Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Bioinformatics
  4. Fast Track Articles
  5. Fast Track Article
image of Integrative Analysis of Single Cell and Bulk RNA Sequencing Data Reveals T-Cell Specific Biomarkers for Diagnosis and Assessment of Celiac Disease: A Comprehensive Bioinformatics Approach

Abstract

Background

Celiac Disease (CD) is a common autoimmune disorder caused by the activation of CD4+ T cells that specifically target gluten and CD8+ T cells, further causing cell death inside the epithelial layer despite no available established biomarkers of CD diagnosis.

Objective

This work aimed to compare scRNA-seq and transcriptome data to find novel gene biomarkers linked to T cells that might potentially be utilized for the diagnosis and assessment of CD.

Methods

Collecting the scRNA and RNAseq datasets from the NCBI database, the Seurat package of R studio, and the statistical analysis tool GREIN server were employed to identify Differentially Expressed Genes (DEGs). Then, DAVID, FunRich, STRING, and NetworkAnalyst tools were utilized to explore significant pathways, key hub proteins, and gene regulators.

Results

After integrating genes and conducting a comparative analysis, a total of 115 genes were identified as DEGs. Exosomes, MHC class II receptor activity, immune response, interferon gamma signaling, and bystander B cell activation within the immune system pathways were the significant Gene Ontology (GO) and metabolic pathways identified. Besides, eleven topological algorithms discovered two hub proteins, namely HLA-DRA and HLA-DRB1, from the PPI network. Through the analysis of the regulatory network, we have identified four crucial Transcription Factors (TFs), including YY1, FOXC1, GATA2, and USF2, and seven significant miRNAs (hsa-mir-129-2-3p, and hsa-mir-155-5p, .) in transcriptionally and post-transcriptionally regulated. Validation of hub proteins and transcription factors using Receiver Operating Characteristic (ROC) analysis indicates the acceptable value of the Area Under the Curve (AUC).

Conclusion

This study utilized single-cell RNA sequencing and transcriptomics data analysis to define unique protein biomarkers associated with T cells throughout the progression of CD. Furthermore, wet lab studies will be needed to validate the potential hub proteins, TFs, and miRNAs as clinical biomarkers.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936353313250123071744
2025-02-10
2025-05-31

  • From This Site

    /content/journals/cbio/10.2174/0115748936353313250123071744
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Cole M.W. Ross B.J. Collins L.K. Imonugo O. Sherman W.F. The impact of celiac disease on complication rates after total joint arthroplasty: A matched cohort study. Arthroplast. Today 2022 17 205 210.e3 10.1016/j.artd.2022.08.001 36254209
    [Google Scholar]
  2. Cichewicz A.B. Mearns E.S. Taylor A. Boulanger T. Gerber M. Leffler D.A. Drahos J. Sanders D.S. Craig T.K.J. Lebwohl B. Diagnosis and treatment patterns in celiac disease. Dig. Dis. Sci. 2019 64 8 2095 2106 10.1007/s10620‑019‑05528‑3 30820708
    [Google Scholar]
  3. Fasano A. Catassi C. Celiac disease. N. Engl. J. Med. 2012 367 25 2419 2426 10.1056/NEJMcp1113994 23252527
    [Google Scholar]
  4. Green P.H.R. Cellier C. Celiac Disease. 2007 www.nejm.org 10.1056/NEJMra071600
    [Google Scholar]
  5. Roy G. Bañares F.F. Corzo M. Aguililla G.S. Hoz G.C. Núñez C. Intestinal and blood lymphograms as new diagnostic tests for celiac disease. Front. Immunol. 2023 13 1081955 10.3389/fimmu.2022.1081955
    [Google Scholar]
  6. Rodrigo L. Celiac disease. World J. Gastroenterol. 2006 12 41 6585 6593 10.3748/wjg.v12.i41.6585 17075969
    [Google Scholar]
  7. Caio G. Celiac disease: A comprehensive current review. BMC Med. 2019 17 1 142 10.1186/s12916‑019‑1380‑z
    [Google Scholar]
  8. Valitutti F. Trovato C. M. Montuori M. Cucchiara S. Pediatric celiac disease: Follow-up in the spotlight. Adv. Nutr. 2017 8 2 356 361 10.3945/an.116.013292
    [Google Scholar]
  9. Tjon J.M.L. Bergen v.J. Koning F. Celiac disease: How complicated can it get? Immunogenetics 2010 62 10 641 651 10.1007/s00251‑010‑0465‑9 20661732
    [Google Scholar]
  10. Huang J. Khademi M. Fugger L. Lindhe Ö. Novakova L. Axelsson M. Malmeström C. Constantinescu C. Lycke J. Piehl F. Olsson T. Kockum I. Inflammation-related plasma and CSF biomarkers for multiple sclerosis. Proc. Natl. Acad. Sci. USA 2020 117 23 12952 12960 10.1073/pnas.1912839117 32457139
    [Google Scholar]
  11. Arneth B. Kraus J. Laboratory biomarkers of multiple sclerosis (MS). Clin. Biochem. 2022 99 1 8 10.1016/j.clinbiochem.2021.10.004 34673037
    [Google Scholar]
  12. Macosko E.Z. Basu A. Satija R. Nemesh J. Shekhar K. Goldman M. Tirosh I. Bialas A.R. Kamitaki N. Martersteck E.M. Trombetta J.J. Weitz D.A. Sanes J.R. Shalek A.K. Regev A. McCarroll S.A. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015 161 5 1202 1214 10.1016/j.cell.2015.05.002 26000488
    [Google Scholar]
  13. Chen X. Teichmann S.A. Meyer K.B. From tissues to cell types and back: Single-cell gene expression analysis of tissue architecture. Annu. Rev. Biomed. Data Sci. 2018 1 1 29 51 10.1146/annurev‑biodatasci‑080917‑013452
    [Google Scholar]
  14. Olsen T.K. Baryawno N. Introduction to single‐cell rna sequencing. Curr. Protoc. Mol. Biol. 2018 122 1 e57 10.1002/cpmb.57 29851283
    [Google Scholar]
  15. Ding J. Systematic comparative analysis of single cell RNA-sequencing methods. Nat. Biotechnol. 2020 38 6 737 746 10.1101/632216
    [Google Scholar]
  16. 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]
  17. Mahi N.A. Najafabadi M.F. Pilarczyk M. Kouril M. Medvedovic M. GREIN: An interactive web platform for re-analyzing geo rna-seq data. Sci. Rep. 2019 9 1 7580 10.1038/s41598‑019‑43935‑8 31110304
    [Google Scholar]
  18. Mayassi T. Ladell K. Gudjonson H. McLaren J.E. Shaw D.G. Tran M.T. Rokicka J.J. Lawrence I. Grenier J.C. Unen v.V. Ciszewski C. Dimaano M. Sayegh H.E. Kumar V. Wijmenga C. Green P.H.R. Gokhale R. Jericho H. Semrad C.E. Guandalini S. Dinner A.R. Kupfer S.S. Reid H.H. Barreiro L.B. Rossjohn J. Price D.A. Jabri B. Chronic inflammation permanently reshapes tissue-resident immunity in celiac disease. Cell 2019 176 5 967 981.e19 10.1016/j.cell.2018.12.039 30739797
    [Google Scholar]
  19. Rahman M.H. Peng S. Hu X. Chen C. Rahman M.R. Uddin S. Quinn J.M.W. Moni M.A. A network-based bioinformatics approach to identify molecular biomarkers for type 2 diabetes that are linked to the progression of neurological diseases. Int. J. Environ. Res. Public Health 2020 17 3 1035 10.3390/ijerph17031035 32041280
    [Google Scholar]
  20. Lytal N. Ran D. An L. Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey. Front. Genet. 2020 11 February 41 10.3389/fgene.2020.00041 32117453
    [Google Scholar]
  21. 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]
  22. 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]
  23. Sultana A. Alam M.S. Liu X. Sharma R. Singla R.K. Gundamaraju R. Shen B. Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches. Transl. Oncol. 2023 27 January 101571 10.1016/j.tranon.2022.101571 36401966
    [Google Scholar]
  24. Levine J.H. Simonds E.F. Bendall S.C. Davis K.L. Amir E.D. Tadmor M.D. Litvin O. Fienberg H.G. Jager A. Zunder E.R. Finck R. Gedman A.L. Radtke I. Downing J.R. Pe’er D. Nolan G.P. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. cell 2015 162 1 184 197 10.1016/j.cell.2015.05.047 26095251
    [Google Scholar]
  25. Huang D. W. DAVID bioinformatics resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids. Res. 2007 35 Web Server issue W169 W175 10.1093/nar/gkm415
    [Google Scholar]
  26. 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]
  27. Tang D. Chen M. Huang X. Zhang G. Zeng L. Zhang G. Wu S. Wang Y. SRplot: A free online platform for data visualization and graphing. PLoS One 2023 18 11 e0294236 10.1371/journal.pone.0294236 37943830
    [Google Scholar]
  28. Szklarczyk D. Gable A.L. Lyon D. Junge A. Wyder S. Cepas H.J. Simonovic M. Doncheva N.T. Morris J.H. Bork P. Jensen L.J. Mering C. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019 47 D1 D607 D613 10.1093/nar/gky1131 30476243
    [Google Scholar]
  29. Paul Shannon Cytoscape: A Software Environment for Integrated Models,”. Genome Res. 1971 13 22 426
    [Google Scholar]
  30. 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]
  31. Xia J. Gill E.E. Hancock R.E.W. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc. 2015 10 6 823 844 10.1038/nprot.2015.052 25950236
    [Google Scholar]
  32. Fornes O. Mondragon C.J.A. Khan A. van der Lee R. Zhang X. Richmond P.A. Modi B.P. Correard S. Gheorghe M. Baranašić D. Garcia S.W. Tan G. Chèneby J. Ballester B. Parcy F. Sandelin A. Lenhard B. Wasserman W.W. Mathelier A. JASPAR 2020: Update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2019 48 D1 gkz1001 10.1093/nar/gkz1001 31701148
    [Google Scholar]
  33. Sethupathy P. Corda B. Hatzigeorgiou A.G. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA 2006 12 2 192 197 10.1261/rna.2239606 16373484
    [Google Scholar]
  34. Walter W. Cabo S.F. Ricote M. GOplot: An R package for visually combining expression data with functional analysis. Bioinformatics 2015 31 17 2912 2914 10.1093/bioinformatics/btv300 25964631
    [Google Scholar]
  35. Rajalahti T. Arneberg R. Kroksveen A.C. Berle M. Myhr K.M. Kvalheim O.M. Discriminating variable test and selectivity ratio plot: Quantitative tools for interpretation and variable (biomarker) selection in complex spectral or chromatographic profiles. Anal. Chem. 2009 81 7 2581 2590 10.1021/ac802514y 19228047
    [Google Scholar]
  36. Mandrekar S.J. Sargent D.J. Clinical trial designs for predictive biomarker validation: Theoretical considerations and practical challenges. J. Clin. Oncol. 2009 27 24 4027 4034 10.1200/JCO.2009.22.3701 19597023
    [Google Scholar]
  37. Geistlinger L. Csaba G. Santarelli M. Ramos M. Schiffer L. Turaga N. Law C. Davis S. Carey V. Morgan M. Zimmer R. Waldron L. Toward a gold standard for benchmarking gene set enrichment analysis. Brief. Bioinform. 2021 22 1 545 556 10.1093/bib/bbz158 32026945
    [Google Scholar]
  38. Kuleshov M.V. Jones M.R. Rouillard A.D. Fernandez N.F. Duan Q. Wang Z. Koplev S. Jenkins S.L. Jagodnik K.M. Lachmann A. McDermott M.G. Monteiro C.D. Gundersen G.W. Ma’ayan A. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016 44 W1 W90 W97 10.1093/nar/gkw377 27141961
    [Google Scholar]
  39. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011 Vol. 8 12 77
    [Google Scholar]
  40. Medrano L.M. Dema B. Larios L.A. Maluenda C. Bodas A. Palacios L.N. Figueredo M.Á. Arquero F.M. Núñez C. HLA and celiac disease susceptibility: New genetic factors bring open questions about the HLA influence and gene-dosage effects. PLoS One 2012 7 10 e48403 10.1371/journal.pone.0048403 23119005
    [Google Scholar]
  41. Aydemir Ö. Polymorphisms in intron 1 of hla-dra differentially associate with type 1 diabetes and celiac disease and implicate involvement of complement system genes C4A and C4B medRxiv 2006 2023 2023
    [Google Scholar]
  42. Sallese M. Lopetuso L.R. Efthymakis K. Neri M. Beyond the hla genes in gluten-related disorders. Front. Nutr. 2020 7 November 575844 10.3389/fnut.2020.575844 33262997
    [Google Scholar]
  43. Megiorni F. Pizzuti A. HLA-DQA1 and HLA-DQB1 in Celiac disease predisposition: Practical implications of the HLA molecular typing. J. Biomed. Sci. 2012 19 1 88 10.1186/1423‑0127‑19‑88 23050549
    [Google Scholar]
  44. Rodero R.S. Rodrigo L. Morera F.J.L. Borra M.J. Vázquez L.A. Fuentes D. Arbesu L.R. Soto L.A. Gonzalez S. Larrea L.C. MHC class I chain-related gene B promoter polymorphisms and celiac disease. Hum. Immunol. 2006 67 3 208 214 10.1016/j.humimm.2006.02.011 16698444
    [Google Scholar]
  45. Poddighe D. Capittini C. The role of HLA in the association between iga deficiency and celiac disease. Dis. Markers 2021 2021 1 8 10.1155/2021/8632861 35186163
    [Google Scholar]
  46. Schweiger S.D. Mendez A. Jamnik K.S. Bratanic N. Bratina N. Battelino T. Brecelj J. Jeras v.B. Genetic risk for co‐occurrence of type 1 diabetes and celiac disease is modified by HLA ‐C and killer immunoglobulin‐like receptors. Tissue Antigens 2014 84 5 471 478 10.1111/tan.12450 25329633
    [Google Scholar]
  47. Jabri B. Selective expansion of intraepithelial lymphocytes expressing the hla-e-specific natural killer receptor cd94 in celiac disease. Gastroenterology 2000 118 5 867 879 10.1016/S0016‑5085(00)70173‑9
    [Google Scholar]
  48. Popat S. Hearle N. Hogberg L. Braegger C.P. O’Donoghue D. Magnusson F.K. Holmes G.K.T. Howdle P.D. Jenkins H. Johnston S. Kennedy N.P. Kumar P.J. Logan R.F.A. Marsh M.N. Mulder C.J. Naluai T.A. Sjoberg K. Stenhammar L. Walters J.R.F. Jewell D.P. Houlston R.S. Variation in the CTLA4/CD28 gene region confers an increased risk of coeliac disease. Ann. Hum. Genet. 2002 66 2 125 137 10.1046/j.1469‑1809.2002.00102.x 12174216
    [Google Scholar]
  49. Anjum N. Baker P.N. Robinson N.J. Aplin J.D. Maternal celiac disease autoantibodies bind directly to syncytiotrophoblast and inhibit placental tissue transglutaminase activity. Reprod. Biol. Endocrinol. 2009 7 1 16 10.1186/1477‑7827‑7‑16 19228395
    [Google Scholar]
  50. Hudec M. Juříčková I. Riegerová K. Ovsepian S.V. Černá M. O’Leary V.B. Enhanced extracellular transfer of hla-dq activates cd3+ lymphocytes towards compromised treg induction in celiac disease. Int. J. Mol. Sci. 2022 23 11 6102 10.3390/ijms23116102 35682780
    [Google Scholar]
  51. Yao Y. Zia A. Neumann R.S. Pavlovic M. Balaban G. Lundin K.E.A. Sandve G.K. Qiao S.W. T cell receptor repertoire as a potential diagnostic marker for celiac disease. Clin. Immunol. 2021 222 108621 10.1016/j.clim.2020.108621 33197618
    [Google Scholar]
  52. Meresse B. Malamut G. Bensussan C.N. Celiac disease: An immunological jigsaw. Immunity 2012 36 6 907 919 10.1016/j.immuni.2012.06.006 22749351
    [Google Scholar]
  53. Qiao S.W. Iversen R. Ráki M. Sollid L.M. The adaptive immune response in celiac disease. Semin. Immunopathol. 2012 34 4 523 540 10.1007/s00281‑012‑0314‑z 22535446
    [Google Scholar]
  54. Camarca M.E. Mozzillo E. Nugnes R. Zito E. Falco M. Fattorusso V. Mobilia S. Buono P. Valerio G. Troncone R. Franzese A. Celiac disease in type 1 diabetes mellitus. Ital. J. Pediatr. 2012 38 1 10 10.1186/1824‑7288‑38‑10 22449104
    [Google Scholar]
  55. Londei M. Ciacci C. Ricciardelli I. Vacca L. Quaratino S. Maiuri L. Gliadin as a stimulator of innate responses in celiac disease. Mol. Immunol. 2005 42 8 913 918 10.1016/j.molimm.2004.12.005 15829281
    [Google Scholar]
  56. Kumar V. Achury G.J. Kanduri K. Almeida R. Hrdlickova B. Zhernakova D.V. Westra H.J. Karjalainen J. Ponce R.I. Li Y. Stachurska A. Tigchelaar E.F. Abdulahad W.H. Lähdesmäki H. Hofker M.H. Zhernakova A. Franke L. Lahesmaa R. Wijmenga C. Withoff S. Systematic annotation of celiac disease loci refines pathological pathways and suggests a genetic explanation for increased interferon-gamma levels. Hum. Mol. Genet. 2015 24 2 397 409 10.1093/hmg/ddu453 25190711
    [Google Scholar]
  57. Comba A. Çaltepe G. Yanık K. Gör U. Yüce Ö. Kalaycı A.G. Assessment of endothelial dysfunction with adhesion molecules in patients with celiac disease. J. Pediatr. Gastroenterol. Nutr. 2016 63 2 247 252 10.1097/MPG.0000000000001138 26835908
    [Google Scholar]
  58. 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
    [Google Scholar]
  59. Zimmer K.P. Poremba C. Weber P. Ciclitira P.J. Harms E. Translocation of gliadin into HLA-DR antigen containing lysosomes in coeliac disease enterocytes. Gut 1995 36 5 703 709 10.1136/gut.36.5.703 7797120
    [Google Scholar]
  60. de la Concha E.G. Arquero F.M. Vigil P. Rubio A. Maluenda C. Polanco I. Fernandez C. Figueredo M.A. Celiac disease and TNF promoter polymorphisms. Hum. Immunol. 2000 61 5 513 517 10.1016/S0198‑8859(99)00187‑1 10773355
    [Google Scholar]
  61. Mohammed M.A. Omar N.M. Shebl A.M. Mansour A.H. Elmasry E. Othman G. Celiac disease prevalence and its HLA-genotypic profile in Egyptian patients with type 1 diabetes mellitus. Trends Med. Res. 2014 9 2 81 97 10.3923/tmr.2014.81.97
    [Google Scholar]
  62. Levine A. Wolfson E. Bujanover Y. Reifen R. Gastroenterology The Wolfson Medical CenterMinistry of Health Israel 2000 May 2014
    [Google Scholar]
  63. Bresnick E.H. Jung M.M. Katsumura K.R. Human GATA2 mutations and hematologic disease: How many paths to pathogenesis? Blood Adv. 2020 4 18 4584 4592 10.1182/bloodadvances.2020002953 32960960
    [Google Scholar]
  64. Shibata N. Genetic association between usf 1 and usf 2 gene polymorphisms and japanese alzheimer’s disease downloaded from. J. Gerontol Biol. Sci. Med. Sci. 2006 61 7 660 662
    [Google Scholar]
  65. Gamboa B.K. A. Quezada A.M. Bravo P.F. MicroRNAs: An epigenetic tool to study celiac disease. Rev. Esp. Enferm. Dig. 2014 106 5 325 333
    [Google Scholar]
  66. Felli C. Baldassarre A. Masotti A. Intestinal and circulating micrornas in coeliac disease. Int. J. Mol. Sci. 2017 18 9 1907 10.3390/ijms18091907
    [Google Scholar]
  67. Kurki A. Kemppainen E. Laurikka P. Kaukinen K. Lindfors K. The use of peripheral blood mononuclear cells in celiac disease diagnosis and treatment 2021 10.1080/17474124.2021.1850262
    [Google Scholar]
  68. Bianchi N. Doneda L. Elli L. Taccioli C. Vaira V. Scricciolo A. Lombardo V. Terrazzan A. Colapietro P. Terranova L. Bergamini C. Vecchi M. Scaramella L. Nandi N. Roncoroni L. Circulating micrornas suggest networks associated with biological functions in aggressive refractory type 2 celiac disease. Biomedicines 2022 10 6 1408 10.3390/biomedicines10061408 35740429
    [Google Scholar]
  69. Bascuñán K.A. Bravo P.F. Gaudioso G. Vaira V. Roncoroni L. Elli L. Monguzzi E. Araya M. A miRNA-based blood and mucosal approach for detecting and monitoring celiac disease. Dig. Dis. Sci. 2020 65 7 1982 1991 10.1007/s10620‑019‑05966‑z 31781909
    [Google Scholar]
  70. Giuffrida P. Sabatino D.A. MicroRNAs in celiac disease diagnosis: A mir curiosity or game-changer? Dig. Dis. Sci. 2020 65 7 1877 1879 10.1007/s10620‑020‑06081‑0
    [Google Scholar]
  71. Tan I.L. Coutinho de Almeida R. Modderman R. Stachurska A. Dekens J. Barisani D. Meijer C.R. Roca M. Ojinaga M.E. Shamir R. Auricchio R. Szabó K.I.R. Castillejo G. Szajewska H. Koletzko S. Zhernakova A. Kumar V. Li Y. Visschedijk M.C. Weersma R.K. Troncone R. Mearin M.L. Wijmenga C. Jonkers I. Withoff S. Circulating miRNAs as potential biomarkers for celiac disease development. Front. Immunol. 2021 12 734763 10.3389/fimmu.2021.734763 34950132
    [Google Scholar]
  72. Hart M. Diener C. Lunkes L. Rheinheimer S. Krammes L. Keller A. Meese E. miR-34a-5p as molecular hub of pathomechanisms in Huntington’s disease. Mol. Med. 2023 29 1 43 10.1186/s10020‑023‑00640‑7 37013480
    [Google Scholar]
  73. He L. Chen Z. Wang J. Feng H. Expression relationship and significance of NEAT1 and miR-27a-3p in serum and cerebrospinal fluid of patients with Alzheimer’s disease. BMC Neurol. 2022 22 1 203 10.1186/s12883‑022‑02728‑9 35659599
    [Google Scholar]
  74. Chen D. Wu X. Xia M. Wu F. Ding J. Jiao Y. Zhan Q. An F. Upregulated exosomic miR-23b-3p plays regulatory roles in the progression of pancreatic cancer. Oncol. Rep. 2017 38 4 2182 2188 10.3892/or.2017.5919 28849236
    [Google Scholar]
  75. Alamro H. Bajic V. Macvanin M.T. Isenovic E.R. Gojobori T. Essack M. Gao X. Type 2 Diabetes Mellitus and its comorbidity, Alzheimer’s disease: Identifying critical microRNA using machine learning. Front. Endocrinol. 2023 13 1084656 10.3389/fendo.2022.1084656 36743910
    [Google Scholar]
  76. Kachare P.H. Sangle S.B. Puri D.V. Khubrani M.M. Shourbaji A.I. STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network. Cogn. Neurodynamics 2024 18 5 3195 3208 10.1007/s11571‑024‑10153‑6 39555263
    [Google Scholar]
  77. Puri D.V. Kachare P.H. Sangle S.B. Kirner R. Jabbari A. Shourbaji A.I. Abdalraheem M. Alameen A. LEADNet: Detection of alzheimer’s disease using spatiotemporal eeg analysis and low-complexity cnn. IEEE Access 2024 12 July 113888 113897 10.1109/ACCESS.2024.3435768
    [Google Scholar]
  78. Shourbaji A.I. Kachare P.H. Abualigah L. Abdelhag M.E. Elnaim B. Anter A.M. Gandomi A.H. A deep batch normalized convolution approach for improving COVID-19 detection from chest x-ray images. Pathogens 2022 12 1 17 10.3390/pathogens12010017 36678365
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936353313250123071744
Loading
Integrative Analysis of Single Cell and Bulk RNA Sequencing Data Reveals T-Cell Specific Biomarkers for Diagnosis and Assessment of Celiac Disease: A Comprehensive Bioinformatics Approach
Bentham Science Publishers ; https://doi.org/10.2174/0115748936353313250123071744
/content/journals/cbio/10.2174/0115748936353313250123071744
/content/journals/cbio/10.2174/0115748936353313250123071744
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: Celiac Disease (CD) ; signaling pathways ; single-cell RNA sequencing ; protein-protein interaction network ; T cell ; protein biomarkers

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