1, IL-1A, IL1F1, IL1-ALPHA -IL1A and Stomach Cancer
5
ING4 12p13.31 my036, p29ING4 -ING4 and Stomach Cancer
5
DIABLO 12q24.31 SMAC, DFNA64 -DIABLO and Stomach Cancer
5
PINX1 8p23 LPTL, LPTS -PINX1 and Stomach Cancer
5
LGALS1 22q13.1 GBP, GAL1 -LGALS1 and Stomach Cancer
5
FH 1q42.1 MCL, FMRD, LRCC, HLRCC, MCUL1 -FH and Stomach Cancer
5
MUC5B 11p15.5 MG1, MUC5, MUC9, MUC-5B -MUC5B and Stomach Cancer
5
ING1 13q34 p33, p47, p33ING1, p24ING1c, p33ING1b, p47ING1a -ING1 Supression in Gastric Cancer
5
GATA5 20q13.33 GATAS, bB379O24.1 -GATA5 and Stomach Cancer
5
TNKS 8p23.1 TIN1, ARTD5, PARPL, TINF1, TNKS1, pART5, PARP5A, PARP-5a -TNKS and Stomach Cancer
5
CD274 9p24 B7-H, B7H1, PDL1, PD-L1, PDCD1L1, PDCD1LG1 -CD274 and Stomach Cancer
5
CTSB 8p22 APPS, CPSB -CTSB and Stomach Cancer
5
CTTN 11q13.3 EMS1 -CTTN and Stomach Cancer
5
NBN 8q21 ATV, NBS, P95, NBS1, AT-V1, AT-V2 -NBN and Stomach Cancer
5
S100A2 1q21 CAN19, S100L -S100A2 and Stomach Cancer
5
TBX21 17q21.32 TBET, T-PET, T-bet, TBLYM -TBX21 and Stomach Cancer
5
CCL2 17q11.2-q12 HC11, MCAF, MCP1, MCP-1, SCYA2, GDCF-2, SMC-CF, HSMCR30 -CCL2 and Stomach Cancer
5
CD83 6p23 BL11, HB15 -CD83 and Stomach Cancer
5
CLDN4 7q11.23 CPER, CPE-R, CPETR, CPETR1, WBSCR8, hCPE-R -CLDN4 and Stomach Cancer
5
NIN 14q22.1 SCKL7 -NIN and Stomach Cancer
5
WNT10B 12q13 SHFM6, WNT-12 -WNT10B and Stomach Cancer
5
PCDH10 4q28.3 PCDH19, OL-PCDH -PCDH10 and Stomach Cancer
5
TP53INP1 8q22 SIP, Teap, p53DINP1, TP53DINP1, TP53INP1A, TP53INP1B -TP53INP1 and Stomach Cancer
5
GPX1 3p21.3 GPXD, GSHPX1 -GPX1 and Stomach Cancer
4
ADH1C 4q23 ADH3 -ADH1C and Stomach Cancer
4
CEACAM6 19q13.2 NCA, CEAL, CD66c -CEACAM6 and Stomach Cancer
4
CCL5 17q12 SISd, eoCP, SCYA5, RANTES, TCP228, D17S136E, SIS-delta -CCL5 and Stomach Cancer
4
SSTR3 22q13.1 SS3R, SS3-R, SS-3-R, SSR-28 -SSTR3 and Stomach Cancer
4
S100A8 1q21 P8, MIF, NIF, CAGA, CFAG, CGLA, L1Ag, MRP8, CP-10, MA387, 60B8AG -S100A8 and Stomach Cancer
4
MCM7 7q21.3-q22.1 MCM2, CDC47, P85MCM, P1CDC47, PNAS146, PPP1R104, P1.1-MCM3 -MCM7 and Stomach Cancer
4
ITGA4 2q31.3 IA4, CD49D -ITGA4 and Stomach Cancer
4
BCL2L11 2q13 BAM, BIM, BOD -BCL2L11 and Stomach Cancer
4
MIRLET7G 3p21.1 LET7G, let-7g, MIRNLET7G, hsa-let-7g -MicroRNA let-7g and Stomach Cancer
4
ST2 11p14.3-p12 -ST2 and Stomach Cancer
4
FOXP1 3p14.1 MFH, QRF1, 12CC4, hFKH1B, HSPC215 -FOXP1 and Stomach Cancer
4
CD55 1q32 CR, TC, DAF, CROM -CD55 and Stomach Cancer
4
HMOX1 22q13.1 HO-1, HSP32, HMOX1D, bK286B10 -HMOX1 and Stomach Cancer
4
MAGEB2 Xp21.3 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Stomach Cancer
4
SOCS1 16p13.13 JAB, CIS1, SSI1, TIP3, CISH1, SSI-1, SOCS-1 -SOCS1 and Stomach Cancer
4
CD86 3q21 B70, B7-2, B7.2, LAB72, CD28LG2 -CD86 and Stomach Cancer
4
S100A11 1q21 MLN70, S100C, HEL-S-43 -S100A11 and Stomach Cancer
4
MTSS1 8p22 MIM, MIMA, MIMB -MTSS1 and Stomach Cancer
4
HBEGF 5q23 DTR, DTS, DTSF, HEGFL -HBEGF and Stomach Cancer
4
SULF1 8q13.2 SULF-1, HSULF-1 -SULF1 and Stomach Cancer
4
HOXD10 2q31.1 HOX4, HOX4D, HOX4E, Hox-4.4 -HOXD10 and Stomach Cancer
4
ANGPT2 8p23.1 ANG2, AGPT2 -ANGPT2 and Stomach Cancer
4
S100A10 1q21 42C, P11, p10, GP11, ANX2L, CAL1L, CLP11, Ca[1], ANX2LG -S100A10 and Stomach Cancer
4
HHIP 4q28-q32 HIP -HHIP and Stomach Cancer
4
SSTR1 14q13 SS1R, SS1-R, SRIF-2, SS-1-R -SSTR1 and Stomach Cancer
4
IQGAP1 15q26.1 SAR1, p195, HUMORFA01 -IQGAP1 and Stomach Cancer
4
MAP2K4 17p12 JNKK, MEK4, MKK4, SEK1, SKK1, JNKK1, SERK1, MAPKK4, PRKMK4, SAPKK1, SAPKK-1 -MAP2K4 and Stomach Cancer
4
CYP1B1 2p22.2 CP1B, GLC3A, CYPIB1, P4501B1 -CYP1B1 and Stomach Cancer
4
PPARD 6p21.2 FAAR, NUC1, NUCI, NR1C2, NUCII, PPARB -PPAR delta and Stomach Cancer
4
PDX1 13q12.1 GSF, IPF1, IUF1, IDX-1, MODY4, PDX-1, STF-1, PAGEN1 -PDX1 and Stomach Cancer
4
NEDD9 6p24.2 CAS2, CASL, HEF1, CAS-L, CASS2 -NEDD9 and Stomach Cancer
4
SST 3q28 SMST -SST and Stomach Cancer
4
JAK3 19p13.1 JAKL, LJAK, JAK-3, L-JAK, JAK3_HUMAN -JAK3 and Stomach Cancer
4
RASAL1 12q23-q24 RASAL -RASAL1 and Stomach Cancer
4
CLDN1 3q28-q29 CLD1, SEMP1, ILVASC -CLDN1 and Stomach Cancer
4
INHBA 7p15-p13 EDF, FRP -INHBA and Stomach Cancer
4
HIC1 17p13.3 hic-1, ZBTB29, ZNF901 -HIC1 and Stomach Cancer
4
AKR1C2 10p15-p14 DD, DD2, TDD, BABP, DD-2, DDH2, HBAB, HAKRD, MCDR2, SRXY8, DD/BABP, AKR1C-pseudo -AKR1C2 and Stomach Cancer
4
FGF3 11q13.3 INT2, HBGF-3 -FGF3 and Stomach Cancer
4
EGR2 10q21.1 AT591, CMT1D, CMT4E, KROX20 -EGR2 and Stomach Cancer
4
LGALS4 19q13.2 GAL4, L36LBP -LGALS4 and Stomach Cancer
4
AQP3 9p13 GIL, AQP-3 -AQP3 and Stomach Cancer
4
CLDN7 17p13.1 CLDN-7, CEPTRL2, CPETRL2, Hs.84359, claudin-1 -CLDN7 and Stomach Cancer
4
IRF1 5q31.1 MAR, IRF-1 -IRF1 and Stomach Cancer
4
CD40 20q12-q13.2 p50, Bp50, CDW40, TNFRSF5 -CD40 and Stomach Cancer
4
COL1A2 7q22.1 OI4 -COL1A2 and Stomach Cancer
4
IL12A 3q25.33 P35, CLMF, NFSK, NKSF1, IL-12A -IL12A and Stomach Cancer
4
MBD2 18q21 DMTase, NY-CO-41 -MBD2 and Stomach Cancer
4
PTPN6 12p13 HCP, HCPH, SHP1, SHP-1, HPTP1C, PTP-1C, SHP-1L, SH-PTP1 -PTPN6 and Stomach Cancer
4
MSI1 12q24 -MSI1 and Stomach Cancer
3
SUZ12 17q11.2 CHET9, JJAZ1 -SUZ12 and Stomach Cancer
3
IFITM1 11p15.5 9-27, CD225, IFI17, LEU13, DSPA2a -IFITM1 and Stomach Cancer
3
ENDOU 12q13.1 P11, PP11, PRSS26 -ENDOU and Stomach Cancer
3
CASP1 11q22.3 ICE, P45, IL1BC -CASP1 and Stomach Cancer
3
SUFU 10q24.32 SUFUH, SUFUXL, PRO1280 -SUFU and Stomach Cancer
3
WNT11 11q13.5 HWNT11 -WNT11 and Stomach Cancer
3
EXO1 1q43 HEX1, hExoI -EXO1 and Stomach Cancer
3
HLA-E 6p21.3 MHC, QA1, EA1.2, EA2.1, HLA-6.2 -HLA-E and Stomach Cancer
3
ROR2 9q22 BDB, BDB1, NTRKR2 -ROR2 and Stomach Cancer
3
S100A9 1q21 MIF, NIF, P14, CAGB, CFAG, CGLB, L1AG, LIAG, MRP14, 60B8AG, MAC387 -S100A9 and Stomach Cancer
3
GSTM3 1p13.3 GST5, GSTB, GTM3, GSTM3-3 -GSTM3 and Stomach Cancer
3
SATB1 3p23 -SATB1 and Stomach Cancer
3
ADIPOR1 1q32.1 CGI45, PAQR1, ACDCR1, CGI-45, TESBP1A -ADIPOR1 and Stomach Cancer
3
ELF3 1q32.2 ERT, ESX, EPR-1, ESE-1 -ELF3 and Stomach Cancer
3
SULF2 20q13.12 HSULF-2 -SULF2 and Stomach Cancer
3
HLA-G 6p21.3 MHC-G -HLA-G and Stomach Cancer
3
NTRK3 15q25 TRKC, gp145(trkC) -NTRK3 and Stomach Cancer
3
WNT5B 12p13.3 -WNT5B and Stomach Cancer
3
SNAI1 20q13.2 SNA, SNAH, SNAIL, SLUGH2, SNAIL1, dJ710H13.1 -SNAI1 and Stomach Cancer
3
PTK7 6p21.1-p12.2 CCK4, CCK-4 -PTK7 and Stomach Cancer
3
CASP10 2q33-q34 MCH4, ALPS2, FLICE2 -CASP10 and Stomach Cancer
3
AKR1B10 7q33 HIS, HSI, ARL1, ARL-1, ALDRLn, AKR1B11, AKR1B12 -AKR1B10 and Stomach Cancer
3
RARRES1 3q25.32 LXNL, TIG1, PERG-1 -RARRES1 and Stomach Cancer
3
PIK3R1 5q13.1 p85, AGM7, GRB1, IMD36, p85-ALPHA -PIK3R1 and Stomach Cancer
3
NEDD4 15q RPF1, NEDD4-1 -NEDD4 and Stomach Cancer
3
SLPI 20q12 ALP, MPI, ALK1, BLPI, HUSI, WAP4, WFDC4, HUSI-I -SLPI and Stomach Cancer
3
CD151 11p15.5 GP27, MER2, RAPH, SFA1, PETA-3, TSPAN24 -CD151 and Stomach Cancer
3
ANXA1 9q21.13 ANX1, LPC1 -ANXA1 and Stomach Cancer
3
MIR124-1 8p23.1 MIR124A, MIR124A1, MIRN124-1, MIRN124A1, mir-124-1 -microRNA 124-1 and Stomach Cancer
3
B2M 15q21.1 -B2M and Stomach Cancer
3
HDAC6 Xp11.23 HD6, JM21, CPBHM, PPP1R90 -HDAC6 and Stomach Cancer
3
YWHAZ 8q23.1 HEL4, YWHAD, KCIP-1, HEL-S-3, 14-3-3-zeta -YWHAZ and Stomach Cancer
3
PAK4 19q13.2 -PAK4 and Stomach Cancer
3
PER1 17p13.1 PER, hPER, RIGUI -PER1 and Stomach Cancer
3
KRT18 12q13 K18, CK-18, CYK18 -KRT18 and Stomach Cancer
3
ADIPOR2 12p13.31 PAQR2, ACDCR2 -ADIPOR2 and Stomach Cancer
3
IL13 5q31 P600, IL-13 -IL13 and Stomach Cancer
3
SERPINB5 18q21.33 PI5, maspin -SERPINB5 and Stomach Cancer
3
RASSF10 11p15.3 -RASSF10 and Stomach Cancer
3
MBL2 10q11.2 MBL, MBP, MBP1, MBPD, MBL2D, MBP-C, COLEC1, HSMBPC -MBL2 and Stomach Cancer
3
HPSE 4q21.3 HPA, HPA1, HPR1, HSE1, HPSE1 -HPSE and Stomach Cancer
3
INHA 2q35 -INHA and Stomach Cancer
3
RARRES3 11q12.3 RIG1, TIG3, HRSL4, HRASLS4, PLA1/2-3 -RARRES3 and Stomach Cancer
3
HCK 20q11-q12 JTK9, p59Hck, p61Hck -HCK and Stomach Cancer
3
ROR1 1p31.3 NTRKR1, dJ537F10.1 -ROR1 and Stomach Cancer
3
MLF1 3q25.1 -MLF1 and Stomach Cancer
3
CBX7 22q13.1 -CBX7 and Stomach Cancer
3
CASP6 4q25 MCH2 -CASP6 and Stomach Cancer
2
DMBT1 10q26.13 GP340, muclin -DMBT1 and Stomach Cancer
2
JAG2 14q32 HJ2, SER2 -JAG2 and Stomach Cancer
2
ABCC4 13q32 MRP4, MOATB, MOAT-B -ABCC4 and Stomach Cancer
2
MT2A 16q13 MT2 -MT2A and Stomach Cancer
2
HSD17B1 17q11-q21 HSD17, EDHB17, EDH17B2, SDR28C1 -HSD17B1 and Stomach Cancer
2
MCM4 8q11.2 NKCD, CDC21, CDC54, NKGCD, hCdc21, P1-CDC21 -MCM4 and Stomach Cancer
2
NOS3 7q36 eNOS, ECNOS -NOS3 and Stomach Cancer
2
ARHGEF1 19q13.13 LSC, GEF1, LBCL2, SUB1.5, P115-RHOGEF -ARHGEF1 and Stomach Cancer
2
PRKCA 17q22-q23.2 AAG6, PKCA, PRKACA, PKC-alpha -PRKCA and Stomach Cancer
2
IL23R 1p31.3 -IL23R and Stomach Cancer
2
DUSP6 12q22-q23 HH19, MKP3, PYST1 -DUSP6 and Stomach Cancer
2
CFLAR 2q33-q34 CASH, FLIP, MRIT, CLARP, FLAME, Casper, FLAME1, c-FLIP, FLAME-1, I-FLICE, c-FLIPL, c-FLIPR, c-FLIPS, CASP8AP1 -CFLAR and Stomach Cancer
2
LRRC3B 3p24 LRP15 -LRRC3B and Stomach Cancer
2
DDX5 17q21 p68, HLR1, G17P1, HUMP68 -DDX5 and Stomach Cancer
2
MTA2 11q12.3 PID, MTA1L1 -MTA2 and Stomach Cancer
2
LAMB3 1q32 AI1A, LAM5, LAMNB1, BM600-125KDA -LAMB3 and Stomach Cancer
2
FRAT2 10q24.1 -FRAT2 and Stomach Cancer
2
MAD1L1 7p22 MAD1, PIG9, TP53I9, TXBP181 -MAD1L1 and Stomach Cancer
2
ADAMTS9 3p14.1 -ADAMTS9 and Stomach Cancer
2
ARNTL 11p15.3 TIC, JAP3, MOP3, BMAL1, PASD3, BMAL1c, bHLHe5 -ARNTL and Stomach Cancer
2
PIN1 19p13 DOD, UBL5 -PIN1 and Stomach Cancer
2
MIR127 14q32.2 MIRN127, mir-127, miRNA127 -MicroRNA miR-127 and Stomach Cancer
2
CDH3 16q22.1 CDHP, HJMD, PCAD -CDH3 and Stomach Cancer
2
CCND3 6p21 -CCND3 and Stomach Cancer
2
XRCC5 2q35 KU80, KUB2, Ku86, NFIV, KARP1, KARP-1 -XRCC5 and Stomach Cancer
2
ADGRB1 8q24.3 BAI1, GDAIF -BAI1 and Stomach Cancer
2
HAVCR2 5q33.3 TIM3, CD366, KIM-3, TIMD3, Tim-3, TIMD-3, HAVcr-2 -HAVCR2 and Stomach Cancer
2
SLC45A3 1q32.1 PRST, IPCA6, IPCA-2, IPCA-6, IPCA-8, PCANAP2, PCANAP6, PCANAP8 -SLC45A3 and Stomach Cancer
2
YWHAE 17p13.3 MDS, HEL2, MDCR, KCIP-1, 14-3-3E -YWHAE and Stomach Cancer
2
CXCL16 17p13 SRPSOX, CXCLG16, SR-PSOX -CXCL16 and Stomach Cancer
2
GNL3 3p21.1 NS, E2IG3, NNP47, C77032 -GNL3 and Stomach Cancer
2
CTAG1B Xq28 CTAG, ESO1, CT6.1, CTAG1, LAGE-2, LAGE2B, NY-ESO-1 -CTAG1B and Stomach Cancer
2
PDK1 2q31.1 -PDK1 and Stomach Cancer
2
ZNF331 19q13.42 RITA, ZNF361, ZNF463 -ZNF331 and Stomach Cancer
2
PVT1 8q24 LINC00079, NCRNA00079, onco-lncRNA-100 -PVT1 and Stomach Cancer
2
HLA-DRA 6p21.3 MLRW, HLA-DRA1 -HLA-DRA and Stomach Cancer
2
MUC3A 7q22 MUC3, MUC-3A -MUC3A and Stomach Cancer
2
MMP13 11q22.2 CLG3, MDST, MANDP1, MMP-13 -MMP13 and Stomach Cancer
2
TNFRSF17 16p13.1 BCM, BCMA, CD269, TNFRSF13A -TNFRSF17 and Stomach Cancer
2
PKD1 16p13.3 PBP, Pc-1, TRPP1 -PKD1 and Stomach Cancer
2
TYK2 19p13.2 JTK1, IMD35 -TYK2 and Stomach Cancer
2
ANGPT1 8q23.1 AGP1, AGPT, ANG1 -ANGPT1 and Stomach Cancer
2
PDCD2 6q27 RP8, ZMYND7 -PDCD2 and Stomach Cancer
2
PTCH2 1p34.1 PTC2 -PTCH2 and Stomach Cancer
2
MMP10 11q22.2 SL-2, STMY2 -MMP10 and Stomach Cancer
2
S100A7 1q21 PSOR1, S100A7c -S100A7 and Stomach Cancer
2
HSD17B2 16q24.1-q24.2 HSD17, SDR9C2, EDH17B2 -HSD17B2 and Stomach Cancer
2
FEZ1 11q24.2 -FEZ1 and Stomach Cancer
2
TNFRSF6B 20q13.3 M68, TR6, DCR3, M68E, DJ583P15.1.1 Amplification
Prognostic
-TNFRSF6B Amplification and Overexpression in Gastric Cancers
2
FOXA2 20p11 HNF3B, TCF3B -FOXA2 and Stomach Cancer
2
WRN 8p12 RECQ3, RECQL2, RECQL3 -WRN and Stomach Cancer
2
BDNF 11p14.1 ANON2, BULN2 -BDNF and Stomach Cancer
2
IGF1 12q23.2 IGFI, IGF-I, IGF1A -IGF1 and Stomach Cancer
2
MLH3 14q24.3 HNPCC7 -MLH3 and Stomach Cancer
2
EP300 22q13.2 p300, KAT3B, RSTS2 -EP300 and Stomach Cancer
2
NOX1 Xq22 MOX1, NOH1, NOH-1, GP91-2 -NOX1 and Stomach Cancer
1
CDK12 17q12 CRK7, CRKR, CRKRS -CDK12 and Stomach Cancer
1
CRY2 11p11.2 HCRY2, PHLL2 -CRY2 and Stomach Cancer
1
LRIG1 3p14 LIG1, LIG-1 -LRIG1 and Stomach Cancer
1
TPM1 15q22.1 CMH3, TMSA, CMD1Y, LVNC9, C15orf13, HTM-alpha -TPM1 and Stomach Cancer
1
PDPK1 16p13.3 PDK1, PDPK2, PDPK2P, PRO0461 -PDPK1 and Stomach Cancer
1
COPS6 7q22.1 CSN6, MOV34-34KD -COPS6 and Stomach Cancer
1
RASSF7 11p15.5 HRC1, HRAS1, C11orf13 -RASSF7 and Stomach Cancer
1
HSD3B1 1p13.1 I, HSD3B, HSDB3, HSDB3A, SDR11E1, 3BETAHSD -HSD3B1 and Stomach Cancer
1
ANXA7 10q22.2 SNX, ANX7, SYNEXIN -ANXA7 and Stomach Cancer
1
SETD2 3p21.31 HYPB, SET2, HIF-1, HIP-1, KMT3A, HBP231, HSPC069, p231HBP -SETD2 and Stomach Cancer
1
NQO2 6p25.2 QR2, DHQV, DIA6, NMOR2 -NQO2 and Stomach Cancer
1
SACS 13q12 SPAX6, ARSACS, DNAJC29, PPP1R138 -SACS and Stomach Cancer
1
MIR10B 2q31.1 MIRN10B, mir-10b, miRNA10B, hsa-mir-10b -MIR10B and Stomach Cancer
1
CYBA 16q24 p22-PHOX -CYBA and Stomach Cancer
1
TCEAL7 Xq22.1 WEX5, MPMGp800C04260Q003 -TCEAL7 and Stomach Cancer
1
DNAJB4 1p31.1 DjB4, HLJ1, DNAJW -DNAJB4 and Stomach Cancer
1
ZNRF3 22q12.1 RNF203, BK747E2.3 -ZNRF3 and Stomach Cancer
1
TM4SF1 3q21-q25 L6, H-L6, M3S1, TAAL6 -TM4SF1 and Stomach Cancer
1
CTCFL 20q13.31 CT27, BORIS, CTCF-T, HMGB1L1, dJ579F20.2 -CTCFL and Stomach Cancer
1
COMT 22q11.21 HEL-S-98n -COMT and Stomach Cancer
1
CHRNA5 15q24 LNCR2 -CHRNA5 and Stomach Cancer
1
CCKBR 11p15.4 GASR, CCK-B, CCK2R -CCKBR and Stomach Cancer
1
CBLB 3q13.11 Cbl-b, RNF56, Nbla00127 -CBLB and Stomach Cancer
1
RASSF5 1q32.1 RAPL, Maxp1, NORE1, NORE1A, NORE1B, RASSF3 -RASSF5 and Stomach Cancer
1
PDCD1LG2 9p24.2 B7DC, Btdc, PDL2, CD273, PD-L2, PDCD1L2, bA574F11.2 -PDCD1LG2 and Stomach Cancer
1
CTSD 11p15.5 CPSD, CLN10, HEL-S-130P -CTSD and Stomach Cancer
1
MIR125A 19q13.41 MIRN125A, miRNA125A -MIR125A and Stomach Cancer
1
SHMT1 17p11.2 SHMT, CSHMT -SHMT1 and Stomach Cancer
1
LMNA 1q22 FPL, IDC, LFP, CDDC, EMD2, FPLD, HGPS, LDP1, LMN1, LMNC, PRO1, CDCD1, CMD1A, FPLD2, LMNL1, CMT2B1, LGMD1B -LMNA and Stomach Cancer
1
NCKIPSD 3p21 DIP, DIP1, ORF1, WISH, VIP54, AF3P21, SPIN90, WASLBP -NCKIPSD and Stomach Cancer
1
IRF8 16q24.1 ICSBP, IRF-8, ICSBP1, IMD32A, IMD32B, H-ICSBP -IRF8 and Stomach Cancer
1
SETD1B 12q24.31 KMT2G, Set1B -SETD1B and Stomach Cancer
1
PDCD1 2q37.3 PD1, PD-1, CD279, SLEB2, hPD-1, hPD-l, hSLE1 -PDCD1 and Stomach Cancer
1
LAPTM4B 8q22.1 LC27, LAPTM4beta -LAPTM4B and Stomach Cancer
1
DOK2 8p21.3 p56DOK, p56dok-2 -DOK2 and Stomach Cancer
1
MMP8 11q22.2 HNC, CLG1, MMP-8, PMNL-CL -MMP8 and Stomach Cancer
1
PLK2 5q12.1-q13.2 SNK, hSNK, hPlk2 -PLK2 and Stomach Cancer
1
SSTR5 16p13.3 SS-5-R -SSTR5 and Stomach Cancer
1
HSP90AB1 6p12 HSP84, HSPC2, HSPCB, D6S182, HSP90B -HSP90AB1 and Stomach Cancer
1
SFRP4 7p14.1 FRP-4, FRPHE, sFRP-4 -SFRP4 and Stomach Cancer
1
S100A3 1q21 S100E -S100A3 and Stomach Cancer
1
SAT2 17p13.1 SSAT2 -SAT2 and Stomach Cancer
1
CRP 1q23.2 PTX1 -CRP and Stomach Cancer
1
BUB3 10q26 BUB3L, hBUB3 -BUB3 and Stomach Cancer
1
PLCD1 3p22-p21.3 NDNC3, PLC-III -PLCD1 and Stomach Cancer
1
MSI2 17q22 MSI2H -MSI2 and Stomach Cancer
1
PITX1 5q31.1 BFT, CCF, POTX, PTX1, LBNBG -PITX1 and Stomach Cancer
1
SOX6 11p15.2 SOXD, HSSOX6 -SOX6 and Stomach Cancer
1
CTCF 16q21-q22.3 MRD21 -CTCF and Stomach Cancer
1
IDO1 8p12-p11 IDO, INDO, IDO-1 -IDO1 and Stomach Cancer
1
NRP1 10p12 NP1, NRP, BDCA4, CD304, VEGF165R -NRP1 and Stomach Cancer
1
PDCD5 19q13.11 TFAR19 -PDCD5 and Stomach Cancer
1
MTA1 14q32.3 -MTA1 and Stomach Cancer
1
CYP2C8 10q23.33 CPC8, CYPIIC8, MP-12/MP-20 -CYP2C8 and Stomach Cancer
1
HINT1 5q31.2 HINT, NMAN, PKCI-1, PRKCNH1 -HINT1 and Stomach Cancer
1
RABEP1 17p13.2 RAB5EP, RABPT5 -RABEP1 and Stomach Cancer
1
HDAC4 2q37.3 HD4, AHO3, BDMR, HDACA, HA6116, HDAC-4, HDAC-A -HDAC4 and Stomach Cancer
1
KLRK1 12p13.2-p12.3 KLR, CD314, NKG2D, NKG2-D, D12S2489E -KLRK1 and Stomach Cancer
1
AFF3 2q11.2-q12 LAF4, MLLT2-like -AFF3 and Stomach Cancer
1
CXADR 21q21.1 CAR, HCAR, CAR4/6 -CXADR and Stomach Cancer
1
HOXD11 2q31.1 HOX4, HOX4F -HOXD11 and Stomach Cancer
1
IL16 15q26.3 LCF, NIL16, PRIL16, prIL-16 -IL16 and Stomach Cancer
1
DUSP4 8p12-p11 TYP, HVH2, MKP2, MKP-2 -DUSP4 and Stomach Cancer
1
AQP1 7p14 CO, CHIP28, AQP-CHIP -AQP1 and Stomach Cancer
1
CCL22 16q13 MDC, ABCD-1, SCYA22, STCP-1, DC/B-CK, A-152E5.1 -CCL22 and Stomach Cancer
1
KDM5A 12p11 RBP2, RBBP2, RBBP-2 -KDM5A and Stomach Cancer

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

Latest Publications

Jiang B, Li S, Jiang Z, Shao P
Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data.
Biomed Res Int. 2017; 2017:7259097 [PubMed] Free Access to Full Article Related Publications
Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding.

Shi XQ, Xue WH, Zhao SF, et al.
Dynamic tracing for epidermal growth factor receptor mutations in urinary circulating DNA in gastric cancer patients.
Tumour Biol. 2017; 39(2):1010428317691681 [PubMed] Related Publications
The mutations of epidermal growth factor receptor are detected in gastric cancer, indicating its suitability as a target for receptor tyrosine kinase inhibitors, as well as a marker for clinical outcome of chemotherapeutic treatments. However, extraction of quality tumor tissue for molecular processes remains challenging. Here, we aimed to examine the clinical relevance of urinary cell-free DNA as an alternative tumor material source used specifically for monitoring epidermal growth factor receptor mutations. Therefore, 120 gastric cancer patients with epidermal growth factor receptor mutations and 100 healthy controls were recruited for the study. The gastric patients also received epidermal growth factor receptor inhibitor treatment for a serial monitoring study. Paired primary tumor specimens were obtained with blood and urine samples, which were taken at a 1-month interval for a duration of 12 months. We found that urinary cell-free DNA yielded a close agreement of 92% on epidermal growth factor receptor mutation status when compared to primary tissue at baseline, and of 99% epidermal growth factor receptor mutation status when compared to plasma samples at different time points. Thus, our data suggest that urinary cell-free DNA may be a reliable source for screening and monitoring epidermal growth factor receptor mutations in the primary gastric cancer.

Negovan A, Iancu M, Moldovan V, et al.
The Interaction between GSTT1, GSTM1, and GSTP1 Ile105Val Gene Polymorphisms and Environmental Risk Factors in Premalignant Gastric Lesions Risk.
Biomed Res Int. 2017; 2017:7365080 [PubMed] Free Access to Full Article Related Publications
The study investigated the possible influence of GSTM1, GSTT1, and GSTP1 gene polymorphisms as predisposing factors for premalignant gastric lesions as well as their interaction with H. pylori infection, gastrotoxic drugs, smoking, and alcohol consumption. In this study, 270 patients with a complet set of gastric biopsies and successfully genotyped were finally included. The GSTM1 gene polymorphism had significant contribution in mild/severe endoscopic lesions (p = 0.01) as well as in premalignant lesions (p = 0.01). The GSTM1 null genotype increased the risk for mucosal defects in H. pylori-negative patients (OR = 2.27, 95% CI: 1.20-4.37) and the risk for premalignant lesions in patients with no alcohol consumption (OR = 2.13, 95% CI: 1.19-3.83). The GSTT1 deleted polymorphism did not significantly increase the risk for premalignant lesions in the absence of gastrotoxic drugs (OR = 1.82, 95% CI: 0.72-4.74). The combined GSTT1T1 and GSTM1 null polymorphisms were borderline correlated with an increased risk for premalignant lesions (OR = 1.72, 95% CI: 1.00-2.97). The wild-type GSTP1 Ile/Ile genotype versus the variant genotypes Ile/Val + Val/Val was significantly associated with a decreased risk of gastric atrophy/intestinal metaplasia (OR = 0.60, 95% CI: 0.37-0.98). In conclusion, the GSTM1 and GSTT1 null genotypes increased the risk for premalignant and endoscopic gastric lesions, modulated by H. pylori, alcohol, or gastrotoxic drug consumption, while the presence of the GSTP1Val allele seemed to reduce the risk for premalignant lesions.

Backman S, Norlén O, Eriksson B, et al.
Detection of Somatic Mutations in Gastroenteropancreatic Neuroendocrine Tumors Using Targeted Deep Sequencing.
Anticancer Res. 2017; 37(2):705-712 [PubMed] Related Publications
Mutations affecting the mechanistic target of rapamycin (MTOR) signalling pathway are frequent in human cancer and have been identified in up to 15% of pancreatic neuroendocrine tumours (NETs). Grade A evidence supports the efficacy of MTOR inhibition with everolimus in pancreatic NETs. Although a significant proportion of patients experience disease stabilization, only a minority will show objective tumour responses. It has been proposed that genomic mutations resulting in activation of MTOR signalling could be used to predict sensitivity to everolimus.
PATIENTS AND METHODS: Patients with NETs that underwent treatment with everolimus at our Institution were identified and those with available tumour tissue were selected for further analysis. Targeted next-generation sequencing (NGS) was used to re-sequence 22 genes that were selected on the basis of documented involvement in the MTOR signalling pathway or in the tumourigenesis of gastroenterpancreatic NETs. Radiological responses were documented using Response Evaluation Criteria in Solid Tumours.
RESULTS: Six patients were identified, one had a partial response and four had stable disease. Sequencing of tumour tissue resulted in a median sequence depth of 667.1 (range=404-1301) with 1-fold coverage of 95.9-96.5% and 10-fold coverage of 87.6-92.2%. A total of 494 genetic variants were discovered, four of which were identified as pathogenic. All pathogenic variants were validated using Sanger sequencing and were found exclusively in menin 1 (MEN1) and death domain associated protein (DAXX) genes. No mutations in the MTOR pathway-related genes were observed.
CONCLUSION: Targeted NGS is a feasible method with high diagnostic yield for genetic characterization of pancreatic NETs. A potential association between mutations in NETs and response to everolimus should be investigated by future studies.

Yuan X, Wang S, Liu M, et al.
Histological and Pathological Assessment of miR-204 and SOX4 Levels in Gastric Cancer Patients.
Biomed Res Int. 2017; 2017:6894675 [PubMed] Free Access to Full Article Related Publications
Gastric cancer is one of the most common cancers and the efficient therapeutic methods are limited. Further study of the exact molecular mechanism of gastric cancer to develop novel targeted therapies is necessary and urgent. We herein systematically examined that miR-204 suppressed both proliferation and metastasis of gastric cancer AGS cells. miR-204 directly targeted SOX4. In clinical tissue research, we determined that miR-204 was expressed much lower and SOX4 expressed much higher in gastric cancer tissues compared with normal gastric tissues. Associated analysis with clinicopathological parameters in gastric cancer patients showed miR-204 was associated with no lymph node metastasis and early tumor stages whereas SOX4 was associated with lymph node metastasis and advanced tumor stages. In addition, miR-204 and SOX4 were negatively correlated in tissues from gastric cancer patients. Our findings examined the important role of miR-204 and SOX4 played in gastric cancer, and they could be used as candidate therapeutic targets for gastric cancer therapy.

Wang Y
Identifying key stage-specific genes and transcription factors for gastric cancer based on RNA-sequencing data.
Medicine (Baltimore). 2017; 96(4):e5691 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: To identify gastric cancer (GC)-associated genes and transcription factors (TFs) using RNA-sequencing (RNA-seq) data of Asians.
MATERIALS AND METHODS: The RNA-seq data (GSE36968) were downloaded from Gene Expression Omnibus database, including 6 noncancerous gastric tissue samples, 5 stage I GC samples, 5 stage II GC samples, 8 stage III GC samples, and 6 stage IV GC samples. The gene expression values in each sample were calculated using Cuffdiff. Following, stage-specific genes were identified by 1-way analysis of variance and hierarchical clustering analysis. Upstream TFs were identified using Seqpos. Besides, functional enrichment analysis of stage-specific genes was performed by DAVID. In addition, the underlying protein-protein interactions (PPIs) information among stage IV-specific genes were extracted from STRING database and PPI network was constructed using Cytoscape software.
RESULTS: A total of 3576 stage-specific genes were identified, including 813 specifically up-regulated genes in the normal gastric tissues, 2224 stage I and II-specific genes, and 539 stage IV-specific genes. Also, a total of 9 and 11 up-regulated TFs were identified for the stage I and II-specific genes and stage IV-specific genes, respectively. Functional enrichment showed SPARC, MMP17, and COL6A3 were related to extracellular matrix. Notably, 2 regulatory pathways HOXA4-GLI3-RUNX2-FGF2 and HMGA2-PRKCA were obtained from the PPI network for stage IV-specific genes. In the PPI network, TFs HOXA4 and HMGA2 might function via mediating other genes.
CONCLUSION: These stage-specific genes and TFs might act in the pathogenesis of GC in Asians.

Zhang Q, Wu S, Zhu J, et al.
Down-regulation of ASIC1 suppressed gastric cancer via inhibiting autophagy.
Gene. 2017; 608:79-85 [PubMed] Related Publications
As autophagy has anti-apoptosis effect and accelerates cell survival, many studies start to target autophagy as a therapeutic strategy for cancer. Acid-sensing ion channels (ASICs) was reported to activate autophagy. However, whether ASICs can regulate gastric cancer through autophagy is unknown. The differentially expressed genes in normal gastric tissue and gastric cancer tissue in patients were investigated by RNA-seq. Expression of ASIC1 and autophagy related 5 (ATG5) was further confirmed by real-time PCR. Effects of knockdown expression of ASIC1 and ATG5 on the growth of gastric SGC-7901 cells were assayed by CCK-8 kit. The animal survival rate and tumor volume in murine heterotopic xenograft model was assayed. The expression of autophagy related genes was enriched in gastric cancer tissue in patients, including ASIC1 and ATG5. Knockdown expression of ASIC1 and ATG5 inhibits the growth of SGC-7901 cells, respectively. ASIC1 regulates ATG5 gene expression in SGC-7901 cells. ASIC1 knockdown extended the survival rate of animals and inhibited the tumor volume in the murine heterotopic xenograft model. This study showed that downregulation of ASIC1 inhibits gastric cancer growth via decreasing autophagy, therefore strongly suggests a therapeutic role for ASIC1 in gastric cancer.

Cao Y, Zhang G, Wang P, et al.
Clinical significance of UGT1A1 polymorphism and expression of ERCC1, BRCA1, TYMS, RRM1, TUBB3, STMN1 and TOP2A in gastric cancer.
BMC Gastroenterol. 2017; 17(1):2 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Individualized therapeutic regimen is a recently intensively pursued approach for targeting diseases, in which the search for biomarkers was considered the first and most important. Thus, the goal of this study was to investigate whether the UGT1A1, ERCC1, BRCA1, TYMS, RRM1, TUBB3, STMN1 and TOP2A genes are underlying biomarkers for gastric cancer, which, to our knowledge, has not been performed.
METHODS: Ninety-eight tissue specimens were collected from gastric cancer patients between May 2012 and March 2015. A multiplex branched DNA liquidchip technology was used for measuring the mRNA expressions of ERCC1, BRCA1, TYMS, RRM1, TUBB3, STMN1 and TOP2A. Direct sequencing was performed for determination of UGT1A1 polymorphisms. Furthermore, correlations between gene expressions, polymorphisms and clinicopathological characteristics were investigated.
RESULTS: The expressions of TYMS, TUBB3 and STMN1 were significantly associated with the clinicopathological characteristics of age, gender and family history of gastric cancer, but not with differentiation, growth patterns, metastasis and TNM staging in patients with gastric cancer. No clinical characteristics were correlated with the expressions of ERCC1, BRCA1, RRM1 and TOP2A. Additionally, patients carrying G allele at -211 of UGT1A1 were predisposed to developing tubular adenocarcinoma, while individuals carrying 6TAA or G allele respectively at *28 or -3156 of UGT1A1 tended to have a local invasion.
CONCLUSIONS: The UGT1A1 polymorphism may be useful to screen the risk population of gastric cancer, while TYMS, TUBB3 and STMN1 may be potential biomarkers for prognosis and chemotherapy guidance.

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.

Fernández C, Bellosillo B, Ferraro M, et al.
MicroRNAs 142-3p, miR-155 and miR-203 Are Deregulated in Gastric MALT Lymphomas Compared to Chronic Gastritis.
Cancer Genomics Proteomics. 2017; 14(1):75-82 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Over the last years, our knowledge on pathogenesis of gastric MALT lymphoma has greatly improved, but its morphological diagnosis is still hampered by overlapping histological features with advanced chronic gastritis. MicroRNAs are deregulated in lymphomas, but their role and usefulness in gastric MALT lymphoma has not been extensively investigated.
MATERIALS AND METHODS: We analyzed the expression of 384 miRNAs using TaqMan microRNA assay in a training series of 10 gastric MALT lymphomas, 3 chronic gastritis and 2 reactive lymph nodes. Then, significantly deregulated miRNAs were individually assessed by real-time PCR in a validation series of 16 gastric MALT lymphomas and 12 chronic gastritis.
RESULTS: Gastric MALT lymphoma is characterized by a specific miRNA expression profile. Among the differentially expressed miRNAs, a significant overexpression of miR-142-3p and miR-155 and down-regulation of miR-203 was observed in gastric MALT lymphoma when compared to chronic gastritis.
CONCLUSION: miR-142-3p, miR-155 and miR-203 expression levels might be helpful biomarkers for the differential diagnosis between gastric MALT lymphomas and chronic gastritis.

Lu C, Shan Z, Li C, Yang L
MiR-129 regulates cisplatin-resistance in human gastric cancer cells by targeting P-gp.
Biomed Pharmacother. 2017; 86:450-456 [PubMed] Related Publications
Development of multiple drug resistance (MDR) to chemotherapy is the major reason for the failure of gastric cancer (GC) treatment. P-glycoprotein (P-gp), which is encoded by MDR gene 1, as one of the mechanisms responsible for MDR. Mounting evidence has demonstrated that the drug-induced dysregulation of microRNAs (miRNAs) function may mediate MDR in cancer cells. However, the underling mechanisms of miRNA-mediated MDR in GC remain unclear. Here, we found that miR-129 was downregulated in cisplatin-resistant GC tissues/cells. Our results also showed that overexpression of miR-129 decreased cisplatin-resistance in cisplatin-resistant GC cells, and miR-129 knockdown reduced chemosensitivity to cisplatin in cisplatin-sensitive GC cells. Furthermore, miR-129 activated the intrinsic apoptotic pathway via upregulating caspase-9 and caspase-3. Most importantly, we further confirmed that P-gp is the functional target of miR-129 by regulating cisplatin-resistance in GC cells. These results suggested that miR-129 reversed cisplatin-resistance through inhibiting the P-gp expression in GC cells.

Nakajima K, Oiso S, Uto T, et al.
Triterpenes suppress octanoylated ghrelin production in ghrelin-expressing human gastric carcinoma cells.
Biomed Res. 2016; 37(6):343-349 [PubMed] Related Publications
Ghrelin is an appetite-stimulating peptide hormone with an octanoyl modification at serine 3 that is essential for its orexigenic effect. Ghrelin O-acyltransferase (GOAT) is the enzyme that catalyzes ghrelin acylation using fatty acyl-coenzyme A as a substrate. We previously developed an assay system based on the AGS-GHRL8 cell line that produces octanoylated ghrelin in the presence of octanoic acid, and demonstrated that some fatty acids suppressed octanoylated ghrelin production. Recent studies have reported that triterpenes have anti-obesity effect. Since such triterpenes, like fatty acids, have a carboxyl group, we speculated that they can suppress octanoylated ghrelin production. To test this hypothesis, we investigated the effect of triterpenes on octanoylated ghrelin production. Asiatic acid, corosolic acid, glycyrrhetinic acid, oleanolic acid and ursolic acid suppressed octanoylated ghrelin levels in AGS-GHRL8 cells without decreasing transcript expression of GOAT or furin, a protease required for ghrelin maturation. β-amyrin had no effect on octanoylated ghrelin level, which was only slightly inhibited by uvaol; the fact that both these triterpenes lack a carboxyl group indicates that this group is important for suppressing octanoylated ghrelin production. These results suggest that triterpenes may have the potential as obesity-preventing agents with suppressive effect on octanoylated ghrelin production.

Chen ZH, Xian JF, Luo LP
Analysis of ADH1B Arg47His, ALDH2 Glu487Lys, and CYP4502E1 polymorphisms in gastric cancer risk and interaction with environmental factors.
Genet Mol Res. 2016; 15(4) [PubMed] Related Publications
Gastric cancer is the fourth commonly diagnosed cancer and the second most frequent cause of cancer death worldwide. Genetic variations in ADH1B and ALDH2 may alter the function and activity of the corresponding enzymes, leading to differences in acetaldehyde exposure between drinkers. Cytochrome P4502E1 (CYP4502E1) is a phase I enzyme that plays an important role in metabolizing nitrosamine compounds and the bioactivation of procarcinogens. During the period of July 2013 to July 2015, 246 patients and 274 controls were enrolled from the First Affiliated Hospital of Jinan University. In the codominant model, the AA genotype of ALDH2 Glu487Lys significantly elevated the risk of gastric cancer in comparison with the GG genotype of ALDH2 Glu487Lys. In the recessive model, the AA genotype of ALDH2 Glu487Lys significantly increased the risk of gastric cancer compared to the GG+GA genotype (OR = 2.34 95%CI = 1.02-5.70). We found in the codominant model that individuals harboring the C2/C2 genotype of CYP4502E1 had a higher risk of developing gastric cancer than those with the C1/C1 genotype. In addition, in the recessive model, we found that the C2/C2 genotype correlated with an elevated risk of gastric cancer in comparison with the C1/C1+C1/C2 genotype (OR = 4.90, 95%CI = 2.04-13.51). However, no significant relationship was measured between ADH1B Arg47His and gastric cancer risk. In summary, the results of our study indicate that ALDH2 Glu487Lys and CYP4502E1 polymorphisms could be risk factors for the development of gastric cancer in the Chinese population.

Li L, Tang XY, Ye LM, et al.
Investigation on the association between IL-10 C819T gene polymorphisms and susceptibility to gastric cancer.
Genet Mol Res. 2016; 15(4) [PubMed] Related Publications
We conducted a case-control study to investigate the association between the interleukin-10 (IL-10) C819T polymorphism and susceptibility to gastric cancer in a Chinese population. A total of 157 patients with gastric cancer and 249 controls were consecutively enrolled from the Guizhou Provincial People's Hospital between October 2012 and February 2015. The polymerase chain reaction-restriction fragment length polymorphism technique was used to genotype for IL-10 C819T. As determined by χ(2)-test, there was a significant difference in genotype distributions of IL-10 C819T between gastric cancer patients and controls (χ(2) = 7.09; P = 0.03). Based on unconditional logistic regression analysis, the TT genotype of IL-10 C819T was significantly associated with increased risk of gastric cancer when compared with that of the CC genotype [odds ratio (OR) = 2.24; 95% confidence interval (CI) = 1.17-4.26; P = 0.008]. In a dominant model, we found that the CT + TT genotype of IL-10 C819T was associated with susceptibility to gastric cancer compared to that of the CC genotype (OR = 1.63; 95%CI = 1.02-2.64). In a recessive model, the TT genotype of IL-10 C819T was correlated with a higher risk of gastric cancer when compared with that of the CC + CT genotype (OR = 1.75; 95%CI = 1.01-3.02). In conclusion, our study suggests that the IL-10 C819T polymorphism is associated with an increased risk of gastric cancer in co-dominant, dominant, and recessive models.

Li S, Zhang H, Ning T, et al.
MiR-520b/e Regulates Proliferation and Migration by Simultaneously Targeting EGFR in Gastric Cancer.
Cell Physiol Biochem. 2016; 40(6):1303-1315 [PubMed] Related Publications
BACKGROUND: MicroRNAs (miRNAs) have been demonstrated to play a crucial role in tumorigenesis. Previous studies have shown that miR-520b/e acts as a tumor suppressor in several tumors. Other studies indicated that epidermal growth factor receptor (EGFR) is highly expressed in many tumors, and involved in the development of tumors, such as cell proliferation, migration, angiogenesis and apoptosis. However, the correlation of miRNAs and EGFR in gastric cancer (GC) has not been adequately investigated. Our aim was to explore the relationship.
METHODS: The expression levels of EGFR and miR-520b/e were examined by RT-PCR and Western blot. We also investigated the relationship between EGFR and miR-520b/e in GC cell lines by relevant experiments.
RESULTS: In this study, we found that miR-520b/e inhibits the protein expression of EGFR by directly binding with the 3'-untranslated region (3'-UTR). And it was shown that the down-regulation of miR-520b/e promotes cell proliferation and migration by negative regulation of the EGFR pathway, while over-expression of miR-520b/e inhibits these properties. In addition, the biological function of EGFR in GC cell lines was validated by silencing and over-expression assays respectively.
CONCLUSIONS: Taken together, our results demonstrate that miR-520b/e acts as a tumor suppressor by regulating EGFR in GC, and provide a novel marker and insight for the potential therapeutic target of GC.

Zhuo C, Li X, Zhuang H, et al.
Elevated THBS2, COL1A2, and SPP1 Expression Levels as Predictors of Gastric Cancer Prognosis.
Cell Physiol Biochem. 2016; 40(6):1316-1324 [PubMed] Related Publications
BACKGROUND/AIMS: Gastric cancer (GC) is an important health problem. Classification based on molecular subtypes may help to determine the prognosis of patients with GC. Tumor invasion and metastasis are important factors affecting the prognosis of cancer. We aimed to identify genes related to tumor invasion and metastasis, which may serve as indicators of good GC prognosis.
METHODS: Tumor tissues and adjacent normal tissues were collected from 105 patients with primary GC who were treated by undergoing radical surgery. Samples were used for tissue microarray analysis. Identified genes with altered expression were further analyzed using the Gene Ontology (Go) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The expression levels of THBS2, COL1A2 and SPP1 were analyzed by RT-PCR, western blot and immunohistochemistry. The overall survival curves of patients with high and low expression of each gene of interest were plotted and compared.
RESULTS: Forty-three genes were identified. THBS2, COL1A2 and SPP1 were selected for further analysis. Altered expression levels of THBS2, COL1A2 and SPP1 in tumor tissues were confirmed. Patients with low THBS2 expression had a better prognosis; the expression of COL1A2 and SPP1 might not affect the prognosis of patients with GC.
CONCLUSION: THBS2, but not COL1A2 and SPP1, may serve as an indicator of GC prognosis.

Senol S, Aydin A, Kosemetin D, et al.
Gastric Adenocarcinoma Biomarker Expression Profiles and their Prognostic Value.
J Environ Pathol Toxicol Oncol. 2016; 35(3):207-222 [PubMed] Related Publications
Expression levels of several molecules implicated in carcinogenesis were examined by immunohistochemical staining, and the prognostic significance of their expression levels in gastric adenocarcinoma (GA) was evaluated. A total of 115 GA and 20 control gastric tissue samples were evaluated by immunohistochemistry using 33 antibodies targeting molecules known to play a part in the development of various tumors. Overexpression of carbonic anhydrase IX (CAIX) and loss of AT-rich interactive domain-containing protein 1A (ARID1A), aldehyde dehydrogenase 1 (ALDH1), and CD44 expression in GA patients were significantly correlated with lymph node (LN) metastasis, advanced tumor stage, and poor prognosis. The results demonstrated that ALDH1A and ARID1A may be strong independent prognostic factors associated with overall survival and recurrence-free survival (p < 0.01 and p < 0.05, respectively). Our results demonstrated that ALDH1, CD44, ARID1A, and CAIX in immunoreactive GA tumor cells exhibit different expression profiles compared with control cells and that these differences are associated with patient survival. The molecules with differential expression profiles were associated with some common functions, including hypoxia, epithelial-to-mesenchymal transition, and SW1/SNF-mediated chromatin remodeling. In addition, the loss of ALDH1, ARID1A, and CD44 and the overexpression of CAIX are important for tumor invasion and metastasis; therefore, they may serve as useful prognostic indicators of long-term survival in patients with GA. In conclusion, our study found that abnormal expression of some of the proteins evaluated in GA tumor cells might have an important role in carcinogenesis and tumor progression and thus may influence the prognosis of patients with GA.

Zhang Y, Chen Z, Li MJ, et al.
Long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 regulates the expression of Gli2 by miR-202 to strengthen gastric cancer progression.
Biomed Pharmacother. 2017; 85:264-271 [PubMed] Related Publications
BACKGROUND: Gastric cancer (GC) is one of the most common malignancies and ranks the second leading cause of cancer death worldwide. Some studies had reported the tumor-promoting effects of long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) as a competing endogenous RNA (ceRNA) by sponging to microRNAs. However, the molecular mechanism of ceRNA regulatory pathway involving MALAT1 in GC remains unclear.
METHODS: MALAT1 and miR -202 expression was detected by quantitative real time PCR (qRT-PCR) in 60 gastric cancer tissues and adjacent normal tissues, CCK8 cell proliferation assays, cell cycle analysis and cell apoptosis assays were performed to detect the GC cell proliferation and apoptosis. The mRNA and protein levels of Gli2 were analyzed by quantitative real-time PCR and Western blotting assays. Furthermore, using online software, luciferase reporter assays, RNA immunoprecipitation (RIP) and RNA pulldown assays demonstrated miR-202 was a target of MALAT1.
RESULTS: We found that MALAT1 was upregulated in GC tissues and higher MALAT1 expression was correlated with larger tumor size, lymph node metastasis, and TNM stage. Moreover, we revealed that MALAT1 was a direct target of miR-202 and knockdown of MALAT1 significantly decreased the expression of Gli2 through negatively regulating miR-202. In addition, knockdown of Malat1 inhibited GC cells proliferation, S-phase cell number, and induced cell apoptosis via negatively regulating miR-202 in vitro.
CONCLUSIONS: Our results elucidated MALAT1/miR-202/Gli2 regulatory pathway, which maybe contribute to a novel therapeutic strategy for GC patients.

Wang B, Yang H, Shen L, et al.
Rs56288038 (C/G) in 3'UTR of IRF-1 Regulated by MiR-502-5p Promotes Gastric Cancer Development.
Cell Physiol Biochem. 2016; 40(1-2):391-399 [PubMed] Related Publications
BACKGROUND/AIMS: Interferon regulatory factor 1 (IRF-1) has been shown to function as a transcriptional activator or repressor of a variety of target genes. However, its upstream, non-coding RNA-related regulatory capacity remains unknown. In this study, we focus on the miRNA-associated single nucleotide polymorphisms (SNPs) in the 3'untranslated region (UTR) of IRF-1 to further investigate the functional relationship and potential diagnostic value of the SNPs and miRNAs among Chinese gastric cancer (GC) patients.
METHODS: We performed a case-control study with 819 GC patients and 756 cancer-free controls. Genotyping by realtime PCR assay, cell transfection, and the dual luciferase reporter assay were used in our study, and the 5-year overall survival rate and relapse-free survival rate in different groups were investigated.
RESULTS: We found that patients suffering from Helicobacter pylori (Hp) infection were the susceptible population compared to controls. SNP rs56288038 (C/G) in IRF-1 3'UTR was involved in the occurrence of GC by acting as a tumor promoter factor. SNP rs56288038 (C/G) could be up-regulated by miR-502-5p, which caused a down-regulation of IRF-1 in cell lines and decreased apoptosis induced by IFN-γ. Carrying the G genotype was related to significantly low expression of IRF-1 and Hp infection, poor differentiation, big tumor size, invasion depth, as well as the high probability of metastasis, and moreover, the C/G SNP was associated with shorter survival of GC patients with five years of follow-up study.
CONCLUSIONS: our findings have shown that the SNP rs56288038 (C/G) in IRF-1 3'UTR acted as a promotion factor in GC development through enhancing the regulatory role of miR-502-5p in IRF-1 expression.

Zhu YW, Yan JK, Li JJ, et al.
Knockdown of Radixin Suppresses Gastric Cancer Metastasis In Vitro by Up-Regulation of E-Cadherin via NF-κB/Snail Pathway.
Cell Physiol Biochem. 2016; 39(6):2509-2521 [PubMed] Related Publications
BACKGROUND/AIMS: Radixin has recently been shown to correlate with the metastasis of gastric cancer, but the pathogenesis is elusive. Adhesion proteins contribute to the regulation of metastasis, and thus this study sought to investigate the role of radixin in the migration, invasion and adhesion of gastric cancer cells, as well as its interaction with adhesion proteins in vitro.
METHODS: Radixin stable knockdown human gastric carcinoma SGC-7901 cells were constructed. Alterations in the migration, invasion and adhesion ability were examined by matrigel-coated plate and transwell assays. The expression pattern of adhesion proteins, including E-cadherin, β-catenin and claudin-1, was determined by quantitative real-time PCR and western blot. Possible involvement of NF-κB/snail pathway was also evaluated.
RESULTS: Stable knockdown of radixin significantly suppressed migration and invasion, but enhanced adhesion in SGC-7901 cells. The expression of E-cadherin was manifestly increased in radixin knockdown cells, whereas the expression of β-catenin and claudin-1 was unchanged. The nuclear exclusion of NF-κB followed by conspicuous reduction of snail expression was involved in the regulation of E-cadherin expression.
CONCLUSIONS: Radixin knockdown suppresses the metastasis of SGC-7901 cells in vitro by up-regulation of E-cadherin. The NF-κB/snail pathway contributes to the regulation of E-cadherin in response to depletion of radixin.

Ren C, Chen H, Han C, et al.
High expression of miR-16 and miR-451 predicating better prognosis in patients with gastric cancer.
J Cancer Res Clin Oncol. 2016; 142(12):2489-2496 [PubMed] Related Publications
PURPOSE: To investigate the expression pattern of miR-16 and miR-451 and evaluate their prognostic value in 180 GC patients undergoing surgery.
METHODS: In our previous study, a panel of five circulating miRNAs (miR-16, miR-25, miR-92a, miR-451 and miR-486-5p) can be used as a potential biomarker for detecting of early-stage gastric carcinoma (GC). Tissue microarrays were constructed from 180 patients with GC after surgery. MiR-16 and miR-451 expression was detected by miRNA-locked nucleic acid in situ hybridization, and their relationship with clinicopathological parameters and overall survival was analyzed.
RESULTS: MiR-16 expression was decreased in 30.6 % (55/180) of GC, increased in 54.4 % (98/180) and unchanged in 15.0 % (27/180), compared with paracancerous normal tissue (P < 0.001). MiR-451 expression was decreased in 17.8 % (32/180), increased in 62.8 % (113/180) and unchanged in 19.4 % (35/180) of GC, compared with paracancerous normal tissue (P < 0.001).Univariate analysis indicated that low miR-16 and miR-451 expression, tumor stage, tumor status, node status and tumor size were significant negative prognostic predictors for overall survival in patients with GC (P < 0.001, P < 0.001, P = 0.002, P < 0.001 and P = 0.001, respectively). Multivariate regression analysis demonstrated that stage [hazard ratio (HR) 1.80; 95 % confidence interval (CI) 1.0-3.26; P = 0.05], low expression of miR-16 (HR 2.26; 95 % CI 1.51-3.40; P < 0.001) and miR-451 (HR 2.01; 95 % CI 1.36-2.96; P < 0.001) predicted shorter OS, while tumor status (HR 1.59; 95 % CI 0.73-3.48 P = 0.242), lymph node metastasis (HR 1.41; 95 % CI 0.71-2.82; P = 0.326) and tumor size (HR 1.53; 95 % CI 0.92-2.55; P = 0.099) were not. Moreover, patients with both miR-16 and miR-451 high expression have better OS than those with two miRNAs unchanged or low expression in GC tissues. Patients with both miR-16 and miR-451 high have better OS than patients with single miR-451 high expression.
CONCLUSIONS: High expression of miR-16 and miR-451 was associated with longer OS in GC patients. Especially patients with miR-16 and miR-451 double high expression will predict better OS. MiR-16 and miR-451 may be used as novel makers to evaluate prognosis and provide a new treatment target in GC.

Liu W, Dong Z, Liang J, et al.
Downregulation of Potential Tumor Suppressor miR-203a by Promoter Methylation Contributes to the Invasiveness of Gastric Cardia Adenocarcinoma.
Cancer Invest. 2016; 34(10):506-516 [PubMed] Related Publications
Like many tumor suppressor genes, some miRNA genes harboring CpG islands undergo methylation-mediated silencing. In the study, we found significant downregulation and proximal promoter methylation of miR-203a and miR-203b in gastric cardia adenocarcinoma (GCA) tissues. The methylation status of miR-203a and miR-203b in tumor tissues was negatively correlated with their expression level. GCA patients in stage III and IV with reduced expression or hypermethylation of miR-203a demonstrated poor patient survival. In all, miR-203a and miR-203b may function as tumor suppressive miRNAs, and reactivation of miR-203a may have therapeutic potential and may be used as prognostic marker for GCA patients.

Li H, Li W, Liu S, et al.
DNMT1, DNMT3A and DNMT3B Polymorphisms Associated With Gastric Cancer Risk: A Systematic Review and Meta-analysis.
EBioMedicine. 2016; 13:125-131 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Increasing studies showed that abnormal changes in single nucleotide polymorphisms (SNPs) of DNMTs (DNMT1, DNMT3A and DNMT3B) were associated with occurrence or decrease of various tumors. However, the associations between DNMTs variations and gastric cancer (GC) risk were still conflicting. We aimed to assess the effect of DNMTs polymorphisms on the susceptibility to GC.
METHODS: Firstly, we did a meta-analysis for 7 SNPs (rs16999593, rs2228611, rs8101866 in DNMT1, rs1550117, rs13420827 in DNMT3A, rs1569686, rs2424913 in DNMT3B). Four genetic models (homozygote, heterozygote, dominant and recessive model) were used. Moreover, a meta-sensitivity and subgroup analysis was performed to clarify heterogeneity source. Lastly, 17 SNPs that couldn't be meta-analyzed were presented in a systematic review.
FINDINGS: 20 studies were included, 13 studies could be meta-analyzed and 7 ones could not. Firstly, a meta-analysis on 13 studies (3959 GC cases and 5992 controls) for 7 SNPs showed that GC risk increased in rs16999593 (heterozygote model: OR 1.36, 95%CI 1.14-1.61; dominant model: OR 1.36, 95%CI 1.15-1.60) and rs1550117 (homozygote model: OR 2.03, 95%CI 1.38-3.00; dominant model: OR 1.20, 95%CI 1.01-1.42; recessive model: OR 1.96, 95%CI 1.33-2.89) but decreased in rs1569686 (dominant model: OR 0.74, 95%CI 0.61-0.90). The remaining SNPs were not found associated with GC risk. Furthermore, the subgroup analysis indicated that for rs1550117 and rs1569686, the significant associations were particularly found in people from Chinese Jiangsu province (rs1550117, OR 1.77, 95%CI 1.25-2.51; rs1569686, OR 0.48, 95%CI 0.36-0.64) and that PCR-RFLP was a sensitive method to discover significant associations (rs1550117, OR 1.77, 95%CI 1.25-2.51; rs1569686, OR 0.49, 95%CI 0.37-0.65). Lastly, a systematic review on 7 studies for 17 SNPs suggested that rs36012910, rs7560488 and rs6087990 might have a potential effect on GC initiation.
CONCLUSION: This meta-analysis demonstrated that rs16999593 and rs1550117 could contribute to GC risk and that rs1569686 might be a protective factor against gastric carcinogenesis. By using these SNPs as biomarkers, it is feasible to estimate the risk of acquiring GC and thus formulate timely preventive strategy.

Jia W, Yu T, Cao X, et al.
Clinical effect of DAPK promoter methylation in gastric cancer: A systematic meta-analysis.
Medicine (Baltimore). 2016; 95(43):e5040 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: The loss of death-associated protein kinase (DAPK) gene expression through promoter methylation is involved in many tumors. However, the relationship between DAPK promoter methylation and clinicopathological features of gastric cancer (GC) remains to be done. Therefore, we performed a meta-analysis to assess the role of DAPK promoter methylation in GC.
METHODS: Literature databases were searched to retrieve eligible studies. The pooled odds ratios (ORs) with its 95% confidence intervals (CIs) were calculated using the Stata 12.0 software.
RESULTS: Final 22 available studies with 1606 GC patients and 1508 nonmalignant controls were analyzed. A significant correlation was found between DAPK promoter methylation and GC (OR = 3.23, 95% CI = 1.70-6.14, P < 0.001), but we did not find any significant association in Caucasian population, and in blood samples in subgroup analyses. DAPK promoter methylation was associated with tumor stage and lymph node status (OR = 0.69, 95% CI = 0.49-0.96, P = 0.03; OR = 1.50, 95% CI = 1.12-2.01, P = 0.007; respectively). However, we did not find that DAPK promoter methylation was associated with gender status and tumor histology.
CONCLUSION: Our findings suggested that DAPK promoter methylation may play a key role in the carcinogenesis and progression of GC. In addition, methylated DAPK was a susceptible gene for Asian population. However, more studies with larger subjects should be done to further evaluate the effect of DAPK promoter methylation in GC patients, especially in blood and Caucasian population subgroup.

Wei S, Wang L, Zhang L, et al.
ZNF143 enhances metastasis of gastric cancer by promoting the process of EMT through PI3K/AKT signaling pathway.
Tumour Biol. 2016; 37(9):12813-12821 [PubMed] Related Publications
The zinc finger protein 143 (ZNF143) is a transcription factor, which regulates many cell cycle-associated genes. ZNF143 expressed strongly in multiple solid tumors. However, the influence of ZNF143 on gastric cancer (GC) remains largely unknown. In this study, we investigated the ZNF143 mRNA level in GC tissues and cells by quantitative real-time PCR (qRT-PCR). The protein expression of ZNF143 in GC cells, and the signaling pathway proteins were detected by Western blotting. Transwell assay and wound healing assay were performed to explore the effects of ZNF143 for the migration ability of GC cells in vitro. We also performed the tail vein injection in nude mice with GC cells to explore the impact of ZNF143 on GC metastasis in vivo. ZNF143 was overexpressed in specimens of GC compared with adjacent normal tissues and increased more significantly in GC tissues of patients who had lymph node metastasis. Ectopic overexpression of ZNF143 enhanced GC migration, whereas ZNF143 knockdown suppressed this effect in vitro. In vivo, ZNF143 knockdown reduced distant metastasis of GC cells in nude mice. In addition, overexpression of ZNF143 reduced the expression of epithelial cell marker (E-cadherin) and induced the expression of mesenchymal cell marker (N-cadherin,Vimentin), Snail and Slug. We also found that ZNF143 enhanced GC cell migration by promoting the process of EMT through PI3K/AKT signaling pathway. In general, our findings show that ZNF143 expressed strongly in GC and enhanced migration of GC cells in vitro and in vivo. It is conceivable that ZNF143 could be a therapeutic genetic target for GC treatment.

Ding WJ, Zhou M, Chen MM, Qu CY
HOXB8 promotes tumor metastasis and the epithelial-mesenchymal transition via ZEB2 targets in gastric cancer.
J Cancer Res Clin Oncol. 2017; 143(3):385-397 [PubMed] Related Publications
PURPOSE: The homeobox B8 (HOXB8) functions as a sequence-specific transcription factor that is involved in development. Increased expression of this gene is associated with a wide variety of tumor; however, its function in gastric cancer has not been clarified. In the present study, the expression of HOXB8 in gastric cancer tissues and influence of HOXB8 on gastric cancer cellular were evaluated.
METHODS: The expression levels of HOXB8 mRNA in human gastric cancer tissues were analyzed through quantitative RT-PCR. To test the role of HOXB8 in gastric cancer metastasis, the cell transwell assay was performed. Microarray, ChIP-qPCR, and Western blot were used to explore the possible mechanism that HOXB8 promotes gastric cancer cells metastasis.
RESULTS: In this study, we found that HOXB8 showed higher expression in metastatic tissues than no-metastatic tissues. Overexpression of HOXB8 can promote gastric cancer cells migration and invasion, while silencing HOXB8 leads to the opposite results. Overexpression of HOXB8 also increases the rate of metastasis in NCI-N87 mice, while silencing HOXB8 has the opposite results. Furthermore, HOXB8 promotes epithelial-mesenchymal transformation of AGS cells. We also found that ZEB2 can interact with HOXB8 and may be a downstream factor of HOXB8 by using microarray. Knockdown of ZEB2 can inhibit HOXB8-induced migration and invasion capacity, as well as the epithelial-mesenchymal transformation in gastric cancer cells.
CONCLUSIONS: The results showed that HOXB8 plays an important role in the development and metastasis of gastric carcinoma.

Chika N, Fukuchi M, Suzuki O, et al.
[Incidence and Characteristics of Mismatch Repair Protein Deficiency in Elderly Gastric Cancer Patients].
Gan To Kagaku Ryoho. 2016; 43(10):1298-1300 [PubMed] Related Publications
The loss of mismatch repair(MMR)function as a result of MLH1 promoter methylation is closely correlated with high frequency microsatellite instability, and tumors with such characteristics are resistant to anticancer drugs such as 5-FU. We examined the incidence and characteristics of deficient MMR(dMMR)in elderly gastric cancer patients by performing a comprehensive immunohistochemical screening. The study was conducted in 199 patients diagnosed with gastric cancer, aged 75 years or older, who underwent a gastrectomy between April 2005 and January 2014. dMMR was detected in 23 patients(12%). All the tumors with dMMR were deficient in MLH1 and PMS2. dMMR was significantly more common compared to proficient MMR(pMMR)in patients with a more advanced age(p=0.03), women(p<0.01), and a tumor location corresponding to the lower region(p<0.01). Considering the incidence of dMMR in elderly gastric cancer patients, only a limited proportion of patients are likely to be eligible for immune checkpoint inhibitor therapy, which is expected to become more popular in the near future.

Gao YW, Zhang CH, Zuo XM, Hui XZ
Genetic Basis of Gastric Cancer.
Chin Med Sci J. 2016; 31(3):192-195 [PubMed] Related Publications
Gastric cancer is the result of multiple risk factors, including environmental factors, genetic factors and the interaction between them. The environmental factors mainly include dietary, Helicobacter pylori infection and family history of gastric cancer. Genetic factors mainly refer to the susceptible genes that cause epigenetic alterations in oncogenes, tumor suppress genes, cell cycle regulators, DNA repair genes and signaling molecules. This paper summarizes the susceptible genes of gastric cancer and explores the genetic basis of it.

Bustos-Carpinteyro AR, Delgado-Figueroa N, Santiago-Luna E, et al.
Association between the CDH1-472delA and -160C>A polymorphisms and diffuse and intestinal gastric cancer in a Mexican population.
Genet Mol Res. 2016; 15(3) [PubMed] Related Publications
Gastric cancer (GC), the third leading cause of cancer-related deaths in Mexico and worldwide, can be classified into diffuse (DGC) or intestinal (IGC) types based on its histological characteristics. DGC is characterized by reduced expression of the cell adhesion protein E-cadherin, which is encoded by CDH1. The -472delA (rs5030625) and -160C>A (rs16260) polymorphisms in CDH1 induce a decrease in gene transcription; in fact, these mutated alleles have been associated with GC in some populations, with conflicting results. The aim of this study was to determine the association between the CDH1 -472delA and -160C>A polymorphisms and DGC and IGC in Mexican patients. The study was conducted in 24, 23, 48, and 93 individuals with DGC and IGC, without GC (control), and belonging to the general Mexican population (GMP), respectively. The genotypes were obtained by polymerase chain reaction - restriction fragment length polymorphism and the obtained data analyzed using Arlequin 3.1. The frequencies of the mutated allele (A) of -472delA were 0.326, 0.318, 0.284, and 0.296 in the DGC, IGC, control, and GMP groups, respectively, and those of the -160C>A polymorphism were 0.174, 0.318, 0.313, and 0.280, respectively. The genotype and allele frequencies of the two polymorphisms did not differ significantly (P > 0.05) among DGC, IGC, and control subjects. Therefore, we concluded that the CDH1 -472delA and -160C>A polymorphisms are not associated with DGC or IGC in patients from western Mexico.

Chen S, Zhu XC, Liu YL, et al.
Investigating the association between XRCC1 gene polymorphisms and susceptibility to gastric cancer.
Genet Mol Res. 2016; 15(3) [PubMed] Related Publications
We carried out a hospital-based case-control study to investigate the role of XRCC1 gene Arg399Gln, Arg280His, and Arg194Trp polymorphisms in susceptibility to gastric cancer. A total of 214 gastric cancer patients and 247 control subjects were recruited between March 2013 and March 2015, and polymorphism genotype frequencies were determined by polymerase chain reaction-restriction fragment length polymorphism. Using the chi-square test, we detected statistically significant differences in age (chi-square = 22.25, P < 0.001), gender (chi-square = 6.74, P = 0.01), and family history of cancer (chi-square = 4.73, P = 0.03) between the case and control groups. Logistic regression analysis revealed that the XRCC1 Arg194Trp TT genotype conferred increased susceptibility to gastric cancer compared to the CC genotype [odds ratio (OR) = 2.38, 95% confidence interval (CI) = 1.28-4.49]. Moreover, individuals carrying the T allele of this variant were found to be at moderately increased risk of this disease (OR = 1.56, 95%CI = 1.16-2.09). However, the XRCC1 Arg399Gln and Arg280His polymorphisms were shown to have no influence on the development of gastric cancer. In conclusion, we suggest that the XRCC1 gene Arg194Trp polymorphism is associated with gastric cancer susceptibility in the Chinese population.

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