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Supplementary information Figure S1 Correlation of infiltrating eosinophils and total IgE in nasal tissues of patients with CRSwNP. The percentage of infiltrating eosinophils and concentrations of total IgE were assessed in nasal tissues of CRSwNP (n=17) and correlations were analyzed by Spearman correlation. CRSwNP: chronic rhinosinusitis with nasal polyps. Figure S2: Differentially expressed mRNAs (DE-mRNAs) and differentially expressed lncRNAs (DE-lncRNAs) in nasal tissues of CRSwNP patients. (A-B) Venn diagrams depicting significant DE-mRNAs and DE-lncRNAs identified by RNA sequencing in CRSwNP with comorbid asthma (CRSwNP+AS) (n=10), CRSwNP-alone (n=10), and control (Ctrl) (n=9) subjects. The number of DE-mRNAs or DE-lncRNAs is shown in the corresponding areas. CRSwNP: chronic rhinosinusitis with nasal polyps; AS: asthma; lncRNA: long non-coding RNA; DE: differentially expressed. Figure S3: Hierarchical clustering of differentially expressed genes. Heat map showing top 500 differentially expressed genes. Background factors (gender, smoking, atopy, disease condition) of subjects are marked in the corresponding areas. CRSwNP+AS (n=10), CRSwNP-alone (n=10), and control (n=9). CRSwNP: chronic rhinosinusitis with nasal polyps; AS: asthma. Figure S4 GO biological processes enriched by common dysregulated genes in CRSwNP with comorbid asthma (CRSwNP+AS) and CRSwNP-alone. The result was visualized by cytoscape network. Node size represented gene number in node and node filled color represented P value. CRSwNP: chronic rhinosinusitis with nasal polyps; AS: asthma; GO: gene ontology. Figure S5 Gene number of modules identified by weighted gene co-expression network analysis (WGCNA). WGCNA was applied to explore the potential functions of 176 common DE-lncRNAs, based on a coexpression network of DE-lncRNAs and DE-mRNAs. Branches of the dendrogram obtained by hierarchical clustering of adjacency based similarity result in 9 modules, labelled with distinct colours. Gene number of each module is shown above the corresponding column. lncRNA: long non-coding RNA; DE: differentially expressed. Figure S6 The expression and potential functions of LINC01146. (A) The expression of LINC01146 in CRSwNP with comorbid asthma (CRSwNP+AS), CRSwNP-alone and control group. ** P<0.01. (B) LINC01146 gene locus and adjacent genes. Spearman correlation analysis was performed between expression of LINC01146 and GALC. (C) Top 10 KEGG pathways significantly enriched by coexpressed mRNAs of LINC01146. Values of P<0.05 were considered statistically significant. CRSwNP: chronic rhinosinusitis with nasal polyps; AS: asthma. Figure S7 Gene modules identified by weighted gene co-expression network analysis (WGCNA) based on expression of differentially expressed mRNAs (DE-mRNAs) and differentially expressed lncRNAs (DE-lncRNAs) in CRSwNP with comorbid asthma (CRSwNP+AS) versus CRSwNP-alone. Branches of the dendrogram obtained by hierarchical clustering of adjacency based similarity demonstrated 7 modules, labelled with distinct colours. CRSwNP: chronic rhinosinusitis with nasal polyps; AS: asthma; lncRNA: long non-coding RNA; DE: differentially expressed. Figure S8 Asthma related genes expressed in nasal tissue of CRSwNP patients with comorbid asthma (CRSwNP+AS) and patients with CRSwNP-alone. CRSwNP+AS (n=10), CRSwNP-alone (n=10), and control (n=9). CRSwNP: chronic rhinosinusitis with nasal polyps; AS: asthma; FPKM: Fragments per kilo-base of exon per million fragments mapped. Table S1. Demographic and clinical characteristics of study subjects Control CRSwNP-alone CRSwNP + AS P value Subjects 31 99 65 NA Sex (male/female) 16/15 61/38 36/29 0.427 Age (years±SD) 49.10±13.82 44.65±11.72 45.60±11.27 0.603 Atopy (yes/no) 5/26 22/77 25/40* 0.025 Smoker (yes/no) 7/24 23/76 14/51 0.796 Recurrence (yes/no) NA 21/78 26/39* 0.009 FEV1/FVC%, median (IQR) NA 88.65 (84.14-90.96) 80.19 (68.53-85.41)* <0.001 FENO, median (IQR) NA 17.00 (12.25-23.00) 33.50 (19.00-57.00)* <0.001 Peripheral blood eosinophils (%), 1.70 (1.10-2.30) 3.00 (1.50-4.90) 6.45 (4.08-8.80)* <0.001 median (IQR) Peripheral blood total IgE (kU/l), 14.40 (22.50-60.80) 53.90 (22.37-90.88) 143.00 (73.15-308.0)* <0.001 median (IQR) Tissue eosinophils (%), NA 9.58 (3.18-34.55) 34.78 (26.34-54.45)* 0.002 median (IQR) Tissue neutrophils (%), NA 10.16 (2.75-16.22) 7.21 (1.58-16.34) 0.425 median (IQR) Tissue lymphocytes (%), NA 56.42 (32.49-69.23) 38.79 (19.20-45.06)* 0.037 median (IQR) Tissue plasma cells (%), NA 12.75 (7.95-21.26) 16.54 (12.74-21.81) 0.515 median (IQR) Tissue total IgE(ng/ml), 3.48 (2.03-8.64) 9.47 (4.63-19.98) 90.98 (39.91-137.55)* <0.001 median (IQR) * CRSwNP+AS vs CRSwNP-alone. CRSwNP: chronic rhinosinusitis with nasal polyps; AS: asthma. Table S2. Common DE-mRNAs and DE-lncRNAs shared by CRSwNP-alone versus control and CRSwNP+AS versus control CRSwNP-alone vs. Control CRSwNP+AS vs. Control Gene identifier Gene name Gene type Log2(Fold Log2(Fold P value P value Change) Change) ENSG00000000938 FGR mRNA 1.363 2.991E-07 2.002 1.330E-12 ENSG00000002587 HS3ST1 mRNA 1.320 5.218E-10 1.194 2.934E-07 ENSG00000002726 AOC1 mRNA 2.582 2.067E-05 2.715 1.269E-05 ENSG00000002933 TMEM176A mRNA 1.542 2.176E-13 1.642 1.457E-12 ENSG00000003249 DBNDD1 mRNA -2.091 1.412E-15 -1.830 2.289E-10 ENSG00000004468 CD38 mRNA 1.502 1.786E-07 1.591 1.429E-04 ENSG00000004660 CAMKK1 mRNA -1.914 9.762E-17 -1.172 1.212E-05 ENSG00000004848 ARX mRNA -2.866 1.009E-04 -2.716 3.124E-04 ENSG00000005059 CCDC109B mRNA 1.647 2.449E-12 1.184 6.296E-04 ENSG00000005379 BZRAP1 mRNA -1.948 1.154E-13 -1.047 1.458E-03 ENSG00000005513 SOX8 mRNA -4.699 6.168E-43 -3.084 6.873E-12 ENSG00000005981 ASB4 mRNA -1.866 1.121E-04 -1.500 1.084E-03 ENSG00000006071 ABCC8 mRNA -2.735 2.813E-09 -2.024 4.787E-04 ENSG00000006074 CCL18 mRNA 6.938 2.438E-12 8.880 5.284E-38 ENSG00000006128 TAC1 mRNA -2.891 5.193E-05 -2.233 4.927E-03 ENSG00000006377 DLX6 mRNA -1.314 1.458E-03 -1.520 1.202E-03 ENSG00000006576 PHTF2 mRNA 1.753 1.203E-08 1.244 1.834E-03 ENSG00000006740 ARHGAP44 mRNA -1.439 8.561E-08 -1.676 7.124E-11 ENSG00000007216 SLC13A2 mRNA -4.510 5.905E-46 -3.832 1.854E-16 ENSG00000007314 SCN4A mRNA -2.535 2.775E-12 -2.756 1.508E-09 ENSG00000007516 BAIAP3 mRNA -1.887 2.231E-04 -1.986 5.195E-05 ENSG00000007968 E2F2 mRNA 1.821 2.022E-05 1.966 4.043E-06 ENSG00000008283 CYB561 mRNA -1.678 1.704E-15 -1.630 3.489E-13 ENSG00000008441 NFIX mRNA -1.903 9.730E-24 -1.456 5.071E-08 ENSG00000008516 MMP25 mRNA 1.553 3.733E-05 2.522 2.782E-08 ENSG00000008735 MAPK8IP2 mRNA -2.214 3.098E-13 -1.312 5.158E-04 ENSG00000009694 TENM1 mRNA -1.790 3.207E-04 -2.639 1.646E-06 ENSG00000009790 TRAF3IP3 mRNA 1.452 2.909E-06 1.352 3.834E-05 ENSG00000010610 CD4 mRNA 1.282 3.592E-07 1.740 6.099E-10 ENSG00000011347 SYT7 mRNA -2.528 1.340E-10 -2.657 2.325E-11 ENSG00000011426 ANLN mRNA 2.990 1.226E-14 2.356 3.240E-07 ENSG00000011600 TYROBP mRNA 2.102 3.467E-16 2.386 1.772E-17 ENSG00000012223 LTF mRNA -5.519 4.364E-32 -4.285 1.084E-11 ENSG00000012660 ELOVL5 mRNA 1.381 7.457E-09 1.399 3.331E-06 ENSG00000012779 ALOX5 mRNA 1.197 5.690E-08 1.668 1.484E-10 ENSG00000015285 WAS mRNA 1.317 6.102E-07 1.644 4.939E-08 ENSG00000016391 CHDH mRNA -2.514 2.192E-22 -1.529 6.757E-08 ENSG00000016490 CLCA1 mRNA 4.994 6.521E-03 8.494 1.837E-10 ENSG00000017373 SRCIN1 mRNA -2.694 2.189E-14 -2.286 5.552E-10 ENSG00000018280 SLC11A1 mRNA 1.581 1.296E-03 1.963 2.199E-05 ENSG00000018625 ATP1A2 mRNA -1.712 1.086E-08 -1.218 1.092E-04 ENSG00000019169 MARCO mRNA 3.442 4.480E-09 7.840 3.180E-15 ENSG00000019505 SYT13 mRNA -3.812 1.160E-10 -3.814 8.564E-14 ENSG00000021300 PLEKHB1 mRNA -2.130 4.145E-11 -1.657 4.254E-08 ENSG00000023445 BIRC3 mRNA 1.512 7.837E-06 1.804 3.521E-04 ENSG00000024526 DEPDC1 mRNA 3.470 6.694E-12 2.725 1.083E-04 ENSG00000026103 FAS mRNA 1.794 1.612E-12 1.251 8.038E-04 ENSG00000026508 CD44 mRNA 1.802 2.109E-09 1.758 3.464E-06 ENSG00000026751 SLAMF7 mRNA 1.426 4.517E-05 1.324 9.837E-04 ENSG00000029153 ARNTL2 mRNA 1.924 9.384E-19 1.612 1.602E-08 ENSG00000029559 IBSP mRNA -4.158 5.171E-05 -3.140 4.729E-03 ENSG00000029993 HMGB3 mRNA 1.300 1.302E-08 1.069 1.485E-04 ENSG00000034063 UHRF1 mRNA 1.666 7.620E-05 1.296 1.491E-03 ENSG00000035499 DEPDC1B mRNA 2.913 1.450E-10 2.530 9.649E-07 ENSG00000037749 MFAP3 mRNA 1.336 4.480E-07 1.071 4.651E-03 ENSG00000038427 VCAN mRNA 1.556 3.348E-09 1.184 2.950E-04 ENSG00000038945 MSR1 mRNA 2.761 2.462E-16 2.410 3.161E-12 ENSG00000041515 MYO16 mRNA 3.342 3.981E-13 1.884 2.325E-04 ENSG00000043039 BARX2 mRNA -3.892 3.110E-23 -3.190 9.519E-11 ENSG00000043462 LCP2 mRNA 2.326 1.943E-14 2.292 8.401E-11 ENSG00000043591 ADRB1 mRNA -1.214 9.870E-04 -1.136 6.207E-03 ENSG00000044524 EPHA3 mRNA -1.995 6.260E-07 -1.922 3.654E-05 ENSG00000048028 USP28 mRNA 1.104 6.469E-07 1.206 9.301E-05 ENSG00000048540 LMO3 mRNA -1.489 1.529E-02 -1.868 2.132E-03 ENSG00000048740 CELF2 mRNA 1.256 1.417E-05 1.284 2.157E-04 ENSG00000049089 COL9A2 mRNA -2.404 5.416E-17 -1.485 3.712E-04 ENSG00000049130 KITLG mRNA 1.591 1.341E-09 1.165 8.655E-04 ENSG00000049192 ADAMTS6 mRNA 1.462 1.401E-04 1.364 9.474E-04 ENSG00000049768 FOXP3 mRNA 2.268 1.465E-10 1.568 1.303E-04 ENSG00000051180 RAD51 mRNA 1.630 3.170E-05 1.065 6.428E-03 ENSG00000052344 PRSS8 mRNA -2.359 1.382E-22 -2.084 1.673E-10 ENSG00000054179 ENTPD2 mRNA -1.438 5.814E-06 -1.537 2.124E-07 ENSG00000054277 OPN3 mRNA 1.112 1.749E-05 1.087 7.740E-04 ENSG00000054803 CBLN4 mRNA -1.874 3.646E-03 -2.306 8.354E-04 ENSG00000057149 SERPINB3 mRNA 2.646 1.878E-24 1.609 1.912E-05 ENSG00000057294 PKP2 mRNA -1.728 7.620E-17 -1.559 9.081E-09 ENSG00000058453 CROCC mRNA -2.541 4.145E-08
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  • Evaluation of Vitamin D Biosynthesis and Pathway Target Genes Reveals

    Evaluation of Vitamin D Biosynthesis and Pathway Target Genes Reveals

    UC Irvine UC Irvine Previously Published Works Title Evaluation of vitamin D biosynthesis and pathway target genes reveals UGT2A1/2 and EGFR polymorphisms associated with epithelial ovarian cancer in African American Women. Permalink https://escholarship.org/uc/item/36m4z61b Journal Cancer medicine, 8(5) ISSN 2045-7634 Authors Grant, Delores J Manichaikul, Ani Alberg, Anthony J et al. Publication Date 2019-05-01 DOI 10.1002/cam4.1996 License https://creativecommons.org/licenses/by/4.0/ 4.0 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Received: 31 July 2018 | Revised: 3 December 2018 | Accepted: 8 January 2019 DOI: 10.1002/cam4.1996 ORIGINAL RESEARCH Evaluation of vitamin D biosynthesis and pathway target genes reveals UGT2A1/2 and EGFR polymorphisms associated with epithelial ovarian cancer in African American Women Delores J. Grant1 | Ani Manichaikul2 | Anthony J. Alberg3 | Elisa V. Bandera4 | Jill Barnholtz‐Sloan5 | Melissa Bondy6 | Michele L. Cote7 | Ellen Funkhouser8 | Patricia G. Moorman9 | Lauren C. Peres2 | Edward S. Peters10 | Ann G. Schwartz7 | Paul D. Terry11 | Xin‐Qun Wang12 | Temitope O. Keku13 | Cathrine Hoyo14 | Andrew Berchuck15 | Dale P. Sandler16 | Jack A. Taylor16 | Katie M. O’Brien16 | Digna R. Velez Edwards17 | Todd L. Edwards18 | Alicia Beeghly‐Fadiel19 | Nicolas Wentzensen20 | Celeste Leigh Pearce21,22 | Anna H. Wu22 | Alice S. Whittemore23,24 | Valerie McGuire23 | Weiva Sieh25,26 | Joseph H. Rothstein25,26 | Francesmary Modugno27,28,29 | Roberta Ness30 | Kirsten Moysich31 | Mary Anne Rossing32,33 | Jennifer A. Doherty34 | Thomas A. Sellers35 | Jennifer B. Permuth‐Way35 | Alvaro N. Monteiro35 | Douglas A. Levine36,37 | Veronica Wendy Setiawan38 | Christopher A. Haiman38 | Loic LeMarchand39 | Lynne R.
  • Cam41996 Am.Pdf

    Cam41996 Am.Pdf

    Evaluation of vitamin D biosynthesis and pathway target genes reveals UGT2A1/2 and EGFR polymorphisms associated with epithelial ovarian cancer in African American Women RUNNING TITLE: Genetic association with variants in the vitamin D pathway KEYWORDS: ovarian cancer, genetic association, vitamin D pathway, African ancestry risk Delores J. Grant1, Ani Manichaikul2, Anthony J. Alberg3, Elisa V. Bandera4, Jill Barnholtz-Sloan5, Melissa Bondy6, Michele L. Cote7, Ellen Funkhouser8, Patricia G. Moorman9, Lauren C. Peres2, Edward S. Peters10, Ann G. Schwartz7, Paul D. Terry11, Xin-Qun Wang2, Temitope O. Keku12, Cathrine Hoyo13, Andrew Berchuck14, Dale P. Sandler15, Jack A. Taylor15, Katie M. O’Brien16, Digna R. Velez Edwards17, Todd L. Edwards18, Alicia Beeghly-Fadiel19, Nicolas Wentzensen20, Celeste Leigh Pearce21,22, Anna H. Wu22, Alice S. Whittemore23,24, Valerie McGuire23, Weiva Sieh25,26, Joseph H. Rothstein25,26, Francesmary Modugno27,28,29, Roberta Ness30, Kirsten Moysich31, Mary Anne Rossing32,33, Jennifer A. Doherty34, Thomas A. Sellers35, Jennifer B. Permuth-Way35, Alvaro N. Monteiro35, Douglas A. Levine36,37, Veronica Wendy Setiawan38, Christopher A. Haiman38, Loic LeMarchand39, Lynne R. Wilkens40, Beth Y. Karlan41, Usha Menon42, Susan Ramus43,44, Simon Gayther45,46, Aleksandra Gentry-Maharaj42, Kathryn L. Terry47,48, Daniel W. Cramer47,48, Ellen L. Goode49, Melissa C. Larson50, Scott H. Kaufmann51, Rikki Cannioto52, Kunle Odunsi53, John L. Etter31, Ruea-Yea Huang54, Marcus Q. Bernardini55, Alicia A. Tone55, Taymaa May55, Marc T. Goodman56,57, Pamela J. Thompson56, Michael E. Carney58, Shelley S. Tworoger59, Elizabeth M. Poole60, Diether Lambrechts61,62, Ignace Vergote63, Adriaan Vanderstichele63, Els Van Nieuwenhuysen63, Hoda Anton-Culver64, Argyrios Ziogas65, James D. Brenton66, Line Bjorge67,68, Helga B.
  • 1 Identifying Clinically-Relevant Population-Specific

    1 Identifying Clinically-Relevant Population-Specific

    Identifying Clinically-Relevant Population-Specific IsomiRs in African Americans and European Americans with Lung Cancer Thesis highlights ● Lung cancer is the leading cause of cancer-related deaths in the United States (U.S.) with over 139,000 deaths per year. African Americans (AAs) have a higher mortality and lower 5-year survival rate than European Americans (EAs). ● AAs smoke less than EAs. When they do smoke, AAs choose menthol cigarettes more than EAs (88.5% vs. 25.7%). ● Smoking-associated biomarkers have been explored at the genomic, metabolomic, and transcriptomic level, providing evidence for miRNA variants (isomiRs) as potential regulators of menthol metabolizing enzymes (MMEs). ● The miRNA and candidate isomiR, miR-374b-5p|3’a-1, had significantly higher expression in AAs that correlated with lower CYP1B1 and UGT2B4 expression. ● The miRNA and candidate isomiR, miR-374b-5p|3’a-1, had significantly higher expression in AAs upon menthol exposure. This did not correlate with lower CYP1B1 and UGT2B4 expression, suggesting other miRNAs, isomiRs, or menthol metabolizing enzymes may be involved. ● Adopting a precision medicine approach to upregulate miR-374b isomiRs may preferentially benefit AAs, and help to reduce mortality and survival disparities. Honors Thesis Proposal by Savanna Touré (’21) For the Academic Year (2020-2021) Advisor: Dr. Khadijah A. Mitchell Committee: Drs. Elaine Reynolds and Daniel Griffith 1 I. TABLE OF CONTENTS II. Biographical Sketch……………………………………………………………………….5 III. Abstract…………………………………………………………………………………....7 IV. Introduction……………………………………………………………………….……….8 A. Racial disparities in lung cancer mortality and survival B. Known behavioral determinants of racial disparities in lung cancer: smoking C. Known biological determinants of racial disparities in lung cancer: genomic and metabolomic level D.
  • (UGT) Genes in Human Cancers and Their Association with Clinical Outcomes

    (UGT) Genes in Human Cancers and Their Association with Clinical Outcomes

    cancers Article The Expression Profiles and Deregulation of UDP-Glycosyltransferase (UGT) Genes in Human Cancers and Their Association with Clinical Outcomes Dong Gui Hu 1,* , Shashikanth Marri 2, Peter I. Mackenzie 1, Julie-Ann Hulin 1, Ross A. McKinnon 1 and Robyn Meech 1 1 Dicipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia; peter.mackenzie@flinders.edu.au (P.I.M.); julieann.hulin@flinders.edu.au (J.-A.H.); ross.mckinnon@flinders.edu.au (R.A.M.); robyn.meech@flinders.edu.au (R.M.) 2 Dicipline of Molecular Medicine and Pathology, College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia; shashikanth.marri@flinders.edu.au * Correspondence: donggui.hu@flinders.edu.au; Tel.: +61-08-82043085 Simple Summary: The human UDP-glycosyltransferase (UGT) superfamily plays a critical role in the metabolism of numerous endogenous and exogenous small lipophilic compounds, including carcinogens, drugs, and bioactive molecules with pro- or anti-cancer activity. Previous studies have documented the expression of UGT genes in several cancers derived from drug-metabolizing organs (e.g., liver, colon, kidney). The present study represents the first to comprehensively assess the expression profiles of UGT genes and their impact on patient survival in nearly 30 different cancers Citation: Hu, D.G.; Marri, S.; primarily derived from non-drug-metabolizing organs. Briefly, our comprehensive analysis of the Mackenzie, P.I.; Hulin, J.-A.; transcriptomic (RNAseq) and clinical datasets of 9514 patients from 33 different cancers shows McKinnon, R.A.; Meech, R. The the widespread expression of UGT genes, indicative of active drug metabolism within the tumor Expression Profiles and Deregulation through the UGT conjugation pathway.