Investigation of the Roles of CRIP1 and IFITM1 as a Transcriptional Marker to Identify Periodontitis with Neural Network
Journal of International Research in Medical and Pharmaceutical Sciences, Volume 18, Issue 1,
Page 20-29
DOI:
10.56557/jirmeps/2023/v18i18244
Abstract
Periodontitis is a severe gum infection that may result in tooth loss, bone loss and other critical pathological complications. The recruitment of immune cells in the affected area creates a unique microenvironment in which diverse cell types can be found. Recently, a group performed single-cell RNA sequencing (scRNA-seq) to profile the transcriptional landscape of PBMCs of periodontitis. The group identified indicators of inflammatory responses and made suggestions on therapeutic targets. Aligned scRNA-seq data was reported in the Gene Expression Omnibus (GEO) database. In this paper, the GEO data was analyzed and constructed a neural network capable of classifying periodontitis patients using CRIP1 and IFITM1 as the input to the model. The model accurately classified (> 90% accuracy) the test dataset with noise added.
- Gene expression omnibus database
- gene marker
- neural network
- PBMCs of periodontitis
- single cell RNA sequencing
How to Cite
References
Arigbede AO, Babatope BO, Bamidele MK. Periodontitis and systemic diseases: A literature review. J Indian Soc Periodontol. 2012;16(4):487-91.
Geneva: World Health Organization. Global oral health status report: towards universal health coverage for oral health by 2030;2022.
Eke PI, Dye BA, Wei L, Thornton-Evans GO, Genco RJ, CDC Periodontal Disease Surveillance workgroup: James Beck (University of North Carolina, Chapel Hill, USA), Gordon Douglass (Past President, American Academy of Periodontology), Roy Page (University of Washin. Prevalence of periodontitis in adults in the United States: 2009 and 2010. J Dent Res. 2012;91(10):914-20.
Eley BM. Periodontics text and evolve e-books package. Saunders/Elsevier; 2011.
Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2018;50(8):1-14.
Lee H, Joo JY, Sohn DH, Kang J, Yu Y, Park HR et al. Single-cell RNA sequencing reveals rebalancing of immunological response in patients with periodontitis after non-surgical periodontal therapy. J Transl Med. 2022;20(1):504.
Hao Y et al. Integrated analysis of multimodal single-cell dat; 2020.
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):411-20.
Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019; 20(1):296.
Chandra V, Bhattacharyya S, Schmiedel BJ, Madrigal A, Gonzalez-Colin C, Fotsing S et al. Promoter-interacting expression quantitative trait loci are enriched for functional genetic variants. Nat Genet. 2021;53(1):110-9.
Schmiedel BJ, Singh D, Madrigal A, Valdovino-Gonzalez AG, White BM, Zapardiel-Gonzalo J et al. Impact of genetic polymorphisms on human immune cell gene expression. Cell. 2018;175(6): 1701-1715.e16.
Kleiveland CR. Peripheral blood mononuclear cells. In: Verhoeckx K et al., editors. The impact of food Bioactives on health: In vitro and ex vivo models. Springer; 2015.
Günther F, Fritsch S. neuralnet: training of Neural Networks. R J. 2010;2(1):30.
CD79a: A novel marker for B-cell neoplasms in routinely processed tissue samples – PubMed.
Available:https://pubmed.ncbi.nlm.nih.gov/7632952/.
Vernau W, Moore PF. An immunophenotypic study of canine leukemias and preliminary assessment of clonality by polymerase chain reaction. Vet Immunol Immunopathol. 1999;69(2-4): 145-64.
Salvadori S, Gansbacher B, Pizzimenti AM, Zier KS. Abnormal signal transduction by T cells of mice with parental tumors is not seen in mice bearing IL-2-secreting tumors. J Immunol Baltim Md. 1994;1950 (153):5176-82.
-
Abstract View: 0 times
PDF Download: 0 times