Gastric Cancer

Overview

Literature Analysis

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Tag cloud generated 29 August, 2019 using data from PubMed, MeSH and CancerIndex

Mutated Genes and Abnormal Protein Expression (487)

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
462
CDH1 16q22.1 UVO, CDHE, ECAD, LCAM, Arc-1, CD324 -CDH1 and Stomach Cancer
345
MET 7q31.2 HGFR, AUTS9, RCCP2, c-Met, DFNB97 Prognostic
-C-MET and Stomach Cancer
293
TNF 6p21.33 DIF, TNFA, TNFSF2, TNLG1F, TNF-alpha -TNF and Stomach Cancer
274
APC 5q22.2 GS, DP2, DP3, BTPS2, DP2.5, PPP1R46 -APC and Stomach Cancer
190
BAX 19q13.33 BCL2L4 -BAX and Stomach Cancer
171
PTGS2 1q31.1 COX2, COX-2, PHS-2, PGG/HS, PGHS-2, hCox-2, GRIPGHS -PTGS2 (COX2) and Stomach Cancer
148
CASP3 4q35.1 CPP32, SCA-1, CPP32B -CASP3 and Stomach Cancer
142
IL10 1q32.1 CSIF, TGIF, GVHDS, IL-10, IL10A -Interleukin-10 and Stomach Cancer
122
KRAS 12p12.1 NS, NS3, CFC2, RALD, KRAS1, KRAS2, RASK2, KI-RAS, C-K-RAS, K-RAS2A, K-RAS2B, K-RAS4A, K-RAS4B, c-Ki-ras2 -KRAS and Stomach Cancer
117
MTOR 1p36.22 SKS, FRAP, FRAP1, FRAP2, RAFT1, RAPT1 -MTOR and Stomach Cancer
112
CEACAM5 19q13.2 CEA, CD66e -CEACAM5 and Stomach Cancer
107
RUNX3 1p36.11 AML2, CBFA3, PEBP2aC -RUNX3 and Stomach Cancer
101
CD44 11p13 IN, LHR, MC56, MDU2, MDU3, MIC4, Pgp1, CDW44, CSPG8, HCELL, HUTCH-I, ECMR-III -CD44 and Stomach Cancer
98
PTEN 10q23.31 BZS, DEC, CWS1, GLM2, MHAM, TEP1, MMAC1, PTEN1, 10q23del -PTEN and Stomach Cancer
93
PCNA 20p12.3 ATLD2 -PCNA and Stomach Cancer
88
MTHFR 1p36.22 -MTHFR and Stomach Cancer
84
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II Prognostic
-MMP2 and Stomach Cancer
83
MUC1 1q22 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?
68
MUC2 11p15.5 MLP, SMUC, MUC-2 -MUC2 and Stomach Cancer
65
VEGFA 6p21.1 VPF, VEGF, MVCD1 -VEGFA and Stomach Cancer
65
KITLG 12q22 SF, MGF, SCF, FPH2, FPHH, KL-1, Kitl, SHEP7 -KITLG and Stomach Cancer
64
PIK3CA 3q26.32 MCM, CWS5, MCAP, PI3K, CLOVE, MCMTC, PI3K-alpha, p110-alpha -PIK3CA and Stomach Cancer
63
MSH2 2p21 FCC1, COCA1, HNPCC, LCFS2, HNPCC1 -MSH2 and Stomach Cancer
62
CDKN1B 12p13.1 KIP1, MEN4, CDKN4, MEN1B, P27KIP1 Prognostic
-CDKN1B and Gastric Cancer
62
FGFR2 10q26.13 BEK, JWS, BBDS, CEK3, CFD1, ECT1, KGFR, TK14, TK25, BFR-1, CD332, K-SAM -FGFR2 and Stomach Cancer
61
TFF1 21q22.3 pS2, BCEI, HPS2, HP1.A, pNR-2, D21S21 -TFF1 and Stomach Cancer
59
ERCC1 19q13.32 UV20, COFS4, RAD10 -ERCC1 and Stomach Cancer
57
HIF1A 14q23.2 HIF1, MOP1, PASD8, HIF-1A, bHLHe78, HIF-1alpha, HIF1-ALPHA, HIF-1-alpha -HIF1A and Stomach Cancer
55
MGMT 10q26.3 -MGMT and Stomach Cancer
55
TGFBR2 3p24.1 AAT3, FAA3, LDS2, MFS2, RIIC, LDS1B, LDS2B, TAAD2, TGFR-2, TGFbeta-RII -TGFBR2 and Stomach Cancer
53
MALT1 18q21.32 MLT, MLT1, IMD12, PCASP1 -MALT1 and Stomach Cancer
53
PSCA 8q24.3 PRO232 -MUC1 polymorphisms and cancer suseptability?
-PSCA and Stomach Cancer
45
MYC 8q24.21 MRTL, MYCC, c-Myc, bHLHe39 -MYC protein, human and Stomach Cancer
52
DCC 18q21.2 CRC18, CRCR1, MRMV1, IGDCC1, NTN1R1 -DCC and Stomach Cancer
51
PDGFRA 4q12 CD140A, PDGFR2, PDGFR-2 -PDGFRA and Stomach Cancer
49
SMAD4 18q21.2 JIP, DPC4, MADH4, MYHRS -SMAD4 and Stomach Cancer
47
MUC5AC 11p15.5 TBM, leB, MUC5, mucin -MUC5AC and Stomach Cancer
46
TLR4 9q33.1 TOLL, CD284, TLR-4, ARMD10 -TLR4 and Stomach Cancer
46
HGF 7q21.11 SF, HGFB, HPTA, F-TCF, DFNB39 -HGF and Stomach Cancer
43
FHIT 3p14.2 FRA3B, AP3Aase -FHIT and Stomach Cancer
40
MUC6 11p15.5 MUC-6 -MUC6 and Stomach Cancer
37
ABCB1 7q21.12 CLCS, MDR1, P-GP, PGY1, ABC20, CD243, GP170 -ABCB1 and Stomach Cancer
37
IL1RN 2q14.2 DIRA, IRAP, IL1F3, IL1RA, MVCD4, IL-1RN, IL-1ra, IL-1ra3, ICIL-1RA -IL1RN and Stomach Cancer
35
FOS 14q24.3 p55, AP-1, C-FOS -FOS and Stomach Cancer
34
VEGFC 4q34.3 VRP, Flt4-L, LMPH1D -VEGFC and Stomach Cancer
31
CYP2E1 10q26.3 CPE1, CYP2E, P450-J, P450C2E -CYP2E1 and Stomach Cancer
31
MDM2 12q15 HDMX, hdm2, ACTFS -MDM2 and Stomach Cancer
29
IL17C 16q24.2 CX2, IL-17C -IL17C and Stomach Cancer
29
ERCC2 19q13.32 EM9, TTD, XPD, TTD1, COFS2, TFIIH -ERCC2 and Stomach Cancer
29
MMP7 11q22.2 MMP-7, MPSL1, PUMP-1 -MMP7 and Stomach Cancer
28
TFF2 21q22.3 SP, SML1 -TFF2 and Stomach Cancer
27
GAPDH 12p13.31 G3PD, GAPD, HEL-S-162eP -GAPDH and Stomach Cancer
27
DAPK2 15q22.31 DRP1, DRP-1 -DAPK2 and Stomach Cancer
24
ALDH2 12q24.12 ALDM, ALDHI, ALDH-E2 -ALDH2 and Stomach Cancer
24
TFF3 21q22.3 ITF, P1B, TFI -TFF3 and Stomach Cancer
24
BCL10 1p22.3 CLAP, mE10, CIPER, IMD37, c-E10, CARMEN -BCL10 and Stomach Cancer
23
CDK6 7q21.2 MCPH12, PLSTIRE -CDK6 and Stomach Cancer
23
H19 11p15.5 ASM, BWS, WT2, ASM1, D11S813E, LINC00008, NCRNA00008 -H19 and Stomach Cancer
23
CHFR 12q24.33 RNF116, RNF196 -CHFR and Stomach Cancer
23
XIAP Xq25 API3, ILP1, MIHA, XLP2, BIRC4, IAP-3, hIAP3, hIAP-3 -XIAP and Stomach Cancer
23
SNAI1 20q13.13 SNA, SNAH, SNAIL, SLUGH2, SNAIL1, dJ710H13.1 -SNAI1 and Stomach Cancer
22
EZH2 7q36.1 WVS, ENX1, EZH1, KMT6, WVS2, ENX-1, EZH2b, KMT6A -EZH2 and Stomach Cancer
22
HLA-A 6p22.1 HLAA -HLA-A and Stomach Cancer
22
CD82 11p11.2 R2, 4F9, C33, IA4, ST6, GR15, KAI1, SAR2, TSPAN27 -CD82 and Stomach Cancer
21
CAMP 3p21.31 LL37, CAP18, CRAMP, HSD26, CAP-18, FALL39, FALL-39 -CAMP and Stomach Cancer
20
FAS 10q23.31 APT1, CD95, FAS1, APO-1, FASTM, ALPS1A, TNFRSF6 -FAS and Stomach Cancer
20
TLR2 4q31.3 TIL4, CD282 -TLR2 and Stomach Cancer
19
S100A4 1q21.3 42A, 18A2, CAPL, FSP1, MTS1, P9KA, PEL98 -S100A4 and Stomach Cancer
19
TIMP3 22q12.3 SFD, K222, K222TA2, HSMRK222 -TIMP3 and Stomach Cancer
19
JAK2 9p24.1 JTK10, THCYT3 -JAK2 and Stomach Cancer
19
MAPK1 22q11.22 ERK, p38, p40, p41, ERK2, ERT1, ERK-2, MAPK2, PRKM1, PRKM2, P42MAPK, p41mapk, p42-MAPK -MAPK1 and Stomach Cancer
17
CBL 11q23.3 CBL2, NSLL, C-CBL, RNF55, FRA11B -Proto-Oncogene Proteins c-cbl and Stomach Cancer
17
GNAS 20q13.32 AHO, GSA, GSP, POH, GPSA, NESP, SCG6, SgVI, GNAS1, PITA3, C20orf45 -GNAS and Stomach Cancer
17
OLFM4 13q14.3 GC1, OLM4, OlfD, GW112, hGC-1, hOLfD, UNQ362, bA209J19.1 -OLFM4 and Stomach Cancer
16
IL17A 6p12.2 IL17, CTLA8, IL-17, CTLA-8, IL-17A -IL17A and Stomach Cancer
16
AURKA 20q13.2 AIK, ARK1, AURA, BTAK, STK6, STK7, STK15, PPP1R47 -AURKA and Stomach Cancer
16
REG4 1p12 GISP, RELP, REG-IV -REG4 and Stomach Cancer
15
MCL1 1q21.2 TM, EAT, MCL1L, MCL1S, Mcl-1, BCL2L3, MCL1-ES, bcl2-L-3, mcl1/EAT -MCL1 and Stomach Cancer
15
MCC 5q22.2 MCC1 -MCC and Stomach Cancer
15
CISH 3p21.2 CIS, G18, SOCS, CIS-1, BACTS2 -CISH and Stomach Cancer
15
CDH17 8q22.1 HPT1, CDH16, HPT-1 -CDH17 and Stomach Cancer
15
FASLG 1q24.3 APTL, FASL, CD178, CD95L, ALPS1B, CD95-L, TNFSF6, TNLG1A, APT1LG1 -FASLG and Stomach Cancer
15
MIR107 10q23.31 MIRN107, miR-107 -MicroRNA mir-107 and Stomach Cancer
14
PRC1 15q26.1 ASE1 -PRC1 and Stomach Cancer
14
CDX1 5q32 -CDX1 and Stomach Cancer
14
PTP4A3 8q24.3 PRL3, PRL-3, PRL-R -PTP4A3 and Stomach Cancer
13
CCND2 12p13.32 MPPH3, KIAK0002 -CCND2 and Stomach Cancer
13
WNT5A 3p14.3 hWNT5A -WNT5A and Stomach Cancer
13
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Stomach Cancer
13
NOS2 17q11.2 NOS, INOS, NOS2A, HEP-NOS -NOS2 and Stomach Cancer
13
IGF2 11p15.5 GRDF, IGF-II, PP9974, C11orf43 -IGF2 and Stomach Cancer
13
MIF 22q11.23 GIF, GLIF, MMIF -MIF 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
WNT2 7q31.2 IRP, INT1L1 -WNT2 and Stomach Cancer
12
TOP2A 17q21.2 TOP2, TP2A -TOP2A and Stomach Cancer
12
MMP1 11q22.2 CLG, CLGN -MMP1 and Stomach Cancer
12
MUC4 3q29 ASGP, MUC-4, HSA276359 -MUC4 and Stomach Cancer
12
PTCH1 9q22.32 PTC, BCNS, HPE7, PTC1, PTCH, NBCCS, PTCH11 -PTCH1 and Stomach Cancer
11
MMP3 11q22.2 SL-1, STMY, STR1, CHDS6, MMP-3, STMY1 -MMP3 and Stomach Cancer
11
PTPN11 12q24.13 CFC, NS1, JMML, SHP2, BPTP3, PTP2C, METCDS, PTP-1D, SH-PTP2, SH-PTP3 -PTPN11 and Stomach Cancer
11
KLF4 9q31.2 EZF, GKLF -KLF4 and Stomach Cancer
11
BMI1 10p12.2 PCGF4, RNF51, FLVI2/BMI1, flvi-2/bmi-1 -BMI1 and Stomach Cancer
11
UGT1A1 2q37 GNT1, UGT1, UDPGT, UGT1A, HUG-BR1, BILIQTL1, UDPGT 1-1 -UGT1A1 and Stomach Cancer
11
VIP 6q25.2 PHM27 -VIP 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
11
CTNNA1 5q31.2 MDPT2, CAP102 -CTNNA1 and Stomach Cancer
11
GRB7 17q12 -GRB7 and Stomach Cancer
11
DPYD 1p21.3 DHP, DPD, DHPDHASE -DPYD and Stomach Cancer
11
IL4 5q31.1 BSF1, IL-4, BCGF1, BSF-1, BCGF-1 -IL4 and Stomach Cancer
11
WNT3A 1q42.13 -WNT3A and Stomach Cancer
11
KRT20 17q21.2 K20, CD20, CK20, CK-20, KRT21 -KRT20 and Stomach Cancer
11
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 and Stomach Cancer
10
ICAM1 19p13.2 BB2, CD54, P3.58 -ICAM1 and Stomach Cancer
10
JAK1 1p31.3 JTK3, JAK1A, JAK1B -JAK1 and Stomach Cancer
10
XAF1 17p13.1 BIRC4BP, XIAPAF1, HSXIAPAF1 -XAF1 and Stomach Cancer
10
SIRT1 10q21.3 SIR2, SIR2L1, SIR2alpha -SIRT1 and Stomach Cancer
10
MAP2K1 15q22.31 CFC3, MEK1, MKK1, MAPKK1, PRKMK1 -MAP2K1 and Stomach Cancer
10
STAR 8p11.23 STARD1 -STAR and Stomach Cancer
10
FSCN1 7p22.1 HSN, SNL, p55, FAN1 -FSCN1 and Stomach Cancer
10
BUB1 2q14 BUB1A, BUB1L, hBUB1 -BUB1 and Stomach Cancer
10
PTK2 8q24.3 FAK, FADK, FAK1, FRNK, PPP1R71, p125FAK, pp125FAK -PTK2 and Stomach Cancer
10
MOS 8q12.1 MSV -MOS and Stomach Cancer
9
ERCC5 13q33.1 XPG, UVDR, XPGC, COFS3, ERCM2, ERCC5-201 -ERCC5 and Stomach Cancer
9
TYMS 18p11.32 TS, TMS, HST422 -TYMS and Stomach Cancer
9
APEX1 14q11.2 APE, APX, APE1, APEN, APEX, HAP1, REF1 -APEX1 and Stomach Cancer
9
ARHGEF1 19q13.2 LSC, GEF1, LBCL2, SUB1.5, P115-RHOGEF -ARHGEF1 and Stomach Cancer
9
IL11 19q13.42 AGIF, IL-11 -IL11 and Stomach Cancer
9
SMO 7q32.1 Gx, CRJS, SMOH, FZD11 -SMO and Stomach Cancer
9
SOX9 17q24.3 CMD1, SRA1, CMPD1, SRXX2, SRXY10 -SOX9 and Stomach Cancer
9
HLA-B 6p21.33 AS, HLAB, B-4901 -HLA-B and Stomach Cancer
8
HMGB1 13q12.3 HMG1, HMG3, HMG-1, SBP-1 -HMGB1 and Stomach Cancer
8
SSTR2 17q25.1 -SSTR2 and Stomach Cancer
8
KLF6 10p15.2 GBF, ZF9, BCD1, CBA1, CPBP, PAC1, ST12, COPEB -KLF6 and Stomach Cancer
8
SFRP2 4q31.3 FRP-2, SARP1, SDF-5 -SFRP2 and Stomach Cancer
8
HLA-DRB1 6p21.32 SS1, DRB1, HLA-DRB, HLA-DR1B -HLA-DRB1 and Stomach Cancer
8
CD24 6q21 CD24A -CD24 and Stomach Cancer
8
HMOX1 22q12.3 HO-1, HSP32, HMOX1D, bK286B10 -HMOX1 and Stomach Cancer
8
DKK1 10q21.1 SK, DKK-1 -DKK1 and Stomach Cancer
8
UCHL1 4p13 NDGOA, PARK5, PGP95, SPG79, PGP9.5, Uch-L1, HEL-117, PGP 9.5, HEL-S-53 -UCHL1 and Stomach Cancer
8
ZEB2 2q22.3 SIP1, SIP-1, ZFHX1B, HSPC082, SMADIP1 -ZEB2 and Stomach Cancer
8
ADH1B 4q23 ADH2, HEL-S-117 -ADH1B and Stomach Cancer
8
CD274 9p24.1 B7-H, B7H1, PDL1, PD-L1, PDCD1L1, PDCD1LG1 -CD274 and Stomach Cancer
8
FADD 11q13.3 GIG3, MORT1 -FADD and Stomach Cancer
8
ODC1 2p25 ODC -ODC1 and Stomach Cancer
8
LGR5 12q22-q23 FEX, HG38, GPR49, GPR67, GRP49 -LGR5 and Stomach Cancer
8
PAK1 11q13.5-q14.1 PAKalpha -PAK1 and Stomach Cancer
8
LGALS3 14q22.3 L31, GAL3, MAC2, CBP35, GALBP, GALIG, LGALS2 -LGALS3 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
GATA4 8p23.1 TOF, ASD2, VSD1, TACHD -GATA4 and Stomach Cancer
7
DAPK1 9q21.33 DAPK -DAPK1 and Stomach Cancer
7
HLTF 3q25.1-q26.1 ZBU1, HLTF1, RNF80, HIP116, SNF2L3, HIP116A, SMARCA3 -HLTF and Stomach Cancer
7
MAD2L1 4q27 MAD2, HSMAD2 -MAD2L1 and Stomach Cancer
7
FLNC 7q32.1 ABPA, ABPL, FLN2, MFM5, MPD4, RCM5, CMH26, ABP-280, ABP280A -FLNC and Stomach Cancer
7
ROCK1 18q11.1 ROCK-I, P160ROCK -ROCK1 and Stomach Cancer
7
DICER1 14q32.13 DCR1, MNG1, Dicer, HERNA, RMSE2, Dicer1e, K12H4.8-LIKE -DICER1 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
TPR 1q31.1 -TPR and Stomach Cancer
7
FH 1q43 MCL, FMRD, HsFH, LRCC, HLRCC, MCUL1 -FH and Stomach Cancer
7
DIABLO 12q24.31 SMAC, DFNA64 -DIABLO and Stomach Cancer
7
MALAT1 11q13.1 HCN, NEAT2, PRO2853, LINC00047, NCRNA00047 -MALAT1 and Stomach Cancer
7
AICDA 12p13.31 AID, ARP2, CDA2, HIGM2, HEL-S-284 -AICDA and Stomach Cancer
7
RASSF2 20p13 CENP-34, RASFADIN -RASSF2 and Stomach Cancer
7
CXCR3 Xq13.1 GPR9, MigR, CD182, CD183, Mig-R, CKR-L2, CMKAR3, IP10-R -CXCR3 and Stomach Cancer
7
PTER 10p13 HPHRP, RPR-1 -PTER and Stomach Cancer
7
PCDH10 4q28.3 PCDH19, OL-PCDH -PCDH10 and Stomach Cancer
7
S100A6 1q21.3 2A9, PRA, 5B10, CABP, CACY, S10A6 -S100A6 and Stomach Cancer
7
MTRR 5p15.31 MSR, cblE -MTRR and Stomach Cancer
7
CCL2 17q12 HC11, MCAF, MCP1, MCP-1, SCYA2, GDCF-2, SMC-CF, HSMCR30 -CCL2 and Stomach Cancer
7
PVT1 8q24.21 MYC, LINC00079, NCRNA00079, onco-lncRNA-100 -PVT1 and Stomach Cancer
6
NFKBIA 14q13.2 IKBA, MAD-3, NFKBI -NFKBIA and Stomach Cancer
6
ING1 13q34 p33, p47, p33ING1, p24ING1c, p33ING1b, p47ING1a -ING1 Supression in Gastric Cancer
6
GDF15 19p13.11 PDF, MIC1, PLAB, MIC-1, NAG-1, PTGFB, GDF-15 -GDF15 and Stomach Cancer
6
SFRP5 10q24.2 SARP3 -SFRP5 and Stomach Cancer
6
IRF1 5q31.1 MAR, IRF-1 -IRF1 and Stomach Cancer
6
DROSHA 5p13.3 RN3, ETOHI2, RNASEN, RANSE3L, RNASE3L, HSA242976 -DROSHA and Stomach Cancer
6
DKK3 11p15.3 RIG, REIC -DKK3 and Stomach Cancer
6
ANO1 11q13.3 DOG1, TAOS2, ORAOV2, TMEM16A -ANO1 and Stomach Cancer
6
FGF7 15q21.2 KGF, HBGF-7 -FGF7 and Stomach Cancer
6
REG1A 2p12 P19, PSP, PTP, REG, ICRF, PSPS, PSPS1 -REG1A and Stomach Cancer
6
CLDN3 7q11.23 RVP1, HRVP1, C7orf1, CPE-R2, CPETR2 -CLDN3 and Stomach Cancer
6
S100P 4p16.1 MIG9 -S100P and Stomach Cancer
6
XIST Xq13.2 SXI1, swd66, DXS1089, DXS399E, LINC00001, NCRNA00001 -XIST and Stomach Cancer
6
GATA6 18q11.2 -GATA6 and Stomach Cancer
6
SCFV 14 -SCFV and Stomach Cancer
6
S100A2 1q21.3 CAN19, S100L -S100A2 and Stomach Cancer
6
HLA-G 6p22.1 MHC-G -HLA-G and Stomach Cancer
6
GATA5 20q13.33 CHTD5, GATAS, bB379O24.1 -GATA5 and Stomach Cancer
6
FZD7 2q33 FzE3 -FZD7 and Stomach Cancer
6
LGALS1 22q13.1 GBP, GAL1 -LGALS1 and Stomach Cancer
6
CD40 20q13.12 p50, Bp50, CDW40, TNFRSF5 -CD40 and Stomach Cancer
6
TFPI2 7q21.3 PP5, REF1, TFPI-2 -TFPI2 and Stomach Cancer
6
COL1A1 17q21.33 OI1, OI2, OI3, OI4, EDSC -COL1A1 and Stomach Cancer
6
GDNF 5p13.2 ATF, ATF1, ATF2, HSCR3, HFB1-GDNF -GDNF and Stomach Cancer
6
NBN 8q21.3 ATV, NBS, P95, NBS1, AT-V1, AT-V2 -NBN and Stomach Cancer
6
SUZ12 17q11.2 CHET9, JJAZ1 -SUZ12 and Stomach Cancer
6
FYN 6q21 SLK, SYN, p59-FYN -FYN and Stomach Cancer
6
CD83 6p23 BL11, HB15 -CD83 and Stomach Cancer
6
AQP3 9p13.3 GIL, AQP-3 -AQP3 and Stomach Cancer
6
SPHK1 17q25.1 SPHK -SPHK1 and Stomach Cancer
6
EP300 22q13.2 p300, KAT3B, MKHK2, RSTS2 -EP300 and Stomach Cancer
6
PLA2G2A 1p36.13 MOM1, PLA2, PLA2B, PLA2L, PLA2S, PLAS1, sPLA2 -PLA2G2A and Stomach Cancer
5
IL1A 2q14 IL1, IL-1A, IL1F1, IL1-ALPHA -IL1A and Stomach Cancer
5
CTTN 11q13.3 EMS1 -CTTN and Stomach Cancer
5
TP53INP1 8q22.1 SIP, Teap, p53DINP1, TP53DINP1, TP53INP1A, TP53INP1B -TP53INP1 and Stomach Cancer
5
IGFBP7 4q12 AGM, PSF, TAF, FSTL2, IBP-7, MAC25, IGFBP-7, RAMSVPS, IGFBP-7v, IGFBPRP1 -IGFBP7 and Stomach Cancer
5
IQGAP1 15q26.1 SAR1, p195, HUMORFA01 -IQGAP1 and Stomach Cancer
5
CD55 1q32.2 CR, TC, DAF, CROM, CHAPLE -CD55 and Stomach Cancer
5
ST7 7q31.2 HELG, RAY1, SEN4, TSG7, ETS7q, FAM4A, FAM4A1 -ST7 and Stomach Cancer
5
S100A11 1q21.3 MLN70, S100C, HEL-S-43 -S100A11 and Stomach Cancer
5
CYP1A2 15q24.1 CP12, P3-450, P450(PA) -CYP1A2 and Stomach Cancer
5
XRCC6 22q13.2 ML8, KU70, TLAA, CTC75, CTCBF, G22P1 -XRCC6 and Stomach Cancer
5
MYH9 22q12.3 MHA, FTNS, EPSTS, BDPLT6, DFNA17, MATINS, NMMHCA, NMHC-II-A, NMMHC-IIA -MYH9 and Stomach Cancer
5
CD86 3q13.33 B70, B7-2, B7.2, LAB72, CD28LG2 -CD86 and Stomach Cancer
5
CTSB 8p23.1 APPS, CPSB -CTSB and Stomach Cancer
5
TNKS 8p23.1 TIN1, ARTD5, PARPL, TINF1, TNKS1, pART5, PARP5A, PARP-5a -TNKS and Stomach Cancer
5
HDAC6 Xp11.23 HD6, JM21, CPBHM, PPP1R90 -HDAC6 and Stomach Cancer
5
POT1 7q31.33 GLM9, CMM10, HPOT1 -POT1 and Stomach Cancer
5
PAK4 19q13.2 -PAK4 and Stomach Cancer
5
PINX1 8p23.1 LPTL, LPTS -PINX1 and Stomach Cancer
5
CLDN4 7q11.23 CPER, CPE-R, CPETR, CPETR1, WBSCR8, hCPE-R -CLDN4 and Stomach Cancer
5
CYP2C19 10q23.33 CPCJ, CYP2C, P450C2C, CYPIIC17, CYPIIC19, P450IIC19 -CYP2C19 and Stomach Cancer
5
FOXA2 20p11.21 HNF3B, TCF3B -FOXA2 and Stomach Cancer
5
COL1A2 7q21.3 OI4 -COL1A2 and Stomach Cancer
5
IL12A 3q25.33 P35, CLMF, NFSK, NKSF1, IL-12A -IL12A and Stomach Cancer
5
ING4 12p13.31 my036, p29ING4 -ING4 and Stomach Cancer
5
TBX21 17q21.32 TBET, T-PET, T-bet, TBLYM -TBX21 and Stomach Cancer
5
MCM7 7q22.1 MCM2, CDC47, P85MCM, P1CDC47, PNAS146, PPP1R104, P1.1-MCM3 -MCM7 and Stomach Cancer
5
MTDH 8q22.1 3D3, AEG1, AEG-1, LYRIC, LYRIC/3D3 -MTDH and Stomach Cancer
5
NIN 14q22.1 SCKL7 -NIN and Stomach Cancer
5
NOTO 2p13.2 -NOTO and Stomach Cancer
5
WNT10B 12q13.12 SHFM6, STHAG8, WNT-12 -WNT10B and Stomach Cancer
5
BCL2L12 19q13.33 -BCL2L12 and Stomach Cancer
5
EXO1 1q43 HEX1, hExoI -EXO1 and Stomach Cancer
5
SOCS1 16p13.13 JAB, CIS1, SSI1, TIP3, CISH1, SSI-1, SOCS-1 -SOCS1 and Stomach Cancer
5
MUC5B 11p15.5 MG1, MUC5, MUC9, MUC-5B -MUC5B and Stomach Cancer
5
MIR124-1 8p23.1 MIR124A, MIR124A1, MIRN124-1, MIRN124A1, mir-124-1 -microRNA 124-1 and Stomach Cancer
4
IL13 5q31.1 P600, IL-13 -IL13 and Stomach Cancer
4
IL23R 1p31.3 -IL23R and Stomach Cancer
4
S100A9 1q21.3 MIF, NIF, P14, CAGB, CFAG, CGLB, L1AG, LIAG, MRP14, 60B8AG, MAC387 -S100A9 and Stomach Cancer
4
SSTR3 22q13.1 SS3R, SS3-R, SS-3-R, SSR-28 -SSTR3 and Stomach Cancer
4
CBX7 22q13.1 -CBX7 and Stomach Cancer
4
ADIPOR1 1q32.1 CGI45, PAQR1, ACDCR1, CGI-45, TESBP1A -ADIPOR1 and Stomach Cancer
4
HIC1 17p13.3 hic-1, ZBTB29, ZNF901 -HIC1 and Stomach Cancer
4
FGF3 11q13.3 INT2, HBGF-3 -FGF3 and Stomach Cancer
4
BCL2L11 2q13 BAM, BIM, BOD -BCL2L11 and Stomach Cancer
4
MBD2 18q21.2 DMTase, NY-CO-41 -MBD2 and Stomach Cancer
4
GPX1 3p21.31 GPXD, GSHPX1 -GPX1 and Stomach Cancer
4
FOXP1 3p13 MFH, QRF1, 12CC4, hFKH1B, HSPC215 -FOXP1 and Stomach Cancer
4
CCL5 17q12 SISd, eoCP, SCYA5, RANTES, TCP228, D17S136E, SIS-delta -CCL5 and Stomach Cancer
4
HHIP 4q31.21 HIP -HHIP and Stomach Cancer
4
PPARD 6p21.31 FAAR, NUC1, NUCI, NR1C2, NUCII, PPARB -PPAR delta and Stomach Cancer
4
IFNG 12q15 IFG, IFI -IFNG and Stomach Cancer
4
CYP1B1 2p22.2 CP1B, GLC3A, CYPIB1, P4501B1 -CYP1B1 and Stomach Cancer
4
PDX1 13q12.2 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
CTAG1B Xq28 CTAG, ESO1, CT6.1, CTAG1, LAGE-2, LAGE2B, NY-ESO-1 -CTAG1B and Stomach Cancer
4
MTSS1 8q24.13 MIM, MIMA, MIMB -MTSS1 and Stomach Cancer
4
RASAL1 12q24.13 RASAL -RASAL1 and Stomach Cancer
4
SULF1 8q13.2-q13.3 SULF-1 -SULF1 and Stomach Cancer
4
SST 3q27.3 SMST -SST and Stomach Cancer
4
HOXD10 2q31.1 HOX4, HOX4D, HOX4E, Hox-4.4 -HOXD10 and Stomach Cancer
4
THBS2 6q27 TSP2 -THBS2 and Stomach Cancer
4
S100A10 1q21.3 42C, P11, p10, GP11, ANX2L, CAL1L, CLP11, Ca[1], ANX2LG -S100A10 and Stomach Cancer
4
HBEGF 5q31.3 DTR, DTS, DTSF, HEGFL -HBEGF and Stomach Cancer
4
NEDD4 15q21.3 RPF1, NEDD4-1 -NEDD4 and Stomach Cancer
4
MAP2K4 17p12 JNKK, MEK4, MKK4, SEK1, SKK1, JNKK1, SERK1, MAPKK4, PRKMK4, SAPKK1, SAPKK-1 -MAP2K4 and Stomach Cancer
4
CEACAM6 19q13.2 NCA, CEAL, CD66c -CEACAM6 and Stomach Cancer
4
ANXA1 9q21.13 ANX1, LPC1 -ANXA1 and Stomach Cancer
4
MIRLET7G 3p21.1 LET7G, let-7g, MIRNLET7G, hsa-let-7g -MicroRNA let-7g and Stomach Cancer
4
PER1 17p13.1 PER, hPER, RIGUI -PER1 and Stomach Cancer
4
CYP19A1 15q21.2 ARO, ARO1, CPV1, CYAR, CYP19, CYPXIX, P-450AROM -CYP19A1 and Stomach Cancer
4
ST2 11p14.3-p12 -ST2 and Stomach Cancer
4
JAK3 19p13.11 JAKL, LJAK, JAK-3, L-JAK, JAK3_HUMAN -JAK3 and Stomach Cancer
4
SLPI 20q13.12 ALP, MPI, ALK1, BLPI, HUSI, WAP4, WFDC4, HUSI-I -SLPI and Stomach Cancer
4
PSMD10 Xq22.3 p28, p28(GANK), dJ889N15.2 -PSMD10 and Stomach Cancer
4
S100A7 1q21.3 PSOR1, S100A7c -S100A7 and Stomach Cancer
4
PTPN1 20q13.13 PTP1B -PTPN1 and Stomach Cancer
4
EGR2 10q21.3 AT591, CMT1D, CMT4E, KROX20 -EGR2 and Stomach Cancer
4
INHBA 7p14.1 EDF, FRP -INHBA and Stomach Cancer
4
PTPN6 12p13 HCP, HCPH, SHP1, SHP-1, HPTP1C, PTP-1C, SHP-1L, SH-PTP1 -PTPN6 and Stomach Cancer
4
ADH1C 4q23 ADH3 -ADH1C and Stomach Cancer
4
CLDN7 17p13.1 CLDN-7, CEPTRL2, CPETRL2, Hs.84359, claudin-1 -CLDN7 and Stomach Cancer
4
AKR1C2 10p15.1 DD, DD2, TDD, BABP, DD-2, DDH2, HBAB, HAKRD, MCDR2, SRXY8, DD/BABP, AKR1C-pseudo -AKR1C2 and Stomach Cancer
4
SSTR1 14q21.1 SS1R, SS1-R, SRIF-2, SS-1-R -SSTR1 and Stomach Cancer
4
S100A8 1q21.3 P8, MIF, NIF, CAGA, CFAG, CGLA, L1Ag, MRP8, CP-10, MA387, 60B8AG -S100A8 and Stomach Cancer
4
LGALS4 19q13.2 GAL4, L36LBP -LGALS4 and Stomach Cancer
4
ITGA4 2q31.3 IA4, CD49D -ITGA4 and Stomach Cancer
4
CLDN1 3q28-q29 CLD1, SEMP1, ILVASC -CLDN1 and Stomach Cancer
4
ROR1 1p31.3 NTRKR1, dJ537F10.1 -ROR1 and Stomach Cancer
4
ANGPT2 8p23.1 ANG2, AGPT2 -ANGPT2 and Stomach Cancer
4
MAGEB2 Xp21.2 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Stomach Cancer
4
CCNE2 8q22.1 CYCE2 -CCNE2 and Stomach Cancer
4
MSI1 12q24 -MSI1 and Stomach Cancer
3
HSD17B2 16q23.3 HSD17, SDR9C2, EDH17B2 -HSD17B2 and Stomach Cancer
3
NR3C1 5q31.3 GR, GCR, GRL, GCCR, GCRST -NR3C1 and Stomach Cancer
3
ROR2 9q22.31 BDB, BDB1, NTRKR2 -ROR2 and Stomach Cancer
3
RARRES3 11q12.3 RIG1, TIG3, HRSL4, HRASLS4, PLA1/2-3 -RARRES3 and Stomach Cancer
3
SATB1 3p23 -SATB1 and Stomach Cancer
3
LIMK1 7q11.23 LIMK, LIMK-1 -LIMK1 and Stomach Cancer
3
IL6R 1q21.3 IL6Q, gp80, CD126, IL6RA, IL6RQ, IL-6RA, IL-6R-1 -IL6R and Stomach Cancer
3
IFITM1 11p15.5 9-27, CD225, IFI17, LEU13, DSPA2a -IFITM1 and Stomach Cancer
3
ELF3 1q32.1 ERT, ESX, EPR-1, ESE-1 -ELF3 and Stomach Cancer
3
KRT18 12q13.13 K18, CK-18, CYK18 -KRT18 and Stomach Cancer
3
B2M 15q21.1 IMD43 -B2M and Stomach Cancer
3
WNT11 11q13.5 HWNT11 -WNT11 and Stomach Cancer
3
HLA-E 6p21.3 MHC, QA1, EA1.2, EA2.1, HLA-6.2 -HLA-E and Stomach Cancer
3
SULF2 20q13.12 HSULF-2 -SULF2 and Stomach Cancer
3
PDCD2 6q27 RP8, ZMYND7 -PDCD2 and Stomach Cancer
3
CCNB2 15q22.2 HsT17299 -CCNB2 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
SPRY4 5q31.3 HH17 -SPRY4 and Stomach Cancer
3
CHGA 14q32.12 CGA -CHGA and Stomach Cancer
3
RASSF10 11p15.3 -RASSF10 and Stomach Cancer
3
CASP10 2q33-q34 MCH4, ALPS2, FLICE2 -CASP10 and Stomach Cancer
3
PIK3R1 5q13.1 p85, AGM7, GRB1, IMD36, p85-ALPHA -PIK3R1 and Stomach Cancer
3
PTK7 6p21.1-p12.2 CCK4, CCK-4 -PTK7 and Stomach Cancer
3
CD151 11p15.5 GP27, MER2, RAPH, SFA1, PETA-3, TSPAN24 -CD151 and Stomach Cancer
3
CCND3 6p21.1 -CCND3 and Stomach Cancer
3
CBLB 3q13.11 Cbl-b, RNF56, Nbla00127 -CBLB and Stomach Cancer
3
SERPINB5 18q21.33 PI5, maspin -SERPINB5 and Stomach Cancer
3
GSTM3 1p13.3 GST5, GSTB, GTM3, GSTM3-3 -GSTM3 and Stomach Cancer
3
FUS 16p11.2 TLS, ALS6, ETM4, FUS1, POMP75, HNRNPP2 -FUS and Stomach Cancer
3
WNT5B 12p13.33 -WNT5B and Stomach Cancer
3
PRKCA 17q24.2 AAG6, PKCA, PRKACA, PKC-alpha -PRKCA and Stomach Cancer
3
AKR1B10 7q33 HIS, HSI, ARL1, ARL-1, ALDRLn, AKR1B11, AKR1B12 -AKR1B10 and Stomach Cancer
3
TNFRSF6B 20q13.33 M68, TR6, DCR3, M68E, DJ583P15.1.1 Amplification
Prognostic
-TNFRSF6B Amplification and Overexpression in Gastric Cancers
3
IL12B 5q33.3 CLMF, NKSF, CLMF2, IMD28, IMD29, NKSF2, IL-12B -IL12B and Stomach Cancer
3
RARRES1 3q25.32 LXNL, TIG1, PERG-1 -RARRES1 and Stomach Cancer
3
SEMA3B 3p21.31 SemA, SEMA5, SEMAA, semaV, LUCA-1 -SEMA3B and Stomach Cancer
3
MIR125A 19q13.41 MIRN125A, mir-125a, miRNA125A -MIR125A and Stomach Cancer
3
ADIPOR2 12p13.31 PAQR2, ACDCR2 -ADIPOR2 and Stomach Cancer
3
INHA 2q35 -INHA and Stomach Cancer
3
PRDX1 1p34.1 PAG, PAGA, PAGB, PRX1, PRXI, MSP23, NKEFA, TDPX2, NKEF-A -PRDX1 and Stomach Cancer
3
PPARG 3p25.2 GLM1, CIMT1, NR1C3, PPARG1, PPARG2, PPARgamma -PPARG and Stomach Cancer
3
HPSE 4q21.23 HPA, HPA1, HPR1, HSE1, HPSE1 -HPSE and Stomach Cancer
3
PLAU 10q22.2 ATF, QPD, UPA, URK, u-PA, BDPLT5 -PLAU and Stomach Cancer
3
YWHAZ 8q22.3 HEL4, YWHAD, KCIP-1, HEL-S-3, HEL-S-93, 14-3-3-zeta -YWHAZ and Stomach Cancer
3
CASP6 4q25 MCH2 -CASP6 and Stomach Cancer
3
ZMYND10 3p21.31 BLU, FLU, CILD22 -ZMYND10 and Stomach Cancer
3
ABCC4 13q32.1 MRP4, MOATB, MOAT-B -ABCC4 and Stomach Cancer
3
MYCBP 1p34.3 AMY-1 -MYCBP and Stomach Cancer
3
SUFU 10q24.32 SUFUH, SUFUXL, PRO1280 -SUFU and Stomach Cancer
3
MBL2 10q21.1 MBL, MBP, MBP1, MBPD, MBL2D, MBP-C, COLEC1, HSMBPC -MBL2 and Stomach Cancer
3
NOS3 7q36.1 eNOS, ECNOS -NOS3 and Stomach Cancer
3
IL32 16p13.3 NK4, TAIF, TAIFa, TAIFb, TAIFc, TAIFd, IL-32beta, IL-32alpha, IL-32delta, IL-32gamma -IL32 and Stomach Cancer
3
MLF1 3q25.32 -MLF1 and Stomach Cancer
3
HCK 20q11.21 JTK9, p59Hck, p61Hck -HCK and Stomach Cancer
3
IL16 15q25.1 LCF, NIL16, PRIL16, prIL-16 -IL16 and Stomach Cancer
2
MUC3A 7q22.1 MUC3, MUC-3A -MUC3A and Stomach Cancer
2
CRY1 12q23.3 PHLL1 -CRY1 and Stomach Cancer
2
JAG2 14q32.33 HJ2, SER2 -JAG2 and Stomach Cancer
2
BDNF 11p14.1 ANON2, BULN2 -BDNF and Stomach Cancer
2
KLK6 19q13.41 hK6, Bssp, Klk7, SP59, PRSS9, PRSS18 -KLK6 and Stomach Cancer
2
MTA1 14q32.33 -MTA1 and Stomach Cancer
2
ARNTL 11p15.3 TIC, JAP3, MOP3, BMAL1, PASD3, BMAL1c, bHLHe5 -ARNTL and Stomach Cancer
2
COL4A6 Xq22.3 DFNX6, DELXq22.3, CXDELq22.3 -COL4A6 and Stomach Cancer
2
XRCC5 2q35 KU80, KUB2, Ku86, NFIV, KARP1, KARP-1 -XRCC5 and Stomach Cancer
2
PDCD5 19q13.11 TFAR19 -PDCD5 and Stomach Cancer
2
ITGB3 17q21.32 GT, CD61, GP3A, BDPLT2, GPIIIa, BDPLT16 -ITGB3 and Stomach Cancer
2
IL6ST 5q11.2 CD130, GP130, CDW130, IL-6RB -IL6ST 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
DDX5 17q23.3 p68, HLR1, G17P1, HUMP68 -DDX5 and Stomach Cancer
2
MT2A 16q13 MT2 -MT2A and Stomach Cancer
2
MAD1L1 7p22.3 MAD1, PIG9, TP53I9, TXBP181 -MAD1L1 and Stomach Cancer
2
SHMT1 17p11.2 SHMT, CSHMT -SHMT1 and Stomach Cancer
2
FEZ1 11q24.2 UNC-76 -FEZ1 and Stomach Cancer
2
ZNF331 19q13.42 RITA, ZNF361, ZNF463 -ZNF331 and Stomach Cancer
2
MAP2K6 17q24.3 MEK6, MKK6, MAPKK6, PRKMK6, SAPKK3, SAPKK-3 -MAP2K6 and Stomach Cancer
2
LAMB3 1q32.2 AI1A, LAM5, LAMNB1, BM600-125KDA -LAMB3 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
HLA-DRA 6p21.32 HLA-DRA1 -HLA-DRA and Stomach Cancer
2
LRRC3B 3p24.1 LRP15 -LRRC3B and Stomach Cancer
2
CDH3 16q22.1 CDHP, HJMD, PCAD -CDH3 and Stomach Cancer
2
CLOCK 4q12 KAT13D, bHLHe8 -CLOCK and Stomach Cancer
2
S100A3 1q21.3 S100E -S100A3 and Stomach Cancer
2
TNFRSF17 16p13.13 BCM, BCMA, CD269, TNFRSF13A -TNFRSF17 and Stomach Cancer
2
MIR1271 5q35.2 MIRN1271, hsa-mir-1271 -MIRN1271 microRNA, human and Stomach Cancer
2
NTRK3 15q25.3 TRKC, GP145-TrkC, gp145(trkC) -NTRK3 and Stomach Cancer
2
SAT2 17p13.1 SSAT2 -SAT2 and Stomach Cancer
2
PKD1 16p13.3 PBP, PC1, Pc-1, TRPP1 -PKD1 and Stomach Cancer
2
PIN1 19p13.2 DOD, UBL5 -PIN1 and Stomach Cancer
2
WNT4 1p36.12 WNT-4, SERKAL -WNT4 and Stomach Cancer
2
DMBT1 10q26.13 SAG, GP340, SALSA, muclin -DMBT1 and Stomach Cancer
2
PDCD1LG2 9p24.1 B7DC, Btdc, PDL2, CD273, PD-L2, PDCD1L2, bA574F11.2 -PDCD1LG2 and Stomach Cancer
2
IMP3 15q24.2 BRMS2, MRPS4, C15orf12 -IMP3 and Stomach Cancer
2
HAVCR2 5q33.3 TIM3, CD366, KIM-3, TIMD3, Tim-3, TIMD-3, HAVcr-2 -HAVCR2 and Stomach Cancer
2
IGF1 12q23.2 IGFI, IGF-I, IGF1A -IGF1 and Stomach Cancer
2
CXCL16 17p13.2 SRPSOX, CXCLG16, SR-PSOX -CXCL16 and Stomach Cancer
2
EDNRB 13q22.3 ETB, ET-B, ETB1, ETBR, ETRB, HSCR, WS4A, ABCDS, ET-BR, HSCR2 -EDNRB and Stomach Cancer
2
HSP90AB1 6p21.1 HSP84, HSPC2, HSPCB, D6S182, HSP90B -HSP90AB1 and Stomach Cancer
2
GNL3 3p21.1 NS, E2IG3, NNP47, C77032 -GNL3 and Stomach Cancer
2
WRN 8p12 RECQ3, RECQL2, RECQL3 -WRN and Stomach Cancer
2
MTA2 11q12.3 PID, MTA1L1 -MTA2 and Stomach Cancer
2
FRAT2 10q24.1 -FRAT2 and Stomach Cancer
2
MIR127 14q32.2 MIRN127, mir-127, miRNA127 -MicroRNA miR-127 and Stomach Cancer
2
ADAMTS9 3p14.1 -ADAMTS9 and Stomach Cancer
2
PDK1 2q31.1 -PDK1 and Stomach Cancer
2
L1CAM Xq28 S10, HSAS, MASA, MIC5, SPG1, CAML1, CD171, HSAS1, N-CAML1, NCAM-L1, N-CAM-L1 -L1CAM and Stomach Cancer
2
TYK2 19p13.2 JTK1, IMD35 -TYK2 and Stomach Cancer
2
CD276 15q24.1 B7H3, B7-H3, B7RP-2, 4Ig-B7-H3 -CD276 and Stomach Cancer
2
MLH3 14q24.3 HNPCC7 -MLH3 and Stomach Cancer
2
PGK1 Xq21.1 PGKA, MIG10, HEL-S-68p -PGK1 and Stomach Cancer
2
MMP13 11q22.2 CLG3, MDST, MANDP1, MMP-13 -MMP13 and Stomach Cancer
2
ADGRB1 8q24.3 BAI1, GDAIF -BAI1 and Stomach Cancer
2
GAGE1 Xp11.23 CT4.1, CT4.4, GAGE4, GAGE-1, GAGE-4 -GAGE1 and Stomach Cancer
2
PDPK1 16p13.3 PDK1, PDPK2, PDPK2P, PRO0461 -PDPK1 and Stomach Cancer
2
NOX1 Xq22.1 MOX1, NOH1, NOH-1, GP91-2 -NOX1 and Stomach Cancer
2
HOXC11 12q13.13 HOX3H -HOXC11 and Stomach Cancer
2
MMP10 11q22.2 SL-2, STMY2 -MMP10 and Stomach Cancer
2
INSR 19p13.2 HHF5, CD220 -INSR and Stomach Cancer
2
PLK2 5q11.2 SNK, hSNK, hPlk2 -PLK2 and Stomach Cancer
2
ANGPT1 8q23.1 AGP1, AGPT, ANG1 -ANGPT1 and Stomach Cancer
2
PER3 1p36.23 GIG13, FASPS3 -PER3 and Stomach Cancer
2
HSD17B1 17q21.2 E2DH, HSD17, EDHB17, EDH17B2, SDR28C1, 17-beta-HSD, 20-alpha-HSD -HSD17B1 and Stomach Cancer
2
SETD2 3p21.31 LLS, HYPB, SET2, HIF-1, HIP-1, KMT3A, HBP231, HSPC069, p231HBP -SETD2 and Stomach Cancer
2
PTCH2 1p34.1 PTC2 -PTCH2 and Stomach Cancer
2
DUSP6 12q21.33 HH19, MKP3, PYST1 -DUSP6 and Stomach Cancer
2
MCM4 8q11.21 NKCD, CDC21, CDC54, NKGCD, hCdc21, P1-CDC21 -MCM4 and Stomach Cancer
2
CDK12 17q12 CRK7, CRKR, CRKRS -CDK12 and Stomach Cancer
1
LRRN2 1q32.1 GAC1, LRRN5, LRANK1, FIGLER7 -LRRN2 and Stomach Cancer
1
LRIG1 3p14 LIG1, LIG-1 -LRIG1 and Stomach Cancer
1
NQO2 6p25.2 QR2, DHQV, DIA6, NMOR2 -NQO2 and Stomach Cancer
1
SOX6 11p15.2 SOXD, HSSOX6 -SOX6 and Stomach Cancer
1
TM4SF1 3q25.1 L6, H-L6, M3S1, TAAL6 -TM4SF1 and Stomach Cancer
1
COMT 22q11.21 HEL-S-98n -COMT and Stomach Cancer
1
CYBA 16q24.2 p22-PHOX -CYBA and Stomach Cancer
1
CHRNA5 15q25.1 LNCR2 -CHRNA5 and Stomach Cancer
1
TPM1 15q22.2 CMH3, TMSA, CMD1Y, LVNC9, C15orf13, HEL-S-265, HTM-alpha -TPM1 and Stomach Cancer
1
CCKBR 11p15.4 GASR, CCK-B, CCK2R -CCKBR and Stomach Cancer
1
COPS6 7q22.1 CSN6, MOV34-34KD -COPS6 and Stomach Cancer
1
MIR106B 7q22.1 MIRN106B, mir-106b -MIR106B and Stomach Cancer
1
HSP90AA1 14q32.31 EL52, HSPN, LAP2, HSP86, HSPC1, HSPCA, Hsp89, Hsp90, LAP-2, HSP89A, HSP90A, HSP90N, Hsp103, HSPCAL1, HSPCAL4, HEL-S-65p -HSP90AA1 and Stomach Cancer
1
HSD3B1 1p12 HSD3B, HSDB3, HSDB3A, SDR11E1, 3BETAHSD -HSD3B1 and Stomach Cancer
1
SACS 13q12.12 SPAX6, ARSACS, DNAJC29, PPP1R138 -SACS and Stomach Cancer
1
IRF8 16q24.1 ICSBP, IRF-8, ICSBP1, IMD32A, IMD32B, H-ICSBP -IRF8 and Stomach Cancer
1
SSTR5 16p13.3 SS-5-R -SSTR5 and Stomach Cancer
1
SFRP4 7p14.1 PYL, FRP-4, FRPHE, sFRP-4 -SFRP4 and Stomach Cancer
1
CTCFL 20q13.31 CT27, BORIS, CTCF-T, HMGB1L1, dJ579F20.2 -CTCFL and Stomach Cancer
1
CRP 1q23.2 PTX1 -CRP and Stomach Cancer
1
MIR10B 2q31.1 MIRN10B, mir-10b, miRNA10B, hsa-mir-10b -MIR10B and Stomach Cancer
1
BUB3 10q26.13 BUB3L, hBUB3 -BUB3 and Stomach Cancer
1
ZNRF3 22q12.1 RNF203, BK747E2.3 -ZNRF3 and Stomach Cancer
1
TCEAL7 Xq22.2 WEX5 -TCEAL7 and Stomach Cancer
1
DNAJB4 1p31.1 DjB4, HLJ1, DNAJW -DNAJB4 and Stomach Cancer
1
LMNA 1q22 FPL, IDC, LFP, CDDC, EMD2, FPLD, HGPS, LDP1, LMN1, LMNC, MADA, PRO1, CDCD1, CMD1A, FPLD2, LMNL1, CMT2B1, LGMD1B -LMNA and Stomach Cancer
1
NCKIPSD 3p21.31 DIP, DIP1, ORF1, WISH, VIP54, AF3P21, SPIN90, WASLBP -NCKIPSD and Stomach Cancer
1
RASSF7 11p15.5 HRC1, HRAS1, C11orf13 -RASSF7 and Stomach Cancer
1
RABEP1 17p13.2 RAB5EP, RABPT5 -RABEP1 and Stomach Cancer
1
LAPTM4B 8q22.1 LC27, LAPTM4beta -LAPTM4B and Stomach Cancer
1
CYP2C8 10q23.33 CPC8, CYPIIC8, MP-12/MP-20 -CYP2C8 and Stomach Cancer
1
HOXB3 17q21.32 HOX2, HOX2G, Hox-2.7 -HOXB3 and Stomach Cancer
1
NRP1 10p11.22 NP1, NRP, BDCA4, CD304, VEGF165R -NRP1 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
ANXA7 10q22.2 SNX, ANX7, SYNEXIN -ANXA7 and Stomach Cancer
1
IL24 1q32.1 C49A, FISP, MDA7, MOB5, ST16, IL10B -IL24 and Stomach Cancer
1
CXADR 21q21.1 CAR, HCAR, CAR4/6 -CXADR and Stomach Cancer
1
TNFRSF8 1p36.22 CD30, Ki-1, D1S166E -TNFRSF8 and Stomach Cancer
1
MSI2 17q22 MSI2H -MSI2 and Stomach Cancer
1
MMP8 11q22.2 HNC, CLG1, MMP-8, PMNL-CL -MMP8 and Stomach Cancer
1
DOK2 8p21.3 p56DOK, p56dok-2 -DOK2 and Stomach Cancer
1
IDO1 8p11.21 IDO, INDO, IDO-1 -IDO1 and Stomach Cancer
1
PITX1 5q31.1 BFT, CCF, POTX, PTX1, LBNBG -PITX1 and Stomach Cancer
1
MIR10A 17q21.32 MIRN10A, mir-10a, miRNA10A, hsa-mir-10a -None and Stomach Cancer
1
DUSP4 8p12 TYP, HVH2, MKP2, MKP-2 -DUSP4 and Stomach Cancer
1
PLCD1 3p22.2 NDNC3, PLC-III -PLCD1 and Stomach Cancer
1
CRY2 11p11.2 HCRY2, PHLL2 -CRY2 and Stomach Cancer
1
ARF1 1q42.13 PVNH8 -ARF1 and Stomach Cancer
1
AFF3 2q11.2-q12 LAF4, MLLT2-like -AFF3 and Stomach Cancer
1
CTCF 16q22.1 MRD21 -CTCF and Stomach Cancer
1
HOXD11 2q31.1 HOX4, HOX4F -HOXD11 and Stomach Cancer
1
RASSF5 1q32.1 RAPL, Maxp1, NORE1, NORE1A, NORE1B, RASSF3 -RASSF5 and Stomach Cancer
1
CTSD 11p15.5 CPSD, CLN10, HEL-S-130P -CTSD 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
AQP1 7p14.3 CO, CHIP28, AQP-CHIP -AQP1 and Stomach Cancer
1
HINT1 5q23.3 HINT, NMAN, PKCI-1, PRKCNH1 -HINT1 and Stomach Cancer
1
BRINP1 9q33.1 DBC1, FAM5A, DBCCR1 -BRINP1 and Stomach Cancer
1
CCL22 16q21 MDC, ABCD-1, SCYA22, STCP-1, DC/B-CK, A-152E5.1 -CCL22 and Stomach Cancer
1
MIB1 18q11.2 MIB, DIP1, ZZZ6, DIP-1, LVNC7, ZZANK2 -MIB1 and Stomach Cancer
1
CAV2 7q31.2 CAV -CAV2 and Stomach Cancer
1
KDM5A 12p13.33 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, Sun Q, Tong Y, et al.
An immune-related gene signature predicts prognosis of gastric cancer.
Medicine (Baltimore). 2019; 98(27):e16273 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Although the outcome of patients with gastric cancer (GC) has improved significantly with the recent implementation of annual screening programs. Reliable prognostic biomarkers are still needed due to the disease heterogeneity. Increasing pieces of evidence revealed an association between immune signature and GC prognosis. Thus, we aim to build an immune-related signature that can estimate prognosis for GC.
METHODS: For identification of a prognostic immune-related gene signature (IRGS), gene expression profiles and clinical information of patients with GC were collected from 3 public cohorts, divided into training cohort (n = 300) and 2 independent validation cohorts (n = 277 and 433 respectively).
RESULTS: Within 1811 immune genes, a prognostic IRGS consisting of 16 unique genes was constructed which was significantly associated with survival (hazard ratio [HR], 3.9 [2.78-5.47]; P < 1.0 × 10). In the validation cohorts, the IRGS significantly stratified patients into high- vs low-risk groups in terms of prognosis across (HR, 1.84 [1.47-2.30]; P = 6.59 × 10) and within subpopulations with stage I&II disease (HR, 1.96 [1.34-2.89]; P = 4.73 × 10) and was prognostic in univariate and multivariate analyses. Several biological processes, including TGF-β and EMT signaling pathways, were enriched in the high-risk group. T cells CD4 memory resting and Macrophage M2 were significantly higher in the high-risk risk group compared with the low-risk group.
CONCLUSION: In short, we developed a prognostic IRGS for estimating prognosis in GC, including stage I&II disease, providing new insights into the identification of patients with GC with a high risk of mortality.

Gao YL, Cui Z, Liu JX, et al.
NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations.
BMC Bioinformatics. 2019; 20(1):353 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Predicting meaningful miRNA-disease associations (MDAs) is costly. Therefore, an increasing number of researchers are beginning to focus on methods to predict potential MDAs. Thus, prediction methods with improved accuracy are under development. An efficient computational method is proposed to be crucial for predicting novel MDAs. For improved experimental productivity, large biological datasets are used by researchers. Although there are many effective and feasible methods to predict potential MDAs, the possibility remains that these methods are flawed.
RESULTS: A simple and effective method, known as Nearest Profile-based Collaborative Matrix Factorization (NPCMF), is proposed to identify novel MDAs. The nearest profile is introduced to our method to achieve the highest AUC value compared with other advanced methods. For some miRNAs and diseases without any association, we use the nearest neighbour information to complete the prediction.
CONCLUSIONS: To evaluate the performance of our method, five-fold cross-validation is used to calculate the AUC value. At the same time, three disease cases, gastric neoplasms, rectal neoplasms and colonic neoplasms, are used to predict novel MDAs on a gold-standard dataset. We predict the vast majority of known MDAs and some novel MDAs. Finally, the prediction accuracy of our method is determined to be better than that of other existing methods. Thus, the proposed prediction model can obtain reliable experimental results.

Zhihua Y, Yulin T, Yibo W, et al.
Hypoxia decreases macrophage glycolysis and M1 percentage by targeting microRNA-30c and mTOR in human gastric cancer.
Cancer Sci. 2019; 110(8):2368-2377 [PubMed] Free Access to Full Article Related Publications
Macrophages are essential inflammatory cells which regulate the features of immune reactions within tumors. Many studies have reported their regulatory roles in immunity through cytokines and cell signaling. However, relatively few studies have focused on their metabolic features and mechanisms. We aimed to determine the signaling pathway regulating cell metabolism and the mechanism related to the regulation of human tumor-associated macrophages (TAMs) in gastric cancer (GC). Tumor-infiltrated macrophages were isolated from human GC tissues using magnetic beads, gene transcription was determined by real-time PCR, protein expression was monitored using western blots, metabolites were determined using HPLC, and transcriptional regulation was analyzed by the luciferase-based reporter gene system. A significant decrease in microRNA (miR)-30c and an increase in regulated in development and DNA damage responses 1 (REDD1) were detected in human GC TAMs, the transcription of miR-30c was negatively correlated with REDD1. MicroRNA-30c expression was suppressed by hypoxia-inducible factor-1α activation and related to decreased mTOR activity as well as glycolysis in human GC TAMs. Hypoxia-regulated miR-30c downregulated REDD-1 expression by targeting its 3'UTR. Overexpression of miR-30c or restored mTOR activity in macrophages with miR-30c

Sai E, Miwa Y, Takeyama R, et al.
Identification of candidates for driver oncogenes in scirrhous-type gastric cancer cell lines.
Cancer Sci. 2019; 110(8):2643-2651 [PubMed] Free Access to Full Article Related Publications
Scirrhous-type gastric cancer (SGC) is one of the most intractable cancer subtypes in humans, and its therapeutic targets have been rarely identified to date. Exploration of somatic mutations in the SGC genome with the next-generation sequencers has been hampered by markedly increased fibrous tissues. Thus, SGC cell lines may be useful resources for searching for novel oncogenes. Here we have conducted whole exome sequencing and RNA sequencing on 2 SGC cell lines, OCUM-8 and OCUM-9. Interestingly, most of the mutations thus identified have not been reported. In OCUM-8 cells, a novel CD44-IGF1R fusion gene is discovered, the protein product of which ligates the amino-terminus of CD44 to the transmembrane and tyrosine-kinase domains of IGF1R. Furthermore, both CD44 and IGF1R are markedly amplified in the OCUM-8 genome and abundantly expressed. CD44-IGF1R has a transforming ability, and the suppression of its kinase activity leads to rapid cell death of OCUM-8. To the best of our knowledge, this is the first report describing the transforming activity of IGF1R fusion genes. However, OCUM-9 seems to possess multiple oncogenic events in its genome. In particular, a novel BORCS5-ETV6 fusion gene is identified in the OCUM-9 genome. BORCS5-ETV6 possesses oncogenic activity, and suppression of its message partially inhibits cell growth. Prevalence of these novel fusion genes among SGC awaits further investigation, but we validate the significance of cell lines as appropriate reagents for detailed genomic analyses of SGC.

Liu J, Song S, Lin S, et al.
Circ-SERPINE2 promotes the development of gastric carcinoma by sponging miR-375 and modulating YWHAZ.
Cell Prolif. 2019; 52(4):e12648 [PubMed] Related Publications
OBJECTIVES: Circular RNAs (circRNAs) exist extensively in the eukaryotic genome. The study aimed to identify the role of hsa_circ_0008365 (Circ-SERPINE2) in gastric carcinoma (GC) cells and its downstream mechanisms.
MATERIALS AND METHODS: Gene Expression Omnibus (GEO) database was applied to screen differentially expressed circRNAs. CircInteractome, TargetScan and miRecords websites were used to predict target relationships. qRT-PCR and RNase R treatment were utilised to detect molecule expression and confirm the existence of circ-SERPINE2. RNA pull-down assay and dual-luciferase reporter assay were performed for interaction between circRNA and miRNA or mRNA. EdU assay, colony formation assay, and flow cytometry for apoptosis and cell cycle detections were utilised to assess cell function. Western blot and immunohistochemistry (IHC) assays were applied for detection of proteins in tissues or cells.
RESULTS: Circ-SERPINE2 and YWHAZ were upregulated, and miR-375 was downregulated in GC tissues and cells. Circ-SERPINE2 and YWHAZ targetedly bound to miR-375. Circ-SERPINE2 promoted cell proliferation and cell cycle progress and inhibited cell apoptosis by sponging miR-375 and regulating YWHAZ expression in vitro. Circ-SERPINE2 repressed solid tumour growth through enhancing miR-375 expression and reducing YWHAZ expression in vivo.
CONCLUSIONS: Circ-SERPINE2 is a novel proliferative promoter through the regulation of miR-375/YWHAZ. Circ-SERPINE2/miR-375/YWHAZ axis might provide a novel therapeutic target of GC.

Zhu C, Huang Q, Zhu H
miR-383 Inhibited the Cell Cycle Progression of Gastric Cancer Cells via Targeting Cyclin E2.
DNA Cell Biol. 2019; 38(8):849-856 [PubMed] Related Publications
Increasing evidence has suggested the key roles of miRNAs in the initiation and progression of human cancers. miR-383 was downregulated and played a suppressive role in a variety of cancers; however, the function of miR-383 in gastric cancer remains unclear. In this study, we found that the expression of miR-383 was significantly reduced in gastric cancer tissues and correlated with the advanced progression of these cancer patients. Functional analysis showed that overexpression of miR-383 inhibited the proliferation and upregulated the apoptosis of gastric cancer cells. Furthermore, cyclin E2 was predicted as the target of miR-383 using the bioinformatics database. miR-383 bound the 3'-untranslated region of cyclin E2 and decreased the expression of cyclin E2 in gastric cancer cells. Upregulation of cyclin E2 was observed in gastric cancer tissues compared with the normal controls. Highly expressed cyclin E2 was inversely correlated with the level of miR-383 in gastric cancer tissues. Consistent with the decreased expression of cyclin E2 with miR-383, transfection of miR-383 induced cell cycle arrest at G1 phase in gastric cancer cells. Restoration of cyclin E2 significantly reversed the inhibitory effect of miR-183 on gastric cancer cell proliferation. Collectively, our results characterized the suppressive role of miR-383 in gastric cancer partially through targeting cyclin E2.

Yoshioka T, Shien K, Takeda T, et al.
Acquired resistance mechanisms to afatinib in HER2-amplified gastric cancer cells.
Cancer Sci. 2019; 110(8):2549-2557 [PubMed] Free Access to Full Article Related Publications
Cancer treatment, especially that for breast and lung cancer, has entered a new era and continues to evolve, with the development of genome analysis technology and the advent of molecular targeted drugs including tyrosine kinase inhibitors. Nevertheless, acquired drug resistance to molecular targeted drugs is unavoidable, creating a clinically challenging problem. We recently reported the antitumor effect of a pan-HER inhibitor, afatinib, against human epidermal growth factor receptor 2 (HER2)-amplified gastric cancer cells. The purpose of the present study was to identify the mechanisms of acquired afatinib resistance and to investigate the treatment strategies for HER2-amplified gastric cancer cells. Two afatinib-resistant gastric cancer cell lines were established from 2 HER2-amplified cell lines, N87 and SNU216. Subsequently, we investigated the molecular profiles of resistant cells. The activation of the HER2 pathway was downregulated in N87-derived resistant cells, whereas it was upregulated in SNU216-derived resistant cells. In the N87-derived cell line, both MET and AXL were activated, and combination treatment with afatinib and cabozantinib, a multikinase inhibitor that inhibits MET and AXL, suppressed the cell growth of cells with acquired resistance both in vitro and in vivo. In the SNU216-derived cell line, YES1, which is a member of the Src family, was remarkably activated, and dasatinib, a Src inhibitor, exerted a strong antitumor effect in these cells. In conclusion, we identified MET and AXL activation in addition to YES1 activation as novel mechanisms of afatinib resistance in HER2-driven gastric cancer. Our results also indicated that treatment strategies targeting individual mechanisms of resistance are key to overcoming such resistance.

Verma R, Agarwal AK, Sakhuja P, Sharma PC
Microsatellite instability in mismatch repair and tumor suppressor genes and their expression profiling provide important targets for the development of biomarkers in gastric cancer.
Gene. 2019; 710:48-58 [PubMed] Related Publications
We evaluated microsatellite instability (MSI) in selected mismatch repair (MMR) and tumor suppressor (TS) genes with a view to exploring genetic changes associated with the occurrence of gastric cancer (GC). Moreover, expression of MSI positive genes was measured to get insights into molecular events operating in the tumor microenvironment. We anticipated discovering new molecular targets with potential as molecular biomarkers of gastric cancer. Of the 13 genes screened, we observed 15% to 52.5% MSI at eight microsatellite loci located in 3' UTR and coding regions of six genes (TGFBR2, PDCD4, MLH3, DLC1, MSH6, and MSH3). The union probability of different combinations of unstable microsatellite loci unveiled a set of four MSI markers from TGFBR2, PDCD4, MLH3, and MSH3 genes that allows detection of up to 85% incidences of GC. Significant downregulation of MLH3, PDCD4, TGFBR2, and DLC1 genes was observed in tumor tissues. Protein structure analyses of two unexplored targets, MSH3 (TG

Rudnicka K, Backert S, Chmiela M
Genetic Polymorphisms in Inflammatory and Other Regulators in Gastric Cancer: Risks and Clinical Consequences.
Curr Top Microbiol Immunol. 2019; 421:53-76 [PubMed] Related Publications
Helicobacter pylori infection is associated with the development of a chronic inflammatory response, which may induce peptic ulcers, gastric cancer (GC), and mucosa-associated lymphoid tissue (MALT) lymphoma. Chronic H. pylori infection promotes the genetic instability of gastric epithelial cells and interferes with the DNA repair systems in host cells. Colonization of the stomach with H. pylori is an important cause of non-cardia GC and gastric MALT lymphoma. The reduction of GC development in patients who underwent anti-H. pylori eradication schemes has also been well described. Individual susceptibility to GC development depends on the host's genetic predisposition, H. pylori virulence factors, environmental conditions, and geographical determinants. Biological determinants are urgently sought to predict the clinical course of infection in individuals with confirmed H. pylori infection. Possible candidates for such biomarkers include genetic aberrations such as single-nucleotide polymorphisms (SNPs) found in various cytokines/growth factors (e.g., IL-1β, IL-2, IL-6, IL-8, IL-10, IL-13, IL-17A/B, IFN-γ, TNF, TGF-β) and their receptors (IL-RN, TGFR), innate immunity receptors (TLR2, TLR4, CD14, NOD1, NOD2), enzymes involved in signal transduction cascades (PLCE1, PKLR, PRKAA1) as well as glycoproteins (MUC1, PSCA), and DNA repair enzymes (ERCC2, XRCC1, XRCC3). Bacterial determinants related to GC development include infection with CagA-positive (particularly with a high number of EPIYA-C phosphorylation motifs) and VacA-positive isolates (in particular s1/m1 allele strains). The combined genotyping of bacterial and host determinants suggests that the accumulation of polymorphisms favoring host and bacterial features increases the risk for precancerous and cancerous lesions in patients.

Vázquez-Ibarra KC, Bustos-Carpinteyro AR, García-Ruvalcaba A, et al.
The ERBB2 gene polymorphisms rs2643194, rs2934971, and rs1058808 are associated with increased risk of gastric cancer.
Braz J Med Biol Res. 2019; 52(5):e8379 [PubMed] Free Access to Full Article Related Publications
Gastric cancer (GC) is the third most lethal type of cancer worldwide. Single nucleotide polymorphisms (SNPs) in regulatory sites or coding regions can modify the expression of genes involved in gastric carcinogenesis, as ERBB2, which encodes for the tyrosine-kinase receptor HER-2. The aim of this work was to analyze the association of the polymorphisms: rs2643194, rs2517951, rs2643195, rs2934971, and rs1058808 with GC, as they have not yet been analyzed in GC patients, as well as to report their frequency in the general Mexican population (GMP). We studied genomic DNA from subjects with GC (n=74), gastric inflammatory diseases (GID, n=76 control subjects), and GMP (n=102). Genotypes were obtained by means of real-time PCR and DNA-sequencing. The risks for GC were estimated through odds ratio (OR) using the Cochran-Armitage trend test and multinomial logistic regression. Increased risk for GC was observed under the dominant inheritance model for the rs2643194 TT or CT genotypes with an OR of 2.75 (95%CI 1.12-6.75, P=0.023); the rs2934971 TT or GT genotypes with an OR of 2.41 (95%CI 1.01-5.76, P=0.043), and the rs1058808 GG or CG genotypes with an OR of 2.21 (95%CI 1.00-4.87, P=0.046). The SNPs rs2643194, rs2934971, and rs1058808 of the ERBB2 gene were associated with increased risk for GC.

Yun X, Bai Y, Li Z, et al.
rs895819 in microRNA-27a increase stomach neoplasms risk in China: A meta-analysis.
Gene. 2019; 707:103-108 [PubMed] Related Publications
BACKGROUND: Across the globe, gastric cancer is a significant public health problem. This meta-analysis was conducted to investigate the association of microRNA-27a (miRNA-27a) rs895819 with gastric cancer risk.
METHODS: The search of databases updated on October 10, 2018 included Pubmed, Embase, Cochrane Library and Web of science. Odds ratio (ORs) and 95% confidence interval (CIs) were calculated to assess the risk of tumor.
RESULTS: Overall meta-analysis suggested the miRNA-27a rs895819 was not related to the gastric carcinogenesis among all model including allele contrast (G vs A, pooled OR: 1.096, 95% CI: 0.962-1.249, P = 0.196), codominant model (GG vs AA, pooled OR: 1.124, 95% CI: 0.794-1.592, P = 0.590; AG vs AA, pooled OR: 1.101, 95% CI: 0.966-1.217, P = 0.060), dominant model (AG + GG vs AA, pooled OR: 1.123, 95% CI: 0.964-1.307, P = 0.136) and recessive model (GG vs AG + AA, pooled OR: 0.927, 95% CI: 0.673-1.278, P = 0.644). Interestingly, among different ethnicity group, significant relation between rs895819 and gastric cancer was observed in co-dominant model among Chinese population (AG vs AA, pooled OR: 1.158, 95% CI: 1.038-1.291, P = 0.008) but not some regions of European population (AG vs AA, pooled OR: 0.852, 95% CI: 0.632-1.148, P = 0.179).
CONCLUSIONS: Our results find that rs895819 contributed to occurrence of gastric cancer in co-dominant model in Chinese population.

Xing R, Zhou Y, Yu J, et al.
Whole-genome sequencing reveals novel tandem-duplication hotspots and a prognostic mutational signature in gastric cancer.
Nat Commun. 2019; 10(1):2037 [PubMed] Free Access to Full Article Related Publications
Genome-wide analysis of genomic signatures might reveal novel mechanisms for gastric cancer (GC) tumorigenesis. Here, we analysis structural variations (SVs) and mutational signatures via whole-genome sequencing of 168 GCs. Our data demonstrates diverse models of complex SVs operative in GC, which lead to high-level amplification of oncogenes. We find varying proportion of tandem-duplications (TDs) among individuals and identify 24 TD hotspots involving well-established cancer genes such as CCND1, ERBB2 and MYC. Specifically, we nominate a novel hotspot involving the super-enhancer of ZFP36L2 presents in approximately 10% GCs from different cohorts, the oncogenic role of which is further confirmed by experimental data. In addition, our data reveal a mutational signature, specifically occurring in noncoding region, significantly enriched in tumors with cadherin 1 mutations, and associated with poor prognoses. Collectively, our data suggest that TDs might serve as an important mechanism for cancer gene activation and provide a novel signature for stratification.

Miceli R, An J, Di Bartolomeo M, et al.
Prognostic Impact of Microsatellite Instability in Asian Gastric Cancer Patients Enrolled in the ARTIST Trial.
Oncology. 2019; 97(1):38-43 [PubMed] Related Publications
BACKGROUND: Caucasian patients with microsatellite instability (MSI)-high gastric cancer (GC) may have better prognosis but worse outcomes.
OBJECTIVE: Here we explored the prognostic role of MSI in Asian patients.
METHODS: This post hoc analysis comprehended radically resected GC patients randomized to XP (capecitabine/cisplatin) or XPRT. MSI status was assessed by combining immunohistochemistry with multiplex polymerase chain reaction. The MSI prognostic effect on disease-free survival (DFS) and overall survival (OS) was evaluated.
RESULTS: 393 tissue samples were analyzed and 35 (9%) were MSI-high. This subgroup was characterized by: older age, Borrmann classification 1-2, antral localization, T3-4 stage, and intestinal type. At univariable analysis, the microsatellite-stable subgroup showed a trend toward a worse prognosis as compared to the MSI-high group: 3-year DFS was 76.3 versus 85.4% (p = 0.122); 3-year OS was 81.7 versus 91.4% (p = 0.046). Multivariable analyses confirmed it in both DFS (hazard ratio, HR = 2.32 [95% CI 0.91, 5.88]; p = 0.077) and OS (HR = 3.17 [95% CI 0.97, 10.43]; p = 0.057).
CONCLUSIONS: MSI-high status was associated with specific clinical-pathological features and a trend toward better outcomes of Asian GC patients.

Rubinstein JC, Nicolson NG, Ahuja N
Next-generation Sequencing in the Management of Gastric and Esophageal Cancers.
Surg Clin North Am. 2019; 99(3):511-527 [PubMed] Related Publications
Next-generation sequencing has enabled genome-wide molecular profiling of gastric and esophageal malignancies at single-nucleotide resolution. The resultant genomic profiles provide information about the specific oncogenic pathways that are the likely driving forces behind tumorigenesis and progression. The abundance of available genomic data has immense potential to redefine management paradigms for these difficult disease processes. The ability to capitalize on the information provided through high-throughput sequencing technologies will define cancer care in the coming decades and could shift the paradigm from current stage-based, organ-specific treatments toward tailored regimens that target the specific culprit pathways driving individual tumors.

Pennathur A, Godfrey TE, Luketich JD
The Molecular Biologic Basis of Esophageal and Gastric Cancers.
Surg Clin North Am. 2019; 99(3):403-418 [PubMed] Related Publications
Esophageal cancer and gastric cancer are leading causes of cancer-related mortality worldwide. In this article, the authors discuss the molecular biology of esophageal and gastric cancer with a focus on esophageal adenocarcinoma. They review data from The Cancer Genome Atlas project and advances in the molecular stratification and classification of esophageal carcinoma and gastric cancer. They also summarize advances in microRNA, molecular staging, gene expression profiling, tumor microenvironment, and detection of circulating tumor DNA. Finally, the authors summarize some of the implications of understanding the molecular basis of esophageal cancer and future directions in the management of esophageal cancer.

Chen S, Chen L, Tan Y, Wang J
Association between rs20417 polymorphism in cyclooxygenase-2 and gastric cancer susceptibility: Evidence from15 case-control studies.
Medicine (Baltimore). 2019; 98(18):e15468 [PubMed] Free Access to Full Article Related Publications
OBJECTIVE: Previous studies have reported an association between cyclooxygenase-2 (COX-2) polymorphism and gastric cancer (GC) susceptibility, but their results are controversial. This meta-analysis was intended to evaluate the relationship between the COX-2 rs20417 polymorphism and GC susceptibility in different ethnic groups.
METHODS: We searched PubMed, EMBASE, Web of Knowledge, and the Chinese Biomedical Database (CBM) for relevant case-control studies published up to October 6, 2018, which reported an association between the COX-2 rs20417 polymorphism and gastric cancer risk. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of this association.
RESULTS: 15 papers detailing case-control studies were included in the analysis, which included a total of 2848 GC cases and 4962 healthy controls. The meta-analysis results indicated that the COX-2 rs20417 polymorphism was associated with increased GC susceptibility under allele (G vs C: OR = 1.67, 95%CI = 1.19-2.35, P = .003), heterozygous (GG vs CG: OR = 1.44, 95%CI = 1.03-2.02, P = .034), dominant (GC+CC vs GG: OR = 1.66, 95%CI = 1.18-2.34, P = .004), homozygous (GG vs CC:OR = 2.20, 95%CI = 1.07-4.54, P = .033), and recessive models (CC vs GG+CG:OR = 2.05, 95%CI = 1.09-3.85, P = .025). An analysis of ethnic subgroups revealed that the COX-2 rs20417 polymorphism was significantly associated with GC susceptibility in Asians under all 5 models (G vs C: OR = 2.22, 95%CI = 1.66-2.96, P < .001; GG vs CC: OR = 4.29, 95%CI = 1.94-9.50, P < .001; GG vs CG: OR = 1.86, 95%CI = 1.34-2.58, P < .001; CC vs GG+CG: OR = 3.73, 95%CI = 1.92-7.24, P < .001; GC+CC vs GG: OR = 2.20, 95%CI = 1.65-2.93, P < .001). Helicobacter pylori positive patients suffered a high risk of GC, compared to H pylori negative patients under the dominant model (OR = 3.09, 95%CI = 1.80-5.32, P < .001).
CONCLUSION: This meta-analysis of 15 case-control studies provides strong evidence that the COX-2 rs20417 polymorphism increases the risk of GC susceptibility in general populations, especially in Asians. Helicobacter pylori positive patients and those with the COX-2 rs20417 polymorphism had a higher risk of developing GC.

Hua RX, Zhuo Z, Zhu J, et al.
LIG3 gene polymorphisms and risk of gastric cancer in a Southern Chinese population.
Gene. 2019; 705:90-94 [PubMed] Related Publications
DNA ligase III (LIG3) has been implicated in the etiology of cancer. However, few studies have accessed the association of LIG3 single nucleotide polymorphisms (SNPs) with gastric cancer risk, especially in Chinese population. The current study was undertaken to investigate contribution of LIG3 gene polymorphisms to gastric cancer risk. We first applied TaqMan assay to genotype three LIG3 gene SNPs (rs1052536 C > T, rs3744356 C > T, rs4796030 A > C) in 1142 patients with gastric cancer and 1173 healthy controls. And then, we adopted unconditional multivariate logistic regression analysis to estimate the association between LIG3 SNP genotypes and gastric cancer risk. In all, no positive association was found between the three LIG3 SNPs and gastric cancer risk in single locus analysis or combined risk genotypes analysis. However, compared with participants with rs4796030 AA genotype, participants with the AC/CC had a decreased risk of developing tumors from cardia at an adjusted OR of 0.68 (95% CI = 0.48-0.96, P = 0.026). In addition, we found that participants harboring 2-3 risk genotypes were at a significantly increased risk of developing tumor from cardia (adjusted OR = 1.63, 95% CI = 1.16-2.28, P = 0.005). These results suggest that genetic variations in LIG3 gene may play a weak role in modifying the risk of gastric cancer. Future functional studies should be performed to elucidate the biological role of LIG3 polymorphisms in gastric cancer carcinogenesis.

Qin P, Wang H, Zhang F, et al.
Targeted silencing of MYCL1 by RNA interference inhibits migration and invasion of MGC-803 gastric cancer cells.
Cell Biochem Funct. 2019; 37(4):266-272 [PubMed] Related Publications
MYCL1 protein expression encoded by a proto-oncogene MYCL1, a member of the MYC family, is correlated with poor prognosis in gastric cancer patients. Nevertheless, the role of MYCL1 in gastric cancer cells remains unknown. In this study, the expression levels of MYCL1 mRNA and protein were downregulated by lentiviral-mediated RNA interference (RNAi) in the MGC-803 gastric cancer cell line. Then, the influence of MYCL1 on the biological behaviour of gastric cancer cells was investigated. Finally, a stable animal model of the MGC-803 human gastric cancer tumour model in nude mice was made successfully. Functionally, silencing of MYCL1 inhibited migration and invasion of the MGC-803 line in vitro and was accompanied with some ultrastructural changes. These results provide some evidences that lentiviral-mediated MYCL1 silencing may be a novel therapeutic strategy for the treatment of gastric cancer. SIGNIFICANCE OF THE STUDY: Gastric cancer is one of the most common malignant tumours worldwide and the second leading cause of cancer-related death in China. Our previous study revealed that expression of MYCL1 in gastric cancer tissue was associated with poor prognosis of patients. However, the potential underlying mechanism is still unclear. In the current study, we displayed the influence of MYCL1 gene on invasion and migration phenotype of gastric cancer cells and provided a possible explanation from the aspect of structural alteration. Our results suggested that downregulation of MYCL1 may be a potential therapeutic strategy for gastric cancer.

Pačínková A, Popovici V
Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer.
Biomed Res Int. 2019; 2019:6763596 [PubMed] Free Access to Full Article Related Publications
The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (

Li XL, Ji YM, Song R, et al.
KIF23 Promotes Gastric Cancer by Stimulating Cell Proliferation.
Dis Markers. 2019; 2019:9751923 [PubMed] Free Access to Full Article Related Publications
Gastric cancer (GC) is one of the most aggressive malignant tumors with low early diagnosis and high metastasis. Despite progress in treatment, to combat this disease, a better understanding of the underlying mechanisms and novel therapeutic targets is needed. KIF23, which belongs to the KIF family, plays a vital role in various cell processes, such as cytoplasm separation and axon elongation. Nowadays, KIF23 has been found to be highly expressed in multiple tumor tissues and cells, suggesting a potential link between KIF23 and tumorigenesis. Herein, we reported that KIF23 expression was correlated with poor prognosis of gastric cancer and found an association between KIF23 and pTNM stage. An in vitro assay proved that the proliferation of gastric cancer cells was significantly inhibited, which is caused by KIF23 depletion. Additionally, knockdown of KIF23 resulted in a marked inhibition of cell proliferation of gastric cancer in mice, with significant downregulation of Ki67 and PCNA expression. In conclusion, these data indicate that KIF23 is a potential therapeutic target for gastric cancer treatment.

Sun B, Dang Y, Zhang F, et al.
Long non‑coding RNA RP1‑163G9.1 is downregulated in gastric adenocarcinoma and is associated with a poor prognosis.
Oncol Rep. 2019; 41(6):3575-3585 [PubMed] Related Publications
The aim of the present study was to investigate the expression, function and underlying molecular mechanism of the long non‑coding (lnc) RNA RP1‑163G9.1 in patients with gastric adenocarcinoma (GA). The expression levels of lncRNA RP1‑163G9.1 were determined in 112 paired clinical GA tissues by reverse transcription‑quantitative polymerase chain reaction analysis. Subsequently, the potential clinical values of lncRNA RP1‑163G9.1 were analyzed with statistical methods. Additionally, the function of lncRNA RP1‑163G9.1 was explored at the cellular level using the Cell Counting Kit‑8 proliferation assay, Transwell experiments, fluorescence in situ hybridization (FISH), colony formation assay and flow cytometry. Furthermore, the function of lncRNA RP1‑163G9.1 was assessed in vivo using subcutaneous tumorigenesis experiments in nude mice. lncRNA RP1‑163G9.1 expression in GA tissues and cells was significantly decreased when compared with that in control gastric tissues (P<0.001) or gastric epithelial cells GES‑1 (P<0.05). This finding was associated with the depth of invasion (P=0.001), lymph node metastasis (P=0.009), tumor size (P=0.037) and immunocytochemistry marker Ki‑67 (P=0.010). FISH detection demonstrated that lncRNA RP1‑163G9.1 was primarily located in the cytoplasm. Notably, overexpression of lncRNA RP1‑163G9.1 significantly decreased cell proliferation (P<0.01), colony formation (P<0.01), invasion (P<0.01) and the number of cells at the S‑phase of the cell cycle (P<0.05); However, it did not exert a significant effect on apoptosis (P>0.05). Furthermore, tumor formation experiments revealed that overexpression of lncRNA RP1‑163G9.1 inhibited cancer cell proliferation in nude mice. The present research indicated that low expression of lncRNA RP1‑163G9.1 may be associated with enhanced tumor proliferation and invasion in GA.

Kim TW, Han SR, Kim JT, et al.
Differential expression of tescalcin by modification of promoter methylation controls cell survival in gastric cancer cells.
Oncol Rep. 2019; 41(6):3464-3474 [PubMed] Related Publications
The EF‑hand calcium binding protein tescalcin (TESC) is highly expressed in various human and mouse cancer tissues and is therefore considered a potential oncogene. However, the underlying mechanism that governs TESC expression remains unclear. Emerging evidence suggests that TESC expression is under epigenetic regulation. In the present study, the relationship between the epigenetic modification and gene expression of TESC in gastric cancer was investigated. To evaluate the relationship between the methylation and expression of TESC in gastric cancer, the methylation status of CpG sites in the TESC promoter was analyzed using microarray with the Illumina Human Methylation27 BeadChip (HumanMethylation27_270596_v.1.2), gene profiles from the NCBI Dataset that revealed demethylated status were acquired, and real‑time methylation‑specific PCR (MSP) in gastric cancer cells was conducted. In the present study, it was demonstrated that the hypermethylation of TESC led to the downregulation of TESC mRNA/protein expression. In addition, 5‑aza‑2c‑deoxycytidine (5'‑aza‑dC) restored TESC expression in the tested gastric cancer cells except for SNU‑620 cells. ChIP assay further revealed that the methylation of the TESC promoter was associated with methyl‑CpG binding domain protein (MBD)1, histone deacetylase (HDAC)2, and Oct‑1 and that treatment with 5'‑aza‑dC facilitated the dissociation of MBD1, HDAC2, and Oct‑1 from the promoter of TESC. Moreover, silencing of TESC increased MBD1 expression and decreased the H3K4me2/3 level, thereby causing transcriptional repression and suppression of cell survival in NCI‑N87 cells; conversely, overexpression of TESC downregulated MBD1 expression and upregulated the H3K4me2 level associated with active transcription in SNU‑638 cells. These results indicated that the differential expression of TESC via the modification status of the promoter and histone methylation controled cell survival in gastric cancer cells. Overall, the present study provided a novel therapeutic strategy for gastric cancer.

Li Y, Zhang Q, Tang X
Long non-coding RNA XIST contributes into drug resistance of gastric cancer cell.
Minerva Med. 2019; 110(3):270-272 [PubMed] Related Publications

Zhang JX, He WL, Feng ZH, et al.
A positive feedback loop consisting of C12orf59/NF-κB/CDH11 promotes gastric cancer invasion and metastasis.
J Exp Clin Cancer Res. 2019; 38(1):164 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Metastasis remains the main cause of cancer-related death for gastric cancer (GC) patients, but the mechanisms are poorly understood. Using The Cancer Genome Atlas (TCGA) data base and bioinformatics analyses, we identified C12orf59 might act as a potential oncogenic protein in GC.
METHODS: We investigate the expression pattern and clinical significance of C12orf59 in two independent cohorts of GC samples. In the training cohort, we used the X-tile program software to generate the optimal cutoff value for C12orf59 expression in order to classify patients accurately according to clinical outcome. In the validation cohort, this derived cutoff score was applied to exam the association of C12orf59 expression with survival outcome. A series of in vivo and in vitro assays were then performed to investigate the function of C12orf59 in GC.
RESULTS: C12orf59 was significantly upregulated, and associated with poor survival outcome in two cohorts of GC samples. Gain- and loss of- function studies demonstrated C12orf59 promotes GC cell invasive and metastatic capacity both in vitro and in vivo, and induces epithelial-mesenchymal transition and angiogenesis. Mechanically, C12orf59 exerts oncogenic functions by up-regulating CDH11 expression via NF-κB signaling. Interesting, CDH11 could in turn promote NF-κB bind to C12orf59's promoter and form a positive feedback loop to sustain the metastatic ability of GC cells. Additionally, downregulation of miR-654-5p is another important mechanism for C12orf59 overexpression in GC.
CONCLUSION: Our finding suggested the newly identified C12orf59/NF-κB/CDH11 feedback loop may represent a new strategy for GC treatment.

Oue N, Sentani K, Sakamoto N, et al.
Molecular carcinogenesis of gastric cancer: Lauren classification, mucin phenotype expression, and cancer stem cells.
Int J Clin Oncol. 2019; 24(7):771-778 [PubMed] Related Publications
Gastric cancer (GC), one of the most common human cancers, is a heterogeneous disease with different phenotypes, prognoses, and responses to treatment. Understanding the pathogenesis of GC at the molecular level is important for prognosis prediction and determining treatments. Microsatellite instability (MSI), silencing of MLH1, MGMT, and CDKN2A genes by DNA hypermethylation, KRAS mutation, APC mutation, and ERBB2 amplification are frequently found in intestinal type GC. Inactivation of CDH1 and RARB by DNA hypermethylation, and amplification of FGFR and MET, are frequently detected in diffuse type GC. In addition, BST2 and PCDHB9 genes are overexpressed in intestinal type GC. Both genes are associated with GC progression. GC can be divided into gastric/intestinal mucin phenotypes according to mucin expression. MSI, alterations of TP73, CDH1 mutation, and DNA methylation of MLH are detected frequently in the gastric mucin phenotype. TP53 mutation, deletion of APC, and DNA methylation of MGMT are detected frequently in the intestinal mucin phenotype. FKTN is overexpressed in the intestinal mucin phenotype, and IQGAP3 is overexpressed in the gastric mucin phenotype. These genes are involved in GC progression. To characterize cancer stem cells, a useful method is spheroid colony formation. KIFC1 and KIF11 genes show more than twofold higher expression in spheroid-forming cells than that in parental cells. Both KIF genes are overexpressed in GC, and knockdown of these genes inhibits spheroid formation. Alterations of these molecules may be useful to understand gastric carcinogenesis. Specific inhibitors of these molecules may also be promising anticancer drugs.

De Scalzi AM, Bonanni B, Galimberti V, et al.
E-cadherin germline mutations in Māori population.
Future Oncol. 2019; 15(12):1291-1294 [PubMed] Related Publications

Ishii T, Kawazoe A, Shitara K
Dawn of precision medicine on gastric cancer.
Int J Clin Oncol. 2019; 24(7):779-788 [PubMed] Related Publications
BACKGROUND: In recent years, a better understanding of tumor biology and molecular features of gastric cancer has been reached. It may serve as a roadmap for patient stratification and trials of targeted therapies. The apparent efficacy of PD-1 blockade might be limited to a relatively small subset of advanced gastric cancer patients.
MATERIALS AND METHODS: In this study, preclinical and clinical studies, which investigated molecular features, promising treatment targets, and immune checkpoint inhibitor in gastric cancer, were reviewed via PubMed and the congress webpages of the American Society of Clinical Oncology and European Society of Medical Oncology.
RESULTS: Next-generation sequencing technologies have defined the genomic landscape of gastric cancer. Indeed, several molecular classifications have been proposed, and distinct molecular subtypes have been identified. Based on these molecular profiles, clinical trials of new agents such as receptor tyrosine kinases inhibitors, antibody-drug conjugates, and IMAB362 (anti-Claudin 18.2) are ongoing. In addition, biomarkers to predict response during immune checkpoint inhibitors and combination therapy have been enthusiastically investigated.
CONCLUSION: Remarkable advances in an understanding of molecular profiles of gastric cancer enable the development of novel agents. The better treatment selection of immune checkpoint inhibitors or combination therapy should be established. These developments could facilitate precision medicine on gastric cancer in the near future.

Cai J, Chen Z, Zuo X
circSMARCA5 Functions as a Diagnostic and Prognostic Biomarker for Gastric Cancer.
Dis Markers. 2019; 2019:2473652 [PubMed] Free Access to Full Article Related Publications
Background: Circular RNAs have been implicated in various malignancies and can function as potential biomarkers for cancers. Reportedly, circSMARCA5 was downregulated in hepatocellular carcinoma and glioblastoma multiforme, but upregulated in prostate cancer. The functional roles and clinical significance of circSMARCA5 still remain unknown in the context of gastric cancer (GC).
Methods: Expression levels of circSMARCA5 were detected by qRT-PCR. Clinical data including patient basic information, clinicopathological features, and survival data were obtained. The Kaplan-Meier methods, multivariate Cox regression models, and the receiver operating characteristic curve were used to assess the clinical significance of circSMARCA5 in GC. Cell proliferation assays and transwell assays were performed to elucidate the functional roles of circSMARCA5 in GC.
Results: The circSMARCA5 level was decreased in GC tissues and cell lines. The low expression level of circSMARCA5 was correlated to poorer overall survival and disease-free survival. Low circSMARCA5 expression was revealed as an independent unfavorable predictive factor for GC. The results indicated that circSMARCA5 had a moderate ability for discrimination between GC patients and controls with an area under the curve of 0.806. Upregulation of circSMARCA5 dampened the proliferation, migration, and invasion of GC cells, whereas circSMARCA5 knockdown promoted GC progression.
Discussion: Our results demonstrated that circSMARCA5 was decreased and exerted tumor-suppressive effects in GC. circSMARCA5 can function as a potential biomarker for GC prognosis and diagnosis.

Seo AN, Kang BW, Bae HI, et al.
Exon 9 Mutation of
Anticancer Res. 2019; 39(4):2145-2154 [PubMed] Related Publications
BACKGROUND: Epstein-Barr virus (EBV)-associated gastric cancer (GC) is known to harbor a significant enrichment of of phosphatidylinositol 4, 5-biphosphate 3- kinase catalytic subunit alpha isoform (PIK3CA). Therefore, this study investigated the clinical relevance and prognostic role of PIK3CA mutations in patients with EBV-GC.
MATERIALS AND METHODS: After reviewing 1,318 consecutive cases of surgically resected GC, 120 patients were identified as EBV-positive using EBV-encoded RNA in situ hybridization. PIK3CA mutations were identified in formalin-fixed and paraffin-embedded surgical specimens from 112 patients with EBV-GC with available tumor tissue samples. Real-time polymerase chain reaction was used to evaluate hot-spot mutations of exons 1, 4, 7, 9, and 20 of PIK3CA.
RESULTS: Among the 112 patients, the frequency of PIK3CA mutations was 25.0% (n=28), and among the 28 patients harboring a PIK3CA mutation, most mutations were identified in exon 9 (n=21, 18.8%). The presence of PIK3CA mutation was also correlated with a higher T category (p<0.001) and N category (p<0.001), as well as the presence of perinueral invasion (p<0.001) and venous invasion (p<0.001). In a univariate analysis, PIK3CA mutation showed no association with overall survival (OS) (p=0.184) or disease-free survival (DFS) (p=0.150). Patients harboring exon 9 PIK3CA mutations exhibited a significantly shorter OS (p=0.023) and DFS (p=0.013) than the patients lacking an exon 9 PIK3CA mutation, yet without statistical significance in the multivariate analysis. Notably, exon 9 E542K mutation of PIK3CA was associated with the worst DFS (p=0.011).
CONCLUSION: The current data show that PIK3CA mutations appear to play an important role in carcinogenesis and tumor aggressiveness in EBV-GC, and also support the concept that exon 9 mutation of PIK3CA is a prognostic indicator for predicting patient outcomes and a rationale for therapeutic targeting in EBV-GC.

Liu JB, Jian T, Yue C, et al.
Chemo-resistant Gastric Cancer Associated Gene Expression Signature: Bioinformatics Analysis Based on Gene Expression Omnibus.
Anticancer Res. 2019; 39(4):1689-1698 [PubMed] Related Publications
BACKGROUND/AIM: This study aimed to identify biomarkers for predicting the prognosis of advanced gastric cancer patients who received docetaxel, cisplatin, and S-1 (DCS).
MATERIALS AND METHODS: Gene expression profiles were obtained from the Gene Expression Omnibus database (GSE31811). Gene-Ontology-enrichment and KEGG-pathway analysis were used for evaluating the biological functions of differentially-expressed genes. Protein-protein interaction (PPI) network and Kaplan-Meier survival analyses were employed to assess the prognostic values of hub genes.
RESULTS: A total of 1,486 differentially expressed genes (DEGs) were identified, including 13 up-regulated and 1,473 down-regulated genes. KEGG pathways such as metabolic pathways, cell adhesion molecules (CAMs), PI3K-Akt signaling pathway and pathways in cancer were significantly represented. In the PPI network, the top ten hub genes ranked by degree were GNG7, PLCB1, CALML5, FGFR4, GRB2, JAK3, ADCY7, ADCY9, GNAS and KDR. Five DEGs, including ANTXR1, EFNA5, GAMT, E2F2 and NRCAM, were associated with relapse-free survival and overall survival.
CONCLUSION: ANTXR1, EFNA5, GAMT, E2F2 and NRCAM are potential biomarkers and therapeutic targets for DCS treatment in GC.

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