Literature DB >> 31737203

Proteomic analysis for identifying the differences in molecular profiling between fanconi anaemia and aplastic anaemia.

Hui Hou1,2, Dan Li3, Yan-Hua Yao1, Jun Lu1, Yi-Na Sun1, Yi-Xin Hu1, Shui-Yan Wu1, Xin-Ran Chu1, Pei-Fang Xiao1, Guo-Qiang Xu3, Shao-Yan Hu1.   

Abstract

Treatment and prognosis of Fanconi anaemia (FA) and acquired aplastic anaemia (AA) differ. However, delayed and inappropriate treatments are administered in FA due to its similarities to AA in presentation. The objective of the current study was to elucidate differences between the molecular mechanisms underlying FA and AA as well as to identify biomarkers and pathways associated with FA via bioinformatics analyses. Proteomic data were obtained from bone marrow samples of patients with FA and AA. Gene ontology analysis was performed using a Database for Annotation, Visualization and Integrated Discovery. KEGG pathway enrichment analyses were conducted using the ClueGO plug-in in Cytoscape. A DEP-associated protein-protein interaction (PPI) network was constructed using STRING and visualized in Cytoscape. A total of 114 DEPs, including 71 upregulated proteins and 43 downregulated proteins, were present in the FA samples, compared with those in the AA samples. Upregulated proteins were enriched in the nucleosome assembly, canonical glycolysis, glycolytic process, and the glycolysis/gluconeogenesis pathway, whereas downregulated proteins were enriched in relation to immune response, negative regulation of apoptosis, proteolysis and CoA biosynthesis. Eight hub proteins with a high degree of connectivity were obtained as follows: alpha-enolase (ENO1), HSP90AA1, phosphoglycerate kinase 1 (PGK1), HSP90AB1, ACTC1, ACTBL2, EEF1A1 and CFL1. Upregulation of ENO1 and CFL1 in patients with FA was confirmed through a WB experiment, and substantiated by the results of data analyses. Bioinformatics analyses are useful for identification of biomarkers and pathways associated with FA and AA. Some crucial DEPs, such as ENO1, PGK1, ACTC1, ACTBL2, EEF1A1 and CFL1, may play an important role in FA and show potential as serological markers for its early diagnosis. AJTR
Copyright © 2019.

Entities:  

Keywords:  Fanconi anemia; aplastic anemia; bioinformatics analysis; biomarker; proteomics

Year:  2019        PMID: 31737203      PMCID: PMC6834519     

Source DB:  PubMed          Journal:  Am J Transl Res            Impact factor:   4.060


  24 in total

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Authors:  Da Wei Huang; Brad T Sherman; Richard A Lempicki
Journal:  Nat Protoc       Date:  2009       Impact factor: 13.491

2.  Telomerase gene therapy rescues telomere length, bone marrow aplasia, and survival in mice with aplastic anemia.

Authors:  Christian Bär; Juan Manuel Povedano; Rosa Serrano; Carlos Benitez-Buelga; Miriam Popkes; Ivan Formentini; Maria Bobadilla; Fatima Bosch; Maria A Blasco
Journal:  Blood       Date:  2016-02-22       Impact factor: 22.113

3.  A new autosomal recessive anomaly mimicking Fanconi's anaemia phenotype.

Authors:  R D Milner; K A Khallouf; R Gibson; A Hajianpour; C G Mathew
Journal:  Arch Dis Child       Date:  1993-01       Impact factor: 3.791

4.  Enhanced TNF-alpha-induced apoptosis in Fanconi anemia type C-deficient cells is dependent on apoptosis signal-regulating kinase 1.

Authors:  Khadijeh Bijangi-Vishehsaraei; M Reza Saadatzadeh; Adam Werne; Kristina A Wilson McKenzie; Reuben Kapur; Hidenori Ichijo; Laura S Haneline
Journal:  Blood       Date:  2005-08-18       Impact factor: 22.113

5.  Differential expression of TP53 associated genes in Fanconi anemia cells after mitomycin C and hydroxyurea treatment.

Authors:  Angélica Martinez; John M Hinz; Laura Gómez; Bertha Molina; Hilda Acuña; Irene M Jones; Sara Frias; Matthew A Coleman
Journal:  Mutat Res       Date:  2008-07-05       Impact factor: 2.433

Review 6.  Alpha-Enolase (ENO1), a potential target in novel immunotherapies.

Authors:  Paola Cappello; Moitza Principe; Sara Bulfamante; Francesco Novelli
Journal:  Front Biosci (Landmark Ed)       Date:  2017-01-01

7.  Identification of Phosphoglycerate Kinase 1 (PGK1) as a reference gene for quantitative gene expression measurements in human blood RNA.

Authors:  Virginia R Falkenberg; Toni Whistler; Janna' R Murray; Elizabeth R Unger; Mangalathu S Rajeevan
Journal:  BMC Res Notes       Date:  2011-09-06

8.  GASOLINE: a Cytoscape app for multiple local alignment of PPI networks.

Authors:  Giovanni Micale; Andrea Continella; Alfredo Ferro; Rosalba Giugno; Alfredo Pulvirenti
Journal:  F1000Res       Date:  2014-07-01

9.  Alpha-enolase (ENO1) controls alpha v/beta 3 integrin expression and regulates pancreatic cancer adhesion, invasion, and metastasis.

Authors:  Moitza Principe; Simone Borgoni; Mariafrancesca Cascione; Michelle Samuel Chattaragada; Sammy Ferri-Borgogno; Michela Capello; Sara Bulfamante; Jennifer Chapelle; Francesca Di Modugno; Paola Defilippi; Paola Nisticò; Paola Cappello; Chiara Riganti; Stefano Leporatti; Francesco Novelli
Journal:  J Hematol Oncol       Date:  2017-01-13       Impact factor: 17.388

10.  ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks.

Authors:  Gabriela Bindea; Bernhard Mlecnik; Hubert Hackl; Pornpimol Charoentong; Marie Tosolini; Amos Kirilovsky; Wolf-Herman Fridman; Franck Pagès; Zlatko Trajanoski; Jérôme Galon
Journal:  Bioinformatics       Date:  2009-02-23       Impact factor: 6.937

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  1 in total

1.  Integrated proteome and phosphoproteome analyses of peripheral blood mononuclear cells in primary Sjögren syndrome patients.

Authors:  Shaoying Huang; Fengping Zheng; Lixiong Liu; Shuhui Meng; Wanxia Cai; Cantong Zhang; Weier Dai; Dongzhou Liu; Xiaoping Hong; Donge Tang; Yong Dai
Journal:  Aging (Albany NY)       Date:  2020-12-03       Impact factor: 5.682

  1 in total

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