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Gene expression profile of the A549 human non-small cell lung carcinoma cell line following treatment with the seeds of Descurainia sophia, a potential anticancer drug Bu-Yeo Kim, Jun Lee, Sung Joon Park, Ok-Sun Bang, and No Soo Kim Supplementary Data Figure Legends Supplementary Figure 1. Electropherogram of total RNA samples used for gene expression profiles. The A549 cells were treated with increasing concentrations of EEDS (0-20 mg/mL). After 24 h drug treatment, total RNAs were extracted from the cells, and their integrities were determined by electropherogram. RIN values range from 10 (intact) to 1(totally degraded). Supplementary Figure 2. Pathways enriched (FDR<0.01) in the Up- and Down-patterns. The genes that are shown in Table 5 and Figure 4, and involved in each pathway are highlighted in green Supplementary Figure 3. The level of similarity is represented in red with a scale bar. The color in the box of a diagonal line or “Activity” on the right panel represents the activity of the pathway. The positions and names of signaling-related pathways are colored red and the metabolism-related pathways are colored blue. Supplementary Table 1. Quality control of total RNA samples used for gene expression analysis. * EEDS (mg/mL) OD260/280 OD260/230 Ratio (28s/18s) RIN Result 0 2.04 2.22 2.3 9.8 Pass 1.25 2.03 2.28 2.2 10.0 Pass 5 2.03 2.28 2.1 10.0 Pass 20 2.04 2.23 2.0 10.0 Pass *RIN values, 10 (intact) to 1 (completely degraded). Supplementary Table 2. Full list of genes regulated by EEDS. Fold induction represents log2 expression ratio of gene compared with that of control. EEDS ( g/mL) 1.25 5 20 symbol 1.25 5 20 Pattern CAPS2 1.58 -0.076 -1.03 GOLIM4 1.033 -0.117 -0.853 DKFZp667F0711 1.172 -0.06 -0.983 AGTR1 1.422 -0.095 -1.096 RORC 1.294 -0.626 -0.624 LOC644587 1.082 0.205 -0.469 ITIH4 1.064 0.147 -0.312 IL11RA 1.007 0.055 -0.111 C2orf73 1.236 0.129 -0.234 C3orf42 1.14 0.2 -0.157 TMF1 1.028 0.118 -0.125 SYT6 1.384 0.181 -0.126 SPRR4 1.47 0.445 -0.159 ITGA10 2.656 0.935 -0.327 OR1N2 1.909 -0.365 -0.488 CYP3A5 1.171 -0.192 -0.269 SLC25A35 1.006 -0.256 -0.303 RAB17 1.087 -0.236 -0.317 LEFTY1 1.854 -0.505 -0.298 HEATR7B1 1.386 -0.402 -0.331 LOC400756 1.302 -0.198 -0.552 FAM167B 1.409 -0.185 -0.568 STAC3 1.285 -0.212 -0.485 KNG1 1.067 -0.314 -0.418 IFITM1 1.473 -0.393 -0.58 C2orf48 1.565 0.021 -0.352 NOSTRIN 1.518 -0.026 -0.541 LOC100129311 1.545 0.002 -0.564 HRCT1 1.577 -0.153 -0.457 LOC727838 1.297 -0.101 0.058 BEX4 1.65 -0.148 -0.004 TRIM42 1.498 -0.007 -0.025 CYP4F3 1.304 -0.023 -0.032 LOC100132483 1.292 -0.044 -0.004 TMEM190 1.211 -0.022 0.078 LOC646139 2.5 -0.011 0.099 PHYHIPL 1.449 -0.219 -0.197 TPPP2 2.148 -0.172 -0.432 STK38L 1.231 -0.079 -0.146 LCT 1.055 -0.217 -0.057 CSMD1 1.229 -0.262 -0.084 GLRA1 1.03 -0.632 -0.005 C3orf66 1.765 -0.6 0.153 ST8SIA2 0.996 -1.226 0.241 PEG10 1.587 -1.379 0.219 TPPP3 1.569 -0.534 0.571 SYT5 1.383 -0.5 0.428 C4orf51 1.213 -0.352 0.569 GLS 1.177 0.033 0.771 FRMD4B 1.778 0.057 1.133 FLJ42392 1.18 0.037 0.572 FGFBP1 1.84 0.082 0.997 WFDC11 1.1 -0.055 0.438 PAK7 1.206 -0.169 0.533 FAM18A 1.007 -0.123 0.503 ACHE 2.703 -0.098 0.437 SERPINA12 1.264 -0.114 0.268 SORCS1 1.395 -0.058 0.291 SLITRK1 1.044 -0.06 0.229 RIPPLY1 1.657 0.038 0.512 FLJ39639 1.094 -0.009 0.337 VENTXP7 1.242 0.293 0.342 DUSP7 2.134 0.407 0.56 LOC100128164 1.62 0.224 0.485 LOC339192 1.515 0.259 0.22 GVIN1 1.052 0.134 0.209 ACRC 1.799 0.268 0.327 CTSG 1.212 0.202 0.268 CLDND2 1.594 0.291 0.298 OAF 1.289 0.37 0.146 TRIM5 1.037 0.268 0.086 LOC100129559 1.768 0.449 0.17 RESP18 1.695 0.485 0.136 LOC154761 1.721 0.47 0.153 LOC399715 1.004 0.2 0 DEFB127 1.344 0.307 0.034 SLC2A13 1.246 0.23 0.091 LOC100130778 1.598 0.378 0.692 C11orf9 1.092 0.298 0.457 SMR3B 1.051 0.221 0.37 RNASE3 1.055 0.172 0.534 LOC100129081 1.022 0.194 0.533 COL5A2 1.028 0.424 0.297 MAP9 1.167 0.543 0.357 ASPM 1.02 0.445 0.35 FIGF 1.874 0.687 0.381 CYP3A7 1.288 0.458 0.306 LOC100132111 1.177 0.392 0.312 FLJ44342 1.068 0.383 0.311 LOC441294 1.726 0.568 0.582 OR5I1 1.159 0.48 0.451 ENTPD1 1.949 0.723 0.75 CEP152 1.314 0.436 0.537 COPG2IT1 1.251 0.587 -0.054 CECR4 1.933 0.974 -0.026 MCTS1 1.108 0.566 0.006 C1orf213 1.277 0.637 0.011 DKFZp434J0226 1.297 0.619 0.056 TNFSF15 1.852 0.716 0.09 TMEM133 1.195 0.44 0.005 C8orf4 1.044 0.687 0.191 LOC645722 1.212 0.858 0.168 KRTCAP3 1.152 0.778 0.142 LOC729570 1.419 0.876 0.066 CEP290 2.084 1.273 0.075 ZNF783 1.055 0.506 0.199 PPP1R1B 1.216 0.55 0.241 LOC728175 1.048 0.476 0.163 HEMGN 1.942 0.908 0.267 DSCAML1 1.066 0.533 0.162 GSDMB 1.521 0.858 0.251 EDEM3 1.468 0.845 0.273 EP400NL 1.164 0.515 0.574 CCL26 1.15 0.524 0.549 PAQR6 1.419 0.622 0.742 C13orf18 1.092 0.448 0.575 ARHGAP20 1.082 0.55 0.613 HS3ST6 1.367 0.703 0.565 FCGR2C 1.082 0.508 0.432 CR1 1.528 0.793 0.735 SMC6 1.383 0.799 0.733 PCYT1B 1.242 0.743 0.676 LOC100130387 2.04 1.213 1.118 FLJ45983 1.416 0.808 0.797 SPAM1 1.543 0.99 0.85 CCDC146 1.229 0.788 0.651 CPEB4 1.129 0.759 0.606 LOC389493 1.098 0.709 0.53 FAM174A 1.207 0.734 0.574 FBLN2 1.037 0.721 0.61 SEMA3A 1.37 0.918 0.811 KIAA1109 1.154 0.772 0.692 FYB 1.878 1.28 1.177 C16orf79 1.96 1.415 1.201 RBM44 1.453 0.869 0.963 LOC729078 1.008 0.607 0.63 LOC285000 1.083 0.647 0.684 CENPJ 1.078 0.657 0.682 ANKRD13C 1.071 0.71 0.707 GOLGA2LY1 1.469 0.623 1.017 CMTM2 1.091 0.466 0.732 LOC730045 1.11 0.508 0.765 LGI4 1.69 0.84 1.168 GPC6 1.108 0.549 0.737 PTGS2 1.257 0.635 0.921 WDR72 1.123 0.786 0.542 LARP7 1.039 0.713 0.498 CYP27C1 1.207 0.913 0.582 FOXC1 1.097 0.701 0.397 EPM2AIP1 1.111 0.72 0.396 SEC31B 2.224 1.431 0.938 AHSA2 2.09 1.294 0.897 JMJD7-PLA2G4B 1.268 0.855 0.521 LOC100130128 1.126 0.799 0.45 AADAC 1.263 0.945 0.513 SLC4A7 1.066 0.835 0.409 AQP7P3 1.576 1.035 0.516 SLCO4A1 1.213 0.781 0.362 GABRE 2.198 1.387 0.644 PP14571 1.772 1.173 0.483 LEPR 1.272 0.731 0.35 DYNLT3 1.016 0.723 0.31 NTS 1.202 0.946 0.398 CCDC88A 1.152 0.879 0.323 MGAT4A 1.441 1.346 0.369 LOC401097 1.447 1.259 0.381 ANKRA2 1.441 1.27 0.441 TTC32 1.632 1.554 0.621 KCNJ11 1.492 1.444 0.528 LIX1 1.32 1.128 0.521 CCDC68 2.178 1.877 0.823 ARSI 1.286 1.088 0.461 APOL6 1.173 0.929 -0.269 CCDC18 1.06 0.751 -0.186 C6orf26 1.95 1.41 -0.298 C16orf73 1.473 0.968 -0.321 MGEA5 1.048 0.68 -0.127 CCPG1 1.342 0.947 -0.121 CCDC46 1.338 0.882 -0.093 IFT74 1.265 0.703 -0.144 EGFL8 2.067 1.529 0.101 SLC26A2 1.062 0.877 -0.009 LAMB2L 1.211 1.024 0.078 POL3S 1.193 1.003 -0.132 CRADD 1.128 1.015 -0.092 LOC388279 1.878 1.792 0.002 HOXB9 0.859 1.057 -0.401 FOXL1 1.428 1.763 -0.399 PTP4A3 1.046 0.948 -0.517 FLJ45244 2.048 1.585 -0.715 ZFYVE28 1.106 1.007 -0.287 PPIL6 1.256 1.176 -0.279 LRRC32 1.237 0.444 -0.474 GRAMD4 1.085 0.402 -0.4 TBX18 1.55 0.762 -0.708 LOC100129354 1.027 0.296 -0.585 IL15 1.167 0.46 -0.64 LOC100128242 1.631 0.853 -1.3 ETAA1 1.114 0.552 -0.763 PCDH21 -0.149 1.706 -0.538 OR2G3 -0.074 1.135 -0.379 GPR112 -0.044 1.02 -0.336 NCRNA00119 0.006 1.036 -0.408 CD1C 0.004 1.334 -0.551 CILP -0.066 1.427 -0.544 INSC -0.049 1.005 -0.239 LOC100130850 -0.04 1.565 -0.135 AAA1 0.01 1.006 -0.133 UNQ9368 -0.096 1.24 -0.105 JPH1 -0.113 1.421 -0.053 LOC100129711 -0.393 1.927 -0.397 MAPK15 -0.189 1.119 -0.159 LOC100129162 -0.185 1.046 -0.159 SLC37A1 -0.161 1.027 -0.089 SULT1C3 -0.338 1.918 0.194 LOC100290415 -0.235 1.254 0.119 LOC100293499 -0.618 1.438 -0.797 DAD1L 0.448 1.601 -0.26 LOC283692 0.188 1.285 -0.485 DCAF4L1 0.421 1.424 -0.47 LOC148189 0.958 1.49 -0.661 LOC284080 0.644 1.1 -0.602 GPT2 1.018 1.754 -0.895 DKFZp686A1627 0.838 1.343 0.034 CHAC1 2.351 3.203 0.026 DMRTA1 2.097 2.331 0.197 USP51 0.693 1.665 0.121 FUT1 1.434 3.636 0.254 PRG2 0.673 1.527 0.073 LOC284561 0.946 1.781 0.022 FOXQ1 0.711 1.447 -0.017 FLNB 0.498 1.161 0.342 C6orf140 0.452 1.039 0.288 SLC1A4 1.195 2.545 0.696 SLC1A5 0.879 1.893 0.48 PSAT1 0.917 2.067 0.507 ABCG1 1.175 2.233 0.728 LOC283713 0.547 1.138 0.212 DRAM2 0.578 1.02 0.192 RPL23AP64 0.592 1.038 0.253 OR52E2 0.896 1.704 0.4 SERPINE2 0.615 1.168 0.531 ITGA3 0.603 1.249 0.549 PKD1L2 1.662 1.907 0.62 LYRM5 1.043 1.2 0.386 LOC158402 1.304 1.445 0.467 FLJ35024 1.402 1.59 0.48 FLJ43663 1.632 1.962 0.585 MUSTN1 1.103 1.142 0.325 KRTAP20-3 0.881 1.095 0.448 KIRREL3 0.868 1.112 0.468 TMEM91 1.13 1.526 0.599 SLC35A1 0.908 1.048 0.436 GUCY1B2 1.091 1.259 0.533 RAET1E 1.046 1.271 0.474 MYH3 1.012 1.163 0.615 C21orf111 0.941 1.07 0.546 hCG_2045830 1.283 1.557 0.783 C5orf28 0.845 1.004 0.494 LOC728510 0.853 1.041 0.479 SIP1 0.998 1.028 0.482 NEDD4 1.066 1.115 0.491 AP1AR 1.281 1.209 0.604 ADRBK2 0.946 1.233 0.206 LOC283501 0.992 1.35 0.277 C20orf70 0.821 1.099 0.212 HPVC1 0.751 1 0.231 MID1IP1 0.869 1.111 0.227 LOC100128086 1.039 1.239 0.248 CEBPG 0.85 1.311 0.266 C9orf167 0.827 1.16 0.335 C8ORFK29 1.039 1.475 0.454 CLN8 1.309 1.791 0.493 ARL6IP6 0.872 1.144 0.299 TRIB3 1.246 1.91 0.594 TPBG 0.719 1.081 0.342 LRP11 0.635 1.054 0.316 LOC57399 0.859 1.333 0.506 ITGA2 1.566 2.309 0.877 LOC644662 0.463 1.694 1.011 ABCC3 0.274 1.108 0.712 FBXO17 0.261 1.049 0.549 BCAT1 0.574 2.123 1.175 SFT2D1 0.193 1.124 0.786 PIGL 0.195 1.005 0.682 PTH 0.249 1.247 0.792 ATP2A3 0.217 1.412 0.899 SLC43A1 0.288 1.498 0.875 ZMAT3 0.573 1.427 0.79 HYI 0.523 1.247 0.702 MAF 1.259 2.881 1.677 BMPER 0.621 1.45 0.856 UHRF1BP1 0.608 1.305 0.755 EXPH5 0.542 1.141 0.697 MT2A 0.499 1.097 0.54 XYLT1 0.56 1.192 0.641 STC2 1.942 4.175 2.263 WNT16 0.537 1.526 0.732 C19orf71 0.515 1.472 0.735 LOC285216 0.497 1.241 0.64 TLR4 0.416 1.124 0.657 ANAPC13 0.449 1.221 0.698 FLJ43315 1.119 3.208 1.847 IL18BP 0.444 1.342 0.727 CDS1 0.342 1.078 0.59 GALC 0.363 1.038 0.696 KRTAP23-1 0.38 1.015 0.724 ASNS 1.26 3.401 2.546 LOC100131397 0.428 1.058 0.754 C12orf39 1.497 3.796 2.58
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  • Genome-Scale Detection of Positive Selection in 9 Primates Predicts Human-Virus Evolutionary Conflicts

    Genome-Scale Detection of Positive Selection in 9 Primates Predicts Human-Virus Evolutionary Conflicts

    bioRxiv preprint doi: https://doi.org/10.1101/131680; this version posted April 27, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Genome-scale detection of positive selection in 9 primates predicts human-virus evolutionary conflicts Robin van der Lee1,*,^, Laurens Wiel1,2, Teunis J.P. van Dam1,#, Martijn A. Huynen1 1Centre for Molecular and Biomolecular Informatics, 2Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Nijmegen, The Netherlands *Correspondence: [email protected] ^Current address: Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children’s Hospital Research Institute, University of British Columbia, Vancouver, Canada #Current address: Theoretical Biology and Bioinformatics, Department of Biology, Faculty of Science, Utrecht University, The Netherlands bioRxiv preprint doi: https://doi.org/10.1101/131680; this version posted April 27, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Abstract Hotspots of rapid genome evolution hold clues about human adaptation. Here, we present a comparative analysis of nine whole-genome sequenced primates to identify high-confidence targets of positive selection. We find strong statistical evidence for positive selection acting on 331 protein-coding genes (3%), pinpointing 934 adaptively evolving codons (0.014%).
  • Mass Spectrometry-Based Proteomics Analysis of Bioactive Proteins in EMD That Modulate Adhesion of Gingival Fibroblast to Improve Bio-Integration of Dental Implants

    Mass Spectrometry-Based Proteomics Analysis of Bioactive Proteins in EMD That Modulate Adhesion of Gingival Fibroblast to Improve Bio-Integration of Dental Implants

    Western University Scholarship@Western Electronic Thesis and Dissertation Repository 3-28-2019 3:00 PM Mass Spectrometry-Based Proteomics Analysis Of Bioactive Proteins In EMD That Modulate Adhesion Of Gingival Fibroblast To Improve Bio-Integration Of Dental Implants David Zuanazzi Machado Jr The University of Western Ontario Supervisor Siqueira, Walter L. The University of Western Ontario Graduate Program in Biochemistry A thesis submitted in partial fulfillment of the equirr ements for the degree in Doctor of Philosophy © David Zuanazzi Machado Jr 2019 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Biochemistry Commons, Dental Materials Commons, Oral Biology and Oral Pathology Commons, and the Periodontics and Periodontology Commons Recommended Citation Zuanazzi Machado, David Jr, "Mass Spectrometry-Based Proteomics Analysis Of Bioactive Proteins In EMD That Modulate Adhesion Of Gingival Fibroblast To Improve Bio-Integration Of Dental Implants" (2019). Electronic Thesis and Dissertation Repository. 6064. https://ir.lib.uwo.ca/etd/6064 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. i ABSTRACT Titanium (Ti) implants are used in dental practice to replace damaged or lost teeth. For effective treatment, the dental implant needs to integrate with the surrounding hard and soft tissues on the implant site. Despite improvements in bone-implant integration that have been achieved through surface modifications, the integration with the soft tissues is still deficient. The oral mucosa that embraces the transmucosal component of the implant only contacts the surface without making a strong attachment with the connective tissue.
  • Syndromic Cases of Hemophilia B and Morbid Obesity Due to Contiguous

    Syndromic Cases of Hemophilia B and Morbid Obesity Due to Contiguous

    Syndromic cases of hemophilia B and morbid obesity due to contiguous gene deletions on Xq26.3-q27.2: unsuspected phenotype-genotype associations by bioinformatics and extensive clinical data mining Pamela Radic1, Miguel Abelleyro1, Betiana Ziegler1, Vanina Marchione1, Julian Nevado2, Pablo Lapunzina2, Gabriela Sciuccati3, Daniela Neme4, Liliana Rossetti1, Mariana Bonduel5, and Carlos De Brasi1 1Instituto de Medicina Experimental (IMEX-CONICET/ANM) 2Instituto de Gen´eticaM´edicay Molecular (INGEMM)-IdiPAZ, Hospital Universitario La Paz 3Hospital de Pediatr´ıaProf Dr Juan P Garrahan Servicio de Hematolog´ıay Oncolog´ıa 4Fundaci´onde la Hemofilia Alfredo Pavlovsky. 5Hospital de Pediatria Prof Dr Juan P Garrahan Servicio de Hematologia y Oncologia August 20, 2020 Abstract Hemophilia B (HB) associates with pathogenic F9-variants. The literature showed that hemizygous deletions encompassing F9 and vicinal genes may express extra-phenotypes suggesting new causal relationships. Aim: Analyze the molecular basis of syndromic cases of HB, obesity (OB), severe global developmental delay (SGDD) and generalized hypotonia (GH). Whole F9-deletions were detected in 3 hemizygous probands with HB. Dense SNP-array and case-specific STS walking strategies al- lowed amplification and characterization of the deletion breakpoints. Bioinformatic/statistical analyses included data mining in HPO (Human Phenotype Ontology), OMIM, STRING (protein-protein interaction networks) databases and estimation of null-hypothesis-based Expected-values. Patients (cases#3-case#1) showed complete F9 deletions involving 0.16-4.34-Mb and 1- 17 additional genes on Xq26.3-q27.2. Bioinformatic/statistical approaches revealed highly significant STRING-associations (P[?]0.00115) between case#1/#2 common deleted genes (SOX3, FGF13, CXorf66) and those HPO associated with OB (20/343), GH (36/923) and SGDD (10/119).
  • Us 2018 / 0305689 A1

    Us 2018 / 0305689 A1

    US 20180305689A1 ( 19 ) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2018 /0305689 A1 Sætrom et al. ( 43 ) Pub . Date: Oct. 25 , 2018 ( 54 ) SARNA COMPOSITIONS AND METHODS OF plication No . 62 /150 , 895 , filed on Apr. 22 , 2015 , USE provisional application No . 62/ 150 ,904 , filed on Apr. 22 , 2015 , provisional application No. 62 / 150 , 908 , (71 ) Applicant: MINA THERAPEUTICS LIMITED , filed on Apr. 22 , 2015 , provisional application No. LONDON (GB ) 62 / 150 , 900 , filed on Apr. 22 , 2015 . (72 ) Inventors : Pål Sætrom , Trondheim (NO ) ; Endre Publication Classification Bakken Stovner , Trondheim (NO ) (51 ) Int . CI. C12N 15 / 113 (2006 .01 ) (21 ) Appl. No. : 15 /568 , 046 (52 ) U . S . CI. (22 ) PCT Filed : Apr. 21 , 2016 CPC .. .. .. C12N 15 / 113 ( 2013 .01 ) ; C12N 2310 / 34 ( 2013. 01 ) ; C12N 2310 /14 (2013 . 01 ) ; C12N ( 86 ) PCT No .: PCT/ GB2016 /051116 2310 / 11 (2013 .01 ) $ 371 ( c ) ( 1 ) , ( 2 ) Date : Oct . 20 , 2017 (57 ) ABSTRACT The invention relates to oligonucleotides , e . g . , saRNAS Related U . S . Application Data useful in upregulating the expression of a target gene and (60 ) Provisional application No . 62 / 150 ,892 , filed on Apr. therapeutic compositions comprising such oligonucleotides . 22 , 2015 , provisional application No . 62 / 150 ,893 , Methods of using the oligonucleotides and the therapeutic filed on Apr. 22 , 2015 , provisional application No . compositions are also provided . 62 / 150 ,897 , filed on Apr. 22 , 2015 , provisional ap Specification includes a Sequence Listing . SARNA sense strand (Fessenger 3 ' SARNA antisense strand (Guide ) Mathew, Si Target antisense RNA transcript, e . g . NAT Target Coding strand Gene Transcription start site ( T55 ) TY{ { ? ? Targeted Target transcript , e .
  • A Novel Transmembrane Glycoprotein Cancer Biomarker Present in the X Chromosome ANA PAULA DELGADO, SHEILIN HAMID, PAMELA BRANDAO and RAMASWAMY NARAYANAN

    A Novel Transmembrane Glycoprotein Cancer Biomarker Present in the X Chromosome ANA PAULA DELGADO, SHEILIN HAMID, PAMELA BRANDAO and RAMASWAMY NARAYANAN

    CANCER GENOMICS & PROTEOMICS 11 : 81-92 (2014) A Novel Transmembrane Glycoprotein Cancer Biomarker Present in the X Chromosome ANA PAULA DELGADO, SHEILIN HAMID, PAMELA BRANDAO and RAMASWAMY NARAYANAN Department of Biological Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, U.S.A. Abstract. Background: The uncharacterized proteins of the genome are of unknown nature (10). These proteins and the human proteome offer an untapped potential for cancer non-coding RNAs are called the ‘dark matter’ of the genome biomarker discovery. Numerous predicted open reading (11-13). Whereas in the past most gene discovery has frames (ORFs) are present in diverse chromosomes. The revolved around known genes due to the ease of follow-up mRNA and protein expression data, as well as the mutational studies (14-17), the dark matter of the genome offers an and variant information for these ORF proteins are available untapped potential (18). Realizing the importance of this area, in the cancer-related bioinformatics databases. Materials the US National Cancer Institute has recently announced a and Methods: ORF proteins were mined using bioinformatics major initiative called illuminating the dark matter for and proteomic tools to predict motifs and domains, and druggable targets (http://commonfund.nih.gov/idg/index). cancer relevance was established using cancer genome, Establishing cancer relevance for novel or uncharacterized transcriptome and proteome analysis tools. Results: A novel proteins is a crucial first step in lead discovery. The cancer testis-restricted ORF protein present in chromosome X called genomes of patients from around the world can be readily CXorf66 was detected in the serum, plasma and neutrophils.
  • Genome-Wide Analysis of Long Non-Coding RNA Expression Profile in Lung Adenocarcinoma Compared to Spinal Metastasis

    Genome-Wide Analysis of Long Non-Coding RNA Expression Profile in Lung Adenocarcinoma Compared to Spinal Metastasis

    1516 Original Article Page 1 of 13 Genome-wide analysis of long non-coding RNA expression profile in lung adenocarcinoma compared to spinal metastasis Houlei Wang#, Annan Hu#, Yun Liang, Ketao Wang, Xiaogang Zhou, Jian Dong Department of Orthopaedic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China Contributions: (I) Conception and design: H Wang, A Hu; (II) Administrative support: J Dong; (III) Provision of study materials or patients: X Zhou; (IV) Collection and assembly of data: K Wang; (V) Data analysis and interpretation: Y Liang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Jian Dong. Department of Orthopaedic Surgery, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai 200032, China. Email: [email protected]. Background: Long non-coding RNAs (lncRNAs) play important roles in tumor metastasis. The aim of the present study was to investigate their expression profile and potential functions in spinal metastasis (SM) of lung adenocarcinoma. Methods: We conducted lncRNA and mRNA expression in lung adenocarcinoma and its SM tissue using microarray analysis. Quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) revealed 10 differentially expressed lncRNAs. Gene ontology and pathway analysis were performed to test the gene effect. Possible target genes of lncRNAs were predicted based on precise algorithms. Results: Microarray analysis found many significantly differentially expressed lncRNAs and mRNAs in lung adenocarcinoma compared with SM. qRT-PCR results aligned with those of the microarray analysis. The expression level of 10 lncRNAs showed the same trend (P<0.05). Biologic pathways known to be involved in cancer were identified among the differentially expressed mRNAs; these include cell adhesion molecules (related to 42 genes), focal adhesion (related to 31 genes), cytokine-cytokine receptor interaction (related to 48 genes), and extracellular matrix-receptor interaction (related to 23 genes).