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Supplementary Tables Supplementary Table 1. Differentially methylated genes in correlation with their expression pattern in the A4 progression model A. Hypomethylated–upregulated Genes (n= 76) ALOX5 RRAD RTN4R DSCR6 FGFR3 HTR7 WNT3A POGK PLCD3 ALPPL2 RTEL1 SEMA3B DUSP5 FOSB ITGB4 MEST PPL PSMB8 ARHGEF4 BST2 SEMA7A SLC12A7 FOXQ1 KCTD12 LETM2 PRPH PXMP2 ARNTL2 CDH3 SHC2 SLC20A2 HSPA2 KIAA0182 LIMK2 NAB1 RASIP1 ASRGL1 CLDN3 DCBLD1 SNX10 SSH1 KREMEN2 LIPE NDRG2 ATF3 CLU DCHS1 SOD3 ST3GAL4 MAL LRRC1 NR3C2 ATP8B3 CYC1 DGCR8 EBAG9 SYNGR1 TYMS MCM2 NRG2 RHOF DAGLA DISP2 FAM19A5 TNNI3 UNC5B MYB PAK6 RIPK4 DAZAP1 DOCK3 FBXO6 HSPA4L WHSC1 PNMT PCDH1 B. Hypermethylated-downregulated Genes (n= 31) ARHGAP22 TNFSF9 KLF6 LRP8 NRP1 PAPSS2 SLC43A2 TBC1D16 ASB2 DZIP1 TPM1 MDGA1 NRP2 PIK3CD SMARCA2 TLL2 C18orf1 FBN1 LHFPL2 TRIO NTNG2 PTGIS SOCS2 TNFAIP8 DIXDC1 KIFC3 LMO1 NR3C1 ODZ3 PTPRM SYNPO Supplementary Table 2. Genes enriched for different histone methylation marks in A4 progression model identified through ChIP-on-chip a. H3K4me3 (n= 978) AATF C20orf149 CUL3 FOXP1 KATNA1 NEGR1 RAN SPIN2B ABCA7 C20orf52 CWF19L1 FRK KBTBD10 NEIL1 RANBP2 SPPL2A ABCC9 C21orf13-SH3BGR CXCL3 FSIP1 KBTBD6 NELF RAPGEF3 SPRY4 ABCG2 C21orf45 CYC1 FUK KCMF1 NFKB2 RARB SPRYD3 ABHD7 C22orf32 CYorf15A FXR2 KCNH7 NGDN RASAL2 SPTLC2 ACA15 C2orf18 DAXX FZD9 KCNMB4 NKAP RASD1 SRFBP1 ACA26 C2orf29 DAZ3 G6PD KCTD18 NKTR RASEF SRI ACA3 C2orf32 DBF4 GABPB2 KDELR2 NNT RASGRF1 SRM ACA48 C2orf55 DBF4B GABRA5 KIAA0100 NOL5A RASSF1 SSH2 ACAT1 C3orf44 DBI GADD45B KIAA0226 NOLC1 RASSF3 SSH3 ACSL5 C5orf15 DCBLD2 GALNT10 KIAA0265 NOVA1 RAVER2 SSSCA1 ACTR10 C5orf34 DCI GALNT4 KIAA0513 NPY1R RB1CC1 STAG1 ADAM10 C5orf39 DCTD GALNT6 KIAA0564 NR4A2 RBJ STARD3NL ADAMTS7 C6orf134 DCUN1D5 GAS1 KIAA0664 NRAS RBL2 STK25 ADH5 C6orf138 DDB1 GBX2 KIAA1012 NRD1 RBM26 STK40 ADHFE1 C6orf166 DDHD1 GCC1-ARF5 KIAA1279 NT5C3 RBPJ STON2 ADRA1D C6orf48 DDX31 GCN1L1 KIAA1344 NT5E RCOR1 STX18 AGPAT5 C6orf66 DEAF1 GDF5 KIAA1429 NTN4 REV3L STXBP1 AGPAT7 C7orf44 DECR2 GDF9-UQCRQ KIAA1715 NUDT13 RFX5 SUCLG1 AGR3 C8orf32 DEPDC1B GDI2 KIAA1804 NUDT2 RHOF SUGT1 AGTPBP1 C8orf37 DGUOK GFRA1 KIAA1826 NUP153 RHPN2 SUMO1 AGTR1 C8orf42 DHX30 GFRA4 KIF27 NUP54 RLF SUMO2 AKR1B1 C8orf55 DIO2 GLRX2 KLF5 NUP93 RNF152 SUPT3H AKR1C3 C8orf58 DIP2A GLS KLHL11 OCLN ROBO4 SWAP70 ALDH1A2 C9orf125 DIP2C GLT8D2 KLHL20 OLFML3 ROCK1 SYNCRIP ALKBH5 C9orf21 DKK3 GM2A KLHL8 OPLAH ROD1 SYT3 ALKBH7 C9orf7 DLX2 GMDS KPNA4 OR1D2 RPL11 TACSTD1 ALS2CR4 C9orf82 DMTF1 GMPR KPNA5 OR2L13 RPL13 TAF10 AMIGO2 CA13 DMXL1 GNS KRT24 OR5R1 RPL27A TAF1B AMMECR1L CABC1 DNAJC10 GOLPH4 Kua ORC4L RPP21 TAGLN2 AMPD2 CALCR DNAJC19 GPAA1 LAMC1 ORC5L RPS6KA2 TARBP2 ANAPC1 CALM1 DNASE2 GPATCH8 LANCL1 OSBP RRM1 TBN ANKRD10 CALN1 DOCK7 GPC3 LASS1 OSGIN2 RRM2B TCEB2 ANKRD11 CAMTA2 DOCK9 GPER LBH OSR2 RSHL3 TCF12 ANKRD45 CAPRIN2 DPPA3 GPKOW LBR PAICS RTCD1 TCFL5 ANXA1 CASC3 DSC2 GPR108 LBX2 PAIP1 RTF1 TDRD5 ANXA3 CASP2 DSG2 GPR113 LCA5 PAQR7 RTN4 TDRKH ANXA4 CBARA1 DTNB GPR125 LDHB PARC RUNX1 TFAP2B AP3B1 CBLB DUSP4 GPR158 LENG8 PATZ1 RUNX3 TFIP11 AP3B2 CBR4 DYNC1H1 GPR162 LEPR PAXIP1 RUSC2 TGS1 AP3M2 CBY1 DZIP1L GPR63 LGR5 PBLD RWDD1 THAP7 AP4E1 CC2D1B ECT2 GRID1 LHFPL1 PCDH9 SAMD4A THOC5 APBA1 CCDC4 EDC3 GRIK2 LHX3 PCDHB11 SAMD9L TIGD2 APOL1 CCDC55 EDN1 GRINL1A LIMD1 PCDHB3 SAP30L TIMELESS APPBP2 CCDC57 EGR2 GRM1 LIN54 PCF11 SAPS1 TMC4 ARF5 CCDC66 EHD1 GRWD1 LOC128977 PCNA SASH1 TMCO1 ARFGEF1 CCDC77 EIF3S3 GSPT1 LOC144097 PDHA1 SATB2 TMED2 ARHGAP29 CCDC88A EIF3S6 GSR LOC196463 PDIK1L SCFD2 TMED5 ARHGDIA CCDC96 EIF4B GSTA4 LOC342897 PDK4 SCN3B TMEFF1 ARHGEF10 CCNG2 EIF4ENIF1 GSTO2 LOC440742 PDSS1 SCPEP1 TMEM1 ARHGEF11 CCNJL ELAC2 GTF2B LOC497661 PECI SCYL1BP1 TMEM132A ARHGEF12 CCR7 ELF2 GUCA1B LRP10 PEX5L SDPR TMEM134 ARIH1 CD151 ELF3 GUK1 LRP12 PFKP SEC24C TMEM139 ARL8B CDAN1 ELOVL1 H2AFX LRRC6 PGM2 SECISBP2 TMEM144 ARMC7 CDC2L5 EML5 H2AFZ LRRC8D PHF1 SELENBP1 TMEM150 ARMC9 CDC42BPA ENO1 HBII-240 LRRTM4 PHF10 SELL TMEM154 ARNTL CDC42BPG ENOSF1 HBII-429 LTV1 PHIP SELT TMEM16D ARRDC2 CDC42EP3 EOMES HBII-55 LYPLA1 PHTF1 SEMA3A TMEM18 ARRDC3 CDCA4 EPHA4 HBS1L MADD PIAS1 SEMA6A TMEM25 ARS2 CDCA7 EPHA7 HEBP2 MAL2 PIGK SENP3 TMEM32 ARTS-1 CDH11 ERBB4 HELB MAN2B2 PIK3CG SENP7 TMEM55A ASB5 CDK10 ERCC2 HERC1 MAP3K7 PIK3R2 SERF2 TMEM60 ASMT CDK8 ERF HERC4 MAP3K8 PIM1 SERINC4-HYPK TMEM63A ATBF1 CDKN2AIP ERGIC2 HES4 MARK4 PKM2 SERTAD3 TMEM87B ATE1 CDKN2B ERGIC3 HIST1H3H MBOAT2 PLSCR1 SETD8 TMEM9 ATP5A1 CDKN2D ERLIN1 HIST1H3I MBTPS1 PMP22 SETDB1 TMEPAI ATP5G3 CEBPA ERO1L HIST1H4K MCFD2 PODN SFXN4 TMPO ATP5J CEBPD ETS1 HLA-F MCM2 POLD2 SH3BP4 TMPRSS3 ATPBD4 CEECAM1 ETV1 HLF MCM3 POLR2C SH3GLB2 TNFSF18 ATXN1 CENPF ETV6 HMGA1 MCRS1 POLS SH3RF2 TNIP1 AVPR2 CEP250 EVA1 HMGA2 MEIS1 POU4F1 SHC1 TNPO2 AXIN1 CFB EVI1 HMGCLL1 MEIS2 PPAP2A SHC4 TOMM70A AZIN1 CFL2 EXOSC2 HNRPC MEN1 PPAP2C SIX4 TOP2A B2M CFLAR FA2H HNRPH1 MESDC1 PPARGC1A SKAP2 TPCN2 B3GALT1 CHD3 FADS1 HOXA6 METRNL PPIC SKIL TPI1 BCDO2 CHID1 FAM120AOS HOXB3 MFAP1-WDR76 PPIG SLC12A2 TPM1 BCL6 CHKB FAM130A1 HOXB7 MFSD2 PPIL3 SLC12A9 TRAP1 BHLHB2 CITED2 FAM13A1 HR MGAT4B PPM1F SLC16A6 TRHDE BIRC2 CKAP2L FAM3A HSD17B4 MGC33894 PPP1CB SLC1A4 TRIM39 BLCAP CLASP1 FAM3C HSDL2 MGC39900 PPP1CC SLC25A22 TRIM68 BMP5 CLCA1 FAM40A HYPK MGC45491 PPP1R13B SLC25A40 TRNT1 BNIP2 CLEC1B FAM83H ICA1 MGC50559 PPP1R9B SLC2A8 TRPM1 BOLL CLPP FAM98B ID3 MLL4 PPP4C SLC30A6 TRPS1 BRUNOL5 CLSTN1 FANCA IDE MLL5 PPRC1 SLC35B1 TSHR BTBD10 CNKSR3 FANCC IER3IP1 MLSTD1 PPTC7 SLC35D3 TSPAN12 BVES CNP FAS IFIT3 MME PRDM5 SLC35E3 TSPYL5 C10orf10 CNTD2 FASTK IFIT5 MRP63 PRDX1 SLC36A1 TTL C10orf137 CNTN6 FAT IFITM1 MRPL39 PREP SLC36A2 TUBB2A C11orf24 COBL FBXL17 IFT74 MRPS26 PREX1 SLC38A2 TUBB4 C11orf52 COL21A1 FBXL4 IGF2BP2 MRPS36 PRIM1 SLC44A1 TUSC4 C12orf10 COL5A1 FBXO21 IGSF2 MSH3 PRKAA1 SLC44A3 U15A C12orf30 COMMD5 FBXO5 IKZF2 MSRB3 PRKAA2 SLC5A4 U21 C14orf2 COMTD1 FBXW7 IL13RA1 MTF2 PRKAB2 SLC5A6 U45B C14orf32 CORO1A FEM1C IL15RA MTMR11 PRKCQ SLC5A8 U54 C14orf37 COX6B1 FER1L3 IL2 MTMR15 PRPF38B SLC6A5 U59A C15orf15 CP FGF12 IL31RA MTMR2 PRPF4B SLC7A5 UAP1 C15orf27 CPNE1 FGF7 ILKAP MTP18 PRPS1L1 SLC8A3 UBE2D2 C15orf37 CPSF1 FH IMPAD1 MTPN PRR13 SLC9A6 UBE2G2 C17orf57 CREBL2 FIBCD1 ING1 MTX2 PSCD2 SLCO3A1 UBE2W C17orf81 CREBZF FKBP2 INHA MVD PSMA5 SLFN11 UBP1 C18orf10 CROP FLJ11286 INPPL1 MVP PSMC6 SMARCAD1 UFM1 C19orf20 CRSP8 FLJ16641 INTS3 MYBPC2 PTN SMARCC1 UFSP1 C1QTNF7 CRTAP FLJ20309 INTS5 MYH7B PTPN13 SNAPC2 UNC13B C1orf114 CSDA FLJ32312 IREB2 MYO9B PTPN9 SNAPC3 UNC45A C1orf121 CSF1R FLJ32447 IRX5 NARS PTPRA SNRPA1 UNC5C C1orf149 CSNK2A1 FLJ37953 ISGF3G NASP PTPRB SNTB1 UNG C1orf151 CTAG2 FLJ38482 ISOC1 NCOA5 PTPRJ SNUPN UNKL C1orf183 CTAGE5 FLNA-EMD ISY1 NDNL2 PWP1 SNX8 UNQ9217 C1orf51 CTGF FLT4 ITGAE NDRG4 RAB11A SPAG9 UPF1 C20orf108 CTSC FLYWCH1 ITGB8 NDST1 RAB30 SPATA4 USP3 C20orf112 CTTNBP2NL FOXC2 JUB NDST2 RAB7L1 SPATA5L1 USP46 C20orf12 CUL1 FOXJ3 K4 NDUFA5 RAI16 SPATA7 VAMP8 VDAC2 WDR53 ZBTB20 ZKSCAN3 ZNF35 ZNF639 hsa-mir-335 VDAC3 WDR77 ZBTB43 ZKSCAN5 ZNF354B ZNF643 hsa-mir-378 VEPH1 WDSUB1 ZBTB7A ZMYM3 ZNF385 ZNF672 hsa-mir-448 VPS37D WEE1 ZBTB8 ZNF217 ZNF391 ZNF691 hsa-mir-542 VPS4B WHSC1 ZCCHC12 ZNF248 ZNF41 ZNF692 hsa-mir-554 VPS53 WNT7A ZDHHC5 ZNF250 ZNF452 ZNF706 hsa-mir-559 VPS72-PIP5K1A WRB ZFAT1 ZNF259 ZNF480 ZNF781 hsa-mir-642 VRK2 XBP1 ZFP30 ZNF271 ZNF488 ZNF786 hsa-mir-9-1 WAPAL XPA ZFP64 ZNF281 ZNF528 ZNF787 hsa-mir-99a WBSCR16 XPO1 ZFYVE20 ZNF285A ZNF533 hsa-mir-137 WDR34 YPEL3 ZHX1 ZNF311 ZNF565 hsa-mir-219-1 WDR51A ZBTB2 ZHX2 ZNF32 ZNF608 hsa-mir-23a b. H3K9me3 ( n= 497) AADACL2 ENDOGL1 LRRC3B RFC3 DYX1C1 LOC399947 RAD54L2 hsa-mir-147 ABCB4 EPB41L4A LYNX1 RFPL4B C1orf128 HAGHL OR13C3 TEKT2 ACA28 EPN1 LYPLAL1 RFXAP C20orf43 HBG2 OR1A2 TFAP2A ACA30 EXOC3L2 M6PR RGS11-ARHGDIG C21orf34 HBII-85-24 OR2A2 TGIF2LY ACA67-C21orf119 FAM107B MAF RHOBTB3 C21orf51 HCN1 OR2T10 TGOLN2 ACCN1 FAM113B MAGEA11 RIT2 C3orf17 HDC OR2T11 TLR2 ACHE FAM117A MAGEA8 RMND5B C3orf19 HDGFRP3 OR4C15 TLR3 ACTRT1 FAM131B MAGEB4 RNF126 C3orf39 HES5 OR4C16 TM6SF2 ADAM12 FAM13C1 MAP2K4 RNF7 C4orf16 HES6 OR4C3 TMEM133 ADCY4 FAM14A MAP3K7IP2 RPL26L1 C5orf29 HIC1 OR4K14 TMEM174 ADH4 FAM47B MARS2 RPS4Y1 C6orf182 HIST1H1B OR4Q3 TMEM5 AEBP2 FAM53A MBNL3 RSAD1 CA2 HIST1H2BE OR51B5 TMEM81 AHR FAM73B MBTPS2 RSPO1 CALML5 HN1L OR51I1 TMLHE AIRE FAM83C MED28 RTBDN CAPN1 HOXA2 OR52N4 TMPIT AKAP12 FASN MFAP1 RUFY1 CASP5 HPS6 OR52N5 TP53I3 AKR1C4 FBN1 MGAM SCN1B CBLN4 HPSE2 OR56A1 TRERF1 AKR7A3 FBN2 MGC27121 SCN3A CBX3 HRK OR5AP2 TRIM33 ALDH1A1 FBXL21 MGC34774 SCN4B CCDC83 HS3ST1 OR5AS1 TRIM5-TRIM22 ALKBH6 FBXO34 MGMT SCUBE2 CCDC89 HSD3B2 OR5D18 TRIM7 ANK2 FBXO44 MINK1 SEMA6D CCR8 IBRDC3 OR5K4 TRPA1 ANKFN1 FCHO1 MLLT1 SERPINF1 CCRL1 IGFBP3 OR5M1 TRPC3 ANKRD17 FDX1 MMP14 SFRP2 CD58 IGJ OR5T2 TRY1 ANKRD54 FHL1 MMP24 SGK CD83 IL13 OR5W2 TSC22D3 ANXA13 FKBP9 MMP27 SGMS1 CDC42 IL5 OR8B3 TSHB AOF2 FLJ20674 MON2 SGOL2 CELSR1 INSL5 OR8J3 TSNARE1 APBB1IP FLJ25439 MS4A5 SLAMF9 CENTG2 INSR OSBPL11 TTC32 APC FLJ30058 MTAC2D1 SLC12A8 CHPF IQUB OSR1 TULP4 API5 FLNA MTMR10 SLC22A16 CHPF-MGC99813 ITGA9 PAQR4 U109 APRT FLT3 MUC15 SLC22A2 CHRNA3 ITM2A PAQR9 U45C ARD1A FMR1 MXRA5 SLC25A28 CHSY1 ITPR2 PCDHGA12 UBE2J1 ARHGDIG FOXG1 MYBPC1 SLC25A33 CILP2 JARID1D PCDHGB7 UBE2V2 ARID2 FOXN3 MYCN SLC34A1 CMBL K8 PCMTD2 UBTF ARPC1A FUT9 MYEOV2 SLITRK5 CMYA5 KCNA3 PDCD5 ULK3 ARRDC1 GAB3-DKC1 MYL9 SLMO2 CNIH2 KCNE4 PDIA4 UNC5D ASB1 GABRA6 MYO1G SMC2 CNKSR1 KCNH5 PDIA6 UNC84B ASCC3 GABRQ NAPE-PLD SNRPE COL11A2 KCTD12 PDLIM3 UPK3A ATF2 GADD45GIP1 NAPG SNURF COPB1 KCTD3 PDLIM4 UTF1 ATG10 GALNACT-2 NCAPG2 SOX11 COPG2 KIAA0280 PFN4 VAMP3 ATP12A GDAP1L1 NCKAP1L SOX13 CPA5 KIAA0776 PHF16 VASH1 ATP2A1 GDF8 NCOA1
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    Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897
  • Seq2pathway Vignette

    Seq2pathway Vignette

    seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway.
  • New Approaches to Functional Process Discovery in HPV 16-Associated Cervical Cancer Cells by Gene Ontology

    New Approaches to Functional Process Discovery in HPV 16-Associated Cervical Cancer Cells by Gene Ontology

    Cancer Research and Treatment 2003;35(4):304-313 New Approaches to Functional Process Discovery in HPV 16-Associated Cervical Cancer Cells by Gene Ontology Yong-Wan Kim, Ph.D.1, Min-Je Suh, M.S.1, Jin-Sik Bae, M.S.1, Su Mi Bae, M.S.1, Joo Hee Yoon, M.D.2, Soo Young Hur, M.D.2, Jae Hoon Kim, M.D.2, Duck Young Ro, M.D.2, Joon Mo Lee, M.D.2, Sung Eun Namkoong, M.D.2, Chong Kook Kim, Ph.D.3 and Woong Shick Ahn, M.D.2 1Catholic Research Institutes of Medical Science, 2Department of Obstetrics and Gynecology, College of Medicine, The Catholic University of Korea, Seoul; 3College of Pharmacy, Seoul National University, Seoul, Korea Purpose: This study utilized both mRNA differential significant genes of unknown function affected by the display and the Gene Ontology (GO) analysis to char- HPV-16-derived pathway. The GO analysis suggested that acterize the multiple interactions of a number of genes the cervical cancer cells underwent repression of the with gene expression profiles involved in the HPV-16- cancer-specific cell adhesive properties. Also, genes induced cervical carcinogenesis. belonging to DNA metabolism, such as DNA repair and Materials and Methods: mRNA differential displays, replication, were strongly down-regulated, whereas sig- with HPV-16 positive cervical cancer cell line (SiHa), and nificant increases were shown in the protein degradation normal human keratinocyte cell line (HaCaT) as a con- and synthesis. trol, were used. Each human gene has several biological Conclusion: The GO analysis can overcome the com- functions in the Gene Ontology; therefore, several func- plexity of the gene expression profile of the HPV-16- tions of each gene were chosen to establish a powerful associated pathway, identify several cancer-specific cel- cervical carcinogenesis pathway.
  • Transcriptomic Analysis of the Aquaporin (AQP) Gene Family

    Transcriptomic Analysis of the Aquaporin (AQP) Gene Family

    Pancreatology 19 (2019) 436e442 Contents lists available at ScienceDirect Pancreatology journal homepage: www.elsevier.com/locate/pan Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma Dimitrios E. Magouliotis a, b, Vasiliki S. Tasiopoulou c, Konstantinos Dimas d, * Nikos Sakellaridis d, Konstantina A. Svokos e, Alexis A. Svokos f, Dimitris Zacharoulis b, a Division of Surgery and Interventional Science, Faculty of Medical Sciences, UCL, London, UK b Department of Surgery, University of Thessaly, Biopolis, Larissa, Greece c Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece d Department of Pharmacology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece e The Warren Alpert Medical School of Brown University, Providence, RI, USA f Riverside Regional Medical Center, Newport News, VA, USA article info abstract Article history: Background: This study aimed to assess the differential gene expression of aquaporin (AQP) gene family Received 14 October 2018 interactome in pancreatic ductal adenocarcinoma (PDAC) using data mining techniques to identify novel Received in revised form candidate genes intervening in the pathogenicity of PDAC. 29 January 2019 Method: Transcriptome data mining techniques were used in order to construct the interactome of the Accepted 9 February 2019 AQP gene family and to determine which genes members are differentially expressed in PDAC as Available online 11 February 2019 compared to controls. The same techniques were used in order to evaluate the potential prognostic role of the differentially expressed genes. Keywords: PDAC Results: Transcriptome microarray data of four GEO datasets were incorporated, including 142 primary Aquaporin tumor samples and 104 normal pancreatic tissue samples.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
  • A Clinicopathological and Molecular Genetic Analysis of Low-Grade Glioma in Adults

    A Clinicopathological and Molecular Genetic Analysis of Low-Grade Glioma in Adults

    A CLINICOPATHOLOGICAL AND MOLECULAR GENETIC ANALYSIS OF LOW-GRADE GLIOMA IN ADULTS Presented by ANUSHREE SINGH MSc A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy Brain Tumour Research Centre Research Institute in Healthcare Sciences Faculty of Science and Engineering University of Wolverhampton November 2014 i DECLARATION This work or any part thereof has not previously been presented in any form to the University or to any other body whether for the purposes of assessment, publication or for any other purpose (unless otherwise indicated). Save for any express acknowledgments, references and/or bibliographies cited in the work, I confirm that the intellectual content of the work is the result of my own efforts and of no other person. The right of Anushree Singh to be identified as author of this work is asserted in accordance with ss.77 and 78 of the Copyright, Designs and Patents Act 1988. At this date copyright is owned by the author. Signature: Anushree Date: 30th November 2014 ii ABSTRACT The aim of the study was to identify molecular markers that can determine progression of low grade glioma. This was done using various approaches such as IDH1 and IDH2 mutation analysis, MGMT methylation analysis, copy number analysis using array comparative genomic hybridisation and identification of differentially expressed miRNAs using miRNA microarray analysis. IDH1 mutation was present at a frequency of 71% in low grade glioma and was identified as an independent marker for improved OS in a multivariate analysis, which confirms the previous findings in low grade glioma studies.