Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Bioinformatics
  4. Fast Track Articles
  5. Fast Track Article
image of Single-Cell RNA Sequence Analysis to Identify Lymphatic Cell-Specific Biomarkers of Guillain-Barre Syndrome by Using Bioinformatics Approaches

Abstract

Background

An uncommon neurological condition known as Guillain-Barre syndrome (GBS) develops when the body's immunological system unintentionally targets peripheral nerves.

Aim

This work aimed to compare scRNA-seq and transcriptome data to find novel gene biomarkers linked to CD4+ T cells and B cells that might potentially be utilized for the diagnosis and assessment of GBS. It aimed to employ scRNA-seq data and bioinformatics tools analysis to identify cell-specific biomarkers for GBS diagnosis and prognosis.

Methodology

scRNA-seq and microarray datasets from the GEO database were utilized to identify differentially expressed genes (DEGs). Pathway enrichment, identification of potential hub genes, and gene regulatory studies were employed using FunRich, DAVID, STRING, and NetworkAnalyst tools.

Results

After integrating the DEGs and performing a comparative analysis, it was discovered that there were 84 DEGs shared between scRNA-seq and microarray datasets. The presence of signal transduction, immune system, cytokine signaling, NOD-like receptor signaling, and focal adhesion was detected in the most significant gene ontology and metabolic pathways. After generating a protein-protein interaction (PPI) network, we used eleven topological algorithms of the cytoHubba plugin for identifying six key hub genes, including CDC42, PTPRC, SRSF1, HNRNPA2B1, NIPBL, and FOS. Several crucial transcription factors (CHD1, IRF1, FOXC1, GATA2, YY1, E2F1, and CREB1) and two significant microRNAs (hsa-mir-20a-5p and hsa-mir-16-5p) were also discovered as hub gene regulators. The receiver operating characteristics (ROC) curve was used to evaluate the prognostic, expression, and diagnostic capabilities of the six major hub genes, indicating a good scoring value.

Conclusion

Finally, functional enrichment pathway analysis, PPI, and regulatory networks analysis demonstrated the critical functions of the identified key hub genes. After further wet lab research is validated, our research work may offer useful predicted potential biomarkers for the diagnosis and prognosis of GBS.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936357032250218072918
2025-02-28
2025-05-07

  • From This Site

    /content/journals/cbio/10.2174/0115748936357032250218072918
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Bragazzi N.L. Kolahi A.A. Nejadghaderi S.A. Lochner P. Brigo F. Naldi A. Lanteri P. Garbarino S. Sullman M.J.M. Dai H. Wu J. Kong J.D. Jahrami H. Sohrabi M.R. Safiri S. Global, regional, and national burden of Guillain–Barré syndrome and its underlying causes from 1990 to 2019. J. Neuroinflammation 2021 18 1 264 10.1186/s12974‑021‑02319‑4 34763713
    [Google Scholar]
  2. Kuwabara S. Yuki N. Axonal Guillain-Barré syndrome: Concepts and controversies. Lancet Neurol. 2013 12 12 1180 1188 10.1016/S1474‑4422(13)70215‑1 24229616
    [Google Scholar]
  3. Willison H. J. Jacobs B. C. van Doorn P. A. Guillain-Barré syndrome. Lancet 2016 388 10045 717 727 10.1016/S0140‑6736(16)00339‑1 26948435
    [Google Scholar]
  4. Huang W.C. Lu C.L. Chen S.C.C. A 15-year nationwide epidemiological analysis of Guillain-Barré syndrome in Taiwan. Neuroepidemiology 2015 44 4 249 254 10.1159/000430917 26088600
    [Google Scholar]
  5. Shahrizaila N. Lehmann H.C. Kuwabara S. Guillain-Barré syndrome. Lancet 2021 397 10280 1214 1228 10.1016/S0140‑6736(21)00517‑1 33647239
    [Google Scholar]
  6. Ozdemir H.H. Analysis of the albumin level, neutrophil-lymphocyte ratio, and platelet-lymphocyte ratio in Guillain-Barré syndrome. Arq. Neuropsiquiatr. 2016 74 9 718 722 10.1590/0004‑282X20160132 27706420
    [Google Scholar]
  7. Zhou Q.L. Li Z.K. Xu F. Liang X.G. Wang X.B. Su J. Tang Y.F. Guillain-Barré syndrome and hemophagocytic syndrome heralding the diagnosis of diffuse large B cell lymphoma: A case report. World J. Clin. Cases 2022 10 26 9502 9509 10.12998/wjcc.v10.i26.9502 36159426
    [Google Scholar]
  8. van den Berg B. Walgaard C. Drenthen J. Fokke C. Jacobs B.C. van Doorn P.A. Guillain–Barré syndrome: Pathogenesis, diagnosis, treatment and prognosis. Nat. Rev. Neurol. 2014 10 8 469 482 10.1038/nrneurol.2014.121 25023340
    [Google Scholar]
  9. Destefano F. Angulo F.J. Iskander J. Shadomy S.V. Weintraub E. Chen R.T. Surveillance for Adverse Events following Immunization from 2008 to 2011 in Zhejiang Province, China. Methods 2004 292 20 12 10.1128/CVI.00541‑12
    [Google Scholar]
  10. Rodríguez Y. Rojas M. Pacheco Y. Acosta-Ampudia Y. Ramírez-Santana C. Monsalve D.M. Gershwin M.E. Anaya J.M. Guillain–Barré syndrome, transverse myelitis and infectious diseases. Cell. Mol. Immunol. 2018 15 6 547 562 10.1038/cmi.2017.142 29375121
    [Google Scholar]
  11. Dahle C. Vrethem M. Ernerudh J. T lymphocyte subset abnormalities in peripheral blood from patients with the Guillain-Barré syndrome. J. Neuroimmunol. 1994 53 2 219 225 10.1016/0165‑5728(94)90032‑9 7520920
    [Google Scholar]
  12. Hartung H-P. Toyka K.V. T‐Cell and macrophage activation in experimental autoimmune neuritis and Guillain‐Barré syndrome. Ann. Neurol. 1990 27 S57 63 10.1002/ana.410270716 2194429
    [Google Scholar]
  13. Siegel G.J. Albers R.W. Basic neurochemistry: Molecular, cellular, and medical aspects. 7th Ed. Raven Press New York 1994 1016
    [Google Scholar]
  14. Wanschitz J. Maier H. Lassmann H. Budka H. Berger T. Distinct time pattern of complement activation and cytotoxic T cell response in Guillain-Barre syndrome. Brain 2003 126 9 2034 2042 10.1093/brain/awg207 12847075
    [Google Scholar]
  15. Chi L. Wang H. Zhang Y. Wang W. Abnormality of circulating CD4+CD25+ regulatory T cell in patients with Guillain–Barré syndrome. J. Neuroimmunol. 2007 192 1-2 206 214 10.1016/j.jneuroim.2007.09.034 17997492
    [Google Scholar]
  16. Safa A. Azimi T. Sayad A. Taheri M. Ghafouri-Fard S. A review of the role of genetic factors in Guillain–Barré syndrome. J. Mol. Neurosci. 2021 71 5 902 920 10.1007/s12031‑020‑01720‑7 33029737
    [Google Scholar]
  17. van Doorn P.A. Ruts L. Jacobs B.C. Clinical features, pathogenesis, and treatment of Guillain-Barré syndrome. Lancet Neurol. 2008 7 10 939 950 10.1016/S1474‑4422(08)70215‑1 18848313
    [Google Scholar]
  18. Lim J.P. Devaux J. Yuki N. Peripheral nerve proteins as potential autoantigens in acute and chronic inflammatory demyelinating polyneuropathies. Autoimmun. Rev. 2014 13 10 1070 1078 10.1016/j.autrev.2014.08.005 25172243
    [Google Scholar]
  19. Willison H. J. Biomarkers in experimental models of antibody-mediated neuropathies. J. Peripher. Nerv. Syst. 2011 16 Suppl 1 60 62 10.1111/j.1529‑8027.2011.00310.x 21696502
    [Google Scholar]
  20. Willison H.J. Goodyear C.S. Glycolipid antigens and autoantibodies in autoimmune neuropathies. Trends Immunol. 2013 34 9 453 459 10.1016/j.it.2013.05.001 23770405
    [Google Scholar]
  21. Blum S. McCombe P.A. Genetics of Guillain‐Barré syndrome (GBS) and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP): Current knowledge and future directions. J. Peripher. Nerv. Syst. 2014 19 2 88 103 10.1111/jns5.12074 25039604
    [Google Scholar]
  22. Chen G. Ning B. Shi T. Single-cell RNA-seq technologies and related computational data analysis. Front. Genet. 2019 10 APR 317 10.3389/fgene.2019.00317 31024627
    [Google Scholar]
  23. Súkeníková L. Mallone A. Schreiner B. Ripellino P. Nilsson J. Stoffel M. Ulbrich S.E. Sallusto F. Latorre D. Autoreactive T cells target peripheral nerves in Guillain–Barré syndrome. Nature 2024 626 7997 160 168 10.1038/s41586‑023‑06916‑6 38233524
    [Google Scholar]
  24. Butler A. Hoffman P. Smibert P. Papalexi E. Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 2018 36 5 411 420 10.1038/nbt.4096 29608179
    [Google Scholar]
  25. Lytal N. Ran D. An L. Normalization methods on single-cell RNA-seq data: An empirical survey. Front. Genet. 2020 11 41 10.3389/fgene.2020.00041
    [Google Scholar]
  26. Cole M.B. Risso D. Wagner A. DeTomaso D. Ngai J. Purdom E. Dudoit S. Yosef N. Performance assessment and selection of normalization procedures for single-cell RNA-Seq. Cell Syst. 2019 8 4 315 328.e8 10.1016/j.cels.2019.03.010 31022373
    [Google Scholar]
  27. Tsuyuzaki K. Sato H. Sato K. Nikaido I. Benchmarking principal component analysis for large-scale single-cell RNA-sequencing. Genome Biol. 2020 21 1 9 10.1186/s13059‑019‑1900‑3 31955711
    [Google Scholar]
  28. Chang K.H. Chuang T.J. Lyu R.K. Ro L.S. Wu Y.R. Chang H.S. Huang C.C. Kuo H.C. Hsu W.C. Chu C.C. Chen C.M. Identification of gene networks and pathways associated with Guillain-Barré syndrome. PLoS One 2012 7 1 e29506 10.1371/journal.pone.0029506 22253732
    [Google Scholar]
  29. Bardou P. Mariette J. Escudié F. Djemiel C. Klopp C. Jvenn: An interactive Venn diagram viewer. BMC Bioinformatics 2014 15 1 293 10.1186/1471‑2105‑15‑293 25176396
    [Google Scholar]
  30. Pathan M. Keerthikumar S. Ang C.S. Gangoda L. Quek C.Y.J. Williamson N.A. Mouradov D. Sieber O.M. Simpson R.J. Salim A. Bacic A. Hill A.F. Stroud D.A. Ryan M.T. Agbinya J.I. Mariadason J.M. Burgess A.W. Mathivanan S. FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics 2015 15 15 2597 2601 10.1002/pmic.201400515 25921073
    [Google Scholar]
  31. Sherman B.T. Hao M. Qiu J. Jiao X. Baseler M.W. Lane H.C. Imamichi T. Chang W. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022 50 W1 W216 W221 10.1093/nar/gkac194 35325185
    [Google Scholar]
  32. Athanasios A. Charalampos V. Vasileios T. Ashraf G. Protein-protein interaction (PPI) network: Recent advances in drug discovery. Curr. Drug Metab. 2017 18 1 5 10 10.2174/138920021801170119204832 28889796
    [Google Scholar]
  33. Szklarczyk D. Gable A.L. Nastou K.C. Lyon D. Kirsch R. Pyysalo S. Doncheva N.T. Legeay M. Fang T. Bork P. Jensen L.J. von Mering C. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021 49 D1 D605 D612 10.1093/nar/gkaa1074 33237311
    [Google Scholar]
  34. Chin C.H. Chen S.H. Wu H.H. Ho C.W. Ko M.T. Lin C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014 8 S4 Suppl. 4 S11 10.1186/1752‑0509‑8‑S4‑S11 25521941
    [Google Scholar]
  35. Mitsis T. Efthimiadou A. Bacopoulou F. Vlachakis D. Chrousos G. Eliopoulos E. Transcription factors and evolution: An integral part of gene expression (Review). World Academy of Sciences Journal 2020 2 1 3 8 10.3892/wasj.2020.32
    [Google Scholar]
  36. Deiuliis J.A. MicroRNAs as regulators of metabolic disease: Pathophysiologic significance and emerging role as biomarkers and therapeutics. Int. J. Obes. 2016 40 1 88 101 10.1038/ijo.2015.170 26311337
    [Google Scholar]
  37. Zhou G. Soufan O. Ewald J. Hancock R.E.W. Basu N. Xia J. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019 47 W1 W234 W241 10.1093/nar/gkz240 30931480
    [Google Scholar]
  38. Turck N. Vutskits L. Sanchez-Pena P. A multiparameter panel method for outcome prediction following aneurysmal subarachnoid hemorrhage. Intens. Care Med. 2011 36 107 115 10.1007/s00134‑009‑1641‑y
    [Google Scholar]
  39. Wang X.M. Yik W.Y. Zhang P. Lu W. Dranchak P.K. Shibata D. Steinberg S.J. Hacia J.G. The gene expression profiles of induced pluripotent stem cells from individuals with childhood cerebral adrenoleukodystrophy are consistent with proposed mechanisms of pathogenesis. Stem Cell Res. Ther. 2012 3 5 39 10.1186/scrt130 23036268
    [Google Scholar]
  40. Breville G. Sukockiene E. Vargas M.I. Lascano A.M. Emerging biomarkers to predict clinical outcomes in Guillain–Barré syndrome. Expert Rev. Neurother. 2023 23 12 1201 1215 10.1080/14737175.2023.2273386 37902064
    [Google Scholar]
  41. Caruso Bavisotto C. Scalia F. Marino Gammazza A. Carlisi D. Bucchieri F. Conway de Macario E. Macario A.J.L. Cappello F. Campanella C. Extracellular vesicle-mediated cell–cell communication in the nervous system: Focus on neurological diseases. Int. J. Mol. Sci. 2019 20 2 434 10.3390/ijms20020434 30669512
    [Google Scholar]
  42. Rink C. Görtzen A. Veh R.W. Prüss H. Serum antibodies targeting neurons of the monoaminergic systems in Guillain-Barré syndrome. J. Neurol. Sci. 2017 372 318 323 10.1016/j.jns.2016.11.078 28017237
    [Google Scholar]
  43. Gao H. Wang S. Duan H. Wang Y. Zhu H. Biological analysis of the potential pathogenic mechanisms of Infectious COVID-19 and Guillain-Barré syndrome. Front. Immunol. 2023 14 December 1290578 10.3389/fimmu.2023.1290578 38115996
    [Google Scholar]
  44. Doets A.Y. Verboon C. van den Berg B. Harbo T. Cornblath D.R. Willison H.J. Islam Z. Attarian S. Barroso F.A. Bateman K. Benedetti L. van den Bergh P. Casasnovas C. Cavaletti G. Chavada G. Claeys K.G. Dardiotis E. Davidson A. van Doorn P.A. Feasby T.E. Galassi G. Gorson K.C. Hartung H.P. Hsieh S.T. Hughes R.A.C. Illa I. Islam B. Kusunoki S. Kuwabara S. Lehmann H.C. Miller J.A.L. Mohammad Q.D. Monges S. Nobile Orazio E. Pardo J. Pereon Y. Rinaldi S. Querol L. Reddel S.W. Reisin R.C. Shahrizaila N. Sindrup S.H. Waqar W. Jacobs B.C. Jacobs B.C. Hughes R A C. Cornblath D.R. Gorson K.C. Hartung H.P. Kusunoki S. van Doorn P.A. Willison H.J. van Woerkom M. van den Berg B. Verboon C. Doets A.Y. Roodbol J. Jacobs B.C. Reisin R.C. Reddel S.W. Islam Z. Islam B. Mohammad Q.D. van den Bergh P. Feasby T.E. Harbo T. Péréon Y. Hartung H.P. Lehmann H.C. Dardiotis E. Nobile-Orazio E. Kusunoki S. Shahrizaila N. Jacobs B.C. van den Berg B. Verboon C. Doets A.Y. Bateman K. Illa I. Querol L. Hsieh S.T. Willison H.J. Chavada G. Davidson A. Gorson K.C. Addington J.M. Ajroud-Driss S. Andersen H. Antonini G. Ariatti A. Attarian S. Badrising U.A. Barroso F.A. Benedetti L. Beronio A. Bianco M. Binda D. Briani C. Bunschoten C. Bürmann J. Bella I.R. Bertorini T.E. Bhavaraju-Sanka R. Brannagan T.H. Busby M. Butterworth S. Casasnovas C. Cavaletti G. Chao C.C. Chetty S. Claeys K.G. Conti M.E. Cosgrove J.S. Dalakas M.C. Derejko M.A. Dimachkie M.M. Doppler K. Dornonville de la Cour C. Echaniz-Laguna A. Eftimov F. Faber C.G. Fazio R. Fujioka T. Fulgenzi E.A. Galassi G. Garcia-Sobrino T. Garnero M. Garssen M.P.J. Gijsbers C.J. Gilchrist J.M. Goldstein J.M. Granit V. Grapperon A. Gutiérrez G. Hadden R.D.M. Holbech J.V. Holt J.K.L. Homedes Pedret C. Htut M. Jericó Pascual I. Kaida K. Karafiath S. Katzberg H.D. Kiers L. Kieseier B.C. Kimpinski K. Kleyweg R.P. Kokubun N. Kolb N.A. Kuitwaard K. Kuwabara S. Kwan J.Y. Ladha S.S. Landschoff Lassen L. Lawson V. Ledingham D. Léon Cejas L. Lucy S.T. Lunn M.P.T. Magot A. Manji H. Marchesoni C. Marfia G.A. Márquez Infante C. Martinez Hernandez E. Mataluni G. McDermott C.J. Meekins G.D. Miller J A L. Monges M.S. Montero M.C.J. Morís de la Tassa G. Mozzoni J. Nascimbene C. Nowak R.J. Orizaloa Balaguer P. Osei-Bonsu M. Lee Pan E.B. Pardo J. Pasnoor M. Rajabally Y.A. Rinaldi S. Ritter C. Roberts R.C. Rojas-Marcos I. Rudnicki S.A. Ruiz M. Sachs G.M. Samijn J.P.A. Santoro L. Schenone A. Schwindling L. Sedano Tous M.J. Sekiguchi Y. Sheikh K.A. Silvestri N.J. Sindrup S.H. Sommer C.L. Stein B. Stino A.M. Spyropoulos A. Srinivasan J. Suzuki H. Tankisi H. Tigner D. Twydell P.T. van Damme P. van der Kooi A.J. van Dijk G.W. van der Ree T. van Koningsveld R. Varrato J.D. Vermeij F.H. Visser L.H. Vytopil M.V. Waheed W. Wilken M. Wilkerson C. Wirtz P.W. Yamagishi Y. Zhou L. Zivkovic S. Regional variation of Guillain-Barré syndrome. Brain 2018 141 10 2866 2877 10.1093/brain/awy232 30247567
    [Google Scholar]
  45. Bischoff J.P. Schulz A. Morrison H. The role of exosomes in intercellular and inter‐organ communication of the peripheral nervous system. FEBS Lett. 2022 596 5 655 664 10.1002/1873‑3468.14274 34990014
    [Google Scholar]
  46. Hagen K.M. Ousman S.S. The neuroimmunology of guillain-barré syndrome and the potential role of an aging immune system. Front. Aging Neurosci. 2021 12 January 613628 10.3389/fnagi.2020.613628 33584245
    [Google Scholar]
  47. Zhu J. Mix E. Link H. Cytokine production and the pathogenesis of experimental autoimmune neuritis and Guillain–Barré syndrome. J. Neuroimmunol. 1998 84 1 40 52 10.1016/S0165‑5728(97)00238‑5 9600707
    [Google Scholar]
  48. Gries M. Davies L. Liu Y. Bachhuber A. Spiegel J. Dillmann U. Hartmann T. Fassbender K. Walter S. Response of Toll-like receptors in experimental Guillain–Barré syndrome: A kinetic analysis. Neurosci. Lett. 2012 518 2 154 160 10.1016/j.neulet.2012.04.077 22579825
    [Google Scholar]
  49. Wang C. Liao S. Wang Y. Hu X. Xu J. Computational identification of guillain-barré syndrome-related genes by an mRNA gene expression profile and a protein–protein interaction network. Front. Mol. Neurosci. 2022 15 March 850209 10.3389/fnmol.2022.850209 35370550
    [Google Scholar]
  50. Raman K. Construction and analysis of protein–protein interaction networks. Autom. Exp. 2010 2 1 2 10.1186/1759‑4499‑2‑2 20334628
    [Google Scholar]
  51. Sevimoglu T. Arga K.Y. The role of protein interaction networks in systems biomedicine. Comput. Struct. Biotechnol. J. 2014 11 18 22 27 10.1016/j.csbj.2014.08.008 25379140
    [Google Scholar]
  52. Evers E.E. Zondag G.C.M. Malliri A. Price L.S. ten Klooster J.P. van der Kammen R.A. Collard J.G. Rho family proteins in cell adhesion and cell migration. Eur. J. Cancer 2000 36 10 1269 1274 10.1016/S0959‑8049(00)00091‑5 10882865
    [Google Scholar]
  53. Laman J.D. Huizinga R. Boons G.J. Jacobs B.C. Guillain-Barré syndrome: Expanding the concept of molecular mimicry. Trends Immunol. 2022 43 4 296 308 10.1016/j.it.2022.02.003 35256276
    [Google Scholar]
  54. Sheikh K.A. Autoantobodies activate small GTPase RhoA to modulate neurite outgrowth. Small GTPases 2011 2 4 233 238 10.4161/sgtp.2.4.17115 22145097
    [Google Scholar]
  55. Al Barashdi M.A. Ali A. McMullin M.F. Mills K. Protein tyrosine phosphatase receptor type C (PTPRC or CD45). J. Clin. Pathol. 2021 74 9 548 552 10.1136/jclinpath‑2020‑206927 34039664
    [Google Scholar]
  56. Costa Lima de Souza D. Basílio Guimarães R. Alves de Siqueira Carvalho A. Guillain-Barre syndrome related to COVID-19: Muscle and nerve biopsy findings. J. Hum. Growth Dev. 2021 31 3 465 469 10.36311/jhgd.v31.12183
    [Google Scholar]
  57. Ning K. Sandoval-Castellanos A.M. Bhargava A. Zhao M. Xu J. Serine and arginine rich splicing factor 1: A potential target for neuroprotection and other diseases. Neural Regen. Res. 2023 18 7 1411 1416 10.4103/1673‑5374.360243 36571335
    [Google Scholar]
  58. Martinez F.J. Pratt G.A. Van Nostrand E.L. Batra R. Huelga S.C. Kapeli K. Freese P. Chun S.J. Ling K. Gelboin-Burkhart C. Fijany L. Wang H.C. Nussbacher J.K. Broski S.M. Kim H.J. Lardelli R. Sundararaman B. Donohue J.P. Javaherian A. Lykke-Andersen J. Finkbeiner S. Bennett C.F. Ares M. Jr Burge C.B. Taylor J.P. Rigo F. Yeo G.W. Protein-RNA networks regulated by normal and ALS-associated mutant HNRNPA2B1 in the nervous system. Neuron 2016 92 4 780 795 10.1016/j.neuron.2016.09.050 27773581
    [Google Scholar]
  59. Puisac B. Teresa-Rodrigo M.E. Hernández-Marcos M. Baquero-Montoya C. Gil-Rodríguez M.C. Visnes T. Bot C. Gómez-Puertas P. Kaiser F. Ramos F. Ström L. Pié J. mRNA quantification of NIPBL isoforms A and B in adult and fetal human tissues, and a potentially pathological variant affecting only isoform a in two patients with Cornelia de Lange syndrome. Int. J. Mol. Sci. 2017 18 3 481 10.3390/ijms18030481 28241484
    [Google Scholar]
  60. Skene P.J. Hernandez A.E. Groudine M. Henikoff S. The nucleosomal barrier to promoter escape by RNA polymerase II is overcome by the chromatin remodeler Chd1. eLife 2014 3 e02042 10.7554/eLife.02042 24737864
    [Google Scholar]
  61. Guzman-Ayala M. Sachs M. Koh F.M. Onodera C. Bulut-Karslioglu A. Lin C.J. Wong P. Nitta R. Song J.S. Ramalho-Santos M. Chd1 is essential for the high transcriptional output and rapid growth of the mouse epiblast. Development 2015 142 1 118 127 10.1242/dev.114843 25480920
    [Google Scholar]
  62. Wei Y. Qi K. Yu Y. Lu W. Xu W. Yang C. Lin Y. Analysis of differentially expressed genes in the dentate gyrus and anterior cingulate cortex in a mouse model of depression. BioMed Res. Int. 2021 2021 1 17 10.1155/2021/5013565 33628784
    [Google Scholar]
  63. Watanabe T. Asano N. Fichtner-Feigl S. Gorelick P.L. Tsuji Y. Matsumoto Y. Chiba T. Fuss I.J. Kitani A. Strober W. NOD1 contributes to mouse host defense against Helicobacter pylori via induction of type I IFN and activation of the ISGF3 signaling pathway. J. Clin. Invest. 2010 120 5 1645 1662 10.1172/JCI39481 20389019
    [Google Scholar]
  64. Zaheer R.S. Proud D. Human rhinovirus-induced epithelial production of CXCL10 is dependent upon IFN regulatory factor-1. Am. J. Respir. Cell Mol. Biol. 2010 43 4 413 421 10.1165/rcmb.2009‑0203OC 19880820
    [Google Scholar]
  65. Ren Z. Wang Y. Liebenson D. Liggett T. Goswami R. Stefoski D. Balabanov R. IRF-1 signaling in central nervous system glial cells regulates inflammatory demyelination. J. Neuroimmunol. 2011 233 1-2 147 159 10.1016/j.jneuroim.2011.01.001 21257209
    [Google Scholar]
  66. Kenzel S. Santos-Sierra S. Deshmukh S.D. Moeller I. Ergin B. Fitzgerald K.A. Lien E. Akira S. Golenbock D.T. Henneke P. Role of p38 and early growth response factor 1 in the macrophage response to group B streptococcus. Infect. Immun. 2009 77 6 2474 2481 10.1128/IAI.01343‑08 19332535
    [Google Scholar]
  67. Li J. Wang Y. Meng X. Liang H. Modulation of transcriptional activity in brain lower grade glioma by alternative splicing. PeerJ 2018 6 MAY e4686 10.7717/peerj.4686 29780667
    [Google Scholar]
  68. Han B. Bhowmick N. Qu Y. Chung S. Giuliano A.E. Cui X. FOXC1: An emerging marker and therapeutic target for cancer. Oncogene 2017 36 28 3957 3963 10.1038/onc.2017.48 28288141
    [Google Scholar]
  69. Xia L. Huang W. Tian D. Zhu H. Qi X. Chen Z. Zhang Y. Hu H. Fan D. Nie Y. Wu K. Overexpression of forkhead box C1 promotes tumor metastasis and indicates poor prognosis in hepatocellular carcinoma. Hepatology 2013 57 2 610 624 10.1002/hep.26029 22911555
    [Google Scholar]
  70. Kurzawski M. Białecka M. Sławek J. Kłodowska-Duda G. Droździk M. Association study of GATA-2 transcription factor gene (GATA2) polymorphism and Parkinson’s disease. Parkinsonism Relat. Disord. 2010 16 4 284 287 10.1016/j.parkreldis.2009.10.006 19864173
    [Google Scholar]
  71. He Y. Casaccia-Bonnefil P. The Yin and Yang of YY1 in the nervous system. J. Neurochem. 2008 106 4 1493 1502 10.1111/j.1471‑4159.2008.05486.x 18485096
    [Google Scholar]
  72. Lu L. Liu L.P. Gui R. Dong H. Su Y.R. Zhou X.H. Liu F.X. Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach. Front. Immunol. 2022 13 August 975848 10.3389/fimmu.2022.975848 36119022
    [Google Scholar]
  73. Ahmed F. F. Reza S. Sarker S. Identification of host transcriptome-guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. PLoS One 2022 17 4 e0266124 10.1371/journal.pone.0266124 35390032
    [Google Scholar]
  74. Fauci A.S. Morens D.M. Zika virus in the americas — yet another arbovirus threat. N. Engl. J. Med. 2016 374 7 601 604 10.1056/NEJMp1600297 26761185
    [Google Scholar]
  75. Tang H. Hammack C. Ogden S.C. Wen Z. Qian X. Li Y. Yao B. Shin J. Zhang F. Lee E.M. Christian K.M. Didier R.A. Jin P. Song H. Ming G. Zika virus infects human cortical neural progenitors and attenuates their growth. Cell Stem Cell 2016 18 5 587 590 10.1016/j.stem.2016.02.016 26952870
    [Google Scholar]
  76. Wu J. Ablation of the transcription factors E2F1-2 limits neuroinflammation and associated neurological deficits after contusive spinal cord injury. Cell Cycle 2015 14 23 3698 3712 10.1080/15384101.2015.1104436 26505089
    [Google Scholar]
  77. Su L. Song X. Xue Z. Zheng C. Yin H. Wei H. Network analysis of microRNAs, transcription factors, and target genes involved in axon regeneration. J. Zhejiang Univ. Sci. B 2018 19 4 293 304 10.1631/jzus.B1700179 29616505
    [Google Scholar]
  78. Liu L. Zhang Y. Chen Y. Zhao Y. Shen J. Wu X. Li M. Chen M. Li X. Sun Y. Gu L. Li W. Wang F. Yao L. Zhang Z. Xiao Z. Du F. Therapeutic prospects of ceRNAs in COVID-19. Front. Cell. Infect. Microbiol. 2022 12 September 998748 10.3389/fcimb.2022.998748 36204652
    [Google Scholar]
  79. Deng X. Luo Y. Guan T. Guo X. Identification of the genetic influence of SARS-CoV-2 infections on IgA nephropathy based on bioinformatics method. Kidney Blood Press. Res. 2023 48 1 367 384 10.1159/000529687 37040729
    [Google Scholar]
  80. Kim W.R. Park E.G. Kang K.W. Lee S.M. Kim B. Kim H.S. Expression analyses of micrornas in hamster lung tissues infected by SARS-CoV-2. Mol. Cells 2020 43 11 953 963 10.14348/molcells.2020.0177 33199671
    [Google Scholar]
  81. Asadi M.R. Talebi M. Gharesouran J. Sabaie H. Jalaiei A. Arsang-Jang S. Taheri M. Sayad A. Rezazadeh M. Analysis of ROQUIN, Tristetraprolin (TTP), and BDNF/miR-16/TTP regulatory axis in late onset Alzheimer’s disease. Front. Aging Neurosci. 2022 14 August 933019 10.3389/fnagi.2022.933019 36016853
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936357032250218072918
Loading
Single-Cell RNA Sequence Analysis to Identify Lymphatic Cell-Specific Biomarkers of Guillain-Barre Syndrome by Using Bioinformatics Approaches
Bentham Science Publishers ; https://doi.org/10.2174/0115748936357032250218072918
/content/journals/cbio/10.2174/0115748936357032250218072918
/content/journals/cbio/10.2174/0115748936357032250218072918
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: CDC42 ; ROC curve ; Guillain-Barre syndrome ; FOXC1 ; pathway ; hub gene ; hsa-mir-16-5p

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test