An All-To-All Approach to the Identification of Sequence-Specific Readers for Epigenetic DNA Modifications on Cytosine
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Figure 2S 4 7 A - C 080125 CSCs 080418 CSCs - + IFN-a 48 h + IFN-a 48 h + IFN-a 72 h 6 + IFN-a 72 h 3 5 MRFI 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 7 B 13 080125 FBS - D 080418 FBS - + IFN-a 48 h 12 + IFN-a 48 h + IFN-a 72 h + IFN-a 72 h 6 080125 FBS 11 10 5 9 8 4 7 6 3 MRFI 5 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 Molecule Molecule FIGURE 4S FIGURE 5S Panel A Panel B FIGURE 6S A B C D Supplemental Results Table 1S. Modulation by IFN-α of APM in GBM CSC and FBS tumor cell lines. Molecule * Cell line IFN-α‡ HLA β2-m# HLA LMP TAP1 TAP2 class II A A HC§ 2 7 10 080125 CSCs - 1∞ (1) 3 (65) 2 (91) 1 (2) 6 (47) 2 (61) 1 (3) 1 (2) 1 (3) + 2 (81) 11 (80) 13 (99) 1 (3) 8 (88) 4 (91) 1 (2) 1 (3) 2 (68) 080125 FBS - 2 (81) 4 (63) 4 (83) 1 (3) 6 (80) 3 (67) 2 (86) 1 (3) 2 (75) + 2 (99) 14 (90) 7 (97) 5 (75) 7 (100) 6 (98) 2 (90) 1 (4) 3 (87) 080418 CSCs - 2 (51) 1 (1) 1 (3) 2 (47) 2 (83) 2 (54) 1 (4) 1 (2) 1 (3) + 2 (81) 3 (76) 5 (75) 2 (50) 2 (83) 3 (71) 1 (3) 2 (87) 1 (2) 080418 FBS - 1 (3) 3 (70) 2 (88) 1 (4) 3 (87) 2 (76) 1 (3) 1 (3) 1 (2) + 2 (78) 7 (98) 5 (99) 2 (94) 5 (100) 3 (100) 1 (4) 2 (100) 1 (2) 070104 CSCs - 1 (2) 1 (3) 1 (3) 2 (78) 1 (3) 1 (2) 1 (3) 1 (3) 1 (2) + 2 (98) 8 (100) 10 (88) 4 (89) 3 (98) 3 (94) 1 (4) 2 (86) 2 (79) * expression of APM molecules was evaluated by intracellular staining and cytofluorimetric analysis; ‡ cells were treatead or not (+/-) for 72 h with 1000 IU/ml of IFN-α; # β-2 microglobulin; § β-2 microglobulin-free HLA-A heavy chain; ∞ values are indicated as ratio between the mean of fluorescence intensity of cells stained with the selected mAb and that of the negative control; bold values indicate significant MRFI (≥ 2). -
Combined Analysis of Chip-Seq and Gene Microarray Datasets Identify the E2-Mediated Genes in Erα-Dependent Manner in Osteosarcoma
ONCOLOGY REPORTS 38: 2335-2342, 2017 Combined analysis of ChIP-seq and gene microarray datasets identify the E2-mediated genes in ERα-dependent manner in osteosarcoma KANGSONG TIAN1, WEI QI1, QIAN YAN1, FEnG ZHanG1, DELEI SONG1, HaIyanG ZHanG2 and MING LV1 1Trauma Department of Orthopedics, 2Microscopic Department of Orthopedics, Zibo Central Hospital, Zibo, Shandong 255036, P.R. China Received March 14, 2017; Accepted August 11, 2017 DOI: 10.3892/or.2017.5914 Abstract. Osteosarcoma is a common bone tumor which is Introduction affected by E2, the most representative estrogen. Gene regula- tion function of E2 is highly dependent on estrogen receptor. ESR1, also known as ER-alpha or ERα, is an important The purpose of this study was to explore the gene regulation estrogen receptor (ER) and involves in the gene regula- patterns of E2 through estrogen receptor α (ESR1) in osteo- tion in various diseases and biological processes, including sarcoma based on the combined analysis of ChIP-seq and breast cancer (1,2), osteosarcoma (3) and cell growth (4). gene microarray. All of the datasets were downloaded from 17β-estradiol (E2) is one of the most representative estrogens the Gene Expression Omnibus (GEO). Differential expression responsible for the development and reproductive capability genes (DEGs) in E2 treated U2OS cells expressing ESR1 of female characteristics (5). Besides, it participates in the (U2OS-ERα) compared with those treated with vehicle were progression of many diseases, for example, E2 could regulate obtained based on R programming software. ESR1-specific the proliferation of breast cancer cells through focal adhesion binding sites (peaks) in E2 treated U2OS cells were identified and chemokine signaling pathways (6); through autophagy through MACS. -
Core Transcriptional Regulatory Circuitries in Cancer
Oncogene (2020) 39:6633–6646 https://doi.org/10.1038/s41388-020-01459-w REVIEW ARTICLE Core transcriptional regulatory circuitries in cancer 1 1,2,3 1 2 1,4,5 Ye Chen ● Liang Xu ● Ruby Yu-Tong Lin ● Markus Müschen ● H. Phillip Koeffler Received: 14 June 2020 / Revised: 30 August 2020 / Accepted: 4 September 2020 / Published online: 17 September 2020 © The Author(s) 2020. This article is published with open access Abstract Transcription factors (TFs) coordinate the on-and-off states of gene expression typically in a combinatorial fashion. Studies from embryonic stem cells and other cell types have revealed that a clique of self-regulated core TFs control cell identity and cell state. These core TFs form interconnected feed-forward transcriptional loops to establish and reinforce the cell-type- specific gene-expression program; the ensemble of core TFs and their regulatory loops constitutes core transcriptional regulatory circuitry (CRC). Here, we summarize recent progress in computational reconstitution and biologic exploration of CRCs across various human malignancies, and consolidate the strategy and methodology for CRC discovery. We also discuss the genetic basis and therapeutic vulnerability of CRC, and highlight new frontiers and future efforts for the study of CRC in cancer. Knowledge of CRC in cancer is fundamental to understanding cancer-specific transcriptional addiction, and should provide important insight to both pathobiology and therapeutics. 1234567890();,: 1234567890();,: Introduction genes. Till now, one critical goal in biology remains to understand the composition and hierarchy of transcriptional Transcriptional regulation is one of the fundamental mole- regulatory network in each specified cell type/lineage. -
Genomic Alterations of Ground-Glass Nodular Lung Adenocarcinoma
www.nature.com/scientificreports OPEN Genomic alterations of ground- glass nodular lung adenocarcinoma Hyun Lee1, Je-Gun Joung2, Hyun-Tae Shin2, Duk-Hwan Kim3, Yujin Kim3, Hojoong Kim1, O. Jung Kwon1, Young Mog Shim4, Ho Yun Lee5, Kyung Soo Lee5, Yoon-La Choi6, 2 7 1 Received: 1 February 2018 Woong-Yang Park , D. Neil Hayes & Sang-Won Um Accepted: 30 April 2018 In-depth molecular pathogenesis of ground-glass nodular lung adenocarcinoma has not been well Published: xx xx xxxx understood. The objectives of this study were to identify genomic alterations in ground-glass nodular lung adenocarcinomas and to investigate whether viral transcripts were detected in these tumors. Nine patients with pure (n = 4) and part-solid (n = 5) ground-glass nodular adenocarcinomas were included. Six were females with a median age of 58 years. We performed targeted exon sequencing and RNA sequencing. EGFR (n = 10), IDH2 (n = 2), TP53 (n = 1), PTEN (n = 1), EPHB4 (n = 1), and BRAF (n = 1) were identifed as driver mutations by targeted exon sequencing. Vasculogenesis-associated genes including NOTCH4 and TGFBR3 expression were signifcantly downregulated in adenocarcinoma tissue versus normal tissue (adjusted P values < 0.001 for both NOTCH4 and TGFBR3). In addition, fve novel fusion gene loci were identifed in four lung adenocarcinomas. However, no signifcant virus-associated transcripts were detected in tumors. In conclusions, EGFR, IDH2, TP53, PTEN, EPHB4, and BRAF were identifed as putative driver mutations of ground-glass nodular adenocarcinomas. Five novel fusion genes were also identifed in four tumors. Viruses do not appear to be involved in the tumorigenesis of ground-glass nodular lung adenocarcinoma. -
Prospective Isolation of NKX2-1–Expressing Human Lung Progenitors Derived from Pluripotent Stem Cells
The Journal of Clinical Investigation RESEARCH ARTICLE Prospective isolation of NKX2-1–expressing human lung progenitors derived from pluripotent stem cells Finn Hawkins,1,2 Philipp Kramer,3 Anjali Jacob,1,2 Ian Driver,4 Dylan C. Thomas,1 Katherine B. McCauley,1,2 Nicholas Skvir,1 Ana M. Crane,3 Anita A. Kurmann,1,5 Anthony N. Hollenberg,5 Sinead Nguyen,1 Brandon G. Wong,6 Ahmad S. Khalil,6,7 Sarah X.L. Huang,3,8 Susan Guttentag,9 Jason R. Rock,4 John M. Shannon,10 Brian R. Davis,3 and Darrell N. Kotton1,2 2 1Center for Regenerative Medicine, and The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA. 3Center for Stem Cell and Regenerative Medicine, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas, USA. 4Department of Anatomy, UCSF, San Francisco, California, USA. 5Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA. 6Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, Massachusetts, USA. 7Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA. 8Columbia Center for Translational Immunology & Columbia Center for Human Development, Columbia University Medical Center, New York, New York, USA. 9Department of Pediatrics, Monroe Carell Jr. Children’s Hospital, Vanderbilt University, Nashville, Tennessee, USA. 10Division of Pulmonary Biology, Cincinnati Children’s Hospital, Cincinnati, Ohio, USA. It has been postulated that during human fetal development, all cells of the lung epithelium derive from embryonic, endodermal, NK2 homeobox 1–expressing (NKX2-1+) precursor cells. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
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 -
A Curated Gene List for Reporting Results of Newborn Genomic Sequencing
Original Research Article © American College of Medical Genetics and Genomics © American College of Medical Genetics and Genomics ORIGINAL RESEARCH ARTICLE A curated gene list for reporting results of newborn genomic sequencing Ozge Ceyhan-Birsoy, PhD1,2,3, Kalotina Machini, PhD1,2,3, Matthew S. Lebo, PhD1,2,3, Tim W. Yu, MD3,4,5, Pankaj B. Agrawal, MD, MMSC3,4,6, Richard B. Parad, MD, MPH3,7, Ingrid A. Holm, MD, MPH3,4, Amy McGuire, PhD8, Robert C. Green, MD, MPH3,9,10, Alan H. Beggs, PhD3,4, Heidi L. Rehm, PhD1,2,3,10; for the BabySeq Project Purpose: Genomic sequencing (GS) for newborns may enable detec- of newborn GS (nGS), and used our curated list for the first 15 new- tion of conditions for which early knowledge can improve health out- borns sequenced in this project. comes. One of the major challenges hindering its broader application Results: Here, we present our curated list for 1,514 gene–disease is the time it takes to assess the clinical relevance of detected variants associations. Overall, 954 genes met our criteria for return in nGS. and the genes they impact so that disease risk is reported appropri- This reference list eliminated manual assessment for 41% of rare vari- ately. ants identified in 15 newborns. Methods: To facilitate rapid interpretation of GS results in new- Conclusion: Our list provides a resource that can assist in guiding borns, we curated a catalog of genes with putative pediatric relevance the interpretive scope of clinical GS for newborns and potentially for their validity based on the ClinGen clinical validity classification other populations. -
Pnas.201413825SI.Pdf
Supporting Information Impens et al. 10.1073/pnas.1413825111 13 15 13 15 SI Methods beling) (Silantes Gmbh), or C6 N2 L-lysine HCl and C6 N4 L- Plasmids. pSG5-His6-SUMO1 plasmid encodes the N-terminal arginine HCl (heavy labeling) (Silantes Gmbh). L-Lysine HCl was His6-tagged mature Small ubiquitin modifier 1 (SUMO1) isoform added at its normal concentration in DMEM (146 mg/L), but the (kind gift of A. Dejean, Institut Pasteur, Paris). The pSG5-His6- concentration of L-arginine HCl was reduced to 25 mg/L (30% of SUMO1 T95R mutat was derived from this plasmid using PCR the normal concentration in DMEM) to prevent metabolic con- mutagenesis. pSG5-His6-SUMO2 was obtained by inserting the version of arginine to proline (4). Cells were kept for at least six cDNA corresponding to the human mature SUMO2 isoform population doublings to ensure complete incorporation of the la- with an N-terminal His6 tag in the pSG5 vector (Stratagene). beled lysine and arginine. 2 The pSG5-His6-SUMO2 T91R mutant was derived from this For transfections, cells were seeded in 75-cm flasks or in 6- or plasmid by PCR mutagenesis. N-terminally HA-tagged human 24-well plates at a density of 2.7 × 106 cells per flask or 3 × 105 or cDNA of ZBTB20 (Zinc finger and BTB domain containing 0.5 × 105 cells per well, respectively. The next day cells were 20) isoform 2 (UniProt identifier Q9HC78-2), HMBOX1 (Ho- transfected with Lipofectamine LTX reagents (Invitrogen) (20 μg meobox containing protein 1) isoform 1 (HMBOX1A) (UniProt of DNA per flask, 3.5 μg per well in the six-well plates, or 0.75 μg identifier Q6NT76-1), NACC1 (Nucleus accumbens-associated per well in the 24-well plates) for 48 h. -
The New Therapeutic Strategies in Pediatric T-Cell Acute Lymphoblastic Leukemia
International Journal of Molecular Sciences Review The New Therapeutic Strategies in Pediatric T-Cell Acute Lymphoblastic Leukemia Marta Weronika Lato 1 , Anna Przysucha 1, Sylwia Grosman 1, Joanna Zawitkowska 2 and Monika Lejman 3,* 1 Student Scientific Society, Laboratory of Genetic Diagnostics, Medical University of Lublin, 20-093 Lublin, Poland; [email protected] (M.W.L.); [email protected] (A.P.); [email protected] (S.G.) 2 Department of Pediatric Hematology, Oncology and Transplantology, Medical University of Lublin, 20-093 Lublin, Poland; [email protected] 3 Laboratory of Genetic Diagnostics, Medical University of Lublin, 20-093 Lublin, Poland * Correspondence: [email protected] Abstract: Childhood acute lymphoblastic leukemia is a genetically heterogeneous cancer that ac- counts for 10–15% of T-cell acute lymphoblastic leukemia (T-ALL) cases. The T-ALL event-free survival rate (EFS) is 85%. The evaluation of structural and numerical chromosomal changes is important for a comprehensive biological characterization of T-ALL, but there are currently no ge- netic prognostic markers. Despite chemotherapy regimens, steroids, and allogeneic transplantation, relapse is the main problem in children with T-ALL. Due to the development of high-throughput molecular methods, the ability to define subgroups of T-ALL has significantly improved in the last few years. The profiling of the gene expression of T-ALL has led to the identification of T-ALL subgroups, and it is important in determining prognostic factors and choosing an appropriate treatment. Novel therapies targeting molecular aberrations offer promise in achieving better first remission with the Citation: Lato, M.W.; Przysucha, A.; hope of preventing relapse. -
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. -
Primate Specific Retrotransposons, Svas, in the Evolution of Networks That Alter Brain Function
Title: Primate specific retrotransposons, SVAs, in the evolution of networks that alter brain function. Olga Vasieva1*, Sultan Cetiner1, Abigail Savage2, Gerald G. Schumann3, Vivien J Bubb2, John P Quinn2*, 1 Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, U.K 2 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK 3 Division of Medical Biotechnology, Paul-Ehrlich-Institut, Langen, D-63225 Germany *. Corresponding author Olga Vasieva: Institute of Integrative Biology, Department of Comparative genomics, University of Liverpool, Liverpool, L69 7ZB, [email protected] ; Tel: (+44) 151 795 4456; FAX:(+44) 151 795 4406 John Quinn: Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK, [email protected]; Tel: (+44) 151 794 5498. Key words: SVA, trans-mobilisation, behaviour, brain, evolution, psychiatric disorders 1 Abstract The hominid-specific non-LTR retrotransposon termed SINE–VNTR–Alu (SVA) is the youngest of the transposable elements in the human genome. The propagation of the most ancient SVA type A took place about 13.5 Myrs ago, and the youngest SVA types appeared in the human genome after the chimpanzee divergence. Functional enrichment analysis of genes associated with SVA insertions demonstrated their strong link to multiple ontological categories attributed to brain function and the disorders. SVA types that expanded their presence in the human genome at different stages of hominoid life history were also associated with progressively evolving behavioural features that indicated a potential impact of SVA propagation on a cognitive ability of a modern human. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7