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Supplemental Materials Figure 1. 80 Genes Most Highly Differentially Expressed Comparing Dedifferentiated to Well- Differentiated Liposarcoma

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Supplemental Materials Figure 1. 80 genes most highly differentially expressed comparing dedifferentiated to well- differentiated liposarcoma Figure 2. Validation of microa CDC2, RACGAP1, FGFR-2, MAD2 and CITED1 200 CDK4 by Liposarcoma Subtype 16 0 12 0 80 Expression40 Level 0 Nl Fat rray data by quantitative RT-P Well Diff 16 0 p16 by Liposarcoma Subtype Dediff 12 0 80 Myxoid Expression40 Level Round Cell 0 12 0 MDM2 by Liposarcoma Subtype 10 0 Pleomorphic Nl Fat 80 60 40 Expression Level 20 RACGAP1 by LiposarcomaWell Diff Subtype 0 70 CR for genes CDK4, MDM2, p16, 60 Dediff 50 Nl Fat 40 30 Myxoid Expression20 Level Well Diff 10 50 0 Round Cell CDC2 by Liposarcoma Subtype Dediff 40 Nl Fat Pleomorphic 30 Myxoid 20 Expression Level Well Diff 10 MAD2L1 by Liposarcoma Subtype Round Cell 60 0 50 Dediff Pleomorphic 40 Nl Fat 30 Myxoid 20 Expression Level Well Diff 10 FGFR-2 by Liposarcoma Subtype 0 Round Cell 200 16 0 Dediff Nl Fat Pleomorphic 12 0 Myxoid 80 Expression Level Well Diff 40 Round Cell 0 Dediff Pleomorphic Nl Fat Myxoid Well Diff CITED1 by Liposarcoma Subtype Round Cell 10 0 80 Dediff Pleomorphic 60 40 Myxoid Expression Level 20 0 Round Cell Nl Fat Pleomorphic Well Diff Dediff Myxoid Round Cell Pleomorphic Table 1. 142 Gene classifier for liposarcoma subtype The genes used for each pair wise subtype comparison are grouped together. The flag column indicates which genes are unique to each subtype comparison. The values show the mean expression levels (actually the mean of the log expression levels was computed and than transformed back to absolute expression level). DD vs MLS Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 202246_s_at CDK4 23988 2401 1225 1873 2565 14153 210437_at MAGEA9 35 1132 33 53 671 29 201570_at CGI-51 * 626 1587 902 614 2069 795 210546_x_at CTAG1B /// CTAG1A 12 7701 9 34 10594 9 209242_at PEG3 251 6756 413 156 5754 500 217339_x_at CTAG1B 18 3441 13 38 5425 15 211674_x_at CTAG1B /// CTAG1A 17 10426 13 51 13213 13 206552_s_at TAC1 33 4452 36 35 2001 53 221928_at ACACB * 240 3181 3868 303 3252 1545 49452_at ACACB * 435 5099 6503 563 5419 2447 206876_at SIM1 37 3567 37 101 2201 84 204780_s_at FAS 1235 219 396 646 191 872 204086_at PRAME 84 2755 88 170 3471 77 215733_x_at CTAG2 20 3010 13 34 5238 17 220948_s_at ATP1A1 1570 577 1426 1934 830 1287 DD vs NF Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 213706_at GPD1 29 638 7680 84 1606 1528 207092_at LEP 13 41 5187 12 20 407 219398_at CIDEC 283 638 16854 318 630 3950 202246_s_at CDK4 23988 2401 1225 1873 2565 14153 203680_at PRKAR2B * 197 739 6908 383 916 2496 204997_at GPD1 * 12 108 2819 21 229 393 214456_x_at SAA1 29 26 13520 26 28 335 205913_at PLIN 47 796 18630 229 906 4635 218865_at MOSC1 * 64 331 2757 60 460 474 201425_at ALDH2 * 1249 1576 12847 1316 1720 3141 209686_at S100B 12 21 858 11 38 194 203571_s_at C10orf116 * 183 586 16948 470 478 3917 207175_at ADIPOQ 72 2295 14223 243 7265 5122 221295_at CIDEA 84 77 4705 87 83 782 219918_s_at ASPM * 320 171 8 554 271 96 DD vs PL Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 202246_s_at CDK4 23988 2401 1225 1873 2565 14153 212656_at TSFM * 3256 405 231 324 502 2192 204027_s_at METTL1 * 2725 502 395 358 564 1756 203227_s_at SAS 7613 792 996 558 645 6776 203445_s_at CTDSP2 * 12495 2796 3108 2650 1991 9964 217373_x_at MDM2 * 1322 142 101 124 136 666 213861_s_at DKFZP586D0919 3079 672 355 414 601 2254 208735_s_at CTDSP2 2587 611 566 591 462 1872 215399_s_at OS9 * 5694 682 603 704 643 3597 200714_x_at OS9 * 16599 2915 2980 2824 2710 11836 203226_s_at SAS * 3678 681 920 512 584 3462 214332_s_at TSFM * 387 127 81 83 184 324 201804_x_at CKAP1 1637 1195 1397 2995 1101 1422 205386_s_at MDM2 * 324 68 65 60 94 220 211759_x_at CKAP1 1674 1186 1341 2893 1158 1561 DD vs RC Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 216590_at GNAT3 76 207 65 73 2179 82 210546_x_at CTAG1B /// CTAG1A 12 7701 9 34 10594 9 213706_at GPD1 29 638 7680 84 1606 1528 211674_x_at CTAG1B /// CTAG1A 17 10426 13 51 13213 13 217339_x_at CTAG1B 18 3441 13 38 5425 15 202246_s_at CDK4 23988 2401 1225 1873 2565 14153 204086_at PRAME 84 2755 88 170 3471 77 204780_s_at FAS 1235 219 396 646 191 872 203549_s_at LPL * 751 19516 14251 1911 22183 10245 215733_x_at CTAG2 20 3010 13 34 5238 17 209242_at PEG3 251 6756 413 156 5754 500 207175_at ADIPOQ 72 2295 14223 243 7265 5122 206876_at SIM1 37 3567 37 101 2201 84 208735_s_at CTDSP2 2587 611 566 591 462 1872 212551_at CAP2 204 2024 575 168 1618 275 DD vs WD Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 200755_s_at CALU 1292 594 561 1626 749 554 205913_at PLIN 47 796 18630 229 906 4635 212271_at MAPK1 * 971 718 611 928 733 567 203929_s_at MAPT * 119 131 548 241 112 383 207175_at ADIPOQ 72 2295 14223 243 7265 5122 203295_s_at ATP1A2 82 175 470 25 241 454 202779_s_at UBE2S * 783 494 300 1477 780 286 220955_x_at RAB23 * 526 70 125 731 52 107 201675_at AKAP1 * 509 2266 1298 959 3644 1214 200757_s_at CALU * 3214 1729 1406 3831 2004 1695 203296_s_at ATP1A2 * 88 390 1107 39 511 1072 213571_s_at EIF4E2 * 1184 853 516 973 852 642 212978_at LRRC8B * 208 503 527 256 973 487 219140_s_at RBP4 * 46 1459 10513 260 2595 3698 206401_s_at MAPT * 25 20 286 65 36 109 MLS vs NF Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 210546_x_at CTAG1B /// CTAG1A 12 7701 9 34 10594 9 211674_x_at CTAG1B /// CTAG1A 17 10426 13 51 13213 13 217339_x_at CTAG1B 18 3441 13 38 5425 15 211700_s_at TRO * 355 656 9 274 444 66 214456_x_at SAA1 29 26 13520 26 28 335 211401_s_at FGFR2 * 22 608 12 30 165 21 204914_s_at SOX11 133 3243 16 81 3229 19 215733_x_at CTAG2 20 3010 13 34 5238 17 204086_at PRAME 84 2755 88 170 3471 77 203639_s_at FGFR2 * 32 1173 7 36 460 47 212265_at QKI * 3195 4251 1337 3637 3722 1941 209242_at PEG3 251 6756 413 156 5754 500 214091_s_at GPX3 1710 1444 20792 3181 1177 7297 209612_s_at ADH1B 150 55 14052 185 51 2035 209613_s_at ADH1B * 56 22 6678 92 38 1198 MLS vs PL Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 219331_s_at FLJ10748 80 854 82 88 542 104 209242_at PEG3 251 6756 413 156 5754 500 206552_s_at TAC1 33 4452 36 35 2001 53 218802_at FLJ20647 * 674 187 575 1089 208 623 213307_at SHANK2 152 864 140 101 1175 141 201804_x_at CKAP1 1637 1195 1397 2995 1101 1422 213252_at SH3MD1 * 443 1129 276 306 1255 460 219046_s_at PKNOX2 * 208 569 167 216 392 277 213308_at SHANK2 66 946 69 17 1347 67 201549_x_at JARID1B * 639 1779 438 435 1174 593 202813_at TARBP1 * 399 848 281 265 596 432 211202_s_at JARID1B * 784 2010 520 494 1453 704 220948_s_at ATP1A1 1570 577 1426 1934 830 1287 220199_s_at C1orf80 * 628 2278 957 536 1702 677 212033_at RBM25 * 1439 3214 1153 1207 2140 1398 MLS vs RC Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 219937_at TRHDE * 45 762 140 84 248 270 208971_at UROD * 611 626 561 697 1079 580 216590_at GNAT3 76 207 65 73 2179 82 219281_at MSRA * 352 431 694 409 859 542 202363_at SPOCK * 658 365 625 1458 1741 618 209137_s_at USP10 * 540 362 635 419 863 426 208836_at ATP1B3 * 3181 2461 4757 3168 4802 2814 210665_at TFPI * 96 105 86 117 50 81 202425_x_at PPP3CA 1063 1351 765 848 2985 795 208131_s_at PTGIS * 1567 1759 1699 576 375 596 202158_s_at CUGBP2 * 395 720 545 462 369 659 201642_at IFNGR2 * 3103 2896 3275 3142 1917 2206 217913_at VPS4A * 782 521 952 661 849 804 202921_s_at ANK2 * 232 428 476 265 190 311 220357_s_at SGK2 * 228 307 477 250 703 371 MLS vs WD Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 210546_x_at CTAG1B /// CTAG1A 12 7701 9 34 10594 9 211674_x_at CTAG1B /// CTAG1A 17 10426 13 51 13213 13 215733_x_at CTAG2 20 3010 13 34 5238 17 217339_x_at CTAG1B 18 3441 13 38 5425 15 204915_s_at SOX11 104 2267 15 123 2065 17 204914_s_at SOX11 133 3243 16 81 3229 19 206552_s_at TAC1 33 4452 36 35 2001 53 213308_at SHANK2 66 946 69 17 1347 67 204913_s_at SOX11 132 2040 25 99 1725 70 204086_at PRAME 84 2755 88 170 3471 77 208792_s_at CLU 1074 152 3716 1331 146 3595 203517_at MTX2 * 470 998 452 759 827 473 212551_at CAP2 204 2024 575 168 1618 275 210437_at MAGEA9 35 1132 33 53 671 29 209242_at PEG3 251 6756 413 156 5754 500 NF vs PL Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 212141_at MCM4 * 281 204 26 469 374 65 209686_at S100B 12 21 858 11 38 194 205046_at CENPE 119 38 10 291 73 27 222036_s_at MCM4 * 556 513 110 841 695 328 213524_s_at G0S2 * 565 2806 18229 1119 4195 5381 207092_at LEP 13 41 5187 12 20 407 205428_s_at CALB2 * 133 155 2933 121 176 622 219398_at CIDEC 283 638 16854 318 630 3950 201291_s_at TOP2A 565 235 4 1072 550 138 222077_s_at RACGAP1 * 664 379 101 1009 513 330 220736_at SLC19A3 * 93 155 2796 105 164 356 39854_r_at PNPLA2 * 892 1612 7035 1182 1698 3841 218124_at RetSat * 569 674 5114 576 916 2024 209086_x_at MCAM * 353 630 2420 440 503 734 222083_at GLYAT * 13 16 522 11 14 44 NF vs RC Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 211674_x_at CTAG1B /// CTAG1A 17 10426 13 51 13213 13 210546_x_at CTAG1B /// CTAG1A 12 7701 9 34 10594 9 217339_x_at CTAG1B 18 3441 13 38 5425 15 ADH1A /// ADH1B /// 209614_at ADH1C * 26 20 1360 62 14 260 204086_at PRAME 84 2755 88 170 3471 77 215733_x_at CTAG2 20 3010 13 34 5238 17 216590_at GNAT3 76 207 65 73 2179 82 205498_at GHR * 635 475 4778 611 371 3064 218039_at NUSAP1 939 573 134 1173 879 380 219398_at CIDEC 283 638 16854 318 630 3950 209612_s_at ADH1B 150 55 14052 185 51 2035 214456_x_at SAA1 29 26 13520 26 28 335 221295_at CIDEA 84 77 4705 87 83 782 214091_s_at GPX3 1710 1444 20792 3181 1177 7297 202503_s_at KIAA0101 * 1149 1401 120 2124 1738 593 NF vs WD Dediff AffyID Gene Symbol Flag LS MLS NF PL RC WD 207039_at CDKN2A * 774 283 82 743 363 735 201291_s_at TOP2A 565 235 4 1072 550 138 37022_at PRELP * 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  • Seromic Profiling of Ovarian and Pancreatic Cancer

    Seromic Profiling of Ovarian and Pancreatic Cancer

    Seromic profiling of ovarian and pancreatic cancer Sacha Gnjatica,1, Erika Rittera, Markus W. Büchlerb, Nathalia A. Gieseb, Benedikt Brorsc, Claudia Freid, Anne Murraya, Niels Halamad, Inka Zörnigd, Yao-Tseng Chene, Christopher Andrewsf, Gerd Rittera, Lloyd J. Olda,1, Kunle Odunsig,2, and Dirk Jägerd,2 aLudwig Institute for Cancer Research Ltd, Memorial-Sloan Kettering Cancer Center, New York, NY 10065; bDepartment of General Surgery, cDepartment of Theoretical Bioinformatics, and dMedizinische Onkologie, Nationales Centrum für Tumorerkrankungen, University Hospital Heidelberg, Heidelberg D-69120, Germany; eDepartment of Pathology, Weill Medical College of Cornell University, New York, NY 10065; and fDepartment of Biostatistics and gDepartment of Gynecologic Oncology, Roswell Park Cancer Institute, Buffalo, NY 14263 Contributed by Lloyd J. Old, December 10, 2009 (sent for review August 20, 2009) Autoantibodies, a hallmark of both autoimmunity and cancer, analyzing a series of lung cancer and healthy control sera on a represent an easily accessible surrogate for measuring adaptive small array (329 proteins) for antigen reactivity using this anti- immune responses to cancer. Sera can now be assayed for re- body profiling method, referred to here as “seromics,” we were activity against thousands of proteins using microarrays, but there able to detect known antigens with sensitivity and specificity is no agreed-upon standard to analyze results. We developed a set comparable to ELISA, as well as new antigens that are now of tailored quality control and normalization procedures based on under further investigation. Contrary to gene microarrays where ELISA validation to allow patient comparisons and determination changes in the pattern of gene expression are detected in clus- of individual cutoffs for specificity and sensitivity.
  • Multi-Functionality of Proteins Involved in GPCR and G Protein Signaling: Making Sense of Structure–Function Continuum with In

    Multi-Functionality of Proteins Involved in GPCR and G Protein Signaling: Making Sense of Structure–Function Continuum with In

    Cellular and Molecular Life Sciences (2019) 76:4461–4492 https://doi.org/10.1007/s00018-019-03276-1 Cellular andMolecular Life Sciences REVIEW Multi‑functionality of proteins involved in GPCR and G protein signaling: making sense of structure–function continuum with intrinsic disorder‑based proteoforms Alexander V. Fonin1 · April L. Darling2 · Irina M. Kuznetsova1 · Konstantin K. Turoverov1,3 · Vladimir N. Uversky2,4 Received: 5 August 2019 / Revised: 5 August 2019 / Accepted: 12 August 2019 / Published online: 19 August 2019 © Springer Nature Switzerland AG 2019 Abstract GPCR–G protein signaling system recognizes a multitude of extracellular ligands and triggers a variety of intracellular signal- ing cascades in response. In humans, this system includes more than 800 various GPCRs and a large set of heterotrimeric G proteins. Complexity of this system goes far beyond a multitude of pair-wise ligand–GPCR and GPCR–G protein interactions. In fact, one GPCR can recognize more than one extracellular signal and interact with more than one G protein. Furthermore, one ligand can activate more than one GPCR, and multiple GPCRs can couple to the same G protein. This defnes an intricate multifunctionality of this important signaling system. Here, we show that the multifunctionality of GPCR–G protein system represents an illustrative example of the protein structure–function continuum, where structures of the involved proteins represent a complex mosaic of diferently folded regions (foldons, non-foldons, unfoldons, semi-foldons, and inducible foldons). The functionality of resulting highly dynamic conformational ensembles is fne-tuned by various post-translational modifcations and alternative splicing, and such ensembles can undergo dramatic changes at interaction with their specifc partners.
  • Genome-Wide Analysis of DNA Methylation, Copy Number Variation, and Gene Expression in Monozygotic Twins Discordant for Primary Biliary Cirrhosis

    Genome-Wide Analysis of DNA Methylation, Copy Number Variation, and Gene Expression in Monozygotic Twins Discordant for Primary Biliary Cirrhosis

    UC Davis UC Davis Previously Published Works Title Genome-wide analysis of DNA methylation, copy number variation, and gene expression in monozygotic twins discordant for primary biliary cirrhosis. Permalink https://escholarship.org/uc/item/34d4m5nk Journal Frontiers in immunology, 5(MAR) ISSN 1664-3224 Authors Selmi, Carlo Cavaciocchi, Francesca Lleo, Ana et al. Publication Date 2014 DOI 10.3389/fimmu.2014.00128 Peer reviewed eScholarship.org Powered by the California Digital Library University of California ORIGINAL RESEARCH ARTICLE published: 28 March 2014 doi: 10.3389/fimmu.2014.00128 Genome-wide analysis of DNA methylation, copy number variation, and gene expression in monozygotic twins discordant for primary biliary cirrhosis Carlo Selmi 1,2*, Francesca Cavaciocchi 1,3, Ana Lleo4, Cristina Cheroni 5, Raffaele De Francesco5, Simone A. Lombardi 1, Maria De Santis 1,3, Francesca Meda1, Maria Gabriella Raimondo1, Chiara Crotti 1, Marco Folci 1, Luca Zammataro1, Marlyn J. Mayo6, Nancy Bach7, Shinji Shimoda8, Stuart C. Gordon9, Monica Miozzo10,11, Pietro Invernizzi 4, Mauro Podda1, Rossana Scavelli 5, Michelle R. Martin12, Michael F. Seldin13,14, Janine M. LaSalle 12 and M. Eric Gershwin2 1 Division of Rheumatology and Clinical Immunology, Humanitas Clinical and Research Center, Milan, Italy 2 Division of Rheumatology, Allergy, and Clinical Immunology, University of California at Davis, Davis, CA, USA 3 BIOMETRA Department, University of Milan, Milan, Italy 4 Liver Unit and Center for Autoimmune Liver Diseases, Humanitas Clinical and Research Center, Milan, Italy 5 National Institute of Molecular Genetics (INGM), Milan, Italy 6 University of Texas Southwestern, Dallas, TX, USA 7 Mt. Sinai University, NewYork, NY, USA 8 Clinical Research Center, National Nagasaki Medical Center, Nagasaki, Japan 9 Henry Ford Hospital, Detroit, MI, USA 10 Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy 11 Division of Pathology, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy 12 Genome Center and M.I.N.D.
  • Downloaded 18 July 2014 with a 1% False Discovery Rate (FDR)

    Downloaded 18 July 2014 with a 1% False Discovery Rate (FDR)

    UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer Permalink https://escholarship.org/uc/item/0t47b9ws Author Spiciarich, David Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer by David Spiciarich A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Chemistry in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Carolyn R. Bertozzi, Co-Chair Professor David E. Wemmer, Co-Chair Professor Matthew B. Francis Professor Amy E. Herr Fall 2017 Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer © 2017 by David Spiciarich Abstract Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer by David Spiciarich Doctor of Philosophy in Chemistry University of California, Berkeley Professor Carolyn R. Bertozzi, Co-Chair Professor David E. Wemmer, Co-Chair Changes in glycosylation have long been appreciated to be part of the cancer phenotype; sialylated glycans are found at elevated levels on many types of cancer and have been implicated in disease progression. However, the specific glycoproteins that contribute to cell surface sialylation are not well characterized, specifically in bona fide human cancer. Metabolic and bioorthogonal labeling methods have previously enabled enrichment and identification of sialoglycoproteins from cultured cells and model organisms. The goal of this work was to develop technologies that can be used for detecting changes in glycoproteins in clinical models of human cancer.