دوره 31، شماره 148 - ( 8-1402 )                   جلد 31 شماره 148 صفحات 487-481 | برگشت به فهرست نسخه ها

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Amini P, Tapak L, Afshar S, Afrasiabi M, Ghasemi M, Alirezaei P. Prediction of Psoriasis from Gene Expression Profiling Using Penalized Logistic Regression Model. J Adv Med Biomed Res 2023; 31 (148) :481-487
URL: http://journal.zums.ac.ir/article-1-7003-fa.html
Prediction of Psoriasis from Gene Expression Profiling Using Penalized Logistic Regression Model. Journal of Advances in Medical and Biomedical Research. 1402; 31 (148) :481-487

URL: http://journal.zums.ac.ir/article-1-7003-fa.html

Prediction of Psoriasis from Gene Expression Profiling Using Penalized Logistic Regression Model
چکیده:   (1282 مشاهده)

Background and Objective: Psoriasis is one of the most common skin disorders in humans and is believed to have genetic foundations. The aim of this study is to identify potential genetic biomarkers for psoriasis using penalized methods.
Materials and Methods: The gene chip GSE55201, which included 74 individuals (34 patients with psoriasis and 30 healthy individuals), was obtained from GEO. Three penalized approaches were used in logistic regression, including Least Absolute Shrinkage Selection Operator, Minimax Concave Penalty, and Smoothing Clipped Absolute Deviation, to identify the most important genes associated with psoriasis. To validate the results, Random Forest was used to assess the predictive power of the selected genes in a validation dataset.
Results: The analysis identified ADORA3 and C16orf72 as two genes that were commonly associated with psoriasis. The independent samples t-test revealed significantly higher expression of ADORA3 and C16orf72 among psoriasis cases (p<0.001). The area under the ROC curve for predicting psoriasis was 0.88 (95% CI: 0.80-0.96) for ADORA3 and 0.75 (95% CI: 0.75-0.94) for C16orf72. The Random Forest analysis showed that the model using these genes had a prediction probability of 0.68 (95% CI: 0.53-0.83).
Conclusion: Among all the methods used, MCP outperformed other penalties, selecting a smaller subset with compatible performance. Two key genes, ADORA3 and C16orf72, were found to be associated with psoriasis and were identified for further study. These genes may serve as genetic biomarkers for predicting psoriasis.

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نوع مطالعه: مقاله پژوهشی | موضوع مقاله: Medical Biology
دریافت: 1401/10/30 | پذیرش: 1402/1/5 | انتشار: 1402/8/7
فهرست منابع
1. Rendon A, Schäkel K. Psoriasis pathogenesis and treatment. Int J Molec Sci. 2019; 20(6):1475. [DOI:10.3390/ijms20061475] [PMID] [PMCID]
2. Stern RS, Nijsten T, Feldman SR,et al. Psoriasis is common, carries a substantial burden even when not extensive, and is associated with widespread treatment dissatisfaction. J Investig Dermatol Symp Proc. 2004;9(2):136-9. [DOI:10.1046/j.1087-0024.2003.09102.x] [PMID]
3. Javitz HS, Ward MM, Farber E, Nail L, Vallow SG. The direct cost of care for psoriasis and psoriatic arthritis in the United States. J Am Acad Dermatol. 2002; 46(6):850-60. [DOI:10.1067/mjd.2002.119669] [PMID]
4. Tapak L, Afshar S, Afrasiabi M, Ghasemi MK, Alirezaei P. Application of genetic algorithm-based support vector machine in identification of gene expression signatures for psoriasis classification: a hybrid model. Bio Med Res Int. 2021;2021:55207. [DOI:10.1155/2021/5520710] [PMID] [PMCID]
5. Parisi R, Iskandar IY, Kontopantelis E, Augustin M, Griffiths CE, Ashcroft DM. National, regional, and worldwide epidemiology of psoriasis: systematic analysis and modelling study. BMJ. 2020;369. [DOI:10.1136/bmj.m1590] [PMID] [PMCID]
6. Parisi R, Symmons DP, Griffiths CE, Ashcroft DM. Global epidemiology of psoriasis: a systematic review of incidence and prevalence. J Investig Dermatol. 2013;133(2):377-85. [DOI:10.1038/jid.2012.339] [PMID]
7. Armstrong AW, Read C. Pathophysiology, clinical presentation, and treatment of psoriasis: a review. JAMA. 2020;323(19):1945-60. [DOI:10.1001/jama.2020.4006] [PMID]
8. Johnson-Huang LM, Lowes MA, Krueger JG. Putting together the psoriasis puzzle: an update on developing targeted therapies. Dis Model Mech. 2012;5(4):423-33. [DOI:10.1242/dmm.009092] [PMID] [PMCID]
9. Hasan BMS, Abdulazeez AM. A review of principal component analysis algorithm for dimensionality reduction. J Soft Comput Data Min. 2021;2(1):20-30. [DOI:10.30880/jscdm.2021.02.01.003]
10. Zhu Y, Tan TL, Cheang WK. Penalized logistic regression for classification and feature selection with its application to detection of two official species of Ganoderma. Chemometr Intell Lab Syst. 2017;171:55-64. [DOI:10.1016/j.chemolab.2017.09.019]
11. Wang CQ, Suarez-Farinas M, Nograles KE, et al. IL-17 induces inflammation-associated gene products in blood monocytes, and treatment with ixekizumab reduces their expression in psoriasis patient blood. J Investig Dermatol. 2014;134(12):2990. [DOI:10.1038/jid.2014.268] [PMID]
12. Zhao Z, Ravid S, Ravid K. Chromosomal mapping of the mouse A3 adenosine receptor gene,adora3. Genomics. 1995;30(1):118-9. [DOI:10.1006/geno.1995.0023] [PMID]
13. Roszkiewicz J, Michałek D, Ryk A, Swacha Z, Szmyd B, Smolewska E. The impact of single nucleotide polymorphisms in ADORA2A and ADORA3 genes on the early response to methotrexate and presence of therapy side effects in children with juvenile idiopathic arthritis: Results of a preliminary study. Int J Rheum Dis. 2020;23(11):1505-13. [DOI:10.1111/1756-185X.13972] [PMID]
14. Stamp LK, Hazlett J, Roberts RL, Frampton C, Highton J, Hessian PA. Adenosine receptor expression in rheumatoid synovium: a basis for methotrexate action. Arthritis Res Ther. 2012;14(3):R138-R. [DOI:10.1186/ar3871] [PMID] [PMCID]
15. Varani K, Padovan M, Vincenzi F, et al. A2A and A3 adenosine receptor expression in rheumatoid arthritis: upregulation, inverse correlation with disease activity score and suppression of inflammatory cytokine and metalloproteinase release. Arthritis Res Ther. 2011;13(6):1-13. [DOI:10.1186/ar3527] [PMID] [PMCID]
16. Ochaion A, Bar-Yehuda S, Cohen S, et al. The anti-inflammatory target A3 adenosine receptor is over-expressed in rheumatoid arthritis, psoriasis and Crohn's disease. Cell Immunol. 2009;258(2):115-22. [DOI:10.1016/j.cellimm.2009.03.020] [PMID]
17. Stamp LK, Hazlett J, Roberts RL, Frampton C, Highton J, Hessian PA. Adenosine receptor expression in rheumatoid synovium: a basis for methotrexate action. Arthritis Res Ther. 2012;14(3):1-9. [DOI:10.1186/ar3871] [PMID] [PMCID]
18. Benslimane Y, Sánchez-Osuna M, Coulombe-Huntington J, et al. A novel p53 regulator, C16ORF72/TAPR1, buffers against telomerase inhibition. Aging Cell. 2021;20(4):e13331-e. [DOI:10.1111/acel.13331] [PMID] [PMCID]
19. Hohensinner PJ, Goronzy JJ, Weyand CM. Telomere dysfunction, autoimmunity and aging. Aging Dis. 2011;2(6):524-37.
20. Georgin-Lavialle S, Aouba A, Mouthon L, et al. The telomere/telomerase system in autoimmune and systemic immune-mediated diseases. Autoimmun Rev. 2010;9(10):646-51. [DOI:10.1016/j.autrev.2010.04.004] [PMID]
21. Fan J, Fan Y, Barut E. Adaptive robust variable selection. Ann Statis. 2014;42(1):324. [DOI:10.1214/13-AOS1191] [PMID] [PMCID]
22. Pang S, Havukkala I, Hu Y, Kasabov N. Classification consistency analysis for bootstrapping gene selection. Neural Comput Applic. 2007;16(6):527-39. [DOI:10.1007/s00521-007-0110-1]
23. Breheny P, Huang J. Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Statis Comput. 2015;25(2):173-87. [DOI:10.1007/s11222-013-9424-2] [PMID] [PMCID]
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