Study on the Selection of the Targets of Esophageal Carcinoma and Interventions of Ginsenosides Based on Network Pharmacology and Bioinformatics
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A Case of Prenatal Diagnosis of 18P Deletion Syndrome Following
Zhao et al. Molecular Cytogenetics (2019) 12:53 https://doi.org/10.1186/s13039-019-0464-y CASE REPORT Open Access A case of prenatal diagnosis of 18p deletion syndrome following noninvasive prenatal testing Ganye Zhao, Peng Dai, Shanshan Gao, Xuechao Zhao, Conghui Wang, Lina Liu and Xiangdong Kong* Abstract Background: Chromosome 18p deletion syndrome is a disease caused by the complete or partial deletion of the short arm of chromosome 18, there were few cases reported about the prenatal diagnosis of 18p deletion syndrome. Noninvasive prenatal testing (NIPT) is widely used in the screening of common fetal chromosome aneuploidy. However, the segmental deletions and duplications should also be concerned. Except that some cases had increased nuchal translucency or holoprosencephaly, most of the fetal phenotype of 18p deletion syndrome may not be evident during the pregnancy, 18p deletion syndrome was always accidentally discovered during the prenatal examination. Case presentations: In our case, we found a pure partial monosomy 18p deletion during the confirmation of the result of NIPT by copy number variation sequencing (CNV-Seq). The result of NIPT suggested that there was a partial or complete deletion of X chromosome. The amniotic fluid karyotype was normal, but result of CNV-Seq indicated a 7.56 Mb deletion on the short arm of chromosome 18 but not in the couple, which means the deletion was de novo deletion. Finally, the parents chose to terminate the pregnancy. Conclusions: To our knowledge, this is the first case of prenatal diagnosis of 18p deletion syndrome following NIPT.NIPT combined with ultrasound may be a relatively efficient method to screen chromosome microdeletions especially for the 18p deletion syndrome. -
ONCOGENOMICS Through the Use of Chemotherapeutic Agents
Oncogene (2002) 21, 7749 – 7763 ª 2002 Nature Publishing Group All rights reserved 0950 – 9232/02 $25.00 www.nature.com/onc Expression profiling of primary non-small cell lung cancer for target identification Jim Heighway*,1, Teresa Knapp1, Lenetta Boyce1, Shelley Brennand1, John K Field1, Daniel C Betticher2, Daniel Ratschiller2, Mathias Gugger2, Michael Donovan3, Amy Lasek3 and Paula Rickert*,3 1Target Identification Group, Roy Castle International Centre for Lung Cancer Research, University of Liverpool, 200 London Road, Liverpool L3 9TA, UK; 2Institute of Medical Oncology, University of Bern, 3010 Bern, Switzerland; 3Incyte Genomics Inc., 3160 Porter Drive, Palo Alto, California, CA 94304, USA Using a panel of cDNA microarrays comprising 47 650 Introduction transcript elements, we have carried out a dual-channel analysis of gene expression in 39 resected primary The lack of generally effective therapies for lung cancer human non-small cell lung tumours versus normal lung leads to the high mortality rate associated with this tissue. Whilst *11 000 elements were scored as common disease. Recorded 5-year survival figures vary, differentially expressed at least twofold in at least one to some extent perhaps reflecting differences in sample, 96 transcripts were scored as over-represented ascertainment methods, but across Europe are approxi- fourfold or more in at least seven out of 39 tumours mately 10% (Bray et al., 2002). Whilst surgery is an and 30 sequences 16-fold in at least two out of 39 effective strategy for the treatment of localized lesions, tumours, including 24 transcripts in common. Tran- most patients present with overtly or covertly dissemi- scripts (178) were found under-represented fourfold in nated disease. -
PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer—A Comprehensive Review
cancers Review Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer—A Comprehensive Review Greta Brezgyte †, Vinay Shah † , Daria Jach and Tatjana Crnogorac-Jurcevic * Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (G.B.); [email protected] (V.S.); [email protected] (D.J.) * Correspondence: [email protected] † Shared first authorship. Simple Summary: Pancreatic ductal adenocarcinoma (PDAC), which represents approximately 90% of all pancreatic cancers, is an extremely aggressive and lethal disease. It is considered a silent killer due to a largely asymptomatic course and late clinical presentation. Earlier detection of the disease would likely have a great impact on changing the currently poor survival figures for this malignancy. In this comprehensive review, we assessed over 4000 reports on non-invasive PDAC biomarkers in the last decade. Applying the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, we selected and reviewed in more detail 49 relevant studies reporting on the most promising candidate biomarkers. In addition, we also highlight the present challenges and complexities of translating novel biomarkers into clinical use. Abstract: Pancreatic ductal adenocarcinoma (PDAC) carries a deadly diagnosis, due in large part to delayed presentation when the disease is already at an advanced stage. CA19-9 is currently the most commonly utilized biomarker for PDAC; however, it lacks the necessary accuracy to detect precursor lesions or stage I PDAC. Novel biomarkers that could detect this malignancy with Citation: Brezgyte, G.; Shah, V.; Jach, improved sensitivity (SN) and specificity (SP) would likely result in more curative resections and D.; Crnogorac-Jurcevic, T. -
Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-Like Mouse Models: Tracking the Role of the Hairless Gene
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 5-2006 Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-like Mouse Models: Tracking the Role of the Hairless Gene Yutao Liu University of Tennessee - Knoxville Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Life Sciences Commons Recommended Citation Liu, Yutao, "Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino- like Mouse Models: Tracking the Role of the Hairless Gene. " PhD diss., University of Tennessee, 2006. https://trace.tennessee.edu/utk_graddiss/1824 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Yutao Liu entitled "Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-like Mouse Models: Tracking the Role of the Hairless Gene." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Life Sciences. Brynn H. Voy, Major Professor We have read this dissertation and recommend its acceptance: Naima Moustaid-Moussa, Yisong Wang, Rogert Hettich Accepted for the Council: Carolyn R. -
Development and Validation of a New Tumor-Based Gene Signature
Zhu et al. J Transl Med (2019) 17:203 https://doi.org/10.1186/s12967-019-1946-8 Journal of Translational Medicine RESEARCH Open Access Development and validation of a new tumor-based gene signature predicting prognosis of HBV/HCV-included resected hepatocellular carcinoma patients Gui‑Qi Zhu1,2†, Yi Yang1,2†, Er‑Bao Chen3†, Biao Wang1,2, Kun Xiao1,2, Shi‑Ming Shi4, Zheng‑Jun Zhou1,2, Shao‑Lai Zhou1,2, Zheng Wang1,2, Ying‑Hong Shi1,2, Jia Fan1,2, Jian Zhou1,2, Tian‑Shu Liu3 and Zhi Dai1,2* Abstract Background: Due to the phenotypic and molecular diversity of hepatocellular carcinomas (HCC), it is still a challenge to determine patients’ prognosis. We aim to identify new prognostic markers for resected HCC patients. Methods: 274 patients were retrospectively identifed and samples collected from Zhongshan hospital, Fudan Uni‑ versity. We analyzed the gene expression patterns of tumors and compared expression patterns with patient survival times. We identifed a “9‑gene signature” associated with survival by using the coefcient and regression formula of multivariate Cox model. This molecular signature was then validated in three patients cohorts from internal cohort (n 69), TCGA (n 369) and GEO dataset (n 80). = = = Results: We identifed 9‑gene signature consisting of ZC2HC1A, MARCKSL1, PTGS1, CDKN2B, CLEC10A, PRDX3, PRKCH, MPEG1 and LMO2. The 9‑gene signature was used, combined with clinical parameters, to ft a multivariable Cox model to the training cohort (concordance index, ci 0.85), which was successfully validated (ci 0.86 for internal cohort; ci 0.78 for in silico cohort). The signature showed= improved performance compared with= clinical parameters alone (ci= 0.70). -
DE-Kupl: Exhaustive Capture of Biological Variation in RNA-Seq Data Through K-Mer Decomposition
Audoux et al. Genome Biology (2017) 18:243 DOI 10.1186/s13059-017-1372-2 METHOD Open Access DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition Jérôme Audoux1, Nicolas Philippe2,3, Rayan Chikhi4, Mikaël Salson4, Mélina Gallopin5, Marc Gabriel5,6, Jérémy Le Coz5,EmilieDrouineau5, Thérèse Commes1,2 and Daniel Gautheret5,6* Abstract We introduce a k-mer-based computational protocol, DE-kupl, for capturing local RNA variation in a set of RNA-seq libraries, independently of a reference genome or transcriptome. DE-kupl extracts all k-mers with differential abundance directly from the raw data files. This enables the retrieval of virtually all variation present in an RNA-seq data set. This variation is subsequently assigned to biological events or entities such as differential long non-coding RNAs, splice and polyadenylation variants, introns, repeats, editing or mutation events, and exogenous RNA. Applying DE-kupl to human RNA-seq data sets identified multiple types of novel events, reproducibly across independent RNA-seq experiments. Background diversity is genomic variation. Polymorphism and struc- Successive generations of RNA-sequencing technologies tural variations within transcribed regions produce RNAs have bolstered the notion that organisms produce a highly with single-nucleotide variations (SNVs), tandem dupli- diverse and adaptable set of RNA molecules. Modern cations or deletions, transposon integrations, unstable transcript catalogs, such as GENCODE [1], now include microsatellites, or fusion events. These events are major hundreds of thousands of transcripts, reflecting pervasive sources of transcript variation that can strongly impact transcription and widespread alternative RNA processing. RNA processing, transport, and coding potential. -
Transcriptional Mechanisms of Resistance to Anti-PD-1 Therapy
Author Manuscript Published OnlineFirst on February 13, 2017; DOI: 10.1158/1078-0432.CCR-17-0270 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Transcriptional mechanisms of resistance to anti-PD-1 therapy Maria L. Ascierto1, Alvin Makohon-Moore2, 11, Evan J. Lipson1, Janis M. Taube3,4, Tracee L. McMiller5, Alan E. Berger6, Jinshui Fan6, Genevieve J. Kaunitz3, Tricia R. Cottrell4, Zachary A. Kohutek7, Alexander Favorov8,10, Vladimir Makarov7,11, Nadeem Riaz7,11, Timothy A. Chan7,11, Leslie Cope8, Ralph H. Hruban4,9, Drew M. Pardoll1, Barry S. Taylor11,12,13, David B. Solit13, Christine A Iacobuzio-Donahue2,11, and Suzanne L. Topalian5 From the 1Departments of Oncology, 3Dermatology, 4Pathology, 5Surgery, 6The Lowe Family Genomics Core, 8Oncology Bioinformatics Core, and the 9 Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine and Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD 21287; the 10Laboratory of System Biology and Computational Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991, Moscow, Russia; and 2Pathology, 7Radiation Oncology, 11Human Oncology and Pathogenesis Program, 12Department of Epidemiology and Biostatistics, and the 13Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York NY 10065. MLA, AM-M, EJL, and JMT contributed equally to this work Running title: Transcriptional mechanisms of resistance to anti-PD-1 Key Words: melanoma, cancer genetics, immunotherapy, anti-PD-1 Financial Support: This study was supported by the Melanoma Research Alliance (to SLT and CI-D), the Bloomberg~Kimmel Institute for Cancer Immunotherapy (to JMT, DMP, and SLT), the Barney Family Foundation (to SLT), Moving for Melanoma of Delaware (to SLT), the 1 Downloaded from clincancerres.aacrjournals.org on October 2, 2021. -
Salivary Exrna Biomarkers to Detect Gingivitis and Monitor Disease Regression
Received: 3 December 2017 | Revised: 14 April 2018 | Accepted: 13 May 2018 DOI: 10.1111/jcpe.12930 OTHER Salivary exRNA biomarkers to detect gingivitis and monitor disease regression Karolina E. Kaczor-Urbanowicz1,2* | Harsh M. Trivedi3* | Patricia O. Lima1,4 | Paulo M. Camargo5 | William V. Giannobile6 | Tristan R. Grogan7 | Frederico O. Gleber-Netto8 | Yair Whiteman9 | Feng Li1 | Hyo Jung Lee10 | Karan Dharia11 | Katri Aro1 | Carmen Martin Carerras-Presas12 | Saarah Amuthan11 | Manjiri Vartak11 | David Akin1 | Hiba Al-adbullah11 | Kanika Bembey11 | Perry R. Klokkevold5 | David Elashoff7 | Virginia M. Barnes13 | Rose Richter13 | William DeVizio13 | James G. Masters3 | David T. W. Wong1 1UCLA School of Dentistry, Center for Oral/Head & Neck Oncology Research, University of California at Los Angeles, Los Angeles, California 2Section of Orthodontics, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, California 3Early Research Oral Care, Colgate Palmolive Co., Piscataway, New Jersey 4Department of Physiological Sciences, Piracicaba Dental School, University of Campinas, Piracicaba, São Paulo, Brazil 5Section of Periodontics, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, California 6Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, Michigan 7Department of Biostatistics, University of California at Los Angeles, Los Angeles, California 8Medical Genomics Laboratory, Centro Internacional de Pesquisa e Ensino (CIPE), AC Camargo Cancer -
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. -
Identification of Crucial Aberrantly Methylated and Differentially
Bioscience Reports (2020) 40 BSR20194365 https://doi.org/10.1042/BSR20194365 Research Article Identification of crucial aberrantly methylated and differentially expressed genes related to cervical cancer using an integrated bioinformatics analysis 1,2,3,* 1,* 1,2,3 1 1 2,3 1 Xiaoling Ma , Jinhui Liu , Hui Wang ,YiJiang,YicongWan, Yankai Xia and Wenjun Cheng Downloaded from http://portlandpress.com/bioscirep/article-pdf/40/5/BSR20194365/879046/bsr-2019-4365.pdf by guest on 28 September 2021 1Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; 2State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; 3State Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China Correspondence: Wenjun Cheng ([email protected]) or Yankai Xia ([email protected]) Methylation functions in the pathogenesis of cervical cancer. In the present study, we ap- plied an integrated bioinformatics analysis to identify the aberrantly methylated and dif- ferentially expressed genes (DEGS), and their related pathways in cervical cancer. Data of gene expression microarrays (GSE9750) and gene methylation microarrays (GSE46306) were gained from Gene Expression Omnibus (GEO) databases. Hub genes were identi- fied by ‘limma’ packages and Venn diagram tool. Functional analysis was conducted by FunRich. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein–protein interaction (PPI) information. Gene Expression Profiling Interac- tive Analysis (GEPIA), immunohistochemistry staining, and ROC curve analysis were con- ducted for validation. Gene Set Enrichment Analysis (GSEA) was also performed to iden- tify potential functions.We retrieved two upregulated-hypomethylated oncogenes and eight downregulated-hypermethylated tumor suppressor genes (TSGs) for functional analysis. -
A Novel Resveratrol Analog: Its Cell Cycle Inhibitory, Pro-Apoptotic and Anti-Inflammatory Activities on Human Tumor Cells
A NOVEL RESVERATROL ANALOG : ITS CELL CYCLE INHIBITORY, PRO-APOPTOTIC AND ANTI-INFLAMMATORY ACTIVITIES ON HUMAN TUMOR CELLS A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Boren Lin May 2006 Dissertation written by Boren Lin B.S., Tunghai University, 1996 M.S., Kent State University, 2003 Ph. D., Kent State University, 2006 Approved by Dr. Chun-che Tsai , Chair, Doctoral Dissertation Committee Dr. Bryan R. G. Williams , Co-chair, Doctoral Dissertation Committee Dr. Johnnie W. Baker , Members, Doctoral Dissertation Committee Dr. James L. Blank , Dr. Bansidhar Datta , Dr. Gail C. Fraizer , Accepted by Dr. Robert V. Dorman , Director, School of Biomedical Sciences Dr. John R. Stalvey , Dean, College of Arts and Sciences ii TABLE OF CONTENTS LIST OF FIGURES……………………………………………………………….………v LIST OF TABLES……………………………………………………………………….vii ACKNOWLEDGEMENTS….………………………………………………………….viii I INTRODUCTION….………………………………………………….1 Background and Significance……………………………………………………..1 Specific Aims………………………………………………………………………12 II MATERIALS AND METHODS.…………………………………………….16 Cell Culture and Compounds…….……………….…………………………….….16 MTT Cell Viability Assay………………………………………………………….16 Trypan Blue Exclusive Assay……………………………………………………...18 Flow Cytometry for Cell Cycle Analysis……………..……………....……………19 DNA Fragmentation Assay……………………………………………...…………23 Caspase-3 Activity Assay………………………………...……….….…….………24 Annexin V-FITC Staining Assay…………………………………..…...….………28 NF-kappa B p65 Activity Assay……………………………………..………….…29