Volume 31, Issue 148 (September & October 2023)                   J Adv Med Biomed Res 2023, 31(148): 481-487 | Back to browse issues page

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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-en.html
Prediction of Psoriasis from Gene Expression Profiling Using Penalized Logistic Regression Model
Payam Amini1 , Leili Tapak *2 , Saeid Afshar3 , Mahlagha Afrasiabi4 , MohammadKazem Ghasemi5 , Pedram Alirezaei6
1- School of Medicine, Keele University, Keele, Staffordshire, ST5 5BG, The United Kingdom
2- Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran , l.tapak06@gmail.com
3- Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Hamadan University of Medical Sciences, Hamadan, Iran
4- Department of Computer, Hamedan University of Technology, Hamedan, Iran
5- Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran
6- Department of Dermatology, Psoriasis Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
Abstract:   (1497 Views)

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.

Keywords: Gene Expression Profiling, Bioinformatics, Psoriasis, Biomarker, Prognosis, Penalized Regression
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✅ 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.


Type of Study: Original Research Article | Subject: Medical Biology
Received: 2023/01/20 | Accepted: 2023/03/25 | Published: 2023/10/29
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