Journal of Advances in Medical and Biomedical Research

en Prediction of Psoriasis from Gene Expression Profiling Using Penalized Logistic Regression Model Medical Biology Medical Biology مقاله پژوهشی Original Research Article 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. Gene Expression Profiling, Bioinformatics, Psoriasis, Biomarker, Prognosis, Penalized Regression 481 487 http://journal.zums.ac.ir/browse.php?a_code=A-10-6472-1&slc_lang=en&sid=1 Payam Amini payam.amini87@gmail.com 5200319475328460078831 5200319475328460078831 No School of Medicine, Keele University, Keele, Staffordshire, ST5 5BG, The United Kingdom Leili Tapak l.tapak06@gmail.com 5200319475328460078832 5200319475328460078832 Yes Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran Saeid Afshar safshar.h@gmail.com 5200319475328460078833 5200319475328460078833 No Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Hamadan University of Medical Sciences, Hamadan, Iran Mahlagha Afrasiabi m.afrasiabi001@gmail.com 5200319475328460078834 5200319475328460078834 No Department of Computer, Hamedan University of Technology, Hamedan, Iran MohammadKazem Ghasemi m.k.ghasemi853@gmail.com 5200319475328460078835 5200319475328460078835 No Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran Pedram Alirezaei prc@umsha.ac.ir 5200319475328460078836 5200319475328460078836 No Department of Dermatology, Psoriasis Research Center, Hamadan University of Medical Sciences, Hamadan, Iran